huashen218/convxai-cia-dataset huggingface dataset:
Title: Jubilee: An Extensible Machine for Multi-tool Fabrication
Authors: family: Vasquez
given: Joshua; family: Twigg-Smith
given: Hannah; family: Tran O'Leary
given: Jasper; family: Peek
given: Nadya
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: We present Jubilee, an open-source hardware machine with automatic tool-changing and interchangeable bed plates. As digital fabrication tools have become more broadly accessible, tailoring those machines to new users and novel workflows has become central to HCI research. However, the lack of hardware infrastructure makes custom application development cumbersome. We identify a need for an extensible platform to allow HCI researchers to develop workflows for fabrication, material exploration, and other applications. Jubilee addresses this need. It can automatically and repeatably change tools in the same operation. It can be built with a combination of simple 3D-printed and readily available parts. It has several standard head designs for a variety of applications including 3D printing, syringe-based liquid handling, imaging, and plotting. We present Jubilee with a comprehensive set of assembly instructions and kinematic mount templates for user-designed tools and bed plates. Finally we demonstrate Jubilee's multi-tool workflow functionality with a series of example applications.
Title: Using Diaries to Probe the Illness Experiences of Adolescent Patients and Parental Caregivers
Authors: family: Hong
given: Matthew K.; family: Lakshmi
given: Udaya; family: Do
given: Kimberly; family: Prahalad
given: Sampath; family: Olson
given: Thomas; family: Arriaga
given: Rosa I.; family: Wilcox
given: Lauren
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Adolescents with chronic conditions must work with family caregivers to manage their illness experiences. To explore how technology can support collaborative documentation of these experiences, we designed and distributed a paper diary probe kit in a two-week field deployment with 12 adolescent-parent dyads (24 participants). Three insights emerged from the study that highlight how technology can support shared illness management: 1) provide scaffolds to recognize physical and emotional experiences in the context of daily activities; 2) help families reconstruct patient experiences; and 3) adapt to individual preferences for capturing, representing and sharing experiences. We discuss opportunities for HCI research that follow from these findings and conclude by reflecting on the benefits and limitations of using diary probes with adolescent patients and their parental caregivers.
Title: Soundr: Head Position and Orientation Prediction Using a Microphone Array
Authors: family: Yang
given: Jackie (Junrui); family: Banerjee
given: Gaurab; family: Gupta
given: Vishesh; family: Lam
given: Monica S.; family: Landay
given: James A.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Although state-of-the-art smart speakers can hear a user's speech, unlike a human assistant these devices cannot figure out users' verbal references based on their head location and orientation. Soundr presents a novel interaction technique that leverages the built-in microphone array found in most smart speakers to infer the user's spatial location and head orientation using only their voice. With that extra information, Soundr can figure out users references to objects, people, and locations based on the speakers' gaze, and also provide relative directions. To provide training data for our neural network, we collected 751 minutes of data (50x that of the best prior work) from human speakers leveraging a virtual reality headset to accurately provide head tracking ground truth. Our results achieve an average positional error of 0.31m and an orientation angle accuracy of 34.3° for each voice command. A user study to evaluate user preferences for controlling IoT appliances by talking at them found this new approach to be fast and easy to use.
Title: Crowdsourcing the Perception of Machine Teaching
Authors: family: Hong
given: Jonggi; family: Lee
given: Kyungjun; family: Xu
given: June; family: Kacorri
given: Hernisa
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Teachable interfaces can empower end-users to attune machine learning systems to their idiosyncratic characteristics and environment by explicitly providing pertinent training examples. While facilitating control, their effectiveness can be hindered by the lack of expertise or misconceptions. We investigate how users may conceptualize, experience, and reflect on their engagement in machine teaching by deploying a mobile teachable testbed in Amazon Mechanical Turk. Using a performance-based payment scheme, Mechanical Turkers (N=100) are called to train, test, and re-train a robust recognition model in real-time with a few snapshots taken in their environment. We find that participants incorporate diversity in their examples drawing from parallels to how humans recognize objects independent of size, viewpoint, location, and illumination. Many of their misconceptions relate to consistency and model capabilities for reasoning. With limited variation and edge cases in testing, the majority of them do not change strategies on a second training attempt.
