huashen218/convxai-cia-dataset huggingface dataset:
Title: FTVR in VR: Evaluation of 3D Perception With a Simulated Volumetric Fish-Tank Virtual Reality Display
Authors: family: Fafard
given: Dylan; family: Stavness
given: Ian; family: Dechant
given: Martin; family: Mandryk
given: Regan; family: Zhou
given: Qian; family: Fels
given: Sidney
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: fish tank virtual reality, 3d perception, spherical display

Abstract: Spherical fish tank virtual reality (FTVR) displays attempt to create a virtual "crystal ball" experience using head-tracked rendering. Almost all of these systems have omitted stereo cues, making them easy to build, but it is not clear how much this omission degrades the 3D experience. In this study, we evaluate performance and subjective effects of stereo on 3D perception and interaction tasks with a spherical FTVR display. To control for calibration error and tracking latency, we perform the evaluation on a simulated spherical display in VR. The results of our study provide a clear recommendation for the design and use of spherical FTVR displays: while omitting stereo may not be readily apparent for users, their performance will be significantly degraded (20% - 91% increase in median task time). Therefore, including stereo viewing in spherical displays is critical for use in FTVR.
Title: Exploring How Privacy and Security Factor into IoT Device Purchase Behavior
Authors: family: Emami-Naeini
given: Pardis; family: Dixon
given: Henry; family: Agarwal
given: Yuvraj; family: Cranor
given: Lorrie Faith
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: consumer disclosures, internet of things (iot), labels, privacy, security, public policy, purchasing behaviors

Abstract: Despite growing concerns about security and privacy of Internet of Things (IoT) devices, consumers generally do not have access to security and privacy information when purchasing these devices. We interviewed 24 participants about IoT devices they purchased. While most had not considered privacy and security prior to purchase, they reported becoming concerned later due to media reports, opinions shared by friends, or observing unexpected device behavior. Those who sought privacy and security information before purchase, reported that it was difficult or impossible to find. We asked interviewees to rank factors they would consider when purchasing IoT devices; after features and price, privacy and security were ranked among the most important. Finally, we showed interviewees our prototype privacy and security label. Almost all found it to be accessible and useful, encouraging them to incorporate privacy and security in their IoT purchase decisions.
Title: An Explanation of Fitts' Law-like Performance in Gaze-Based Selection Tasks Using a Psychophysics Approach
Authors: family: Schuetz
given: Immo; family: Murdison
given: T. Scott; family: MacKenzie
given: Kevin J.; family: Zannoli
given: Marina
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: fitts' law, gaze input, gaze selection, user interfaces, saccades, eye movements

Abstract: Eye gaze as an input method has been studied since the 1990s, to varied results: some studies found gaze to be more efficient than traditional input methods like a mouse, others far behind. Comparisons are often backed up by Fitts' Law without explicitly acknowledging the ballistic nature of saccadic eye movements. Using a vision science-inspired model, we here show that a Fitts'-like distribution of movement times can arise due to the execution of secondary saccades, especially when targets are small. Study participants selected circular targets using gaze. Seven different target sizes and two saccade distances were used. We then determined performance across target sizes for different sampling windows ("dwell times") and predicted an optimal dwell time range. Best performance was achieved for large targets reachable by a single saccade. Our findings highlight that Fitts' Law, while a suitable approximation in some cases, is an incomplete description of gaze interaction dynamics.
Title: MultiTrack: Multi-User Tracking and Activity Recognition Using Commodity WiFi
Authors: family: Tan
given: Sheng; family: Zhang
given: Linghan; family: Wang
given: Zi; family: Yang
given: Jie
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: human tracking, activity recognition, wifi sensing

Abstract: This paper presents MultiTrack, a commodity WiFi based human sensing system that can track multiple users and recognize activities of multiple users performing them simultaneously. Such a system can enable easy and large-scale deployment for multi-user tracking and sensing without the need for additional sensors through the use of existing WiFi devices (e.g., desktops, laptops and smart appliances). The basic idea is to identify and extract the signal reflection corresponding to each individual user with the help of multiple WiFi links and all the available WiFi channels at 5GHz. Given the extracted signal reflection of each user, MultiTrack examines the path of the reflected signals at multiple links to simultaneously track multiple users. It further reconstructs the signal profile of each user as if only a single user has performed activity in the environment to facilitate multi-user activity recognition. We evaluate MultiTrack in different multipath environments with up to 4 users for multi-user tracking and up to 3 users for activity recognition. Experimental results show that our system can achieve decimeter localization accuracy and over 92% activity recognition accuracy under multi-user scenarios.
Title: What is Mixed Reality?
Authors: family: Speicher
given: Maximilian; family: Hall
given: Brian D.; family: Nebeling
given: Michael
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: taxonomy, mixed reality, literature review, virtual reality, expert interviews, augmented reality, conceptual framework

