While multimodal generative AI can support creative activities, users often struggle to prompt models to achieve desired aesthetic, acoustic, or stylistic characteristics. Besides, existing generative models are predominantly driven by text-based prompts regardless of their output modality, i.e., using text-based prompts for creating images, videos, and sounds, which often leads to high prompt uncertainty. In response, recent multimodal AI systems introduce interaction strategies to better align model interpretations with users’ creative intent, but the growing variety of strategies makes it hard to judge what works for a given use case. We address this gap with a systematic literature review (n=71) that categorizes prompt-uncertainty-reduction strategies into six types: Guiding Prompt Construction, System Refining of the Prompt, Direct Manipulations of Output Elements, Explaining Reasoning about Prompt Interpretation, Displaying Multiple Outputs, and Controlling Modifier Contribution. For each type, we summarize mechanisms, benefits, and challenges, enabling more efficient navigation of the prompt-support design space.

Conversational agents that participate in or mediate group interaction introduce challenges that extend beyond supporting individual users, raising new questions about how agents participate in and influence groups. To characterise this emerging design space, we present a systematic review of 53 peer-reviewed studies on group conversational agents (GCAs). We analyse how GCAs intervene in group-level processes, including participation regulation, conflict mediation, task alignment, and execution support. Using concepts from group research as an analytic lens, we organise prior GCA work around recurring group interactional challenges (orientation, conflict, alignment, and execution), and examine the roles agents are designed to play in addressing these challenges. We find that GCAs are predominantly designed as short-term, role-bounded interventions targeting isolated challenges in bounded interactional contexts. We further identify recurring structural tensions in GCA design, including tradeoffs between visibility and discretion, proactivity and group autonomy, and agent authority and group ownership. Together, these findings clarify how current GCAs are positioned within group interaction, surface the implicit assumptions embedded in their designs, and outline open questions for future research on conversational agents as group-level interventions.

Conversational agents have been studied as tools to scaffold planning and self-reflection for productivity and well-being. While prior work has demonstrated positive outcomes, we still lack a clear understanding of what drives these results and how users behave and communicate with agents that act as coaches rather than assistants. Such understanding is critical for designing interactions in which agents foster meaningful behavioral change. We conducted a 14-day longitudinal study with 12 participants using a proactive agent that initiated regular check-ins to support daily planning and reflection. Our findings reveal diverse interaction patterns: participants accepted or negotiated suggestions, developed shared mental models, reported progress, and at times resisted or disengaged. We also identified problematic aspects of the agent’s behavior, including rigidity, premature turn-taking, and overpromising. Our work contributes to understanding how people interact with a proactive, coach-like agent and offers design considerations for facilitating effective behavioral change.

As large language models (LLMs) become widespread, students increasingly turn to systems like ChatGPT for writing tasks. Educators worry that this reliance may reduce critical engagement with writing and hinder students’ learning processes. Although datasets exist on students’ use of LLMs for writing, how they functionally use ChatGPT in detail—and how this usage shapes their writing and perceptions—remains underexplored. We conducted an online study (n=77) in which students wrote an essay using an in-house ChatGPT we developed to capture their queries. Through qualitative analysis, we identified the types of assistance students sought and presented patterns of use, ranging from asking for opinions on a topic to delegating the entire writing task to ChatGPT. We also found that students’ writing self-efficacy influenced their querying patterns and that levels of ownership and creativity varied depending on how they used ChatGPT. This study contributes empirical data to ongoing discussions about how writing education should incorporate or regulate LLM-powered tools.

University research labs often rely on chat-based platforms for communication and project management, where valuable knowledge surfaces but is easily lost in message streams. Documentation can preserve knowledge, but it requires ongoing maintenance and is challenging to navigate. Drawing on formative interviews that revealed organizational memory challenges in labs, we designed CHOIR, an LLM-based chatbot that supports organizational memory through four key functions: document-grounded Q&A, Q&A sharing for follow-up discussion, knowledge extraction from conversations, and AI-assisted document updates. We deployed CHOIR in four research labs for one month (n=21), where the lab members asked 107 questions and lab directors updated documents 38 times in the organizational memory. Our findings reveal a privacy-awareness tension: questions were asked privately, limiting directors’ visibility into documentation gaps. Students often avoided contribution due to challenges in generalizing personal experiences into universal documentation. We contribute design implications for privacy-preserving awareness and supporting context-specific knowledge documentation.

