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Broadening Privacy and Surveillance: Eliciting Interconnected Values with a Scenarios Workbook on Smart Home Cameras arXiv.cs.HC Pub Date : 2024-05-17 Richmond Y. Wong, Jason Caleb Valdez, Ashten Alexander, Ariel Chiang, Olivia Quesada, James Pierce
We use a design workbook of speculative scenarios as a values elicitation activity with 14 participants. The workbook depicts use case scenarios with smart home camera technologies that involve surveillance and uneven power relations. The scenarios were initially designed by the researchers to explore scenarios of privacy and surveillance within three social relationships involving "primary" and "non-primary"
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Evaluating Saliency Explanations in NLP by Crowdsourcing arXiv.cs.HC Pub Date : 2024-05-17 Xiaotian Lu, Jiyi Li, Zhen Wan, Xiaofeng Lin, Koh Takeuchi, Hisashi Kashima
Deep learning models have performed well on many NLP tasks. However, their internal mechanisms are typically difficult for humans to understand. The development of methods to explain models has become a key issue in the reliability of deep learning models in many important applications. Various saliency explanation methods, which give each feature of input a score proportional to the contribution of
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The AI Collaborator: Bridging Human-AI Interaction in Educational and Professional Settings arXiv.cs.HC Pub Date : 2024-05-16 Mohammad Amin Samadi, Spencer JaQuay, Jing Gu, Nia Nixon
AI Collaborator, powered by OpenAI's GPT-4, is a groundbreaking tool designed for human-AI collaboration research. Its standout feature is the ability for researchers to create customized AI personas for diverse experimental setups using a user-friendly interface. This functionality is essential for simulating various interpersonal dynamics in team settings. AI Collaborator excels in mimicking different
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Tell me more: Intent Fulfilment Framework for Enhancing User Experiences in Conversational XAI arXiv.cs.HC Pub Date : 2024-05-16 Anjana Wijekoon, David Corsar, Nirmalie Wiratunga, Kyle Martin, Pedram Salimi
The evolution of Explainable Artificial Intelligence (XAI) has emphasised the significance of meeting diverse user needs. The approaches to identifying and addressing these needs must also advance, recognising that explanation experiences are subjective, user-centred processes that interact with users towards a better understanding of AI decision-making. This paper delves into the interrelations in
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Air Signing and Privacy-Preserving Signature Verification for Digital Documents arXiv.cs.HC Pub Date : 2024-05-17 P. Sarveswarasarma, T. Sathulakjan, V. J. V. Godfrey, Thanuja D. Ambegoda
This paper presents a novel approach to the digital signing of electronic documents through the use of a camera-based interaction system, single-finger tracking for sign recognition, and multi commands executing hand gestures. The proposed solution, referred to as "Air Signature," involves writing the signature in front of the camera, rather than relying on traditional methods such as mouse drawing
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ChatGPT in Classrooms: Transforming Challenges into Opportunities in Education arXiv.cs.HC Pub Date : 2024-05-17 Harris Bin Munawar, Nikolaos Misirlis
In the era of exponential technology growth, one unexpected guest has claimed a seat in classrooms worldwide, Artificial Intelligence. Generative AI, such as ChatGPT, promises a revolution in education, yet it arrives with a double-edged sword. Its potential for personalized learning is offset by issues of cheating, inaccuracies, and educators struggling to incorporate it effectively into their lesson
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Beyond static AI evaluations: advancing human interaction evaluations for LLM harms and risks arXiv.cs.HC Pub Date : 2024-05-17 Lujain Ibrahim, Saffron Huang, Lama Ahmad, Markus Anderljung
Model evaluations are central to understanding the safety, risks, and societal impacts of AI systems. While most real-world AI applications involve human-AI interaction, most current evaluations (e.g., common benchmarks) of AI models do not. Instead, they incorporate human factors in limited ways, assessing the safety of models in isolation, thereby falling short of capturing the complexity of human-model
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CNER: A tool Classifier of Named-Entity Relationships arXiv.cs.HC Pub Date : 2024-05-17 Jefferson A. Peña Torres, Raúl E. Gutiérrez De Piñerez
