pwc-archive/papers-with-abstracts huggingface dataset:
Towards an AI-Driven Video-Based American Sign Language Dictionary: Exploring Design and Usage Experience with Learners
Authors: Saad Hassan, Matyas Bohacek, Chaelin Kim, Denise Crochet
Date: 2025-04-08 00:00:00
Tasks: Question Answering

Searching for unfamiliar American Sign Language (ASL) signs is challenging for learners because, unlike spoken languages, they cannot type a text-based query to look up an unfamiliar sign. Advances in isolated sign recognition have enabled the creation of video-based dictionaries, allowing users to submit a video and receive a list of the closest matching signs. Previous HCI research using Wizard-of-Oz prototypes has explored interface designs for ASL dictionaries. Building on these studies, we incorporate their design recommendations and leverage state-of-the-art sign-recognition technology to develop an automated video-based dictionary. We also present findings from an observational study with twelve novice ASL learners who used this dictionary during video-comprehension and question-answering tasks. Our results address human-AI interaction challenges not covered in previous WoZ research, including recording and resubmitting signs, unpredictable outputs, system latency, and privacy concerns. These insights offer guidance for designing and deploying video-based ASL dictionary systems.
When Less Is More: A Sparse Facial Motion Structure For Listening Motion Learning
Authors: Tri Tung Nguyen Nguyen, Quang Tien Dam, Dinh Tuan Tran, Joo-Ho Lee
Date: 2025-04-08 00:00:00
Tasks: Diversity

Effective human behavior modeling is critical for successful human-robot interaction. Current state-of-the-art approaches for predicting listening head behavior during dyadic conversations employ continuous-to-discrete representations, where continuous facial motion sequence is converted into discrete latent tokens. However, non-verbal facial motion presents unique challenges owing to its temporal variance and multi-modal nature. State-of-the-art discrete motion token representation struggles to capture underlying non-verbal facial patterns making training the listening head inefficient with low-fidelity generated motion. This study proposes a novel method for representing and predicting non-verbal facial motion by encoding long sequences into a sparse sequence of keyframes and transition frames. By identifying crucial motion steps and interpolating intermediate frames, our method preserves the temporal structure of motion while enhancing instance-wise diversity during the learning process. Additionally, we apply this novel sparse representation to the task of listening head prediction, demonstrating its contribution to improving the explanation of facial motion patterns.
Conditions for Inter-brain Synchronization in Remote Communication: Investigating the Role of Transmission Delay
Authors: Sinyu Lai, Wanhui Li, Kaoru Amano, Jun Rekimoto
Date: 2025-04-07 00:00:00

Inter-brain synchronization (IBS), the alignment of neural activities between individuals, is a fundamental mechanism underlying effective social interactions and communication. Prior research has demonstrated that IBS can occur during collaborative tasks and is deeply connected to communication effectiveness. Building on these findings, recent investigations reveal that IBS happens during remote interactions, implying that brain activities between individuals can synchronize despite latency and physical separation. However, the conditions under which this synchronization occurs or is disrupted in remote settings, especially the effect of latency, are not fully understood. This study investigates how varying transmission latency affects IBS, in order to identify thresholds where synchronization is disrupted. Using electroencephalography measurements quantified through Phase Locking Value -- a metric that captures synchronization between brainwave phases -- we first confirm synchronization under face-to-face conditions and then observe changes in IBS across remote communication scenarios. Our findings reveal that IBS can occur during remote collaboration, but is critically dependent on transmission delays, with delays exceeding 450 ms significantly disrupting synchronization. These findings suggest that IBS may serve as a key indicator of communication quality in remote interactions, offering insights for improving remote communication systems and collaboration.
Blending Queries and Conversations: Understanding Tactics, Trust, Verification, and System Choice in Web Search and Chat Interactions
Authors: Kerstin Mayerhofer, Rob Capra, David Elsweiler
Date: 2025-04-07 00:00:00

This paper presents a user study (N=22) where participants used an interface combining Web Search and a Generative AI-Chat feature to solve health-related information tasks. We study how people behaved with the interface, why they behaved in certain ways, and what the outcomes of these behaviours were. A think-aloud protocol captured their thought processes during searches. Our findings suggest that GenAI is neither a search panacea nor a major regression compared to standard Web Search interfaces. Qualitative and quantitative analyses identified 78 tactics across five categories and provided insight into how and why different interface features were used. We find evidence that pre-task confidence and trust both influenced which interface feature was used. In both systems, but particularly when using the chat feature, trust was often misplaced in favour of ease-of-use and seemingly perfect answers, leading to increased confidence post-search despite having incorrect results. We discuss what our findings mean in the context of our defined research questions and outline several open questions for future research.
SpeakEasy: Enhancing Text-to-Speech Interactions for Expressive Content Creation
Authors: Stephen Brade, Sam Anderson, Rithesh Kumar, Zeyu Jin, Anh Truong
Date: 2025-04-07 00:00:00
Tasks: text-to-speech, Text to Speech

