相关论文: Characterizing the Quality of Insight by Interacti…
Graphical perception studies are a key element of visualization research, forming the basis of design recommendations and contributing to our understanding of how people make sense of visualizations. However, graphical perception studies…
We present a review on the current state of publicly available datasets within the human action recognition community; highlighting the revival of pose based methods and recent progress of understanding person-person interaction modeling.…
We introduce a dataset for classifying wellness dimensions in social media user posts, covering six key aspects: physical, emotional, social, intellectual, spiritual, and vocational. The dataset is designed to capture these dimensions in…
The degree of concentration, enthusiasm, optimism, and passion displayed by individual(s) while interacting with a machine is referred to as `user engagement'. Engagement comprises of behavioral, cognitive, and affect related cues. To…
The experience and adoption of conversational search is tied to the accuracy and completeness of users' mental models -- their internal frameworks for understanding and predicting system behaviour. Thus, understanding these models can…
The frequency with which people interact with technology means that users may develop interface habits, i.e. fast, automatic responses to stable interface cues. Design guidelines often assume that interface habits are beneficial. However,…
Web search is among the most frequent online activities. Whereas traditional information retrieval techniques focus on the information need behind a user query, previous work has shown that user behaviour and interaction can provide…
Users increasingly face multiple interface features on one hand, and constraints on available resources (e.g., time, attention) on the other. Understanding the sensitivity of users' well-being to feature type and resource constraints, is…
This paper describes a novel approach to systematically improve information interactions based solely on its wording. Following an interdisciplinary literature review, we recognized three key attributes of words that drive user engagement:…
Collective robotic systems are biologically inspired and advantageous due to their apparent global intelligence and emergent behaviors. Many applications can benefit from the incorporation of collectives, including environmental monitoring,…
We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point…
Science has long been viewed as a key driver of economic growth and rising standards of living. Knowledge about how scientific advances support marketplace inventions is therefore essential for understanding the role of science in…
Assessing drivers' interaction capabilities is crucial for understanding human driving behavior and enhancing the interactive abilities of autonomous vehicles. In scenarios involving strong interaction, existing metrics focused on…
Understanding how players interact with the mobile game app on smartphone devices is important for game experts to develop and refine their app products. Conventionally, the game experts achieve their purposes through intensive user studies…
Privacy policies are intended to support informed consent, yet users rarely read them fully. This study examines how common privacy policy interface structures influence attention allocation, reading behavior, and perceived experience.…
Privacy Policies are a cornerstone of informed consent, yet a persistent gap exists between their legal intent and practical efficacy. Despite decades of Human-Computer Interaction (HCI) research proposing various visualizations, user…
In an era of AI's growing capabilities and influences, recent advancements are reshaping HCI and CSCW's view of AI. Playful interactions emerged as an important way for users to make sense of the ever-changing AI technologies, yet remained…
Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable. In this work, we propose a method to both interpret and augment the…
Large language models (LLMs) have demonstrated the potential to mimic human social intelligence. However, most studies focus on simplistic and static self-report or performance-based tests, which limits the depth and validity of the…
The automobile is always a point of interest where new technology has been deployed. Because of this interest, human-vehicle interaction has been an appealing area for much research in recent years. The current in-vehicle design has been…