We introduce CNER, an ensemble of capable tools for extraction of semantic relationships between named entities in Spanish language. Built upon a container-based architecture, CNER integrates different Named entity recognition and relation extraction tools with a user-friendly interface that allows users to input free text or files effortlessly, facilitating streamlined analysis. Developed as a prototype
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IntelliExplain: Enhancing Interactive Code Generation through Natural Language Explanations for Non-Professional Programmers arXiv.cs.HC Pub Date : 2024-05-16 Hao Yan, Thomas D. Latoza, Ziyu Yao
Large language models (LLMs) have exhibited a strong promise in automatically generating executable code from natural language descriptions, particularly with interactive features that allow users to engage in the code-generation process by instructing the LLM with iterative feedback. However, existing interaction paradigms often assume that users have expert knowledge to debug source code and are
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A Design Trajectory Map of Human-AI Collaborative Reinforcement Learning Systems: Survey and Taxonomy arXiv.cs.HC Pub Date : 2024-05-16 Zhaoxing Li
Driven by the algorithmic advancements in reinforcement learning and the increasing number of implementations of human-AI collaboration, Collaborative Reinforcement Learning (CRL) has been receiving growing attention. Despite this recent upsurge, this area is still rarely systematically studied. In this paper, we provide an extensive survey, investigating CRL methods based on both interactive reinforcement
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How To Save A World: The Go-Along Interview as Game Preservation Methodology in Wurm Online arXiv.cs.HC Pub Date : 2024-05-16 Florence Smith Nicholls, Michael Cook
Massively multiplayer online (MMO) games boomed in the late 1990s to 2000s. In parallel, ethnographic studies of these communities emerged, generally involving participant observation and interviews. Several decades on, many MMOs have been reconfigured, remastered or are potentially no longer accessible at all, which presents challenges for their continued study and long-term preservation. In this
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"The Death of Wikipedia?" -- Exploring the Impact of ChatGPT on Wikipedia Engagement arXiv.cs.HC Pub Date : 2024-05-16 Neal Reeves, Wenjie Yin, Elena Simperl, Miriam Redi
Wikipedia is one of the most popular websites in the world, serving as a major source of information and learning resource for millions of users worldwide. While motivations for its usage vary, prior research suggests shallow information gathering -- looking up facts and information or answering questions -- dominates over more in-depth usage. On the 22nd of November 2022, ChatGPT was released to the
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Firefighters' Perceptions on Collaboration and Interaction with Autonomous Drones: Results of a Field Trial arXiv.cs.HC Pub Date : 2024-05-16 Moyi Li, Dzmitry Katsiuba, Mateusz Dolata, Gerhard Schwabe
Applications of drones in emergency response, like firefighting, have been promoted in the past decade. As the autonomy of drones continues to improve, the ways in which they are integrated into firefighting teams and their impact on crews are changing. This demands more understanding of how firefighters perceive and interact with autonomous drones. This paper presents a drone-based system for emergency
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Mental Well-being Opportunities in Interacting and Reflecting with Personal Data Sculptures of EEG arXiv.cs.HC Pub Date : 2024-05-16 Maria Teresa Ortoleva, Rita Borgo, Alfie Abdul-Rahman
Data physicalization is a research area in quick expansion whose necessity and popularity are motivated by the pervasiveness of data in our everyday. While the reflective ability of personal data physicalization has been vastly documented, their mental health and emotional well-being benefits remain largely unexplored. We present a qualitative study where we create personal data sculptures of electroencephalograms
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Discussing Risks and Benefits in the Future of Hybrid Rehabilitation and Fitness in Mixed Reality arXiv.cs.HC Pub Date : 2024-05-16 Jana Franceska Funke, Enrico Rukzio
In a world where in-person context transitions more into remote and hybrid concepts, we should consider new concepts of interaction in health and rehabilitation and what advantages and disadvantages they bring. One of the rising topics is mixed reality, where we can use the advantages of immersive 3D, 360-degree environments. Meanwhile, physical activity is further decreasing and with it negative effects