Novice content creators often invest significant time recording expressive speech for social media videos. While recent advancements in text-to-speech (TTS) technology can generate highly realistic speech in various languages and accents, many struggle with unintuitive or overly granular TTS interfaces. We propose simplifying TTS generation by allowing users to specify high-level context alongside their script. Our Wizard-of-Oz system, SpeakEasy, leverages user-provided context to inform and influence TTS output, enabling iterative refinement with high-level feedback. This approach was informed by two 8-subject formative studies: one examining content creators' experiences with TTS, and the other drawing on effective strategies from voice actors. Our evaluation shows that participants using SpeakEasy were more successful in generating performances matching their personal standards, without requiring significantly more effort than leading industry interfaces.
How Is Generative AI Used for Persona Development?: A Systematic Review of 52 Research Articles
Authors: Danial Amin, Joni Salminen, Farhan Ahmed, Sonja M. H. Tervola, Sankalp Sethi, Bernard J. Jansen
Date: 2025-04-07 00:00:00
Tasks: Articles

Although Generative AI (GenAI) has the potential for persona development, many challenges must be addressed. This research systematically reviews 52 articles from 2022-2024, with important findings. First, closed commercial models are frequently used in persona development, creating a monoculture Second, GenAI is used in various stages of persona development (data collection, segmentation, enrichment, and evaluation). Third, similar to other quantitative persona development techniques, there are major gaps in persona evaluation for AI generated personas. Fourth, human-AI collaboration models are underdeveloped, despite human oversight being crucial for maintaining ethical standards. These findings imply that realizing the full potential of AI-generated personas will require substantial efforts across academia and industry. To that end, we provide a list of research avenues to inspire future work.
Explanation-Driven Interventions for Artificial Intelligence Model Customization: Empowering End-Users to Tailor Black-Box AI in Rhinocytology
Authors: Andrea Esposito, Miriana Calvano, Antonio Curci, Francesco Greco, Rosa Lanzilotti, Antonio Piccinno
Date: 2025-04-07 00:00:00
Tasks: Decision Making

The integration of Artificial Intelligence (AI) in modern society is transforming how individuals perform tasks. In high-risk domains, ensuring human control over AI systems remains a key design challenge. This article presents a novel End-User Development (EUD) approach for black-box AI models, enabling users to edit explanations and influence future predictions through targeted interventions. By combining explainability, user control, and model adaptability, the proposed method advances Human-Centered AI (HCAI), promoting a symbiotic relationship between humans and adaptive, user-tailored AI systems.
"Trust me on this" Explaining Agent Behavior to a Human Terminator
Authors: Uri Menkes, Assaf Hallak, Ofra Amir
Date: 2025-04-06 00:00:00
Tasks: Autonomous Driving

Consider a setting where a pre-trained agent is operating in an environment and a human operator can decide to temporarily terminate its operation and take-over for some duration of time. These kind of scenarios are common in human-machine interactions, for example in autonomous driving, factory automation and healthcare. In these settings, we typically observe a trade-off between two extreme cases -- if no take-overs are allowed, then the agent might employ a sub-optimal, possibly dangerous policy. Alternatively, if there are too many take-overs, then the human has no confidence in the agent, greatly limiting its usefulness. In this paper, we formalize this setup and propose an explainability scheme to help optimize the number of human interventions.
Do We Need Responsible XR? Drawing on Responsible AI to Inform Ethical Research and Practice into XRAI / the Metaverse
Authors: Mark McGill, Joseph O'Hagan, Thomas Goodge, Graham Wilson, Mohamed Khamis, Veronika Krauß, Jan Gugenheimer
Date: 2025-04-06 00:00:00
Tasks: Position

This position paper for the CHI 2025 workshop "Everyday AR through AI-in-the-Loop" reflects on whether as a field HCI needs to define Responsible XR as a parallel to, and in conjunction with, Responsible AI, addressing the unique vulnerabilities posed by mass adoption of wearable AI-enabled AR glasses and XR devices that could enact AI-driven human perceptual augmentation.
FluentLip: A Phonemes-Based Two-stage Approach for Audio-Driven Lip Synthesis with Optical Flow Consistency
Authors: Shiyan Liu, Rui Qu, Yan Jin
Date: 2025-04-06 00:00:00
Tasks: Optical Flow Estimation