Capturing professionals’ decision-making in creative workflows (e.g., UI/UX) is essential for reflection, collaboration, and knowledge sharing, yet existing methods often leave rationales incomplete and implicit decisions hidden. To address this, we present the Clear approach, which structures reasoning into cognitive decision steps—linked units of actions, artifacts, and explanations making decisions traceable with generative AI. Building on Clear, we introduce ClearFairy, a think-aloud AI assistant for UI design that detects weak explanations, asks lightweight clarifying questions, and infers missing rationales. In a study with twelve professionals, 85% of ClearFairy’s inferred rationales were accepted (as-is or with revisions). Notably, the system increased “strong explanations”—rationales providing sufficient causal reasoning—from 14% to 83% without adding cognitive demand. Furthermore, exploratory applications demonstrate that captured steps can enhance generative AI agents in Figma, yielding predictions better aligned with professionals and producing coherent outcomes. We release a dataset of 417 decision steps to support future research.

Peer agents can supplement real-time collaborative learning in asynchronous online courses. Constructive Controversy (CC) theory suggests that humans deepen their understanding of a topic by confronting and resolving controversies. This study explores whether CC’s benefits apply to LLM-based peer agents, focusing on the impact of agents’ disputatious behaviors and disclosure of agents’ behavior designs on the learning process. In our mixed-method study (n=144), we compare LLMs that follow detailed CC guidelines (regulated) to those guided by broader goals (unregulated) and examine the effects of disclosing the agents’ design to users (transparent vs. opaque). Findings show that learners’ values influence their agent interaction: those valuing control appreciate unregulated agents’ willingness to cease push-back upon request, while those valuing intellectual challenges favor regulated agents for stimulating creativity. Additionally, design transparency lowers learners’ perception of agents’ abilities. Our findings lay the foundation for designing effective collaborative peer agents in isolated educational settings.

Autistic individuals sometimes disclose autism when asking LLMs for social advice, hoping for more personalized responses. However, they also recognize that these systems may reproduce stereotypes, raising uncertainty about the risks and benefits of disclosure. We conducted a mixed-methods study combining a large-scale LLM audit experiment with interviews involving 11 autistic participants. We developed a six-step pipeline operationalizing 12 documented autism stereotypes into decision-making scenarios framed as users requesting advice (e.g., “Should I do A or B?”). We generated 345,000 responses from six LLMs and measured how advice shifted when prompts disclosed autism versus when they did not. When autism was disclosed, LLMs disproportionately recommended avoiding stereotypically stressful situations, including social events, confrontations, new experiences, and romantic relationships. While some participants viewed this as affirming, others criticized it as infantilizing or undermining opportunities for growth. Our study illuminates how the intermingling of affirmation and stereotyping complicates the personalization of LLMs.1

Journaling can potentially serve as an effective method for autistic adolescents to improve narrative skills. However, its text-centric nature and high executive functioning demands present barriers to practice. We present Autiverse, an AI-guided multimodal journaling app for tablets that scaffolds daily narratives through conversational prompts and visual supports. Autiverse elicits key details of an adolescent-selected event through a stepwise dialogue with peer-like, customizable AI and composes them into an editable four-panel comic strip. Through a two-week deployment study with 10 autistic adolescent-parent dyads, we examine how Autiverse supports autistic adolescents to organize their daily experience and emotion. Our findings show Autiverse scaffolded adolescents’ coherent narratives, while enabling parents to learn additional details of their child’s events and emotions. Moreover, the customized AI peer created a comfortable space for sharing, fostering enjoyment and a strong sense of agency. Drawing on these results, we discuss implications for adaptive scaffolding across autism profiles, socio-emotionally appropriate AI peer design, and balancing autonomy with parental involvement.