Abstract: What is Mixed Reality (MR)? To revisit this question given the many recent developments, we conducted interviews with ten AR/VR experts from academia and industry, as well as a literature survey of 68 papers. We find that, while there are prominent examples, there is no universally agreed on, one-size-fits-all definition of MR. Rather, we identified six partially competing notions from the literature and experts' responses. We then started to isolate the different aspects of reality relevant for MR experiences, going beyond the primarily visual notions and extending to audio, motion, haptics, taste, and smell. We distill our findings into a conceptual framework with seven dimensions to characterize MR applications in terms of the number of environments, number of users, level of immersion, level of virtuality, degree of interaction, input, and output. Our goal with this paper is to support classification and discussion of MR applications' design and provide a better means to researchers to contextualize their work within the increasingly fragmented MR landscape.
Title: Machine Heuristic: When We Trust Computers More than Humans with Our Personal Information
Authors: family: Sundar
given: S. Shyam; family: Kim
given: Jinyoung
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: automation bias, machine heuristic, virtual agent, cognitive heuristics, main model, secure and trustworthy computing

Abstract: In this day and age of identity theft, are we likely to trust machines more than humans for handling our personal information? We answer this question by invoking the concept of "machine heuristic," which is a rule of thumb that machines are more secure and trustworthy than humans. In an experiment (N = 160) that involved making airline reservations, users were more likely to reveal their credit card information to a machine agent than a human agent. We demonstrate that cues on the interface trigger the machine heuristic by showing that those with higher cognitive accessibility of the heuristic (i.e., stronger prior belief in the rule of thumb) were more likely than those with lower accessibility to disclose to a machine, but they did not differ in their disclosure to a human. These findings have implications for design of interface cues conveying machine vs. human sources of our online interactions.
Title: Critter: Augmenting Creative Work with Dynamic Checklists, Automated Quality Assurance, and Contextual Reviewer Feedback
Authors: family: Bharadwaj
given: Aditya; family: Siangliulue
given: Pao; family: Marcus
given: Adam; family: Luther
given: Kurt
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: creative work, quality assurance, feedback, checklists

Abstract: Checklists and guidelines have played an increasingly important role in complex tasks ranging from the cockpit to the operating theater. Their role in creative tasks like design is less explored. In a needfinding study with expert web designers, we identified designers' challenges in adhering to a checklist of design guidelines. We built Critter, which addressed these challenges with three components: Dynamic Checklists that progressively disclose guideline complexity with a self-pruning hierarchical view, AutoQA to automate common quality assurance checks, and guideline-specific feedback provided by a reviewer to highlight mistakes as they appear. In an observational study, we found that the more engaged a designer was with Critter, the fewer mistakes they made in following design guidelines. Designers rated the AutoQA and contextual feedback experience highly, and provided feedback on the tradeoffs of the hierarchical Dynamic Checklists. We additionally found that a majority of designers rated the AutoQA experience as excellent and felt that it increased the quality of their work. Finally, we discuss broader implications for supporting complex creative tasks.
Title: Do People Consume the News they Trust?
Authors: family: Taneja
given: Harsh; family: Yaeger
given: Katie
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: trustworthiness, online news, media choice, rational choice, news media, trust, media use

Abstract: It is reasonable to expect trusted news organizations to have more engaged users. However, given the lowest levels of trust in media and the several intermediaries involved in digital news consumption, recent studies posit that trust and usage may not be related. We argue that while trust may not relate to overall news usage, given that much of it is incidental, but it could still explain intentional usage. We correlated passively metered usage from digital trace data on 35 national news outlets in the US with their trustworthiness from a nationally representative survey, for three discrete months. We find no association between trust and overall user engagement, but a positive relationship between trustworthiness and direct visits, the latter a measure of intentional usage. These relationships held for outlets despite their partisan leanings, multi-platform presence and their mainstream nature.
Title: Saliency Deficit and Motion Outlier Detection in Animated Scatterplots
Authors: family: Veras
given: Rafael; family: Collins
given: Christopher
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: visualization, saliency, perception

Abstract: We report the results of a crowdsourced experiment that measured the accuracy of motion outlier detection in multivariate, animated scatterplots. The targets were outliers either in speed or direction of motion, and were presented with varying levels of saliency in dimensions that are irrelevant to the task of motion outlier detection (e.g., color, size, position). We found that participants had trouble finding the outlier when it lacked irrelevant salient features and that visual channels contribute unevenly to the odds of an outlier being correctly detected. Direction of motion contributes the most to accurate detection of speed outliers, and position contributes the most to accurate detection of direction outliers. We introduce the concept of saliency deficit in which item importance in the data space is not reflected in the visualization due to a lack of saliency. We conclude that motion outlier detection is not well supported in multivariate animated scatterplots.
Title: Understanding Affective Experiences with Conversational Agents
Authors: family: Yang
given: Xi; family: Aurisicchio
given: Marco; family: Baxter
given: Weston
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: positive design, conversational agents, user experience, emotional design, affect

