Human traces scattered across urban landscapes can signify our everyday lives and societal vibrancy in subtle and poetic forms. In this paper, we explore how designed technology can engage biodata as evocative traces. To this end, we present the design, implementation, and evaluation of HeartSway, an interactive hammock that captures a user’s heart rate and micro-movements as traces and replays them as an embodied experience for the next visitor. Through a qualitative field study (N=10), we find that HeartSway evokes feelings of connection, curiosity about prior users, and appreciation for shared human vitality. Our work contributes to understanding anonymous archival biodata as a design material for experiential urban traces. We offer design considerations for intimate asynchronous encounters between strangers in public spaces and for reimagining public amenities.

Recent advances in generative AI have introduced AI-extended characters, which refer to AI-generated personas grounded in pre-existing human or fictional referents. While prior research focuses on direct social interaction, their capacity to foster parasocial interaction (PSI) in media remains underexplored. We analyzed 1,460 audience comments from 299 AI talk show videos to investigate this gap. Our findings identify three distinct objects of PSI within AI-extended characters: referents, AI proxies, and blended characters. Although referents remain the primary focus, PSI toward AI proxies and blended characters suggests that audience engagement with AI media may extend beyond the original referents. We further found that humanlikeness and AI awareness appeared as recurring themes in how audiences interpreted these relationships. This work provides a preliminary understanding of human engagement with AI-extended characters and offers design implications for future AI-mediated creative media content.

While conversational agents’ (CAs) semantic and syntactic capabilities have advanced, their pragmatic skills, using language appropriately in context, have emerged as a critical focus in practical applications. Hence, scholars integrate conversational skills derived from human-human interaction into CA designs. However, existing research mainly adopts an empirical approach and focuses on specific CA deployment, making it challenging to identify overarching patterns or develop a comprehensive methodology for transferring human pragmatic skills to CA design. Thus, we conducted a systematic review of 85 studies from primary databases (e.g., ACM, IEEE, etc.), focusing on designing CAs with human-derived conversational skills. We identified skill categories (verbal, paralinguistic, nonverbal), transfer strategies (from dialog data, theories, and via co-design), implementations, and evaluation metrics. We consolidated these insights into a four-stage design process: human skill exploration, definition, transfer, and iterative evaluation. Future research can leverage this to design CAs that achieve conversational goals through contextually appropriate language use.

Parkinson’s disease (PD) commonly leads to gait disorders that necessitate long-term rehabilitation dependent on specialists and clinic-based interventions. To reduce dependence on clinicians and investigate how wearable technology can provide continuous guidance for rehabilitation training. We distilled key design principles from patient–clinician interviews and co-designed a gait training system. The system employs inertial measurement units (IMUs) to capture kinematic data, then delivers multimodal cueing (visual, auditory, and somatosensory) aligned with walking features. Two user studies (N = 16 PD patients) evaluated the effectiveness of multimodal cueing, examining strategies for information delivery and gait correction. Results indicated that visual and auditory cueing were more effective for process-oriented adjustments, whereas somatosensory stimulation better supported periodic cueing. Moreover, a dissociation between performance outcomes and user preferences was observed. These findings highlight the potential of wearable technology to provide continuous, daily training guidance for PD patients.

AI has been increasingly integrated into screenwriting practice. In refinement, screenwriters expect AI to provide feedback that supports reflection across the internal perspective of characters and the external perspective of the overall story. However, existing AI tools cannot sufficiently coordinate the two perspectives to meet screenwriters’ needs. To address this gap, we present DuoDrama, an AI system that generates feedback to assist screenwriters’ reflection in refinement. To enable DuoDrama, based on performance theories and a formative study with nine professional screenwriters, we design the Experience-Grounded Feedback Generation Workflow for Human Reflection (ExReflect). In ExReflect, an AI agent adopts an experience role to generate experience and then shifts to an evaluation role to generate feedback based on the experience. A study with fourteen professional screenwriters shows that DuoDrama improves feedback quality and alignment and enhances the effectiveness, depth, and richness of reflection. We conclude by discussing broader implications and future directions.

Generative AI has greatly transformed creative work in various domains, such as screenwriting. To understand this transformation, prior research often focused on capturing a snapshot of human-AI co-creation practice at a specific moment, with less attention to how humans mobilize, regulate, and reflect to form the practice gradually. Motivated by Bandura’s theory of human agency, we conducted a two-week study with 19 professional screenwriters to investigate how they embraced AI in their creation process. Our findings revealed that screenwriters not only mindfully planned, foresaw, and responded to AI usage, but, more importantly, through reflections on practice, they developed themselves and human-AI co-creation paradigms, such as cognition, strategies, and workflows. They also expressed various expectations for how future AI should better support their agency. Based on our findings, we conclude this paper with extensive discussion and actionable suggestions to screenwriters, tool developers, and researchers for sustainable human-AI co-creation.

Visual designers often seek inspiration from Chinese paintings when tasked with creating Chinese-style illustrations, posters, etc. Our formative study (N=10) reveals that during ideation, designers learn the cultural symbols, emotions, compositions, and styles in Chinese paintings but face challenges in searching, analyzing, and integrating these dimensions. This paper leverages multi-modal large models to annotate the value of each dimension in 16,315 Chinese paintings, built on which we propose InkIdeator, an ideation support system for Chinese-style visual designs. InkIdeator suggests cultural symbols associated with the task theme, provides dimensional keywords to help analyze Chinese paintings, and generates visual examples integrating user-selected keywords. Our within-subjects study (N=12) using a baseline system without extracted dimensional keywords, along with two extended use cases by Chinese painters, indicates InkIdeator's effectiveness in creative ideation support, helping users efficiently explore cultural dimensions in Chinese paintings and visualize their ideas. We discuss implications for supporting culture-related visual design ideation with generative AI.

Perceptual grouping enables people to organize elements into units according to intrinsic (e.g., proximity) and extrinsic (e.g., common region) principles. However, the role of physical surfaces as extrinsic grouping cues for virtual elements in Augmented Reality (AR) remains unclear. To provide a deeper understanding, we conducted two within-subject studies. The first study (N = 24) using repetition discrimination tasks revealed that surfaces can be common-region cues in 3D, with their influence depending on their distance to target objects along the viewing direction. Building on these findings, the second study (N = 24) employed both objective and subjective measures to capture the interaction between proximity and common-region cues in AR. Results indicate that competing cues reduce group clarity. They also enable us to distill people’s strategies for improving the clarity by leveraging their physical and virtual environments. Finally, we propose design recommendations for future AR systems in assisted grouping tasks.

Toxic behavior in Multiplayer Online Battle Arena (MOBA) games has become a major issue. While previous studies have examined factors influencing toxic behavior, few have captured the cognitive and emotional states of the aggressors at the point of emergence of toxic behavior, or traced its evolution across an entire match. To fill the gap, we conducted replay-based semi-structured interviews with 18 players who recently initiated toxic behavior during matches. With adapted retrospective think-aloud protocols and players’ emotional journey maps, we collected their subjective perceptions and dynamic changes of emotion. Through thematic analysis, we identified a multi-dimensional criterion for evaluating toxicity severity and a three-layer cognition–emotion association structure, and described recurring persistent and single-instance patterns of toxic behavior observed in our matches. Based on our findings, we contribute to understanding the internal evolution of player toxicity and discuss implications for preventive intervention strategies and designs aiming at mitigating toxic behavior.

Asynchronous online discussions enable diverse participants to co-construct knowledge beyond individual contributions. This process ideally evolves through sequential phases, from superficial information exchange to deeper synthesis. However, many discussions stagnate in the early stages. Existing AI interventions typically target isolated phases, lacking mechanisms to progressively advance knowledge co-construction, and the impacts of different intervention styles in this context remain unclear and warrant investigation. To address these gaps, we conducted a design workshop to explore AI intervention strategies (task-oriented and/or relationship-oriented) throughout the knowledge co-construction process, and implemented them in an LLM-powered agent capable of facilitating progression while consolidating foundations at each phase. A within-subject study (N=60) involving five consecutive asynchronous discussions showed that the agent consistently promoted deeper knowledge progression, with different styles exerting distinct effects on both content and experience. These findings provide actionable guidance for designing adaptive AI agents that sustain more constructive online discussions.

Everyday digital feminism refers to the ordinary, often pragmatic ways women articulate lived experiences and cultivate solidarity in online spaces. In China, such practices flourish on RedNote through discussions under hashtags like “women’s growth”. Recently, DeepSeek-generated content has been taken up as a new voice in these conversations. Given widely recognized gender biases in LLMs, this raises critical concerns about how LLMs interact with everyday feminist practices. Through an analysis of 430 RedNote posts, 139 shared DeepSeek responses, and 3211 comments, we found that users predominantly welcomed DeepSeek’s advice. Yet feminist critical discourse analysis revealed that these responses primarily encouraged women to self-optimize and pursue achievements within prevailing norms rather than challenge them. By interpreting this case, we discuss the opportunities and risks that LLMs introduce for everyday feminism as a pathway toward women’s empowerment, and offer design implications for leveraging LLMs to better support such practices.

Existing research has examined how artificial teammates influence collaboration within teams, but far less is known about their role in shaping interactions between teams. In particular, it remains unclear how transparent integration of AI teammates influences intergroup biases in competitive contexts. To investigate this, we designed StarHarvest, an online game where two hybrid teams (each consisting of one human and one bot, either concealed or revealed) competed for resources while bots elicited prosocial or antisocial behaviors. Drawing on data from 240 participants, we analyzed behavioral choices, evaluations, and resource allocations toward ingroup and outgroup members. Our findings show that hidden bots fostered stronger within-team coordination but also allowed asymmetric retribution toward weaker opponents. By contrast, revealed bots were treated as secondary teammates, reducing cohesion and shifting responsibility onto human partners. We conclude with design implications for socially responsible integration of artificial teammates, highlighting tensions between group-level and agent-level identities.

Effectively providing emotional support is a critical yet intricate interpersonal skill. Supporters often lack accessible and practical training opportunities to develop this competency. To address this gap, we introduce ESR-Coach, a Large Language Model (LLM)-based coaching system designed to train individuals in emotionally supportive communication. ESR-Coach leverages multiple AI agents to generate practice scenarios, demonstrate reference responses, and provide assessments on user practice replies. We evaluate the proficiency of our system on these three tasks, demonstrating high-fidelity case generation, helpful exemplary responses, and valid response assessments. In our user study (N=20), ESR-Coach helped participants achieve an average improvement of 17% in response helpfulness. After training, participants also employed more diverse and effective strategies. We further discuss the social intelligence of LLMs and their potential to foster humans’ interpersonal skills in real-world scenarios.

From purchasing a gift to deciding on a hobby, unfamiliar decisions—decisions without domain knowledge and experience—are frequent and significant. The complexity and uncertainty of such decisions demand unique approaches to information seeking, understanding, and decision-making. Our formative study highlights that in the current workflow, users want to start by discovering broad and relevant domain information evenly and simultaneously, quickly address emerging inquiries, and gain personalized standards to assess information found. We present ChoiceMates, an interactive multi-agent system designed to address these needs by enabling users to engage with a dynamic set of LLM agents each presenting a unique experience in the domain. Unlike existing multi-agent systems that automate tasks with agents, the user orchestrates agents to assist their decision-making process in each turn, through chatting with all agents, with a tagged subset of agents, or calling in new agents into the space. By comparing ChoiceMates with a web search condition and a multi-agent framework (n=12), we show that ChoiceMates enables a more confident, satisfactory decision-making with better situation understanding than web search, and higher decision quality than a commercial multi-agent framework. We further illustrate how participants utilized ChoiceMates to make unfamiliar decisions, providing insights into designing a more controllable and collaborative multi-agent system.

Growing awareness of wellness has prompted people to consider whether their dietary patterns align with their health and fitness goals. In response, researchers have introduced various wearable dietary monitoring systems and dietary assessment approaches. However, these solutions are either limited to identifying foods with simple ingredients or insufficient in providing an analysis of individual dietary behaviors with domain-specific knowledge. In this article, we present DietGlance , a system that automatically monitors dietary behaviors in daily routines and delivers personalized analysis from knowledge sources. DietGlance first detects ingestive episodes from multimodal inputs using eyeglasses, capturing privacy-preserving meal images of various dishes being consumed. Based on the inferred food items and consumed quantities from these images, DietGlance further provides nutritional analysis and personalized dietary suggestions, empowered by the retrieval-augmented generation module on a reliable nutrition library. A short-term user study (N = 33) and a 4-week longitudinal study (N = 16) demonstrate the usability and effectiveness of DietGlance , offering insights and implications for future AI-assisted dietary monitoring and personalized healthcare intervention systems using eyewear.

Collective memory—community members' interconnected memories and impressions of the group—is essential to the community's culture and identity. Its development requires members' continuous participatory contribution and sensemaking. However, existing works mainly adopt a holistic sociological perspective to analyze well-developed collective memory, less focusing on member-level conceptualization of this possession or what the co-contribution practices can be. Therefore, this work alternatively adopts the latter perspective and probes such interpretative and interactional patterns with two mobile systems. With one being a locative narrative and exploration system condensed from existing literature's design frameworks, and the other being a conventional online forum representing current practices, they served as the anchors of observation for our two-week, mixed-methods field study (n=38) on a university campus. A core debate we have identified was to retrospectively contemplate or document the presence as a history for the future. This also subsequently impacted the narrative focuses, expectations of collective memory constituents, and the ways participants seek inspiration from the group. We further extracted design considerations that could better embrace the diverse conceptualizations of collective memory and bond different community members together. Lastly, revisiting and reflecting on our design, we provided extra insights on designing devoted locative narrative experiences for community-driven UGC platforms.

We are again thrilled to be able to present the Computer-Supported Cooperative Work and Social Computing (CSCW) community with an issue of the Proceedings of the ACM on Human-Computer Interaction, containing very interesting and relevant scholarship from its members. This issue includes 42 papers from the May 2025 cycle, selected from a total of 637 submissions and following two rounds of reviews and one revision. 209 submissions from this round will be further revised, reviewed again, and may appear in another issue of the journal later this year. Our external reviewers and track editorial board have together conducted a rigorous review process to select contributions of the highest quality advancing the CSCW field. As Track Chairs, we are grateful for the community’s collective efforts to continue shaping and sharing CSCW’s tradition of high-quality scholarship across the years.

This paper examines how informal care work in community-based aging systems is assetized through the institutionalization of timebanking. Drawing on long-term ethnography following the implementation of digital timebanking systems as part of government-led aging-in-community initiatives in Shanghai, this paper teases out the labor of navigating multi-layered temporal tensions in informal caregiving, from interactional to personal to generational. We demonstrate how present informal care work has been transformed into exchangeable, bankable, and quantifiable assets, promising future social and economic benefits. We analyze how timebanking assetizes, institutionalizes, and governs informal care work by mobilizing younger generations of older adults (i.e., those recently retired) to perform unpaid caregiving for older generations in the communities under the rhetoric of mutual support and uncertain promises of future care. We discuss how CSCW can critically engage with the temporal navigations that are integral to informal care work, particularly in the context of demographic shifts, labor shortages, and the broken formal aging care infrastructure. From there, we question whether timebanking constitutes a sustainable sociotechnical alternative to formal care systems, or if it is increasingly co-opted for labor extraction that reproduces the precarity of aging futures.

While mesh networking for edge settings (e.g., smart buildings, farms, battlefields, etc.) has received much attention, the layer of control over such meshes remains largely centralized and cloud-based. This paper focuses on applications with commonplace sense-trigger-actuate (STA) workloads—like the abstraction of routines popular now in smart homes, but applied to larger-scale edge IoT deployments. We present CoMesh, which tackles the challenge of building a decentralized mesh-based control plane for local, non-cloud, and hubless management of sense-trigger-actuate applications. CoMesh builds atop an abstraction called the coterie, which spreads STA load in a finegrained way both across space and across time. A coterie uses a novel combination of techniques such as zero-message-exchange protocols (for fast proactive member selection), quorum-based agreement, and locality-sensitive hashing. We analyze and theoretically prove safety and liveness properties of CoMesh. Our evaluation with both a Raspberry Pi-4 deployment and largerscale simulations, using real building maps and real routine workloads, shows that CoMesh is load-balanced, fast, faulttolerant, and scalable.

In redirected walking techniques, curvature gain and bending gain, which are referred to as curvature manipulation, are important redirection gains. The applied gains can differ when multiple paths are mapped, and sudden changes in gain may cause discomfort. This study proposes quadratic curvature manipulation (QCM) based on the habituation mechanism to effectively reduce discomfort. This method quadratically adjusts the path curvature, thereby reducing user's perception of curvature changes. Furthermore, we introduce the segmented curvature change (SCC) mode that combines QCM with linear curvature manipulation to facilitate more natural gain transitions, thereby reducing discomfort. Two experiments were conducted. Experiment 1 examined the relationship between QCM parameters and gains at which users felt discomfort. Experiment 2 further examined the effects of different curvature change modes on discomfort. The results indicate that using the SCC mode in curvature manipulations is more effective than other methods in reducing discomfort.

Peer Reviewed

The rise of generative AI technology has sparked interest in using digital information to create AI-generated agents as digital legacy.These agents, often referred to as "AI Afterlives", present unique challenges compared to traditional digital legacy.Yet, there is limited human-centered research on "AI Afterlife" as digital legacy, especially from the perspectives of the individuals being represented by these agents.This paper presents a qualitative study examining users' perceptions, expectations, and concerns regarding AI-generated agents as digital legacy.We identify factors shaping people's attitudes, their perceived differences compared with the traditional digital legacy, and concerns they might have in real practices.We also examine the design aspects throughout the life cycle and interaction process.Based on these findings, we situate "AI Afterlife" in digital legacy, and delve into design implications for maintaining identity consistency and balancing intrusiveness and support in "AI Afterlife" as digital legacy.

CHI ’25, Yokohama, Japan

User-centered design necessitates researchers deeply understanding target users throughout the design process. However, during early-stage user interviews, researchers may misinterpret users due to time constraints, incorrect assumptions, and communication barriers. To address this challenge, we introduce InsightBridge , a tool that supports real-time, AI-assisted information synthesis and visual-based verification. InsightBridge automatically organizes relevant information from ongoing interview conversations into an empathy map. It further allows researchers to specify elements to generate visual abstracts depicting the selected information, and then review these visuals with users to refine the visuals as needed. We evaluated the effectiveness of InsightBridge through a within-subject study (N=32) from both the researchers' and users' perspectives. Our findings indicate that InsightBridge can assist researchers in note-taking and organization, as well as in-time visual checking, thereby enhancing mutual understanding with users. Additionally, users' discussions of visuals prompt them to recall overlooked details and scenarios, leading to more insightful ideas.

Hospital admission interviews are critical for patient care but strain nurses' capacity due to time constraints and staffing shortages. While LLM-powered conversational agents (CAs) offer automation potential, their rigid sequencing and lack of humanized communication skills risk misunderstandings and incomplete data capture. Through participatory design with clinicians and volunteers, we identified essential communication strategies and developed a novel CA that implements these strategies through: (1) dynamic topic management using graph-based conversation flows, and (2) context-aware scaffolding with few-shot prompt tuning. Technical evaluation on an admission interview dataset showed our system achieving performance comparable to or surpassing human-written ground truth, while outperforming prompt-engineered baselines. A between-subject study (N=44) demonstrated significantly improved user experience and data collection accuracy compared to existing solutions. We contribute a framework for humanizing medical CAs by translating clinician expertise into algorithmic strategies, alongside empirical insights for balancing efficiency and empathy in healthcare interactions, and considerations for generalizability.