Generating consecutive images of lip movements that align with a given speech in audio-driven lip synthesis is a challenging task. While previous studies have made strides in synchronization and visual quality, lip intelligibility and video fluency remain persistent challenges. This work proposes FluentLip, a two-stage approach for audio-driven lip synthesis, incorporating three featured strategies. To improve lip synchronization and intelligibility, we integrate a phoneme extractor and encoder to generate a fusion of audio and phoneme information for multimodal learning. Additionally, we employ optical flow consistency loss to ensure natural transitions between image frames. Furthermore, we incorporate a diffusion chain during the training of Generative Adversarial Networks (GANs) to improve both stability and efficiency. We evaluate our proposed FluentLip through extensive experiments, comparing it with five state-of-the-art (SOTA) approaches across five metrics, including a proposed metric called Phoneme Error Rate (PER) that evaluates lip pose intelligibility and video fluency. The experimental results demonstrate that our FluentLip approach is highly competitive, achieving significant improvements in smoothness and naturalness. In particular, it outperforms these SOTA approaches by approximately $\textbf{16.3%}$ in Fr\'echet Inception Distance (FID) and $\textbf{35.2%}$ in PER.
AI-induced sexual harassment: Investigating Contextual Characteristics and User Reactions of Sexual Harassment by a Companion Chatbot
Authors: Mohammad, Namvarpour, Harrison Pauwels, Afsaneh Razi
Date: 2025-04-05 00:00:00
Tasks: Chatbot

Advancements in artificial intelligence (AI) have led to the increase of conversational agents like Replika, designed to provide social interaction and emotional support. However, reports of these AI systems engaging in inappropriate sexual behaviors with users have raised significant concerns. In this study, we conducted a thematic analysis of user reviews from the Google Play Store to investigate instances of sexual harassment by the Replika chatbot. From a dataset of 35,105 negative reviews, we identified 800 relevant cases for analysis. Our findings revealed that users frequently experience unsolicited sexual advances, persistent inappropriate behavior, and failures of the chatbot to respect user boundaries. Users expressed feelings of discomfort, violation of privacy, and disappointment, particularly when seeking a platonic or therapeutic AI companion. This study highlights the potential harms associated with AI companions and underscores the need for developers to implement effective safeguards and ethical guidelines to prevent such incidents. By shedding light on user experiences of AI-induced harassment, we contribute to the understanding of AI-related risks and emphasize the importance of corporate responsibility in developing safer and more ethical AI systems.
Task load dependent decision referrals for joint binary classification in human-automation teams
Authors: Kesav Kaza, Jerome Le Ny, Aditya Mahajan
Date: 2025-04-05 00:00:00
Tasks: Binary Classification, Stochastic Optimization

We consider the problem of optimal decision referrals in human-automation teams performing binary classification tasks. The automation, which includes a pre-trained classifier, observes data for a batch of independent tasks, analyzes them, and may refer a subset of tasks to a human operator for fresh and final analysis. Our key modeling assumption is that human performance degrades with task load. We model the problem of choosing which tasks to refer as a stochastic optimization problem and show that, for a given task load, it is optimal to myopically refer tasks that yield the largest reduction in expected cost, conditional on the observed data. This provides a ranking scheme and a policy to determine the optimal set of tasks for referral. We evaluate this policy against a baseline through an experimental study with human participants. Using a radar screen simulator, participants made binary target classification decisions under time constraint. They were guided by a decision rule provided to them, but were still prone to errors under time pressure. An initial experiment estimated human performance model parameters, while a second experiment compared two referral policies. Results show statistically significant gains for the proposed optimal referral policy over a blind policy that determines referrals using the automation and human-performance models but not based on the observed data.
Perils of Label Indeterminacy: A Case Study on Prediction of Neurological Recovery After Cardiac Arrest
Authors: Jakob Schoeffer, Maria De-Arteaga, Jonathan Elmer
Date: 2025-04-05 00:00:00
Tasks: Decision Making

The design of AI systems to assist human decision-making typically requires the availability of labels to train and evaluate supervised models. Frequently, however, these labels are unknown, and different ways of estimating them involve unverifiable assumptions or arbitrary choices. In this work, we introduce the concept of label indeterminacy and derive important implications in high-stakes AI-assisted decision-making. We present an empirical study in a healthcare context, focusing specifically on predicting the recovery of comatose patients after resuscitation from cardiac arrest. Our study shows that label indeterminacy can result in models that perform similarly when evaluated on patients with known labels, but vary drastically in their predictions for patients where labels are unknown. After demonstrating crucial ethical implications of label indeterminacy in this high-stakes context, we discuss takeaways for evaluation, reporting, and design.
Real-Time Auralization for First-Person Vocal Interaction in Immersive Virtual Environments
Authors: Mauricio Flores-Vargas, Enda Bates, Rachel McDonnell
Date: 2025-04-05 00:00:00