Non-native English speakers performing English-related tasks at work struggle to sustain EFL learning, despite their motivation. Often, study materials are disconnected from their work context. Our formative study revealed that reviewing work-related English becomes burdensome with current systems, especially after work. Although workers rely on LLM-based assistants to address their immediate needs, these interactions may not directly contribute to their English skills. We present LingoQ, an AI-mediated system that allows workers to practice English using quizzes generated from their LLM queries during work. LingoQ leverages these on-the-fly queries using AI to generate personalized quizzes that workers can review and practice on their smartphones. We conducted a three-week deployment study with 28 EFL workers to evaluate LingoQ. Participants valued the quality-assured, work-situated quizzes and constantly engaging with the app during the study. This active engagement improved self-efficacy and led to learning gains for beginners and, potentially, for intermediate learners. Drawing on these results, we discuss design implications for leveraging workers’ growing reliance on LLMs to foster proficiency and engagement while respecting work boundaries and ethics.

Computer Vision (CV) models can become outdated after deployment as real-world data evolves, requiring intensive attention from AI engineers to address degraded performance through tasks like data relabeling to update models with new human perceptions. Interactive human-in-the-loop systems have considerable potential to enhance model-steering practices. However, such workflows reveal two challenges: (1) scalability, where labor demands increase with data size, and (2) actionability, where human insights do not readily transform into model revisions. Based on our formative study (S1) on the current challenges faced by CV professionals, we developed De-Decay, an end-to-end Human-Data Alignment system offering scalable label-less assessment and actionable insight transformation . This enables engineers to investigate degradation and auto-retrain models with AI support, such as image clustering and regeneration. Our summative study (S2) showed that De-Decay helped engineers effectively identify and address CV degradation. We discuss how future research can enhance scalability and actionability in AI evaluation systems for aligning AI behaviors with human mental models.

In subjective decision-making, where decisions are based on contextual interpretation, Large Language Models (LLMs) can be integrated to present users with additional rationales to consider. The diversity of these rationales is mediated by the ability to consider the perspectives of different social actors. However, it remains unclear whether and how models differ in the distribution of perspectives they provide. We compare the perspectives taken by humans and different LLMs when assessing subtle sexism scenarios. We show that these perspectives can be classified within a finite set (perpetrator, victim, decision-maker), consistently present in argumentations produced by humans and LLMs, but in different distributions and combinations, demonstrating differences and similarities with human responses, and between models. We argue for the need to systematically evaluate LLMs' perspective-taking to identify the most suitable models for a given decision-making task. We discuss the implications for model evaluation.

Peer Reviewed

This work sheds light on whether and how creative writers' needs are met by existing research and commercial writing support tools (WST). We conducted a need finding study to gain insight into the writers' process during creative writing through a qualitative analysis of the response from an online questionnaire and Reddit discussions on r/Writing. Using a systematic analysis of 115 tools and 67 research papers, we map out the landscape of how digital tools facilitate the writing process. Our triangulation of data reveals that research predominantly focuses on the writing activity and overlooks pre-writing activities and the importance of visualization. We distill 10 key takeaways to inform future research on WST and point to opportunities surrounding underexplored areas. Our work offers a holistic and up-to-date account of how tools have transformed the writing process, guiding the design of future tools that address writers' evolving and unmet needs.

To provide long-term personalized assistance to users, AI agents must have effective long-term memory (LTM). However, there is little understanding of users’ perceptions, practices, and challenges with LTM in agents. We interviewed 21 users of agents such as ChatGPT and Claude to understand people’s everyday experiences with agent LTM. Our findings shed light on the flow of memory in agents as a three-stage process consisting of (1) information intake, (2) storage and management, and (3) retrieval and interpretation. Users’ perceptions of agent LTM are mainly influenced by Stage 3, and thus users’ interactions with agent LTM are mainly attempts at influencing and understanding how the agent retrieves and interprets information from memory. Therefore, we recommend that technological approaches to user interaction with agent LTM focus at least as much on memory retrieval and interpretation as they do on memory intake, storage, and management.

