huashen218/convxai-cia-dataset huggingface dataset:
Title: Investigating Representation of Text and Audio in Educational VR using Learning Outcomes and EEG
Authors: family: Baceviciute
given: Sarune; family: Mottelson
given: Aske; family: Terkildsen
given: Thomas; family: Makransky
given: Guido
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: This paper reports findings from a between-subjects experiment that investigates how different learning content representations in virtual environments (VE) affect the process and outcomes of learning. Seventy-eight participants were subjected to an immersive virtual reality (VR) application, where they received identical instructional information, rendered in three different formats: as text in an overlay interface, as text embedded semantically in a virtual book, or as audio. Learning outcome measures, self-reports, and an electroencephalogram (EEG) were used to compare conditions. Results show that reading was superior to listening for the learning outcomes of retention, self-efficacy, and extraneous attention. Reading text from a virtual book was reported to be less cognitively demanding, compared to reading from an overlay interface. EEG analyses show significantly lower theta and higher alpha activation in the audio condition. The findings provide important considerations for the design of educational VR environments.
Title: "Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
Authors: family: Lai
given: Vivian; family: Liu
given: Han; family: Tan
given: Chenhao
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans. While existing approaches focus on explaining machine predictions with real-time assistance, we explore model-driven tutorials to help humans understand these patterns in a train- ing phase. We consider both tutorials with guidelines from scientific papers, analogous to current practices of science communication, and automatically selected examples from training data with explanations. We use deceptive review detection as a testbed and conduct large-scale, randomized human-subject experiments to examine the effectiveness of such tutorials. We find that tutorials indeed improve human performance, with and without real-time assistance. In particular, although deep learning provides superior predictive performance than simple models, tutorials and explanations from simple models are more useful to humans. Our work suggests future directions for human-centered tutorials and explanations towards a synergy between humans and AI.
Title: Automated Class Discovery and One-Shot Interactions for Acoustic Activity Recognition
Authors: family: Wu
given: Jason; family: Harrison
given: Chris; family: Bigham
given: Jeffrey P.; family: Laput
given: Gierad
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Acoustic activity recognition has emerged as a foundational element for imbuing devices with context-driven capabilities, enabling richer, more assistive, and more accommodating computational experiences. Traditional approaches rely either on custom models trained in situ, or general models pre-trained on preexisting data, with each approach having accuracy and user burden implications. We present Listen Learner, a technique for activity recognition that gradually learns events specific to a deployed environment while minimizing user burden. Specifically, we built an end-to-end system for self-supervised learning of events labelled through one-shot interaction. We describe and quantify system performance 1) on preexisting audio datasets, 2) on real-world datasets we collected, and 3) through user studies which uncovered system behaviors suitable for this new type of interaction. Our results show that our system can accurately and automatically learn acoustic events across environments (e.g., 97% precision, 87% recall), while adhering to users' preferences for non-intrusive interactive behavior.
Title: Understanding the Heisenberg Effect of Spatial Interaction: A Selection Induced Error for Spatially Tracked Input Devices
Authors: family: Wolf
given: Dennis; family: Gugenheimer
given: Jan; family: Combosch
given: Marco; family: Rukzio
given: Enrico
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Virtual and augmented reality head-mounted displays (HMDs) are currently heavily relying on spatially tracked input devices (STID) for interaction. These STIDs are all prone to the phenomenon that a discrete input (e.g. button press) will disturb the position of the tracker, resulting in a different selection point during ray-cast interaction (Heisenberg Effect of Spatial Interaction). Besides the knowledge of its existence, there is currently a lack of a deeper understanding of its severity, structure and impact on throughput and angular error during a selection task. In this work, we present a formal evaluation of the Heisenberg effect and the impact of body posture, arm position and STID degrees of freedom on its severity. In a Fitt's Law inspired user study (N=16), we found that the Heisenberg effect is responsible for 30.45% of the overall errors occurring during a pointing task, but can be reduced by 25.4% using a correction function.
Title: How to Trick AI: Users' Strategies for Protecting Themselves from Automatic Personality Assessment
Authors: family: Völkel
given: Sarah Theres; family: Haeuslschmid
given: Renate; family: Werner
given: Anna; family: Hussmann
given: Heinrich; family: Butz
given: Andreas
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Psychological targeting tries to influence and manipulate users' behaviour. We investigated whether users can protect themselves from being profiled by a chatbot, which automatically assesses users' personality. Participants interacted twice with the chatbot: (1) They chatted for 45 minutes in customer service scenarios and received their actual profile (baseline). (2) They then were asked to repeat the interaction and to disguise their personality by strategically tricking the chatbot into calculating a falsified profile. In interviews, participants mentioned 41 different strategies but could only apply a subset of them in the interaction. They were able to manipulate all Big Five personality dimensions by nearly 10%. Participants regarded personality as very sensitive data. As they found tricking the AI too exhaustive for everyday use, we reflect on opportunities for privacy protective designs in the context of personality-aware systems.