As social service robots become commonplace, it is essential for them to effectively interpret human signals, such as verbal, gesture, and eye gaze, when people need to focus on their primary tasks to minimize interruptions and distractions. Toward such a socially acceptable Human-Robot Interaction, we conducted a study ($N=24$) in an AR-simulated context of a coffee chat. Participants elicited social cues to signal intentions to an anthropomorphic, zoomorphic, grounded technical, or aerial technical robot waiter when they were speakers or listeners. Our findings reveal common patterns of social cues over intentions, the effects of robot morphology on social cue position and conversational role on social cue complexity, and users' rationale in choosing social cues. We offer insights into understanding social cues concerning perceptions of robots, cognitive load, and social context. Additionally, we discuss design considerations on approaching, social cue recognition, and response strategies for future service robots.

Traditional AI-assisted decision-making systems often provide fixed recommendations that users must either accept or reject entirely, limiting meaningful interaction - especially in cases of disagreement. To address this, we introduce Human-AI Deliberation, an approach inspired by human deliberation theories that enables dimension-level opinion elicitation, iterative decision updates, and structured discussions between humans and AI. At the core of this approach is Deliberative AI, an assistant powered by large language models (LLMs) that facilitates flexible, conversational interactions and precise information exchange with domain-specific models. Through a mixed-methods user study, we found that Deliberative AI outperforms traditional explainable AI (XAI) systems by fostering appropriate human reliance and improving task performance. By analyzing participant perceptions, user experience, and open-ended feedback, we highlight key findings, discuss potential concerns, and explore the broader applicability of this approach for future AI-assisted decision-making systems.

Decomposition is a fundamental skill in algorithmic programming, requiring learners to break down complex problems into smaller, manageable parts. However, current self-study methods, such as browsing reference solutions or using LLM assistants, often provide excessive or generic assistance that misaligns with learners' decomposition strategies, hindering independent problem-solving and critical thinking. To address this, we introduce Decomposition Box (DBox), an interactive LLM-based system that scaffolds and adapts to learners' personalized construction of a step tree through a "learner-LLM co-decomposition"approach, providing tailored support at an appropriate level. A within-subjects study (N=24) found that compared to the baseline, DBox significantly improved learning gains, cognitive engagement, and critical thinking. Learners also reported a stronger sense of achievement and found the assistance appropriate and helpful for learning. Additionally, we examined DBox's impact on cognitive load, identified usage patterns, and analyzed learners' strategies for managing system errors. We conclude with design implications for future AI-powered tools to better support algorithmic programming education.

Intelligent agents coexisting with humans often need to interact with human-shared objects in environments. Thus, agents should plan their interactions based on objects' affordances and the current situation to achieve acceptable outcomes. How to support intelligent agents' planning of affordance-based interactions compatible with human perception and values in real-world contexts remains under-explored. We conducted a formative study identifying the physical, intrapersonal, and interpersonal contexts that count to household human-agent interaction. We then proposed ACKnowledge, a computational framework integrating a dynamic knowledge graph, a large language model, and a vision language model for affordance-based interaction planning in dynamic human environments. In evaluations, ACKnowledge generated acceptable planning results with an understandable process. In real-world simulation tasks, ACKnowledge achieved a high execution success rate and overall acceptability, significantly enhancing usage-rights respectfulness and social appropriateness over baselines. The case study's feedback demonstrated ACKnowledge's negotiation and personalization capabilities toward an understandable planning process.

Educational inequalities in disadvantaged areas have long been a global concern. While Information and Communication Technologies (ICTs) have shown great potential in addressing this issue, the unique challenges in disadvantaged areas often hinder the practical effectiveness of such technologies. This paper examines live-streaming-based dual-teacher classes (LSDC) through a qualitative study in disadvantaged regions of China. Our findings indicate that, although LSDC offers students in these regions access to high-quality educational resources, its practical implementation is fraught with challenges. Specifically, we foreground the pivotal role of local teachers in mitigating these challenges. Through a series of situated efforts, local teachers contextualize high-quality lectures to the local classroom environment, ensuring the expected educational outcomes. Based on our findings, we argue that greater recognition and support for the situational practices of local teachers is essential for fostering a more equitable, sustainable, and scalable technology-driven educational model in disadvantaged areas.

With the rise of AI technologies and their growing influence in the screenwriting field, understanding the opportunities and concerns related to AI's role in screenwriting is essential for enhancing human-AI co-creation. Through semi-structured interviews with 23 screenwriters, we explored their creative practices, attitudes, and expectations in collaborating with AI for screenwriting. Based on participants' responses, we identified the key stages in which they commonly integrated AI, including story structure & plot development, screenplay text, goal & idea generation, and dialogue. Then, we examined how different attitudes toward AI integration influence screenwriters' practices across various workflow stages and their broader impact on the industry. Additionally, we categorized their expected assistance using four distinct roles of AI: actor, audience, expert, and executor. Our findings provide insights into AI's impact on screenwriting practices and offer suggestions on how AI can benefit the future of screenwriting.

Danmaku, a system of scene-aligned, time-synced, floating comments, can augment video content to create g'collective knowledge'. However, its chaotic nature often hinders viewers from effectively assimilating the collective knowledge, especially in knowledge-intensive science videos. With a formative study, we examined viewers' practices for processing collective knowledge and the specific barriers they encountered. Building on these insights, we designed a processing pipeline to filter, classify, and cluster danmaku, leading to the development of CoKnowledge - a tool incorporating a video abstract, knowledge graphs, and supplementary danmaku features to support viewers' assimilation of collective knowledge in science videos. A within-subject study (N=24) showed that CoKnowledge significantly enhanced participants' comprehension and recall of collective knowledge compared to a baseline with unprocessed live comments. Based on our analysis of user interaction patterns and feedback on design features, we presented design considerations for developing similar support tools.

Digital journaling offers a means for older adults to express themselves, document their lives, and engage in self-reflection, contributing to the maintenance of cognitive function and social connectivity. Although previous works have investigated the motivations and benefits of digital journaling for older adults, little technical support has been designed to offer assistance. We conducted a formative study with older adults and uncovered their encountered challenges and preferences for technical support. Informed by the findings, we designed a Large Language Model (LLM) empowered tool, JournalAIde, which provides vicarious experience, idea organization, sample text generation, and visual editing cues to enhance older adults' confidence, writing ability, and sustained attention during digital journaling. Through a between-subjects study and a field deployment, we demonstrated the JournalAIde's significant effectiveness compared to a baseline system in empowering older adults in digital journaling. We further investigated older adults' experiences and perceptions of LLM writing assistance.

With increasing sizes of DNN (Deep Neural Network) models making them exceed the memory of a single device (GPU), model parallelism-based training has become paramount, splitting a model across multiple devices. Unfortunately, today’s model parallelism approaches often result in memory-unbalanced allocations of the model across the multiple devices, with some devices’ memory heavily utilized while others remain underused. This imbalance limits deployments from reaching high batch sizes, triggers Out of Memory (OOM) errors earlier, and underutilizes resources. We present Wainscot, a model parallelism solution that produces memory-balanced placements of a DNN model across multiple devices, without noticeable increases in step time. We explore and empirically compare different granularities of rebalancing: operators, operator groups, and subgraphs. Experiments with diverse DNNs across a wide range of batch sizes demonstrate that compared to state-of-the-art model parallelism systems, Wainscot reduces maximal peak memory (across devices) by 47.94%, with a modest increase of 1.62% in step time.

The ability to coordinate turn taking during spoken dialogue is crucial for an embodied spoken dialogue system (SDS), e.g., in a humanoid robot. The SDS needs to model transitions in the conversational floor, which describes each party’s stance (either speaking or listening). Further, the SDS needs to signal its perception of the floor to the human, so that they can coordinate floor transitions and resolve conflicts. Conventional SDS employ standalone modules to control floor transitions but do not produce timely and appropriate responses. Recent end-to-end audio LLMs generate responses quickly, but do not coordinate floor transitions as accurately. In this work, we propose an SDS architecture that dynamically adjusts its prompts to an end-to-end audio LLM based upon its perception of the conversational floor state. The LLM output determines not only the audio output, but also the perceived floor state. This enables the system to signal its stance to the human, both when listening and when speaking. We conducted an experiment where a humanoid robot administered a semi-structured interview with human subjects. Results show that, compared with baseline systems using static prompts, dynamic prompting enables the LLM to model floor transitions more accurately, to generate more appropriate signalling, and to interrupt less, leading to smoother turn-taking in dialogue.

Researchers frequently need to synthesize their own publications into coherent narratives that demonstrate their scholarly contributions.To suit diverse communication contexts, exploring alternative ways to organize one's work while maintaining coherence is particularly challenging, especially in interdisciplinary fields like HCI where individual researchers' publications may span diverse domains and methodologies.In this paper, we present PaperBridge, a human-AI co-exploration system informed by a formative study and content analysis.PaperBridge assists researchers in exploring diverse perspectives for organizing their publications into coherent narratives.At its core is a bi-directional analysis engine powered by large language models, supporting iterative exploration through both top-down user intent (e.g., determining organization structure) and bottom-up refinement on narrative components (e.g., thematic paper groupings).Our user study (N=12) demonstrated PaperBridge's usability and effectiveness in facilitating the exploration of alternative research narratives.Our findings also provided empirical insights into how interactive systems can scaffold academic communication tasks.

Abstract In heterogeneous graphs (HGs), which offer richer network and semantic insights compared to homogeneous graphs, the Metapath technique serves as an essential tool for data mining. This technique facilitates the specification of sequences of entity connections, elucidating the semantic composite relationships between various node types for a range of downstream tasks. Nevertheless, selecting the most appropriate metapath from a pool of candidates and assessing its impact presents significant challenges. To address this issue, our study introduces MetapathVis, an interactive visual analytics system designed to assist machine learning (ML) practitioners in comprehensively understanding and comparing the effects of metapaths from multiple fine‐grained perspectives. MetapathVis allows for an in‐depth evaluation of various models generated with different metapaths, aligning HG network information at the individual level with model metrics. It also facilitates the tracking of aggregation processes associated with different metapaths. The effectiveness of our approach is validated through three case studies and a user study, with feedback from domain experts confirming that our system significantly aids ML practitioners in evaluating and comprehending the viability of different metapath designs.

The rise of AI-generated content (AIGC) is transforming online creative communities (OCCs) and posing challenges to their regulation. The interacting behaviors, such as sharing artworks with descriptions, commenting on creations, and creators' subsequent replying are the essential components of user engagement in these communities. Understanding the influence of AIGC on the evolving user engagement could be helpful for community regulation. In this work, we collect 235K posts and their associated 255K comments from DeviantArt, a large creative community allowing uploading AIGC. Through open coding, we identify five categories of practices in describing and commenting on artworks, respectively. A set of deep learning models are applied to classify the posts and comments. We then combine time series regression analysis, causal inference analysis, and logistic regression analysis, to examine the impact of the surge of AIGC on user engagement. Results suggest that AI-generated artworks show a decreasing emphasis on the content of creations but an increasing trend toward commercial and promotion purposes. AI-generated artworks emphasize less on IP issues than human-created ones, while the awareness of IP issues drops for human-created artworks with the growth of AIGC as well. Although comments with high sentiment valence, for peer bonding or for requesting usage positively predict the reply behavior for human-created artworks, community members are less likely to maintain these interactions as AIGC rises. Finally, we discuss insights and design implications for OCCs.

We are again thrilled to be able to present the Computer-Supported Cooperative Work and Social Computing (CSCW) community with an issue of the Proceedings of the ACM on Human-Computer Interaction, containing very interesting and relevant scholarship from its members. This issue includes 211 papers, of which 192 were accepted from the January 2024 cycle, and 19 were accepted from the July 2024 cycle. It reflects great efforts and contributions from external reviewers, Associate Chairs and Editors, who together have conducted a rigorous review process to select contributions of the highest quality advancing the CSCW field. As Track Chairs, we are grateful for the community's collective efforts to continue shaping and sharing CSCW's tradition of high-quality scholarship across the years.

We are again thrilled to be able to present the Computer-Supported Cooperative Work and Social Computing (CSCW) community with an issue of the Proceedings of the ACM on Human-Computer Interaction, containing very interesting and relevant scholarship from its members. This issue includes 313 papers, of which 45 were accepted from the January 2024 cycle, 85 were accepted from the July 2025 cycle, and 183 were accepted from the October 2025 cycle. It reflects great efforts and contributions from external reviewers, Associate Chairs and Editors, who together have conducted a rigorous review process to select contributions of the highest quality advancing the CSCW field. As Track Chairs, we are grateful for the community's collective efforts to continue shaping and sharing CSCW's tradition of high-quality scholarship across the years.

Aging in place refers to the enabling of individuals to age comfortably and securely within their own homes and communities. Aging in place relies on robust infrastructure, prompting the development and implementation of both human-led care services and information and communication technologies to provide support. Through a long-term ethnographic study that includes semi-structured interviews with 24 stakeholders, we consider these human- and technology-driven care infrastructures for aging in place, examining their origins, deployment, interactions with older adults, and challenges. In doing so, we reconsider the value of these different forms of older adult care, highlighting the various issues associated with using, for instance, health monitoring technology or appointment scheduling systems to care for older adults aging in place. We suggest that technology should take a supportive, not substitutive role in older adult care infrastructure. Furthermore, we note that designing for aging in place should move beyond a narrow focus on independence in one's home to instead encompass the broader community and its dynamics.

Traditional Medicine (TM) is the oldest healthcare form and has been increasingly adopted as the primary or complementary medical therapy in the world. However, TM's practical development remains highly challenging. While artificial intelligence (AI) has become powerful in advancing modern medicine, limited attention has been paid to its potential and usage in TM. This study addresses this gap through a probe-based interview study with 16 TM clinicians, examining their experiences, perceptions, and expectations of AI-empowered clinical support systems. Our findings reveal that despite numerous AI-CDS systems, their practical usage in TM settings was still limited. We identify a series of practical challenges when integrating AI-CDS into TM clinical scenarios, largely due to TM's unique features and the significant data work challenges these features present. We end by critically discussing the potential issues that may arise when integrating AI into practical TM scenarios, and proposing a series of practical recommendations for future studies.

An unequitable material allocation scheme will cause secondary disasters and directly affect relief efficiency. Post-disaster relief equity allocation-transportation problem is a key issue in humanitarian logistics. However, existing studies on relief material allocation lack the characterization of equity, and have not considered the hierarchical relationship between two decision-makers and uncertain costs caused by disasters. For this purpose, this paper addresses a satisfaction objective to measure equity, and constructs a bilevel multi-objective distributionally robust (BMDR) method. On the one hand, our work focuses on the hierarchical relationship, with the local government as the upper decision-maker and the affected population as the lower decision-maker. On the other hand, our work constructs chance constraints under ambiguity set to characterize uncertain costs with sub-Gaussian distribution information. In particular, we show that robust optimal solution obtained by our method has not only a priori probability guarantee but also a posteriori probability guarantee. Furthermore, we transform our model into a mixed integer second-order cone programming (MISOCP) model and design a customized branch-and-cut (B&C) algorithm. Finally, some experiments are carried out via a real tornado case in Yancheng. The empirical findings show that the relief scheme obtained by our BMDR model has better out-of-sample performance and an improved posteriori probability bound guarantee, and the proposed algorithm has better solving efficiency. Our method can be used to guide the allocation and transportation of post-disaster relief materials under incomplete cost distribution information and improve the robustness of relief.

Heads-up computing aims to provide synergistic digital assistance that minimally interferes with users' on-the-go daily activities. Currently, the input modalities of heads-up computing are mainly voice and finger gestures. In this work, we propose and evaluate the AmplitudeArrow (AA) technique designed for on-the-go AR menu selection to demonstrate that consecutive simple head gestures can also be an effective input modality for heads-up computing. Specifically, AA arranges menu icons into one/two row(s). To select a target icon, the user first makes their head yaw to pre-select the target icon or the column containing it and then makes their head pitch to make the arrow in the target icon expand until the arrow covers the target icon completely, i.e., the pitch amplitude surpasses the selection confirmation threshold. User studies indicated that AA demonstrated robust resistance to walking-caused head perturbation and external factors such as other people/obstacles, delivering high accuracy (error rate $< $< 5$\%$%) and fast speed ($< $< 1.5s per selection) when there were no more than six icon columns (twelve icons) distributed horizontally and evenly in a menu area with a horizontal visual angle of $43^{\circ }$43∘.

The burgeoning online video game industry has sparked intense competition among providers to both expand their user base and retain existing players, particularly within social interaction genres. To anticipate player churn, there is an increasing reliance on machine learning (ML) models that focus on social interaction dynamics. However, the prevalent opacity of most ML algorithms poses a significant hurdle to their acceptance among domain experts, who often view them as "opaque models". Despite the availability of eXplainable Artificial Intelligence (XAI) techniques capable of elucidating model decisions, their adoption in the gaming industry remains limited. This is primarily because non-technical domain experts, such as product managers and game designers, encounter substantial challenges in deciphering the "explicit" and "implicit" features embedded within computational models. This study proposes a reliable, interpretable, and actionable solution for predicting player churn by restructuring model inputs into explicit and implicit features. It explores how establishing a connection between explicit and implicit features can assist experts in understanding the underlying implicit features. Moreover, it emphasizes the necessity for XAI techniques that not only offer implementable interventions but also pinpoint the most crucial features for those interventions. Two case studies, including expert feedback and a within-subject user study, demonstrate the efficacy of our approach.

Composite visualization represents a widely embraced design that combines multiple visual representations to create an integrated view. However, the traditional approach of creating composite visualizations in immersive environments typically occurs asynchronously outside of the immersive space and is carried out by experienced experts. In this work, we aim to empower users to participate in the creation of composite visualization within immersive environments through embodied interactions. This could provide a flexible and fluid experience with immersive visualization and has the potential to facilitate understanding of the relationship between visualization views. We begin with developing a design space of embodied interactions to create various types of composite visualizations with the consideration of data relationships. Drawing inspiration from people's natural experience of manipulating physical objects, we design interactions based on the combination of 3D manipulations in immersive environments. Building upon the design space, we present a series of case studies showcasing the interaction to create different kinds of composite visualizations in virtual reality. Subsequently, we conduct a user study to evaluate the usability of the derived interaction techniques and user experience of creating composite visualizations through embodied interactions. We find that empowering users to participate in composite visualizations through embodied interactions enables them to flexibly leverage different visualization views for understanding and communicating the relationships between different views, which underscores the potential of several future application scenarios.