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Enhancing Saliency Prediction in Monitoring Tasks: The Role of Visual Highlights arXiv.cs.HC Pub Date : 2024-05-15 Zekun Wu, Anna Maria Feit
This study examines the role of visual highlights in guiding user attention in drone monitoring tasks, employing a simulated interface for observation. The experiment results show that such highlights can significantly expedite the visual attention on the corresponding area. Based on this observation, we leverage both the temporal and spatial information in the highlight to develop a new saliency model:
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Modeling User Preferences via Brain-Computer Interfacing arXiv.cs.HC Pub Date : 2024-05-15 Luis A. Leiva, Javier Ttraver, Alexandra Kawala-Sterniuk, Tuukka Ruotsalo
Present Brain-Computer Interfacing (BCI) technology allows inference and detection of cognitive and affective states, but fairly little has been done to study scenarios in which such information can facilitate new applications that rely on modeling human cognition. One state that can be quantified from various physiological signals is attention. Estimates of human attention can be used to reveal preferences
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Beyond Repetition: The Role of Varied Questioning and Feedback in Knowledge Generalization arXiv.cs.HC Pub Date : 2024-05-15 Gautam Yadav, Paulo F. Carvalho, Elizabeth A. McLaughlin, Kenneth R. Koedinger
This study examines the effects of question type and feedback on learning outcomes in a hybrid graduate-level course. By analyzing data from 32 students over 30,198 interactions, we assess the efficacy of unique versus repeated questions and the impact of feedback on student learning. The findings reveal students demonstrate significantly better knowledge generalization when encountering unique questions
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AKN_Regie: a bridge between digital and performing arts arXiv.cs.HC Pub Date : 2024-05-14 Georges GagneréINREV
In parallel with the dissemination of information technology, we note the persistence of frontiers within creative practices, in particular between the digital arts and the performing arts. Crossings of these frontiers brought to light the need for a common appropriation of digital issues. As a result of this appropriation, the AvatarStaging platform and its software dimension AKN_Regie will be described
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Societal Adaptation to Advanced AI arXiv.cs.HC Pub Date : 2024-05-16 Jamie Bernardi, Gabriel Mukobi, Hilary Greaves, Lennart Heim, Markus Anderljung
Existing strategies for managing risks from advanced AI systems often focus on affecting what AI systems are developed and how they diffuse. However, this approach becomes less feasible as the number of developers of advanced AI grows, and impedes beneficial use-cases as well as harmful ones. In response, we urge a complementary approach: increasing societal adaptation to advanced AI, that is, reducing
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Revisiting OPRO: The Limitations of Small-Scale LLMs as Optimizers arXiv.cs.HC Pub Date : 2024-05-16 Tuo Zhang, Jinyue Yuan, Salman Avestimehr
Numerous recent works aim to enhance the efficacy of Large Language Models (LLMs) through strategic prompting. In particular, the Optimization by PROmpting (OPRO) approach provides state-of-the-art performance by leveraging LLMs as optimizers where the optimization task is to find instructions that maximize the task accuracy. In this paper, we revisit OPRO for automated prompting with relatively small-scale
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Investigating the Effect of Operation Mode and Manifestation on Physicalizations of Dynamic Processes arXiv.cs.HC Pub Date : 2024-05-15 Daniel Pahr, Henry Ehlers, Hsiang-Yun Wu, Manuela Waldner, Renata G. Raidou
We conducted a study to systematically investigate the communication of complex dynamic processes along a two-dimensional design space, where the axes represent a representation's manifestation (physical or virtual) and operation (manual or automatic). We exemplify the design space on a model embodying cardiovascular pathologies, represented by a mechanism where a liquid is pumped into a draining vessel
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Evaluation scheme for children-centered language interaction competence of AI-driven robots arXiv.cs.HC Pub Date : 2024-05-15 Siqi Xie, Jiantao Li
