<link>https://pc.inf.usi.ch/</link></image><item><title>Open Positions</h1> <article> <h1>Open Positions</h1> <p>Wed, 01 Jul 2026 00:00:00 +0000</p> </article> <article> <h1>A foundation model for electrodermal activity data</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Diagnosing Constraint Awareness for Trustworthy Reinforcement Learning in MEC</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Diversity-Augmented Negative Sampling for Implicit Collaborative Filtering</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>ERIS: Enhancing Privacy and Communication Efficiency in Serverless Federated Learning</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Federated Concept-Based Models: Interpretable models with distributed supervision</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Federated Learning with Energy-Based Structured Probabilistic Inference</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Federated Learning with Profile Mapping under Distribution Shifts and Drifts</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Illocutionary Explanation Planning for Source-Faithful Explanations in Retrieval-Augmented Language Models</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Interpretability in Deep Time Series Models Demands Semantic Alignment</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Keynote - Beyond Accuracy: The Good, the Bad, and the Unknown in Sensor-Based Human Behavior Modeling</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Longitudinal assessment of chorea in Huntington's disease using digital passive monitoring</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Measured Enough: How Academic Knowledge Workers Negotiate Data Sharing and Control</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineering</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Multi-modal physiological markers of arousal induced by CO2 inhalation in Virtual Reality</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>Platform and Study Design for Adaptive EMA Using Smart Glasses to Monitor Affective States</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>The Power of 10: New Rules for the Digital World</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>The price of precision: the cost of preprocessing for automated code revision in code review</h1> <p>Thu, 01 Jan 2026 00:00:00 +0000</p> </article> <article> <h1>LIFETRACE</h1> <p>Tue, 02 Dec 2025 00:00:00 +0000</p> <p>Digital technologies and wearable devices have emerged as critical tools for developing personalized well-being/health interventions, closely supported by psychological experience sampling instruments. While existing public datasets often focus on clinical populations and controlled environments, this paper presents a comprehensive dataset integrating anthropometric and health measurements, in-the-wild longitudinal physical activity monitoring, environmental context data, socio-demographic factors, and standardized psychological well-being metrics. By monitoring relevant behavioral and health data, this dataset investigates those relevant determinants to form long-term physical activity habits. Over a 14-week period, we collected data from 88 participants. Of these, 59 followed a daily walking protocol during the first 7 weeks, with the remaining 29 participants serving as a control group, with no physical activity required. This paper presents an exploratory data analysis (EDA) to test the reliability and completeness of the collected data, together with a baseline machine learning model to assess the predictive power of included variables, relative to the well-being metrics. Moreover, by using the feature importance method, a preliminary explainability approach is applied to evaluate the soundness of predictions regarding feature contributions. Several applications are envisioned, including the impact of environmental conditions on behavior, exploring the contribution of each observed variable to habit formation, and providing a benchmark for future machine learning models in this field.</p> </article> <article> <h1>Data from \"LIFETRACE: A Longitudinal Multimodal Dataset on Daily Physical Activity, Well-Being, and Habits\" (Version 1)</h1> <p>Mon, 01 Dec 2025 00:00:00 +0000</p> </article> <article> <h1>Data from \"LIFETRACE: A Longitudinal Multimodal Dataset on Daily Physical Activity, Well-Being, and Habits\" (Version 1)</h1> <p>Mon, 01 Dec 2025 00:00:00 +0000</p> </article> <article> <h1>XAI-FinCrime</h1> <p>Mon, 01 Sep 2025 00:00:00 +0000</p> <p><strong>Funding:</strong> Innosuisse · September 2025 – August 2028</p> <p>The project aims to transform financial crime prevention by leveraging machine learning, generative AI, and explainable AI (XAI) to enhance fraud detection and compliance management. It uses ML models for transactional behavior analysis and multimodal LLMs for unstructured data processing, with counterfactual explainers to improve AI decision transparency.</p> <h2 id="objectives">Objectives</h2> <ul> <li>Significantly reduce the typical False Positive rate of 95% found in current rule-based industry standards</li> <li>Improve F1-scores, accuracy, and True Positive rates compared to existing solutions</li> <li>Enhance user understanding of AI systems to reduce investigation effort per alert</li> <li>Validate innovations through pilot studies with financial institutions</li> </ul> </article> <article> <h1>EXperiMental: Wearable Technology and EXplainable AI for Mental Health and Inclusivity in Schools</h1> <p>Tue, 01 Jul 2025 00:00:00 +0000</p> <p><strong>Funding:</strong> SNF, grant 230189 · July 2025 – December 2028</p> <p>Mental health is a vital part of our overall well-being, especially during early adolescence, a time of rapid growth and emotional development. The initiative explores how AI, smartphones, and smartwatches can promote mental well-being in schools through collaboration among psychology, sports science, computer science, and ethics experts across Romania, Croatia, and Switzerland.</p> <p>The research involves two major studies: (1) observing daily routines using digital tools and mental health check-ins, and (2) applying AI methods to identify patterns signaling mood or mental health changes. The aim is creating personalized, understandable suggestions for young people, teachers, and school psychologists.</p> <h2 id="objectives">Objectives</h2> <ul> <li>Understand how physical activity, sleep, and phone use relate to adolescent mental health</li> <li>Design youth-friendly, privacy-protective monitoring methods via mobile devices</li> <li>Develop personalised suggestions, such as encouraging a walk or improving sleep habits</li> <li>Create ethically sound guidelines for technology use supporting adolescent mental well-being in schools</li> </ul> </article> <article> <h1>PhD position: XAI-FinCrime</h1> <p>Mon, 20 Jan 2025 00:00:00 +0000</p> <p>The People-Centered Computing Lab is looking for a PhD student in machine learning and explainable AI applied to financial crime detection.</p> <p>Source: .</p> </article> <article> <h1>Postdoc position: XAI-FinCrime</h1> <p>Mon, 20 Jan 2025 00:00:00 +0000</p> <p>The People-Centered Computing Lab is looking for a postdoctoral researcher in machine learning and explainable AI for financial crime detection and prevention.</p> <p>Source: .</p> </article> <article> <h1>A Comparative Analysis of Retrieval-Augmented Generation and Crowdsourcing for Fact-Checking</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>A Review of Methods for Unobtrusive Measurement of Work-Related Well-Being</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>A Survey on Federated Learning in Human Sensing</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>All You Need for Counterfactual Explainability Is Principled and Reliable Estimate of Aleatoric and Epistemic Uncertainty</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gap</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Beyond the lab: An in-depth analysis of real-world practices in government-to-citizen software user documentation</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Blue Mondays and Happy Meals: Investigating the Impact of Contextual Factors on Mood States</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Can Global XAI Methods Reveal Injected Bias in LLMs? SHAP vs Rule Extraction vs RuleSHAP</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Categorical Explaining Functors: Ensuring Coherence in Logical Explanations</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Causally Reliable Concept Bottleneck Models</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Causally Reliable Concept Bottleneck Models</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Comprehension is a double-edged sword: Over-interpreting unspecified information in intelligible machine learning explanations</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Counterfactual Concept Bottleneck Models</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Deep Learning for Facial Expression and Human Activity Recognition Using Smart Glasses</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Detecting Semantic Clones of Unseen Functionality</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Detecting Semantic Clones of Unseen Functionality</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>DiscoLQA: zero-shot discourse-based legal question answering on European Legislation</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Diverse Negative Sampling for Implicit Collaborative Filtering</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Evaluating and Addressing Fairness Across User Groups in Negative Sampling for Recommender Systems</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Evaluating the Quality of Counterfactual Explanations in Multivariate Time-Series</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Explicit vs. Implicit Biographies: Evaluating and Adapting LLM Information Extraction on Wikidata-Derived Texts</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Exploring Generalist Foundation Models for Time Series of Electrodermal Activity Data</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>FLUX: Efficient Descriptor-Driven Clustered Federated Learning under Arbitrary Distribution Shifts</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Foundations of Interpretable Models</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>From Research to Certification with Data-Driven Medical Decision Support Systems (Dagstuhl Seminar 25052)</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Generating Explanations for Models Predicting Student Exam Performance</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Generative models for grid-based and image-based pathfinding</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>How robust is your fair model? Exploring the robustness of prominent fairness strategies</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>How to Improve the Explanatory Power of an Intelligent Textbook: a Case Study in Legal Writing</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>If Concept Bottlenecks are the Question, are Foundation Models the Answer?</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Interpretable Link Prediction via Neural-Symbolic Reasoning</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Is General-Purpose AI Reasoning Sensitive to Data-Induced Cognitive Biases? Dynamic Benchmarking on Typical Software Engineering Dilemmas</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Large Language Models for In-File Vulnerability Localization Can Be \"Lost in the End\"</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Large Language Models for In-File Vulnerability Localization Can Be \"Lost in the End\"</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Leveraging Complementary AI Explanations to Mitigate Misunderstanding in XAI</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Leveraging Complementary AI Explanations to Mitigate Misunderstanding in XAI</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>LIFETRACE: A Longitudinal Multimodal Dataset on Daily Physical Activity, Well-Being, and Habits</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Linearly-interpretable concept embedding models for text analysis</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Logic Explanation of AI Classifiers by Categorical Explaining Functors</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Mitigating Interpersonal Variability in Machine Learning Models for Human Behavior Monitoring</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Navigating explanatory multiverse through counterfactual path geometry</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Neural Interpretable Reasoning</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>On the interplay of Explainability, Privacy and Predictive Performance with Explanation-assisted Model Extraction</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>On the Interplay of Explainability, Privacy and Predictive Performance with Explanation-assisted Model Extraction</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Perfect counterfactuals in imperfect worlds: modelling noisy implementation of actions in sequential algorithmic recourse</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Robust PPG Authentication: The Role of Temporal, Individual, and State Variability</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Scalable Evaluation and Neural Models for Compositional Generalization</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Simplifying software compliance: AI technologies in drafting technical documentation for the AI Act</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Towards Better Generalization and Interpretability in Unsupervised Concept-Based Models</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Towards Better Generalization and Interpretability in Unsupervised Concept-Based Models</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Towards Transparent Reasoning: What Drives Faithfulness in Large Language Models?</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Unobtrusive Perceived Sleep Quality Monitoring in the Wild</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>Using AI explainable models and handwriting/drawing tasks for psychological well-being</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>XAI for U 2025: 2$nd$ International Workshop on Explainable AI for Ubiquitous, Pervasive and Wearable Computing</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> </article> <article> <h1>A Feature-Based Approach for Subtle Emotion Recognition in Realistic Scenarios</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Categorical Foundation of Explainable AI: A Unifying Theory</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Causal Concept Embedding Models: Beyond Causal Opacity in Deep Learning</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Climbing the Ladder of Interpretability with Counterfactual Concept Bottleneck Models</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Contact</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> <p>We are located on USI’s Lugano East Campus, Sector D, 2nd floor.</p> <h2 id="contact-info">Contact Info</h2> <p>Faculty of Informatics<br> USI Lugano, Campus Est - Sector D.2<br> Via La Santa 1<br> 6962 Lugano-Viganello, Switzerland</p> <p>People-Centered Computing Research Group<br> c/o Profs. Silvia Santini and Marc Langheinrich<br> Institute of Computer Systems<br> Università della Svizzera italiana (USI)<br> Via G. Buffi 13<br> 6900 Lugano, Switzerland</p> <p>For individual offices, email addresses, and phone numbers, see the People page.</p> <h2 id="how-to-find-us">How to Find Us</h2> <p>By public transport from Lugano train station, take Bus No. 6 toward Università / Cornaredo and get off at “Campus Universitario”. Bus No. 5 from the Genzana stop also reaches “Campus Universitario” via the city center.</p> </article> <article> <h1>Counterfactual Explanations for Clustering Models</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Courses</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> <p>The group offers courses within the Faculty of Informatics. Students interested in a bachelor’s thesis, master’s thesis, or summer internship can also review the page.</p> <h2 id="master-courses">Master Courses</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <h2 id="bachelor-courses">Bachelor Courses</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <h2 id="past-courses">Past Courses</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> </ul> </article> <article> <h1>DC3DO: Diffusion Classifier for 3D Objects</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Differential Privacy for Anomaly Detection: Analyzing the Trade-Off Between Privacy and Explainability</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Education</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Evaluating Explanations Through LLMs: Beyond Traditional User Studies</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>FedMMA-HAR: Federated Learning for Human Activity Recognition With Missing Modalities Using Head-Worn Wearables</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Generative Models for Counterfactual Explanations</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Gradient-Based Competitive Learning: Theory</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>How to Validate XAI in Longitudinal Studies?</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Interpretable Concept-Based Memory Reasoning</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Interpretable Concept-Based Memory Reasoning</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Interpretable representations in explainable AI: from theory to practice</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Large Language Models for Wearable Data Analysis and Interpretation</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Lateralization Effects in Electrodermal Activity Data Collected Using Wearable Devices</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Leveraging Simulation Data to Understand Bias in Predictive Models of Infectious Disease Spread</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Modeling multiple sclerosis using mobile and wearable sensor data</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Modeling Perceived Sleep Quality Through Objective and Subjective Data</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Object-Centric Learning with Slot Mixture Module</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Perfect Counterfactuals in Imperfect Worlds: Modelling Noisy Implementation of Actions in Sequential Algorithmic Recourse</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Proceedings of the First Multimodal, Affective and Interactive eXplainable AI Workshop (MAI-XAI24 2024) co-located with 27th European Conference On Artificial Intelligence 19-24 October 2024 (ECAI 2024), Santiago de Compostela, Spain, October 19, 2024</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Proceedings of the KDD Workshop on Human-Interpretable AI 2024 co-located with 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024), Centre de Convencions Internacional de Barcelona, Spain, August 26, 2024</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Relational Concept Bottleneck Models</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Representation Learning for Wearable-Based Applications in the Case of Missing Data</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Self-supervised Interpretable Concept-based Models for Text Classification</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Student Projects</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Systematic Evaluation of Self-Supervised Learning Approaches for Wearable-Based Fatigue Recognition</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>WellComp 2024: Seventh International Workshop on Computing for Well-Being</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>Workshop on Human-Interpretable AI</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>XAI for U: Explainable AI for Ubiquitous, Pervasive and Wearable Computing</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> </article> <article> <h1>XAI-PAC: Towards Explainable and Private Affective Computing</h1> <p>Mon, 01 Jan 2024 00:00:00 +0000</p> <p><strong>Funding:</strong> SNF · January 2024 – January 2028</p> <p>Wearable devices combined with AI methods can improve lives through affective computing, which tracks factors related to emotional states and stress for remote mental-health management. However, current AI methods are “black-boxed” and require vast sensitive data. The project addresses the need for explainable and privacy-aware affective computing.</p> <h2 id="objectives">Objectives</h2> <ul> <li>Develop novel eXplainable AI (XAI) tools for affective-computing methods based on wearable sensor data with multimodal, interactive capabilities</li> <li>Develop privacy-aware ML methods for wearable sensor data, including privacy-aware personalization and domain-adaptation</li> <li>Fuse privacy awareness and explainability into a single integrated approach</li> </ul> </article> <article> <h1>TRUST-ME: TRUstworthy enhancement of job SaTisfaction and productivity using Micro-sensing in work Environments</h1> <p>Sun, 01 Oct 2023 00:00:00 +0000</p> <p><strong>Funding:</strong> SNF · October 2023 – October 2026</p> <p>The TRUST-ME initiative addresses employee well-being in modern workplaces by examining the complex relationship between employee productivity, job satisfaction, and well-being. The endeavor combines sensing technology and AI to create personalized monitoring tools, emphasizing multimodal monitoring and modeling of job satisfaction and productivity, as well as secure and private AI for productivity assessment. The approach relies on workplace sensors and privacy-preserving techniques, such as federated learning, to deliver insights while protecting employee autonomy.</p> </article> <article> <h1>LAUREATE</h1> <p>Wed, 27 Sep 2023 00:00:00 +0000</p> <p>The latest developments in wearable sensors have resulted in a wide range of devices available to consumers, allowing users to monitor and improve their physical activity, sleep patterns, cognitive load, and stress levels. However, the lack of out-of-the-lab labelled data hinders the development of advanced machine learning models for predicting affective states. Furthermore, to the best of our knowledge, there are no publicly available datasets in the area of Human Memory Augmentation. This paper presents a dataset we collected during a 13-week study in a university setting. The dataset, named LAUREATE, contains the physiological data of 42 students during 26 classes (including exams), daily self-reports asking the students about their lifestyle habits (e.g. studying hours, physical activity, and sleep quality) and their performance across multiple examinations. In addition to the raw data, we provide expert features from the physiological data, and baseline machine learning models for estimating self-reported affect, models for recognising classes vs breaks, and models for user identification. Besides the use cases presented in this paper, among which Human Memory Augmentation, the dataset represents a rich resource for the UbiComp community in various domains, including affect recognition, behaviour modelling, user privacy, and activity and context recognition.</p> </article> <article> <h1>SmartCHANGE</h1> <p>Mon, 01 May 2023 00:00:00 +0000</p> <p><strong>Funding:</strong> Horizon Europe · May 2023 – May 2027</p> <p>The SmartCHANGE project addresses the shift from communicable to non-communicable chronic diseases (NCDs) in developed countries. According to WHO, more than 70% of deaths worldwide are attributed to NCDs, with projected costs rising from 5.5 trillion € (2010) to over 12 trillion € by 2030. Most NCDs share risk factors including obesity, low physical fitness, poor nutrition, and sedentary behavior. The project aims to create trustworthy, AI-based decision-support tools for assessing NCD risk in children and youth.</p> <h2 id="objectives">Objectives</h2> <ul> <li>Build accurate machine learning models for predicting lifetime NCD risk (cardiovascular and metabolic disease) in children and youth</li> <li>Ensure risk-prediction models and AI tools are trustworthy</li> <li>Develop tools for health professionals and citizens to improve health outcomes</li> <li>Engage users in requirements elicitation and participatory design</li> <li>Conduct proof-of-concept studies across four real-world healthcare scenarios in different countries</li> <li>Develop implementation recommendations and dissemination strategies</li> <li>Create an exploitation and sustainability plan</li> </ul> </article> <article> <h1>(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Explainability</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>A Federated Unsupervised Personalisation for Cognitive Workload Estimation</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>BiHeartS</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> <p>Sleep is the primary mean of recovery from accumulated fatigue and thus plays a crucial role in fostering people’s mental and physical well-being. Sleep quality monitoring systems are often implemented using wearables that leverage their sensing capabilities to provide sleep behaviour insights and recommendations to users. Building models to estimate sleep quality from sensor data is a challenging task, due to the variability of both physiological data, perception of sleep quality, and the daily routine across users. This challenge gauges the need for a comprehensive dataset that includes information about the daily behaviour of users, physiological signals as well as the perceived sleep quality. In this paper, we try to narrow this gap by proposing Bilateral Heart rate from multiple devices and body positions for Sleep measurement (BiHeartS) dataset. The dataset is collected in the wild from 10 participants for 30 consecutive nights. Both research-grade and commercial wearable devices are included in the data collection campaign. Also, comprehensive self-reports are collected about the sleep quality and the daily routine.</p> </article> <article> <h1>BiHeartS: Bilateral Heart Rate from multiple devices and body positions for Sleep measurement Dataset</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Bridging Equational Properties and Patterns on Graphs: an AI-Based Approach</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Can Users Correctly Interpret Machine Learning Explanations and Simultaneously Identify Their Limitations?</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Categorical Foundations of Explainable AI: A Unifying Formalism of Structures and Semantics</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Concept Distillation in Graph Neural Networks</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Deep learning approach for accurate prostate cancer identification and stratification using combined immunostaining of cytokeratin, p63, and racemase</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Enhancing XGBoost with Heuristic Smoothing for Transportation Mode and Activity Recognition</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Explainable Depression Detection Using Handwriting Features</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Exploring Transformer and Graph Convolutional Networks for Human Mobility Modeling</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Federated Learning for Privacy-aware Cognitive Workload Estimation</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>From charts to atlas: Merging latent spaces into one</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>From Charts to Atlas: Merging Latent Spaces into One</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>GANs for Privacy-Aware Mobility Modeling</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>GCI: A (G)raph (C)oncept (I)nterpretation Framework</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Global Explainability of GNNs via Logic Combination of Learned Concepts</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>HeartS</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Interpretable Graph Networks Formulate Universal Algebra Conjectures</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Interpretable Graph Networks Formulate Universal Algebra Conjectures</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Interpretable Neural-Symbolic Concept Reasoning</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Interpretable Neural-Symbolic Concept Reasoning</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Interpretable Neural-Symbolic Concept Reasoning</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>LAUREATE: A Dataset for Supporting Research in Affective Computing and Human Memory Augmentation</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Learning Logic Explanations by Neural Networks</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Logic Explained Networks</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Logic Explained Networks</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>More Is Less: When Do Recommenders Underperform for Data-rich Users?</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Navigating Explanatory Multiverse Through Counterfactual Path Geometry</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>OCOsense Glasses - Monitoring Facial Gestures and Expressions for Augmented Human-Computer Interaction: OCOsense Glasses for Monitoring Facial Gestures and Expressions</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Privacy-aware Human Activity Recognition with Smart Glasses for Digital Therapeutics</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Recognition of Engagement from Electrodermal Activity Data Across Different Contexts</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Recognizing Activities of Daily Living using Multi-sensor Smart Glasses</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Relational Concept Based Models</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>SHARCS: Shared Concept Space for Explainable Multimodal Learning</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Simply Logical - The First Three Decades</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Smart Glasses for Gait Analysis of Parkinson's Disease Patients</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Towards Robust Metrics for Concept Representation Evaluation</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Towards Robust Metrics for Concept Representation Evaluation</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Video-Based Emotion Estimation Using Deep Neural Networks: A Comparative Study</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>WellComp 2023: Sixth International Workshop on Computing for Well-Being</h1> <p>Sun, 01 Jan 2023 00:00:00 +0000</p> </article> <article> <h1>Prof. Silvia Santini’s contribution on La Regione</h1> <p>Mon, 21 Nov 2022 00:00:00 +0000</p> <p>Professor Silvia Santini discussed how digital devices and apps are increasingly integrated into daily life and how this creates both concerns and opportunities for well-being.