As AI technology continues to advance, the importance of human-AI collaboration becomes increasingly evident, with numerous studies exploring its potential in various fields. One vital field is data science, including feature engineering (FE), where both human ingenuity and AI capabilities play pivotal roles. Despite the existence of AI-generated recommendations for FE, there remains a limited understanding of how to effectively integrate and utilize humans’ and AI’s knowledge. To address this gap, we design a readily-usable prototype, human&AI-assisted FE in Jupyter notebooks. It harnesses the strengths of humans and AI to provide feature suggestions to users, seamlessly integrating these recommendations into practical workflows. Using the prototype as a research probe, we conducted an exploratory study to gain valuable insights into data science practitioners’ perceptions, usage patterns, and their potential needs when presented with feature suggestions from both humans and AI. Through qualitative analysis, we discovered that the “Creator” of the feature (i.e., AI or human) significantly influences users’ feature selection, and the semantic clarity of the suggested feature greatly impacts its adoption rate. Furthermore, our findings indicate that users perceive both differences and complementarity between features generated by humans and those generated by AI. Lastly, based on our study results, we derived a set of design recommendations for future human&AI FE design. Our findings show the collaborative potential between humans and AI in the field of FE.

Reading and repeatedly retelling a short story is a common and effective approach to learning the meanings and usages of target words. However, learners often struggle with comprehending, recalling, and retelling the story contexts of these target words. Inspired by the Cognitive Theory of Multimedia Learning, we propose a computational workflow to generate relevant images paired with stories. Based on the workflow, we work with learners and teachers to iteratively design an interactive vocabulary learning system named RetAssist. It can generate sentence-level images of a story to facilitate the understanding and recall of the target words in the story retelling practices. Our within-subjects study (N=24) shows that compared to a baseline system without generative images, RetAssist significantly improves learners’ fluency in expressing with target words. Participants also feel that RetAssist eases their learning workload and is more useful. We discuss insights into leveraging text-to-image generative models to support learning tasks.

Situated visualization blends data into the real world to fulfill individuals’ contextual information needs. However, interacting with situated visualization in public environments faces challenges posed by users’ acceptance and contextual constraints. To explore appropriate interaction design, we first conduct a formative study to identify users’ needs for data and interaction. Informed by the findings, we summarize appropriate interaction modalities with eye-based, hand-based and spatially-aware object interaction for situated visualization in public environments. Then, through an iterative design process with six users, we explore and implement interactive techniques for activating and analyzing with situated visualization. To assess the effectiveness and acceptance of these interactions, we integrate them into an AR prototype and conduct a within-subjects study in public scenarios using conventional hand-only interactions as the baseline. The results show that participants preferred our prototype over the baseline, attributing their preference to the interactions being more acceptable, flexible, and practical in public.

Regular screening is critical for individuals at risk of neurocognitive disorders (NCDs) to receive early intervention. Conversational agents (CAs) have been adopted to administer dialog-based NCD screening tests for their scalability compared to human-administered tests. However, unique communication skills are required for CAs during NCD screening, e.g., clinicians often apply scaffolding to ensure subjects’ understanding of and engagement in screening tests. Based on scaffolding theories and analysis of clinicians’ practices from human-administered test recordings, we designed a scaffolding framework for the CA. In an exploratory wizard-of-Oz study, the CA empowered by ChatGPT administered tasks in the Grocery Shopping Dialog Task with 15 participants (10 diagnosed with NCDs). Clinical experts verified the quality of the CA’s scaffolding and we explored its effects on task understanding of the participants. Moreover, we proposed implications for the future design of CAs that enable scaffolding for scalable NCD screening.

On online video platforms, viewers often lack a channel to sense others’ and express their affective state on the fly compared to co-located group-viewing. This study explored the design of complementary affective communication specifically for effortless, spontaneous sharing of frissons during video watching. Also known as aesthetic chills, frissons are instant psycho-physiological reactions like goosebumps and shivers to arousing stimuli. We proposed an approach that unobtrusively detects viewers’ frissons using skin electrodermal activity sensors and presents the aggregated data alongside online videos. Following a design process of brainstorming, focus group interview (N=7), and design iterations, we proposed three different designs to encode viewers’ frisson experiences, namely, ambient light, icon, and vibration. A mixed-methods within-subject study (N=48) suggested that our approach offers a non-intrusive and efficient way to share viewers’ frisson moments, increases the social presence of others as if watching together, and can create affective contagion among viewers.

Elicitation diary studies, a type of qualitative, longitudinal research method, involve participants to self-report aspects of events of interest at their occurrences as memory cues for providing details and insights during post-study interviews. However, due to time constraints and lack of motivation, participants’ diary entries may be vague or incomplete, impairing their later recall. To address this challenge, we designed an automatic contextual information recording agent, DiaryHelper, based on the theory of episodic memory. DiaryHelper can predict five dimensions of contextual information and confirm with participants. We evaluated the use of DiaryHelper in both the recording period and the elicitation interview through a within-subject study (N=12) over a period of two weeks. Our results demonstrated that DiaryHelper can assist participants in capturing abundant and accurate contextual information without significant burden, leading to a more detailed recall of recorded events and providing greater insights.

In AI-assisted decision-making, it is crucial but challenging for humans to achieve appropriate reliance on AI. This paper approaches this problem from a human-centered perspective, “human self-confidence calibration”. We begin by proposing an analytical framework to highlight the importance of calibrated human self-confidence. In our first study, we explore the relationship between human self-confidence appropriateness and reliance appropriateness. Then in our second study, We propose three calibration mechanisms and compare their effects on humans’ self-confidence and user experience. Subsequently, our third study investigates the effects of self-confidence calibration on AI-assisted decision-making. Results show that calibrating human self-confidence enhances human-AI team performance and encourages more rational reliance on AI (in some aspects) compared to uncalibrated baselines. Finally, we discuss our main findings and provide implications for designing future AI-assisted decision-making interfaces.

Designing and using technologies to support Non-Pharmacological Interventions (NPI) for People with Dementia (PwD) has drawn increasing attention in HCI, with the potential expectations of higher user engagement and positive outcomes. Yet, technologies for NPI can only be valuable if practitioners successfully incorporate them into their ongoing intervention practices beyond a limited research period. Currently, we know little about how practitioners experience and perceive these technologies in practical NPI for PwD. In this paper, we investigate this question through observations of five in-person NPI activities and interviews with 11 therapists and 5 caregivers. Our findings elaborate the practical NPI workflow process and characteristics, and practitioners’ attitudes, experiences, and perceptions to technology-mediated NPI in practice. Generally, our participants emphasized practical NPI is a complex and professional practice, needing fine-grained, personalized evaluation and planning, and the practical executing process is situated, and multi-stakeholder collaborative. Yet, existing technologies often fail to consider these specific characteristics, which leads to limitations in practical effectiveness or sustainable use. Drawing on our findings, we discuss the possible implications for designing more useful and practical NPI intervention technologies.

Semantic typographic logos harmoniously blend typeface and imagery to represent semantic concepts while maintaining legibility. Conventional methods using spatial composition and shape substitution are hindered by the conflicting requirement for achieving seamless spatial fusion between geometrically dissimilar typefaces and semantics. While recent advances made AI generation of semantic typography possible, the end-to-end approaches exclude designer involvement and disregard personalized design. This paper presents TypeDance, an AI-assisted tool incorporating design rationales with the generative model for personalized semantic typographic logo design. It leverages combinable design priors extracted from uploaded image exemplars and supports type-imagery mapping at various structural granularity, achieving diverse aesthetic designs with flexible control. Additionally, we instantiate a comprehensive design workflow in TypeDance, including ideation, selection, generation, evaluation, and iteration. A two-task user evaluation, including imitation and creation, confirmed the usability of TypeDance in design across different usage scenarios.

Chinese traditional opera (Xiqu) performers often experience skin problems due to the long-term use of heavy-metal-laden face paints. To explore the current skincare challenges encountered by Xiqu performers, we conducted an online survey (N=136) and semi-structured interviews (N=15) as a formative study. We found that incomplete makeup removal is the leading cause of human-induced skin problems, especially the difficulty in removing eye makeup. Therefore, we proposed EyeVis, a prototype that can visualize the residual eye makeup and record the time make-up was worn by Xiqu performers. We conducted a 7-day deployment study (N=12) to evaluate EyeVis. Results indicate that EyeVis helps to increase Xiqu performers’ awareness about removing makeup, as well as boosting their confidence and security in skincare. Overall, this work also provides implications for studying the work of people who wear makeup on a daily basis, and helps to promote and preserve the intangible cultural heritage of practitioners.

Students’ increasing use of Artificial Intelligence (AI) presents new challenges for assessing their mastery of knowledge and skills in project-based learning (PBL). This paper introduces a co-design study to explore the potential of students’ AI usage data as a novel material for PBL assessment. We conducted workshops with 18 college students, encouraging them to speculate an alternative world where they could freely employ AI in PBL while needing to report this process to assess their skills and contributions. Our workshops yielded various scenarios of students’ use of AI in PBL and ways of analyzing such usage grounded by students’ vision of how educational goals may transform. We also found that students with different attitudes toward AI exhibited distinct preferences in how to analyze and understand their use of AI. Based on these findings, we discuss future research opportunities on student-AI interactions and understanding AI-enhanced learning.

Preference-based learning aims to align robot task objectives with human values. One of the most common methods to infer human preferences is by pairwise comparisons of robot task trajectories. Traditional comparison-based preference labeling systems seldom support labelers to digest and identify critical differences between complex trajectories recorded in videos. Our formative study (N = 12) suggests that individuals may overlook non-salient task features and establish biased preference criteria during their preference elicitation process because of partial observations. In addition, they may experience mental fatigue when given many pairs to compare, causing their label quality to deteriorate. To mitigate these issues, we propose FARPLS, a Feature-Augmented Robot trajectory Preference Labeling System. FARPLS highlights potential outliers in a wide variety of task features that matter to humans and extracts the corresponding video keyframes for easy review and comparison. It also dynamically adjusts the labeling order according to users’ familiarities, difficulties of the trajectory pair, and level of disagreements. At the same time, the system monitors labelers’ consistency and provides feedback on labeling progress to keep labelers engaged. A between-subjects study (N = 42, 105 pairs of robot pick-and-place trajectories per person) shows that FARPLS can help users establish preference criteria more easily and notice more relevant details in the presented trajectories than the conventional interface. FARPLS also improves labeling consistency and engagement, mitigating challenges in preference elicitation without raising cognitive loads significantly.

Humanoid robots are promising approach to automating patient interviews routinely conducted by medical staff. Their human-like appearance enables them to use the full gamut of verbal and behavioral cues that are critical to a successful interview. On the other hand, anthropomorphism can induce expectations of human-level performance by the robot. Not meeting such expectations degrades the quality of interaction. Specifically, humans expect rich real-time interactions during speech exchange, such as backchanneling and barge-ins. The nature of the patient interview task differs from most other scenarios where task oriented dialogue systems have been used, as there is increased potential of engagement breakdown during interaction. We describe a dialogue system architecture that improves the performance of humanoid robots on the patient interview task. Our architecture adds a nested inner real-time control loop to improve the timeliness of the robot’s responses based on the notion of "stance", an elaboration of the concept of a "turn", common in most existing dialogue systems. It also expands the dialogue state to monitor not only task progress, but also human engagement. Experiments using a humanoid robot running our proposed architecture reveal improved performance on interview tasks in terms of the perceived timeliness of responses and users’ impressions of the system.

Interdisciplinary studies often require researchers to explore literature in diverse branches of knowledge. Yet, navigating through the highly scattered knowledge from unfamiliar disciplines poses a significant challenge. In this paper, we introduce DiscipLink, a novel interactive system that facilitates collaboration between researchers and large language models (LLMs) in interdisciplinary information seeking (IIS). Based on users’ topic of interest, DiscipLink initiates exploratory questions from the perspectives of possible relevant fields of study, and users can further tailor these questions. DiscipLink then supports users in searching and screening papers under selected questions by automatically expanding queries with disciplinary-specific terminologies, extracting themes from retrieved papers, and highlighting the connections between papers and questions. Our evaluation, comprising a within-subject comparative experiment and an open-ended exploratory study, reveals that DiscipLink can effectively support researchers in breaking down disciplinary boundaries and integrating scattered knowledge in diverse fields. The findings underscore the potential of LLM-powered tools in fostering information-seeking practices and bolstering interdisciplinary research.

In healthcare, AI techniques are widely used for tasks like risk assessment and anomaly detection. Despite AI’s potential as a valuable assistant, its role in complex medical data analysis often over-simplifies human-AI collaboration dynamics. To address this, we collaborated with a local hospital, engaging six physicians and one data scientist in a formative study. From this collaboration, we propose a framework integrating two-phase interactive visualization systems: one for Human-Led, AI-Assisted Retrospective Analysis and another for AI-Mediated, Human-Reviewed Iterative Modeling. This framework aims to enhance understanding and discussion around effective human-AI collaboration in healthcare.

Satellite Internet, as an important component of the integrated space-ground information network, is a hot research hotspot nowadays. Many scholars have undertaken research in the areas of constellation networking design, network protocol design, and communication performance assessment, and their main research tool is software simulation. Traditional stand-alone network simulation simulators based on OPNET or NS3 are constrained in the simulation efficiency of mega satellite networks because of the limitations of computer hardware conditions and software performance. Based on the above characteristics, we propose a parallel network simulation architecture based on low correlation between different areas of the global satellite network, and in order to improve the parallel network simulation performance, the network topology needs to be divided effectively. Therefore, firstly we consider CPU and memory resource consumption as a measure of topology partitioning performance indicators, propose a resource assessment algorithm and use the result of this assessment as the topology partitioning optimization objective; secondly, we propose a load balancing based intelligent topology partitioning algorithm (LBTP); thirdly, we propose a time slice algorithm (TSA) for parallel simulation in each time cycle. To demonstrate the algorithm proposed in this paper, we built a simulation platform based on the combination of STK (Satellite Tool Kit), OPNET and Proxmox VE, and experimentally verified that the proposed architecture and algorithm significantly improve the simulation efficiency.

Conversational Agents (CAs) can facilitate information elicitation in various scenarios, such as semi-structured interviews. Current CAs can ask predetermined questions but lack skills for asking follow-up questions. Thus, we designed three approaches for CAs to automatically ask follow-up questions, i.e., follow-ups on concepts, follow-ups on related concepts, and general follow-ups. To investigate their effects, we conducted a user study (N=26) in which a CA interviewer asked follow-up questions generated by algorithms and crafted by human wizards. Our results showed that the CA's follow-up questions were readable and effective in information elicitation. The follow-ups on concepts and related concepts achieved a lower drop rate and better relevance, while the general follow-ups elicited more informative responses. Further qualitative analysis of the human-CA interview data revealed algorithm drawbacks and identified follow-up question techniques used by the human wizards. We provided design implications for improving information elicitation of future CAs based on the results.

Online design communities, where members exchange free-form views on others' designs, offer a space for beginners to learn visual design. However, the content of these communities is often unorganized for learners, containing many redundancies and irrelevant comments. In this paper, we propose a computational approach for leveraging online design communities to run a conversational agent that assists informal learning of visual elements (e.g., color and space). Our method extracts critiques, suggestions, and rationales on visual elements from comments. We present DesignQuizzer, which asks questions about visual design in UI examples and provides structured comment summaries. Two user studies demonstrate the engagement and usefulness of DesignQuizzer compared with the baseline (reading reddit.com/r/UI_design). We also showcase how effectively novices can apply what they learn with DesignQuizzer in a design critique task and a visual design task. We discuss how to use our approach with other communities and offer design considerations for community-powered learning support tools.

Interactive storytelling is vital for preschooler development. While children's interactive partners have traditionally been their parents and teachers, recent advances in artificial intelligence (AI) have sparked a surge of AI-based storytelling and reading technologies. As these technologies become increasingly ubiquitous in preschoolers' lives, questions arise regarding how they function in practical storytelling and reading scenarios and, how parents, the most critical stakeholders, experience and perceive these technologies. This paper investigates these questions through a qualitative study with 17 parents of children aged 3-6. Our findings suggest that even though AI-based storytelling and reading technologies provide more immersive and engaging interaction, they still cannot meet parents' expectations due to a series of interactive and algorithmic challenges. We elaborate on these challenges and discuss the possible implications of future AI-based interactive storytelling technologies for preschoolers.

Choice of color is critical to creating effective charts with an engaging, enjoyable, and informative reading experience. However, designing a good color palette for a chart is a challenging task for novice users who lack related design expertise. For example, they often find it difficult to articulate their abstract intentions and translate these intentions into effective editing actions to achieve a desired outcome. In this work, we present NL2Color, a tool that allows novice users to refine chart color palettes using natural language expressions of their desired outcomes. We first collected and categorized a dataset of 131 triplets, each consisting of an original color palette of a chart, an editing intent, and a new color palette designed by human experts according to the intent. Our tool employs a large language model (LLM) to substitute the colors in original palettes and produce new color palettes by selecting some of the triplets as few-shot prompts. To evaluate our tool, we conducted a comprehensive two-stage evaluation, including a crowd-sourcing study ( N=71) and a within-subjects user study ( N=12). The results indicate that the quality of the color palettes revised by NL2Color has no significantly large difference from those designed by human experts. The participants who used NL2Color obtained revised color palettes to their satisfaction in a shorter period and with less effort.

Online critique communities (OCCs) provide a convenient space for creators to solicit feedback on their artifacts and improve skills. Creators’ behavioral, emotional, and cognitive engagement with comments on their works contribute to their skill development. However, what kinds of critique creators feel engaging may change with the creation stage of their shared artifacts. In this paper, we first model three dimensions of engagement expressed in creators’ replies to peer comments. Then we quantitatively examine how their engagement is affected by artifacts’ stage and feedback characteristics via regression analysis. Results show that creators sharing works-in-progress tend to exhibit lower behavioral and emotional engagement, but higher cognitive engagement than those sharing complete works. The increase in the valence of the feedback is associated with a stronger increase in behavior engagement for seekers sharing complete works than works-in-progress. Finally, we discuss how our insights could benefit OCCs and other online help-seeking platforms.

Automatic stress tracking has become increasingly available on wearable devices. Research has investigated its use for individual stress management, largely within the traditional data-as-care framing. However, its use for stress sharing in social relationships, particularly close relationships, is still under explored. Inspired by the idea of “caring-through-data”, which focuses on mediating the social and emotional experiences of the collective “us” with data, this paper presents a design study with a prototype called IntimaSea, a display featuring illustrative stress data in collective forms to be shared among close relationships. The field trials with nine groups of intimately-connected users (N=19) highlight its potential on stress awareness, interpretation and management, as well as intimacy promotion. We end by discussing sharing stress for social ways of stress management, stress data as a meaningful social cue mediating relationships, as well as design implications for caring-through-data.

In Community-Based Question Answering (CQA) platforms, people can participate in discussions about non-factoid topics by marking their stances, providing premises, or arguing for the opinions they support, which forms “collective arguments”. The sustainable development of collective arguments relies on a big contributor base, yet most of the frequent CQA users are lurkers who seldom speak out. With a formative study, we identified detailed obstacles preventing lurkers from contributing to collective arguments. We consequently designed a processing pipeline for extracting and summarizing augmentative elements from question threads. Based on this we built CoArgue, a tool with navigation and chatbot features to support CQA lurkers’ motivation and ability in making contributions. Through a within-subject study (N=24), we found that, compared to a Quora-like baseline, participants perceived CoArgue as significantly more useful in enhancing their motivation and ability to join collective arguments and found the experience to be more engaging and productive.