By simply composing prompts, developers can prototype novel generative applications with Large Language Models (LLMs). To refine prototypes into products, however, developers must iteratively revise prompts by evaluating outputs to diagnose weaknesses. Formative interviews (N=8) revealed that developers invest significant effort in manually evaluating outputs as they assess context-specific and subjective criteria. We present EvalLM, an interactive system for iteratively refining prompts by evaluating multiple outputs on user-defined criteria. By describing criteria in natural language, users can employ the system’s LLM-based evaluator to get an overview of where prompts excel or fail, and improve these based on the evaluator’s feedback. A comparative study (N=12) showed that EvalLM, when compared to manual evaluation, helped participants compose more diverse criteria, examine twice as many outputs, and reach satisfactory prompts with 59% fewer revisions. Beyond prompts, our work can be extended to augment model evaluation and alignment in specific application contexts.

Recent large language models (LLMs) offer the potential to support public health monitoring by facilitating health disclosure through open-ended conversations but rarely preserve the knowledge gained about individuals across repeated interactions. Augmenting LLMs with long-term memory (LTM) presents an opportunity to improve engagement and self-disclosure, but we lack an understanding of how LTM impacts people’s interaction with LLM-driven chatbots in public health interventions. We examine the case of CareCall—an LLM-driven voice chatbot with LTM—through the analysis of 1,252 call logs and interviews with nine users. We found that LTM enhanced health disclosure and fostered positive perceptions of the chatbot by offering familiarity. However, we also observed challenges in promoting self-disclosure through LTM, particularly around addressing chronic health conditions and privacy concerns. We discuss considerations for LTM integration in LLM-driven chatbots for public health monitoring, including carefully deciding what topics need to be remembered in light of public health goals.

Large Language Models (LLMs) offer promising opportunities in mental health domains, although their inherent complexity and low controllability elicit concern regarding their applicability in clinical settings. We present MindfulDiary, an LLM-driven journaling app that helps psychiatric patients document daily experiences through conversation. Designed in collaboration with mental health professionals, MindfulDiary takes a state-based approach to safely comply with the experts’ guidelines while carrying on free-form conversations. Through a four-week field study involving 28 patients with major depressive disorder and five psychiatrists, we examined how MindfulDiary facilitates patients’ journaling practice and clinical care. The study revealed that MindfulDiary supported patients in consistently enriching their daily records and helped clinicians better empathize with their patients through an understanding of their thoughts and daily contexts. Drawing on these findings, we discuss the implications of leveraging LLMs in the mental health domain, bridging the technical feasibility and their integration into clinical settings.

With their generative capabilities, large language models (LLMs) have transformed the role of technological writing assistants from simple editors to writing collaborators. Such a transition emphasizes the need for understanding user perception and experience, such as balancing user intent and the involvement of LLMs across various writing domains in designing writing assistants. In this study, we delve into the less explored domain of personal writing, focusing on the use of LLMs in introspective activities. Specifically, we designed DiaryMate, a system that assists users in journal writing with LLM. Through a 10-day field study (N=24), we observed that participants used the diverse sentences generated by the LLM to reflect on their past experiences from multiple perspectives. However, we also observed that they are over-relying on the LLM, often prioritizing its emotional expressions over their own. Drawing from these findings, we discuss design considerations when leveraging LLMs in a personal writing practice.

Children typically learn to identify and express their emotions by sharing stories and feelings with others, particularly family members. However, it is challenging for parents or siblings to have effective emotion communication with children since children are still developing their communication skills. We present ChaCha, a chatbot that encourages and guides children to share personal events and associated emotions. ChaCha combines a state machine and large language models (LLMs) to keep the dialogue on track while carrying on free-form conversations. Through an exploratory study with 20 children (aged 8–12), we examine how ChaCha prompts children to share personal events and guides them to describe associated emotions. Participants perceived ChaCha as a close friend and shared their stories on various topics, such as family trips and personal achievements. Based on the findings, we discuss opportunities for leveraging LLMs to design child-friendly chatbots to support children in sharing emotions.

Designers rely on visual search to explore and develop ideas in early design stages. However, designers can struggle to identify suitable text queries to initiate a search or to discover images for similarity-based search that can adequately express their intent. We propose GenQuery, a novel system that integrates generative models into the visual search process. GenQuery can automatically elaborate on users’ queries and surface concrete search directions when users only have abstract ideas. To support precise expression of search intents, the system enables users to generatively modify images and use these in similarity-based search. In a comparative user study (N=16), designers felt that they could more accurately express their intents and find more satisfactory outcomes with GenQuery compared to a tool without generative features. Furthermore, the unpredictability of generations allowed participants to uncover more diverse outcomes. By supporting both convergence and divergence, GenQuery led to a more creative experience.

