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As concerns about employee burnout and skilled staff shortages in cybersecurity grow, our study aims to better understand the contributing factors to burnout in this field. Utilizing a mixed-methods approach, we analyze self-reported job and personal characteristics, along with digital activity data from 35 incident responders, identifying several factors such as high workload, time pressure, and lack of support from management. Our findings reveal that over half of the participants experience burnout (N=19), which is linked to increased workload, limited control, poor teamwork, and inadequate recognition. Burned-out responders often work more than 40 hours per week, have poor sleep quality, and engage in more email activities, meetings, and after-hour collaborations. Through our research, we also identify coping strategies individuals use to mitigate these stressors. Based on our findings, we provide practical recommendations to help organizations better support their cybersecurity incident response teams. While our study acknowledges limitations and suggests future research directions, it contributes significantly to understanding the challenges faced by cybersecurity incident responders. Our insights offer a comprehensive understanding of burnout factors in this domain and have broader implications for other high-stress work environments consistent with the interdisciplinary nature of CSCW.

MindScape aims to study the benefits of integrating time series behavioral patterns (e.g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-reflection and well-being. We argue that integrating behavioral sensing in LLMs will likely lead to a new frontier in AI. In this Late-Breaking Work paper, we discuss the MindScape contextual journal App design that uses LLMs and behavioral sensing to generate contextual and personalized journaling prompts crafted to encourage self-reflection and emotional development. We also discuss the MindScape study of college students based on a preliminary user study and our upcoming study to assess the effectiveness of contextual AI journaling in promoting better well-being on college campuses. MindScape represents a new application class that embeds behavioral intelligence in AI.

We present a comprehensive, user-centric approach to understand preferences in AI-based productivity agents and develop personalized solutions tailored to users' needs. Utilizing a two-phase method, we first conducted a survey with 363 participants, exploring various aspects of productivity, communication style, agent approach, personality traits, personalization, and privacy. Drawing on the survey insights, we developed a GPT-4 powered personalized productivity agent that utilizes telemetry data gathered via Viva Insights from information workers to provide tailored assistance. We compared its performance with alternative productivity-assistive tools, such as dashboard and narrative, in a study involving 40 participants. Our findings highlight the importance of user-centric design, adaptability, and the balance between personalization and privacy in AI-assisted productivity tools. By building on the insights distilled from our study, we believe that our work can enable and guide future research to further enhance productivity solutions, ultimately leading to optimized efficiency and user experiences for information workers.

Re-finding information is an essential activity, however, it can be difficult when people struggle to express what they are looking for. Through a need-finding survey, we first seek opportunities for improving re-finding experiences, and explore one of these opportunities by implementing the FoundWright system. The system leverages recent advances in language transformer models to expand people's ability to express what they are looking for, through the interactive creation and manipulation of concepts contained within documents. We use FoundWright as a design probe to understand (1) how people create and use concepts, (2) how this expanded ability helps re-finding, and (3) how people engage and collaborate with FoundWright's machine learning support. Our probe reveals that this expanded way of expressing re-finding goals helps people with the task, by complementing traditional searching and browsing. Finally, we present insights and recommendations for future work aiming at developing systems to support re-finding., Comment: 26 pages

Traditional interventions for academic procrastination often fail to capture the nuanced, individual-specific factors that underlie them. Large language models (LLMs) hold immense potential for addressing this gap by permitting open-ended inputs, including the ability to customize interventions to individuals' unique needs. However, user expectations and potential limitations of LLMs in this context remain underexplored. To address this, we conducted interviews and focus group discussions with 15 university students and 6 experts, during which a technology probe for generating personalized advice for managing procrastination was presented. Our results highlight the necessity for LLMs to provide structured, deadline-oriented steps and enhanced user support mechanisms. Additionally, our results surface the need for an adaptive approach to questioning based on factors like busyness. These findings offer crucial design implications for the development of LLM-based tools for managing procrastination while cautioning the use of LLMs for therapeutic guidance.