Title: How Relevant is Hick's Law for HCI?
Authors: family: Liu
given: Wanyu; family: Gori
given: Julien; family: Rioul
given: Olivier; family: Beaudouin-Lafon
given: Michel; family: Guiard
given: Yves
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Hick's law is a key quantitative law in Psychology that relates reaction time to the logarithm of the number of stimulus-response alternatives in a task. Its application to HCI is controversial: Some believe that the law does not apply to HCI tasks, others regard it as the cornerstone of interface design. The law, however, is often misunderstood. We review the choice-reaction time literature and argue that: (1) Hick's law speaks against, not for, the popular principle that 'less is better'; (2) logarithmic growth of observed temporal data is not necessarily interpretable in terms of Hick's law; (3) the stimulus-response paradigm is rarely relevant to HCI tasks, where choice-reaction time can often be assumed to be constant; and (4) for user interface design, a detailed examination of the effects on choice-reaction time of psychological processes such as visual search and decision making is more fruitful than a mere reference to Hick's law.
Title: MUBS: A Personalized Recommender System for Behavioral Activation in Mental Health
Authors: family: Rohani
given: Darius A.; family: Quemada Lopategui
given: Andrea; family: Tuxen
given: Nanna; family: Faurholt-Jepsen
given: Maria; family: Kessing
given: Lars V.; family: Bardram
given: Jakob E.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Depression is a leading cause of disability worldwide, which has inspired the design of mobile health (mHealth) applications for disease monitoring, prediction, and diagnosis. Less mHealth research has, however, focused on the treatment of depressive disorders. Clinical evidence shows that depressive symptoms can be reduced through a behavior change method known as Behavioral Activation (BA). This paper presents MUBS; a smartphone-based system for BA, which specifically contributes a personalized content-based activity recommendation model using a unique list of validated activities. An 8-week feasibility study with 17 depressive patients provided detailed insight into how MUBS provided inspiration and motivation for planning and engaging in more pleasant activities, thereby facilitating the core components of BA. Based on this study, the paper discusses how recommender technology can be used in the design of mHealth technology for BA.
Title: Dziban: Balancing Agency & Automation in Visualization Design via Anchored Recommendations
Authors: family: Lin
given: Halden; family: Moritz
given: Dominik; family: Heer
given: Jeffrey
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Visualization recommender systems attempt to automate design decisions spanning choices of selected data, transformations, and visual encodings. However, across invocations such recommenders may lack the context of prior results, producing unstable outputs that override earlier design choices. To better balance automated suggestions with user intent, we contribute Dziban, a visualization API that supports both ambiguous specification and a novel anchoring mechanism for conveying desired context. Dziban uses the Draco knowledge base to automatically complete partial specifications and suggest appropriate visualizations. In addition, it extends Draco with chart similarity logic, enabling recommendations that also remain perceptually similar to a provided "anchor" chart. Existing APIs for exploratory visualization, such as ggplot2 and Vega-Lite, require fully specified chart definitions. In contrast, Dziban provides a more concise and flexible authoring experience through automated design, while preserving predictability and control through anchored recommendations.
Title: Understanding Cybersecurity Practices in Emergency Departments
Authors: family: Stobert
given: Elizabeth; family: Barrera
given: David; family: Homier
given: Valérie; family: Kollek
given: Daniel
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Emergency departments (EDs) have unique operational requirements within hospitals. They have strong availability demands, are staffed by rotating personnel, and must provide services as quickly as possible. Modern EDs are also heavily computerized, and as such cybersecurity practices play a key role in meeting the expected operational standards. To better understand the cybersecurity challenges in EDs, we conducted a survey asking 347 ED personnel across Canada about their cybersecurity practices. The survey collected information relating to authentication and password management, use of personal devices for handling patient data, Internet connectivity on personal and hospital systems, and institutional security policies. Our results show that across multiple hospitals, deployed computer security systems fail to integrate with the requirements of staff and patients, leading to interruptions and inefficiencies.