In AI-assisted decision-making, it is critical for human decision-makers to know when to trust AI and when to trust themselves. However, prior studies calibrated human trust only based on AI confidence indicating AI’s correctness likelihood (CL) but ignored humans’ CL, hindering optimal team decision-making. To mitigate this gap, we proposed to promote humans’ appropriate trust based on the CL of both sides at a task-instance level. We first modeled humans’ CL by approximating their decision-making models and computing their potential performance in similar instances. We demonstrated the feasibility and effectiveness of our model via two preliminary studies. Then, we proposed three CL exploitation strategies to calibrate users’ trust explicitly/implicitly in the AI-assisted decision-making process. Results from a between-subjects experiment (N=293) showed that our CL exploitation strategies promoted more appropriate human trust in AI, compared with only using AI confidence. We further provided practical implications for more human-compatible AI-assisted decision-making.

Multi-step retrosynthetic route planning (MRRP) is the core task in synthetic chemistry, in which chemists recursively deconstruct a target molecule to find a set of reactants that make up the target. MRRP is challenging in that the search space is vast, and chemists are often lost in the process. Existing AI models can achieve automatic MRRP fast, but they only work on relatively simple targets, which leaves complex molecules under chemists’ expertise. To facilitate MRRP of complex molecules, we proposed a human-AI collaborative system, RetroLens, through a participatory design process. AI can contribute by two approaches: joint action and algorithm-in-the-loop. Deconstruction steps are allocated to chemists or AI based on their capabilities and AI recommends candidate revision steps to fix problems along the way. A within-subjects study (N=18) showed that chemists who used RetroLens reported faster MRRP, broader design space exploration, higher confidence in their planning, and lower cognitive load.

Pop culture is an important aspect of communication. On social media people often post pop culture reference images that connect an event, product or other entity to a pop culture domain. Creating these images is a creative challenge that requires finding a conceptual connection between the users’ topic and a pop culture domain. In cognitive theory, this task is called conceptual blending. We present a system called PopBlends that automatically suggests conceptual blends. The system explores three approaches that involve both traditional knowledge extraction methods and large language models. Our annotation study shows that all three methods provide connections with similar accuracy, but with very different characteristics. Our user study shows that people found twice as many blend suggestions as they did without the system, and with half the mental demand. We discuss the advantages of combining large language models with knowledge bases for supporting divergent and convergent thinking.

Existing research on human-AI collaborative decision-making focuses mainly on the interaction between AI and individual decision-makers. There is a limited understanding of how AI may perform in group decision-making. This paper presents a wizard-of-oz study in which two participants and an AI form a committee to rank three English essays. One novelty of our study is that we adopt a speculative design by endowing AI equal power to humans in group decision-making. We enable the AI to discuss and vote equally with other human members. We find that although the voice of AI is considered valuable, AI still plays a secondary role in the group because it cannot fully follow the dynamics of the discussion and make progressive contributions. Moreover, the divergent opinions of our participants regarding an “equal AI” shed light on the possible future of human-AI relations.

Vocabulary learning support tools have widely exploited existing materials, e.g., stories or video clips, as contexts to help users memorize each target word. However, these tools could not provide a coherent context for any target words of learners’ interests, and they seldom help practice word usage. In this paper, we work with teachers and students to iteratively develop Storyfier, which leverages text generation models to enable learners to read a generated story that covers any target words, conduct a story cloze test, and use these words to write a new story with adaptive AI assistance. Our within-subjects study (N=28) shows that learners generally favor the generated stories for connecting target words and writing assistance for easing their learning workload. However, in the read-cloze-write learning sessions, participants using Storyfier perform worse in recalling and using target words than learning with a baseline tool without our AI features. We discuss insights into supporting learning tasks with generative models.

Learning to read scientific papers critically, which requires first grasping their main ideas and then raising critical thoughts, is important yet challenging for novice researchers. The traditional ways to develop critical paper reading (CPR) skills, e.g., checking general tutorials or taking reading courses, often can not provide individuals with adaptive and accessible support. In this paper, we first derive user requirements of a CPR training tool based on literature and a survey study (N=52). Then, we develop CriTrainer , an interactive tool for CPR training. It leverages text summarization techniques to train readers’ skills in grasping the paper’s main ideas. It further utilizes template-based generated questions to help them learn how to raise critical thoughts. A mixed-design study (N=24) shows that compared to a baseline tool with general CPR guidance, students trained by CriTrainer perform better in independently raising critical thinking questions on a new paper. We conclude with design considerations for CPR training tools.

Online fandom communities (OFCs) provide a convenient space for fans to create, collect, and discuss the content of their mutual interest (e.g., music artists). Real-world events could frequently attract outsiders to join OFCs, providing both the opportunity to expand the fan base and challenges to manage the community. However, it is unclear that how influxes of newcomers would influence the development of OFCs and what user behaviors may be correlated with their future engagement. To fill this gap, we took the music OFCs as the focus, and quantitatively analyzed user behaviors and their correlations with users' future engagement in the community. Results suggested that 1) event-induced newcomers expressed more hate speech and negative sentiment, praised less celebrity-related content (e.g., song, album), and interacted with narrower cohorts than existing members; 2) Although existing members tended to receive more upvotes during the events than before and after the events, newcomers showed an opposite trend; 3) keeping users' activeness, expressing positive sentiments, and having diverse interactions during periods of influx were helpful when maintaining members' future levels of engagement. This work deepened the understanding of fan behaviors in the dynamic period, and we discussed how our insights could benefit OFCs.

This paper zooms in on a particularly precarious and largely invisibilized group of care workers: middle-aged and less-educated female migrant workers from rural China. Drawing from a mixed-methods study, we specifically examine how the extensive use of data-driven technologies impacts care workers' wellbeing in the workplace. Our findings suggest the extensive use of data-driven healthcare technologies are eroding care workers' workplace wellbeing, especially their sense of identity, agency, and perceived justice. Specifically, in the data-driven workplace, care workers are treated as a servant to data, instead of a human with agency and knowledge. They are no merely care workers who provide various care services for care receivers, but also data workers, whose practices and agency are greatly limited by data. This aggravates preexisting hardship of care workers, and reproduces new social injustice. We suggest CSCW researchers and practitioners take into account how pre-existing social structures shaped the designs of socio-technological systems, and reconceptualize the paradigm of "data-drivenness" for more just and ethical data-driven healthcare technologies.

Using data and data technologies to support healthcare has drawn significant attention recently. While CSCW and HCI have largely celebrated the tremendous promise of 'data-driven healthcare' in reforming the healthcare sector, this paper reveals 'labor-driven reality' of this promised data-driven future. Drawing from a qualitative study in a real-world data-driven long-term care (LTC) facility in China, we demonstrate how data-driven technologies work in practice, and especially how frontline workers, as the crux of this data-driven configuration, conduct a tremendous amount of "data work" to make data-drivenness work. This data work, we argue, goes beyond the "clerical work" and functions as a labor of maintenance, articulation, and repair, that both guarantees data technologies' functionalities and acts as an interface between stakeholders. We conclude by discussing the practices, problems and opportunities of this data work in a boarder socio-cultural context.

Urban vibrancy describes the prosperity, diversity, and accessibility of urban areas, which is vital to a city’s socio-economic development and sustainability. While many efforts have been made for statically measuring and evaluating urban vibrancy, there are few studies on the evolutionary process of urban vibrancy, yet we know little about the relationship between urban vibrancy evolution and sophisticated spatiotemporal dynamics. In this article, we make use of multi-sourced urban data to develop a data-driven framework, U-Evolve , to investigate urban vibrancy evolution. Specifically, we first exploit the spatiotemporal characteristics of urban areas to create multi-view time-dependent graphs. Then, we analyze the contextual features and graph patterns of multi-view time-dependent graphs in terms of informing future urban vibrancy variations. Our analysis validates the informativeness of multi-view time-dependent graphs for characterizing and informing future urban vibrancy evolution. After that, we construct a feature based model to forecast future urban vibrancy evolution and quantify each feature’s importance. Moreover, to further enhance the forecasting effectiveness, we propose a graph learning based model to capture spatiotemporal autocorrelation of urban areas based on multi-view time-dependent graphs in an end-to-end manner. Finally, extensive experiments on two metropolises, Beijing and Shanghai, demonstrate the effectiveness of our forecasting models. The U-Evolve framework has also been deployed in the production environment to deliver real-world urban development and planning insights for various cities in China.

Traffic violations have become one of the major threats to urban transportation systems, undermining human safety and causing economic losses. To alleviate this problem, crowd-based patrol forces including traffic police and voluntary participants have been employed in many cities. To adaptively optimize patrol routes with limited manpower, it is essential to be aware of traffic violation hotspots. Traditionally, traffic violation hotspots are directly inferred from experiences, and existing patrol routes are usually fixed. In this paper, we propose a mobile crowdsensing-based framework to dynamically infer traffic violation hotspots and adaptively schedule crowd patrol routes. Specifically, we first extract traffic violation-prone locations from heterogeneous crowd-sensed data and propose a spatiotemporal context-aware self-adaptive learning model (CSTA) to infer traffic violation hotspots. Then, we propose a tensor-based integer linear problem modeling method (TILP) to adaptively find optimal patrol routes under human labor constraints. Experiments on real-world data from two Chinese cities (Xiamen and Chengdu) show that our approach accurately infers traffic violation hotspots with F1-scores above 90% in both cities, and generates patrol routes with relative coverage ratios above 85%, significantly outperforming baseline methods.

Robot Learning from Demonstration (RLfD) allows non-expert users to teach a robot new skills or tasks directly through demonstrations. Although modeled after human–human learning and teaching, existing RLfD methods make robots act as passive observers without the feedback of their learning statuses in the demonstration gathering stage. To facilitate a more transparent teaching process, we propose two mechanisms of Learning Engagement , Z2O-Mode and D2O-Mode, to dynamically adapt robots’ attentional and behavioral engagement expressions to their actual learning status. Through an online user experiment with 48 participants, we find that, compared with two baselines, the two kinds of Learning Engagement can lead to users’ more accurate mental models of the robot’s learning progress, more positive perceptions of the robot, and better teaching experience. Finally, we provide implications for leveraging engagement expression to facilitate transparent human-AI (robot) communication based on our key findings.

Projection and ranking are frequently used analysis techniques in multi-attribute data exploration. Both families of techniques help analysts with tasks such as identifying similarities between observations and determining ordered subgroups, and have shown good performances in multi-attribute data exploration. However, they often exhibit problems such as distorted projection layouts, obscure semantic interpretations, and non-intuitive effects produced by selecting a subset of (weighted) attributes. Moreover, few studies have attempted to combine projection and ranking into the same exploration space to complement each other's strengths and weaknesses. For this reason, we propose RankAxis, a visual analytics system that systematically combines projection and ranking to facilitate the mutual interpretation of these two techniques and jointly support multi-attribute data exploration. A real-world case study, expert feedback, and a user study demonstrate the efficacy of RankAxis.

Interdisciplinary experimental science (e.g., medicinal chemistry) refers to the disciplines that integrate knowledge from different scientific backgrounds and involve experiments in the research process. Deciding "in what direction to proceed" is critical for the success of the research in such disciplines, since the time, money, and resource costs of the subsequent research steps depend largely on this decision. However, such a direction identification task is challenging in that researchers need to integrate information from large-scale, heterogeneous materials from all associated disciplines and summarize the related publications of which the core contributions are often showcased in diverse formats. The task also requires researchers to estimate the feasibility and potential in future experiments in the selected directions. In this work, we selected medicinal chemistry as a case and presented an interactive visual tool, MedChemLens, to assist medicinal chemists in choosing their intended directions of research. This task is also known as drug target (i.e., disease-linked proteins) selection. Given a candidate target name, MedChemLens automatically extracts the molecular features of drug compounds from chemical papers and clinical trial records, organizes them based on the drug structures, and interactively visualizes factors concerning subsequent experiments. We evaluated MedChemLens through a within-subjects study (N=16). Compared with the control condition (i.e., unrestricted online search without using our tool), participants who only used MedChemLens reported faster search, better-informed selections, higher confidence in their selections, and lower cognitive load.

Promotions are commonly used by e-commerce merchants to boost sales. The efficacy of different promotion strategies can help sellers adapt their offering to customer demand in order to survive and thrive. Current approaches to designing promotion strategies are either based on econometrics, which may not scale to large amounts of sales data, or are spontaneous and provide little explanation of sales volume. Moreover, accurately measuring the effects of promotion designs and making bootstrappable adjustments accordingly remains a challenge due to the incompleteness and complexity of the information describing promotion strategies and their market environments. We present PromotionLens, a visual analytics system for exploring, comparing, and modeling the impact of various promotion strategies. Our approach combines representative multivariant time-series forecasting models and well-designed visualizations to demonstrate and explain the impact of sales and promotional factors, and to support "what-if" analysis of promotions. Two case studies, expert feedback, and a qualitative user study demonstrate the efficacy of PromotionLens.

An effective task-oriented chatbot should be able to exert a certain level of Social Intelligence (SI), the ability to emulate human social behaviors to reduce user frustration and dissatisfaction. However, few studies explored using humor, a common rhetorical device in human-human interactions, to improve chatbots’ overall SI. To fill this gap, we proposed to apply self-mockery humor to a customer service chatbot in different interaction stages with users. We proposed a pipeline to create situated self-mockery for the chatbot and conducted a within-subject experiment (N=28) to compare it with a chatbot without self-mockery utterance. Results showed that the self-mockery chatbot was perceived as significantly funnier, more satisfactory, and delivering higher performance in two out of the five measured characteristics of SI with comparable performance in the rest. We further discussed how participants’ individual factors might affect the perceived helpfulness of self-mockery on SI and concluded with design considerations.

Engaging local communities is essential to safeguarding intangible cultural heritage (ICH). To better encourage the participation of local communities, especially younger generations, e.g., students, ICH experience and education workshops are widely adopted by academia, museums, governments, and non-profit organizations. The expected outcomes of these workshops, such as archives, documents, and even creative design solutions, can benefit the promotion and preservation of ICH. However, because of the steep learning curve of using traditional ICH tools and the lack of interactions between students and ICH practitioners, many ICH workshops currently fail to engage students in learning ICH-related knowledge, developing empathy with ICH, or designing novel artifacts with ICH elements. To bridge this gap, we designed a workshop, which integrated ICH in China, digital fabrication, creative technology, and making, to engage Chinese students with ICH and creative design. We conducted empirical studies to collect feedback from students (N = 30) and ICH professionals (N=6). The application of digital fabrication tools successfully piqued students’ interest in ICH and enabled them to create interactive ICH artifacts through quick prototyping. However, the ICH professionals pointed out several issues of using digital fabrication, especially regarding tacit knowledge, use of traditional tools, and cultural authenticity. We discuss the importance of these factors in students’ acquisition of ICH knowledge and ICH-oriented design, and provide implications for future ICH design workshops.

Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Generating educational questions of fairytales or storybooks is vital for improving children's literacy ability.However, it is challenging to generate questions that capture the interesting aspects of a fairytale story with educational meaningfulness.In this paper, we propose a novel question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions.To train the event-centric summarizer, we finetune a pre-trained transformer-based sequenceto-sequence model using silver samples composed by educational question-answer pairs.On a newly proposed educational questionanswering dataset FairytaleQA, we show good performance of our method on both automatic and human evaluation metrics.Our work indicates the necessity of decomposing question type distribution learning and event-centric summary generation for educational question generation.

Misinformation has disruptive effects on our lives. Many researchers have looked into means to identify and combat misinformation in text or data visualization. However, there is still a lack of under-standing of how misinformation can be introduced when text and visualization are combined to tell data stories, not to mention how to improve the lay public's awareness of possible misperceptions about facts in narrative visualization. In this paper, we first analyze where misinformation could possibly be injected into the production-consumption process of data stories through a literature survey. Then, as a first step towards combating misinformation in data stories, we explore possible defensive design methods to enhance the reader's awareness of information misalignment when data facts are scripted and visualized. More specifically, we conduct a between-subjects crowdsourcing study to investigate the impact of two design methods enhancing text-visualization integration, i.e., explanatory annotation and interactive linking, on users' awareness of misinformation in data stories. The study results show that although most participants still can not find misinformation, the two design methods can significantly lower the perceived credibility of the text or visualizations. Our work informs the possibility of fighting an infodemic through defensive design methods.

Conversational agents (CAs) have the great potential in mitigating the clinicians’ burden in screening for neurocognitive disorders among older adults. It is important, therefore, to develop CAs that can be engaging, to elicit conversational speech input from older adult participants for supporting assessment of cognitive abilities. As an initial step, this paper presents research in developing the backchanneling ability in CAs in the form of a verbal response to engage the speaker. We analyzed 246 conversations of cognitive assessments between older adults and human assessors, and derived the categories of reactive backchannels (e.g. “hmm”) and proactive backchannels (e.g. “please keep going”). This is used in the development of TalkTive, a CA which can predict both timing and form of backchanneling during cognitive assessments. The study then invited 36 older adult participants to evaluate the backchanneling feature. Results show that proactive backchanneling is more appreciated by participants than reactive backchanneling.

Online medical crowdfunding campaigns (OMCCs) help patients seek financial support. First impressions (FIs) of an OMCC, including perceived empathy, credibility, justice, impact, and attractiveness, could affect viewers’ donation decisions. Images play a crucial role in manifesting FIs, and it is beneficial for fundraisers to understand how viewers may judge their selected images for OMCCs beforehand. This work proposes a data-driven approach to assessing whether an OMCC image conveys appropriate FIs. We first crowdsource viewers’ perception of OMCC images. Statistical analysis confirms that agreement on all five dimensions of FIs exists, and these FIs positively correlate with donation intention. We compute image content, color, texture, and composition features, then analyze the correlation between these visual features and FIs. We further predict FIs based on these features, and the best model achieves an overall F1-score of 0.727. Finally, we discuss how our insights could benefit fundraisers and possible ethical concerns.

Synchronous online learning has become a trend in recent years. However, instructors often face the challenge of inferring audiences’ reactions and learning status without seeing their faces in video feeds, which prevents instructors from establishing connections with students. To solve this problem, based on a need-finding survey with 67 college instructors, we propose Glancee, a real-time interactive system with adaptable configurations, sidebar-based visual displays, and comprehensive learning status detection algorithms. Then, we conduct a within-subject user study in which 18 college instructors deliver lectures online with Glancee and two baselines, EngageClass and ZoomOnly. Results show that Glancee can effectively support online teaching and is perceived to be significantly more helpful than the baselines. We further investigate how instructors’ emotions, behaviors, attention, cognitive load, and trust are affected during the class. Finally, we offer design recommendations for future online teaching assistant systems.

It is challenging for customers to select appearance building products (e.g., skincare products, weight loss programs) that suit them personally as such products usually demonstrate efficacy only after long-term usage. Although e-retailers generally provide product descriptions or other customers’ reviews, users often find it hard to relate to their own situations. In this work, we proposed a pipeline to display envisioned users’ appearance after long-term use of appearance building products to deliver their efficacy on each individual visually. We selected skincare as a case and developed SkincareMirror which predicts skincare effects on users’ facial images by analyzing product function labels, efficacy ratings, and skin models’ images. The results of a between-subjects study (N=48) show that (1) SkincareMirror outperforms the baseline shopping site in terms of perceived usability, usefulness, user satisfaction and helps users select products faster; (2) SkincareMirror is especially effective to males and users with limited product domain knowledge.