The rise of AI conversational agents has broadened opportunities to enhance human capabilities across various domains. As these agents become more prevalent, it is crucial to investigate the impact of different affective abilities on their performance and user experience. In this study, we surveyed 745 respondents to understand the expectations and preferences regarding affective skills in various applications. Specifically, we assessed preferences concerning AI agents that can perceive, respond to, and simulate emotions across 32 distinct scenarios. Our results indicate a preference for scenarios that involve human interaction, emotional support, and creative tasks, with influences from factors such as emotional reappraisal and personality traits. Overall, the desired affective skills in AI agents depend largely on the application's context and nature, emphasizing the need for adaptability and context-awareness in the design of affective AI conversational agents.

QC 20211103

QC 20211103

Artificial agents' smiling behaviour is likely to influence their likeability and the quality of user experience. While studies of human interaction highlight the importance of smile dynamics, this feature is often lacking in artificial agents, presenting a design opportunity. We developed a virtual motivational therapist with four smiling behaviours, varying in terms of quality and dynamism. We video-recorded experimental sessions with participants who posed as patients in a therapy session. The data were analysed combining a mix of quantitative and qualitative methods, focusing on participants' own facial expressions during the interaction. Results suggest that the condition driven using data from a real therapist, where smiles are dynamic and occur at specific moments, is the most effective. We further discuss the particular importance of smile as a multipurpose emotional display in human-machine interaction., Part of proceedings: ISBN 978-1-6654-0019-0QC 20220602

Artificial agents' smiling behaviour is likely to influence their likeability and the quality of user experience. While studies of human interaction highlight the importance of smile dynamics, this feature is often lacking in artificial agents, presenting a design opportunity. We developed a virtual motivational therapist with four smiling behaviours, varying in terms of quality and dynamism. We video-recorded experimental sessions with participants who posed as patients in a therapy session. The data were analysed combining a mix of quantitative and qualitative methods, focusing on participants' own facial expressions during the interaction. Results suggest that the condition driven using data from a real therapist, where smiles are dynamic and occur at specific moments, is the most effective. We further discuss the particular importance of smile as a multipurpose emotional display in human-machine interaction., Part of proceedings: ISBN 978-1-6654-0019-0QC 20220602

Artificial agents' smiling behaviour is likely to influence their likeability and the quality of user experience. While studies of human interaction highlight the importance of smile dynamics, this feature is often lacking in artificial agents, presenting a design opportunity. We developed a virtual motivational therapist with four smiling behaviours, varying in terms of quality and dynamism. We video-recorded experimental sessions with participants who posed as patients in a therapy session. The data were analysed combining a mix of quantitative and qualitative methods, focusing on participants' own facial expressions during the interaction. Results suggest that the condition driven using data from a real therapist, where smiles are dynamic and occur at specific moments, is the most effective. We further discuss the particular importance of smile as a multipurpose emotional display in human-machine interaction., Part of proceedings: ISBN 978-1-6654-0019-0QC 20220602

Artificial agents' smiling behaviour is likely to influence their likeability and the quality of user experience. While studies of human interaction highlight the importance of smile dynamics, this feature is often lacking in artificial agents, presenting a design opportunity. We developed a virtual motivational therapist with four smiling behaviours, varying in terms of quality and dynamism. We video-recorded experimental sessions with participants who posed as patients in a therapy session. The data were analysed combining a mix of quantitative and qualitative methods, focusing on participants' own facial expressions during the interaction. Results suggest that the condition driven using data from a real therapist, where smiles are dynamic and occur at specific moments, is the most effective. We further discuss the particular importance of smile as a multipurpose emotional display in human-machine interaction., Part of proceedings: ISBN 978-1-6654-0019-0QC 20220602

Artificial agents' smiling behaviour is likely to influence their likeability and the quality of user experience. While studies of human interaction highlight the importance of smile dynamics, this feature is often lacking in artificial agents, presenting a design opportunity. We developed a virtual motivational therapist with four smiling behaviours, varying in terms of quality and dynamism. We video-recorded experimental sessions with participants who posed as patients in a therapy session. The data were analysed combining a mix of quantitative and qualitative methods, focusing on participants' own facial expressions during the interaction. Results suggest that the condition driven using data from a real therapist, where smiles are dynamic and occur at specific moments, is the most effective. We further discuss the particular importance of smile as a multipurpose emotional display in human-machine interaction., Part of proceedings: ISBN 978-1-6654-0019-0QC 20220602