Abstract: While previous studies of Conversational Agents (e.g. Siri, Google Assistant, Alexa and Cortana) have focused on evaluating usability and exploring capabilities of these systems, little work has examined users' affective experiences. In this paper we present a survey study with 171 participants to examine CA users' affective experiences. Specifically, we present four major usage scenarios, users' affective responses in these scenarios, and the factors which influenced the affective responses. We found that users' overall experience was positive with interest being the most salient positive emotion. Affective responses differed depending on the scenarios. Both pragmatic and hedonic qualities influenced affect. The factors underlying pragmatic quality are: helpfulness, proactivity, fluidity, seamlessness and responsiveness. The factors underlying hedonic quality are: comfort in human-machine conversation, pride of using cutting-edge technology, fun during use, perception of having a human-like assistant, concern about privacy and fear of causing distraction.
Title: Rehumanized Crowdsourcing: A Labeling Framework Addressing Bias and Ethics in Machine Learning
Authors: family: Barbosa
given: Natã M.; family: Chen
given: Monchu
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: ethics, machine learning, bias, crowdsourcing

Abstract: The increased use of machine learning in recent years led to large volumes of data being manually labeled via crowdsourcing microtasks completed by humans. This brought about dehumanization effects, namely, when task requesters overlook the humans behind the task, leading to issues of ethics (e.g., unfair payment) and amplification of human biases, which are transferred into training data and affect machine learning in the real world. We propose a framework that allocates microtasks considering human factors of workers such as demographics and compensation. We deployed our framework to a popular crowdsourcing platform and conducted experiments with 1,919 workers collecting 160,345 human judgments. By routing microtasks to workers based on demographics and appropriate pay, our framework mitigates biases in the contributor sample and increases the hourly pay given to contributors. We discuss potential extensions and how it can promote transparency in crowdsourcing.
Title: What Can We Learn from Augmented Reality (AR)? Benefits and Drawbacks of AR for Inquiry-based Learning of Physics
Authors: family: Radu
given: Iulian; family: Schneider
given: Bertrand
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: augmented reality, collaborative learning, physics education

Abstract: Emerging technologies such as Augmented Reality (AR), have the potential to radically transform education by making challenging concepts visible and accessible to novices. In this project, we have designed a Hololens-based system in which collaborators are exposed to an unstructured learning activity in which they learned about the invisible physics involved in audio speakers. They learned topics ranging from spatial knowledge, such as shape of magnetic fields, to abstract conceptual knowledge, such as relationships between electricity and magnetism. We compared participants' learning, attitudes and collaboration with a tangible interface through multiple experimental conditions containing varying layers of AR information. We found that educational AR representations were beneficial for learning specific knowledge and increasing participants' self-efficacy (i.e., their ability to learn concepts in physics). However, we also found that participants in conditions that did not contain AR educational content, learned some concepts better than other groups and became more curious about physics. We discuss learning and collaboration differences, as well as benefits and detriments of implementing augmented reality for unstructured learning activities.
Title: Serpentine: A Self-Powered Reversibly Deformable Cord Sensor for Human Input
Authors: family: Shahmiri
given: Fereshteh; family: Chen
given: Chaoyu; family: Waghmare
given: Anandghan; family: Zhang
given: Dingtian; family: Mittal
given: Shivan; family: Zhang
given: Steven L.; family: Wang
given: Yi-Cheng; family: Wang
given: Zhong Lin; family: Starner
given: Thad E.; family: Abowd
given: Gregory D.
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: ubiquitous computing, tangible interfaces, triboelectric nanogenerator, self-powered sensor, soft electronics, input devices

Abstract: We introduce Serpentine, a self-powered sensor that is a reversibly deformable cord capable of sensing a variety of human input. The material properties and structural design of Serpentine allow it to be flexible, twistable, stretchable and squeezable, enabling a broad variety of expressive input modalities. The sensor operates using the principle of Triboelectric Nanogenerators (TENG), which allows it to sense mechanical deformation without an external power source. The affordances of the cord include six interactions---Pluck, Twirl, Stretch, Pinch, Wiggle and Twist. Serpentine demonstrates the ability to simultaneously recognize these inputs through a single physical interface. A 12-participant user study illustrates 95.7% accuracy for a user-dependent recognition model using a realtime system and 92.17% for user-independent offline detection. We conclude by demonstrating how Serpentine can be employed in everyday ubiquitous computing applications.
Title: VRsneaky: Increasing Presence in VR Through Gait-Aware Auditory Feedback
Authors: family: Hoppe
given: Matthias; family: Karolus
given: Jakob; family: Dietz
given: Felix; family: Woźniak
given: Paweł W.; family: Schmidt
given: Albrecht; family: Machulla
given: Tonja-Katrin
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: gait awareness, presence, virtual reality, auditory feedback