This article explores the evaluation method for the language communication proficiency of AI-driven robots engaging in interactive communication with children. The utilization of AI-driven robots in children's everyday communication is swiftly advancing, underscoring the importance of evaluating these robots'language communication skills. Based on 11 Chinese families' interviews and thematic analysis
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Cross-Cultural Validation of Partner Models for Voice User Interfaces arXiv.cs.HC Pub Date : 2024-05-15 Katie Seaborn, Iona Gessinger, Suzuka Yoshida, Benjamin R. Cowan, Philip R. Doyle
Recent research has begun to assess people's perceptions of voice user interfaces (VUIs) as dialogue partners, termed partner models. Current self-report measures are only available in English, limiting research to English-speaking users. To improve the diversity of user samples and contexts that inform partner modelling research, we translated, localized, and evaluated the Partner Modelling Questionnaire
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Impact of Design Decisions in Scanpath Modeling arXiv.cs.HC Pub Date : 2024-05-14 Parvin Emami, Yue Jiang, Zixin Guo, Luis A. Leiva
Modeling visual saliency in graphical user interfaces (GUIs) allows to understand how people perceive GUI designs and what elements attract their attention. One aspect that is often overlooked is the fact that computational models depend on a series of design parameters that are not straightforward to decide. We systematically analyze how different design parameters affect scanpath evaluation metrics
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Analyzing Nursing Assistant Attitudes Towards Empathic Geriatric Caregiving Using Quantitative Ethnography arXiv.cs.HC Pub Date : 2024-05-14 Behdokht Kiafar, Salam Daher, Shayla Sharmin, Asif Ahmmed, Ladda Thiamwong, Roghayeh Leila Barmaki
An emergent challenge in geriatric care is improving the quality of care, which requires insight from stakeholders. Qualitative methods offer detailed insights, but they can be biased and have limited generalizability, while quantitative methods may miss nuances. Network-based approaches, such as quantitative ethnography (QE), can bridge this methodological gap. By leveraging the strengths of both
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The Impact of 2D and 3D Gamified VR on Learning American Sign Language arXiv.cs.HC Pub Date : 2024-05-14 Jindi Wang, Ioannis Ivrissimtzis, Zhaoxing Li, Lei Shi
Sign language has been extensively studied as a means of facilitating effective communication between hearing individuals and the deaf community. With the continuous advancements in virtual reality (VR) and gamification technologies, an increasing number of studies have begun to explore the application of these emerging technologies in sign language learning. This paper describes a user study that
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fNIRS Analysis of Interaction Techniques in Touchscreen-Based Educational Gaming arXiv.cs.HC Pub Date : 2024-05-14 Shayla Sharmin, Elham Bakhshipour, Behdokht Kiafar, Md Fahim Abrar, Pinar Kullu, Nancy Getchell, Roghayeh Leila Barmaki
Touchscreens are becoming increasingly widespread in educational games, enhancing the quality of learner experience. Traditional metrics are often used to evaluate various input modalities, including hand and stylus. However, there exists a gap in understanding the cognitive impacts of these modalities during educational gameplay, which can be addressed through brain signal analysis to gain deeper
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Theorizing Deception: A Scoping Review of Theory in Research on Dark Patterns and Deceptive Design arXiv.cs.HC Pub Date : 2024-05-14 Weichen Joe Chang, Katie Seaborn, Andrew A. Adams
The issue of dark patterns and deceptive designs (DPs) in everyday interfaces and interactions continues to grow. DPs are manipulative and malicious elements within user interfaces that deceive users into making unintended choices. In parallel, research on DPs has significantly increased over the past two decades. As the field has matured, epistemological gaps have also become a salient and pressing
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Deceptive, Disruptive, No Big Deal: Japanese People React to Simulated Dark Commercial Patterns arXiv.cs.HC Pub Date : 2024-05-14 Katie Seaborn, Tatsuya Itagaki, Mizuki Watanabe, Yijia Wang, Ping Geng, Takao Fujii, Yuto Mandai, Miu Kojima, Suzuka Yoshida
Dark patterns and deceptive designs (DPs) are user interface elements that trick people into taking actions that benefit the purveyor. Such designs are widely deployed, with special varieties found in certain nations like Japan that can be traced to global power hierarchies and the local socio-linguistic context of use. In this breaking work, we report on the first user study involving Japanese people