Facial action unit recognition has many applications from market research to psychotherapy and from image captioning to entertainment. Despite its recent progress, deployment of these models has been impeded due to their limited generalization to unseen people and demographics. This work conducts an in-depth analysis of performance across several dimensions: individuals(40 subjects), genders (male and female), skin types (darker and lighter), and databases (BP4D and DISFA). To help suppress the variance in data, we use the notion of self-supervised denoising autoencoders to design a method for deep face normalization(DeepFN) that transfers facial expressions of different people onto a common facial template which is then used to train and evaluate facial action recognition models. We show that person-independent models yield significantly lower performance (55% average F1 and accuracy across 40 subjects) than person-dependent models (60.3%), leading to a generalization gap of 5.3%. However, normalizing the data with the newly introduced DeepFN significantly increased the performance of person-independent models (59.6%), effectively reducing the gap. Similarly, we observed generalization gaps when considering gender (2.4%), skin type (5.3%), and dataset (9.4%), which were significantly reduced with the use of DeepFN. These findings represent an important step towards the creation of more generalizable facial action unit recognition systems.

We now turn to understanding the impact that COVID-19 had on the personal productivity and well-being of information workers as their work practices were impacted by remote work. This chapter overviews people's productivity, satisfaction, and work patterns, and shows that the challenges and benefits of remote work are closely linked. Looking forward, the infrastructure surrounding work will need to evolve to help people adapt to the challenges of remote and hybrid work., Comment: In The New Future of Work: Research from Microsoft on the Impact of the Pandemic on Work Practices, edited by Jaime Teevan, Brent Hecht, and Sonia Jaffe, 1st ed. Microsoft, 2021. https://aka.ms/newfutureofwork

Virtual meetings are critical for remote work because of the need for synchronous collaboration in the absence of in-person interactions. In-meeting multitasking is closely linked to people's productivity and wellbeing. However, we currently have limited understanding of multitasking in remote meetings and its potential impact. In this paper, we present what we believe is the most comprehensive study of remote meeting multitasking behavior through an analysis of a large-scale telemetry dataset collected from February to May 2020 of U.S. Microsoft employees and a 715-person diary study. Our results demonstrate that intrinsic meeting characteristics such as size, length, time, and type, significantly correlate with the extent to which people multitask, and multitasking can lead to both positive and negative outcomes. Our findings suggest important best-practice guidelines for remote meetings (e.g., avoid important meetings in the morning) and design implications for productivity tools (e.g., support positive remote multitasking)., Comment: In ACM CHI 2021

The ability to monitor audience reactions is critical when delivering presentations. However, current videoconferencing platforms offer limited solutions to support this. This work leverages recent advances in affect sensing to capture and facilitate communication of relevant audience signals. Using an exploratory survey (N = 175), we assessed the most relevant audience responses such as confusion, engagement, and head-nods. We then implemented AffectiveSpotlight, a Microsoft Teams bot that analyzes facial responses and head gestures of audience members and dynamically spotlights the most expressive ones. In a within-subjects study with 14 groups (N = 117), we observed that the system made presenters significantly more aware of their audience, speak for a longer period of time, and self-assess the quality of their talk more similarly to the audience members, compared to two control conditions (randomly-selected spotlight and default platform UI). We provide design recommendations for future affective interfaces for online presentations based on feedback from the study.