Title: High Water Pants: Designing Embodied Environmental Speculation
Authors: family: Biggs
given: Heidi R.; family: Desjardins
given: Audrey
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: In this paper, we present the High Water Pants: speculative wearable technology which makes climate change tangible for everyday cyclists. The pants work by mechanically shortening when a cyclist wearing the pants enters an area of Seattle, USA, which is projected to be impacted by sea-level rise in 30-80 years. This interaction 'bends time' by allowing cyclists to feel future climate change data in the present. First, we discuss the research through design process of creating the High Water Pants including foundational research, a description of the design concept and results of a preliminary study with the pants. Second, we discuss three implications of the pants for human-computer interaction (HCI): (1) they offer the concept of a 'present/future' paradigm for embodied speculation, (2) our research process demonstrates how to successfully involve more-than-human perspectives, and (3) we articulate how the High Water Pants respond to shifts in HCI's framing of sustainability.
Title: The Designer's Body as Resource in Design: Exploring Combinations of Point-of-view and Tense
Authors: family: Svanæs
given: Dag; family: Barkhuus
given: Louise
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: The design of wearable, tangible and embedded interactive products requires a focus on bodily/kinesthetic aspects of the user experience, that is, how the product "feels" in use. Although best practice in user-centered design (such as iterative design, prototyping, user testing) also applies for this new type of product, the designer's skill set needs to be supplemented with design methods and practices that utilize bodily intelligence and empathy with the user. We present a framework for categorizing such body-centered design practices based on two dimensions: point-of-view (1st, 2nd, 3rd person) and tense (past, present, future). Inspired by Merleau-Ponty's phenomenology of the body, Shusterman's work on somaesthetics, and Buber's theories on intersubjectivity, the framework provides a language for talking about different ways designers and co-designers can utilize their body as a design resource. The intention is not to be prescriptive on method, but to provide guidance during planning, execution and analysis.
Title: Approach Matters: Linking Practitioner Approaches to Technology Design for People with Dementia
Authors: family: Dixon
given: Emma; family: Lazar
given: Amanda
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Technology design for dementia is an active and growing area. Though work to date has largely addressed functional needs, there is a growing recognition of the importance of supporting meaningful activities. However, technology for active, rather than passive, engagement is relatively novel beyond specific applications (e.g., music or reminiscence therapy). To better understand how to support active engagement of people with dementia in activities, we interviewed nineteen practitioners. Our findings reveal differing approaches to making sense of the actions of people with dementia, as well as to engaging them in activities. We discuss the importance of tracing epistemological understandings of dementia to different configurations of technology for people living with dementia and provide a practical guide to support designers to do so. Finally, we discuss considerations for the design of dementia technologies around facilitating self-actualization and managing emotional exposure for care-providers.
Title: Designing and Evaluating 'In the Same Boat', A Game of Embodied Synchronization for Enhancing Social Play
Authors: family: Robinson
given: Raquel Breejon; family: Reid
given: Elizabeth; family: Fey
given: James Collin; family: Depping
given: Ansgar E.; family: Isbister
given: Katherine; family: Mandryk
given: Regan L.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Social closeness is important for health and well-being, but is difficult to maintain over a distance. Games can help connect people by strengthening existing relationships or creating new ones through shared playful experiences. We present the design and evaluation of 'In the Same Boat' (ITSB), a two-player infinite runner designed to foster social closeness in distributed dyads. ITSB leverages the synchronization of both players' input to steer a canoe down a river and avoid obstacles. We created two versions: embodied controls, which use players' physiological signals (breath rate, facial expressions), and standard keyboard controls. Results from a study with 35 dyads indicate that ITSB fostered affiliation, and while embodied controls were less intuitive, people enjoyed them more. Further, photos of the dyads were rated as happier and closer in the embodied condition, indicating the potential of embodied controls to foster social closeness in synchronized play over a distance.
Title: Camera Adversaria
Authors: family: Browne
given: Kieran; family: Swift
given: Ben; family: Nurmikko-Fuller
given: Terhi
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: In this paper we introduce Camera Adversaria; a mobile app designed to disrupt the automatic surveillance of personal photographs by technology companies. The app leverages the brittleness of deep neural networks with respect to high-frequency signals, adding generative adversarial perturbations to users' photographs. These perturbations confound image classification systems but are virtually imperceptible to human viewers. Camera Adversaria builds on methods developed by machine learning researchers as well as a growing body of work, primarily from art and design, which transgresses contemporary surveillance systems. We map the design space of responses to surveillance and identify an under-explored region where our project is situated. Finally we show that the language typically used in the adversarial perturbation literature serves to affirm corporate surveillance practices and malign resistance. This raises significant questions about the function of the research community in countenancing systems of surveillance.