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Using ChatGPT for Thematic Analysis arXiv.cs.HC Pub Date : 2024-05-13 Aleksei Turobov, Diane Coyle, Verity Harding
The utilisation of AI-driven tools, notably ChatGPT, within academic research is increasingly debated from several perspectives including ease of implementation, and potential enhancements in research efficiency, as against ethical concerns and risks such as biases and unexplained AI operations. This paper explores the use of the GPT model for initial coding in qualitative thematic analysis using a
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Comparing the Efficacy of GPT-4 and Chat-GPT in Mental Health Care: A Blind Assessment of Large Language Models for Psychological Support arXiv.cs.HC Pub Date : 2024-05-15 Birger Moell
Background: Rapid advancements in natural language processing have led to the development of large language models with the potential to revolutionize mental health care. These models have shown promise in assisting clinicians and providing support to individuals experiencing various psychological challenges. Objective: This study aims to compare the performance of two large language models, GPT-4
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ViSTooth: A Visualization Framework for Tooth Segmentation on Panoramic Radiograph arXiv.cs.HC Pub Date : 2024-05-14 Shenji Zhu, Miaoxin Hu, Tianya Pan, Yue Hong, Bin Li, Zhiguang Zhou, Ting Xu
Tooth segmentation is a key step for computer aided diagnosis of dental diseases. Numerous machine learning models have been employed for tooth segmentation on dental panoramic radiograph. However, it is a difficult task to achieve accurate tooth segmentation due to complex tooth shapes, diverse tooth categories and incomplete sample set for machine learning. In this paper, we propose ViSTooth, a visualization
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Why Larp?! A Synthesis Paper on Live Action Roleplay in Relation to HCI Research and Practice arXiv.cs.HC Pub Date : 2024-05-14 Karin Johansson, Raquel Breejon Robinson, Jon Back, Sarah Lynne Bowman, James Fey, Elena Márquez Segura, Annika Waern, Katherine Isbister
Live action roleplay (larp) has a wide range of applications, and can be relevant in relation to HCI. While there has been research about larp in relation to topics such as embodied interaction, playfulness and futuring published in HCI venues since the early 2000s, there is not yet a compilation of this knowledge. In this paper, we synthesise knowledge about larp and larp-adjacent work within the
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AI-Resilient Interfaces arXiv.cs.HC Pub Date : 2024-05-14 Elena L. Glassman, Ziwei Gu, Jonathan K. Kummerfeld
AI is powerful, but it can make choices that result in objective errors, contextually inappropriate outputs, and disliked options. We need AI-resilient interfaces that help people be resilient to the AI choices that are not right, or not right for them. To support this goal, interfaces need to help users notice and have the context to appropriately judge those AI choices. Existing human-AI interaction
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Establishing Heuristics for Improving the Usability of GUI Machine Learning Tools for Novice Users arXiv.cs.HC Pub Date : 2024-05-14 Asma Yamani, Haifa Alshammare, Malak Baslyman
Machine learning (ML) tools with graphical user interfaces (GUI) are facing demand from novice users who do not have the background of their underlying concepts. These tools are frequently complex and pose unique challenges in terms of interaction and comprehension by novice users. There is yet to be an established set of usability heuristics to guide and assess GUI ML tool design. To address this
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Kawaii Computing: Scoping Out the Japanese Notion of Cute in User Experiences with Interactive Systems arXiv.cs.HC Pub Date : 2024-05-14 Yijia Wang, Katie Seaborn
Kawaii computing is a new term for a steadily growing body of work on the Japanese notion of "cute" in human-computer interaction (HCI) research and practice. Kawaii is distinguished from general notions of cute by its experiential and culturally-sensitive nature. While it can be designed into the appearance and behaviour of interactive agents, interfaces, and systems, kawaii also refers to certain
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No Joke: An Embodied Conversational Agent Greeting Older Adults with Humour or a Smile Unrelated to Initial Acceptance arXiv.cs.HC Pub Date : 2024-05-14 Ge "Rikaku" Li, Katie Seaborn