Advances in machine intelligence have enabled conversational interfaces that have the potential to radically change the way humans interact with machines. However, even with the progress in the abilities of these agents, there remain critical gaps in their capacity for natural interactions. One limitation is that the agents are often monotonic in behavior and do not adapt to their partner. We built two end-to-end conversational agents: a voice-based agent that can engage in naturalistic, multi-turn dialogue and align with the interlocutor's conversational style, and a 2nd, expressive, embodied conversational agent (ECA) that can recognize human behavior during open-ended conversations and automatically align its responses to the visual and conversational style of the other party. The embodied conversational agent leverages multimodal inputs to produce rich and perceptually valid vocal and facial responses (e.g., lip syncing and expressions) during the conversation. Based on empirical results from a set of user studies, we highlight several significant challenges in building such systems and provide design guidelines for multi-turn dialogue interactions using style adaptation for future research., Comment: Conversational Agents: Acting on the Wave of Research and Development, CHI 2019 Workshop

A natural conversational interface that allows longitudinal symptom tracking would be extremely valuable in health/wellness applications. However, the task of designing emotionally-aware agents for behavior change is still poorly understood. In this paper, we present the design and evaluation of an emotion-aware chatbot that conducts experience sampling in an empathetic manner. We evaluate it through a human-subject experiment with N=39 participants over the course of a week. Our results show that extraverts preferred the emotion-aware chatbot significantly more than introverts. Also, participants reported a higher percentage of positive mood reports when interacting with the empathetic bot. Finally, we provide guidelines for the design of emotion-aware chatbots for potential use in mHealth contexts., Comment: Accepted for presentation at 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII). arXiv admin note: substantial text overlap with arXiv:1812.11423

We present an end-to-end voice-based conversational agent that is able to engage in naturalistic multi-turn dialogue and align with the interlocutor's conversational style. The system uses a series of deep neural network components for speech recognition, dialogue generation, prosodic analysis and speech synthesis to generate language and prosodic expression with qualities that match those of the user. We conducted a user study (N=30) in which participants talked with the agent for 15 to 20 minutes, resulting in over 8 hours of natural interaction data. Users with high consideration conversational styles reported the agent to be more trustworthy when it matched their conversational style. Whereas, users with high involvement conversational styles were indifferent. Finally, we provide design guidelines for multi-turn dialogue interactions using conversational style adaptation.

Humans use a host of signals to infer the emotional state of others. In general, computer systems that leverage signals from multiple modalities will be more robust and accurate in the same task. We present a multimodal affect and context sensing platform. The system is composed of video, audio and application analysis pipelines that leverage ubiquitous sensors (camera and microphone) to log and broadcast emotion data in real-time. The platform is designed to enable easy prototyping of novel computer interfaces that sense, respond and adapt to human emotion. This paper describes the different audio, visual and application processing components and explains how the data is stored and/or broadcast for other applications to consume. We hope that this platform helps advance the state-of-the-art in affective computing by enabling development of novel human-computer interfaces.

The delivery of mental health interventions via ubiquitous devices has shown much promise. A conversational chatbot is a promising oracle for delivering appropriate just-in-time interventions. However, designing emotionally-aware agents, specially in this context, is under-explored. Furthermore, the feasibility of automating the delivery of just-in-time mHealth interventions via such an agent has not been fully studied. In this paper, we present the design and evaluation of EMMA (EMotion-Aware mHealth Agent) through a two-week long human-subject experiment with N=39 participants. EMMA provides emotionally appropriate micro-activities in an empathetic manner. We show that the system can be extended to detect a user's mood purely from smartphone sensor data. Our results show that our personalized machine learning model was perceived as likable via self-reports of emotion from users. Finally, we provide a set of guidelines for the design of emotion-aware bots for mHealth., Comment: Accepted for presentation at 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)

Automated passive sensing applications and self-reported smart diaries seem to hold promise for the management of anxiety in autism and other mental health conditions. However, passive sensing often struggles with noisy data, ambiguous feedback and weak user agency over the device, whilst self-reporting relies on user-entered data which can be time consuming and cognitively demanding. To address these limitations, we explore a different approach, whereby individuals consciously actuate personal data capture and are in control of it at all times; yet, the interaction solely involves clicking a button, thus avoiding cognitive overload whilst supporting immediate reflection. We call this approach intentive computing. Through our initial investigations we found that conscious interactions cannot only provide real-time relief in anxiety management, but can also function as memory anchors irrespective of the content captured and even prior to data visualization.