Title: QMaps: Engaging Students in Voluntary Question Generation and Linking
Authors: family: Yeckehzaare
given: Iman; family: Barghi
given: Tirdad; family: Resnick
given: Paul
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Generating multiple-choice questions is known to improve students' critical thinking and deep learning. Visualizing relationships between concepts enhances meaningful learning, students' ability to relate new concepts to previously learned concepts. We designed and deployed a collaborative learning process through which students generate multiple-choice questions and represent the prerequisite knowledge structure between questions as visual links in a shared map, using a variation of Concept Maps that we call "QMap." We conducted a four-month study with 19 undergraduate students. Students sustained voluntary contributions, creating 992 good questions, and drawing 1,255 meaningful links between the questions. Through analyzing self-reports, observations, and usage data, we report on the technical and social design features that led students to sustain their motivation.
Title: Who Are You Asking?: Qualitative Methods for Involving AAC Users as Primary Research Participants
Authors: family: Beneteau
given: Erin
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: When trying to understand people's perspectives, qualitative researchers in HCI often use methods which assume participants can easily communicate verbally. There are few dedicated resources in HCI which provide an overview of qualitative methods to effectively gather the perspectives of people who cannot easily communicate verbally; specifically, people who use Augmentative and Alternative Communication (AAC). As a result, AAC users might be excluded from studies using methods such as interviews or focus groups, even if they fit the researcher's target population. To address this problem, I review literature from both HCI and therapeutic AAC research fields to discuss methods used with AAC users. In addition, I present relevant case examples from my own qualitative research and propose a framework to guide HCI researchers on choosing appropriate methods when involving AAC users as central research participants. I also identify design opportunities for HCI researchers to innovate on the tools and methods used for qualitative research with AAC users. This paper provides an easily accessible overview of qualitative methods HCI researchers can use with AAC users as participants.
Title: Autonomous Vehicle-Cyclist Interaction: Peril and Promise
Authors: family: Hou
given: Ming; family: Mahadevan
given: Karthik; family: Somanath
given: Sowmya; family: Sharlin
given: Ehud; family: Oehlberg
given: Lora
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Autonomous vehicles (AVs) will redefine interactions between road users. Presently, cyclists and drivers communicate through implicit cues (vehicle motion) and explicit but imprecise signals (hand gestures, horns). Future AVs could consistently communicate awareness and intent and other feedback to cyclists based on their sensor data. We present an exploration of AV-cyclist interaction, starting with preliminary design studies which informed the implementation of an immersive VR AV-cyclist simulator, and the design and evaluation of a number of AV-cyclist interfaces. Our findings suggest that AV-cyclist interfaces can improve rider confidence in lane merging scenarios. We contribute an AV-cyclist immersive simulator, insights on trade-offs of various aspects of AV-cyclist interaction design including modalities, location, and complexity, and positive results suggesting improved rider confidence due to AV-cyclist interaction. While we are encouraged by the potential positive impact AV-cyclist interfaces can have on cyclist culture, we also emphasize the risks over-reliance can pose to cyclists.
Title: Metro Futures: Experience-Centred Co-Design at Scale
Authors: family: Bowen
given: Simon; family: Wright
given: Peter; family: Wilson
given: Alexander; family: Dow
given: Andy; family: Bartindale
given: Tom; family: Anderson
given: Robert
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: This paper discusses how characteristics of experience-centred and collaborative design can be translated to larger scales. We describe Metro Futures, a region-wide public consultation on the design of new light rail trains, where we followed an experience-centred co-design approach supported by digital media and tools to develop findings with a core group of 20 ?co-researchers' and ~4000 public participants. The paper discusses how the characteristics of a focus on experience, and collaborative design exploration were achieved with co-researchers and, at scale, through online and face-to-face interactions using various digital media and tools. Whilst not at the depth of smaller scales, there are opportunities to retain characteristics of experience-centred co-design at scale to produce findings that can usefully inform ensuing design work, and avoid the averaging of public contributions often evident in large scale public consultations.