Embodied conversation agents (ECAs) are increasingly being developed for older adults as assistants or companions. Older adults may not be familiar with ECAs, influencing uptake and acceptability. First impressions can correlate strongly with subsequent judgments, even of computer agents, and could influence acceptance. Using the circumplex model of affect, we developed three versions of an ECA --
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Play Across Boundaries: Exploring Cross-Cultural Maldaimonic Game Experiences arXiv.cs.HC Pub Date : 2024-05-13 Katie Seaborn, Satoru Iseya, Shun Hidaka, Sota Kobuki, Shruti Chandra
Maldaimonic game experiences occur when people engage in personally fulfilling play through egocentric, destructive, and/or exploitative acts. Initial qualitative work verified this orientation and experiential construct for English-speaking Westerners. In this comparative mixed methods study, we explored whether and how maldaimonic game experiences and orientations play out in Japan, an Eastern gaming
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Silver-Tongued and Sundry: Exploring Intersectional Pronouns with ChatGPT arXiv.cs.HC Pub Date : 2024-05-13 Takao Fujii, Katie Seaborn, Madeleine Steeds
ChatGPT is a conversational agent built on a large language model. Trained on a significant portion of human output, ChatGPT can mimic people to a degree. As such, we need to consider what social identities ChatGPT simulates (or can be designed to simulate). In this study, we explored the case of identity simulation through Japanese first-person pronouns, which are tightly connected to social identities
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LLM Theory of Mind and Alignment: Opportunities and Risks arXiv.cs.HC Pub Date : 2024-05-13 Winnie Street
Large language models (LLMs) are transforming human-computer interaction and conceptions of artificial intelligence (AI) with their impressive capacities for conversing and reasoning in natural language. There is growing interest in whether LLMs have theory of mind (ToM); the ability to reason about the mental and emotional states of others that is core to human social intelligence. As LLMs are integrated
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cVIL: Class-Centric Visual Interactive Labeling arXiv.cs.HC Pub Date : 2024-05-13 Matthias Matt, Matthias Zeppelzauer, Manuela Waldner
We present cVIL, a class-centric approach to visual interactive labeling, which facilitates human annotation of large and complex image data sets. cVIL uses different property measures to support instance labeling for labeling difficult instances and batch labeling to quickly label easy instances. Simulated experiments reveal that cVIL with batch labeling can outperform traditional labeling approaches
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LLAniMAtion: LLAMA Driven Gesture Animation arXiv.cs.HC Pub Date : 2024-05-13 Jonathan Windle, Iain Matthews, Sarah Taylor
Co-speech gesturing is an important modality in conversation, providing context and social cues. In character animation, appropriate and synchronised gestures add realism, and can make interactive agents more engaging. Historically, methods for automatically generating gestures were predominantly audio-driven, exploiting the prosodic and speech-related content that is encoded in the audio signal. In
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Layout Generation Agents with Large Language Models arXiv.cs.HC Pub Date : 2024-05-13 Yuichi Sasazawa, Yasuhiro Sogawa
In recent years, there has been an increasing demand for customizable 3D virtual spaces. Due to the significant human effort required to create these virtual spaces, there is a need for efficiency in virtual space creation. While existing studies have proposed methods for automatically generating layouts such as floor plans and furniture arrangements, these methods only generate text indicating the
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A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systems arXiv.cs.HC Pub Date : 2024-05-13 Lixi Zhu, Xiaowen Huang, Jitao Sang
Conversational Recommender System (CRS) leverages real-time feedback from users to dynamically model their preferences, thereby enhancing the system's ability to provide personalized recommendations and improving the overall user experience. CRS has demonstrated significant promise, prompting researchers to concentrate their efforts on developing user simulators that are both more realistic and trustworthy
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PyZoBot: A Platform for Conversational Information Extraction and Synthesis from Curated Zotero Reference Libraries through Advanced Retrieval-Augmented Generation arXiv.cs.HC Pub Date : 2024-05-13 Suad Alshammari, Lama Basalelah, Walaa Abu Rukbah, Ali Alsuhibani, Dayanjan S. Wijesinghe