</p> <p>Source: .</p> </article> <article> <h1>Our group members ranked among the world’s top 2% of scientists</h1> <p>Wed, 26 Oct 2022 00:00:00 +0000</p> <p>Marc Langheinrich and Martin Gjoreski were included in Stanford University and Elsevier science-wide citation indicator lists for top scientists.</p> <p>Source: .</p> </article> <article> <h1>Daniel Gatica-Perez presents Towards Increasing Diversity in Mobile Sensing Research</h1> <p>Tue, 18 Oct 2022 00:00:00 +0000</p> <p>Professor Silvia Santini hosted Daniel Gatica-Perez from EPFL for a seminar on diversity and mobile sensing research.</p> <p>Source: .</p> </article> <article> <h1>PROSELF: Semi-automated Self-Tracking Systems to Improve Personal Productivity</h1> <p>Sat, 01 Oct 2022 00:00:00 +0000</p> <p><strong>Funding:</strong> SNF, grant 197242 · October 2022 – September 2025</p> <p>PROSELF aims at investigating how emerging mobile and wearable technology can help provide an understanding of what makes people feel (and be) productive, and subsequently, to assist users with managing their productivity on a daily basis.</p> <p>The project addresses concerns about workplace monitoring by focusing on knowledge workers’ empowerment rather than employer control. While existing “productivity apps” help monitor work activities, PROSELF advocates for systems that consider environmental, behavioral, and psychological factors in one’s feeling of (and indeed, observed) performance at work.</p> <h2 id="objectives">Objectives</h2> <ul> <li>Provide novel methods to enable self-monitoring at work in a semi-automated manner</li> <li>Develop a sound modeling framework to identify proxies of specific performance indicators in users’ context data</li> <li>Devise an adequate architecture to support the definition and execution of anticipatory decisions</li> <li>Critically evaluate these approaches in-situ in a range of field studies</li> </ul> </article> <article> <h1>What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components (Version 2022-jose)</h1> <p>Sat, 01 Oct 2022 00:00:00 +0000</p> </article> <article> <h1>Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition) (Version v2.0)</h1> <p>Mon, 01 Aug 2022 00:00:00 +0000</p> </article> <article> <h1>M2Sleep</h1> <p>Fri, 01 Apr 2022 00:00:00 +0000</p> <p>Personal informatics systems can help people promote their health and well-being. Recent studies have shown that such systems can be used to infer relevant health indicators such as, e.g., stress, anxiety, and sleeping habits. While automatic detection of sleep has been studied extensively, there is a lack of studies exploring how population and personalized models influence the performance of sleep detection. In this article, we address this challenge by investigating the recognition of sleep/wake stages and high/low sleep quality with a focus on the impact of personalized models. To evaluate our approach, we collect a dataset of physiological signals and self-reports about sleep/wake times and sleep quality score. The dataset contains 6557 hours of sensor data collected using wristbands from 16 participants over one month. Our results show that personalized models perform significantly better than population models for sleep quality recognition, and are comparably good for sleep stage detection. The balanced accuracy for sleep/wake and high/low sleep quality are 92.2% and 61.51%, which are significantly higher than baseline classifiers.</p> </article> <article> <h1>Prof. Marc Langheinrich’s contribution on La Regione</h1> <p>Mon, 21 Feb 2022 00:00:00 +0000</p> <p>Professor Marc Langheinrich discussed digital privacy and the evolution of personal data handling in a feature with La Regione.</p> <p>Source: .</p> </article> <article> <h1>Affect and Learning in the LAUREATE Dataset</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Analysing Donors' Behaviour in Non-profit Organisations for Disaster Resilience: The 2019-2020 Australian Bushfires Case Study</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>BAG-DSM: A Method for Generating Alternatives for Hierarchical Multi-Attribute Decision Models Using Bayesian Optimization</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>BayCon: Model-agnostic Bayesian Counterfactual Generator</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>CONFIG.SYS</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Ethical and Fairness Implications of Model Multiplicity</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Exploring LAUREATE - the Longitudinal multimodAl stUdent expeRience datasEt for AffecT and mEmory research</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Facial Expression Recognition using Facial Mask with EMG Sensors</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>FAT Forensics: A Python toolbox for algorithmic fairness, accountability and transparency</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Federated Learning for Privacy-Aware Human Mobility Modeling</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Handling Missing Data For Sleep Monitoring Systems</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Heart Rate During Sleep Measured Using Finger-, Wrist- and Chest-Worn Devices: A Comparison Study</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Hello, Goodbye</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Introduction of the LAUREATE dataset: the Longitudinal multimodAl stUdent expeRience datasEt for AffecT and mEmory research</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Monitoring Valence with Facial EMG Sensors using Machine Learning</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Multi-task Learning for Stress Recognition</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>OCOsense Glasses for Facial Expressions Recognition and Contextual Affective Computing in Real World and Augmented Reality</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>On the Impact of Lateralization in Physiological Signals from Wearable Sensors</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>On the mismatch between measured and perceived sleep quality</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition)</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Software-Based Remote Network Attestation</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Standing on the Platforms of Giants</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Technobubble</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>The Role of Model Personalization for Sleep Stage and Sleep Quality Recognition Using Wearables</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>Toward privacy-aware federated analytics of cohorts for smart mobility</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>WellComp 2022: Fifth International Workshop on Computing for Well-Being</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>What Actually Works for Activity Recognition in Scenarios with Significant Domain Shift: Lessons Learned from the 2019 and 2020 Sussex-Huawei Challenges</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components</h1> <p>Sat, 01 Jan 2022 00:00:00 +0000</p> </article> <article> <h1>HAFAR</h1> <p>Mon, 18 Oct 2021 00:00:00 +0000</p> <p>Head gestures and facial expressions – like, e.g., nodding or smiling – are important indicators of the quality of human interactions in physical meetings as well as in computer-mediated settings. Computer systems able to recognize such behavioral cues can support and improve human interactions. Several researchers have thus tackled the problem of automatically recognizing head gestures and facial expressions, mainly leveraging video data. In this paper, we instead consider inertial signals collected from unobtrusive, ear-mounted devices. We focus on typical activities performed during social interactions – head shaking, nodding, smiling, talking and yawning – and propose a hierarchical classification approach to discriminate them from each other. Further, we investigate whether the transfer of knowledge learned from publicly available datasets leads to further performance improvements. Our results show that the combined use of our hierarchical approach and transfer learning allows the classifier to discriminate head and mouth activities with an F1 score of 84.79, smile, talk and yawn with an F1 score of 45.42, and nodding and head shaking with an F1 score of 88.24, outperforming shallow classifiers by 2-9 percentage points.</p> </article> <article> <h1>Tutorial at the Neuroergonomics 2021 conference in Munich</h1> <p>Mon, 23 Aug 2021 00:00:00 +0000</p> <p>Martin Gjoreski co-organized a tutorial on machine learning for cognitive load inference from physiological signals at Neuroergonomics 2021.</p> <p>Source: .</p> </article> <article> <h1>Martin to win Jožef Stefan Prize!</h1> <p>Fri, 26 Mar 2021 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p>Our very own just received the Slovenian “ ” for outstanding PhD thesis!</p> <p>Up to 3 such awards are given annually for outstanding PhD dissertations of young researchers (up to 32 years) who have graduated in the last 3 years in Slovenia, in the fields of natural sciences, mathematics, and technical sciences (incl. medicine and biotechnology). It’s a pretty competitive award – the number of young researchers that graduated in the past 3 years is close to (in those scientific fields in Slovenia) – so that’s a top 1% selection!</p> <p>If you’re interested, his thesis “A fusion of classical and deep machine learning for mobile health and behavior monitoring with wearable sensors” is available .</p> <p>Congrats Martin!</p> </article> <article> <h1>A Longitudinal Study of Pervasive Display Personalisation</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>A Method for Combining Classical and Deep Machine Learning for Mobile Health and Behavior Monitoring</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>Analysis of Deep Transfer Learning Using DeepConvLSTM for Human Activity Recognition from Wearable Sensors</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>Automatic Recognition of Flow During Work Activities Using Context and Physiological Signals</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>Biometric recognition using wearable devices in real-life settings</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>Cognitive Load Monitoring With Wearables-Lessons Learned From a Machine Learning Challenge</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>Dependence Day</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>Detecting Verbal and Non-Verbal Gestures Using Earables</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>emteqPRO: Face-mounted Mask for Emotion Recognition and Affective Computing</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>Hierarchical Classification and Transfer Learning to Recognize Head Gestures and Facial Expressions Using Earbuds</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>Learning comprehensible and accurate hybrid trees</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>Prepare for Impact!</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>Synchronous Transmissions in Low-Power Wireless: A Survey of Communication Protocols and Network Services</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>The Internet of Thugs?</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>To FLoC or Not?</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>Towards the Design of a Privacy-preserving Attribute Based Credentials-based Digital ID in Denmark - Usefulness, Barriers, and Recommendations</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> </article> <article> <h1>IEEE Pervasive: Oct-Dec 2020 issue is out!</h1> <p>Sat, 19 Dec 2020 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p>The latest issue of is out! The Oct-Dec 2020 issue features Guest Editors , , , and and their selection of articles on Edge Computing!</p> <p>Edge computing offers the promise of supporting the demanding requirements of the next generation of pervasive computing and IoT systems.</p> <p>You can access their and ‘s EIC message (“ “) free of charge on the .</p> </article> <article> <h1>BASE: Behavioral Analytics for Smart Environments</h1> <p>Tue, 01 Dec 2020 00:00:00 +0000</p> <p><strong>Funding:</strong> SNF · December 2020 – November 2023</p> <p>Behavioral analytics is widely recognized as being key to providing new services and solutions in many application domains. The key to behavioral analytics is to be able to identify and track individual user interactions across time and space (e.g., Web server logs). However, currently there are no viable mechanisms for service providers to track user interactions in the real world without raising significant privacy concerns. BASE develops a new privacy-aware method for understanding human behavior through activity traces (e.g., GPS, smartphone app launches). The work is based on state-of-the-art federated machine-learning approaches.</p> </article> <article> <h1>EDArtifact</h1> <p>Mon, 15 Jun 2020 00:00:00 +0000</p> <p>Recent wearable devices enable continuous and unobtrusive monitoring of human’s physiological parameters, like e.g., electrodermal activity and heart rate, over long periods of time in everyday life settings. Continuous monitoring of these parameters enables the creation of systems able to predict affective states and stress with the goal of providing feedback to improve them. Deployment of such systems in everyday life settings is still complex and prone to errors due to the low quality of the collected data impacted by the presence of artifacts. In this paper we present an automatic approach to detect artifacts in electrodermal activity (EDA) signals collected in-the-wild over long periods of time. To this end we first perform a systematic literature review and compile a set of guidelines for human annotators to label artifacts manually and we use these labels as ground-truth to test our automatic approach. To evaluate our approach, we collect physiological data from 13 participants in-the-wild and two human annotators label 107.56 hours of this data set. We make the data set publicly available to other researchers upon request. Our model achieves a recall of 98% for clean and shape artifacts classification on data collected in-the-wild using leave-one-subject-out cross-validation, which is 42 percentage points higher than the baseline. We show that state of the art approaches do not generalize well when tested with completely in-the-wild data and identify only 17% of the artifacts present in our data set, even after manual adaption. We further test the robustness of our approach over time using leave-one-day-out and achieve very similar performance. We then introduce a new metric to evaluate the quality of EDA segments that considers the impact of not only artifacts in the shape of EDA but also artifacts generated by environmental temperature changes or user’s high intensity movement. Our results imply that we can eliminate the need for human annotators or significantly reduce the time they need to label data. Also, our approach can be used in an online manner to automatically detect artifacts in EDA signals.</p> </article> <article> <h1>A Multi-Sensor Approach to Automatically Recognize Breaks and Work Activities of Knowledge Workers in Academia</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>Classical and deep learning methods for recognizing human activities and modes of transportation with smartphone sensors</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>Designing an Intelligent Cognitive Assistant as Persuasive Technology for Stress, Anxiety and Depression Relief</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>Designing for conflict: a design space for multi-viewer support in future display networks</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>Detection of Artifacts in Ambulatory Electrodermal Activity Data</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>Engineering Privacy by Design: Are engineers ready to live up to the challenge?</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>First Contact!</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>IoT '20 Companion: 10th International Conference on the Internet of Things Companion, Malmö Sweden, October 6-9, 2020</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>IoT '20: 10th International Conference on the Internet of Things, Malmö, Sweden, October 6-9, 2020</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>Is Privacy Regulation Slowing Down Research on Pervasive Computing?</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>Is There an App for That?</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>Long Live the IoT</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>Machine Learning and End-to-End Deep Learning for Monitoring Driver Distractions From Physiological and Visual Signals</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>Machine Learning and End-to-End Deep Learning for the Detection of Chronic Heart Failure From Heart Sounds</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>PerMEASS - Personal Mental Health Virtual Assistant with Novel Ambient Intelligence Integration</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>Synchronous Transmissions in Low-Power Wireless: A Survey of Communication Protocols and Network Services</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>Tackling the SHL challenge 2020 with person-specific classifiers and semi-supervised learning</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>The New Normal</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>Toward Cognitive Load Inference for Attention Management in Ubiquitous Systems</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>UbiTtention 2020: 5th International Workshop on Smart & Ambient Notification and Attention Management</h1> <p>Wed, 01 Jan 2020 00:00:00 +0000</p> </article> <article> <h1>USILaughs</h1> <p>Mon, 20 May 2019 00:00:00 +0000</p> <p>A growing number of pervasive systems integrate emotion recognition capabilities with the aim of fostering and promoting human health and wellbeing. In this paper, we argue that enhancing these systems with the ability to detect laughter episodes automatically would improve their effectiveness. This is because laughter is one of the most expressive behavioral cues for positive emotions. However, the existing approaches for laughter recognition rely on the use of obtrusive devices or of video and audio cues and have thus limited applicability in real-world settings. To overcome this limitation, we evaluate the feasibility of a novel, multi-modal approach to recognize laughter episodes using physiological and body movement data gathered with unobtrusive, wrist-worn devices. To assess the performance of our method, we collect an extensive data set of laughter episodes, which we also make publicly available. Our results show that laughter episodes can be distinguished from non-laughter episodes with an accuracy of 81%. Further, we demonstrate that the signatures left by laughter episodes on physiological and body-movement data differ significantly from those caused by slightly intense motions or cognitive load tasks.</p> </article> <article> <h1>A Plan B for Plan S?</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>Cooperativism and Human-Computer Interaction</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>Energy-Aware Coflow and Antenna Scheduling for Hybrid Server-Centric Data Center Networks</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>Engineering Privacy by Design: Are engineers ready to live up to the challenge?</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>Evaluating the accuracy of heart rate sensors based on photoplethysmography for in-the-wild analysis</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>INFAS: In-Network Flow mAnagement Scheme for SDN Control Plane Protection</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>Inside the Organization: Why Privacy and Security Engineering Is a Challenge for Engineers</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>Laughter Recognition Using Non-invasive Wearable Devices</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>Let's Talk About the Weather</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>Movie+: Towards Exploring Social Effects of Emotional Fingerprints for Video Clips and Movies</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>Scheduling Content in Pervasive Display Systems</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>Securely Storing and Sharing Memory Cues in Memory Augmentation Systems: A Practical Approach</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>Sharing Economy Design Cards</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>The Internet of Data</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>The Paperless Magazine</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>UPA'19: 4th international workshop on ubiquitous personal assistance</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>Using Unobtrusive Wearable Sensors to Measure the Physiological Synchrony Between Presenters and Audience Members</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>Whisper: Fast Flooding for Low-Power Wireless Networks</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> </article> <article> <h1>6th DACH+ Conference on Energy informatics (EnInf 2017)</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Analysis of users' heart rate data and self-reported perceptions to understand effective virtual reality characteristics</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Augmenting Humans</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Augmenting Humans on the Slope: Two Electronic Devices That Enhance Safety and Decision Making</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Augmenting Memory Recall in Work Meetings: Establishing a Quantifiable Baseline</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Bibliometrics - Measuring Pervasive Computing's Impact</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Calmify: Measuring the Effectiveness of Personalized Meditation Techniques Using Mobile Technologies</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Co-creation and Risk-Taking - In Pursuit of New Technology for Human Augmentation: An Interview with Pranav Mistry</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Emotion-Aware Systems for Promoting Human Well-being</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Energy-efficient SDN control and visualization</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>For the Benefit of Humanity</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Got Change?</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Memstone: a tangible interface for controlling capture and sharing of personal memories</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Moodbook: An Application for Continuous Monitoring of Social Media Usage and Mood</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Plantxel: Towards a Plant-based Controllable Display</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Privacy in Mobile and Pervasive Computing</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Proceedings of the 7th ACM International Symposium on Pervasive Displays, PerDis 2018, Munich, Germany, June 6-8, 2018</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>REMO: Resource efficient distributed network monitoring</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Roaming Objects: Encoding Digital Histories of Use into Shared Objects and Tools</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Selecting Individual and Population Models for Predicting Human Mobility</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Sharing physical objects using smart contracts</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Supporting the design of sharing economy services: learning from technology-mediated sharing practices of both digital and physical artifacts</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>The Golden Age of Privacy?</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Unobtrusive Assessment of Students' Emotional Engagement during Lectures Using Electrodermal Activity Sensors</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Unobtrusive Recognition of Socio-Affective Dynamics During Human Interactions Using Wearables and Smartphones</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>UPA'18: 3rd International Workshop on Ubiquitous Personal Assistance</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Using Students' Physiological Synchrony to Quantify the Classroom Emotional Climate</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>Whisper: Fast Flooding for Low-Power Wireless Networks</h1> <p>Mon, 01 Jan 2018 00:00:00 +0000</p> </article> <article> <h1>From Sharing to Caring: Examining Socio-Technical Aspects of the Collaborative Economy</h1> <p>Wed, 01 Mar 2017 00:00:00 +0000</p> <p><strong>Funding:</strong> COST · March 2017 – March 2021</p> <p>The terms “Sharing Economy” or “Collaborative Economy” have been commonly used in recent years to refer to a proliferation of initiatives, business models and forms of work. The main objective of this action is to develop a European network of actors (including scholars, practitioners, communities and policy makers) focusing on the development of collaborative economy models and platforms and on social and technological implications of the collaborative economy through a practice-focused approach.</p> <h2 id="objectives">Objectives</h2> <ul> <li>To develop deeper understanding of the collaborative economy phenomenon by studying sociotechnical systems and human practices across local, regional, national and international initiatives</li> <li>To discuss and critique current discourse elements on the collaborative economy, proposing richer definitions and characterization</li> <li>To formulate a European research agenda for socio-technical aspects, including technological platform design, legal, ethical and financial implications</li> <li>To articulate a European research perspective aligned with EU values of social innovation and Europe 2020 strategy objectives for a smart, sustainable and inclusive economy</li> </ul> </article> <article> <h1>Amplifying human cognition: bridging the cognitive gap between human and machine</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Audience monitor: an open source tool for tracking audience mobility in front of pervasive displays</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Augmenting skiers' peripheral perception</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Can Less be More?: Contrasting Limited, Unlimited, and Automatic Picture Capture for Augmenting Memory Recall</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Editorial</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Energy-aware coflow and antenna scheduling for hybrid server-centric data center networks</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Every Byte Counts: Selective Prefetching for Mobile Applications</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>From Travel Plans to Magic Wands: A Cross-Domain Study of Practices and Privacy Requirements for Sharing Emerging Types of Online Content</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>In-classroom self-tracking for teachers and students: preliminary findings from a pilot study</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Investigating the effect of user profile during training for BCI-based games</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Keep the Beat: On-The-Fly Clock Offset Compensation for Synchronous Transmissions in Low-Power Networks</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Measuring the Media Effects of a Tourism-Related Virtual Reality Experience Using Biophysical Data</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Proceedings of the 6th ACM International Symposium on Pervasive Displays, PerDis 2017, Lugano, Switzerland, June 7-9, 2017</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Promoting CARE: Changes via Awareness, Recognition and Experience</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Secure and energy-efficient interconnects for board-to-board communication</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Soil, Rock, and Snow: On Designing for Information Sharing in Outdoor Sports</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Understanding the potential of human-machine crowdsourcing for weather data</h1> <p>Sun, 01 Jan 2017 00:00:00 +0000</p> </article> <article> <h1>Uncovering Privacy Concerns for Public Display Personalisation</h1> <p>Fri, 01 Jul 2016 00:00:00 +0000</p> <p><strong>Funding:</strong> Google Faculty Grant · July 2016 – June 2017</p> <p>The focus of this project is to understand personalization and privacy needs of actual public display users. The project will deploy a rich set of personalizable applications on an existing public display network deployed across a university campus, and distribute mobile phone applications (apps) to participants that let them personalize displays with various information feeds (e.g., bus schedules, Google+ feeds) automatically, i.e., as soon as the phone comes close to a display without manual intervention.