Activity tracking has the potential to promote active lifestyles among older adults. However, current activity tracking technologies may inadvertently perpetuate ageism by focusing on age-related health risks. Advocating for a personalized approach in activity tracking technology, we sought to understand what activities older adults find meaningful to track and the underlying values of those activities. We conducted a reflective interview study following a 7-day activity journaling with 13 participants. We identified various underlying values motivating participants to track activities they deemed meaningful. These values, whether competing or aligned, shape the desirability of activities. Older adults appreciate low-exertion activities, but they are difficult to track. We discuss how these activities can become central in designing activity tracking systems. Our research offers insights for creating value-driven, personalized activity trackers that resonate more fully with the meaningful activities of older adults.

Transesophageal echocardiography (TEE) plays a pivotal role in cardiology for diagnostic and interventional procedures. However, using it effectively requires extensive training due to the intricate nature of image acquisition and interpretation. To enhance the efficiency of novice sonographers and reduce variability in scan acquisitions, we propose a novel ultrasound (US) navigation assistance method based on contrastive learning as goal-conditioned reinforcement learning (GCRL). We augment the previous framework using a novel contrastive patient batching method (CPB) and a data-augmented contrastive loss, both of which we demonstrate are essential to ensure generalization to anatomical variations across patients. The proposed framework enables navigation to both standard diagnostic as well as intricate interventional views with a single model. Our method was developed with a large dataset of 789 patients and obtained an average error of 6.56 mm in position and 9.36 degrees in angle on a testing dataset of 140 patients, which is competitive or superior to models trained on individual views. Furthermore, we quantitatively validate our method’s ability to navigate to interventional views such as the Left Atrial Appendage (LAA) view used in LAA closure. Our approach holds promise in providing valuable guidance during transesophageal ultrasound examinations, contributing to the advancement of skill acquisition for cardiac ultrasound practitioners.

Human-AI collaborative decision-making can achieve better outcomes than either party individually. The success of this collaboration can depend on whether the human decision-maker perceives the AI contribution as beneficial to the decision-making process. Beneficial AI explanations are often described as relevant, convincing, and trustworthy. Yet, we know little about the characteristics of explanations that result in these perceptions. Focusing on collaborative subjective decision-making, using the context of subtle sexism, where explanations can surface new interpretations, we conducted a user study (N=20) to explore the structural and content characteristics that affect perceptions of human and AI-generated verbal (text and audio) explanations. We find four groups of characteristics ( Tone, Grammatical Elements, Argumentative Sophistication and Relation to User ), and that the effect of these characteristics on the perception of explanations for subtle sexism depends on the perceived author. Thus, we also identify which explanation characteristics participants use to identify the author of an explanation. Demonstrating the relationship between these characteristics and explanation perceptions, we present a categorized set of characteristics that system builders can leverage to produce the appropriate perception of an explanation for various sensitive contexts. We also highlight human perception biases and associated issues resulting from these perceptions.

Large language models (LLMs) provide a new way to build chatbots by accepting natural language prompts. Yet, it is unclear how to design prompts to power chatbots to carry on naturalistic conversations while pursuing a given goal such as collecting self-report data from users. We explore what design factors of prompts can help steer chatbots to talk naturally and collect data reliably. To this aim, we formulated four prompt designs with different structures and personas. Through an online study (N = 48) where participants conversed with chatbots driven by different designs of prompts, we assessed how prompt designs and conversation topics affected the conversation flows and users' perceptions of chatbots. Our chatbots covered 79% of the desired information slots during conversations, and the designs of prompts and topics significantly influenced the conversation flows and the data collection performance. We discuss the opportunities and challenges of building chatbots with LLMs.

People struggle with the overflow of smartphone notifications but often face two challenges: (1) prioritizing the informative notifications as they wish and (2) retaining the delivered information as long as they want to utilize it. In this paper, we present DataHalo, a customizable notification visualization system that represents notifications as prolonged ambient visualizations on the home screen. DataHalo supports keyword-based filtering and categorization, and draws graphical marks based on time-varying importance model to enable longitudinal interaction with the notifications. We evaluated DataHalo through a usability study (N = 17), from which we improved the interface. We then conducted a three-week deployment study (N = 12) to assess how people use DataHalo in their domestic contexts. Our study revealed that people generated various visualization settings for different kinds of apps. Drawing on both quantitative and qualitative findings, we discussed implications for supporting effective notification management through customizable ambient visualizations.