The exponential growth of scientific literature has resulted in information overload, challenging researchers to effectively synthesize relevant publications. This paper explores the integration of traditional reference management software with advanced computational techniques, including Large Language Models and Retrieval-Augmented Generation. We introduce PyZoBot, an AI-driven platform developed
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AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments arXiv.cs.HC Pub Date : 2024-05-13 Samuel Schmidgall, Rojin Ziaei, Carl Harris, Eduardo Reis, Jeffrey Jopling, Michael Moor
Diagnosing and managing a patient is a complex, sequential decision making process that requires physicians to obtain information -- such as which tests to perform -- and to act upon it. Recent advances in artificial intelligence (AI) and large language models (LLMs) promise to profoundly impact clinical care. However, current evaluation schemes overrely on static medical question-answering benchmarks
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Open-vocabulary Auditory Neural Decoding Using fMRI-prompted LLM arXiv.cs.HC Pub Date : 2024-05-13 Xiaoyu Chen, Changde Du, Che Liu, Yizhe Wang, Huiguang He
Decoding language information from brain signals represents a vital research area within brain-computer interfaces, particularly in the context of deciphering the semantic information from the fMRI signal. However, many existing efforts concentrate on decoding small vocabulary sets, leaving space for the exploration of open vocabulary continuous text decoding. In this paper, we introduce a novel method
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Understanding Data Understanding: A Framework to Navigate the Intricacies of Data Analytics arXiv.cs.HC Pub Date : 2024-05-13 Joshua Holstein, Philipp Spitzer, Marieke Hoell, Michael Vössing, Niklas Kühl
As organizations face the challenges of processing exponentially growing data volumes, their reliance on analytics to unlock value from this data has intensified. However, the intricacies of big data, such as its extensive feature sets, pose significant challenges. A crucial step in leveraging this data for insightful analysis is an in-depth understanding of both the data and its domain. Yet, existing
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G-VOILA: Gaze-Facilitated Information Querying in Daily Scenarios arXiv.cs.HC Pub Date : 2024-05-13 Zeyu Wang, Yuanchun Shi, Yuntao Wang, Yuchen Yao, Kun Yan, Yuhan Wang, Lei Ji, Xuhai Xu, Chun Yu
Modern information querying systems are progressively incorporating multimodal inputs like vision and audio. However, the integration of gaze -- a modality deeply linked to user intent and increasingly accessible via gaze-tracking wearables -- remains underexplored. This paper introduces a novel gaze-facilitated information querying paradigm, named G-VOILA, which synergizes users' gaze, visual field
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AIris: An AI-powered Wearable Assistive Device for the Visually Impaired arXiv.cs.HC Pub Date : 2024-05-13 Dionysia Danai Brilli, Evangelos Georgaras, Stefania Tsilivaki, Nikos Melanitis, Konstantina Nikita
Assistive technologies for the visually impaired have evolved to facilitate interaction with a complex and dynamic world. In this paper, we introduce AIris, an AI-powered wearable device that provides environmental awareness and interaction capabilities to visually impaired users. AIris combines a sophisticated camera mounted on eyewear with a natural language processing interface, enabling users to
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Comparing Perceptions of Static and Adaptive Proactive Speech Agents arXiv.cs.HC Pub Date : 2024-05-13 Justin Edwards, Philip R. Doyle, Holly Brannigan, Benjamin R. Cowan
A growing literature on speech interruptions describes how people interrupt one another with speech, but these behaviours have not yet been implemented in the design of artificial agents which interrupt. Perceptions of a prototype proactive speech agent which adapts its speech to both urgency and to the difficulty of the ongoing task it interrupts are compared against perceptions of a static proactive
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Chronoblox: Chronophotographic Sequential Graph Visualization arXiv.cs.HC Pub Date : 2024-05-13 Quentin LobbéCMB, Camille RothCAMS, CMB, Lena MangoldCAMS, CMB
We introduce Chronoblox, a system for visualizing dynamic graphs. Chronoblox consists of a chronophotography of a sequence of graph snapshots based on a single embedding space common to all time periods. The goal of Chronoblox is to project all snapshots onto a common visualization space so as to represent both local and global dynamics at a glance. In this short paper, we review both the embedding