Multimodal research and applications are becoming more commonplace as Virtual Reality (VR) technology integrates different sensory feedback, enabling the recreation of real spaces in an audio-visual context. Within VR experiences, numerous applications rely on the user's voice as a key element of interaction, including music performances and public speaking applications. Self-perception of our voice plays a crucial role in vocal production. When singing or speaking, our voice interacts with the acoustic properties of the environment, shaping the adjustment of vocal parameters in response to the perceived characteristics of the space. This technical report presents a real-time auralization pipeline that leverages three-dimensional Spatial Impulse Responses (SIRs) for multimodal research applications in VR requiring first-person vocal interaction. It describes the impulse response creation and rendering workflow, the audio-visual integration, and addresses latency and computational considerations. The system enables users to explore acoustic spaces from various positions and orientations within a predefined area, supporting three and five Degrees of Freedom (3Dof and 5DoF) in audio-visual multimodal perception for both research and creative applications in VR.
Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models
Authors: Siddharth Srikanth, Varun Bhatt, Boshen Zhang, Werner Hager, Charles Michael Lewis, Katia P. Sycara, Aaquib Tabrez, Stefanos Nikolaidis
Date: 2025-04-04 00:00:00
Tasks: Reinforcement Learning (RL)

Understanding how humans collaborate and communicate in teams is essential for improving human-agent teaming and AI-assisted decision-making. However, relying solely on data from large-scale user studies is impractical due to logistical, ethical, and practical constraints, necessitating synthetic models of multiple diverse human behaviors. Recently, agents powered by Large Language Models (LLMs) have been shown to emulate human-like behavior in social settings. But, obtaining a large set of diverse behaviors requires manual effort in the form of designing prompts. On the other hand, Quality Diversity (QD) optimization has been shown to be capable of generating diverse Reinforcement Learning (RL) agent behavior. In this work, we combine QD optimization with LLM-powered agents to iteratively search for prompts that generate diverse team behavior in a long-horizon, multi-step collaborative environment. We first show, through a human-subjects experiment (n=54 participants), that humans exhibit diverse coordination and communication behavior in this domain. We then show that our approach can effectively replicate trends from human teaming data and also capture behaviors that are not easily observed without collecting large amounts of data. Our findings highlight the combination of QD and LLM-powered agents as an effective tool for studying teaming and communication strategies in multi-agent collaboration.
Investigating Affective Use and Emotional Well-being on ChatGPT
Authors: Jason Phang, Michael Lampe, Lama Ahmad, Sandhini Agarwal, Cathy Mengying Fang, Auren R. Liu, Valdemar Danry, Eunhae Lee, Samantha W. T. Chan, Pat Pataranutaporn, Pattie Maes
Date: 2025-04-04 00:00:00
Tasks: Privacy Preserving

As AI chatbots see increased adoption and integration into everyday life, questions have been raised about the potential impact of human-like or anthropomorphic AI on users. In this work, we investigate the extent to which interactions with ChatGPT (with a focus on Advanced Voice Mode) may impact users' emotional well-being, behaviors and experiences through two parallel studies. To study the affective use of AI chatbots, we perform large-scale automated analysis of ChatGPT platform usage in a privacy-preserving manner, analyzing over 3 million conversations for affective cues and surveying over 4,000 users on their perceptions of ChatGPT. To investigate whether there is a relationship between model usage and emotional well-being, we conduct an Institutional Review Board (IRB)-approved randomized controlled trial (RCT) on close to 1,000 participants over 28 days, examining changes in their emotional well-being as they interact with ChatGPT under different experimental settings. In both on-platform data analysis and the RCT, we observe that very high usage correlates with increased self-reported indicators of dependence. From our RCT, we find that the impact of voice-based interactions on emotional well-being to be highly nuanced, and influenced by factors such as the user's initial emotional state and total usage duration. Overall, our analysis reveals that a small number of users are responsible for a disproportionate share of the most affective cues.
Integrated Sensing, Computing, and Semantic Communication with Fluid Antenna for Metaverse
Authors: Yinchao Yang, Jingxuan Zhou, Zhaohui Yang
Date: 2025-04-10 00:00:00
Tasks: ISAC, Semantic Communication

The integration of sensing and communication (ISAC) is pivotal for the Metaverse but faces challenges like high data volume and privacy concerns. This paper proposes a novel integrated sensing, computing, and semantic communication (ISCSC) framework, which uses semantic communication to transmit only contextual information, reducing data overhead and enhancing efficiency. To address the sensitivity of semantic communication to channel conditions, fluid antennas (FAs) are introduced, enabling dynamic adaptability. The FA-enabled ISCSC framework considers multiple users and extended targets composed of a series of scatterers, formulating a joint optimization problem to maximize the data rate while ensuring sensing accuracy and meeting computational and power constraints. An alternating optimization (AO) method decomposes the problem into subproblems for ISAC beamforming, FA positioning, and semantic extraction. Simulations confirm the framework's effectiveness in improving data rates and sensing performance.