Massively multiplayer online role-playing games create virtual communities that support heterogeneous “social roles” determined by gameplay interaction behaviors under a specific social context. For all social roles, formal roles are pre-defined, obvious, and explicitly ascribed to the people holding the roles, whereas informal roles are not well-defined and unspoken. Identifying the informal roles and understanding their subtle changes are critical to designing sociability mechanisms. However, it is nontrivial to understand the existence and evolution of such roles due to their loosely defined, interconvertible, and dynamic characteristics. We propose a visual analytics system, RoleSeer, to investigate informal roles from the perspectives of behavioral interactions and depict their dynamic interconversions and transitions. Two cases, experts’ feedback, and a user study suggest that RoleSeer helps interpret the identified informal roles and explore the patterns behind role changes. We see our approach’s potential in investigating informal roles in a broader range of social games.

During recent crises like COVID-19, microblogging platforms have become popular channels for affected people seeking assistance such as medical supplies and rescue operations from emergency responders and the public. Despite this common practice, the affordances of microblogging services for help-seeking during crises that needs immediate attention are not well understood. To fill this gap, we analyzed 8K posts from COVID-19 patients or caregivers requesting urgent medical assistance on Weibo, the largest microblogging site in China. Our mixed-methods analyses suggest that existing microblogging functions need to be improved in multiple aspects to sufficiently facilitate help-seeking in emergencies, including capabilities of search and tracking requests, ease of use, and privacy protection. We also find that people tend to stick to certain well-established functions for publishing requests, even after better alternatives emerge. These findings have implications for designing microblogging tools to better support help requesting and responding during crises.

Creating presentation slides is a critical but time-consuming task for data scientists. While researchers have proposed many AI techniques to lift data scientists’ burden on data preparation and model selection, few have targeted the presentation creation task. Based on the needs identified from a formative study, this paper presents NB2Slides, an AI system that facilitates users to compose presentations of their data science work. NB2Slides uses deep learning methods as well as example-based prompts to generate slides from computational notebooks, and take users’ input (e.g., audience background) to structure the slides. NB2Slides also provides an interactive visualization that links the slides with the notebook to help users further edit the slides. A follow-up user evaluation with 12 data scientists shows that participants believed NB2Slides can improve efficiency and reduces the complexity of creating slides. Yet, participants questioned the future of full automation and suggested a human-AI collaboration paradigm.

More and more learning apps like Duolingo are using some form of gamification (e.g., badges, points, and leaderboards) to enhance user learning. However, they are not always successful. Gamification misuse is a phenomenon that occurs when users become too fixated on gamification and get distracted from learning. This undesirable phenomenon wastes users' precious time and negatively impacts their learning performance. However, there has been little research in the literature to understand gamification misuse and inform future gamification designs. Therefore, this paper aims to fill this knowledge gap by conducting the first extensive qualitative research on gamification misuse in a popular learning app called Duolingo. Duolingo is currently the world's most downloaded learning app used to learn languages. This study consists of two phases: (I)a content analysis of data from Duolingo forums (from the past nine years) and (II)semi-structured interviews with 15 international Duolingo users. Our research contributes to the Human-Computer Interaction (HCI) and Learning at Scale ([email protected]) research communities in three ways: (1) elaborating the ramifications of gamification misuse on user learning, well-being, and ethics, (2) identifying the most common reasons for gamification misuse (e.g., competitiveness, overindulgence in playfulness, and herding), and (3) providing designers with practical suggestions to prevent (or mitigate) the occurrence of gamification misuse in their future designs of gamified learning apps.

With the rise of deep learning and intelligent vehicles, the smart assistant has become an essential in-car component to facilitate driving and provide extra functionalities. In-car smart assistants should be able to process general as well as car-related commands and perform corresponding actions, which eases driving and improves safety. However, there is a data scarcity issue for low resource languages, hindering the development of research and applications. In this paper, we introduce a new dataset, Cantonese In-car Audio-Visual Speech Recognition (CI-AVSR), for in-car command recognition in the Cantonese language with both video and audio data. It consists of 4,984 samples (8.3 hours) of 200 in-car commands recorded by 30 native Cantonese speakers. Furthermore, we augment our dataset using common in-car background noises to simulate real environments, producing a dataset 10 times larger than the collected one. We provide detailed statistics of both the clean and the augmented versions of our dataset. Moreover, we implement two multimodal baselines to demonstrate the validity of CI-AVSR. Experiment results show that leveraging the visual signal improves the overall performance of the model. Although our best model can achieve a considerable quality on the clean test set, the speech recognition quality on the noisy data is still inferior and remains an extremely challenging task for real in-car speech recognition systems. The dataset and code will be released at https://github.com/HLTCHKUST/CI-AVSR.

Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation. Despite the spontaneous nature of code-switching in conversational spoken language, most existing works collect code-switching data from read speech instead of spontaneous speech. ASCEND (A Spontaneous Chinese-English Dataset) is a high-quality Mandarin Chinese-English code-switching corpus built on spontaneous multi-turn conversational dialogue sources collected in Hong Kong. We report ASCEND’s design and procedure for collecting the speech data, including annotations. ASCEND consists of 10.62 hours of clean speech, collected from 23 bilingual speakers of Chinese and English. Furthermore, we conduct baseline experiments using pre-trained wav2vec 2.0 models, achieving a best performance of 22.69% character error rate and 27.05% mixed error rate.

Automatic speech recognition (ASR) on low resource languages improves the access of linguistic minorities to technological advantages provided by artificial intelligence (AI). In this paper, we address the problem of data scarcity for the Hong Kong Cantonese language by creating a new Cantonese dataset. Our dataset, Multi-Domain Cantonese Corpus (MDCC), consists of 73.6 hours of clean read speech paired with transcripts, collected from Cantonese audiobooks from Hong Kong. It comprises philosophy, politics, education, culture, lifestyle and family domains, covering a wide range of topics. We also review all existing Cantonese datasets and analyze them according to their speech type, data source, total size and availability. We further conduct experiments with Fairseq S2T Transformer, a state-of-the-art ASR model, on the biggest existing dataset, Common Voice zh-HK, and our proposed MDCC, and the results show the effectiveness of our dataset. In addition, we create a powerful and robust Cantonese ASR model by applying multi-dataset learning on MDCC and Common Voice zh-HK.

The availability of effective instrumental support may affect the physical and mental wellness of postpartum mothers. Through an online survey with 84 new mothers in Hong Kong and six-month follow-up interviews with one family, we investigate the practices of our participants mobilizing their support network to help with childcare, their experiences with infant-centric family informatics in the process, and the barriers, needs, and expectations that emerged. Our findings suggest that postpartum mothers may offload different types of babysitting tasks to different caregivers; they try to orchestrate the whole process through assorted communication media but may face a variety of practical and relational challenges. New mothers demand affordable, usable, and accessible information and communication supports to streamline infant-centric family informatics, if applicable. We thus propose a set of design considerations as to how a (connected) system of low-, medium-, and/or high-tech informatics tools could better foster mobilization of instrumental childcare support.

Community-based Question Answering (CQA) platforms can provide rich experience and suggestions for people who seek to construct Activity Plans (AP), such as bodybuilding or sightseeing. However, answer posts in CQA platforms could be too unstructured and overwhelming to be easily applied to AP construction, as validated by our formative study for understanding relevant user challenges. We therefore proposed an answer-post processing pipeline, based on which we built PlanHelper, a tool assisting users in processing the CQA information and constructing AP interactively. We conducted a within-subject study (N=24) with a Quora-like interface as the baseline. Results suggested that when creating AP with PlanHelper, users were significantly more satisfied with the informational support and more engaged during the interaction. Moreover, we performed an in-depth analysis on the user behaviors with PlanHelper and summarized the design considerations for such supporting tools.

Community Question Answering Websites (CQAs) like Stack Overflow rely on continuous user contributions to keep their services active. Nevertheless, they often undergo a sharp decline in their user participation during the holiday season, undermining their performance. To address this issue, some CQAs have developed their own special promotional gamification schemes to incentivize users to maintain their contributions throughout the holiday season. These promotional gamification schemes are often time-limited, optional, and run alongside the default gamification schemes of their websites. However, the impact of such promotional gamification schemes on user behavior remains largely unexplored in the existing literature. This paper takes the first steps toward filling this knowledge gap by conducting a large-scale empirical study of a particular promotional gamification scheme called Winter Bash (WB) on the CQA of Stack Overflow. According to our findings, promotional gamification schemes may not be the panacea they are portrayed to be. For example, in the case of WB, we find that the scheme is not effective for improving the collective engagement of all users. Only some particular user types (i.e., experienced and reputable users) are often provoked under WB. Most novice users, who comprise the majority of Stack Overflow website's user base, seem to be indifferent to such a gamification scheme. Our research also shows the importance of studying the quantity and quality of user engagement in unison to better understand the effectiveness of a gamification scheme. Previous gamification studies in the literature have focused predominantly on studying the quantity of user engagement alone. Last but not least, we conclude our paper by presenting some practical considerations for improving the design of future promotional gamification schemes in CQAs and similar platforms.

In recent years, studies on the user experience have emerged as an indispensable part of any gamification research. The study of user experience enables gamification designers and practitioners to design or adapt their gamification schemes in a more knowledgeable and efficacious manner. However, one popular gamification scheme that has largely remained under-researched in terms of user experience is promotional gamification, which refers to an optional and time-limited gamification program that usually mounts an already gamified platform to increase user incentive and engagement for a short span of time (e.g., during the holiday season). The current study undertakes the first steps necessary to explore users' experiences of working with a promotional gamification scheme in a large-scale online community. To this end, we conduct an extensive qualitative case study of users' experiences with a promotional gamification scheme on the Community Question Answering Website (CQA) of Stack Exchange, called Winter Bash (WB). Notably, the purpose of WB is to operate as a makeshift solution that prevents the decline in user contributions during the holiday season. However, like many other gamification schemes, WB is not devoid of issues, and our research helps identify those issues without overlooking the WB's strengths. Our study denotes not only the first (empirical) typology of users' affective responses to promotional gamification schemes but also the first classification of (de)motivational factors involved in user engagement. At its core, this study comprises two salient parts: (1) a content analysis of user-generated data regarding WB (from the past eight years), and (2) a series of semi-structured interviews with 17 international users who are familiar with WB. We triangulate our findings from (1) and (2) by performing a similar content analysis for two other promotional gamification schemes, namely "Answerathon" (from Travel Meta) and "Discussion Tournament" (from Reddit). Based on the findings of this study, we present certain guidelines for gamification designers and practitioners, enabling them to deploy or adapt their promotional gamification schemes in a more knowledgeable and effective manner. Finally, our work is concluded by highlighting a few novel research opportunities for researchers invested in the fields of Human-Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW).

Crowdsourcing is rapidly gaining popularity among academic and business communities. Yet, our understanding of this work way is still in its incipient stage, in particular regarding the increasingly large and diverse crowdworkers. As such, we aim to understand crowdworkers' perception and experience to themselves and their work from their own perspective. We explore this by a mix-methods study of crowdworkers in Ali, one of prominent micro-task crowdsourcing platforms in China. Our findings highlight crowdworker in Ali is not only a coded name, but also an identity with some positive attitudes and beliefs towards work and life. In particular, this identity provides many socio-psychological benefits for crowdworkers, which further contributes to their consistent engagement in Ali and proactive practices to improve crowdworker communities and Ali platform collaboratively. We according suggest that taking crowdworker identity as a lens for crowdsourcing research, and turning attention towards construction and expressions of crowdworkers' identity and values in their own context.

Persuading people to change their opinions is a common practice in online discussion forums on topics ranging from political campaigns to relationship consultation. Enhancing people's ability to write persuasive arguments could not only practice their critical thinking and reasoning but also contribute to the effectiveness and civility in online communication. It is, however, not an easy task in online discussion settings where written words are the primary communication channel. In this paper, we derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions through a survey with 123 online forum users and interviews with five debating experts. To satisfy these design goals, we analyzed and built a labeled dataset of fine-grained persuasive strategies (i.e., logos, pathos, ethos, and evidence) in 164 arguments with high ratings on persuasiveness from ChangeMyView, a popular online discussion forum. We then designed an interactive visual system, Persua, which provides example-based guidance on persuasive strategies to enhance the persuasiveness of arguments. In particular, the system constructs portfolios of arguments based on different persuasive strategies applied to a given discussion topic. It then presents concrete examples based on the difference between the portfolios of user input and high-quality arguments in the dataset. A between-subjects study shows suggestive evidence that Persua encourages users to submit more times for feedback and helps users improve more on the persuasiveness of their arguments than a baseline system. Finally, a set of design considerations was summarized to guide future intelligent systems that improve the persuasiveness in text.

People often take user ratings/reviews into consideration when shopping for products or services online. However, such user-generated data contains self-selection bias that could affect people's decisions and it is hard to resolve this issue completely by algorithms. In this work, we propose to raise people's awareness of the self-selection bias by making three types of information concerning user ratings/reviews transparent. We distill these three pieces of information, i.e., reviewers' experience, the extremity of emotion, and reported aspect(s), from the definition of self-selection bias and exploration of related literature. We further conduct an online survey to assess people's perceptions of the usefulness of such information and identify the exact facets (e.g., negative emotion) people care about in their decision process. Then, we propose a visual design to make such details behind user reviews transparent and integrate the design into an experimental website for evaluation. The results of a between-subjects study demonstrate that our bias-aware design significantly increases people's awareness of bias and their satisfaction with decision-making. We further offer a series of design implications for improving information transparency and awareness of bias in user-generated content.

Traffic violations have become one of the major threats to urban transportation systems, undermining road safety and causing economic losses. Although various methods have been proposed by road authorities and researchers to find out the possible causes of traffic violations, existing methods often fail to diagnose traffic violations from drivers’ perspectives and contexts or consider their visual and comprehension loads while driving. In this work, we propose a driver-centered simulation platform to inspect drivers’ loads in traffic violation hotspots. Specifically, we first build a driving simulator based on the 3D point clouds of real-world traffic violation hotspots. We then recruit drivers to simulate driving in designated traffic scenes. Indicators for drivers’ visual and comprehension loads are derived based on drivers’ feedback. Upon this basis, we build an explainable model to automatically indicate drivers’ visual and comprehension loads under various crowd-sensed traffic scenes. Experiments using real-world data from a Chinese City (Xiamen) and case studies show that our approach successfully derives a set of prominent indicators to effectively diagnose drivers’ visual and comprehension loads in real-world traffic violation hotspots.

Previous user experience research emphasizes meaning in interaction design beyond conventional interactive gestures. However, existing exemplars that successfully reify abstract meanings through interactions are usually case-specific, and it is currently unclear how to systematically create or extend meanings for general gesture-based interactions. We present Metaphoraction, a creativity support tool that formulates design ideas for gesture-based interactions to show metaphorical meanings with four interconnected components: gesture , action , object , and meaning . To represent the interaction design ideas with these four components, Metaphoraction links interactive gestures to actions based on the similarity of appearances, movements, and experiences; relates actions to objects by applying the immediate association; bridges objects and meanings by leveraging the metaphor TARGET-SOURCE mappings. We build a dataset containing 588,770 unique design idea candidates through surveying related research and conducting two crowdsourced studies to support meaningful gesture-based interaction design ideation. Five design experts validate that Metaphoraction can effectively support creativity and productivity during the ideation process. The paper concludes by presenting insights into meaningful gesture-based interaction design and discussing potential future uses of the tool.

As a decentralized training approach, horizontal federated learning (HFL) enables distributed clients to collaboratively learn a machine learning model while keeping personal/private information on local devices. Despite the enhanced performance and efficiency of HFL over local training, clues for inspecting the behaviors of the participating clients and the federated model are usually lacking due to the privacy-preserving nature of HFL. Consequently, the users can only conduct a shallow-level analysis of potential abnormal behaviors and have limited means to assess the contributions of individual clients and implement the necessary intervention. Visualization techniques have been introduced to facilitate the HFL process inspection, usually by providing model metrics and evaluation results as a dashboard representation. Although the existing visualization methods allow a simple examination of the HFL model performance, they cannot support the intensive exploration of the HFL process. In this article, strictly following the HFL privacy-preserving protocol, we design an exploratory visual analytics system for the HFL process termed HFLens, which supports comparative visual interpretation at the overview, communication round, and client instance levels. Specifically, the proposed system facilitates the investigation of the overall process involving all clients, the correlation analysis of clients' information in one or different communication round(s), the identification of potential anomalies, and the contribution assessment of each HFL client. Two case studies confirm the efficacy of our system. Experts' feedback suggests that our approach indeed helps in understanding and diagnosing the HFL process better.

Past research recognized that paper has many advantages over digital devices, such as affordability, tangibility, and flexibility. Paper, however, also lacks many of the functionalities available in digital technologies, such as access to online resources and the ability to display interactive content. Prior research therefore identified opportunities for fusing the two mediums into a combined interface. This work presents a literature review on this form of innovation - technologies that bridge the paper-digital gap. First, we synthesize an understanding of paper and its relationship with digital devices through the lens of past works. Then, we outline the state-of-the-art for paper-digital interfaces and highlight possible use cases and implementation approaches. Last, we discuss design considerations and future work for developing paper-digital interfaces. Our work may be beneficial for HCI researchers interested in the development of hybrid paper-digital interfaces, and more broadly in embedding digital functionalities in everyday objects.

Visual metaphors, which are widely used in graphic design, can deliver messages in creative ways by fusing different objects. The keys to creating visual metaphors are diverse exploration and creative combinations, which is challenging with conventional methods like image searching. To streamline this ideation process, we propose to use a mind-map-like structure to recommend and assist users to explore materials. We present MetaMap, a supporting tool which inspires visual metaphor ideation through multi-dimensional example-based exploration. To facilitate the divergence and convergence of the ideation process, MetaMap provides 1) sample images based on keyword association and color filtering; 2) example-based exploration in semantics, color, and shape dimensions; and 3) thinking path tracking and idea recording. We conduct a within-subject study with 24 design enthusiasts by taking a Pinterest-like interface as the baseline. Our evaluation results suggest that MetaMap provides an engaging ideation process and helps participants create diverse and creative ideas.

Online mental health communities (OMHCs) are prominent resources for improving people’s mental wellbeing. An immediate cue of such improvement is support-seekers’ satisfaction expressed in their replies to the received comments. However, the comments that seekers find satisfying may change with their community knowledge, e.g., measured by tenure and posting experience in that community. In this paper, we first model the amount of satisfaction conveyed in the support-seekers’ replies to the received comments. Then we quantitatively examine how seekers’ expressed satisfaction is affected by their community knowledge, sought and received support in an OMHC. Results show that support-seekers with more posting experience generally display less contentment to the received comments. Compared to newcomers, higher tenured members express less satisfaction when receiving informational support. We also found that support matching positively predicts seekers’ satisfaction regardless of their community knowledge. Our findings have implications for OMHCs to satisfy support-seekers through their community knowledge.

As more and more face-to-face classes move to online environments, it becomes increasingly important to explore any emerging barriers to students' learning. This work focuses on characterizing student barriers to active learning in synchronous online environments. The aim is to help novice educators develop a better understanding of those barriers and prepare more student-centered course plans for their active online classes. Towards this end, we adopt a qualitative research approach and study information from different sources: social media content, interviews, and surveys from students and expert educators. Through a thematic analysis, we craft a nuanced list of students' online active learning barriers within the themes of human-side, technological, and environmental barriers. Each barrier is explored from the three aspects of frequency, importance, and exclusiveness to active online classes. Finally, we conduct a summative study with 12 novice educators and explain the benefits of using our barrier list for course planning in active online classes.