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How Non-native English Speakers Use, Assess, and Select AI-Generated Paraphrases with Information Aids arXiv.cs.HC Pub Date : 2024-05-13 Yewon Kim, Thanh-Long V. Le, Donghwi Kim, Mina Lee, Sung-Ju Lee
Non-native English speakers (NNESs) often face challenges in achieving fluency in their written English. AI paraphrasing tools have the potential to improve their writing by suggesting more fluent paraphrases to their original sentences. Yet, the effectiveness of these tools depends on the user's ability to accurately assess and select context-appropriate suggestions, which is a significant challenge
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Examining Humanness as a Metaphor to Design Voice User Interfaces arXiv.cs.HC Pub Date : 2024-05-13 Smit Desai, Mateusz Dubiel, Luis A. Leiva
Voice User Interfaces (VUIs) increasingly leverage 'humanness' as a foundational design metaphor, adopting roles like 'assistants,' 'teachers,' and 'secretaries' to foster natural interactions. Yet, this approach can sometimes misalign user trust and reinforce societal stereotypes, leading to socio-technical challenges that might impede long-term engagement. This paper explores an alternative approach
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From traces to measures: Large language models as a tool for psychological measurement from text arXiv.cs.HC Pub Date : 2024-05-13 Joseph J. P. Simons, Wong Liang Ze, Prasanta Bhattacharya, Brandon Siyuan Loh, Wei Gao
Digital trace data provide potentially valuable resources for understanding human behaviour, but their value has been limited by issues of unclear measurement. The growth of large language models provides an opportunity to address this limitation in the case of text data. Specifically, recognizing cases where their responses are a form of psychological measurement (the use of observable indicators
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Maximizing Information Gain in Privacy-Aware Active Learning of Email Anomalies arXiv.cs.HC Pub Date : 2024-05-13 Mu-Huan Miles Chung, Sharon Li, Jaturong Kongmanee, Lu Wang, Yuhong Yang, Calvin Giang, Khilan Jerath, Abhay Raman, David Lie, Mark Chignell
Redacted emails satisfy most privacy requirements but they make it more difficult to detect anomalous emails that may be indicative of data exfiltration. In this paper we develop an enhanced method of Active Learning using an information gain maximizing heuristic, and we evaluate its effectiveness in a real world setting where only redacted versions of email could be labeled by human analysts due to
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Towards improved software visualisation of parameterised REE patterns: Introducing REEkit for geological analysis arXiv.cs.HC Pub Date : 2024-05-13 Jaxon Kneipp, Alex Potanin, Michael Anenburg
Modern geological studies and mineral exploration techniques rely heavily on being able to digitally visualise and interpret data. Rare earth elements (REEs) are vital for renewable energy technologies. REE concentrations, when normalised to a standard material, show unique geometric curves (or patterns) in geological samples due to their similar chemical properties. The lambda technique can be used
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Exploring the Effects of User-Agent and User-Designer Similarity in Virtual Human Design to Promote Mental Health Intentions for College Students arXiv.cs.HC Pub Date : 2024-05-13 Pedro Guillermo Feijóo-García, Chase Wrenn, Alexandre Gomes de Siqueira, Rashi Ghosh, Jacob Stuart, Heng Yao, Benjamin Lok
Virtual humans (i.e., embodied conversational agents) have the potential to support college students' mental health, particularly in Science, Technology, Engineering, and Mathematics (STEM) fields where students are at a heightened risk of mental disorders such as anxiety and depression. A comprehensive understanding of students, considering their cultural characteristics, experiences, and expectations
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Hell is Paved with Good Intentions: The Intricate Relationship Between Cognitive Biases and Dark Patterns arXiv.cs.HC Pub Date : 2024-05-12 Thomas Mildner, Albert Inkoom, Rainer Malaka, Jasmin Niess
Throughout the past decade, research in HCI has identified numerous instances of dark patterns in digital interfaces. These efforts have led to a well-fostered typology describing harmful strategies users struggle to navigate. However, an in-depth understanding of the underlying mechanisms that deceive, coerce, or manipulate users is missing. We explore the interplay between cognitive biases and dark