Title: How Well Do People Report Time Spent on Facebook? An Evaluation of Established Survey Questions with Recommendations
Authors: family: Ernala
given: Sindhu Kiranmai; family: Burke
given: Moira; family: Leavitt
given: Alex; family: Ellison
given: Nicole B.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Many studies examining social media use rely on self-report survey questions about how much time participants spend on social media platforms. Because they are challenging to answer accurately and susceptible to various biases, these self-reported measures are known to contain error -- although the specific contours of this error are not well understood. This paper compares data from ten self-reported Facebook use survey measures deployed in 15 countries (N = 49,934) against data from Facebook's server logs to describe factors associated with error in commonly used survey items from the literature. Self-reports were moderately correlated with actual Facebook use (r = 0.42 for the best-performing question), though participants significantly overestimated how much time they spent on Facebook and underestimated the number of times they visited. People who spent a lot of time on the platform were more likely to misreport their time, as were teens and younger adults, which is notable because of the high reliance on college-aged samples in many fields. We conclude with recommendations on the most accurate ways to collect time-spent data via surveys.
Title: Augmenting Static Visualizations with PapARVis Designer
Authors: family: Chen
given: Zhutian; family: Tong
given: Wai; family: Wang
given: Qianwen; family: Bach
given: Benjamin; family: Qu
given: Huamin
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: This paper presents an authoring environment for augmenting static visualizations with virtual content in augmented reality.Augmenting static visualizations can leverage the best of both physical and digital worlds, but its creation currently involves different tools and devices, without any means to explicitly design and debug both static and virtual content simultaneously. To address these issues, we design an environment that seamlessly integrates all steps of a design and deployment workflow through its main features: i) an extension to Vega, ii) a preview, and iii) debug hints that facilitate valid combinations of static and augmented content. We inform our design through a design space with four ways to augment static visualizations. We demonstrate the expressiveness of our tool through examples, including books, posters, projections, wall-sized visualizations. A user study shows high user satisfaction of our environment and confirms that participants can create augmented visualizations in an average of 4.63 minutes.
Title: Temporal Segmentation of Creative Live Streams
Authors: family: Fraser
given: C. Ailie; family: Kim
given: Joy O.; family: Shin
given: Hijung Valentina; family: Brandt
given: Joel; family: Dontcheva
given: Mira
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Many artists broadcast their creative process through live streaming platforms like Twitch and YouTube, and people often watch archives of these broadcasts later for learning and inspiration. Unfortunately, because live stream videos are often multiple hours long and hard to skim and browse, few can leverage the wealth of knowledge hidden in these archives. We present an approach for automatic temporal segmentation of creative live stream videos. Using an audio transcript and a log of software usage, the system segments the video into sections that the artist can optionally label with meaningful titles. We evaluate this approach by gathering feedback from expert streamers and comparing automatic segmentations to those made by viewers. We find that, while there is no one "correct" way to segment a live stream, our automatic method performs similarly to viewers, and streamers find it useful for navigating their streams after making slight adjustments and adding section titles.
Title: Outline Pursuits: Gaze-assisted Selection of Occluded Objects in Virtual Reality
Authors: family: Sidenmark
given: Ludwig; family: Clarke
given: Christopher; family: Zhang
given: Xuesong; family: Phu
given: Jenny; family: Gellersen
given: Hans
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: In 3D environments, objects can be difficult to select when they overlap, as this affects available target area and increases selection ambiguity. We introduce Outline Pursuits which extends a primary pointing modality for gaze-assisted selection of occluded objects. Candidate targets within a pointing cone are presented with an outline that is traversed by a moving stimulus. This affords completion of the selection by gaze attention to the intended target's outline motion, detected by matching the user's smooth pursuit eye movement. We demonstrate two techniques implemented based on the concept, one with a controller as the primary pointer, and one in which Outline Pursuits are combined with head pointing for hands-free selection. Compared with conventional raycasting, the techniques require less movement for selection as users do not need to reposition themselves for a better line of sight, and selection time and accuracy are less affected when targets become highly occluded.
Title: Engaging the Commons in Participatory Sensing: Practice, Problems, and Promise in the Context of Dockless Bikesharing
Authors: family: Gao
given: Ge; family: Sun
given: Yuling; family: Zhang
given: Yongle
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Participatory sensing refers to the sensing paradigm where human participants use personal mobile devices to generate and share data from their surroundings. It holds the promise of providing information that is otherwise challenging to access, which sets the stage for understanding and resolving various social issues. However, difficulties in engaging participants often hinder the fulfillment of this promise. The current paper presents a qualitative study in the context of dockless bikesharing, where participatory sensing constitutes a backbone of the bike status monitoring system. We conducted in-depth interviews with 30 participants. These participants came from different emergent groups who took part in filing status reports for shared bikes. Our analysis indicated close associations among participants' models of engagement, their perceived (dis)connections with the sensing data, and their situated interpretation of the incentives. Based on these findings, we propose ways to engage the commons in participatory sensing for dockless bikesharing and beyond.