</p> </article> <article> <h1>\"Fun place within a serious space\": stimulating community interaction and engagement through situated snapshots in a university setting</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>A good balance of costs and benefits: convincing a university administration to support the installation of an interactive multi-application display system on campus</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Collecting Shared Experiences through Lifelogging: Lessons Learned</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Collocated Interaction: New Challenges in 'Same Time, Same Place' Research</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Concurrent Transmissions for Communication Protocols in the Internet of Things</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Design and evaluation of a wearable AR system for sharing personalized content on ski resort maps</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Developing a Networked Public Display System</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Evaluating Engagement in Reading: Comparing Children and Adult Assessors</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Experiences from developing networked public display applications on 3rd party infrastructures</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Follow My Recommendations: A Personalized Privacy Assistant for Mobile App Permissions</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>How Many Roles Can Children Play?</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Longitudinal, cross-site and \"in the Wild\": a study of public displays user communities' situated snapshots</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Measuring the effect of cued recall on work meetings</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Mobile first?: understanding device usage practices in novel content sharing services</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Multimedia Memory Cues for Augmenting Human Memory</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Pervasive Displays [Guest editors' introduction]</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Proceedings of the 5th ACM International Symposium on Pervasive Displays, PerDis 2016, Oulu, Finland, June 20 - 22, 2016</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Quantifying the Uncertainty of Next-Place Predictions</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Remembering through lifelogging: A survey of human memory augmentation</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>s-Helmet: A Ski Helmet for Augmenting Peripheral Perception</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Secure Personal Memory-Sharing with Co-located People and Places</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>SkiAR: Wearable Augmented Reality System for Sharing Personalized Content on Ski Resort Maps</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>The Creation and Analysis of a Website Privacy Policy Corpus</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>The TRiTS model: teacher roles in introducing digital technology into a school curriculum</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>Towards understanding digital sharing practices in outdoor sports</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>UbiBeam++: Augmenting Interactive Projection with Head-Mounted Displays</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>WAHM 2016: 3rd workshop on ubiquitous technologies for augmenting the human mind</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>WeatherUSI: User-Based Weather Crowdsourcing on Public Displays</h1> <p>Fri, 01 Jan 2016 00:00:00 +0000</p> </article> <article> <h1>PersonAAL: Improving Quality of Life and Remote Care</h1> <p>Thu, 01 Oct 2015 00:00:00 +0000</p> <p><strong>Funding:</strong> EU AAL Programme · October 2015 – September 2018</p> <p>The PersonAAL project aims at extending the time older people can live in their home environment by increasing their autonomy and assisting them in carrying out activities of daily living by means of intelligent and intuitive web applications enabling users to receive personalized and context dependent assistance directly in their own homes with the goal to improve quality of life and decrease healthcare delivery cost.</p> <p>The initiative also seeks to provide older adults with useful and usable means for better managing their lifestyle by increasing awareness and control through tailored information delivered intuitively.</p> </article> <article> <h1>A Metric for Adaptive Routing on Trustworthy Paths</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>A population model for predicting human mobility</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>A step towards a protocol-independent measurement framework for dynamic networks</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Air Tandem: A Collaborative Bodily Game Exploring Interpersonal Synchronization</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>An analysis of the YouNow live streaming platform</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Automated customer segmentation based on smart meter data with temperature and daylight sensitivity</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Beyond the Individual: The Contextual Wheel of Practice as a Research Framework for Sustainable HCI</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Capture the Moment: \"In the Wild\" Longitudinal Case Study of Situated Snapshots Captured Through an Urban Screen in a Community Setting</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Construction on Display: Exploring the Use of Public Displays on Construction Sites</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>CulTech2015: cultural diversity and technology design</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Digitally Enhanced Reality</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>EmoSnaps: a mobile application for emotion recall from facial expressions</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>eMotion: retrospective in-car user experience evaluation</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Everyday commuting: prediction, actual experience and recall of anger and frustration in the car</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>From Fishing Trips in Alaska to Cherry Blossoms in Japan: Martin Källström's Take on Wearable Cameras</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>From Start to Finish: Understanding Group Sharing Behavior in a Backcountry Skiing Community</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Household occupancy monitoring using electricity meters</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Privacy and Security [Guest editors' introduction]</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, UbiComp/ISWC Adjunct 2015, Osaka, Japan, September 7-11, 2015</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015, Osaka, Japan, September 7-11, 2015</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Proceedings of the 2015 ACM International Symposium on Wearable Computers, ISWC 2015, Osaka, Japan, September 7-11, 2015</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Public displays for monitoring and improving community wellbeing</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Public Photos, Private Concerns: Uncovering Privacy Concerns of User Generated Content Created Through Networked Public Displays</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>PulseCam: Biophysically Driven Life Logging</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Re-Live the Moment: Visualizing run experiences to motivate future exercises</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Security and Privacy Implications of Pervasive Memory Augmentation</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>ShadowTouch: a Multi-user Application Selection Interface for Interactive Public Displays</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Supporting creativity in designing story authoring tools</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Supporting interactivity on a ski lift</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>The UbiDCC Data Set: Collecting Wi-Fi and Bluetooth Scans During a Large-scale Conference</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Towards enabling concurrent transmissions in heterogeneous networks</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Understanding Display Blindness in Future Display Deployments</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Understanding future challenges for networked public display systems in community settings</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Understanding usage control requirements in pervasive memory augmentation systems</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Weather with you: evaluating report reliability in weather crowdsourcing</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>WeatherUSI: Crowdsourcing Weather Experience on Public Displays</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>Workshop on Mobile Cognition: Using Mobile Devices to Enhance Human Cognition</h1> <p>Thu, 01 Jan 2015 00:00:00 +0000</p> </article> <article> <h1>SHARING21: Future Digital Sharing Interfaces</h1> <p>Wed, 01 Oct 2014 00:00:00 +0000</p> <p><strong>Funding:</strong> SNF · October 2014 – September 2018</p> <p>The SHARING21 project aims at understanding current practices of sharing personal content, both explicitly (e.g., with friends and family) and implicitly (e.g., with service providers through the use of their services), and to uncover corresponding end-user concerns across a wide variety of content, in order to map the design space for future user interfaces that empower end-users to stay in control of their shared data in a world full of autonomous IoT devices.</p> </article> <article> <h1>CYBERPARKS: ICT and Public Spaces</h1> <p>Tue, 01 Apr 2014 00:00:00 +0000</p> <p><strong>Funding:</strong> COST Action TU1306 · April 2014 – March 2018</p> <p>The Action deals with opportunities and risks ICTs offer to the user, via the appreciation, design and usage of public spaces. It exploits the benefits of interweaving a green experience with digital engagement via sharing knowledge, experiences and ideas, and analysing public spaces.</p> </article> <article> <h1>Atmos: a hybrid crowdsourcing approach to weather estimation</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Community is the message: viewing networked public displays through McLuhan's lens of figure and ground</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Déjà vu - technologies that make new situations look familiar: position paper</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Demo: An Ecosystem for Open Display Networks</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Design guidelines for more engaging electronic books: insights from a cooperative inquiry study</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Fiabot!: design and evaluation of a mobile storytelling application for schools</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Hacking HCI3P: second workshop on human computer interaction in third places</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>How the availability of Wi-Fi connections influences the use of mobile devices</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>I can wait a minute: uncovering the optimal delay time for pre-moderated user-generated content on public displays</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Integrating digital storytelling in formal educational settings: a design framework</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Integrating interactive applications with digital signage: Towards a scheduling framework for pervasive displays</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Lifelogging: You're Wearing a Camera?</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Locator: a self-adaptive framework for the recognition of relevant places</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Monitoring the impact of stress on the sleep patterns of pilgrims using wearable sensors</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>My good old kodak: understanding the impact of having only 24 pictures to take</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Personal informatics for non-geeks: lessons learned from ordinary people</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Personal or social?: designing mobile interactions for co-located interaction</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Personalisation and privacy in future pervasive display networks</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Pervasive Displays 2014</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Rethinking Third Places: Contemporary Design With Technology</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Scheduling Interactive and Concurrently Running Applications in Pervasive Display Networks</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Smart heating control with occupancy prediction: how much can one save?</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Tandem Browsing Toolkit: Distributed Multi-Display Interfaces with Web Technologies</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>The ECO data set and the performance of non-intrusive load monitoring algorithms</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Trust and privacy in mobile experience sharing: future challenges and avenues for research</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>UbiComp 2013</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>Understanding aspects of pilgrimage using social networks derived from smartphones</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>UniDisplay - A research prototype to investigate expectations towards public display applications</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>WAHM 2014: workshop on ubiquitous technologies for augmenting the human mind</h1> <p>Wed, 01 Jan 2014 00:00:00 +0000</p> </article> <article> <h1>RECALL: Enhanced Human Memory</h1> <p>Fri, 01 Nov 2013 00:00:00 +0000</p> <p><strong>Funding:</strong> EU FP7, FET Open, grant 612933 · Nov 2013 – Oct 2016</p> <p>RECALL aims to re-think and re-define the notion of memory augmentation. The project will harness recent developments in capture technology and information retrieval and develop a new paradigm for memory augmentation technologies that are technically feasible, desired by users, and beneficial to society.</p> <p>The initiative combines technological interventions with basic research questions in memory psychology to advance memory augmentation from niche clinical use to mainstream adoption, developing novel capture systems and feedback mechanisms through displays and mobile devices.</p> <h2 id="objectives">Objectives</h2> <ul> <li>Automate the acquisition of personal memories</li> <li>Investigate how technological feedback can enhance memory retention and attenuation</li> </ul> </article> <article> <h1>'My Life, Shared' - Trust and Privacy in the Age of Ubiquitous Experience Sharing (Dagstuhl Seminar 13312)</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>A practical framework for ethics: the PD-net approach to supporting ethics compliance in public display studies</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>A site properties assessment framework for wireless sensor networks</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Automatic socio-economic classification of households using electricity consumption data</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Back-of-device authentication on smartphones</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Connecting Wireless Sensor Networks to the Robot Operating System</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Control and scheduling interface for public displays</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Design Considerations for Application Selection and Control in Multi-user Public Displays</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Design recommendations for the development of a digital storytelling mobile application</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Flexible development of variable software features for mobile business applications</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>For some eyes only: protecting online information sharing</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>How long are you staying?: predicting residence time from human mobility traces</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Interactive technologies that enhance children's creativity</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Key challenges in application and content scheduling for Open Pervasive Display Networks</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Modelling the role of teachers in introducing portable technology to the school curriculum</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Moment Machine: Opportunities and Challenges of Posting Situated Snapshots onto Networked Public Displays</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Occupancy Detection from Electricity Consumption Data</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>On the selection of testbeds for the evaluation of sensor network protocols and applications</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>On the use of instantaneous entropy to measure the momentary predictability of human mobility</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>P-LAYERS - A Layered Framework Addressing the Multifaceted Issues Facing Community-Supporting Public Display Deployments</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Participatory Design for Cultural Representation: A Cultural Transparency Perspective</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Privacy By Design</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Social networked displays: integrating networked public displays with social media</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>SOFTec 2013: second workshop on computer mediated social offline interactions</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Tethered or free to roam: the design space of limiting content access on community displays</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp '13, Zurich, Switzerland, September 8-12, 2013</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp '13, Zurich, Switzerland, September 8-12, 2013 - Adjunct Publication</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>The influence of temporal and spatial features on the performance of next-place prediction algorithms</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>The SITA principle for location privacy - Conceptual model and architecture</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Towards the benchmarking of ultra-low latency communication protocols for wireless sensor and actuator networks</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>Using unlabeled Wi-Fi scan data to discover occupancy patterns of private households</h1> <p>Tue, 01 Jan 2013 00:00:00 +0000</p> </article> <article> <h1>APD: Attentive Public Displays</h1> <p>Tue, 01 May 2012 00:00:00 +0000</p> <p><strong>Funding:</strong> Hasler Foundation · May 2012 – Apr 2016</p> <p>The goal of the project is to create a system of attentive displays deployed in a crowded place that are able to grasp the situational context of its viewers and use this information to adapt their behavior.</p> <p>The system employs 3D sensors for depth imaging and computer vision algorithms to infer crowd behavioral patterns, then adapts display behavior accordingly.</p> </article> <article> <h1>2012 IEEE International Conference on Pervasive Computing and Communications, Lugano, Switzerland, March 19-23, 2012</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>A privacy-by-design approach to location sharing</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>CBFR: Bloom filter routing with gradual forgetting for tree-structured wireless sensor networks with mobile nodes</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>Demo: using mobile devices to personalize pervasive displays</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>Designing \"interacting places\" for a student community using a communicative ecology approach</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>How to evaluate public displays</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>Improving device-level electricity consumption breakdowns in private households using ON/OFF events</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>Interacting places - A framework for promoting community interaction and place awareness through public displays</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>Open Display Networks: A Communications Medium for the 21st Century</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>Selection and Control of Applications in Pervasive Displays</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>The interacting places framework: conceptualizing public display applications that promote community interaction and place awareness</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>Towards automatic classification of private households using electricity consumption data</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>Understanding reading experience to inform the design of ebooks for children</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>Using Extracted Behavioral Features to Improve Privacy for Shared Route Tracks</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>Using mobile devices to personalize pervasive displays</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>Using public displays to stimulate passive engagement, active engagement, and discovery in public spaces</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>Workshop on Computer Mediated Social Offline Interactions (SOFTec 2012)</h1> <p>Sun, 01 Jan 2012 00:00:00 +0000</p> </article> <article> <h1>SAPIENT: Supporting Fundamental Rights, Privacy and Ethics in Surveillance Technologies</h1> <p>Tue, 01 Mar 2011 00:00:00 +0000</p> <p><strong>Funding:</strong> EU FP7, SEC · Mar 2011 – Jun 2014</p> <p>SAPIENT is a 40-month Collaborative Project which aims to specify how and when smart surveillance should be used (or not) and its characteristics to be effective and scalable to rapidly adapt to changing situations.</p> <p>The project will provide the Commission, data protection authorities and other stakeholders with a set of criteria for data protection and integrity that could be used to verify that surveillance systems and the sharing of information respect the privacy of citizens.</p> <h2 id="objectives">Objectives</h2> <ul> <li>Establish guidelines for smart surveillance deployment</li> <li>Create criteria for data protection and integrity verification in surveillance systems</li> <li>Ensure privacy respect in information-sharing practices</li> </ul> </article> <article> <h1>CATS: using scenario dramatization to rapidly design public displays for stimulating community interaction</h1> <p>Sat, 01 Jan 2011 00:00:00 +0000</p> </article> <article> <h1>Connecting things to the web using programmable low-power WiFi modules</h1> <p>Sat, 01 Jan 2011 00:00:00 +0000</p> </article> <article> <h1>Designing Shared Public Display Networks - Implications from Today's Paper-Based Notice Areas</h1> <p>Sat, 01 Jan 2011 00:00:00 +0000</p> </article> <article> <h1>DISSense: An adaptive ultralow-power communication protocol for wireless sensor networks</h1> <p>Sat, 01 Jan 2011 00:00:00 +0000</p> </article> <article> <h1>FunSquare: first experiences with autopoiesic content</h1> <p>Sat, 01 Jan 2011 00:00:00 +0000</p> </article> <article> <h1>Perception beyond the Here and Now</h1> <p>Sat, 01 Jan 2011 00:00:00 +0000</p> </article> <article> <h1>Talking to Strangers: Using Large Public Displays to Facilitate Social Interaction</h1> <p>Sat, 01 Jan 2011 00:00:00 +0000</p> </article> <article> <h1>The Impact of Network Topology on Collection Performance</h1> <p>Sat, 01 Jan 2011 00:00:00 +0000</p> </article> <article> <h1>Towards a New Classification of Location Privacy Methods in Pervasive Computing</h1> <p>Sat, 01 Jan 2011 00:00:00 +0000</p> </article> <article> <h1>Towards a New Privacy-Aware Location Sharing Platform</h1> <p>Sat, 01 Jan 2011 00:00:00 +0000</p> </article> <article> <h1>PD-NET: Towards Future Pervasive Display Networks</h1> <p>Sat, 01 May 2010 00:00:00 +0000</p> <p><strong>Funding:</strong> EU FP7, FET Open · May 2010 – Jun 2013</p> <p>The PD-NET project aims to lay the scientific foundations for a new form of communications medium with the same potential impact on society as radio, television and the Internet.</p> <p>The initiative seeks to investigate scientific challenges and emerging technologies necessary for establishing extensive networks of pervasive public displays with integrated sensors.</p> </article> <article> <h1>PALS: Privacy-Aware Location Sharing</h1> <p>Thu, 01 Apr 2010 00:00:00 +0000</p> <p><strong>Funding:</strong> SNF · Apr 2010 – Aug 2013</p> <p>Many consumers today willingly give up their current location information in exchange for location-based social services. This development poses great privacy risks. PALS aims at designing, implementing, and evaluating novel methods and tools to facilitate the privacy-aware sharing of location data with friends, strangers, and operators. Its goal is to offer a decentralized approach that supports all needed features of location sharing systems without the need to centrally collect location data.</p> </article> <article> <h1>10011 Report - Pervasive Public Displays</h1> <p>Fri, 01 Jan 2010 00:00:00 +0000</p> </article> <article> <h1>Architectural mismatch issues in identity management deployment</h1> <p>Fri, 01 Jan 2010 00:00:00 +0000</p> </article> <article> <h1>BIT - A framework and architecture for providing digital services for physical products</h1> <p>Fri, 01 Jan 2010 00:00:00 +0000</p> </article> <article> <h1>Identity Management Mismatch Challenges in the Danish Municipality Administration System</h1> <p>Fri, 01 Jan 2010 00:00:00 +0000</p> </article> <article> <h1>Influence of user perception, security needs, and social factors on device pairing method choices</h1> <p>Fri, 01 Jan 2010 00:00:00 +0000</p> </article> <article> <h1>Let the play set come alive: Supporting playful learning through the digital augmentation of a traditional toy environment</h1> <p>Fri, 01 Jan 2010 00:00:00 +0000</p> </article> <article> <h1>Pervasive Public Displays, 03.01. - 08.01.2010</h1> <p>Fri, 01 Jan 2010 00:00:00 +0000</p> </article> <article> <h1>Social networking and the risk to companies and institutions</h1> <p>Fri, 01 Jan 2010 00:00:00 +0000</p> </article> <article> <h1>Sorting out smart surveillance</h1> <p>Fri, 01 Jan 2010 00:00:00 +0000</p> </article> <article> <h1>Threads2Events: an automatic code generation approach</h1> <p>Fri, 01 Jan 2010 00:00:00 +0000</p> </article> <article> <h1>Towards understanding ATM security: a field study of real world ATM use</h1> <p>Fri, 01 Jan 2010 00:00:00 +0000</p> </article> <article> <h1>UbiComp 2010: Ubiquitous Computing, 12th International Conference, UbiComp 2010, Copenhagen, Denmark, September 26-29, 2010, Adjunct Papers Proceedings</h1> <p>Fri, 01 Jan 2010 00:00:00 +0000</p> </article> <article> <h1>UbiComp 2010: Ubiquitous Computing, 12th International Conference, UbiComp 2010, Copenhagen, Denmark, September 26-29, 2010, Proceedings</h1> <p>Fri, 01 Jan 2010 00:00:00 +0000</p> </article> <article> <h1>LiSS: Living in Surveillance Societies</h1> <p>Wed, 01 Apr 2009 00:00:00 +0000</p> <p><strong>Funding:</strong> COST · Apr 2009 – Feb 2013</p> <p>The primary objective of the Action is to increase and deepen knowledge about living and working in the surveillance age, in order to better understand the consequences and impacts of enhanced surveillance, and subsequently to make recommendations about its future governance and practice.</p> </article> <article> <h1>A survey of RFID privacy approaches</h1> <p>Thu, 01 Jan 2009 00:00:00 +0000</p> </article> <article> <h1>Adaptive random sensor selection for field reconstruction in wireless sensor networks</h1> <p>Thu, 01 Jan 2009 00:00:00 +0000</p> </article> <article> <h1>An Infrastructure for Interactive and Playful Learning in Augmented Toy Environments</h1> <p>Thu, 01 Jan 2009 00:00:00 +0000</p> </article> <article> <h1>An update on privacy in ubiquitous computing</h1> <p>Thu, 01 Jan 2009 00:00:00 +0000</p> </article> <article> <h1>Encountering SenseCam: personal recording technologies in everyday life</h1> <p>Thu, 01 Jan 2009 00:00:00 +0000</p> </article> <article> <h1>Improving Location Fingerprinting through Motion Detection and Asynchronous Interval Labeling</h1> <p>Thu, 01 Jan 2009 00:00:00 +0000</p> </article> <article> <h1>Kingdom of the Knights: evaluation of a seamlessly augmented toy environment for playful learning</h1> <p>Thu, 01 Jan 2009 00:00:00 +0000</p> </article> <article> <h1>Privacy, trust and policy-making: Challenges and responses</h1> <p>Thu, 01 Jan 2009 00:00:00 +0000</p> </article> <article> <h1>SPARK Rapid Prototyping Environment - Mobile Phone Development Made Easy</h1> <p>Thu, 01 Jan 2009 00:00:00 +0000</p> </article> <article> <h1>W41K: digitally augmenting traditional game environments</h1> <p>Thu, 01 Jan 2009 00:00:00 +0000</p> </article> <article> <h1>Building RFID-Based Augmented Dice with Perfect Recognition Rates</h1> <p>Tue, 01 Jan 2008 00:00:00 +0000</p> </article> <article> <h1>Data Collection in Wireless Sensor Networks for Noise Pollution Monitoring</h1> <p>Tue, 01 Jan 2008 00:00:00 +0000</p> </article> <article> <h1>First experiences using wireless sensor networks for noise pollution monitoring</h1> <p>Tue, 01 Jan 2008 00:00:00 +0000</p> </article> <article> <h1>RFIDice - Augmenting Tabletop Dice with RFID</h1> <p>Tue, 01 Jan 2008 00:00:00 +0000</p> </article> <article> <h1>The Internet of Things, First International Conference, IOT 2008, Zurich, Switzerland, March 26-28, 2008. Proceedings</h1> <p>Tue, 01 Jan 2008 00:00:00 +0000</p> </article> <article> <h1>Towards guidelines for designing augmented toy environments</h1> <p>Tue, 01 Jan 2008 00:00:00 +0000</p> </article> <article> <h1>Using a Movable RFID Antenna to Automatically Determine the Position and Orientation of Objects on a Tabletop</h1> <p>Tue, 01 Jan 2008 00:00:00 +0000</p> </article> <article> <h1>Adaptive model selection for time series prediction in wireless sensor networks</h1> <p>Mon, 01 Jan 2007 00:00:00 +0000</p> </article> <article> <h1>- Prototyping Sensor-Based, Location-Aware Applications in Java</h1> <p>Sun, 01 Jan 2006 00:00:00 +0000</p> </article> <article> <h1>A Classification Scheme of Lifelogging User Interfaces</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed September 2015 <strong>Student:</strong> Daniel Cardozo</p> <p>Nowadays lifelogging is increasingly becoming part of people’s everyday life. The continuous advancements in different sensing and information technologies are facilitating the application of lifelogging to several domains. These domains describe scenarios where human memory can be supported to recall information of past experiences. However, one of the main challenges of lifelogging concerns how to access information when this is located in complex and vast depositories of data called lifelogs. Without the appropriate user interface to interact with lifelogs, the retrieval and visualization of information becomes overwhelming. What’s more, the different applications of lifelogging pose diverse contexts and particular needs of information.