The use of digital examples plays a critical role in mobile UI design. Yet, it remains unclear how UX/UI designers manage (i.e., collect, archive, and utilize) examples to facilitate their design processes at different stages, and what possible challenges are imposed on the design of proper tools to support these practices. In this paper, we conduct a qualitative interview study with mobile UI/UX designers (12 experts and 12 novices), deriving the commonality in practices and analyzing possible differences across four design phases (Discover, Define, Develop, and Deliver) and expertise. In brief, we find that there is more diverse and frequent use of examples in the Discover and Develop phases, and that experts take more diverse advantage of the information from examples compared to novices. We further identify the challenges faced by designers when using existing example management services, and propose potential design implications for the development of more supportive design tools in the future.

Problem-Based Learning (PBL) is a popular approach to instruction that supports students to get hands-on training by solving problems. Question Pool websites (QPs) such as LeetCode, Code Chef, and Math Playground help PBL by supplying authentic, diverse, and contextualized questions to students. Nonetheless, empirical findings suggest that 40% to 80% of students registered in QPs drop out in less than two months. This research is the first attempt to understand and predict student dropouts from QPs via exploiting students' engagement moods. Adopting a data-driven approach, we identify five different engagement moods for QP students, which are namely challenge-seeker, subject-seeker, interest-seeker, joy-seeker, and non-seeker. We find that students have collective preferences for answering questions in each engagement mood, and deviation from those preferences increases their probability of dropping out significantly. Last but not least, this paper contributes by introducing a new hybrid machine learning model (we call Dropout-Plus) for predicting student dropouts in QPs. The test results on a popular QP in China, with nearly 10K students, show that Dropout-Plus can exceed the rival algorithms' dropout prediction performance in terms of accuracy, F1-measure, and AUC. We wrap up our work by giving some design suggestions to QP managers and online learning professionals to reduce their student dropouts.

Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community. In this paper, we develop a generalizable chatbot architecture (CASS) to provide social support for community members in an online health community. The CASS architecture is based on advanced neural network algorithms, thus it can handle new inputs from users and generate a variety of responses to them. CASS is also generalizable as it can be easily migrate to other online communities. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support. Our work also contributes to fill the research gap on how a chatbot may influence the whole community's engagement.

In mobile crowdsourcing, organizers usually need participants' precise locations for optimal task allocation, e.g., minimizing selected workers' travel distance to task locations. However, the exposure of users' locations raises privacy concerns. In this paper, we propose a location privacy-preserving task allocation framework with geo-obfuscation to protect users' locations during task assignments. More specifically, we make participants obfuscate their reported locations under the guarantee of two rigorous privacy-preserving schemes, differential and distortion privacy, without the need to involve any third-party trusted entity. In order to achieve optimal task allocation with the differential-and-distortion geo-obfuscation, we formulate a mixed-integer non-linear programming problem to minimize the expected travel distance of the selected workers under the constraints of differential and distortion privacy. Moreover, a worker may be willing to accept multiple tasks, and a task organizer may be concerned with multiple utility objectives such as task acceptance ratio in addition to travel distance. Against this background, we also extend our solution to the multi-task allocation and multi-objective optimization cases. Evaluation results on both simulation and real-world user mobility traces verify the effectiveness of our framework. Particularly, our framework outperforms Laplace obfuscation, a state-of-the-art geo-obfuscation mechanism, by achieving up to 47 percent shorter average travel distance on real-world data under the same level of privacy protection.

With the rapid development of online education in recent years, there has been an increasing number of learning platforms that provide students with multi-step questions to cultivate their problem-solving skills. To guarantee the high quality of such learning materials, question designers need to inspect how students' problem-solving processes unfold step by step to infer whether students' problem-solving logic matches their design intent. They also need to compare the behaviors of different groups (e.g., students from different grades) to distribute questions to students with the right level of knowledge. The availability of fine-grained interaction data, such as mouse movement trajectories from the online platforms, provides the opportunity to analyze problem-solving behaviors. However, it is still challenging to interpret, summarize, and compare the high dimensional problem-solving sequence data. In this paper, we present a visual analytics system, QLens, to help question designers inspect detailed problem-solving trajectories, compare different student groups, distill insights for design improvements. In particular, QLens models problem-solving behavior as a hybrid state transition graph and visualizes it through a novel glyph-embedded Sankey diagram, which reflects students' problem-solving logic, engagement, and encountered difficulties. We conduct three case studies and three expert interviews to demonstrate the usefulness of QLens on real-world datasets that consist of thousands of problem-solving traces.

Food-posting, a pervasive practice on social media platforms, opens a window for introspection on personal food intake, physical health, and mental well-being. Existing self-reflection tools on food intake usually require manual logging of dietary information and inadequately support retrospective reviews beyond the data. To facilitate in-depth, non-judgmental self-reflection on information hidden in food-posting, we propose a design to transform general food posts into "a postcard from a past food journey". The postcards are procedurally created from food posts, and encode nutritional values together with the user's emotional status extracted from photos and texts. After validating the visual design, we evaluate the auto-generated postcards with 20 participants to explore how they reflect on the data, context, action, and value subjects. Qualitative feedback indicates that our designs encourage users to review their physical and mental well-being differently from conventional visualization. We conclude by discussing issues identified with the non-judgmental postcard design.

Online Ask the Doctor (AtD) services allow access to health professionals anytime anywhere beyond existing patient-provider relationships. Recently, many free-market AtD platforms have emerged and been adopted by a large scale of users. However, it is still unclear how people make use of these AtD platforms in practice. In this paper, we present an interview study with 12 patients/caregivers who had experience using AtD in China, highlighting patient agency in seeking more reliable and cost-effective healthcare beyond clinic settings. Specifically, we illustrate how they make strategic choices online on AtD platforms, and how they strategically integrate online and offline services together for healthcare. This paper contributes an empirical study of the use of large-scale AtD platforms in practice, demonstrates patient agency for healthcare beyond clinic settings, and recommends design implications for online healthcare services.

In Dunhuang Mogao Grottoes, unique Buddhist murals of ancient China are preserved. Unfortunately, the exquisite murals are suffering from degradation. Experts have been trying to enhance public's awareness of mural protection, but there's no efficacious means to attract interest and popularize knowledge yet. In this paper, we propose RestoreVR, an interactive virtual reality (VR) system engaging users to experience Dunhuang mural restoration in a digital tour in the cave. Based on an online survey with the public and in-depth interviews with five Dunhuang experts, we derive a set of design requirements for generating embodied knowledge and situated experience in VR to bridge the gap between highly specialized experts and general audiences. Accordingly, we design RestoreVR and conduct a between-subjects user study to compare our system with traditional methods. The results suggest that RestoreVR significantly improves user experience and awareness of CH protection over existing methods.

There is an increased use of Internet-of-Things and wearable sensing devices in the urban marathon to ensure effective response to unforeseen medical needs. However, the massive amount of real-time, heterogeneous movement and psychological data of runners impose great challenges on prompt medical incident analysis and intervention. Conventional approaches compile such data into one dashboard visualization to facilitate rapid data absorption but fail to support joint decision-making and operations in medical encounters. In this paper, we present MaraVis, a real-time urban marathon visualization and coordinated intervention system. It first visually summarizes real-time marathon data to facilitate the detection and exploration of possible anomalous events. Then, it calculates an optimal camera route with an arrangement of shots to guide offline effort to catch these events in time with a smooth view transition. We conduct a within-subjects study with two baseline systems to assess the efficacy of MaraVis.

Automatic generation of Virtual Reality (VR) worlds which adapt to physical environments have been proposed to enable safe walking in VR. However, such techniques mainly focus on the avoidance of physical objects as obstacles and overlook their interaction affordances as passive haptics. Current VR experiences involving interaction with physical objects in surroundings still require verbal instruction from an assisting partner. We present ARchitect, a proof-of-concept prototype that allows flexible customization of a VR experience with human-in-the-loop. ARchitect brings in an assistant to map physical objects to virtual proxies of matching affordances using Augmented Reality (AR). In a within-subjects study (9 user pairs) comparing ARchitect to a baseline condition, assistants and players experienced decreased workload and players showed increased VR presence and trust in the assistant. Finally, we defined design guidelines of ARchitect for future designers and implemented three demonstrative experiences.

Textual comments from peers with informational and emotional support are beneficial to members of online mental health communities (OMHCs). However, many comments are not of high quality in reality. Writing support technologies that assess (AS) the text or recommend (RE) writing examples on the fly could potentially help support providers to improve the quality of their comments. However, how providers perceive and work with such technologies are under-investigated. In this paper, we present a technological prototype MepsBot which offers providers in-situ writing assistance in either AS or RE mode. Results of a mixed-design study with 30 participants show that both types of MepsBots improve users' confidence in and satisfaction with their comments. The AS-mode MepsBot encourages users to refine expressions and is deemed easier to use, while the RE-mode one stimulates more support-related content re-editions. We report concerns on MepsBot and propose design considerations for writing support technologies in OMHCs.

Storyboarding is an important ideation technique that uses sequential art to depict important scenarios of user experience. Existing data-driven support for storyboarding focuses on constructing user stories, but fail to address its benefit as a graphic narrative device. Instead, we propose to develop a data-driven design support tool that increases the expressiveness of user stories by facilitating sketching storyboards. To explore this, we focus on supporting the sketching of emotional expressions of characters in storyboards. In this paper, we present EmoG, an interactive system that generates sketches of characters with emotional expressions based on input strokes from the user. We evaluated EmoG with 21 participants in a controlled user study. The results showed that our tool has significantly better performance in usefulness, ease of use, and quality of results than the baseline system.

The modulation of voice properties, such as pitch, volume, and speed, is crucial for delivering a successful public speech. However, it is challenging to master different voice modulation skills. Though many guidelines are available, they are often not practical enough to be applied in different public speaking situations, especially for novice speakers. We present VoiceCoach, an interactive evidence-based approach to facilitate the effective training of voice modulation skills. Specifically, we have analyzed the voice modulation skills from 2623 high-quality speeches (i.e., TED Talks) and use them as the benchmark dataset. Given a voice input, VoiceCoach automatically recommends good voice modulation examples from the dataset based on the similarity of both sentence structures and voice modulation skills. Immediate and quantitative visual feedback is provided to guide further improvement. The expert interviews and the user study provide support for the effectiveness and usability of VoiceCoach.

Animation, a common design element in user interfaces (UI), can impact user engagement (UE) with mobile applications. To avoid impairing UE due to improper design of animation, designers rely on resource-intensive evaluation methods like user studies or expert reviews. To alleviate this burden, we propose a data-driven approach to assisting designers in examining UE issues with their animation designs. We first crowdsource UE assessments of mobile UI animations. Based on the collected data, we then build a novel deep learning model that captures both spatial and temporal features of animations to predict their UE levels. Evaluations show that our model achieves a reasonable accuracy. We further leverage the animation feature encoded by our model and a sample set of expert reviews to derive potential UE issues of a particular animation. Finally, we develop a proof-of-concept tool and evaluate its potential usage in actual design practices with experts

Physical common sense plays an essential role in the cognition abilities of robots for humanrobot interaction.Machine learning methods have shown promising results on physical commonsense learning in natural language processing but still suffer from model generalization.In this paper, we formulate physical commonsense learning as a knowledge graph completion problem to better use the latent relationships among training samples.Compared with completing general knowledge graphs, completing a physical commonsense knowledge graph has three unique characteristics: training data are scarce, not all facts can be mined from existing texts, and the number of relationships is small.To deal with these problems, we first use a pre-training language model BERT to augment training data, and then employ constrained tucker factorization to model complex relationships by constraining types and adding negative relationships.We compare our method with existing state-ofthe-art knowledge graph embedding methods and show its superior performance.

"Gaming the system" is the phenomenon where students attempt to perform well by systematically exploiting properties of the learning system, rather than learning the material. Frequent gaming tends to cause bad learning outcomes. Though existing studies tackle the problem by redesigning the system workflow to change students' behaviors automatically, gaming students discover new ways to game. We instead propose a novel way, reflective nudge, to reflectively influence students' attitudes by conveying reasons not to game via information visualizations. Particularly, we identify three common gaming contexts and involve students and instructors in co-designing three context-specific persuasive visualizations. We deploy our information visualizations in a real online learning platform. Through embedded surveys and in-person interviews, we find some evidence that the designs can promote students' reflection on gaming, and suggestive data that two of them can reduce gaming compared with control groups. Furthermore, we present insights into reflective nudge designs and practical issues concerning deployment.

Abstract Problem‐solving dynamics refers to the process of solving a series of problems over time, from which a student's cognitive skills and non‐cognitive traits and behaviors can be inferred. For example, we can derive a student's learning curve (an indicator of cognitive skill) from the changes in the difficulty level of problems solved, or derive a student's self‐regulation patterns (an example of non‐cognitive traits and behaviors) based on the problem‐solving frequency over time. Few studies provide an integrated overview of both aspects by unfolding the problem‐solving process. In this paper, we present a visual analytics system named SeqDynamics that evaluates students ‘problem‐solving dynamics from both cognitive and non‐cognitive perspectives. The system visualizes the chronological sequence of learners’ problem‐solving behavior through a set of novel visual designs and coordinated contextual views, enabling users to compare and evaluate problem‐solving dynamics on multiple scales. We present three scenarios to demonstrate the usefulness of SeqDynamics on a real‐world dataset which consists of thousands of problem‐solving traces. We also conduct five expert interviews to show that SeqDynamics enhances domain experts’ understanding of learning behavior sequences and assists them in completing evaluation tasks efficiently.

Sparse Mobile Crowdsensing (MCS) has become a compelling approach to acquire and infer urban-scale sensing data. However, participants risk their location privacy when reporting data with their actual sensing positions. To address this issue, we propose a novel location obfuscation mechanism combining E-differential-privacy and δ-distortion-privacy in Sparse MCS. More specifically, differential privacy bounds adversaries' relative information gain regardless of their prior knowledge, while distortion privacy ensures that the expected inference error is larger than a threshold under an assumption of adversaries' prior knowledge. To reduce the data quality loss incurred by location obfuscation, we design a differential-and-distortion privacy-preserving framework with three components. First, we learn a data adjustment function to fit the original sensing data to the obfuscated location. Second, we apply a linear program to select an optimal location obfuscation function. The linear program aims to minimize the uncertainty in data adjustment under the constraints of E-differential-privacy, δ-distortion-privacy, and evenly-distributed obfuscation. We also design an approximated method to reduce the required computation resources. Third, we propose an uncertainty-aware inference algorithm to improve the inference accuracy for the obfuscated data. Evaluations with real environment and traffic datasets show that our optimal method reduces the data quality loss by up to 42% compared to the state-of-the-art methods with the same level of privacy protection; the approximated method incurs <; 3% additional quality loss than the optimal method, but only needs <; 1% of the computation time.

The integration of the fifth generation (5G) networks and artificial intelligence (AI) benefits to create a more holistic and better connected ecosystem for industries. User profiling has become an important issue for industries to improve company profit. In the 5G era, smartphone applications have become an indispensable part in our everyday lives. Users determine what apps to install based on their personal needs, interests, and tastes, which is likely shaped by their genders-the behavioral, cultural, or psychological traits typically associated with their sex. It is possible to profile users' gender based simply on a single snapshot of apps installed on their smartphones. With this inference based on easy to access data, we can make smartphone systems more user-friendly, and provide better personalized products and services. In this article, we explore such possibilities through an empirical study on a large-scale dataset of installed app lists from 15 000 Android users. More specifically, we investigate the following research questions: 1) What differences between females and males can be explored from installed app lists? 2) Can user gender be reliably inferred from a snapshot of apps installed? Which snapshot feature(s) are the most predictive? What is the best combination of features for building the gender prediction model? 3) What are the limitations of a gender prediction model based solely on a snapshot of apps installed on a smartphone? We find significant gender differences in app type, function, and icon design. We then extract the corresponding features from a snapshot of apps installed to infer the gender of each user. We assess the gender predictive ability of individual features and combinations of different features. We achieve an accuracy of 76.62% and area under the curve of 84.23% with the best set of features, outperforming the existing work by around 5% and 10%, respectively. Finally, we perform an error analysis on misclassified users and discussed the implications and limitations of this article.

Social media, such as Facebook and WeChat, empowers millions of users to create, consume, and disseminate online information on an unprecedented scale. The abundant information on social media intensifies the competition of WeChat Public Official Articles (i.e., posts) for gaining user attention due to the zero-sum nature of attention. Therefore, only a small portion of information tends to become extremely popular while the rest remains unnoticed or quickly disappears. Such a typical "long-tail" phenomenon is very common in social media. Thus, recent years have witnessed a growing interest in predicting the future trend in the popularity of social media posts and understanding the factors that influence the popularity of the posts. Nevertheless, existing predictive models either rely on cumbersome feature engineering or sophisticated parameter tuning, which are difficult to understand and improve. In this paper, we study and enhance a point process-based model by incorporating visual reasoning to support communication between the users and the predictive model for a better prediction result. The proposed system supports users to uncover the working mechanism behind the model and improve the prediction accuracy accordingly based on the insights gained. We use realistic WeChat articles to demonstrate the effectiveness of the system and verify the improved model on a large scale of WeChat articles. We also elicit and summarize the feedback from WeChat domain experts.

Fact sheets with vivid graphical design and intriguing statistical insights are prevalent for presenting raw data. They help audiences understand data-related facts effectively and make a deep impression. However, designing a fact sheet requires both data and design expertise and is a laborious and time-consuming process. One needs to not only understand the data in depth but also produce intricate graphical representations. To assist in the design process, we present DataShot which, to the best of our knowledge, is the first automated system that creates fact sheets automatically from tabular data. First, we conduct a qualitative analysis of 245 infographic examples to explore general infographic design space at both the sheet and element levels. We identify common infographic structures, sheet layouts, fact types, and visualization styles during the study. Based on these findings, we propose a fact sheet generation pipeline, consisting of fact extraction, fact composition, and presentation synthesis, for the auto-generation workflow. To validate our system, we present use cases with three real-world datasets. We conduct an in-lab user study to understand the usage of our system. Our evaluation results show that DataShot can efficiently generate satisfactory fact sheets to support further customization and data presentation.

Understanding how much users disclose personal information in Online Social Networks (OSN) has served various scenarios such as maintaining social relationships and customer segmentation. Prior studies on self-disclosure have relied on surveys or users' direct social networks. These approaches, however, cannot represent the whole population nor consider user dynamics at the community level.

As service robots are envisioned to provide decision-making support (DMS) in public places, it is becoming essential to design the robot's manner of offering assistance. For example, robot shop assistants that proactively or reactively give product recommendations may impact customers' shopping experience. In this paper, we propose an anticipation-autonomy policy framework that models three levels of proactivity (high, medium and low) of service robots in DMS contexts. We conduct a within-subject experiment with 36 participants to evaluate the effects of DMS robot's proactivity on user perceptions and interaction behaviors. Results show that a highly proactive robot is deemed inappropriate though people can get rich information from it. A robot with medium proactivity helps reduce the decision space while maintaining users' sense of engagement. The least proactive robot grants users more control but may not realize its full capability. We conclude the paper with design considerations for service robot's manner.