Sighted and blind and low vision (BLV) creators alike use videos to communicate with broad audiences. Yet, video editing remains inaccessible to BLV creators. Our formative study revealed that current video editing tools make it difficult to access the visual content, assess the visual quality, and efficiently navigate the timeline. We present AVscript, an accessible text-based video editor. AVscript enables users to edit their video using a script that embeds the video’s visual content, visual errors (e.g., dark or blurred footage), and speech. Users can also efficiently navigate between scenes and visual errors or locate objects in the frame or spoken words of interest. A comparison study (N=12) showed that AVscript significantly lowered BLV creators’ mental demands while increasing confidence and independence in video editing. We further demonstrate the potential of AVscript through an exploratory study (N=3) where BLV creators edited their own footage.

Recent large language models (LLMs) have advanced the quality of open-ended conversations with chatbots. Although LLM-driven chatbots have the potential to support public health interventions by monitoring populations at scale through empathetic interactions, their use in real-world settings is underexplored. We thus examine the case of CareCall, an open-domain chatbot that aims to support socially isolated individuals via check-up phone calls and monitoring by teleoperators. Through focus group observations and interviews with 34 people from three stakeholder groups, including the users, the teleoperators, and the developers, we found CareCall offered a holistic understanding of each individual while offloading the public health workload and helped mitigate loneliness and emotional burdens. However, our findings highlight that traits of LLM-driven chatbots led to challenges in supporting public and personal health needs. We discuss considerations of designing and deploying LLM-driven chatbots for public health intervention, including tensions among stakeholders around system expectations.

The local explanation provides heatmaps on images to explain how Convolutional Neural Networks (CNNs) derive their output. Due to its visual straightforwardness, the method has been one of the most popular explainable AI (XAI) methods for diagnosing CNNs. Through our formative study (S1), however, we captured ML engineers' ambivalent perspective about the local explanation as a valuable and indispensable envision in building CNNs versus the process that exhausts them due to the heuristic nature of detecting vulnerability. Moreover, steering the CNNs based on the vulnerability learned from the diagnosis seemed highly challenging. To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations. DeepFuse helps CNN engineers to systemically search "unreasonable" local explanations and annotate the new boundaries for those identified as unreasonable in a labor-efficient manner. Next, it steers the model based on the given annotation such that the model doesn't introduce similar mistakes. We conducted a two-day study (S2) with 12 experienced CNN engineers. Using DeepFuse, participants made a more accurate and "reasonable" model than the current state-of-the-art. Also, participants found the way DeepFuse guides case-based reasoning can practically improve their current practice. We provide implications for design that explain how future HCI-driven design can move our practice forward to make XAI-driven insights more actionable.

Current activity tracking technologies are largely trained on younger adults’ data, which can lead to solutions that are not well-suited for older adults. To build activity trackers for older adults, it is crucial to collect training data with them. To this end, we examine the feasibility and challenges with older adults in collecting activity labels by leveraging speech. Specifically, we built MyMove, a speech-based smartwatch app to facilitate the in-situ labeling with a low capture burden. We conducted a 7-day deployment study, where 13 older adults collected their activity labels and smartwatch sensor data, while wearing a thigh-worn activity monitor. Participants were highly engaged, capturing 1,224 verbal reports in total. We extracted 1,885 activities with corresponding effort level and timespan, and examined the usefulness of these reports as activity labels. We discuss the implications of our approach and the collected dataset in supporting older adults through personalized activity tracking technologies.

Speech as a natural and low-burden input modality has great potential to support personal data capture. However, little is known about how people use speech input, together with traditional touch input, to capture different types of data in self-tracking contexts. In this work, we designed and developed NoteWordy, a multimodal self-tracking application integrating touch and speech input, and deployed it in the context of productivity tracking for two weeks (N = 17). Our participants used the two input modalities differently, depending on the data type as well as personal preferences, error tolerance for speech recognition issues, and social surroundings. Additionally, we found speech input reduced participants' diary entry time and enhanced the data richness of the free-form text. Drawing from the findings, we discuss opportunities for supporting efficient personal data capture with multimodal input and implications for improving the user experience with natural language input to capture various self-tracking data.