</p> <p>This thesis regards the construction of a classification scheme of lifelogging user interfaces. The approach of reference to the construction of the scheme was Faceted Analysis. This approach permitted to integrate different perspectives from which lifelogging user interfaces can be described. The domain where lifelogging is applied was defined as the main classification feature. The rest of features were derived from the combination of design principles of lifelogging systems and design goals for presentation and visualization of lifelogs. The criterion to define and develop these features was the scope of lifelogging user interfaces, which was defined in terms of their boundaries when supporting the synergy between human memory and lifelog. The result of this thesis was a classification scheme composed of seven features that allow characterizing lifelogging user interfaces for different application domains.</p> <p>For more information contact: </p> </article> <article> <h1>A Mobile Privacy Assistant for Sharing Personal Memories</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Draft <strong>Student:</strong></p> <p>Recent improvements in mobile technologies (better and richer sensors, cheaper and faster storage) allow us to record different aspects of our daily activities, e.g. work meetings, discussions during coffee breaks, family moments, etc. Within the EU research project we investigated how such data captured from mobile and wearable devices can serve as “memory cues” to help individuals remember certain past events – e.g., a set of images can help one remember an evening with friends or pictures showing white board content can help one remember what was discussed in the last work meeting.</p> <p>Having already developed the RECALL system for collecting and storing such personal memories, now we are interested to understand how such data can be safely shared with others without violating users’ privacy.</p> <p>The goal of this project is to develop a mobile application for allowing users to control (at some extent) how such data is shared with others. For example, one can specify that they want to share all their work-related data with work colleagues, or data captured during last hiking trip with close friends, etc. We envision the following three functionalities/features of the Mobile Privacy Assistant app:</p> <ol> <li>an interface for allowing users to specify what parts of their captured data can be shared with which people and under what conditions;</li> <li>create a model for storing users privacy specifications from step (1) – using e.g. JSON or XML – such that they can be easily processed by other components of our RECALL system</li> <li>since this app will not have any list of friends that a user can share data with, we envision to start out by connecting it with the user’s social network accounts (e.g. Facebook, Linkedin) and use their APIs to retrieve user’s social connections together with their classification (e.g. “friends”, “family”, “colleagues”).</li> </ol> <p>The app development will be done in Android, hence it is preferred if the student has already some basic Android programming skills (if not then willingness to quickly learn it).</p> <p>In case for a master thesis, the student will first perform a literature investigation (on the different privacy interfaces for controlling data sharing) and come up with a set of requirements that can be then added to the Android app.</p> <p>For more information contact: <strong>No items found</strong></p> </article> <article> <h1>A Novel Approach Using Bilateral Data Fusion for EDA Data Classification</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Available</p> <p>This thesis addresses the impact of lateralization on Electrodermal Activity (EDA) sensors in wearable devices. Lateralization, influenced by brain hemisphere activation, affects the accuracy of EDA readings based on the device’s placement on a specific body side. Despite recent studies highlighting this issue, there is limited exploration of the potential benefits of using EDA devices on both sides simultaneously. The research aims to fill this gap by investigating how leveraging data from both sides concurrently can enhance classifier accuracy through machine learning. The focus is on datasets in the lab, with implications for medical-grade applications affected by lateralization. Success in demonstrating improved accuracy may revolutionize the field, particularly in sensitive medical tasks, offering more reliable predictions for tasks impacted by lateralization.</p> <p>For more information contact: , </p> </article> <article> <h1>A tangible interface for controlling capture and sharing of personal data</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed September 2017 <strong>Student:</strong> Matteo Pontiggia</p> <p>The goal of this project is to design and prototype a tangible device for controlling the capture and exchange of “lifelog” data – e.g., photos captured by a wearable camera, or audio recorded by a wrist-worn audio-capture device – with other, co-located peers. The overall vision of lifelogging is that captured experiences can help us remember better our past, and thus improve our cognitive skills and overall memory performance. By supporting the dynamic exchange of such captured data when co-located with other (e.g., in a meeting) we can also have access to our own experiences from someone else’s vantage point. The to-be-designed device would allow one to control both the capture and the subsequent sharing of experiences not only in a tangible way, but also act as a social marker that would allow all parties involved in a meeting to understand when their discussions would be captured and shared. For example, recording would only take place if the device is placed on a table; exchange with others would only proceed if other devices are placed next to it; shaking the device would delete the last minute of captured data, etc. The first stage of this project will study the different requirements for a privacy friendly data recording device. The second part will be about applying those requirements and constructing a first prototype of a physical recording gadget. Willingness to learn advanced prototyping skills (3D printing, embedded systems development) is required, prior actual experience with embedded systems programming (Arduino or Raspberry Pi) is a plus.</p> <p>For more information contact: </p> </article> <article> <h1>A Toolchain for Visual Memory</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> UROP <strong>Status:</strong> Completed August 2015 <strong>Student:</strong> Lucas Pennati</p> <p>Within the EU research project RECALL we are investigating how captured images can serve as “memory cues” to help individuals remember certain past events (e.g., meetings, or an evening with friends). Existing wearable “lifelogging” devices allow for the automated capture of photographs throughout the day, e.g., every 30 seconds. However, in order to use these images as memory cues, we need to have an idea of what’s “on” these pictures. This UROP project will investigate opportunities to automatically classify such images along various dimensions, e.g., the amount of blurriness that they contain, their light levels, but also higher-level contextual information such as the number of people (faces) in the image, or if it contains a cup, etc. The student will develop a modular toolbox using OpenCV that can run an arbitrary set of sensors over a folder of images in order to extract contextual information from these images.</p> <p>For more information contact: </p> </article> <article> <h1>A Visualization Tool for Smartphone-acquired Activity Traces</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Draft <strong>Student:</strong></p> <p>Every day we spend an increasing amount of time interacting with our smartphones, while chatting, checking e-mails or simply browsing the web. Even if smartphone usage is highly segmented, it nevertheless results in a considerable volume of personal data, such us call, text, app and GPS logs, pictures, videos etc. This combined with the strong habit-forming aspect of mobile devices can reveal precious insights of one’s daily life and habits. However, what is particularly intriguing is the potential of such information to support memory recall. For example, it has been found that selfies hold a significant amount of visual cues that can lead to richer recollections. This bachelor project should create a smartphone app to collect and visualize one’s day through the lens of smartphone activity, e.g., GPS traces, SMS and call logs, slide-ins, USB dockings, app invocations, etc.). Aggregated data should be clustered and visualized in different ways (e.g. along a timeline). Basic Android programming skills required, strong Web programming skills an asset. Strong interest in visualization a plus. Data collection will be done using an existing mobile phone collection framework such as funf.</p> <p>For more information contact: <strong>No items found</strong></p> </article> <article> <h1>A Web-based Application for Visualizing Personal Memories</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Completed February 2018 <strong>Student:</strong> Federico Pfahler</p> <p>Using wearable and mobile devices people now can fully log their life in pictures, audio or even video recordings. Reviewing such “life-logs” can support users in recollecting memories of past events and potentially improve their overall cognitive abilities. In this project, we focus on visual logs, namely images, as this type of data offers the strongest influence in recalling past memories. In addition to first-person view images that one can capture with their wearable camera (e.g., Narrative Clip or Microsoft SenseCam), users can also obtain first-person view images captured by others or even third-person view images recorded by fixed infrastructure cameras.</p> <p>Once such data is captured, they have to be displayed to the users in useful and attractive visualizations. The aim of this project is in designing and developing an interface for visualizing the pool of visual traces as captured by users’ wearable cameras as well as images obtained from other sources. Starting from a pre-designed mock-up and the results from a usability test of the mock-up, the student will develop a web-based application that visualizes captured images in a compact but still useful way. The application should follow a responsive design, i.e., render well in both big sized screens (e.g., laptop and desktop) and smaller variants (tablets and phones). Moreover, the application should be integrated with the back-end database that stores captured images. Solid web-development skills required, while strong interest in data visualization is a plus.</p> <p>For more information contact: </p> </article> <article> <h1>Activity recognition using ear-worn sensors and machine learning</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> UROP <strong>Status:</strong> Completed September 2022 <strong>Student:</strong> Davide Casnici</p> <p>Working toward context recognition for human memory augmentation systems, we developed Human Activity Recognition (HAR) machine learning pipeline using data from earable devices. In this paper, we analyze how the earables can be used to detect different types of verbal (e.g. speaking) and non-verbal (e.g. head shaking) interactions and other activities (e.g. standing still). We collected a dataset of ear-located IMU sensors from 30 participants and compared classical machine learning methods with state-of-the-art deep learning models to classify the raw data into a set of predefined activities. We explored how different parameters — including sampling frequency, window size, and type of sensors — influence the models’ performance. The best-performing model in the leave-one-subject-out evaluation, a spectro-temporal residual network (STResNet), achieved an F1-score of 0.69 (in recognising seven different activities: nodding, speaking, eating, staying, head shaking, walking, and walking while speaking.</p> <p>For more information contact: </p> </article> <article> <h1>Affect and Learning in the LAUREATE Dataset</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed March 2024 <strong>Student:</strong> Enqi Fu</p> <p>Affective states and cognition share a close relationship, influencing each other in several ways. In particular, our affect, i.e. our feelings and mood, impacts our perception of the world, our memory, and our decisions. This relationship also influences the cognitive process of learning. Working towards personalised student interventions for improving learning and mental well-being, we conducted a longitudinal study during a university semester. The resulting dataset, called LAUREATE (the Longitudinal multimodAl student expeRience datasEt for AffecT and mEmory research), consists of daily self-reports from students, audiovisual material from lectures, physiological signals from students and lecturers, students’ grades, and more. In this work, we present an initial analysis of lecturers’ and students’ experiences during lectures in terms of self-reported affect, especially positive activation (PA), negative activation (NA), valence (VA), and engagement.</p> <p>For more information contact: </p> </article> <article> <h1>Analysis of Human Memory and Physiological Signals</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Completed June 2023 <strong>Student:</strong> Andrea Prato</p> <p>Memory Augmentation Systems could be capable of selecting specific to-be-remembered events during an experience, e.g., by detecting the individual as distracted or unengaged. Such information could then be used to generate memory cues for the specific periods during which the user was distracted. One way used to detect the cognitive state of users, e.g. if the user is focused on a task, is through physiological signals, like electrodermal activity (EDA) and interbeat interval (IBI). The goal of this thesis is to evaluate the potential of physiological signals captured by a wrist-worn device to detect the cognitive load of individuals presented with a memory task. The final purpose is to use this work as an additional input in future Memory Augmentation Systems. The specific tasks of the project are: (i) Overview methods for physiological signal processing and review memory augmentation literature; (ii) Develop the necessary code for processing the EDA and IBI data; (iii) Design and run an experiment to test the setup and pipeline.</p> <p>For more information contact: </p> </article> <article> <h1>Attentive Public Displays: Understanding Audience Moving Patterns in front of Interactive Public Displays</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed September 2016 <strong>Student:</strong> Cristian Gomez Mora</p> <p>Public displays are used in many public places such as city squares, universities, malls, libraries or bus stations. The public display content may vary depending on their purpose and the type of presentation. However, it is not always clear how public displays and their content influence everyday activities in the environment where they are deployed; how people behave, move around, and use the displays and their content; and how effective is the display content according to requirements of different stakeholders.</p> <p>This thesis presents Attentive Public Displays (APD) – a tool that captures movements of people in front of public displays using depth sensor data and provides visualization and descriptive statistics of the audience movements. First, the thesis shows the design and implementation of the tool. It describes the software components and algorithms for capturing depth information, detecting people and their movements from the collected depth data, and representing and storing the movements in a database. The database entries containing people movements are further structured in a more understandable way, using additional filtering and data mining techniques to present the paths in a simple to use Web interface. Then, the thesis presents the evaluation of the performance of the developed tool based on three metrics: Accuracy, Precision and Sensitivity. The evaluation of the tool is based on 15 captured videos, each of 5 minutes duration. The number of people in the videos is manually counted and contrasted with the data captured by the tool. In total the tool correctly detected 211 people out of 226 people counted manually in front of the display. The average accuracy of the tool is 90.53%, average precision is 95.97%, and sensitivity is 94.21%. Then, the thesis presents a real-world deployment of the tool in front of a public display located in a busy hall in front of the university canteen, where the tool collected 6 weeks of data. Finally, the thesis discusses the limitations of the tool and present possible improvements.</p> <p> </p> <p>For more information contact: </p> </article> <article> <h1>Business Intelligence</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p>Business Intelligence is a course taught within the . See the USI Search link below for more information.</p> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) Business Intelligence 2016 Spring - - Business Intelligence 2015 Spring - -</p> <p>USI Search Directory: </p> </article> <article> <h1>Co-Located AR Based Display Sharing</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> UROP <strong>Status:</strong> Completed August 2015 <strong>Student:</strong> Alexander North</p> <p>Winter sports like skiing and snowboarding are often group activities. Groups of skiers and snowboarders traditionally use folded paper maps or board-mounted larger-scale maps near ski lifts to aid decision making: which slope to take next, where to have lunch, or what hazards to avoid when going off-piste. To enrich those static maps with personal content (e.g., pictures, prior routes taken, or hazards encountered), we developed a wearable augmented reality system that allows groups of skiers and snowboarders to share such content in-situ on a printed resort map while on the slope.</p> <p>For more information contact: </p> </article> <article> <h1>Collaborative Economy Practices and Communities in Switzerland – A Case Study</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> UROP <strong>Status:</strong> Completed June 2018 <strong>Student:</strong> Masako Kitazaki</p> <p>The general principle of “collaborative consumption” enables the effective and efficient coordination, acquisition, distribution, and sharing of many kind of different resources, e.g., vehicles, housing, or fertile land. Apart from the well-known for-profit sharing services such as Airbnb, Uber, and TaskRabbit, an increasing amount of community groups and organizations have established not-for-profit cooperatives that often prioritize environmental, social, and cultural values within their local communities.</p> <p>The goals of the master project are (1) to conduct an empirical research study in the form of in-depth interviews and observations in both commercial and non-for-profit organizations (e.g. in the context of sharing personal artifacts and/or bike sharing); and (2) to compare and contrast sharing practices throughout these services and provide a comprehensive interpretation of the results. Particularly, we would be interested in utilizing practice-based approaches (e.g. Shove’s theory of social practice) within the data analysis. This project requires strong analytical skills and a willingness to learn about a novel and emerging research field. Experience with empirical research is a plus (e.g., ethnography, seminar work in human-computer interaction) though supervising guidance is available. Proficiency in any of the Swiss official languages (mostly German, French, or Italian) is an asset.</p> <p>For more information contact: </p> </article> <article> <h1>Computer Architecture</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p>The overall goal of this course is to understand the logical structure of a modern computer, from its basic building blocks to its more complex components. This knowledge is intended to provide the basis for system design and performance analysis, both for hardware and software systems. The concepts learned in this course are also foundational elements for the design of compilers and operating systems.</p> <h5 id="contents">Contents</h5> <p>Computer systems are a combination of their hardware and software. Whether you intend to design the next breed of microprocessor, the next Java compiler, or the greatest C++ application, a computer scientist is best served by understanding what is going on under the hood of the computer. Why? Simply put, knowing how a program is translated into the electronics allows it to be more efficiently written. This course will address issues such as digital logic, machine representation of data, basic CPU and instruction-set architecture, memory organization, I/O interfaces, and architecture alternatives.</p> <h5 id="teaching-mode">Teaching Mode</h5> <p>This course consists of a theory and a laboratory component. Both components involve classroom sessions and significant amounts of homework. Students are expected to read the assigned readings before class. During theory sessions we discuss remaining questions and problems. The homework assignments of the laboratory component complement the theory and allow students to practice and deepen their skills by solving well-focused problems. During the weekly laboratory sessions we introduce and discuss upcoming homework assignments and we review past assignments to clarify possible misunderstandings. Students can benefit most from this course if they thoroughly prepare for and actively participate in each session.</p> <h5 id="references">References</h5> <p>Andrew S. Tanenbaum, Structured Computer Organization, 6th Edition (required), Prentice Hall, 2012, ISBN: 978-0132916523.</p> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) Computer Architecture 2022 Fall Marc Langheinrich Matías Laporte Computer Architecture 2019 Fall Marc Langheinrich Ilaria Scarabottolo | Matías Laporte Computer Architecture 2018 Fall Marc Langheinrich Lorenzo Ferretti | Agon Bexheti Computer Architecture 2017 Fall Marc Langheinrich Enrique Fynn | Pietro Bressana Computer Architecture 2016 Fall Marc Langheinrich Anna Marchenko | Diego Ulisse Pizzagalli Computer Architecture 2015 Fall Giovanni Ansaloni Agon Bexheti Computer Architecture 2014 Fall Marc Langheinrich Agon Bexheti Computer Architecture 2013 Fall Marc Langheinrich Michele Papalini | Leandro Pacheco Computer Architecture 2012 Fall Marc Langheinrich Grigory Fedyukovich | Valentina Poletti Computer Architecture 2011 Fall Marc Langheinrich Marcello Paolo Scipioni Computer Architecture 2010 Fall Marc Langheinrich Ivan Elhart Computer Architecture 2009 Fall Marc Langheinrich Jochen Wuttke Computer Architecture 2008 Fall Marc Langheinrich Konstantin Rubinov</p> <p>USI Search Directory: </p> </article> <article> <h1>Computer Networking</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p>Goal of the course is to provide students with theoretical and practical knowledge of human-centered design of interfaces for computing systems.</p> <p>The course starts with an introduction to ethics that investigates the many ways and venues information and communication technologies can be used maliciously. The course moves on to introduce the concept of user-centred design to produce usable, useful and used tools. The different stages from ideation to paper prototyping are covered in theory and practice.</p> <p>Teaching Mode</p> <p>Theoretical concepts will be provided through instructor-led lectures. Theory will be put to good use in practice, by letting students engage in group work to deliver a project, developed through progressive stages of design and prototyping.</p> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) Computer Networking 2023 Spring Silvia Santini Lidia Alecci | Matías Laporte Computer Networking 2022 Spring Silvia Santini Lidia Alecci | Matías Laporte Computer Networking 2021 Spring Silvia Santini Fabio Di Lauro | Matías Laporte Computer Networking 2020 Spring Antonio Carzaniga | Silvia Santini Pietro Giuseppe Bressana | Matías Laporte Computer Networking 2018 Fall Antonio Carzaniga | Silvia Santini Theodore Jepsen | Ali Fattaholmanan Computer Networking 2017 Fall Antonio Carzaniga -</p> <p>USI Search Directory: [https://search.usi.ch/en/courses/35265699/computer-networking]([types field=)</p> </article> <article> <h1>Counterfactual Explanations for Unsupervised Learning</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed <strong>Student:</strong> Aurora Spagnol</p> <p>Artificially Intelligence (AI) is slowly but surely becoming an integrated part of our daily lives. However, with decisions derived from AI systems ultimately affecting human lives (e.g., medicine and law), there is an emerging need for understanding how such decisions are made by AI methods. Furthermore, the “right to explanation” foreshadowed by the General Data Protection Regulation (GDPR) [1] challenged the Machine Learning (ML) community to build explainability into predictive models and their outputs. This paradigm shift – where predictive performance is no longer the only (and main) objective – gives rise to two distinct viewpoints. One argues that algorithmic black boxes should continue to be optimised for predictive power with explainability needs, possibly, fulfilled through post-hoc methods due to an apparent incompatibility of these two goals, thus forcing one of them to be sacrificed for the other [2]. The second standpoint disputes this trade-off as purely anecdotal and persuasively argues for building inherently transparent models, especially for high-stakes decisions [3].</p> <p>Counterfactuals are an explainability approach uniquely positioned in this space as they can be generated post-hoc but remain truthful with respect to the underlying black box (i.e., exhibit full fidelity). They enable ML users to understand what the output of a predictive model would have been had the instance in question changed in a particular way. This type of counterfactual analysis helps the explainees to simulate certain aspects of the ML model, thus improving its interpretability [4]. Notably, evidence from psychology and cognitive sciences suggests that people use counterfactual reasoning daily to analyse what could have happened had they acted differently [5].</p> <p>The goal of this project is to explore existing XAI methods and possibly develop a new method that would improve existing solutions, with a focus on counterfactual-based XAI.</p> <p>Specifically, this Master thesis has four main tasks:</p> <ol> <li>Explore the related scientific studies (papers) on: Counterfactual explanations for unsupervised models; and counterfactual explanations in wearable sensor data.</li> <li>Preprocessing and exploratory data analysis over the LAUREATE dataset [6].</li> <li>Develop Unsupervised BayCon – an unsupervised version of the supervised counterfactual generator BayCon [6]</li> <li>Perform a small user-study with 5-10 participants to evaluate the quality of the explanations generated by Unsupervised BayCon</li> </ol> <h5 id="references">References:</h5> <ol> <li>Goodman, S.Flaxman, European union regulations on algorithmic decision-making and a “right to explanation”, AI Magazine 38 (3) (2017) 50–57.</li> <li> </li> <li>Rudin, C. Stop explaining black-box machine learning models for high-stakes decisions and use interpretable models instead. Nature Machine Intelligence 1, no. 5:206-215, 2019.</li> <li>Robert Hoffman, Tim Miller, Shane T. Mueller, Gary Klein, and William J. Clancey. Explaining explanation, part 4: A deep dive on deep nets. IEEE Intelligent Sys-tems 33, no. 3:87-95, 2018.</li> <li>Ruth M. J. Byrne. The rational imagination: How people create alternatives to reality. MIT Press, Cambridge, Massachusetts, 2005.</li> <li>Romashov, P., Gjoreski, M., Sokol, K., Martinez, M. V., & Langheinrich, M. BayCon: Model-agnostic Bayesian Counterfactual Generator.</li> <li>In-house dataset (30+ participants) that contain longitudinal data from physiological sensors (heart rate, sweating rate, skin temperature and acceleration). To be provided by the supervisors: </li> </ol> <p>For more information contact: </p> </article> <article> <h1>Creating an Experimental Apparatus for a Digital Health Literacy Experiment</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Completed January 2021 <strong>Student:</strong> Robert Jans</p> <p>The faculty of communication science is planning an experiment to investigate the digital health literacy of participants in the area of sleeping disorders. As part of this project, we are seeking a student to help implement the experimental apparatus that will allow the research team to record data and test its hypotheses.