The term 'patient experience' is currently part of a global discourse on ways to improve healthcare. This study empirically explores what patient experience is in cardiac remote monitoring and considers the implications for user experience (UX). Through interviews around the deployment of a mobile app that enables patients to collaborate with clinicians, we unpack experiences in six themes and present narratives of patients' lifeworlds. We find that patients' emotions are grounded in negative feelings (uncertainty, anxiety, loss of hope) and that positive experiences (relief, reassurance, safety) arise from getting feedback on symptoms and from continuous and comforting interaction with clinicians. With this paper, we aim to sensitise UX researchers and designers of patient-centred e-health by proposing three UX dimensions: connectedness, comprehension, and compassion.

The main problem with remote monitoring of cardiac device patients relates to inefficient communication. This is because patients and clinicians are separated in space and time. In the SCAUT project (2014-2018) we experiment with asynchronous interaction and explore how different types of patient-generated data can improve collaboration. The types of data that patients generate using the SCAUT patient app includes symptom experiences (categories/audio/numeric values), context (activity level/audio), medication list and travel information. We find that it is very important to consider how the data that patients enter can become useful for patients and clinicians simultaneously.

The objective of this panel is to advance knowledge in areas related to the “dark” side of IT use in organizations. Emerging and as yet nascent academic research is beginning to focus on negative impacts of IT use. The panel seeks to (1) introduce a new area of exploration and (2) disseminate new points of view. The panelists will do so by drawing upon their research and practice related insights in five areas that characterize the dark side of IT use, namely - IT usage related stress, addiction, misuse, work overload, and interruptions. These are clearly important areas to examine, given that the ubiquitous and functionally pervasive nature of IT use in organizations is expected to expose users to ever greater levels of conditions that are potent for experiencing this dark side. The proposed panel is thus of relevance and interest to the IS research and practice communities.

The objective of this panel is to advance knowledge in areas related to the “dark” side of IT use in organizations. Emerging and as yet nascent academic research is beginning to focus on negative impacts of IT use. The panel seeks to (1) introduce a new area of exploration and (2) disseminate new points of view. The panelists will do so by drawing upon their research and practice related insights in five areas that characterize the dark side of IT use, namely - IT usage related stress, addiction, misuse, work overload, and interruptions. These are clearly important areas to examine, given that the ubiquitous and functionally pervasive nature of IT use in organizations is expected to expose users to ever greater levels of conditions that are potent for experiencing this dark side. The proposed panel is thus of relevance and interest to the IS research and practice communities.

This program explored the movement of information generated in realms of scientific expertise across not only disciplinary lines but into the realms of personal life and public policy, where specialized knowledge can have enormous impact in such areas as health literacy, communication, food safety, and the environment. Questions asked during the presentation included: What are the challenges in translation from one information culture to another? Where are the opportunities? The program featured a panel of accomplished faculty members (Aldoory, Buchanan, Espy-Wilson, Palmer) who discussed these issues with "Visiting Future-ists" Chowdhury, Czerwinski and Russell.

This program explored both the promise and peril of an era in which free-flowing information raises issues of security, privacy, ownership, in which the response of governments has included both improved access and obstacles to transparency, and in which the use of social media has been been able to breach the walls erected by repressive regimes in some places but not in others. The program featured a panel of accomplished faculty members (Bederson, Khamis and Jaeger) with expertise ranging from e-government to mobile computing to the role of social media in the unfolding "Arab Spring." Following their presentations, the speakers discussed the issues of transparency and boundaries with "Visiting Future-ists" Chowdhury, Czerwinski and Russell.

Presenters at the program were three “Visiting Future-ists”: Dan Russell, Google's "director of user happiness," who leads efforts to improve the effectiveness of web searching and keynote speaker at the campus-wide Future of Information Forum held in November 2010; Mary Czerwinski, who manages the research on human-computer interaction at Microsoft and focuses on information visualization, group awareness and lifelogging; and Abdur Chowdhury, former chief scientist at Twitter, who has been working toward improving the ability to separate "signal" from "noise" in the explosion of information on the Web. The Visiting Future-ists described their work in helping to innovate for the future information environment, and discussed the opportunities and challenges that lie ahead. They also engaged the audience in brainstorming aimed at identifying information-related issues that can best be addressed through interdisciplinary research.