Designers strive to make their mobile apps stand out in a competitive market by creating a distinctive brand personality. However, it is unclear whether users can form a consistent impression of brand personality by looking at a few user interface (UI) screenshots in the app store, and if this process can be modeled computationally. To bridge this gap, we first collect crowd assessment on brand personalities depicted by the UIs of 318 applications, and statistically confirm that users can reach substantial agreement. To further model how users process mobile UI visually, we compute UI descriptors including Color, Organization, and Texture at both element and page levels. We feed these descriptors to a computational model, achieving a high accuracy of predicting perceived brand personality (MSE = 0.035 and R^2 = 0.78). This work could benefit designers by highlighting contributing visual factors to brand personality creation and providing quick, low-cost design feedback.

Online question pools like LeetCode provide hands-on exercises of skills and knowledge. However, due to the large volume of questions and the intent of hiding the tested knowledge behind them, many users find it hard to decide where to start or how to proceed based on their goals and performance. To overcome these limitations, we present PeerLens, an interactive visual analysis system that enables peer-inspired learning path planning. PeerLens can recommend a customized, adaptable sequence of practice questions to individual learners, based on the exercise history of other users in a similar learning scenario. We propose a new way to model the learning path by submission types and a novel visual design to facilitate the understanding and planning of the learning path. We conducted a within-subject experiment to assess the efficacy and usefulness of PeerLens in comparison with two baseline systems. Experiment results show that users are more confident in arranging their learning path via PeerLens and find it more informative and intuitive.

Zhenjie Zhao, Andrew Cattle, Evangelos Papalexakis, Xiaojuan Ma. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Zhenjie Zhao, Xiaojuan Ma. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Planar object tracking (POT) is the basis of many indoor AR applications. However, there still lacks a systematic way to assess AR trackers. Existing benchmarks usually focus on the tracking accuracy of an algorithm without sufficient details about its sensitivity to various object properties and user behaviors, shedding limited light on possible resulting usability issues. We therefore propose a comprehensive POT benchmark system to understand the weakness of a tracker and derive cues for system improvement. We first identify a set of objects that are commonly used as indoor mobile AR markers and specify their vision-related properties. We then construct a video collection to record typical user interactions with these markers, and statistically quantify the consequent changes as a result of individual or multiple basic manipulations. Evaluation shows that this work can expose a tracker's sensitiveness to different object properties and user behaviors, drawing insights for system improvement and algorithm design.

Imitation learning targets deriving a mapping from states to actions, a.k.a. policy, from expert demonstrations. Existing methods for imitation learning typically require any actions in the demonstrations to be fully available, which is hard to ensure in real applications. Though algorithms for learning with unobservable actions have been proposed, they focus solely on state information and over- look the fact that the action sequence could still be partially available and provide useful information for policy deriving. In this paper, we propose a novel algorithm called Action-Guided Adversarial Imitation Learning (AGAIL) that learns a pol- icy from demonstrations with incomplete action sequences, i.e., incomplete demonstrations. The core idea of AGAIL is to separate demonstrations into state and action trajectories, and train a policy with state trajectories while using actions as auxiliary information to guide the training whenever applicable. Built upon the Generative Adversarial Imitation Learning, AGAIL has three components: a generator, a discriminator, and a guide. The generator learns a policy with rewards provided by the discriminator, which tries to distinguish state distributions between demonstrations and samples generated by the policy. The guide provides additional rewards to the generator when demonstrated actions for specific states are available. We com- pare AGAIL to other methods on benchmark tasks and show that AGAIL consistently delivers com- parable performance to the state-of-the-art methods even when the action sequence in demonstrations is only partially available.

Spatio-temporal prediction is a key type of tasks in urban computing, e.g., traffic flow and air quality. Adequate data is usually a prerequisite, especially when deep learning is adopted. However, the development levels of different cities are unbalanced, and still many cities suffer from data scarcity. To address the problem, we propose a novel cross-city transfer learning method for deep spatio-temporal prediction tasks, called RegionTrans. RegionTrans aims to effectively transfer knowledge from a data-rich source city to a data-scarce target city. More specifically, we first learn an inter-city region matching function to match each target city region to a similar source city region. A neural network is designed to effectively extract region-level representation for spatio-temporal prediction. Finally, an optimization algorithm is proposed to transfer learned features from the source city to the target city with the region matching function. Using citywide crowd flow prediction as a demonstration experiment, we verify the effectiveness of RegionTrans. Results show that RegionTrans can outperform the state-of-the-art fine-tuning deep spatio-temporal prediction models by reducing up to 10.7% prediction error.

An effective virtual agent (VA) that serves humans not only completes tasks efficaciously, but also manages its interpersonal relationships with users judiciously. Although past research has studied how agents apologize or seek help appropriately, there lacks a comprehensive study of how to design an emotionally intelligent (EI) virtual agent. In this paper, we propose to improve a VA's perceived EI by equipping it with personality-driven responsive expression of emotions. We conduct a within-subject experiment to verify this approach using a medical assistant VA. We ask participants to observe how the agent (displaying a dominant or submissive trait, or having no personality) handles user challenges when issuing reminders and rate its EI. Results show that simply being emotionally expressive is insufficient for suggesting VAs as fully emotionally intelligent. Equipping such VAs with a consistent, distinctive personality trait (especially submissive) can convey a significantly stronger sense of EI in terms of the ability to perceive, use, understand, and manage emotions, and can better mitigate user challenges.

Collaborative crowd computing, e.g., human computation and crowdsourcing, involves a team of workers jointly solving tasks of varying difficulties. In such settings, the ability to manage the workflow based on workers' skills and task strains can improve output quality. However, many practical systems employ a simple additive scoring scheme to measure worker performance, and do not consider the task difficulty or worker interaction. Some prior works have looked at ways of measuring worker performance or task difficulty in collaborative settings, but usually assume sophisticated models. In our work, we address this question by taking a competitive perspective and leveraging the vast prior work on competitive games. We adapt TrueSkill's standard competitive model by treating the task as a fictitious worker that the team of humans jointly plays against. We explore two fast online approaches to estimate the worker and task ratings: (1) an ELO rating system, and (2) approximate inference with the Expectation Propagation algorithm. To assess the strengths and weaknesses of the various rating methods, we conduct a human study on Amazon's Mechanical Turk with a simulated ESP game. Our experimental design has the novel element of pairing a carefully designed bot with human workers; these encounters can be used, in turn, to generate a larger set of simulated encounters, yielding more data. Our analysis confirms that our ranking scheme performs consistently and robustly, and outperforms the traditional additive scheme in terms of predicted accuracy.

Affectionate communication, the conveyance of closeness, care, and fondness for another, plays a key role in romantic relationships. While the pervasive use of digital technology for communication limits affectionate interaction through nonverbal cues -- a major channel of expression in face-to-face settings, there have been few approaches which scaffold couples' romantic text conversations. To bridge this gap, we propose a novel interactive system Lily which gives users inspirations to enrich their romantic expressions in text messaging. It first listens to users' original input and then recommends romantic lyrics holding the closest meaning in real-time during chats with partners. After a three-day empirical study, participants who are real-life couples reported that they not only received useful cues from Lily in terms of how to polish their affectionate expressions, but also learnt to enrich the conversation with topics enlightened by its recommendations. Based on our findings, we finally provide several design considerations for actual deployment of such an application.

To ensure the playability of Multiplayer Online Battle Arena (MOBA) games, designers strive to balance different game occurrences. Although machine learning (ML) can help classify matches into different occurrence categories, designers demand more flexible input, interpretable output, and interactive collaboration with ML to facilitate analysis in breadth and depth. To this end, we work closely with a game company to design a visual occurrence analytics system through a stepwise co-design process. We first identify bottlenecks in game designers' conventional practices and their concerns about ML via an observational study. Then, we develop the single-match module of the visualization system to familiarize users with interactive analytics. Next, we incorporate ML models to recommend match segments of interest during occurrence classification and streamline the cross-match analysis. Empirical studies confirm the efficacy of our system. Experts' feedback suggests that our stepwise co-design process indeed helps them better embrace collaboration with machines.

For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locations in a future period. To find an optimal group of users, existing methods often require information about users' mobility history, which may cause privacy breaches. In this paper, we propose a method to maximize mobile crowd's future location coverage under a guaranteed location privacy protection scheme. In our approach, users only need to upload one of their frequently visited locations, and more importantly, the uploaded location is obfuscated using a geographic differential privacy policy. We propose both analytic and practical solutions to this problem. Experiments on real user mobility datasets show that our method significantly outperforms the state-of-the-art geographic differential privacy methods by achieving a higher coverage under the same level of privacy protection.

Eyes-free target acquisition is a basic and important human ability to interact with the surrounding physical world, relying on the sense of space and proprioception. In this research, we leverage this ability to improve interaction in virtual reality (VR), by allowing users to acquire a virtual object without looking at it. We expect this eyes-free approach can effectively reduce head movements and focus changes, so as to speed up the interaction and alleviate fatigue and VR sickness. We conduct three lab studies to progressively investigate the feasibility and usability of eyes-free target acquisition in VR. Results show that, compared with the eyes-engaged manner, the eyes-free approach is significantly faster, provides satisfying accuracy, and introduces less fatigue and sickness; Most participants (13/16) prefer this approach. We also measure the accuracy of motion control and evaluate subjective experience of users when acquiring targets at different locations around the body. Based on the results, we make suggestions on designing appropriate target layout and discuss several design issues for eyes-free target acquisition in VR.

We propose VirtualGrasp, a novel gestural approach to retrieve virtual objects in virtual reality. Using VirtualGrasp, a user retrieves an object by performing a barehanded gesture as if grasping its physical counterpart. The object-gesture mapping under this metaphor is of high intuitiveness, which enables users to easily discover, remember the gestures to retrieve the objects. We conducted three user studies to demonstrate the feasibility and effectiveness of the approach. Progressively, we investigated the consensus of the object-gesture mapping across users, the expressivity of grasping gestures, and the learnability and performance of the approach. Results showed that users achieved high agreement on the mapping, with an average agreement score [35] of 0.68 (SD=0.27). Without exposure to the gestures, users successfully retrieved 76% objects with VirtualGrasp. A week after learning the mapping, they could recall the gestures for 93% objects.

This paper attempts to marry the interpretability of statistical machine learning approaches with the more robust models of joke structure and joke semantics capable of being learned by neural models. Specifically, we explore the use of semantic relatedness features based on word associations, rather than the more common Word2Vec similarity, on a binary humour identification task and identify several factors that make word associations a better fit for humour. We also explore the effects of using joke structure, in the form of humour anchors (Yang et al., 2015), for improving the performance of semantic features and show that, while an intriguing idea, humour anchors contain several pitfalls that can hurt performance.

Engagement, the key construct that describes the synergy between human (users) and technology (computing systems), is gaining increasing attention in academia and industry. Human-Engaged AI (HEAI) is an emerging research paradigm that aims to jointly advance the capability and capacity of human and AI technology. In this paper, we first review the key concepts in HEAI and its driving force from the integration of Artificial Intelligence (AI) and Human-Computer Interaction (HCI). Then we present an HEAI framework developed from our own work.

Timely access to professional medical advice is crucial for patient health outcomes. Traditional offline, one-on-one patient-provider interactions are time consuming and costly. As a result, online "Ask the doctor" (AtD) services have become increasingly popular, where patients and caregivers can obtain advice from medical professionals at a lower information and transaction cost. In this paper, we present an empirical study of Fenda, an innovative AtD platform recently introduced in China where patients and caregivers can consult a wide variety of healthcare professionals for a small fee. Using qualitative research methods, we analyzed how patients and caregivers interact with medical professionals on this platform, focusing on the nature of the questions asked and user strategies to optimize the usage of the platform. We further derived implications for designing better online AtD services connecting patients and caregivers to medical professionals.

Despite the popularity of photo editors used to improve image attractiveness and expressiveness on social media, many users have trouble making sense of color filter effects and locating a preferred filter among a set of designer-crafted candidates. The problem gets worse when more computer-generated filters are introduced. To enhance filter findability, we semantically name and organize color effects leveraging data curated by creative communities online. We first model semantic mappings between color themes and keywords in everyday language. Next, we index and organize each filter by the derived semantic information. We conduct three separate studies to investigate the benefit of the semantic features on filter exploration. Our results indicate that color theme semantics constructed through social curation enhances filter findability, providing important implications into how to use the wisdom of the crowd to improve user experience with image editors.

Massively Multiplayer Online Role-playing Games (MMORPGs) feature a large number of players socially interacting with one another in an immersive gaming environment. A successful MMORPG should engage players and meet their needs to achieve different categories of gratifications. Research on the evolution of player social interaction network and the dynamics of inter-player intimacy could provide insights into players' gratification-oriented behaviors in MMORPGs. Such understanding could in turn guide game designs for better engaging existing players and marketing strategies for attracting newcomers. Conventional dynamic network analysis may help investigate game-based social interactions at the macroscopic level. However, current dynamic network visualization techniques mainly focus on illustrating topological changes of the entire network, which are unsuitable for analyzing player-specific social interactions in the virtual world from an egocentric perspective. In general, game designers and operators find it difficult to analyze the way players with different gratification needs may interact with one another and the consequences on their relationships with direct ties, using a decentralized social graph with complicated time-varying structures. In this paper, we present MMOSeer, a visual analytics system for exploring the evolution of egocentric player intimacy network. MMOSeer focuses on the relationship between a player (ego) and his/her directly-linked friends (alters). We follow a user-centered design process to develop the system with game analysts and apply novel visualization techniques in conjunction with well-established algorithms to depict the evolution of intimacy egocentric network. We also derive a centrality change metric to infer how the impact of changes in an ego's interactive behaviors may propagate through the intimacy network, reshaping the structure of the alters' social circles at both micro and macro levels. Finally, we validate the usability of MMOSeer by discovering different user interaction patterns and the corresponding ego-network structural changes in a real-world gameplay dataset from a commercial MMORPG.

When human partners attend to peripheral computing devices while interacting with conversational robots, the inability of the robots to determine the actual engagement level of the human partners after gaze shift may cause communication breakdown. In this paper, we propose a real-time perception model for robots to estimate human partners' engagement dynamics, and investigate different robot behavior strategies to handle ambiguities in humans' status and ensure the flow of the conversation. In particular, we define four novel types of engagement status and propose a real-time engagement inference model that weighs humans' social signals dynamically according to the involvement of the computing devices. We further design two robot behavior strategies (explicit and implicit) to help resolve uncertainties in engagement inference and mitigate the impact of uncoupling, based on an annotated human-human interaction video corpus. We conducted a within-subject experiment to assess the efficacy and usefulness of the proposed engagement inference model and behavior strategies. Results show that robots with our engagement model can deliver better service and smoother conversations as an assistant, and people find the implicit strategy more polite and appropriate.

Research on Computer-Assisted Language Learning (CALL) has shown that the use of multimedia materials such as images and videos can facilitate interpretation and memorization of new words and phrases by providing richer cues than text alone. We present ViVo, a novel video-augmented dictionary that provides an inexpensive, convenient, and scalable way to exploit huge online video resources for vocabulary learning. ViVo automatically generates short video clips from existing movies with the target word highlighted in the subtitles. In particular, we apply a word sense disambiguation algorithm to identify the appropriate movie scenes with adequate contextual information for learning. We analyze the challenges and feasibility of this approach and describe our interaction design. A user study showed that learners were able to retain nearly 30% more new words with ViVo than with a standard bilingual dictionary days after learning. They preferred our video-augmented dictionary for its benefits in memorization and enjoyable learning experience.

Danmaku is an emerging socio-digital media paradigm that puts anonymous, asynchronous user-generated scrolling comments on videos. (How) can danmaku afford the illusion and realization of social interactions, if at all possible given its interactional incoherence nature? To answer this question, we collect Chinese danmaku users' reflection on their motivations to use this social service and explore the actual practices that meet the needs. According to a preliminary danmaku usage survey, users consider it as an information seeking and emotion venting channel. Through archival analysis of real-world data, we find that danmaku commentaries are relatively short, video-centric, saturated with emotions, and similar in syntactic and semantic features. Users have developed a set of mechanisms adapted to the medium, to leverage such text-based messages to foster interpersonal and hyperpersonal communication for sharing of facts, thoughts, and feelings.

This paper looks at the task of predicting word association strengths across three datasets; WordNet Evocation (Boyd-Graber et al., 2006), University of Southern Florida Free Association norms (Nelson et al., 2004), and Edinburgh Associative Thesaurus (Kiss et al., 1973).We achieve results of r = 0.357 and ρ = 0.379, r = 0.344 and ρ = 0.300, an ρ = 0.292 and ρ = 0.363, respectively.We find Word2Vec (Mikolov et al., 2013) and GloVe (Pennington et al., 2014) cosine similarities, as well as vector offsets, to be the highest performing features.Furthermore, we examine the usefulness of Gaussian embeddings (Vilnis and McCallum, 2014) for predicting word association strength, the first work to do so.

In traditional mobile crowdsensing applications, organizers need participants' precise locations for optimal task allocation, e.g., minimizing selected workers' travel distance to task locations. However, the exposure of their locations raises privacy concerns. Especially for those who are not eventually selected for any task, their location privacy is sacrificed in vain. Hence, in this paper, we propose a location privacy-preserving task allocation framework with geo-obfuscation to protect users' locations during task assignments. Specifically, we make participants obfuscate their reported locations under the guarantee of differential privacy, which can provide privacy protection regardless of adversaries' prior knowledge and without the involvement of any third-part entity. In order to achieve optimal task allocation with such differential geo-obfuscation, we formulate a mixed-integer non-linear programming problem to minimize the expected travel distance of the selected workers under the constraint of differential privacy. Evaluation results on both simulation and real-world user mobility traces show the effectiveness of our proposed framework. Particularly, our framework outperforms Laplace obfuscation, a state-of-the-art differential geo-obfuscation mechanism, by achieving 45% less average travel distance on the real-world data.

In this research, we conducted an elicitation study with 54 participants to evaluate user-defined mobile gestures given three types of operations, handheld postures, and interaction spaces, respectively, to investigate the effects of these factors on mental model. The results of the elicitation study revealed that each of the three factors significantly affected the users’ mental models, which could be reflected by the characteristics (e.g., nature, number of steps, and spatial utility) of the user-defined mobile gestures. In addition, we found that users tended to assign different roles to mid-air and on-screen gestures based on their motor ability given a handheld gesture and their interpretation of the characteristics and requirements of an operation. We further derived a set of gestures commonly defined in the elicitation study for a list of representative operations, and we share our insights into users’ mental models and the implications on future mobile-gesture design in different contexts.

Mobile crowd photographing (MCP) is an emerging area of interest for researchers as the built-in cameras of mobile devices are becoming one of the commonly used visual logging approaches in our daily lives. In order to meet diverse MCP application requirements and constraints of sensing targets, a multifacet task model should be defined for a generic MCP data collection framework. Furthermore, MCP collects pictures in a distributed way in which a large number of contributors upload pictures whenever and wherever it is suitable. This inevitably leads to evolving picture streams. This paper investigates the multiconstraint-driven data selection problem in MCP picture aggregation and proposes a pyramid-tree (PTree) model which can efficiently select an optimal subset from the evolving picture streams based on varied coverage needs of MCP tasks. By utilizing the PTree model in a generic MCP data collection framework, which is called CrowdPic, we test and evaluate the effectiveness, efficiency, and flexibility of the proposed framework through crowdsourcing-based and simulation-based experiments. Both the theoretical analysis and simulation results indicate that the PTree-based framework can effectively select a subset with high utility coverage and low redundancy ratio from the streaming data. The overall framework is also proved flexible and applicable to a wide range of MCP task scenarios.