</p> <p>Specifically, this Bachelor projects has three main tasks</p> <ol> <li>Spidering and indexing (e.g., using Apache Solr/Lucene) a pre-defined set of website that provide sleeping disorder information as an experimental corpus</li> <li>Creating a Google-like search interface to the corpus that can be configured to selectively show/rank different sets of corpus sites, according to the experimental conditions under investigation</li> <li>Setting up an experimental environment (e.g., using the SafeExamBrowser) to conduct controlled experiments using the corpus (e.g., to prevent participants from accessing non-corpus sites) and to record salient data (e.g., search logs, clickstream)</li> </ol> <p>For more information contact: </p> </article> <article> <h1>Deep learning models for human mobility modeling</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Completed June 2021 <strong>Student:</strong> Roland Holenstein</p> <p>Human mobility modeling is widely recognized as being key to providing new services and solutions in many application domains. For example, tracking viral diseases dynamics (e.g., the COVID-19 pandemics), tracking changes in behavior which can be used to recognize impending mental health episodes, deliver more effective advertising and retail experiences, enhance security and shape the provision of urban services, etc.</p> <p>In the context of a BSc project, you will implement an existing state-of-the-art method for human mobility modeling (e.g., [5],[6]) and you will apply the method on a new dataset (e.g., [1] or [2]). Also, you will analyze the behavior of the method for a variety of parameters (e.g., dataset size, model size, etc.). Finally, you will report on the results and propose possible improvements/changes that could be applied on the method for improving the results.</p> <p>In the context of an MSc thesis or UROP project, you will: (i) research datasets and machine learning methods for human mobility modeling (e.g., [1][2][3][4][5][6]); (ii) pre-preprocess/normalize several mobility datasets to a common format (e.g., [1][2]); (iii) implement advanced deep-learning methods for human mobility modeling (e.g., [5],[6]) and compare their results on at least two datasets; (iv) summarize the results and propose future work.</p> <h5 id="references">References:</h5> <ol> <li>Mokhtar, Sonia Ben, Antoine Boutet, Louafi Bouzouina, Patrick Bonnel, Olivier Brette, Lionel Brunie, Mathieu Cunche et al. “PRIVA’MOV: Analysing Human Mobility Through Multi-Sensor Datasets.” 2017 [https://hal.inria.fr/hal-01578557/document]</li> <li>Moro, Arielle, Vaibhav Kulkarni, Pierre-Adrien Ghiringhelli, Bertil Chapuis, and Benoit Garbinato. “Breadcrumbs: A Feature Rich Mobility Dataset with Point of Interest Annotation.” arXiv preprint arXiv:1906.12322 (2019) [https://arxiv.org/pdf/1906.12322.pdf]</li> <li>Luca, Massimiliano, Gianni Barlacchi, Bruno Lepri, and Luca Pappalardo. “Deep Learning for Human Mobility: a Survey on Data and Models.” arXiv preprint arXiv:2012.02825 (2020). [https://arxiv.org/pdf/2012.02825.pdf]</li> <li>Feng, Jie, Zeyu Yang, Fengli Xu, Haisu Yu, Mudan Wang, and Yong Li. “Learning to Simulate Human Mobility.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3426-3433. 2020. [https://www.youtube.com/watch?v=sj4UCW0P6Ks&ab_channel=AssociationforComputingMachinery%28ACM%29]</li> <li>Feng, Jie, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. “Deepmove: Predicting human mobility with attentional recurrent networks.” In Proceedings of the 2018 world wide web conference, pp. 1459-1468. 2018. [https://github.com/vonfeng/DeepMove]</li> <li>Yu, Lantao, Weinan Zhang, Jun Wang, and Yong Yu. “Seqgan: Sequence generative adversarial nets with policy gradient.” In Thirty-first AAAI conference on artificial intelligence. 2017. [https://github.com/LantaoYu/SeqGAN]</li> <li>scikit-mobility: mobility analysis in Python</li> <li>Brown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan et al. “Language models are few-shot learners.” arXiv preprint arXiv:2005.14165 (2020). [https://arxiv.org/abs/2005.14165]</li> <li>Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. “Attention is all you need.” In Advances in neural information processing systems, pp. 5998-6008. 2017. [https://arxiv.org/abs/1706.03762]</li> <li>Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. “Bert: Pre-training of deep bidirectional transformers for language understanding.” arXiv preprint arXiv:1810.04805 (2018). [https://arxiv.org/pdf/1810.04805.pdf?source=post_elevate_sequence_page—————————]</li> <li>“Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing”. Google AI Blog. Retrieved 2019-11-27.</li> <li> </li> <li> </li> <li> </li> <li> </li> </ol> <p>For more information contact: </p> </article> <article> <h1>Design and Evaluation of a Smartphone App to Support Sharing Physical Objects (SHA21.C)</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Draft <strong>Student:</strong></p> <p>Many persons are willing to contribute to the community by sharing objects they own, such as household items, tools and media items. The goal of the project is to develop an application that supports this by connecting lenders and borrowers in an easy way. Beyond the obvious features of a standard “classifieds” app (i.e., a potential lender provides information about objects that he is willing to lend; the application provides an easy way for the borrower to find the items he is interested in and, once found, contact the lender) the app should focus on supporting the interaction inherent in such physical lending, i.e., the physical exchange of the item in question. For example, a simple “bump” could allow a borrower to acknowledge receipt of an item, or an embedded NFC tag in the item itself could be used. Optionally, the app should be evaluated within a short study with several participants. Willingness to learn qualitative research methods in Human-Computer Interaction, as well as basic iOS programming skills required; strong Web programming skills an asset. Hardware such as a mobile phone and a smartwatch will be provided.</p> <p>For more information contact: <strong>No items found</strong></p> </article> <article> <h1>Detection of Social Engagement Interaction using Smartphones</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Draft <strong>Student:</strong></p> <p>Recent improves in mobile technologies (better and richer sensors, cheaper and faster storage) allow us to record different aspects of our daily activities, e.g. work meetings, discussions during coffee breaks, family moments, etc. Within the EU research project RECALL we are investigating how such data captured from mobile and wearable devices can serve as “memory cues” to help individuals remember certain past events – e.g., a set of images can help one remember an evening with friends or pictures showing white board content can help one remember what was discussed in the last work meeting.</p> <p>We are interested to understand how such data can be safely shared with other co-located people – e.g. with all the colleagues that participated in the meeting – in order to construct a better representation of what really happened in an event. Hence, our bigger goal is to build a solution for peer2peer exchange of captured memory cues or moments. The goal of this bachelor project is to develop an Android mobile app that uses audio data (and/or other mobile sensors) to detect when two people are socially interacting (e.g. speaking). This will be used as a trigger to decide when to start our peer2peer moment sharing. If time permits the student can also experiment and try to construct a solution for detecting the presence of nearby peers using audio signals. The project requires solid Android programming skills, preferably some knowledge on how to work with audio data and willingness to quickly learn new things.</p> <p>For more information contact: <strong>No items found</strong></p> </article> <article> <h1>Displays Mobile Application: A Mobile Interface for Customizing Public Displays</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Completed June 2015 <strong>Student:</strong> Talal El Afchal</p> <p>Display blindness is a known problem for interactive public displays since passers-by ignore or pay little attention to displays in public. Various theories argue that people ignore displays not because they are not interested in interacting with them, but because they are not interested in all the displayed information. A mobile application that personalizes the displays could increase people’s attention to the displayed information. In order to allow the displays personalization this project implements an android mobile application where users can choose applications to be displayed on public displays.</p> <p>For more information contact: </p> </article> <article> <h1>EDA Lateralization during Sleep</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor Master <strong>Status:</strong> Draft <strong>Student:</strong></p> <p>Advances in wearable technologies have made possible the ubiquitous sensing of physiological signals, like Blood Volume Pressure (BVP) or ElectroDermal Activity (EDA). However, some signals, like EDA, can change the value that is recorded depending on which side of the body the sensor is placed on. The medical literature has explored extensively this phenomenon, called Lateralization, in both EDA and other physiological recordings, but it is not clear the impact it might have on wearable-based applications. In this thesis, the objective is thus to analyse the lateralization effect from physiological signals recorded while people are sleeping. As such, the first a data collection will be run, to integrate some existing dataset. Then, the student will analyse the raw signal as well as extract some hand-crafted features. A Machine Learning task will be then investigated, to gauge the impact of lateralization, e.g., higher or lower performance of the classifier depending on which side the data is trained on.</p> <p>This project is available as a MSc thesis or as a BSc thesis (with reduced tasks).</p> <p>For more information contact: </p> </article> <article> <h1>Embodied Large Language Models for Personalized MeetingSummarization</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Available</p> <p>The rapid advancement of pervasive systems and wearables has paved the way for personal informatics systems, e.g., personal assistants and chatbots. Such systems gear towards enhancing users’ productivity in both work setting and personal life. In work settings, users leverage personal informatics systems in managing their tasks, tracking their progress and scheduling their meetings.</p> <p>Meetings are crucial for communication, decision-making, and brainstorming. The effectiveness of meetings relies on participants’ ability to remember key topics, often using meeting minutes or notes as memory cues. However, this approach is time-consuming, demands high attention levels, and may lead to varied perceptions among participants, causing a lack of synchronization.</p> <p>In this project, we aim at developing a personalized meeting summarization tool that aims at optimizing the meeting experience. The proposed tool integrates users’ affection from wearables with large language models, e.g., ChatGPT.</p> <p>This project is available as a MSc thesis, as a BSc thesis, or as a UROP internship project.</p> <p>For more information contact: , </p> </article> <article> <h1>Empathetic Virtual Agents using Large Language Models</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Available</p> <p>The rapid advancement of pervasive systems and wearables has paved the way for personal informatics systems, e.g., personal assistants and chatbots. These systems can be deployed in different personal and professional settings. Such personal assistants aim at personalizing the user ‘s experience by taking into consideration the user’s affection status and respond based on the user’s emotions and mental state status.</p> <p>In this project, we aim at developing an empathetic personal assistant tool that provides an affection-aware user experience. The proposed tool integrates users’ affection from wearables with large language models, e.g., ChatGPT.</p> <p>This project is available as a MSc thesis, as a BSc thesis, or as a UROP internship project.</p> <p>For more information contact: , </p> </article> <article> <h1>Evaluating a Human Memory Augmentation App (RECALL.C)</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed June 2017 <strong>Student:</strong> Matias Laporte</p> <p>For more information contact: </p> </article> <article> <h1>Evaluating Secure Personal Memory Sharing with Co-Located People</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed February 2018 <strong>Student:</strong> Jacob Jeyara</p> <p>Using smartphones and wearable devices people now can fully log their life in pictures, audio or even video recordings. Such data – “life-logs” – can help evoke past memories and potentially improve our overall cognitive abilities. One interesting opportunity in highly networked environments is the ability to share parts of one’s life-logs with others, in order to benefit from recordings of each-other (e.g., by having access to a third person view of oneself in a meeting). In order to avoid any privacy violations, life-logs should only be shared with co-located people – as soon as people leave or join the meeting, the exchange of lifelogs should be stopped or initiated, respectively. In prior work we have designed an initial prototype of this system, running on several Nexus 5X smartphones. The aim of this project is to evaluate the Android app – called ‘MemShare’, in order to understand usability requirements and use, and to further refine the overall system (including the backend server and web-based control and inspection tools) based on collected feedback and observed use. This project required a highly motivated student that will co-design, develop, and trial the MemShare system. Basic Android programming skills and willingness to learn about human subject research required; knowledge of human-computer interaction methodologies and visual design skills are a plus.</p> <p>For more information contact: , </p> </article> <article> <h1>Evaluation and Experimentation</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p>Computer scientists build complex systems or choose among existing systems to satisfy perceived needs and requirements. The system is then deployed in an environment consisting of humans and other systems. How do we know the impact of the system on the environment and how well it meets the perceived requirements? A fundamental skill in informatics is the ability to design experiments for evaluating computer systems. In this course, the students will acquire a basic understanding of how to design such experiments and what pitfalls to avoid during design and experimentation. Basic concepts of experimental design, data measurement, qualitative and quantitative evaluation, and evaluation with and without users will be covered.</p> <h5 id="teaching-mode">Teaching Mode</h5> <p>Lectures, presentations, homework assignments and in-class discussions.</p> <h5 id="references">References</h5> <p>No mandatory textbook will be used. Handouts will be provided. However, students are encouraged to complement their reading with one or more of the following sources:</p> <ul> <li>E. J. Davidson: Evaluation Methodology Basics. Sage 2004, 280 pages.</li> <li>A. Field, G. Hole: How to Design and Report Experiments. Sage 2003, 384 pages.</li> <li>Claes Pohlin et al.: Experimentation in Software Engineering. Springer-Verlag 2012, ISBN-13: 978-3642290435.</li> </ul> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) Evaluation and Experimentation 2022 Fall Gabriele Bavota Leonardo Alchieri Evaluation and Experimentation 2021 Fall Gabriele Bavota Antonio Mastropaolo Evaluation and Experimentation in Informatics 2018 Fall Matthias Hauswirth | Marc Langheinrich Anton Fedosov Evaluation and Experimentation in Informatics 2017 Fall Matthias Hauswirth | Marc Langheinrich Anton Fedosov Evaluation and Experimentation in Informatics 2015 Spring Matthias Hauswirth | Marc Langheinrich Anton Fedosov Evaluation and Experimentation in Informatics 2013 Fall Matthias Hauswirth | Marc Langheinrich Steven Mudda Evaluation and Experimentation in Informatics 2012 Fall - -</p> </article> <article> <h1>Explainable AI for Federated Models in Wearable Sensing</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master UROP <strong>Status:</strong> Available</p> <p>Federated learning and its combination with differential privacy is the latest technique for building privacy-aware machine-learning models [1]. Its primary assumption – no data leaves the local data storage, has enabled its application in a variety of privacy-sensitive domains: mobile keyboard prediction [2], human mobility modeling based on GPS data [3], modeling from electronic health records [4] , etc.</p> <p>Artificial Intelligence (AI) methods can bring significant and sustainable improvements to our lives. However, end-users must be able to understand those systems. Unfortunately, today’s groundbreaking AI methods are black-boxed (i.e., the decision model and the process are not understandable). The increased complexity of AI algorithms has made previous eXplainable AI (XAI) tools unsuitable, including the fact that most of the XAI solutions are not designed to operate under privacy constraints.</p> <p>This project will investigate XAI techniques compatible with privacy-aware approaches (e.g., federated learning). The focus will be on counterfactual explainers [55] for wearable sensing data. Specific project tasks are:</p> <ol> <li>Analyze XAI tools that can operate under privacy constraints, focusing on counterfactuals.</li> <li>Pre-process one dataset from wearable sensing systems. Example datasets include emotion recognition, activity recognition and energy expenditure estimation [6, 7, 8].</li> <li>Develop machine learning models for one of the datasets in step 2, and apply existing XAI tools on the models developed, including the method for generating counterfactual explanations, BayCon [10, 11].</li> <li>Develop XAI tool for counterfactual explanations that can operate under privacy constraints.</li> </ol> <p>This project is available as a MSc thesis and as a UROP internship project.</p> <h5 id="literature">Literature:</h5> <p>[1] J. Konečný, H. B. McMahan, F. X. Yu, P. Richtárik, A. Theertha Suresh, and D. Bacon. “Federated learning: Strategies for improving communication efficiency.” arXiv preprint arXiv:1610.05492 (2016). [2] Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. Federated learning for mobile keyboard prediction. CoRR, abs/1811.03604, 2018. URL 1811.03604. [3] Ezequiel, C. E. J., Gjoreski, M., & Langheinrich, M. (2022). Federated Learning for Privacy-Aware Human Mobility Modeling. Frontiers in Artificial Intelligence, 5, 867046. [4] Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W. (2018). Federated learning of predictive models from federated electronic health records. International journal of medical informatics, 112, 59-67. [5] Jiang, J. C., Kantarci, B., Oktug, S., & Soyata, T. (2020). Federated learning in smart city sensing: Challenges and opportunities. Sensors, 20(21), 6230. [6] T. Miller, “Explanation in artificial intelligence: Insights from the social sciences,” Artificial intelligence 267: 1-38, 2019. [7] M. Laporte, D. Gasparini, M. Gjoreski, and M. Langheinrich. “Exploring LAUREATE-the Longitudinal multimodAl stUdent expeRience datasEt for AffecT and mEmory research.” in the UbiComp/ISWC’22 Adjunct Proceedings, 2022. [8] Gashi, S., Min, C., Montanari, A., Santini, S., & Kawsar, F. (2022). A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices. Scientific Data, 9(1), 1-14. [9] Gjoreski, M., Kiprijanovska, I., Stankoski, S., Mavridou, I., Broulidakis, M. J., Gjoreski, H., & Nduka, C. (2022). Facial EMG sensing for monitoring affect using a wearable device. Scientific reports. [10] M. Gjoreski M, V. Kuzmanovski V, and M. Bohanec “BAG-DSM: A Method for Generating Alternatives for Hierarchical Multi-Attribute Decision Models Using Bayesian Optimization,” Algorithms, 2022. [11] P. Romashov, M. Gjoreski, K. Sokol, M. V. Martinez, and M. Langheinrich. “BayCon: Model-agnostic Bayesian Counterfactual Generator,”. In IJCAI 2022.</p> <p>For more information contact: </p> </article> <article> <h1>Explainable AI through counterfactual explanations</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Completed August 2022 <strong>Student:</strong> Piotr Romashov</p> <p>Artificially Intelligence (AI) is slowly but surely becoming an integrated part of our daily lives. However, with decisions derived from AI systems ultimately affecting human lives (e.g. medicine and law), there is an emerging need for understanding how such decisions are made by AI methods [11]. Furthermore, the recent General Data Protection Regulation (“GDPR”) [1] tasks the machine-learning (ML) community to enable explainability of the models and their output. More specifically, according to the GDPR, the ML models should offer the possibility to answer/provide explanation such as: “You were denied a loan because your annual income was £30,000. If your income had been £45,000, you would have been offered a loan.” [1]. In the ML domain, this task is referred to as “search for counterfactual explanations”. The idea is that, besides the model’s output, additional counterfactual information should be provided of how the world would have to be different for another (e.g., more desirable) outcome to occur. This new requirement has some researchers argue that a model’s accuracy should be sacrificed, and interpretable models should be preferred over black-box ML models for high-stake decisions [2].</p> <p>In order to augment existing AI systems with explainability, “Explainable Artificial Intelligence (XAI)” methods are being developed actively both in academia [3-9] and industry (e.g., IBM, Microsoft, Facebook and Google). XAI deals with the creation of machine learning techniques that enable end-users to understand, trust, and possibly manage the emerging generation of so-called “artificially intelligent partners” [10].</p> <p>The goal of this project is to explore existing XAI methods and possibly develop a new method that would improve existing solutions, with a focus on counterfactual-based XAI.</p> <p>Specifically, this Master thesis has four main tasks:</p> <ol> <li>Surveying existing algorithms for generating counterfactual explanations (e.g., [3-9, 12]);</li> <li>Implementing at least one XAI method and analyzing its performance (benefits and drawbacks) on several datasets (preferably in comparison with at least one related method from related studies).</li> <li>Publicly available XAI service (or python library) to be used with scikit-learn models.</li> </ol> <h5 id="references">References:</h5> <p>[1] Wachter, S., Brent M., and Chris R. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harv. JL & Tech. 31 : 841, 2017.</p> <p>[2] Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1, no. 5:206-215, 2019.</p> <p>[3] Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1, no. 5:206-215, 2019.</p> <p>[4] Joshi, S., Oluwasanmi K., Warut V., Been K., and Joydeep G. Towards realistic individual recourse and actionable explanations in black-box decision making systems. arXiv preprint arXiv:1907.09615, 2019.</p> <p>[5] Karimi, A.-H., Gilles B., Borja B., and Isabel V. Model-agnostic counterfactual explanations for consequential decisions. arXiv preprint arXiv:1905.11190, 2019.</p> <p>[6] Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Viégas, F., and Wilson, J. The what-if tool: Interactive probing of machine learning models. IEEE transactions on visualization and computer graphics, 26(1):56–65, 2019.</p> <p>[7] Tolomei, G., Silvestri, F., Haines, A., and Lalmas, M. Interpretable predictions of tree-based ensembles via actionable feature tweaking. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 465–474. ACM, 2017.</p> <p>[8] Ustun, B., Spangher, A., and Liu, Y. Actionable recourse in linear classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency, pages 10–19. ACM, 2019.</p> <p>[9] Dandl, Susanne, Christoph Molnar, Martin Binder, and Bernd Bischl. “Multi-objective counterfactual explanations.” In International Conference on Parallel Problem Solving from Nature, pp. 448-469. Springer, Cham, 2020.</p> <p>[10] D.Gunning, Explainableartificialintelligence(xAI),TechnicalReport,DefenseAd- vanced Research Projects Agency (DARPA), 2017.</p> <p>[11] B.Goodman,S.Flaxman,Europeanunionregulationsonalgorithmicdecision-mak- ing and a “right to explanation”, AI Magazine 38 (3) (2017) 50–57.</p> <p>[12] Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., … & Herrera, F., “Concepts, taxonomies, opportunities and challenges toward responsible AI,” Information Fusion, 58, 82-115, 2020</p> <p>For more information contact: </p> </article> <article> <h1>Exploring LAUREATE- the Longitudinal multimodAl stUdent expeRience datasEt for AffecT and mEmory research</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> UROP <strong>Status:</strong> Completed June 2022 <strong>Student:</strong> Daniele Gasparini</p> <p>Studies in the lab have shown that affect recognition using physiological data is feasible with machine learning methods. Datasets collected in-the-wild can further improve such methods’ robustness and applicability. This study presents LAUREATE, a Longitudinal mUltimodal student expeRience datasEt for AffecT and mEmory research. The dataset was collected throughout a university semester with 44 participants (including two lecturers) in two courses totalling 52 sessions, including classes, quizzes, and exams. We recorded participants’ physiological signals with a wristband device and collected daily survey answers about participants’ behaviour (e.g., study hours, smoking habits, physical activity, caffeine and food intake) and their perceived engagement, attention, and emotions after class. As a proxy for evaluating the quality of the physiological data, we present preliminary findings about the relation between the physiological signals and the different session types.</p> <p>For more information contact: </p> </article> <article> <h1>Fed-CogLoad: Federated Cognitive Load Estimation</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Assigned March 2023 <strong>Student:</strong> Mattias Formo</p> <p>Federated learning (FL) is a state-of-the-art machine-learning technique developed by Google, where the users’ privacy is guaranteed by implementing one simple rule: “No personal data leaves the user-device”. This project will investigate FL techniques for cognitive load estimate. Cognitive load can be estimated through the analysis of from pupillometry data, brain activation data (EEG), breathing rate, heart rate, heart rate variability and other related physiological responses.</p> <p>Specifically, this thesis has four main tasks:</p> <p>overview excising datasets for cognitive load estimation;</p> <p>develop a centralized machine learning pipeline for cognitive load estimation;</p> <p>develop a FL pipeline for cognitive load estimation;</p> <p>The main challenge of the work will be to develop enhanced privacy-aware models for cognitive-load modeling using federated learning.</p> <p>For more information contact: </p> </article> <article> <h1>Field Project (MMI)</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p>The Field Project consists in a consultancy study on behalf of a firm, conducted by a group of 2-3 students under the supervision of a professor (“tutor”) for a standard duration of 4 months.</p> <p>The aim of the Field Project is to provide students with:</p> <ul> <li>experience in project management</li> <li>contact with a real “client”</li> <li>experience in group projects (mixed groups!)</li> <li>It is meant to provide a bridge between theoretical and practical knowledge.</li> </ul> <p>The field project is carried out during the 3rd semester of the program, from September to mid-December. Students work on the project in parallel with the regular study program.</p> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) MMI Field Project 2022 Fall Marc Langheinrich - MMI Field Project 2021 Fall Marc Langheinrich - MMI Field Project 2020 Fall Marc Langheinrich - MMI Field Project 2019 Fall Marc Langheinrich - MMI Field Project 2018 Fall Marc Langheinrich - MMI Field Project 2017 Fall Marc Langheinrich - MMI Field Project 2016 Fall Marc Langheinrich - MMI Field Project 2015 Fall Mauro Pezzè - MMI Field Project 2014 Fall Marc Langheinrich -</p> <p>USI Search Directory: </p> </article> <article> <h1>Fintech Seminar</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Class is inactive.</strong></p> <p>The Fintech Seminar was offered only in AY17/18 and AY18/19.</p> <p>This seminar features speakers from a range of backgrounds (practitioners and researchers, entrepreneurs and representatives from large financial institutions) offering insights into the current trends and topics in Fintech. Students will use a set of references as a starting point for an in-depth term paper on a topic covered by a speaker, and complete the report with additional information from the speaker’s presentation.</p> <p>Past/current speakers include representatives from UBS, Avaloq, Melonport, Tendermint, EY, and the University of Pavia.</p> <h5 id="teaching-mode">Teaching Mode</h5> <p>Seminar speakers will present for 45-60 minutes, followed by 30-45 minutes of Q&A. The presenters will provide a list of references at the beginning of the semester, allowing students to seed their report with an overview of current research in the area.</p> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) Fintech Seminar 2019 Fall - - Fintech Seminar 2018 Fall - -</p> <p>USI Search Directory: </p> </article> <article> <h1>FunSquare: The Context Collector</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Completed June 2011 <strong>Student:</strong> Matteo Bellan</p> <p>In a world where social networks diffusion is limiting face-to-face interactions, a dedicated technology can bring them back in the place where they originated: squares and cities. Public Displays networks, as a growing research field with a very high potential, are the perfect tool for such a purpose. Within the FunSquare project, the goal is to build a public display application that provides conversational topics and, by becoming a special feature of the place, helps stimulating socialization through triangulation, an effect where a special feature of a place acts as a link between people.</p> <p>The FunSquare Context Collector (FCC) is a core module of the FunSquare project. Its goal is to automatically gather -from different sources across a city and on the web- information about the places where the displays are located, in order to attract people with something they feel to be part of.