Abstract: While Virtual Reality continues to increase in fidelity, it remains an open question how to effectively reflect the user's movements and provide congruent feedback in virtual environments. We present VRsneaky, a system for producing auditory movement feedback, which helps participants orient themselves in a virtual environment by providing footstep sounds. The system reacts to the user's specific gait features and adjusts the audio accordingly. In a user study with 28 participants, we found that VRsneaky increases users' sense of presence as well as awareness of their own posture and gait. Additionally, we find that increasing auditory realism significantly influences certain characteristics of participants' gait. Our work shows that gait-aware audio feedback is a means to increase presence in virtual environments. We discuss opportunities and design requirements for future scenarios where users walk through immersive virtual worlds.
Title: Enabling Identification and Behavioral Sensing in Homes using Radio Reflections
Authors: family: Hsu
given: Chen-Yu; family: Hristov
given: Rumen; family: Lee
given: Guang-He; family: Zhao
given: Mingmin; family: Katabi
given: Dina
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: behavioral sensing, smart environments, passive rf sensing, wireless sensing, user identification

Abstract: Understanding users' behavior at home is central to behavioral research. For example, social researchers are interested in studying domestic abuse, and healthcare professionals are interested in caregiver-patient interaction. Today, such studies rely on diaries and questionnaires, which are subjective, erroneous, and hard to sustain in longitudinal studies. We introduce Marko, a system that automatically collects behavior-related data, without asking people to write diaries or wear sensors. Marko transmits a low power wireless signal and analyses its reflections from the environment. It maps those reflections to how users interact with the environment (e.g., access to medication cabinet) and with each other (e.g., watch TV together). It provides novel algorithms for identifying who-does-what, and bootstrapping the system in new homes without asking users for new annotations. We evaluate Marko with a one-month deployment in six homes, and demonstrate its value for studying couple relationships and caregiver-patient interaction.
Title: I (Don't) See What You Typed There! Shoulder-surfing Resistant Password Entry on Gamepads
Authors: family: Mayer
given: Peter; family: Gerber
given: Nina; family: Reinheimer
given: Benjamin; family: Rack
given: Philipp; family: Braun
given: Kristoffer; family: Volkamer
given: Melanie
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: user study, shoulder-surfng, gamepads, authentication

Abstract: Using gamepad-driven devices like games consoles is an activity frequently shared with others. Thus, shoulder-surfing is a serious threat. To address this threat, we present the first investigation of shoulder-surfing resistant text password entry on gamepads by (1) identifying the requirements of this context; (2) assessing whether shoulder-surfing resistant authentication schemes proposed in non-gamepad contexts can be viably adapted to meet these requirements; (3) proposing "Colorwheels", a novel shoulder-surfing resistant authentication scheme specifically geared towards this context; (4) using two different methodologies proposed in the literature for evaluating shoulder-surfing resistance to compare "Colorwheels", on-screen keyboards (the de facto standard in this context), and an existing shoulder-surfing resistant scheme which we identified during our assessment and adapted for the gamepad context; (5) evaluating all three schemes regarding their usability. Having applied different methodologies to measure shoulder-surfing resistance, we discuss their strengths and pitfalls and derive recommendations for future research.
Title: NVGaze: An Anatomically-Informed Dataset for Low-Latency, Near-Eye Gaze Estimation
Authors: family: Kim
given: Joohwan; family: Stengel
given: Michael; family: Majercik
given: Alexander; family: De Mello
given: Shalini; family: Dunn
given: David; family: Laine
given: Samuli; family: McGuire
given: Morgan; family: Luebke
given: David
Venue: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Keywords: eye tracking, machine learning, virtual reality, dataset

Abstract: Quality, diversity, and size of training data are critical factors for learning-based gaze estimators. We create two datasets satisfying these criteria for near-eye gaze estimation under infrared illumination: a synthetic dataset using anatomically-informed eye and face models with variations in face shape, gaze direction, pupil and iris, skin tone, and external conditions (2M images at 1280x960), and a real-world dataset collected with 35 subjects (2.5M images at 640x480). Using these datasets we train neural networks performing with sub-millisecond latency. Our gaze estimation network achieves 2.06(±0.44)° of accuracy across a wide 30°×40° field of view on real subjects excluded from training and 0.5° best-case accuracy (across the same FOV) when explicitly trained for one real subject. We also train a pupil localization network which achieves higher robustness than previous methods.