The factors influencing people’s food decisions, such as one’s mood and eating environment, are important information to foster self-reflection and to develop personalized healthy diet. But, it is difficult to consistently collect them due to the heavy data capture burden. In this work, we examine how speech input supports capturing everyday food practice through a week-long data collection study (N = 11). We deployed FoodScrap, a speech-based food journaling app that allows people to capture food components, preparation methods, and food decisions. Using speech input, participants detailed their meal ingredients and elaborated their food decisions by describing the eating moments, explaining their eating strategy, and assessing their food practice. Participants recognized that speech input facilitated self-reflection, but expressed concerns around re-recording, mental load, social constraints, and privacy. We discuss how speech input can support low-burden and reflective food journaling and opportunities for effectively processing and presenting large amounts of speech data.

Finding the propagation scope for various types of issues in Software Product Lines (SPLs) is a complicated Multi-Criteria Decision Making (MCDM) problem. This task often requires human-in-the-loop data analysis, which covers not only multiple product attributes but also contextual information (e.g., internal policy, customer requirements, exceptional cases, cost efficiency). We propose an interactive visualization tool to support MCDM tasks in software issue propagation based on the user's mental model. Our tool enables users to explore multiple criteria with their insight intuitively and find the appropriate propagation scope.

Most mobile health apps employ data visualization to help people view their health and activity data, but these apps provide limited support for visual data exploration. Furthermore, despite its huge potential benefits, mobile visualization research in the personal data context is sparse. This work aims to empower people to easily navigate and compare their personal health data on smartphones by enabling flexible time manipulation with speech. We designed and developed [email protected], a mobile app that leverages the synergy of two complementary modalities: speech and touch. Through an exploratory study with 13 long-term Fitbit users, we examined how multimodal interaction helps participants explore their own health data. Participants successfully adopted multimodal interaction (i.e., speech and touch) for convenient and fluid data exploration. Based on the quantitative and qualitative findings, we discuss design implications and opportunities with multimodal interaction for better supporting visual data exploration on mobile devices.

Git metadata contains rich information for developers to understand the overall context of a large software development project. Thus it can help new developers, managers, and testers understand the history of development without needing to dig into a large pile of unfamiliar source code. However, the current tools for Git visualization are not adequate to analyze and explore the metadata: They focus mainly on improving the usability of Git commands instead of on helping users understand the development history. Furthermore, they do not scale for large and complex Git commit graphs, which can play an important role in understanding the overall development history. In this paper, we present Githru, an interactive visual analytics system that enables developers to effectively understand the context of development history through the interactive exploration of Git metadata. We design an interactive visual encoding idiom to represent a large Git graph in a scalable manner while preserving the topological structures in the Git graph. To enable scalable exploration of a large Git commit graph, we propose novel techniques (graph reconstruction, clustering, and Context-Preserving Squash Merge (CSM) methods) to abstract a large-scale Git commit graph. Based on these Git commit graph abstraction techniques, Githru provides an interactive summary view to help users gain an overview of the development history and a comparison view in which users can compare different clusters of commits. The efficacy of Githru has been demonstrated by case studies with domain experts using real-world, in-house datasets from a large software development team at a major international IT company. A controlled user study with 12 developers comparing Githru to previous tools also confirms the effectiveness of Githru in terms of task completion time.

Productivity tracking tools often determine productivity based on the time interacting with work-related applications. To deconstruct productivity's diverse and nebulous nature, we investigate how knowledge workers conceptualize personal productivity and delimit productive tasks in both work and non-work contexts. We report a 2-week diary study followed by a semi-structured interview with 24 knowledge workers. Participants captured productive activities and provided the rationale for why the activities were assessed to be productive. They reported a wide range of productive activities beyond typical desk-bound work-ranging from having a personal conversation with dad to getting a haircut. We found six themes that characterize the productivity assessment-work product, time management, worker's state, attitude toward work, impact & benefit, and compound task and identified how participants interleaved multiple facets when assessing their productivity. We discuss how these findings could inform the design of a comprehensive productivity tracking system that covers a wide range of productive activities.