</p> <p>For more information contact: </p> </article> <article> <h1>FunSquare: The Fun Fact Generator</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Completed June 2011 <strong>Student:</strong> Thomas Selber</p> <p>Funsquare is a public-display service to facilitate social interaction between strangers, acquaintances and friends. It displays context-aware “fun facts” about a place, using an arbitrary range of topics, e.g., sustainability, history, science. By providing conversational topics and becoming a special feature of the place, Funsquare can help stimu- lating socialization through “triangulation”, an effect where special feature of the place acts as a link between people.</p> <p>The goal of this project is to implement the F3G module of the FunSquare application, i.e., an engine that is able to pair various “facts” from a database with “context information” and generate lists of “fun facts” that are written back into the database, so that another module can display them.</p> <p>For more information contact: , </p> </article> <article> <h1>Generative Adversarial Networks (GANs) for human mobility trajectory generation</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed July 2022 <strong>Student:</strong> Ivan Fontana</p> <p>GANs are a type of neural networks that once trained on a specific dataset, they can generate new samples, similar but not same, as the samples in the training dataset (e.g., see 15). This generative characteristic can be used to generate human mobility trajectories, and thus enhance existing labeled human mobility datasets, in order to improve the performance of existing methods for human mobility modeling.</p> <p>In the context of a BSc project, you will: (i) overview methods and datasets for trajectory generation (e.g., [4]); (ii) use an existing GAN method for trajectory generation on new datasets; (iii) compare the generated trajectories with original trajectories and analyze the behavior of the network for a variety of parameters; (iv) summarize the results and propose future work.</p> <p>In the context of an MSc thesis or UROP project, you will: (i) overview methods and datasets for trajectory generation (e.g., [4]); (ii) pre-process datasets appropriate for the task (e.g., [1][2]); (iii) create a novel GAN method for trajectory generation; (iv) use the generated trajectories to enhance existing datasets and evaluate machine-learning models for next-task prediction on enhanced vs. original dataset; (v) summarize the results and propose future work.</p> <h5 id="references">References:</h5> <ol> <li>Mokhtar, Sonia Ben, Antoine Boutet, Louafi Bouzouina, Patrick Bonnel, Olivier Brette, Lionel Brunie, Mathieu Cunche et al. “PRIVA’MOV: Analysing Human Mobility Through Multi-Sensor Datasets.” 2017 [https://hal.inria.fr/hal-01578557/document]</li> <li>Moro, Arielle, Vaibhav Kulkarni, Pierre-Adrien Ghiringhelli, Bertil Chapuis, and Benoit Garbinato. “Breadcrumbs: A Feature Rich Mobility Dataset with Point of Interest Annotation.” arXiv preprint arXiv:1906.12322 (2019) [https://arxiv.org/pdf/1906.12322.pdf]</li> <li>Luca, Massimiliano, Gianni Barlacchi, Bruno Lepri, and Luca Pappalardo. “Deep Learning for Human Mobility: a Survey on Data and Models.” arXiv preprint arXiv:2012.02825 (2020). [https://arxiv.org/pdf/2012.02825.pdf]</li> <li>Feng, Jie, Zeyu Yang, Fengli Xu, Haisu Yu, Mudan Wang, and Yong Li. “Learning to Simulate Human Mobility.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3426-3433. 2020. [https://www.youtube.com/watch?v=sj4UCW0P6Ks&ab_channel=AssociationforComputingMachinery%28ACM%29]</li> <li>Feng, Jie, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. “Deepmove: Predicting human mobility with attentional recurrent networks.” In Proceedings of the 2018 world wide web conference, pp. 1459-1468. 2018. [https://github.com/vonfeng/DeepMove]</li> <li>Yu, Lantao, Weinan Zhang, Jun Wang, and Yong Yu. “Seqgan: Sequence generative adversarial nets with policy gradient.” In Thirty-first AAAI conference on artificial intelligence. 2017. [https://github.com/LantaoYu/SeqGAN]</li> <li>scikit-mobility: mobility analysis in Python</li> <li>Brown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan et al. “Language models are few-shot learners.” arXiv preprint arXiv:2005.14165 (2020). [https://arxiv.org/abs/2005.14165]</li> <li>Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. “Attention is all you need.” In Advances in neural information processing systems, pp. 5998-6008. 2017. [https://arxiv.org/abs/1706.03762]</li> <li>Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. “Bert: Pre-training of deep bidirectional transformers for language understanding.” arXiv preprint arXiv:1810.04805 (2018). [https://arxiv.org/pdf/1810.04805.pdf?source=post_elevate_sequence_page—————————]</li> <li>“Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing”. Google AI Blog. Retrieved 2019-11-27.</li> <li> </li> <li> </li> <li> </li> <li> </li> </ol> <p>For more information contact: </p> </article> <article> <h1>Group Activity Journal – Shared Feed for Outdoor Sports</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Draft <strong>Student:</strong></p> <p>Skiing and snowboarding are highly social activities. Winter enthusiasts capture and share vast amount of pictures and videos during outdoor vacations. To support information exchange among groups of skiers and snowboarders we propose a group daily feed that could be automatically populated with media, POIs, and contextual and statistical details of the skiing activity, and which would be accessible online through a mobile phone app or a web-page. The feed would show user-captured events and present them on a timeline. Events could be added instantly via a simple tap using a smartwatch and/or using mobile phone interface. Following the recent trend in instant messaging services such as Snapchat or iMessage, where a message can expire after some time, we would like to associate an expiration tag to any events added to the feed. This “group feed” populated by custom events of participants could be used as a trip report or a blog to create a narrative about ski vacations for the group members. The feed would be automatically shared among participants with an option to grant access to external observers who want to follow a particular participant or the entire group in near real time.</p> <p>Basic iOS programming skills required, strong Web programming skills an asset. Hardware such as a mobile phone and a smartwatch will be provided.</p> <p>For more information contact: <strong>No items found</strong></p> </article> <article> <h1>Information Security</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p>This class exposes students to the fundamental concepts of computer security and network security. The growing importance of networks and distributed systems, and their use to support safety-critical applications, has made computer and communication security a central issue for systems today. The class is built on three main parts: security foundations (which includes security terminology, core cryptograhic principles, and secure protocols); applied security (which discusses software security and web security); and privacy (which covers both technical and social aspects of privacy). Students learn to critically assess the security properties of a system and make informed decisions about implementing secure processes. A somewhat shorter version of this class ( ) was previously taught in the Bachelor program.</p> <h5 id="teaching-mode">Teaching Mode</h5> <p>Two lectures per week with weekly homework and reading assignments, interspersed with lab sessions that allow students to gain basic knowledge in information security.</p> <h5 id="references">References</h5> <p><strong>Required:</strong></p> <ul> <li>Cryptography and Network Security – Principles and Practices; William Stalling; 7th Ed., Pearson Intl., 2016</li> </ul> <p><strong>Supplemental Reading:</strong></p> <p>Additional handouts will be provided as needed, e.g., from:</p> <ul> <li>Network Security: Private Communication in a Public World; Charlie Kaufman, Radia Perlman, Mike Speciner; 2nd Ed., Prentice Hall, 2002</li> <li>Security Engineering: A Guide to Building Dependable Distributed Systems; Ross Anderson; 2nd Ed., Wiley, 2008</li> </ul> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) Information Security 2023 Spring Marc Langheinrich - Information Security 2022 Spring Marc Langheinrich - Information Security 2021 Spring Marc Langheinrich - Information Security 2020 Spring Marc Langheinrich - Information Security 2019 Spring Marc Langheinrich - Information Security 2018 Spring Marc Langheinrich - Information Security 2017 Spring Marc Langheinrich - Information Security 2016 Spring Marc Langheinrich -</p> <p>USI Search Directory: </p> </article> <article> <h1>Introduction to Information Security</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Class is inactive.</strong></p> <p>This class has been expanded and is now taught in the Master program.</p> <p>The goal of this class is to expose students to the fundamental concepts of computer and communications security. The growing importance of networks and distributed systems, and their use to support safety-critical applications, has made computer and communications security a central issue for systems today. Additionally, the rise of social networks and location-based services has increased the amount of our personal data held by third parties significantly. It is therefore imperative that students know the technical foundations of computer and communications security, as well as the basic goals and mechanisms of privacy-aware data processing.</p> <h5 id="contents">Contents</h5> <p>This course provides an overview of the available technologies for achieving security and privacy in an electronic world. Topics discussed include encryption; authentication; security protocols; computer, network, and web security; anonymity; and privacy enhancing technologies.</p> <h5 id="teaching-mode">Teaching Mode</h5> <p>Two lectures per week with weekly homework and reading assignments, interspersed with individual lab sessions that discuss homework and provide hands-on experience with developing security solutions.</p> <h5 id="references">References</h5> <ul> <li>William Stalling: Cryptography and Network Security – Principles and Practices, 57th Ed., Pearson Intl., 2011.</li> </ul> <p>Additional handouts will be provided as needed, e.g., from:</p> <ul> <li>Charlie Kaufman, Radia Perlman, Mike Speciner: Network Security: Private Communication in a Public World, 2nd Ed., Prentice Hall, 2002.</li> <li>Ross Anderson: Security Engineering: A Guide to Building Dependable Distributed Systems, 2nd Ed., Wiley, 2008.</li> </ul> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) Introduction to Information Security 2014 Spring - - Introduction to Information Security 2013 Spring - - Introduction to Information Security 2012 Spring - - Introduction to Information Security 2011 Spring - - Introduction to Information Security 2010 Spring - -</p> </article> <article> <h1>Investigating the dichotomy of sharing practices in virtual and physical realms: from theoretical overview to design considerations (SHA21.A)</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed July 2017 <strong>Student:</strong> Jeremías Albano</p> <p>Online social networks have made sharing personal experiences with others – mostly in form of photos and comments – a common activity. Nowadays the scope of user-generated and shared content on the net varies vastly from personal media to individual preferences to physiological information (e.g. in form of daily workouts). Popular “sharing economy” services (e.g. AirBnB, Uber) and connected devices are expanding the set of “things” one can share. Given that a new generation of sharing services is about to emerge, it is of crucial importance to understand how traditional sharing practices inform and support designers of those services. This project will look into consolidating the existing body of work on both sharing personal digital content (e.g., on social networking sites, through photo sharing apps) and personal physical possessions (e.g., apartment sharing). The project aims to identify commonalities and differences between digital and non-digital context sharing, in particular summarizing existing research on motivations to share, audience management, privacy and trust issues, and user experience requirements. If possible, these findings should be connected to contemporary theories of social psychology and practice theory. The final results of this project would be: (1) a comprehensive account of the existing body of knowledge on content and resource sharing practices; (2) a set of design considerations that allows designers and developers to build future sharing services to enable sharing activities bridging virtual and physical realms. This projects requires strong analytical skills and a willingness to learn about a novel and emerging research field. Experience with interdisciplinary research literature a plus (e.g., seminar work in human-computer interaction) though supervising guidance is available.</p> <p>For more information contact: </p> </article> <article> <h1>Just Share It: A Decentralized Autonomous System to Support Sharing Physical Objects Using Blockchain and Smart Contracting</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> UROP <strong>Status:</strong> Completed May 2018 <strong>Student:</strong> Egor Ermolaev</p> <p>Many persons are willing to contribute to the community by sharing objects they own, such as household items, tools and media items. For example in Switzerland an online service provides a set of stickers for a mailbox to let people see what household items one can borrow from their neighbours. However, the service does not support the actual act of sharing those items – how borrower and lender meet, agree, and exchange. “Just Share It” is meant as an application that provides such a service, by connecting lenders and borrowers through mobile technology.</p> <p>A potential lender provides information about objects that he is willing to lend. The application provides an easy way for borrowers to find the items they are interested in. Once a borrower has found an item, the application provides a way for the lender and the borrower to communicate and come to an arrangement. An underlying layer of blockchain-driven smart contracting technology facilitates online contractual agreements (e.g., to record sharing transactions). In order to maintain a positive and friendly environment, “Just Share It” will also need to provide means by which users can build up trust. For example, the application should allow the borrower to leave a short feedback about the experience with the item in the form of a short notice and a picture.</p> <p>This project is part of the Swiss National Science Foundation funded SHARING21 research project, where we are looking into new ways of supporting sharing both digital information and physical objects. The goal of this project is to implement the “Just Share It” application on a mobile platform, incorporating blockchain and smart contracting technologies (e.g., using Etherium, an open-source distributed computing platform). Master students applying for this project in addition to developing the prototype are expected to conduct its evaluation with a dozen of users.</p> <p>Strong Web programming skills (Ajax frameworks) are required, basic Solidity and/or C++ programming skills are an asset, prior experience with iOS and/or multi-platform smartphone app development (e.g. Ionic framework) is desirable. Hardware such as a mobile phone and a smartwatch will be provided. Supervising guidance on qualitative research methodology (Anton) and distributed information system architecture (Agon) is available.</p> <p>For more information contact: , </p> </article> <article> <h1>Mobile and Wearable Computing</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p>Mobile devices such as mobile phones, smart watches, and other wearable devices can interact seamlessly by relying on available communication infrastructures. This course focuses on challenges and opportunities arising from the use of systems of mobile devices or “mobile sensing systems”.</p> <p>Following an overview of applications enabled by mobile sensing systems the focus will be devoted to the most significant technologies, including hardware platforms, programming environments and tools.</p> <p>Relevant aspects related to the design and development of a mobile sensing system, including the handling of sensors, the design of user interfaces, the management of local and remote sensor data storage, privacy and security issues will be investigated and addressed. In order to gain practical hands-on experience, students will learn in the lab sessions how to design, implement, and demonstrate Android-based mobile sensing applications.</p> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) Mobile and Wearable Computing 2023 Fall Silvia Santini Nouran Abdalazim | Leonardo Alchieri Mobile and Wearable Computing 2022 Fall Silvia Santini Nouran Abdalazim Mobile and Wearable Computing 2021 Fall Silvia Santini Shkurta Gashi Mobile and Wearable Computing 2020 Fall Silvia Santini Shkurta Gashi | Elena Di Lascio Mobile and Wearable Computing 2019 Fall Silvia Santini Elena Di Lascio Mobile Computing 2018 Fall Silvia Santini Elena Di Lascio Mobile Computing 2017 Fall Ivan Elhart Elena Di Lascio Mobile Computing 2016 Fall Silvia Santini Agon Bexheti</p> <p>USI Search Directory: </p> </article> <article> <h1>MoviePlus – A Platform for Sharing Digital Content For Augmented Reality Displays</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> UROP <strong>Status:</strong> Completed September 2018 <strong>Student:</strong> Bianca Stancu</p> <p>Nowadays people are using tablets, laptops and various digital devices to work collaboratively on the same project while being present physically or remotely. Imagine to work on the same design or analytical task, or just simply watch an entertaining video using Google Glass. Annotating and interacting with virtual content became a daily activity of many architects, engineers, designers, artists, etc. We offer a student to join on exciting opportunity to design a prototype, which enables digital content sharing and interaction in Augmented Reality. The student design and develop the shared movie viewing system using VideoPlus, a platform for augmented video content ( .</p> <p>We propose to use the combination of a head-mounted display (e.g., iPhone X/ Pixel 2 with Daydream or Microsoft Hololens) and a wrist-worn controller (e.g., Apple Watch) to develop an interactive system, which would enable tagging favorite scenes, sharing subtitles, sending ephemeral messages among viewers and/or taking collaborative trivia quizzes. We envision prototyping on the mobile device as part of the project work, so we would encourage students with iOS/Android or C#/C++ development skills to apply. Ultimately, we would like to conduct controlled experiments to evaluate a designed system.</p> <p>This project is hosted by Faculty of Informatics and Institute of Computational Science. Thus, the student will be co-supervised by the responsible investigators from both sides.</p> <p>For more information contact: </p> </article> <article> <h1>Multimodal Federated Learning for Sensor Data</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Assigned February 2023 <strong>Student:</strong> Alessandro Gobbetti</p> <p>Federated learning and its combination with differential privacy is the latest technique for building privacy-aware machine-learning models [1]. Its primary assumption – no data leaves the local data storage, has enabled its application in a variety of privacy-sensitive domains: mobile keyboard prediction [2], human mobility modeling based on GPS data [3], modeling from electronic health records [4] , etc.</p> <p>This project will investigate single modality vs. multi-modality federated models. This is an important issue for wearable sensing systems that utilize multiple sensing devices, e.g., smartphone and smartwatch. Each device, and each sensor in the devices, may have a different availability –– data coming from the smartwatch may be unavailable at certain periods (e.g., while charging). To enable the collaborative learning of joint models between users with a variable data/modality availability, we will investigate several multi-modal schemes.</p> <p>Project tasks:</p> <ol> <li>Pre-process one dataset from wearable sensing systems. Example datasets include emotion recognition, activity recognition and energy expenditure estimation [6, 7, 8].</li> <li>Build centralized multimodal and single-modal models using the dataset from step 1.</li> <li>Build federated multimodal and single-modal models using the dataset from step 1 and compare them with the centralized models from 2.</li> <li>Develop novel multi-modal federated learning method considering device/sensor availability, computational cost, model accuracy.</li> </ol> <p>This project is available as a MSc thesis (all four tasks), as a BSs thesis (only task 2 and task 3), or as a UROP internship project (all 4 tasks).</p> <h5 id="literature">Literature:</h5> <p>[1] Konečný, Jakub, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. “Federated learning: Strategies for improving communication efficiency.” arXiv preprint arXiv:1610.05492 (2016). [2] Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. Federated learning for mobile keyboard prediction. CoRR, abs/1811.03604, 2018. URL 1811.03604. [3] Ezequiel, C. E. J., Gjoreski, M., & Langheinrich, M. (2022). Federated Learning for Privacy-Aware Human Mobility Modeling. Frontiers in Artificial Intelligence, 5, 867046. [4] Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W. (2018). Federated learning of predictive models from federated electronic health records. International journal of medical informatics, 112, 59-67. [5] Jiang, J. C., Kantarci, B., Oktug, S., & Soyata, T. (2020). Federated learning in smart city sensing: Challenges and opportunities. Sensors, 20(21), 6230. [6] M. Laporte, D. Gasparini, M. Gjoreski, and M. Langheinrich. “Exploring LAUREATE-the Longitudinal multimodAl stUdent expeRience datasEt for AffecT and mEmory research.” in the UbiComp/ISWC’22 Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2022. [7] Gashi, S., Min, C., Montanari, A., Santini, S., & Kawsar, F. (2022). A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices. Scientific Data, 9(1), 1-14. [8] Gjoreski, M., Kiprijanovska, I., Stankoski, S., Mavridou, I., Broulidakis, M. J., Gjoreski, H., & Nduka, C. (2022). Facial EMG sensing for monitoring affect using a wearable device. Scientific reports, 12(1), 1-12.</p> <p>For more information contact: </p> </article> <article> <h1>NDA: Developing Networked Public Display Analytics Based on Classifying the Audience in Front of a Display and Mapping Them to Interaction Zones</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed September 2014 <strong>Student:</strong> Miloš Nikolić</p> <p>Public display systems are increasingly becoming part of the urban landscape, with systems being deployed in venues such as railway stations, shopping malls and city squares. It is not hard to imagine that soon these displays will be networked and empowered with interaction technology, e.g., touch, thus constituting a novel and powerful communication medium – networked public displays. One of the biggest problems this medium is facing is determining measures of success for different application that will be running on these displays. In other words: How many people pass by the display? Out of those how many glance at the screen? Out of those how many actually interact with the application? Is there any influence on the audience size on the interactions with the screen?</p> <p>The aim of this project is to develop a set of tools and metrics that will allow automatic detection and clustering of the audience in front of the display into groups such as 1) passers-by, 2) people who glance at the screen, and 3) people who interact with the screen. As these groups can emerge all at once around the scree in different display zones it will be important to map the audience/users to different interaction zones.</p> <p>At the outset, 1) using the Kinect sensor and live video feed a simple algorithm that detects the number of people in front of the screen should be developed. Then using the information from depth-camera a classifier should be developed that clusters detected users into different groups, i.e., 1) passers-by, 2) people who glance at the screen, and 3) people who interact with it. At a certain point in time users should receive IDs and transitions between the interaction zones and user groups should be detected (e.g., a person passing by the screen then moving a bit closer and finally interacting with a display application. In order to confirm the accuracy of the cluster video recordings from another camera will be used to manually label and classify the audience and their interaction zones.</p> <p>For more information contact: </p> </article> <article> <h1>Personalized Public Displays: Designing Interactive Display Applications for Active and Walk-by Content Personalization</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Completed June 2017 <strong>Student:</strong> Francesco Saverio Zuppichini</p> <p>Public display systems are increasingly becoming part of the urban landscape, with systems being deployed in venues such as railway stations, shopping malls, city squares, and universities. Most public displays today are simple slide-show systems that broadcast content based on a pre-selected schedule. However, public displays envisioned in the near future will provide a platform for running diverse interactive applications with highly personalized content. These applications will allow viewers to express their preferences both explicitly through interactive touch interfaces (active personalization) and implicitly using mobile handsets (walk-by personalization).</p> <p>The aim of this project is to explore the development, deployment, and actual use of web-based interactive display applications that can show highly personalized content such as content from online social platforms on public displays. The main focus of this project is on 1) designing and implementing minimum three <strong>web-based applications</strong> that can show personalized and user generated content (e.g., Facebook stream) and show such a content on public displays based on a set of personalization parameters, 2) implementing <strong>mobile interfaces</strong> for the developed apps that can be “loaded” into an existing Android-based “controller app” prototype (called “Tacita”) that allows users to set the personalization parameters, and 3) extending the existing Tacita application with the <strong>indoor localization</strong> using Bluetooth Low Energy (BLE) devices that will support implicit walk-by personalization.</p> <p>For more information contact: </p> </article> <article> <h1>Personalized Public Displays: Uncovering Personalization Needs</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Draft <strong>Student:</strong></p> <p>With the significant price drops of large LCD panels, public displays are increasingly dominating the urban landscape. It is not hard to imagine that in the near future, public displays will shift from showing predefined still images or videos to becoming more interactive and highly personalized. By providing custom content to individual passers-by such as upcoming bus schedules, relevant news items, and even personalized messages (e.g., Twitter, Facebook, or Google+ posts), public displays may increase their utility and become more appreciated in our environment. However, showing such a contextualized and personalized content increases privacy concerns and may impact the use of public displays.</p> <p>The main goal of this project is to explore, uncover, and understand personalization needs of potential and actual users of public displays. The project will start with an online survey/questionnaire for assessing current understanding of personalization needs of public displays in a university environment. Following the initial survey, the student will conduct in-depth interviews with the student community members using snow-ball sampling method or conduct interviews next to the displays at the point of interaction. The main task is to identify different “work roles” of the community members and uncover their information needs and technologies used while “on the go” on-campus. The initial survey and in-depth interviews will be followed by an extensive data analysis looking into personalization needs and concerns of the display users and understanding what technologies are used, when, and how to satisfy the information needs on-campus. A possibility to strengthen the data analysis would be to conduct a second set of interviews after installation and initial use of implicit personalization extensions into the existing display system. These personalization extensions will allow users to personalize display applications and their content using a mobile application called “Tacita”.</p> <p>For more information contact: <strong>No items found</strong></p> </article> <article> <h1>Personalized Shared Public Displays: Supporting Multi-user Walk-by Content Personalization</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed September 2018 <strong>Student:</strong> Filip Zivkovic</p> <p>Public display systems are increasingly becoming part of the urban landscape, with systems being deployed in venues such as railway stations, shopping malls, city squares, and universities. Most public displays today are simple slide-show systems that broadcast content based on a pre-selected schedule. However, public displays envisioned in the near future will provide a platform for running diverse interactive applications with highly personalized content. These applications will allow viewers to express their preferences both explicitly through interactive touch interfaces (active personalization) and implicitly using mobile handsets (walk-by personalization).