This program cast a wide net -- from performance to art to entrepreneurship -- in considering the information-seeking and sharing activities involved in creative endeavors. Faculty members Elahi, Epstein and Wilson Smiley made presentations about what they saw as the role of information in creativity, and talked with "Visiting Future-ists" Chowdhury, Czerwinski and Russell about the challenges and opportunities ahead at the intersection of information, creativity and culture.

We present WIPDash, a visualization for software development teams designed to increase group awareness of work items and code base activity. WIPDash was iteratively designed by working with two development teams, using interviews, observations, and focus groups, as well as sketches of the prototype. Based on those observations and feedback, we prototyped WIPDash and deployed it with two software teams for a one week field study. We summarize the lessons learned, and include suggestions for a future version.

We present WIPDash, a visualization for software development teams designed to increase group awareness of work items and code base activity. WIPDash was iteratively designed by working with two development teams, using interviews, observations, and focus groups, as well as sketches of the prototype. Based on those observations and feedback, we prototyped WIPDash and deployed it with two software teams for a one week field study. We summarize the lessons learned, and include suggestions for a future version.

Calendar applications for small handheld devices such as PDAs are growing in popularity. This led us to develop FishCal, a novel calendar interface for PDAs. It supports users in performing planning and analysis tasks by using a fisheye representation of dates coupled with compact overviews, user control over the visible time period, and integrated search. This enables users to see overviews and to easily navigate the calendar structure, and to discover patterns and outliers. FishCal was evaluated in a benchmark usability study comparing it to Microsoft's Pocket PC 2002(tm) calendar. Eleven users performed complex tasks significantly faster and completed them more often with FishCal. Task by task user satisfaction data showed a significant advantage for FishCal as well. A number of usability issues were identified to aid in the iterative refinement of FishCal. Keywords Fisheye Views, Information Visualization, Calendar Interfaces, PDAs, Animation, Graphics. (UMIACS-TR-2002-48) (HCIL-TR-2002-09)

Calendar applications for small handheld devices such as PDAs are growing in popularity. This led us to develop FishCal, a novel calendar interface for PDAs. It supports users in performing planning and analysis tasks by using a fisheye representation of dates coupled with compact overviews, user control over the visible time period, and integrated search. This enables users to see overviews and to easily navigate the calendar structure, and to discover patterns and outliers. FishCal was evaluated in a benchmark usability study comparing it to Microsoft's Pocket PC 2002(tm) calendar. Eleven users performed complex tasks significantly faster and completed them more often with FishCal. Task by task user satisfaction data showed a significant advantage for FishCal as well. A number of usability issues were identified to aid in the iterative refinement of FishCal. Keywords Fisheye Views, Information Visualization, Calendar Interfaces, PDAs, Animation, Graphics. (UMIACS-TR-2002-48) (HCIL-TR-2002-09)

Calendar applications for small handheld devices such as PDAs are growing in popularity. This led us to develop FishCal, a novel calendar interface for PDAs. It supports users in performing planning and analysis tasks by using a fisheye representation of dates coupled with compact overviews, user control over the visible time period, and integrated search. This enables users to see overviews and to easily navigate the calendar structure, and to discover patterns and outliers. FishCal was evaluated in a benchmark usability study comparing it to Microsoft's Pocket PC 2002(tm) calendar. Eleven users performed complex tasks significantly faster and completed them more often with FishCal. Task by task user satisfaction data showed a significant advantage for FishCal as well. A number of usability issues were identified to aid in the iterative refinement of FishCal. Keywords Fisheye Views, Information Visualization, Calendar Interfaces, PDAs, Animation, Graphics. (UMIACS-TR-2002-48) (HCIL-TR-2002-09)