Despite the great demand on and attempts at package express shipping services, online retailers have not yet had a practical solution to make such services profitable. In this paper, we propose an economical approach to express package delivery, i.e., exploiting relays of taxis with passengers to help transport package collectively, without degrading the quality of passenger services. Specifically, we propose a two-phase framework called crowddeliver for the package delivery path planning. In the first phase, we mine the historical taxi trajectory data offline to identify the shortest package delivery paths with estimated travel time given any Origin-Destination pairs. Using the paths and travel time as the reference, in the second phase we develop an online adaptive taxi scheduling algorithm to find the near-optimal delivery paths iteratively upon real-time requests and direct the package routing accordingly. Finally, we evaluate the two-phase framework using the real-world data sets, which consist of a point of interest, a road network, and the large-scale trajectory data, respectively, that are generated by 7614 taxis in a month in the city of Hangzhou, China. Results show that over 85% of packages can be delivered within 8 hours, with around 4.2 relays of taxis on average.

To design a successful Multiplayer Online Battle Arena (MOBA) game, the ratio of snowballing and comeback occurrences to all matches played must be maintained at a certain level to ensure its fairness and engagement. Although it is easy to identify these two types of occurrences, game developers often find it difficult to determine their causes and triggers with so many game design choices and game parameters involved. In addition, the huge amounts of MOBA game data are often heterogeneous, multi-dimensional and highly dynamic in terms of space and time, which poses special challenges for analysts. In this paper, we present a visual analytics system to help game designers find key events and game parameters resulting in snowballing or comeback occurrences in MOBA game data. We follow a user-centered design process developing the system with game analysts and testing with real data of a trial version MOBA game from NetEase Inc. We apply novel visualization techniques in conjunction with well-established ones to depict the evolution of players' positions, status and the occurrences of events. Our system can reveal players' strategies and performance throughout a single match and suggest patterns, e.g., specific player' actions and game events, that have led to the final occurrences. We further demonstrate a workflow of leveraging human analyzed patterns to improve the scalability and generality of match data analysis. Finally, we validate the usability of our system by proving the identified patterns are representative in snowballing or comeback matches in a one-month-long MOBA tournament dataset.

This paper explores why and how accidental breakage of technologies can promote humans to interact and ultimately lead to positive behavioral, emotional, and relational change. Through a set of research activities, including meta-synthesis of daily anecdotes, design workshops, and a case study, we gain insights into what may hinder or trigger human-human communication, and propose the conceptual and actionable process of Breakage-to-Icebreaker (B2I) design. Instead of intentionally breaking a technology, B2I design embeds mechanisms into existing products and services, creating opportunities for users to interpersonally interact online and/or offline while enjoying the original features and functionalities. Finally, we envision a broader and extended use of B2I thinking in everyday design research and practices.

Engagement is a key reason for introducing gamification to learning and thus serves as an important measurement of its effectiveness. Based on a literature review and meta-synthesis, this paper proposes a comprehensive framework of engagement in gamification for learning. The framework sketches out the connections among gamification strategies, dimensions of engagement, and the ultimate learning outcome. It also elicits other task - and user - related factors that may potentially impact the effect of gamification on learner engagement. To verify and further strengthen the framework, we conducted a user study to demonstrate that: 1) different gamification strategies can trigger different facets of engagement; 2) the three dimensions of engagement have varying effects on skill acquisition and transfer; and 3) task nature and learner characteristics that were overlooked in previous studies can influence the engagement process. Our framework provides an in-depth understanding of the mechanism of gamification for learning, and can serve as a theoretical foundation for future research and design.

Using coding education to promote computational thinking and nurture problem-solving skills in children has become an emerging global trend. However, how different input and output modalities in coding tools affect coding as a problem-solving process remains unclear. Of interest are the advantages and disadvantages of graphical and tangible interfaces for teaching coding to children. We conducted four kids coding workshops to study how different input and output methods in coding affected the problem-solving process and class dynamics. Results revealed that graphical input could keep children focused on problem solving better than tangible input, but it was less provocative for class discussion. Tangible output supported better schema construction and casual reasoning and promoted more active class engagement than graphical output but offered less affordance for analogical comparison among problems. We also derived insights for designing new tools and teaching methods for kids coding.

Social product discovery is an emerging paradigm that enables users to seek information and inspiration from peer-contributed contents. Researchers have observed herd behaviors in social discovery, i.e., basing beliefs and decisions on what similarly situated others have done. In this paper, we explore the effects of content layout and motivation on users' herd behaviors in social discovery. We conduct an eye-tracking study with 120 participants to compare goal- and action-oriented users' behaviors on a grid versus waterfall style social discovery site. The results show that users have a higher tendency to herd on a grid-style website, more so for goal-oriented users.

Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context dependent. In addition, the fact that bike usage pattern is affected not only by common contextual factors (e.g., time and weather) but also by opportunistic contextual factors (e.g., social and traffic events) poses a great challenge. To address these issues, we propose a dynamic cluster-based framework for over-demand prediction. Depending on the context, we construct a weighted correlation network to model the relationship among bike stations, and dynamically group neighboring stations with similar bike usage patterns into clusters. We then adopt Monte Carlo simulation to predict the over-demand probability of each cluster. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately predicts over-demand clusters and outperforms the baseline methods significantly.

Sparse Mobile Crowdsensing (MCS) has become a compelling approach to acquire and make inference on urban-scale sensing data. However, participants risk their location privacy when reporting data with their actual sensing positions. To address this issue, we adopt e-differential-privacy in Sparse MCS to provide a theoretical guarantee for participants' location privacy regardless of an adversary's prior knowledge. Furthermore, to reduce the data quality loss caused by differential location obfuscation, we propose a privacypreserving framework with three components. First, we learn a data adjustment function to fit the original sensing data to the obfuscated location. Second, we apply a linear program to select an optimal location obfuscation function, which aims to minimize the uncertainty in data adjustment. We also propose a fast approximated variant. Third, we propose an uncertaintyaware inference algorithm to improve the inference accuracy of obfuscated data. Evaluations with real environment and traffic datasets show that our optimal method reduces the data quality loss by up to 42% compared to existing differential privacy methods.

Computational Thinking (CT) has been widely introduced and investigated in recent years, particularly in the U.S. since the born of visual, block-based, drag-drop programming environments such as Kodu, Scratch, Minecraft and App Inventor. Although the user interface is mainly in English, the characteristics of these easy-to-use, game-based, and interactive tools attract many teachers and researchers in the world to pay much attention to the possibilities and opportunities of introducing these tools to students. Recently, some primary school teachers in Hong Kong begin to independently introduce some of these programming tools to students at age 7 - 11 as a part of learning activities in their computer lessons. Their motives are similar but not the same, such as making a fun learning and teaching experience, motivating students for active and collaborative participation, and introducing CT concepts to develop generic skills (e.g. problem solving skills, creativity, and critical thinking). However, there is an absence of well-developed and planned curriculum for "coding education" to introduce computational thinking systematically to students in the local context with expected learning outcomes. Due to the uniqueness of K-12 curriculum in Hong Kong, the existing curriculum model in the U.S. may need to be customized and redesigned to become suitable for integrating into the curriculum in Hong Kong. In this poster, it describes the first proposed coding education curriculum in Hong Kong primary education (Primary 4 to Primary 6) with relevant objectives, structures, contents, and learning outcomes. A new pedagogical design framework for CT is introduced in this poster, which could be generalizable and yet to be evaluated. This new curriculum will serve as the curriculum guide to local teachers, and is the first research initiative of a three-year longitudinal study investigating the impact of CT activities to students particularly in Hong Kong. The experience of this curriculum development for CT concepts in K-12 education can inspire teachers and researchers in other parts of the world when adopting and internationalizing CT activities based on the curriculum model developed under the U.S. education.

Reading academic paper is a daily task for researchers and graduate students. However, reading effectively can be challenging, particularly for novices in scientific research. For example, when readers are reading the related work section that cites a fair number of references in limited page space, they often need to flip back and forth between the text and the references and may also frequently search elsewhere for more information about the references. This increases the difficulty of understanding a paper. In this paper, we propose a narrative visualization system that helps the reading of academic papers. As a first step, we adopt narrative visualization to present literature review as interactive slides. Specifically, we propose a narrative structure with three levels of granularities that the reader can drill down or roll up freely. The logic flow of a slideshow can be organized based on the paper's presentation or citations. We demonstrate the effectiveness of our system through several case studies and user studies. The results show that the system allows users to quickly track and glance related work, making paper reading more effective and enjoyable.

Poor health literacy, or the ability to interpret and make judgments about one's health, affects the effectiveness of healthcare services and people's quality of life. We explored design principles on how to design visually salient, legible and interpretable medical graphics to promote comprehension and recall of electronic medical diagnostic information, an essential component of personal health records, for populations with limited health literacy. We first conducted a two-stage investigation to confirm that people can comprehend pictorial representations of medical conditions and treatments and improve their health literacy. We then designed a set of medical diagnostics graphics, based on the principles of information design derived from a large set of information graphics in various domains. Evaluated in a lab study, our medical diagnostic graphics were found to be capable of improving people's perceived comprehension and recall, and were considered easy to understand and very useful for individuals with limited health literacy.

This paper presents a group-aware mobile crowd sensing system called MobiGroup, which supports group activity organization in real-world settings. Acknowledging the complexity and diversity of group activities, this paper introduces a formal concept model to characterize group activities and classifies them into four organizational stages. We then present an intelligent approach to support group activity preparation, including a heuristic rule-based mechanism for advertising public activity and a context-based method for private group formation. In addition, we leverage features extracted from both online and offline communities to recommend ongoing events to attendees with different needs. Compared with the baseline method, people preferred public activities suggested by our heuristic rule-based method. Using a dataset collected from 45 participants, we found that the context-based approach for private group formation can attain a precision and recall of over 80%, and the usage of spatial-temporal contexts and group computing can have more than a 30% performance improvement over considering the interaction frequency between a user and related groups. A case study revealed that, by extracting the features such as dynamic intimacy and static intimacy, our cross-community approach for ongoing event recommendation can meet different user needs.

Container ports are generally measured and compared using performance indicators such as container throughput and facility productivity. Being able to measure the performance of container ports quantitatively is of great importance for researchers to design models for port operation and container logistics. Instead of relying on the manually collected statistical information from different port authorities and shipping companies, we propose to leverage the pervasive ship GPS traces and maritime open data to derive port performance indicators, including ship traffic, container throughput, berth utilization, and terminal productivity. These performance indicators are found to be directly related to the number of container ships arriving at the terminals and the number of containers handled at each ship. Therefore, we propose a framework that takes the ships' container-handling events at terminals as the basis for port performance measurement. With the inferred port performance indicators, we further compare the strengths and weaknesses of different container ports at the terminal level, port level, and region level, which can potentially benefit terminal productivity improvement, liner schedule optimization, and regional economic development planning. In order to evaluate the proposed framework, we conduct extensive studies on large-scale real-world GPS traces of container ships collected from major container ports worldwide through the year, as well as various maritime open data sources concerning ships and ports. Evaluation results confirm that the proposed framework not only can accurately estimate various port performance indicators but also effectively produces port comparison results such as port performance ranking and port region comparison.

Eye tracking is a compelling tool for revealing people's spatial-temporal distribution of visual attention. But quality eye tracking hardware is expensive and can only be used with one person at a time. Further, webcam eye tracking systems have significant limitations on head movement and lighting conditions that result in significant data loss and inaccuracies. To address these drawbacks, we introduce a new approach that harnesses the crowd to understand allocation of visual attention. In our approach, crowdsourcing participants use mouse clicks to self-report the positions and trajectory for the following valuable eye tracking measures: first gaze, last gaze and all gazes. We validate our crowdsourcing approach with a user study, which demonstrated good accuracy when compared to a real eye tracker. We then deployed our prototype, GazeCrowd, in a crowdsourcing setting, and showed that it accurately generated gaze heatmaps and trajectory maps. Such an approach will allow designers to evaluate and refine their visual design without requiring the use of limited/expensive eye trackers.

Online collaboration tools enable developers of interactive systems to quickly reach potential users for usability testing. Can these technologies serve designers who seek feedback on user needs during the earliest stages of design? Online needfinding may help designers create products and services that can target a more diverse user population. To explore this, we conducted a feasibility study to compare face-to-face methods with online needfinding sessions. We found that video can sufficiently capture nuanced reactions to preliminary concept storyboards, but that feedback providers need guidance and structure. We then introduce a tool for collecting early-stage design feedback from online participants and conduct a case study with a professional design team. The team conducted needfinding activities with local participants, as well as a cost-equivalent number of online participants The case study demonstrates that combining online crowdsourcing with a video survey tool provides a simple and cost-efficient way to collect early-stage feedback.

Bike sharing systems have been deployed in many cities to promote green transportation and a healthy lifestyle. One of the key factors for maximizing the utility of such systems is placing bike stations at locations that can best meet users' trip demand. Traditionally, urban planners rely on dedicated surveys to understand the local bike trip demand, which is costly in time and labor, especially when they need to compare many possible places. In this paper, we formulate the bike station placement issue as a bike trip demand prediction problem. We propose a semi-supervised feature selection method to extract customized features from the highly variant, heterogeneous urban open data to predict bike trip demand. Evaluation using real-world open data from Washington, D.C. and Hangzhou shows that our method can be applied to different cities to effectively recommend places with higher potential bike trip demand for placing future bike stations.

Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TripPlanner, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TripPlanner works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TripPlanner applies heuristic algorithms to add user's preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.

Food is more than just a means of survival; it is also a form of communication. In this paper, we investigate the potential of food as a social message carrier (a.k.a., food messaging). To investigate how people accept, use, and perceive food messaging, we conducted exploratory interviews, a field study, and follow-up interviews over four weeks in a large information technology (IT) company. We collected 904 messages sent by 343 users. Our results suggest strong acceptance of food messaging as an alternative message channel. Further analysis implies that food messaging embodies characteristics of both text messaging and gifting. It is preferred in close relationships for its evocation of positive emotions. As the first field study on edible social messaging, our empirical findings provide valuable insights into the uniqueness of food as a message carrier and its capabilities to promote greater social bonding.

Collaborative online shopping, an emerging paradigm in e-commerce, allows remote shoppers to extend purchase-oriented social interactions into the digital environment. Online vendors have been experimenting ways to facilitate this activity. However, more research needs to be done on identifying what feature can create a pleasing shopping experience and ultimately encourage spending. In this paper, we present an exploration of the impact of co-navigation supports, including location cue, split screen, and shared view, on the experiences and performance of 60 co-shopper dyads. We also studied if status composition of shopping companions played a role in this process. By analyzing about 1800 minutes of eye-tracking data, video footages, and web logs, we found that split screen encouraged more diverse product search, shared view enabled better coordination, and location cue was the least distracting. Co-buyers achieved better factual and inference understanding, though buyer-advisor dyads were more likely to stay together.

Traditionally, the port container throughput, a crucial measurement of regional economic development, was manually collected by port authorities. This requires a large amount of human effort and often delays publication of this important figure. In this paper, by leveraging ubiquitous positioning techniques and open data, we propose a two-phase approach to estimation of port container throughput in real-time. First, we obtain the number of container ships arriving at berth by analyzing the ships' GPS traces. Then we estimate the throughput of each ship, in terms of number of containers transshipped, by considering the ship's berthing time, capacity, length, breadth, and crane operation performance, as extracted from different data sources. Evaluation results using real-world datasets from Hong Kong and Singapore show that the proposed approach not only estimates the container throughput quite accurately, but also outperforms the baseline method significantly.

Volunteering is an important activity that brings great benefits to societies. However, encouraging volunteerism is difficult due to the altruistic nature of volunteer activities and the high resource demand in carrying them out. We have created a Facebook game called "Farmer's Tale" to attract and make it easier for people to volunteer. We evaluated people's acceptance to this novel idea and the results revealed great potential in such type of games.

Auditory displays have been used in both human-machine and computer interfaces. However, the use of non-speech audio in assistive communication for people with language disabilities, or in other applications that employ visual representations, is still under-investigated. In this paper, we introduce SoundNet, a linguistic database that associates natural environmental sounds with words and concepts. A sound labeling study was carried out to verify SoundNet associations and to investigate how well the sounds evoke concepts. A second study was conducted using the verified SoundNet data to explore the power of environmental sounds to convey concepts in sentence contexts, compared with conventional icons and animations. Our results show that sounds can effectively illustrate (especially concrete) concepts and can be applied to assistive interfaces.

It is challenging to search a dictionary consisting of thousands of entries in order to select appropriate words for building written communication. This is true both for people trying to communicate in a foreign language who have not developed a full vocabulary, for school children learning to write, for authors who wish to be more precise and expressive, and especially for people with lexical access disorders. We make vocabulary navigation and word finding easier by augmenting a basic vocabulary with links between words based on human judgments of semantic similarity. In this paper, we report the results from a user study evaluating how our system named ViVA performs compared to a widely used assistive vocabulary in which words are organized hierarchically into common categories.

People with aphasia, a condition that impairs the ability to understand or generate written or spoken language, are aided by assistive technology that helps them communicate through a vocabulary of icons. These systems are akin to language translation systems, translating icon arrangements into spoken or written language and vice versa. However, these icon-based systems have little vocabulary breadth or depth, making it difficult for people with aphasia to apply their usage to multiple real world situations. Pictures from the web are numerous, varied, and easily accessible and thus, could potentially address the small size issues of icon-based systems. We present results from two studies that investigate this potential and demonstrate that images can be as effective as icons when used as a replacement for English language communication. The first study uses elderly subjects to investigate the efficacy of images vs. icons in conveying word meaning; the second study examines the retention of word-level meaning by both images and icons with a population of aphasics. We conclude that images collected from the web are as functional as icons in conveying information and thus, are feasible to use in assistive technology that supports people with aphasia.

In this paper we study how verbs are visually conveyed in daily communication contexts for both young and old adults. Four visual modes are compared: a single static image, a panel of four static images, an animation, and a video clip. The results reveal age effects, as well as performance differences introduced by lexical verb properties and visual cues. We also suggest guidelines for visual verb creation.

In this paper, we introduce W2ANE, an Online Multimedia Language Assistant for individuals with aphasia, a language disorder that affects millions of people. W2ANE offers a rich online multimedia library (OMLA) supported by an adaptable and adaptive vocabulary scaffold (ViVA). The system, accessible over the Internet, provides a platform for applications such as looking up unknown words, constructing phrases for communication, practicing pronunciations, and accessing content. W2ANE also enables resource sharing and remote collaboration.

Icons and digital images used in augmentative and alternative communication (AAC) are not as effective in illustrating verbs, especially for people with cognitive degeneration or impairment. Realistic videos have possible advantages for conveying verbs, as verified in our studies with young and old adults comparing single image, multiple images, animations, and video clips. Videos are especially more effective for verbs that show concrete movements or actions. Based on our studies, we propose rules for filming video verb representations, exploring possible visual cues and other factors that may affect people's perception and interpretation.