</p> <p>The aim of this project is to explore the development, deployment, and evaluation of web-based interactive display applications that can show personalized content influenced by the presence and preferences of multiple mobile phone users. The main focus of this project is on: 1) Designing and implementing two web-based applications, based on Public Transportation and USI Class Schedule information available through online API services, that can visualize and personalize such a content on public displays based on a set of personalization parameters. The apps should provide novel content visualization and scheduling algorithms that can support multiple, potentially conflicting, walk-by content personalization requests on shared public displays. 2) Integrating and deploying the new display applications within an existing touch-enabled display system at USI and supporting an existing Tacita personalization framework by implementing mobile interfaces that can be “loaded” into an existing iOS “controller app” and can be used to set personalization content parameters within the apps. 3) Evaluating the stability, latency, and overall performance of the apps within the existing display network. The evaluation will be performed based on the system log-files, time-stamps of events and personalization requests that occur within the apps, performance of the developed content visualization and scheduling algorithms, and the usage of system resources, e.g., CPU usage. The applications should be developed using HTML5 technologies within Play (Java) framework.</p> <p>For more information contact: </p> </article> <article> <h1>Personalized Sleep Quality Prediction Model Using Wearable Devices</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor Master <strong>Status:</strong> Draft <strong>Student:</strong></p> <p>Mobile and wearable devices can nowadays be used as highly available, non-invasive tools to monitor human behavior. Their ubiquitous characteristics encourage their employment in personal health monitoring systems. Such systems provide the user with continuous feedback about daily behavior, productivity, well-being, etc. Daily sleep is considered a pivotal factor in daily routine due to its role in the rejuvenate of brain and body from accumulated daily basis fatigue. The continuous quantification of sleep quality helps in assessing human health and life patterns. However, the interpersonal variability hinders this task by creating a significant gap between the user-reported sleep quality and the objective sleep quality. The goal of this thesis is to analyze the impact of different personal characteristics on the user-reported sleep quality. Then, to make use of this knowledge along with physiological signals from wearable devices to build a personalized sleep quality predication model.</p> <p>This project is available as a MSc thesis or as a BSc thesis (with reduced tasks).</p> <p>For more information contact: </p> </article> <article> <h1>Physical Computing</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Class is inactive.</strong></p> <p>This class was discontinued in 2018.</p> <p>Physical Computing is about integrating the real world with sensing, communication, and computation. It is about rapidly prototyping devices that can react and interact directly with their environment, rather than being accessed through a keyboard and monitor. The class introduces students to the idea of using small, programmable microcomputers to build self-contained, physical systems that help automate everyday tasks. The course exposes students to basic electronics, microcontroller programming (using the “Processing” language), short-range wireless networking (e.g., Bluetooth), mobile interfaces (smartphones), and embedded sensing. The class centers on Arduino development boards that allow one to rapidly build reactive and/or interactive everyday items, without the need for attaching a Mac or PC to them.</p> <h5 id="teaching-mode">Teaching Mode</h5> <p>Two hands-on practical lab sessions per week. Each session starts with a short tutorial, followed be practical exercises. Weekly group homework lets students apply the gained knowledge in a self-chosen project. The final deliverable is a context-aware artifact comprised of one or more of these above-mentioned technologies, together with a report describing its architecture and implementation.</p> <h5 id="references">References</h5> <p>There is no required textbook. Handouts are provided as needed, though students are encouraged to peruse additional literature, such as:</p> <ul> <li>Getting Started with Arduino; Massimo Banzi; 2nd Edition; O’Reilly; 2012</li> <li>Getting Started with Processing; Casey Reas and Ben Fry; O’Reilly, 2010</li> <li>Programming Your Home. The Pragmatic Programmers; Mike Riley; 2012</li> <li>Hacking Electronics. Simon Monk. McGraw Hill, 2013</li> <li>Practical Electronics For Inventors (3rd edition). Paul Scherz and Simon Monk. McGraw Hill, 2013</li> <li>Make: Electronics; Charles Platt; O’Reilly; 2009</li> </ul> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) Physical Computing 2018 Spring - - Physical Computing 2017 Spring - - Physical Computing 2016 Spring - - Physical Computing 2015 Spring - - Physical Computing 2013 Fall - -</p> <p>USI Search Directory: </p> </article> <article> <h1>Physiological Synchrony and its Effects on Recall in Peer Meetings</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed March 2021 <strong>Student:</strong> Luca Albinati</p> <p>Meetings form an integral (if often boring) part of our work. A common approach to improve the effectiveness of such meetings is to create a record of it for later review, thus allowing participants to prepare for the next meeting. Being able to effectively follow up a meeting is crucial to its success. Technology can make record creation easier, e.g., by automatically creating a meeting transcript from a video recording. However, reading a full summary of a meeting can be cumbersome. Furthermore, such an “objective” view of the meeting does not consider the individual experience of each participant.</p> <p>An alternative approach is to provide participants with so-called “memory cues” – either semantic (e.g., words clouds showing key concepts discussed in the meeting) or episodic (e.g., short audio clips, pictures) – that allow them to recall, from their own memory, what transpired during the meeting. Today’s technology makes both the collection, preparation, and presentation of memory cues possible. The ubiquitous availability of screens (e.g., laptop screen savers, tablet and smartphone lock screens, wall-mounted displays) enables the ambient presentation of memory cues, while wearable recorders and sensors facilitate the comprehensive capture of one’s meeting experience. Wearable biophysical sensors (e.g., as integrated in smart watches) allow for a highly personalized capture process that “understands” how a meeting affects each participant.</p> <p>One interesting social process in a meeting is called “synchrony” – a cognitive, affective, and behavioral connection between two or more participants that creates a sort of “connection” or “vibe”. This connection has been shown to greatly improve the effectiveness of a meeting, as well as one’s recall. Social synchrony manifests itself physiologically in our brains: “synchronized” people have their brains react to a task or event simultaneously. While using an fMRI machine to measure each participant’s brain activity clearly is infeasible, prior research has shown that electrodermal activity (EDA) is a good proxy for brain activity. People who are synchronized not only have “synchronized” brain activity, but also show simultaneous changes in EDA.</p> <p>The goal of this Master’s thesis is to design, develop, and execute a small pilot experiment to study how the physiological synchrony of peers during a meeting affect their memory of the event, and how this can be used to create a recall-based productivity system.</p> <p>For more information contact: </p> </article> <article> <h1>Privacy-aware human mobility modeling</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Completed June 2022 <strong>Student:</strong> Jose Castro Elizondo Ezequiel</p> <p>Human mobility can be modeled using smartphone data, however, such data represents sensitive information, and the collection of those threatens the privacy of the users involved. The recent introduction of federated learning, a privacy-preserving approach to build machine and deep learning models, represents a promising technique to solve the privacy issue.</p> <p>In the context of a BSc project, you will: overview methods based on federated-learning [8][9][10] with the aim to use them for human mobility modeling; (ii) use an existing dataset to train a deep learning model for human mobility modeling (e.g., next task prediction) [12][13] using federated learning; (iii) analyze the behavior of the model for a variety of parameters (e.g., dataset size, model size, etc.); (iv) summarize the results and propose future work.</p> <p>In the context of an MSc thesis or UROP project, you will: (i) create an overview of privacy-aware human mobility modeling approaches; (ii) pre-preprocess/normalize datasets to a common format (e.g., [1][2]); ; (iii) create advanced privacy-aware models for human mobility modeling (e.g., Transformer networks [8][9][10] using federated learning [14]); (iv) compare the results to existing baselines (e.g., baselines based on RNNs [5],[6]); (v) summarize the results and propose future work.</p> <h5 id="references">References:</h5> <ol> <li>Mokhtar, Sonia Ben, Antoine Boutet, Louafi Bouzouina, Patrick Bonnel, Olivier Brette, Lionel Brunie, Mathieu Cunche et al. “PRIVA’MOV: Analysing Human Mobility Through Multi-Sensor Datasets.” 2017 [https://hal.inria.fr/hal-01578557/document]</li> <li>Moro, Arielle, Vaibhav Kulkarni, Pierre-Adrien Ghiringhelli, Bertil Chapuis, and Benoit Garbinato. “Breadcrumbs: A Feature Rich Mobility Dataset with Point of Interest Annotation.” arXiv preprint arXiv:1906.12322 (2019) [https://arxiv.org/pdf/1906.12322.pdf]</li> <li>Luca, Massimiliano, Gianni Barlacchi, Bruno Lepri, and Luca Pappalardo. “Deep Learning for Human Mobility: a Survey on Data and Models.” arXiv preprint arXiv:2012.02825 (2020). [https://arxiv.org/pdf/2012.02825.pdf]</li> <li>Feng, Jie, Zeyu Yang, Fengli Xu, Haisu Yu, Mudan Wang, and Yong Li. “Learning to Simulate Human Mobility.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3426-3433. 2020. [https://www.youtube.com/watch?v=sj4UCW0P6Ks&ab_channel=AssociationforComputingMachinery%28ACM%29]</li> <li>Feng, Jie, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. “Deepmove: Predicting human mobility with attentional recurrent networks.” In Proceedings of the 2018 world wide web conference, pp. 1459-1468. 2018. [https://github.com/vonfeng/DeepMove]</li> <li>Yu, Lantao, Weinan Zhang, Jun Wang, and Yong Yu. “Seqgan: Sequence generative adversarial nets with policy gradient.” In Thirty-first AAAI conference on artificial intelligence. 2017. [https://github.com/LantaoYu/SeqGAN]</li> <li>scikit-mobility: mobility analysis in Python</li> <li>Brown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan et al. “Language models are few-shot learners.” arXiv preprint arXiv:2005.14165 (2020). [https://arxiv.org/abs/2005.14165]</li> <li>Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. “Attention is all you need.” In Advances in neural information processing systems, pp. 5998-6008. 2017. [https://arxiv.org/abs/1706.03762]</li> <li>Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. “Bert: Pre-training of deep bidirectional transformers for language understanding.” arXiv preprint arXiv:1810.04805 (2018). [https://arxiv.org/pdf/1810.04805.pdf?source=post_elevate_sequence_page—————————]</li> <li>“Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing”. Google AI Blog. Retrieved 2019-11-27.</li> <li> </li> <li> </li> <li> </li> <li> </li> </ol> <p>For more information contact: </p> </article> <article> <h1>Privacy-aware Video-based Sensing for Affect Recognition</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Assigned February 2024 <strong>Student:</strong> Eleonora Bardelli</p> <p>Affective computing is an interdisciplinary field that aims at the development of computer science techniques that enable machines to recognize, understand and simulate human affective states [1, 2]. A fundamental assumption is that different mental states (e.g., emotions and stress), and different intensities of those states, manifest through physiological and behavioral changes. A variety of sensing modalities can capture these changes [3, 4].</p> <p>Video-based sensing is one promising approach for affect recognition, however, it is also privacy intrusive. Thus, this project will develop a method for privacy-aware personal-video sensing for affect recognition. The method will utilize personal camera (e.g., smartphone or laptop camera) and will include privacy-aware features such as: to record only when the users grant permission, to record only when a specific user is in front of the camera (user identification). Once the video is collected in a privacy-aware manner, existing video-based affect recognition software will be sued to extract affect related information.</p> <p>Project tasks:</p> <ol> <li>Review existing software (e.g., GitHub and Google scholar) for user identification, software for counting faces in a video, and software for Facial Action Coding System (FACS) [5].</li> <li>Implement privacy-aware user identification;</li> <li>Implement privacy-aware method for extracting Facial Action Units (based on FACS);</li> <li>Test the overall processing pipeline is a small user-study (e.g., 5 to 10 participants).</li> </ol> <p>This project is available as a MSc thesis (all four tasks), as a BSc thesis (only task two and task three), or as a UROP internship project (all four tasks).</p> <h5 id="literaturel">LiteratureL</h5> <p>[1] R. Picard. “Affective Computing,” Cambridge, MA: MIT Press., 1997. [2] J. Tao, J., and T. Tan, “Affective computing: A review. In International Conference on Affective computing and intelligent interaction,” Springer, Berlin, Heidelberg, 2005. [3] Y. Wang, W. Song, W. Tao, A. Liotta, D. Yang, X. Li, S. Gao, Y. Sun, W. Ge, W. Zhang, and W. Zhang, “A systematic review on affective computing: Emotion models, databases, and recent advances.” Information Fusion, 2022. [4] R. Arya, S. Jaiteg Singh, and A. Kumar. “A survey of multidisciplinary domains contributing to affective computing.” Computer Science Review 40 (2021): 100399. [5] </p> <p>For more information contact: </p> </article> <article> <h1>Professional Ethics</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) Professional Ethics 2014 Fall - -</p> </article> <article> <h1>Programming Fundamentals I</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) Programming Fundamentals I 2014 Fall - -</p> </article> <article> <h1>Protect privacy in wearable devices using data anonymization</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Master <strong>Status:</strong> Available</p> <p>Wearables facilitate continuous data collection to monitor diverse human behaviors, covering activity, health, and stress. This personal data, including electrocardiogram, movements, and heart rate, is often accessible online for research. Despite the common practice of masking names with random identifiers, studies show that this is insufficient for user identity protection due to the subject-dependent nature of physiological data. This research utilizes existing data and models to implement and assess anonymization techniques such as noise addition and synthetic data generation. The objective is to determine the extent of effective user identity protection while minimizing disruption to human behavior prediction.</p> <p>This project is available as a MSc thesis or as a BSc thesis (with reduced tasks).</p> <p>For more information contact: , </p> </article> <article> <h1>Public Reactive Displays</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Completed September 2010 <strong>Student:</strong> Federico Caputo</p> <p>In the modern age of communication and transport, it’s an increasingly common scenario for the average person to have friends scattered all around a country, if not around the world. Despite the modern social networking platforms and the increasingly common use of VoIP software, it is still rather difficult to keep in touch with friends and colleagues, mainly due to the differences in people daily schedules. The goal of this system is to help “friends” to remain in touch despite distances and time constraints, using a same-time-free-time approach.</p> <p>The same-time-free-time approach is based on the chance of two friends having some free time in the same moment, thus having the opportunity to (virtually) meet each other; what it’s missing is a system that is able to exploit these free-time at the same time occurrences, allowing friends to keep in touch. The basic idea is to have a computer connected to a motion sensor, a blue-tooth antenna and a webcam; this way, the system is able, respectively, to detect users’ presence, to recognize users through their bluetooth devices’ unique MAC address and to automatically open a sort of “virtual window” with another similar system in a different location where another user, recognized as a friend of the first user, is recognized through the same process.</p> <p>For more information contact: </p> </article> <article> <h1>PulseCam: Capture your truly significant moments</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Completed May 2015 <strong>Student:</strong> Christian Vuerich</p> <p>Life logging emerged as a way to capture and remember more mainly through pictures. However, as life log- ging becomes increasingly mainstream, the volume of captured content also increases but our capacity for reviewing diminishes. In order to limit picture taking on such devices to only the most memorable moments, we propose a biophysical driven capture process that adapts the camera capture rate based on one’s heart rate. In our prototype called PulseCam an Android smart phone worn on the body acts as the picture capture device, adjusting its capture rate based on one’s heart rate as measured by an Android-based smart watch.</p> <p>The purpose of this work is two fold: a) we examine the potential of PulseCam to capture pictures of significant moments and b) investigate the potential of such pictures to improve one’s ability to remember. This paper introduces the general approach, describes our current prototype, and outlines the planned study design.</p> <p>For more information contact: </p> </article> <article> <h1>Re-Live the Moment: Visualizing Run Experiences to Motivate Future Exercises</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> UROP <strong>Status:</strong> Completed August 2014 <strong>Student:</strong> Jesper Findahl</p> <p>Contemporary psychology theory emphasizes that people are more likely to achieve planned behavior if they are reminded of previous good experiences of that behavior. Within the EU research project RECALL, we are investigating the effect of past memories on behavior change, i.e. make people run more by reminding them the fun moments they experienced during their previous run activities.</p> <p>This project focuses on existing technological improvements in data collection (advanced mobile and wearable sensors) and data visualization in order to create an experimental prototype for capturing and visualizing ones physical activities. The outcome will be an an Android app that records one’s physical workouts in the form of pictures and music one was listening to at the time, and, process those data to create a slide show of the experience.</p> <p>For more information contact: , </p> </article> <article> <h1>Reactive Displays: Supporting Dynamic Display Behavior Using a Rescheduling Approach</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed September 2014 <strong>Student:</strong> Alessandro Gusmeroli</p> <p>Public display systems are increasingly becoming part of the urban landscape, with systems being deployed in venues such as railway stations, shopping malls and city squares. Most of these public displays are simple slide show systems that broadcast content based on a predefined schedule, or a playlist, that does not change during the runtime. However, supporting interactive applications and more relevant content, public displays must be reactive to the user input and their preferences.</p> <p>As a follow-up to the international research project “PD-Net”, we are deploying a number of large public displays on the USI campus. These displays will form a part of an open display network that can show interactive and concurrently running applications and allow display users to influence application and content scheduling. Spontaneous user interactions are hard to predict and can occur at any moment in time requiring public displays to adjust, or reschedule, applications and their content. Thus, the displays need to produce a new schedule, or a playlist, every time there is an input from the users.</p> <p>The main goal of this project is to develop and evaluate the performance of a “rescheduling component” for public displays that produces a new schedule every time a user starts interacting with a display based on a given set of applications, associated constraints, and previous states of the display.</p> <p>The software basis for this work should be a) a web-based template for developing public display applications called “WE-BAT” developed at USI, and b) the SMT solving framework for expressing and solving scheduling problems. SMT solvers combine a rich and flexible high-level modeling language with efficient decision procedures. In the course of this project, an SMT solver, such as OpenSMT developed at USI, should be used to produce a display schedule based on the WE-BAT application template.</p> <p>For more information contact: </p> </article> <article> <h1>Roaming Objects – Object-encoded Digital Experiences</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> UROP <strong>Status:</strong> Completed August 2016 <strong>Student:</strong> Lucas Pennati</p> <p>In the era of Internet of Things traditionally “disconnected” objects such as household items, domestic electronics and even vehicles will have their own unique identity and can be addressed through the Web anywhere, anytime. With the advent of “sharing economy” services those objects could be easily found and shared among users (e.g., book sharing, ). Once shared the object provides a unique experience with a borrower, often could be described with a story. Remember, those checkout cards from the library, that indicates a name of a previous owner? The character of the Miyazaki’s ”Whisper of the heart” turned this simple discovery into a fascinating quest. Using this metaphor we would like to embed a digital tag (e.g. using NFC, QR code, augmented reality marker less tracking) on a “roamed” physical objects. The tags could be addressed digitally through a mobile phone or a web interface. Within the SHARING21 research project, we are looking into new ways of supporting sharing both digital information and physical objects. The bachelor project require to setup a mechanism to add, browse and retrieve digital experiences encoded into physical objects. Those experiences can be represented in form of textual, media or contextual (e.g. location, weather, mood) information associated with the object.</p> <p>Strong Web programming skills required, basic iOS programming skills an asset. All hardware will be provided.</p> <p>For more information contact: </p> </article> <article> <h1>Self-supervised Domain Adaptation for Sensor Data</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master UROP <strong>Status:</strong> Available</p> <p>Wearable devices, combined with Artificial Intelligence (AI) methods, can bring significant and sustainable improvements to our lives – from improved patient monitoring and decreased healthcare costs to enhanced sports performance and improved quality of life. Standard approaches involve Machine Learning (ML) techniques applied to the data captured from body-worn sensing devices. The ML techniques can be based on classical (feature-based) ML, or Deep Learning (DL) applied on the raw sensor data (end-to-end learning). A typical weakness that all ML-based HAR systems have, regardless of whether they are classical or DL-based, is the domain shift that can be caused by different sensor placements [1, 2].</p> <p>This project will explore personalization and domain-adaptation techniques to address important challenges in wearable computing: noisy data, limited data, and domain shifts in the labels and the sensor data due to subjectivity. ML processing pipelines (including deep learning techniques) will be augmented with the latest unsupervised and self-supervised learning techniques, including contrastive learning [3]. These advanced techniques should produce more robust and data-efficient models (i.e., requiring fewer person-specific labels). Diffusion-based approaches [4, 5] could also be considered.</p> <p>Project tasks:</p> <ol> <li>Overview of existing self-supervised learning approaches [6, 7, 8, 9, 10].</li> <li>Pre-process one dataset from wearable sensing systems. Example datasets include emotion recognition, activity recognition and energy expenditure estimation [11, 12, 13]</li> <li>Build baseline ML models using the dataset from step 2.</li> <li>Develop self-supervised ML approach and compare self-supervised models with the baseline ML models from step 3.</li> </ol> <p>This project is available as a MSc thesis and as a UROP internship project.</p> <h5 id="literature">Literature:</h5> <p>[1] Gjoreski, M.; Gjoreski, H.; Luštrek, M.; Gams, M. How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls? Sensors 2016, 16, 800. . [2] Kalabakov, S.; Stankoski, S.; Kiprijanovska, I.; Andova, A.; Rešˇciˇc, N.; Janko, V.; Gjoreski, M.; Gams, M.; Luštrek, M. What Actually Works for Activity Recognition in Scenarios with Significant Domain Shift: Lessons Learned from the 2019 and 2020 Sussex-Huawei Challenges. Sensors 2022, 22, 3613. . [3] Haresamudram, H., Essa, I., & Plötz, T. (2021). Contrastive predictive coding for human activity recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(2), 1-26. [4] K. Rasul, C. Seward, I. Schuster, and R. Vollgraf. Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In International Conference on Machine Learning, 2021. [5] L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, Y. Shao, W. Zhang, B. Cui, and M.-H. Yang. “Diffusion models: A comprehensive survey of methods and applications.” arXiv preprint arXiv:2209.00796 (2022). [6] J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch et al. “Bootstrap your own latent-a new approach to self-supervised learning.” Advances in neural information processing systems 33 (2020): 21271-21284. [7] D. Wei, J. J. Lim, A. Zisserman, and W. T. Freeman. “Learning and using the arrow of time.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8052-8060. 2018. [8] C. Tong, J. Ge, and N.D. Lane. “Zero-Shot Learning for IMU-Based Activity Recognition Using Video Embeddings.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 4 (2021): 1-23. [9] H. Kwon, C. Tong, H, Haresamudram, Y. Gao, G. D. Abowd, N.D. Lane, and T. Ploetz. “IMUTube: Automatic extraction of virtual on-body accelerometry from video for human activity recognition.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, no. 3, 2020. [10] Y. Jain, C. I. Tang, C. Min, F. Kawsar, and A. Mathur. “ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 1 (2022): 1-28. [11] M. Laporte, D. Gasparini, M. Gjoreski, and M. Langheinrich. “Exploring LAUREATE-the Longitudinal multimodAl stUdent expeRience datasEt for AffecT and mEmory research.” in the UbiComp/ISWC’22 Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2022. [12] Gashi, S., Min, C., Montanari, A., Santini, S., & Kawsar, F. (2022). A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices. Scientific Data, 9(1), 1-14. [13] Gjoreski, M., Kiprijanovska, I., Stankoski, S., Mavridou, I., Broulidakis, M. J., Gjoreski, H., & Nduka, C. (2022). Facial EMG sensing for monitoring affect using a wearable device. Scientific reports, 12(1), 1-12.</p> <p>For more information contact: </p> </article> <article> <h1>Shadow: An Application Selection Interface for Pervasive Public Displays</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed August 2014 <strong>Student:</strong> Federico Scacchi</p> <p>Public display systems are increasingly becoming part of the urban landscape. Most public displays today are simple slide-show systems that broadcast content based on a pre-selected schedule. However, public displays envisioned in the near future will provide a rich platform for running diverse interactive and concurrently running applications that display viewers can actively select for presentation.</p> <p>The aim of this project is to explore the development of an application selection interface for public displays in the form of user’s silhouette based on depth data and a touch screen interface. The main focus of this project is on: a) visualizing the presence of viewers in front of a display using depth information, b) adding icons of available display application to the user’s silhouette and change their size (silhouette and icons) depending on the users distance, and c) evaluating the interface. Application icons could be placed on the edges of the silhouette and by touching the icons viewers could select individual applications for an immediate on-demand presentation. The project should be developed using advanced web technologies that allow for streaming depth data and overlaying the shadow interface over the existing display content.