To help people enhance their personal productivity by providing effective feedback, we designed and developed TimeAware, a self-monitoring system for capturing and reflecting on personal computer usage behaviors. TimeAware employs an ambient widget to promote self-awareness and to lower the feedback access burden, and web-based information dashboard to visualize people's detailed computer usage. To examine the effect of framing on individual's productivity, we designed two versions of TimeAware, each with a different framing setting-one emphasizing productive activities (positive framing) and the other emphasizing distracting activities (negative framing), and conducted an eight-week deployment study (N = 24). We found a significant effect of framing on participants' productivity: only participants in the negative framing condition improved their productivity. The ambient widget seemed to help sustain engagement with data and enhance self-awareness. We discuss how to leverage framing effects to help people enhance their productivity, and how to design successful productivity monitoring tool.

This paper addresses issues existing in situated language understanding in a moving car. Particularly, we propose a method for understanding user queries regarding specific target buildings in their surroundings based on the driver's head pose and speech information. To identify a meaningful head pose motion related to the user query that is among spontaneous motions while driving, we construct a model describing the relationship between sequences of a driver's head pose and the relative direction to an interest point using the Gaussian process regression. We also consider time-varying interest point using kernel density estimation. We collected situated queries from subject drivers by using our research system embedded in a real car. The proposed method achieves an improvement in the target identification rate by 14% in the user-independent training condition and 27% in the user-dependent training condition over the method that uses the head motion at the start-of-speech timing.

Environmental monitoring, an important application for robots, has begun to be addressed recently with linear least squares regression techniques because they estimate the values of measured attributes and their uncertainty. But several challenges remain when performing adaptive sampling in a communication-constrained distributed multi-robot setting. When the attributes of interest evolve over time (as is natural for many environments) any non-homogeneous spatial variability may necessitate continual re-modeling of the field dynamics and/or re-sampling of the field. This raises questions about the robots' division of labor and workload balance that can be difficult to address when sample information is not stored centrally. This paper tackles these coordination problems efficiently by introducing a sub-division-based modeling technique appropriate for distributed decision-making. We augment Ordinary Kriging to enable representation of a field's (potentially non-homogeneous) evolution through Bayes filtering that characterize the underlying dynamics. This approach not only enables adaptive path planning in the field, but the sub-divided areas lead to a straightforward formulation of the optimal workload distribution through modification of an approximate graph partitioning algorithm. Using a simulated multi-robot sampling scenario, we demonstrate and validate the approach. The experiments show good performance in terms of cross-validation using real values and illustrate how hotspots are identified and modeled, in turn affecting the division of labor.

In this paper, high efficiency control method for interleaved flyback inverter with synchronous rectifier based on photovoltaic AC modules is proposed. In this control method, using synchronous rectifier, a main switch is operated with the soft switching in the region that ACC(Active Clamp Circuit) isn't operated for reducing loss of the ACC. Therefore, a switching loss of the main switch can be reduced. A theoretical analysis and the design principle of the proposed method are provided and its validity is confirmed through simulation results.

In this paper, we present a novel integrated bio cell processor to handle individual embryo cells. Its functions are composed of transporting, isolation, orientation, and immobilization of cells. These functions are essential for biomanipulation of single cells, and have been typically carried out by a proficient operator. The purpose of this study is the automation of these functions for effective cell manipulation using a MEMS based bio cell processor. This device is realized with relatively simple design and fabrication process. To transport cells, microfluidic channel is employed. The isolation of a cell is performed by actuation of polypyrrole (PPy) valves. The orientation control of cells is accomplished by dielectrophoresis (DEP). By the suction from the micro-hole, the target embryo cell is immobilized. Experimental results show that this device can substitute the essential but very tiresome and repeatable embryo cell manipulation and contribute significantly to the improvement of speed and success rate of operation by facilitating the cell manipulation. The cell viability test for the device is studied through the distribution of mitochondria in mouse (B6CBA) embryo cells and cultivation of cells for 86 h after cell was manipulated by DEP.