Title: Evaluating the End-User Experience of Private Browsing Mode
Authors: family: Abu-Salma
given: Ruba; family: Livshits
given: Benjamin
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: In this paper, we investigate why users of private browsing mode misunderstand the benefits and limitations of private browsing. We design and conduct a three-part study: (1) an analytic evaluation of the user interface of private mode in different browsers; (2) a qualitative user study to explore user mental models of private browsing; (3) a participatory design study to investigate why existing browser disclosures, the in-browser explanations of private mode, do not communicate the actual protection of private mode. We find the user interface of private mode in different browsers violated well-established design guidelines and heuristics. Further, most participants had incorrect mental models of private browsing, influencing their understanding and usage of private mode. We also find existing browser disclosures did not explain the primary security goal of private mode. Drawing from the results of our study, we extract a set of recommendations to improve the design of disclosures.
Title: PalmBoard: Leveraging Implicit Touch Pressure in Statistical Decoding for Indirect Text Entry
Authors: family: Yi
given: Xin; family: Wang
given: Chen; family: Bi
given: Xiaojun; family: Shi
given: Yuanchun
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: We investigated how to incorporate implicit touch pressure, finger pressure applied to a touch surface during typing, to improve text entry performance via statistical decoding. We focused on one-handed touch-typing on indirect interface as an example scenario. We first collected typing data on a pressure-sensitive touchpad, and analyzed users' typing behavior such as touch point distribution, key-to-finger mappings, and pressure images. Our investigation revealed distinct pressure patterns for different keys. Based on the findings, we performed a series of simulations to iteratively optimize the statistical decoding algorithm. Our investigation led to a Markov-Bayesian decoder incorporating pressure image data into decoding. It improved the top-1 accuracy from 53% to 74% over a naive Bayesian decoder. We then implemented PalmBoard, a text entry method that implemented the Markov-Bayesian decoder and effectively supported one-handed touch-typing on indirect interfaces. A user study showed participants achieved an average speed of 32.8 WPM with 0.6% error rate. Expert typists could achieve 40.2 WPM with 30 minutes of practice. Overall, our investigation showed that incorporating implicit touch pressure is effective in improving text entry decoding.
Title: Wrex: A Unified Programming-by-Example Interaction for Synthesizing Readable Code for Data Scientists
Authors: family: Drosos
given: Ian; family: Barik
given: Titus; family: Guo
given: Philip J.; family: DeLine
given: Robert; family: Gulwani
given: Sumit
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Data wrangling is a difficult and time-consuming activity in computational notebooks, and existing wrangling tools do not fit the exploratory workflow for data scientists in these environments. We propose a unified interaction model based on programming-by-example that generates readable code for a variety of useful data transformations, implemented as a Jupyter notebook extension called Wrex. User study results demonstrate that data scientists are significantly more effective and efficient at data wrangling with Wrex over manual programming. Qualitative participant feedback indicates that Wrex was useful and reduced barriers in having to recall or look up the usage of various data transform functions. The synthesized code allowed data scientists to verify the intended data transformation, increased their trust and confidence in Wrex, and fit seamlessly within their cell-based notebook workflows. This work suggests that presenting readable code to professional data scientists is an indispensable component of offering data wrangling tools in notebooks.
Title: Automatic Annotation Synchronizing with Textual Description for Visualization
Authors: family: Lai
given: Chufan; family: Lin
given: Zhixian; family: Jiang
given: Ruike; family: Han
given: Yun; family: Liu
given: Can; family: Yuan
given: Xiaoru
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: In this paper, we propose a technique for automatically annotating visualizations according to the textual description. In our approach, visual elements in the target visualization, along with their visual properties, are identified and extracted with a Mask R-CNN model. Meanwhile, the description is parsed to generate visual search requests. Based on the identification results and search requests, each descriptive sentence is displayed beside the described focal areas as annotations. Different sentences are presented in various scenes of the generated animation to promote a vivid step-by-step presentation. With a user-customized style, the animation can guide the audience's attention via proper highlighting such as emphasizing specific features or isolating part of the data. We demonstrate the utility and usability of our method through a user study with use cases.