</p> <p>For more information contact: </p> </article> <article> <h1>Smart Group Activity Journal – Creating an Automated Activity Feed for Outdoor Sports (SHA21.D)</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master <strong>Status:</strong> Draft <strong>Student:</strong></p> <p>Skiing and snowboarding are highly social activities. Winter enthusiasts capture and share vast amount of pictures and videos during outdoor vacations. To support information exchange among groups of skiers and snowboarders, this project seeks to create a semi-automated group activity journal. The journal would be automatically shared among group members with an option to grant access to external observers who want to follow a particular participant or the entire group in near real time. The student should implement an app that allows one to both post to and visualize a shared “event stream”. Events posted should be supported with a simple plug-in system, e.g., one could imagine a “slope tracker” that posts to the stream whenever one has finished a ski run. Types of content added to the shared stream should include, but are not limited to (1) a pin on a map with geolocation information to setup a meeting point while on the slope; (2) captured media content (photos, videos, an optional live-stream) of the run; (3) reference information in a form of text necessary for descent (e.g., the time left until sunset, the operational hours of a ski lift at a particular location, conditions of the slope with detailed information about potential hazards during descent). Following the recent trend in instant messaging services such as Snapchat or iMessage, where a message can expire after some time, optionally one would be able to associate an expiration tag to any events added to the feed. An additional smartwatch interface should support quick entry of items, e.g., one could post a “hazard” on the slopes by selecting a hazard type and the system automatically adding time and location. The shared activity journal will be hosted on a central server and will be accessible by group participants or observers through the app. An optional integration with an optical head-mounted display for augmented reality (RideOn, Recon Snow 2) is encouraged. Intermediate to strong iOS/Android programming skills required, strong Web programming skills an asset. Hardware such as a smartphone, smartwatch, and augmented reality gear will be provided.</p> <p>For more information contact: <strong>No items found</strong></p> </article> <article> <h1>SmartHelmet: Using LIDAR sensing to alert skiers</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Completed September 2015 <strong>Student:</strong> Filippo Ferrario</p> <p>Helmets are a necessity on today’s ski slopes, yet it still happens that one is not aware of an approaching skier from behind (in particular when one listens to music on headphones), leading to potentially dangerous collisions. The SmartHelmet project should explore the use of multiple LIDAR sensors (e.g., the LIDAR Lite by PulsedLight) in order to provide skiers with visual (LEDs) and/or auditory (headphones) warning of skiers approaching from behind.</p> <p>For more information contact: </p> </article> <article> <h1>Software Atelier 2: Human Computer Interaction</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p>Goal of the course is to provide students with theoretical and practical knowledge of human-centered design of interfaces for computing systems.</p> <p>The course starts with an introduction to ethics that investigates the many ways and venues information and communication technologies can be used maliciously. The course moves on to introduce the concept of user-centred design to produce usable, useful and used tools. The different stages from ideation to paper prototyping are covered in theory and practice.</p> <p>Teaching Mode</p> <p>Theoretical concepts will be provided through instructor-led lectures. Theory will be put to good use in practice, by letting students engage in group work to deliver a project, developed through progressive stages of design and prototyping.</p> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) Software Atelier II 2023 Spring Silvia Santini Irene Zanardi | Nouran Abdalazim Software Atelier II 2022 Spring Silvia Santini Leandro Soares Guedes | Nouran Abdalazim Software Atelier II 2021 Spring Silvia Santini Leandro Soares Guedes | Rodrigo Benedito Otoni Software Atelier II 2020 Spring Silvia Santini | Monica Landoni Rodrigo Benedito Otoni | Ilaria Scarabottolo Software Atelier II 2019 Spring Monica Landoni -</p> <p>USI Search Directory: [https://search.usi.ch/en/courses/35265708/software-atelier-2-human-computer-interaction]([types field=)</p> </article> <article> <h1>Statistical Analysis for Affect and Learning in the LAUREATE Dataset</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> UROP <strong>Status:</strong> Completed September 2022 <strong>Student:</strong> Cindy Guerrero Toro</p> <p>Affective states and cognition share a close relationship, influencing each other in several ways. In particular, our affect, i.e. our feelings and mood, impacts our perception of the world, our memory, and our decisions. This relationship also influences the cognitive process of learning. Working towards personalised student interventions for improving learning and mental well-being, we conducted a longitudinal study during a university semester. The resulting dataset, called LAUREATE (the Longitudinal multimodAl student expeRience datasEt for AffecT and mEmory research), consists of daily self-reports from students, audiovisual material from lectures, physiological signals from students and lecturers, students’ grades, and more. In this work, we present an initial analysis of lecturers’ and students’ experiences during lectures in terms of self-reported affect, especially positive activation (PA), negative activation (NA), valence (VA), and engagement.</p> <p>For more information contact: </p> </article> <article> <h1>Table Tennis Paddle Detection Library for Microsoft Kinect</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Completed June 2011 <strong>Student:</strong> Fabio Rambone</p> <p>The commercial introduction of a technology as the Microsoft Kinect at its relatively low cost continues the rev- olution of how humans can interact with Computers. This revolution has originated with the Nintendo Wii, allowing a user to interact with the a system with hand gestures through a controller called “WiiMote”. The WiiMote is held in one hand and through the use of an accelerometer and optical sensors it is capable of motion sensing.</p> <p>The Microsoft Kinect brought these capabilities to a new level, eliminating the need of a hand-held controller and allowing a precise position detection of the user in 3D space. This evolution extended greatly the possibilities of such a device from simple video games to much more, including its usage in virtual reality simulation. The goal of this bachelor project is to implement a table tennis paddle detection and tracking library for the Mi- crosoft Kinect. Which in turn will be used to make a realistic virtual table tennis application.</p> <p>For more information contact: Randolf Schärfig</p> </article> <article> <h1>Tacita-Mobile: Controlling Content Presentation on Personalized Public Displays Through Mobile Phones</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Completed June 2012 <strong>Student:</strong> Mattia Candeloro</p> <p>Public display systems are increasingly becoming part of the urban landscape, with systems being deployed in venues such as railway stations, shopping malls and city squares. Most public displays today are simple slide-show systems that broadcast content based on a pre-selected schedule. However, public displays envisioned in the near future will provide a rich platform for running diverse interactive applications with highly contextualized and personal- ized content. These applications will allow viewers to express their preferences both explicitly through interactive touch interfaces and implicitly using mobile handsets. This project explores the development of an Android mobile application that allows viewers to influence the content of the application displayed on public displays. The mobile application that has been developed is based on an HTML5 component that is the user interface in conjunction with native Android components that monitors locations of the mobile phone, implements the interaction with the displays using the WebSocket protocol and store users data.</p> <p>For more information contact: </p> </article> <article> <h1>The Human Dash Cam: A Human Memory Prosthesis in the Era of Ubiquitous Computing</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Draft <strong>Student:</strong> TBA</p> <p>The goal of this project is to develop a mobile app that collects continuously pics and contextual info from any possible source (e.g., a wearable camera, a smartwatch) and makes it immediately accessible to the user in a “messenger style” UI (i.e. using bubbles on the screen) that stacks context up to a 5 min buffer. When something “significant” happens, the user can go back and revisit the most recent pics/data captured and augment the data explicitly by adding tags, or by semi-automatically adding further context information (e.g., add GPS data, people vicinity as indicated by Bluetooth, or one’s emotional state as measured by a medical wristband). The so-tagged “memory” will be moved to storage directory, where meaningful events are stored. Eventually, this will form part of a larger endeavor to help people to better remember the significant moments in their day. This project requires excellent mobile development skills, and the willingness to quickly learn novel APIs that allow one to connect the mobile phone to wireless devices, such as a medical wristband (Empatica E3) or a wearable camera (Narrative Clip2). All hardware will be provided.</p> <p>For more information contact: <strong>No items found</strong></p> </article> <article> <h1>Transformers for human mobility modeling</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed <strong>Student:</strong> Riccardo Corrias</p> <p>In this BSc project you will implement a state-of-the-art model for human mobility modeling. The goal is to use Transformer networks (e.g., GPT-3, BERT), which are deep learning language/sequence modeling methods that have a potential to improve upon the previously used RNN- and LSTM-based methods. More specifically, in this project you will: (i) overview methods based on Transformer networks [8][9][10] with the aim to use them for human mobility modeling; (ii) use an existing dataset to train a Transformer network for human mobility modeling (e.g., next task prediction) [12][13]; (iii) analyze the behavior of the network for a variety of parameters (e.g., dataset size, embedding dimension, number of heads, etc.); (iv) summarize the results and propose future work.</p> <p>In the context of an MSc thesis or UROP project, you will develop state-of-the-art models for human mobility modeling. The goal is to use Transformer networks (e.g., GPT-3, BERT), which are deep learning language/sequence modeling methods that have a potential to improve upon the previously used RNN- and LSTM-based methods. More specifically, in this project you will: (i) overview methods based on Transformer networks [8][9][10] with the aim to use them for human mobility modeling; (ii) pre-preprocess two mobility datasets (e.g., [1][2]); build a novel Transformer network for human mobility modeling (e.g., next task prediction) [12][13]; (iii) compare the Transformer network to an existing baseline method for human mobility modeling [5][6]; (iv) summarize the results and propose future work.</p> <h5 id="references">References:</h5> <ol> <li>Mokhtar, Sonia Ben, Antoine Boutet, Louafi Bouzouina, Patrick Bonnel, Olivier Brette, Lionel Brunie, Mathieu Cunche et al. “PRIVA’MOV: Analysing Human Mobility Through Multi-Sensor Datasets.” 2017 [https://hal.inria.fr/hal-01578557/document]</li> <li>Moro, Arielle, Vaibhav Kulkarni, Pierre-Adrien Ghiringhelli, Bertil Chapuis, and Benoit Garbinato. “Breadcrumbs: A Feature Rich Mobility Dataset with Point of Interest Annotation.” arXiv preprint arXiv:1906.12322 (2019) [https://arxiv.org/pdf/1906.12322.pdf]</li> <li>Luca, Massimiliano, Gianni Barlacchi, Bruno Lepri, and Luca Pappalardo. “Deep Learning for Human Mobility: a Survey on Data and Models.” arXiv preprint arXiv:2012.02825 (2020). [https://arxiv.org/pdf/2012.02825.pdf]</li> <li>Feng, Jie, Zeyu Yang, Fengli Xu, Haisu Yu, Mudan Wang, and Yong Li. “Learning to Simulate Human Mobility.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3426-3433. 2020. [https://www.youtube.com/watch?v=sj4UCW0P6Ks&ab_channel=AssociationforComputingMachinery%28ACM%29]</li> <li>Feng, Jie, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. “Deepmove: Predicting human mobility with attentional recurrent networks.” In Proceedings of the 2018 world wide web conference, pp. 1459-1468. 2018. [https://github.com/vonfeng/DeepMove]</li> <li>Yu, Lantao, Weinan Zhang, Jun Wang, and Yong Yu. “Seqgan: Sequence generative adversarial nets with policy gradient.” In Thirty-first AAAI conference on artificial intelligence. 2017. [https://github.com/LantaoYu/SeqGAN]</li> <li>scikit-mobility: mobility analysis in Python</li> <li>Brown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan et al. “Language models are few-shot learners.” arXiv preprint arXiv:2005.14165 (2020). [https://arxiv.org/abs/2005.14165]</li> <li>Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. “Attention is all you need.” In Advances in neural information processing systems, pp. 5998-6008. 2017. [https://arxiv.org/abs/1706.03762]</li> <li>Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. “Bert: Pre-training of deep bidirectional transformers for language understanding.” arXiv preprint arXiv:1810.04805 (2018). [https://arxiv.org/pdf/1810.04805.pdf?source=post_elevate_sequence_page—————————]</li> <li>“Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing”. Google AI Blog. Retrieved 2019-11-27.</li> <li> </li> <li> </li> <li> </li> <li> </li> </ol> <p>For more information contact: </p> </article> <article> <h1>Ubiquitous Computing</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Class is inactive.</strong></p> <p>This class was last offered in 2013. The class offers a hands-on experience of Ubiquitous Computing technologies.</p> <p>The goal of this class is to introduce the vision of ubiquitous computing and expose students to the fundamental concepts and technologies in the area. The term “ubiquitous computing” describes the vision of incorporating sensing, computation, and communication into everyday things in order to make them and their surroundings “smart”. Smart things can detect where they are, sense what is around them, detect and communicate with other smart things, remember what they were used for previously, and reason about the kind of future actions they might likely be used for. Interaction with smart things does not happen through keyboards and mice, but through tangible interfaces, contactless ID chips, gesture and movement detection, or through wearable sensors.</p> <p>Originally conceived in the early 1990s, this vision of a disappearing computer is rapidly attaining levels of both technical and economical feasibility. Organic displays (OLEDs), electronic ink (eInk), Near Field Communication (NFC), radio frequency identification (RFID), Zigbee, and Bluetooth are but a few examples of the recent technological developments that drive this trend; Pay-per-use insurances, smart homes, and mobile payments but a few examples of recent economic developments.</p> <h5 id="contents">Contents</h5> <p>This lecture (2h per week) will offer an overview of various technological, economic, and social topics in the area of ubiquitous computing. Specific topics covered may include, but are not limited to:</p> <ul> <li>The vision of “ubiquitous computing”</li> <li>Technology trends</li> <li>Wireless communication (WiFi, Bluetooth) and ad-hoc networking</li> <li>Location awareness and location-based services</li> <li>Smart labels (RFIDs)</li> <li>Embedded systems and wireless sensor networks</li> <li>Energy issues</li> <li>Interaction with invisible computers</li> <li>Wearable computing</li> <li>Activity recognition</li> <li>Application scenarios, economic implications</li> <li>Social implication and public policy debate</li> </ul> <h5 id="teaching-mode">Teaching Mode</h5> <p>One lecture per week with weekly homework assignment, ranging from reading assignments, to simulations, to concept videos.</p> <h5 id="references">References</h5> <p>John Krum (Ed.): Ubiquitous Computing. CRC Press, October 2009.</p> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) Ubiquitous Computing 2013 Fall - - Ubiquitous Computing 2012 Fall - - Ubiquitous Computing 2011 Fall - - Ubiquitous Computing 2010 Fall - - Ubiquitous Computing 2009 Fall - -</p> </article> <article> <h1>Ubiquitous Computing Lab</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Class is inactive.</strong></p> <p>This class was last offered in 2012.</p> <p>The goal of this class is to provide a hands-on introduction to ubiquitous computing applications. It is meant to be taken in conjunction with the Ubiquitous Computing lecture, which will provide the theoretical background. Topics covered are:</p> <ul> <li>Embedded Systems programming (Arduino)</li> <li>Mobile programming (Android)</li> <li>RFID</li> </ul> <p>The students work in teams to build a ubiquitous computing application consisting of sensing, communicating, and actuating.</p> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) Ubiquitous Computing Lab 2012 Fall - - Ubiquitous Computing Lab 2011 Fall - - Ubiquitous Computing Lab 2010 Fall - -</p> </article> <article> <h1>Uncertainty-aware Deep Learning in digital healthcare</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Master UROP <strong>Status:</strong> Available</p> <p>In this thesis, the objective is to investigate the use of Monte Carlo Dropout, a Bayesian Deep Learning technique, to make uncertainty prediction on Neural Network outputs when dealing with physiological data. In particular, the student will investigate the creation of a system using Early Exit to adjust the computational power based on the uncertainty prediction. The student will leverage state-of-the-art Deep Learning models to make predictions on publicly available datasets for health applications, e.g., ECG data from healthy and unhealthy individuals.</p> <p>This project is available as a MSc thesis or as a EUROP project.</p> <p>For more information contact: , </p> </article> <article> <h1>Uncovering needs and requirements for data sharing activities in sports</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed August 2015 <strong>Student:</strong> Nadeen Alkaydi</p> <p>Digital cameras, mobile phones, portable music players, activity trackers, and similar devices make creating, uploading, and interacting with digital content easier today than ever before. Users are increasingly interested in sharing content across computers and devices and across an ever-increasing number of online platforms (e.g, YouTube, Facebook, Google Docs, Instagram) that make data potentially available anywhere.</p> <p>The proposed master thesis aims to discover needs and requirements of sportspeople for sharing activity-related personal data, both biometrical and non-biometrical. We invite a master student to conduct an empirical study within the extended USI community, encompassing students, their families and friends. The student should focus on comparing sharing requirements and practices across different sport activities (e.g. runners vs alpine skiers), document differences in sharing habits. The ultimate research question to be answered is: How do sportspeople practices and the context of an activity influence the communication needs and practices of sharing digital sports-related data?</p> <p>For more information contact: </p> </article> <article> <h1>Understanding Practices and Motivations for Sharing Physical Resources through Digital Services (SHA21.B)</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master <strong>Status:</strong> Draft <strong>Student:</strong></p> <p>Today, vast amounts of user-generated and user-mediated content populates social networks. Current research has focused extensively on needs, practices, and concerns surrounding the sharing of photos and videos, textual information (e.g., status updates), and documents. However, in recent years, the scope of what is “shareable” has greatly increased, comprising not only audio-visual content but also preferences and tastes (e.g., playlists, food), physiological data (e.g., workouts), trips, and even information about and access to real-world artifacts (e.g., “couchsurfing”). A recent market trend is to share personal physical possessions, initially rooms and apartments (e.g., Airbnb), but more recently rides (Uber), cars (Getaround) and household items (Snapgoods). The goal of this project is to design, conduct and subsequently analyze an online survey that attempts to elicit current practices of usage of selected sharing economy services, as well as identify motivations to participate in such services. Additionally, the student should conduct contextual interviews involving different stakeholders of such service (e.g. users, non-users, owners, suppliers) to further understand the economic role of using such sharing-economy services. This projects requires strong analytical skills and a willingness to learn about a novel and emerging research field. Experience with performing survey research and/or interviews a plus.</p> <p>For more information contact: <strong>No items found</strong></p> </article> <article> <h1>Understanding Purpose-Driven Use of Location Sharing Services</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Bachelor Master UROP <strong>Status:</strong> Draft <strong>Student:</strong></p> <p>So-called “geosocial” applications allow one to share one’s current location with friends, families, or even the public. This project investigates how the particular location sharing functionalities of a geosocial app, namely continuous, proximity-based and ad-hoc sharing, affect its actual use in daily life. To this end, we drew up a Location Sharing Acceptance Model (LSAM) that comprises user acceptance factors for location sharing from previous works, and conducted its initial validation by means of a 4-week study involving 36 participants where we collected survey data and actual application use. In a second step, we now seek to validate these findings in a larger follow-up study, conducted via a crowdsourcing platform. This requires a highly motivated student that is (a) willing to learn about survey research and the statistical models involved, and (b) devises, creates, conducts, and analysis a crowdsourcing study using an online survey platform.</p> <p>For more information contact: <strong>No items found</strong></p> </article> <article> <h1>User Experience Design</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p>This class aims at familiarizing students with both the theory behind the discipline of Human Computer Interaction (HCI) and the practical process of User eXperience (UX) design. Students not only develop an awareness and appreciation of the crucial implications of good interfaces in terms of overall system performance and user satisfaction, but also learn core skills needed in order to identify user requirements, envision interfaces and processes, and evaluate competing design options. Students will work in small teams of 3-5 to drive a design project from start to finish. Core skills are introduced in hands-on classes, interspersed with lectures and discussions about the underlying theory.</p> <h5 id="contents">Contents</h5> <p>Definitions and Formalisms; User requirement Gathering and Analysis; Focus Groups, Interviews and Questionnaires; Scenario Based Approach; Interface Design; Use of Metaphors and Models; Mock-ups and Prototypes; Evaluation Techniques: Heuristic vs. Empirical; Formative Evaluation.</p> <h5 id="teaching-mode">Teaching Mode</h5> <p>Students will be encouraged to take an active role and explore the importance of HCI on software design, development, testing and evaluation. Classes will alternate traditional lecture based ones with problem based learning sessions when students will be working in small groups to find solutions to problems generated by poor interface design.</p> <h5 id="references">References</h5> <p>The course textbook is</p> <ul> <li>The UX Book: Process and Guidelines for Ensuring a Quality User Experience; Rex Hartson and Pardha Pyla; Morgan Kaufmann, 2012, ISBN: 0123852412.</li> </ul> <p>However, students are encouraged to also look at other textbooks in his area, including:</p> <ul> <li>Carroll, J., M. (2002) Human-computer interaction in the new millennium, Addison Wesley</li> <li>Carroll, J., M. (2003) HCI Models, Theories, and Frameworks: Toward a Multidisciplinary Science (Interactive Technologies)</li> <li>Lazar J. Dr.. (2007) Universal Usability: Designing Computer Interfaces for Diverse User Populations, John Wiley and Sons.</li> <li>Raskin J. (2000) The Humane Interface: New Directions for Designing Interactive Systems, ACM Press.</li> <li>Shneiderman, B., Plaisant, C. (2004) Designing the User Interface: Strategies for Effective Human-Computer Interaction; Addison-Wesley, 1987. Fourth edition with Catherine Plaisant as co-author 2004.</li> </ul> <p>Students are strongly encouraged to use online sources and journals, starting from the very extensive HCI bibliography web site, by Gary Perlman. It is highly recommended to browse USI library looking at the HCI dedicated shelf, Dewey code 004.019 where students will find all relevant textbooks. Lecturer will point at additional relevant papers for students to read and discuss weekly in class.</p> <h5 id="editions">Editions</h5> <p>Class Semester Instructor Teaching Assistant(s) UX Design 2022 Fall Marc Langheinrich - UX Design 2018 Fall - - UX Design 2017 Fall - - HCI Design 2016 Fall - - HCI Design 2015 Fall - - HCI Design 2014 Spring - - HCI Design 2013 Spring - - HCI Design 2012 Spring - - HCI Design 2011 Spring - -</p> <p>USI Search Directory: </p> </article> <article> <h1>User identification in real-world settings using wearables</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p><strong>Type:</strong> Master <strong>Status:</strong> Assigned January 2023 <strong>Student:</strong> Emilio Lingenthal</p> <p>Wearable devices provide the means for continuously identify users by performing biometric recognition. The highly subject-dependent nature of physiological data (e.g., heart rate, electrodermal activity, skin temperature) makes it possible to identify the user by tracking those traits over time, as it is already done nowadays with the fingerprint or iris recognition. This project aims to understand to what extent user identification is possible and if different condition can hamper that capability. After performing data exploration on an existing data, provided by the supervisor, the student will develop a machine learning model to identify users from wearable data. In addition, he/she will test the efficiency of common anonymization techniques, such as noise addition and synthetic data generation.</p> <p>For more information contact: </p> </article> <article> <h1>USIDisplays: Designing a Multi-Application Display Experience</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Bachelor <strong>Status:</strong> Completed June 2012 <strong>Student:</strong> Marcello Romanelli</p> <p>Public display systems are increasingly becoming part of the urban landscape, with systems being deployed in venues such as railway stations, shopping malls and city squares. Most public displays today are simple slide-show systems that broadcast content based on a pre-selected schedule. However, public displays envisioned in the near future will provide a rich platform for running diverse interactive applications with highly contextualized and personalized content. These applications will allow viewers to express their preferences both explicitly through interactive touch interfaces and implicitly using mobile handsets.</p> <p>The main focus of this thesis is to create a new architecture capable of supporting a wide range of applications that can be deployed on a distributed display network. Moreover, users will have a chance to influence applications displayed the screens either with their presence or by direct interaction with the screens.</p> <p>In this report, I will explain the system’s architecture and how we used the latest web 2.0 technologies provided by HTML5 API in order to provide the desired functionalities. The components that have been developed are a display manager, an application repository, an initial set of applications and of course a display component.</p> <p>The result of this work has been an initial set of application that can run concurrently inside a single display. An additional characteristic of these applications, is that they can be influenced both in their aspect and in their content via the interaction with the mobiles phones of the nearby users.</p> <p>For more information contact: </p> </article> <article> <h1>WeatherUSI: Crowd Sourcing the Weather on Public Displays</h1> <p>Mon, 01 Jan 0001 00:00:00 +0000</p> <p> <figure > <div class="flex justify-center "> <div class="w-full" ></div> </div></figure> </p> <p><strong>Type:</strong> Master <strong>Status:</strong> Completed January 2016 <strong>Student:</strong> Ahmed Fouad</p> <p>Public display systems are increasingly becoming part of the urban landscape. Most public displays today are simple slide-show systems that broadcast content in the form of static images. However, public displays envisioned in the near future will not only integrate content from a number of different sources, but also serve as data collection stations. Indeed, research has shown that public displays may hold a significant potential in “crowd sourcing”, when motivational design and feedback validation mechanisms are employed.</p> <p>Crowd sourcing is a process where a large number of volunteer users contribute information through an online platform. A well-known example of crowd sourcing is Wikipedia. In the context of this thesis, the student should explore the design, implementation, and evaluation of a crowd sourcing public display application for weather information. This application builds upon the existing infrastructure of “Atmos”, a participatory sensing app that allows users to contribute subjective weather experiences, as well as their personal predictions, through their mobile phones. WeatherUSI should port the main principle of Atmos to a public display app and use the Atmos backend services to store user feedback, ideally merging both mobile (i.e., through a smartphone) and stationary (i.e., at a public display) Atmos reports. The main challenge of the work will be: (a) a design that will draw in passery-by, e.g., by using gamification elements; (b) an implementation that cleverly supports the multi-region public display engine in use at USI; and (c) an evaluation that verifies the feasibility of crowd sourcing weather information through public displays.</p> <p>For more information contact: </p> </article> </main></body></html>