Title: Auditory Display in Interactive Science Simulations: Description and Sonification Support Interaction and Enhance Opportunities for Learning
Authors: family: Tomlinson
given: Brianna J.; family: Walker
given: Bruce N.; family: Moore
given: Emily B.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Science simulations are widely used in classrooms to support inquiry-based learning of complex science concepts. These tools typically rely on interactive visual displays to convey relationships. Auditory displays, including verbal description and sonification (non-speech audio), combined with alternative input capabilities, may provide an enhanced experience for learners, particularly learners with visual impairment. We completed semi-structured interviews and usability testing with eight adult learners with visual impairment for two audio-enhanced simulations. We analyzed trends and edge cases in participants' interaction patterns, interpretations, and preferences. Findings include common interaction patterns across simulation use, increased efficiency with second use, and the complementary role that description and sonification play in supporting learning opportunities. We discuss how these control and display layers work to encourage exploration and engagement with science simulations. We conclude with general and specific design takeaways to support the implementation of auditory displays for accessible simulations.
Title: Pushing the (Visual) Narrative: The Effects of Prior Knowledge Elicitation in Provocative Topics
Authors: family: Heyer
given: Jeremy; family: Raveendranath
given: Nirmal Kumar; family: Reda
given: Khairi
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Narrative visualization is a popular style of data-driven storytelling. Authors use this medium to engage viewers with complex and sometimes controversial issues. A challenge for authors is to not only deliver new information, but to also overcome people's biases and misconceptions. We study how people adjust their attitudes toward (or away from) a message experienced through a narrative visualization. In a mixed-methods analysis, we investigate whether eliciting participants' prior beliefs, and visualizing those beliefs alongside actual data, can increase narrative persuasiveness. We find that incorporating priors does not significantly affect attitudinal change. However, participants who externalized their beliefs expressed greater surprise at the data. Their comments also indicated a greater likelihood of acquiring new information, despite the minimal change in attitude. Our results also extend prior findings, showing that visualizations are more persuasive than equivalent textual data representations for exposing contentious issues. We discuss the implications and outline future research directions.
Title: Squeeze the Ball: Designing an Interactive Playground towards Aiding Social Activities of Children with Low-Function Autism
Authors: family: Wu
given: Qin; family: Yu
given: Chenmei; family: Chen
given: Yanjun; family: Yao
given: Jiayu; family: Wu
given: Xi; family: Peng
given: Xiaolan; family: Han
given: Teng
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Most intervention methods used for social skills training in children with autism are dedicated to high-functioning autism (HFA). However, extensive neurological and developmental disorders of low-functioning autism (LFA) have hampered their adoption. In this study, we observed and interviewed children with LFA, and their teachers, from a local educational institution, to better understand the children's social needs and barriers. Then, with the aim of aiding the children with their social activities, we illustrate the design process of SqueeBall, an interactive playground equipment. We evaluated the design with 18 children (16 with LFA and 2 with HFA) between 2.5 and 7 years of age. Results showed that these children had a pleasant game experience when the group bonded, and the equipment had a positive effect on aiding them in various ways. Finally, we discuss the challenges and opportunities of multimedia interaction techniques in aiding children with LFA.
Title: Under Surveillance: Technology Practices of those Monitored by the State
Authors: family: Sanches
given: Pedro; family: Tsaknaki
given: Vasiliki; family: Rostami
given: Asreen; family: Brown
given: Barry
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: This paper documents the experiences of those living under state surveillance. We interviewed our participants about how they lived under threat, and how it changed their technology practices. Our participants spanned three groups - journalists who reported from countries where their activities were illegal; activists who took part in civil disobedience, and individuals who worked in illegal activities that would have likely led to prosecution. In our analysis we cover four themes: first, 'the imagined surveillant'. Second, the danger and dependencies of technology use, third, their coping strategies, and lastly how belonging to a group can protect but also expose. In our discussion we cover how we can design for dissidents, and how to deal with the difficult questions this raises. We conclude by advocating for research that takes into account a critical view of the state in HCI and more broadly for an anti-surveillance stance in the design of technologies.
Title: LipType: A Silent Speech Recognizer Augmented with an Independent Repair Model
Authors: family: Pandey
given: Laxmi; family: Arif
given: Ahmed Sabbir
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: text input., deep learning, language modeling, Silent speech recognition

Abstract: Speech recognition is unreliable in noisy places, compromises privacy and security when around strangers, and inaccessible to people with speech disorders. Lip reading can mitigate many of these challenges but the existing silent speech recognizers for lip reading are error prone. Developing new recognizers and acquiring new datasets is impractical for many since it requires enormous amount of time, effort, and other resources. To address these, first, we develop LipType, an optimized version of LipNet for improved speed and accuracy. We then develop an independent repair model that processes video input for poor lighting conditions, when applicable, and corrects potential errors in output for increased accuracy. We then test this model with LipType and other speech and silent speech recognizers to demonstrate its effectiveness.