相关论文: "Chasing Shadows": Understanding Personal Data Ext…
Many individuals especially those with autism spectrum disorder (ASD), alexithymia, or other neurodivergent profiles face challenges in recognizing, expressing, or interpreting emotions. To support more inclusive and personalized emotion…
Emotion recognition is a complex task due to the inherent subjectivity in both the perception and production of emotions. The subjectivity of emotions poses significant challenges in developing accurate and robust computational models. This…
Self-tracking can improve people's awareness of their unhealthy behaviors and support reflection to inform behavior change. Increasingly, new technologies make tracking easier, leading to large amounts of tracked data. However, much of that…
User experience research often uses surveys and interviews, which may miss subconscious user interactions. This study explores eye-tracking and biometric feedback as tools to assess user engagement and cognitive load in digital interfaces.…
How do different brains create unique visual experiences from identical sensory input? While neural representations vary across individuals, the fundamental architecture underlying these differences remains poorly understood. Here, we…
Mental health disorders create profound personal and societal burdens, yet conventional diagnostics are resource-intensive and limit accessibility. Advances in artificial intelligence, particularly natural language processing and multimodal…
Mental health assessments are of central importance to individuals' well-being. Conventional assessment methodologies predominantly depend on clinical interviews and standardised self-report questionnaires. Nevertheless, the efficacy of…
The difficulty intrinsic to a given example, rooted in its inherent ambiguity, is a key yet often overlooked factor in evaluating neural NLP models. We investigate the interplay and divergence among various metrics for assessing intrinsic…
Digital technologies are reshaping how people experience their surroundings, often pulling focus toward virtual spaces and making it harder to stay present and engaged. Wearable augmented reality (AR), by embedding digital information into…
Multiple people tracking is a key problem for many applications such as surveillance, animation or car navigation, and a key input for tasks such as activity recognition. In crowded environments occlusions and false detections are common,…
Personal data cover multiple aspects of our daily life and activities, including health, finance, social, Internet, Etc. Personal data visualisations aim to improve the user experience when exploring these large amounts of personal data and…
Data visualizations are increasingly seen as socially constructed, with several recent studies positing that perceptions and interpretations of visualization artifacts are shaped through complex sets of interactions between members of a…
Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations. This paper investigates anonymization methods based on representation…
Differential privacy protects an individual's privacy by perturbing data on an aggregated level (DP) or individual level (LDP). We report four online human-subject experiments investigating the effects of using different approaches to…
Human-like AI agents such as robots and chatbots are becoming increasingly popular, but they present a variety of ethical concerns. The first concern is in how we define humanness, and how our definition impacts communities historically…
The progression from novice to disciplinary expert is a longstanding area of inquiry in educational research. Studies investigating such progressions have often resorted to participants' self-assessments or other qualitative indicators as a…
Data sparsity is a key challenge limiting the power of AI tools across various domains. The problem is especially pronounced in domains that require active user input rather than measurements derived from automated sensors. It is a critical…
Research on Cognitive Personal Informatics (CPI) is steadily growing as new wearable cognitive tracking technologies emerge on the consumer market, claiming to measure stress, focus, and other cognitive factors. At the same time, with…
The cognitive framework of conceptual spaces bridges the gap between symbolic and subsymbolic AI by proposing an intermediate conceptual layer where knowledge is represented geometrically. There are two main approaches for obtaining the…
Affective computing - combining sensor technology, machine learning, and psychology - have been studied for over three decades and is employed in AI-powered technologies to enhance emotional awareness in AI systems, and detect symptoms of…