huashen218/convxai-cia-dataset huggingface dataset:
Title: ConceptScope: Organizing and Visualizing Knowledge in Documents based on Domain Ontology
Authors: family: Zhang
given: Xiaoyu; family: Chandrasegaran
given: Senthil; family: Ma
given: Kwan-Liu
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Visualization, Ontology, Knowledge Representation

Abstract: Current text visualization techniques typically provide overviews of document content and structure using intrinsic properties such as term frequencies, co-occurrences, and sentence structures. Such visualizations lack conceptual overviews incorporating domain-relevant knowledge, needed when examining documents such as research articles or technical reports. To address this shortcoming, we present ConceptScope, a technique that utilizes a domain ontology to represent the conceptual relationships in a document in the form of a Bubble Treemap visualization. Multiple coordinated views of document structure and concept hierarchy with text overviews further aid document analysis. ConceptScope facilitates exploration and comparison of single and multiple documents respectively. We demonstrate ConceptScope by visualizing research articles and transcripts of technical presentations in computer science. In a comparative study with DocuBurst, a popular document visualization tool, ConceptScope was found to be more informative in exploring and comparing domain-specific documents, but less so when it came to documents that spanned multiple disciplines.
Title: Tessera: Discretizing Data Analysis Workflows on a Task Level
Authors: family: Yan
given: Jing Nathan; family: Gu
given: Ziwei; family: Rzeszotarski
given: Jeffrey M
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Interaction Log Analysis, Visualization, Data Analytics

Abstract: Researchers have investigated a number of strategies for capturing and analyzing data analyst event logs in order to design better tools, identify failure points, and guide users. However, this remains challenging because individual- and session-level behavioral differences lead to an explosion of complexity and there are few guarantees that log observations map to user cognition. In this paper we introduce a technique for segmenting sequential analyst event logs which combines data, interaction, and user features in order to create discrete blocks of goal-directed activity. Using measures of inter-dependency and comparisons between analysis states, these blocks identify patterns in interaction logs coupled with the current view that users are examining. Through an analysis of publicly available data and data from a lab study across a variety of analysis tasks, we validate that our segmentation approach aligns with users’ changing goals and tasks. Finally, we identify several downstream applications for our approach.
Title: Modeling and Leveraging Analytic Focus During Exploratory Visual Analysis
Authors: family: Zhou
given: Zhilan; family: Wen
given: Ximing; family: Wang
given: Yue; family: Gotz
given: David
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: User Modeling, Insight Provenance, Visual Analytics, Analytic Focus

Abstract: Visual analytics systems enable highly interactive exploratory data analysis. Across a range of fields, these technologies have been successfully employed to help users learn from complex data. However, these same exploratory visualization techniques make it easy for users to discover spurious findings. This paper proposes new methods to monitor a user’s analytic focus during visual analysis of structured datasets and use it to surface relevant articles that contextualize the visualized findings. Motivated by interactive analyses of electronic health data, this paper introduces a formal model of analytic focus, a computational approach to dynamically update the focus model at the time of user interaction, and a prototype application that leverages this model to surface relevant medical publications to users during visual analysis of a large corpus of medical records. Evaluation results with 24 users show that the modeling approach has high levels of accuracy and is able to surface highly relevant medical abstracts.
Title: Showing Academic Performance Predictions during Term Planning: Effects on Students’ Decisions, Behaviors, and Preferences
Authors: family: Mendez
given: Gonzalo; family: Galárraga
given: Luis; family: Chiluiza
given: Katherine
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: framing effects, academic performance predictions, course recommendation., course selection, Visual learning analytics

Abstract: Course selection is a crucial activity for students as it directly impacts their workload and performance. It is also time-consuming, prone to subjectivity, and often carried out based on incomplete information. This task can, nevertheless, be assisted with computational tools, for instance, by predicting performance based on historical data. We investigate the effects of showing grade predictions to students through an interactive visualization tool. A qualitative study suggests that in the presence of predictions, students may focus too much on maximizing their performance, to the detriment of other factors such as the workload. A follow-up quantitative study explored whether these effects are mitigated by changing how predictions are conveyed. Our observations suggest the presence of a framing effect that induces students to put more effort into course selection when faced with more specific predictions. We discuss these and other findings and outline considerations for designing better data-driven course selection tools.
Title: mTSeer: Interactive Visual Exploration of Models on Multivariate Time-series Forecast
Authors: family: Xu
given: Ke; family: Yuan
given: Jun; family: Wang
given: Yifang; family: Silva
given: Claudio; family: Bertini
given: Enrico
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: visualization, model evaluation, multivariate time series, feature extraction, machine learning, forecasting

Abstract: Time-series forecasting contributes crucial information to industrial and institutional decision-making with multivariate time-series input. Although various models have been developed to facilitate the forecasting process, they make inconsistent forecasts. Thus, it is critical to select the model appropriately. The existing selection methods based on the error measures fail to reveal deep insights into the model’s performance, such as the identification of salient features and the impact of temporal factors (e.g., periods). This paper introduces mTSeer, an interactive system for the exploration, explanation, and evaluation of multivariate time-series forecasting models. Our system integrates a set of algorithms to steer the process, and rich interactions and visualization designs to help interpret the differences between models in both model and instance level. We demonstrate the effectiveness of mTSeer through three case studies with two domain experts on real-world data, qualitative interviews with the two experts, and quantitative evaluation of the three case studies.
Title: CAST: Authoring Data-Driven Chart Animations
Authors: family: Ge
given: Tong; family: Lee
given: Bongshin; family: Wang
given: Yunhai
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: chart animation authoring, Chart animation, data visualization, interactive system, chart animation specification

Abstract: We present CAST, an authoring tool that enables the interactive creation of chart animations. It introduces the visual specification of chart animations consisting of keyframes that can be played sequentially or simultaneously, and animation parameters (e.g., duration, delay). Building on Canis [19], a declarative chart animation grammar that leverages data-enriched SVG charts, CAST supports auto-completion for constructing both keyframes and keyframe sequences. It also enables users to refine the animation specification (e.g., aligning keyframes across tracks to play them together, adjusting delay) with direct manipulation and other parameters for animation effects (e.g., animation type, easing function) using a control panel. In addition to describing how CAST infers recommendations for auto-completion, we present a gallery of examples to demonstrate the expressiveness of CAST and a user study to verify its learnability and usability. Finally, we discuss the limitations and potentials of CAST as well as directions for future research.
Title: IGScript: An Interaction Grammar for Scientific Data Presentation
Authors: family: Liu
given: Richen; family: Gao
given: Min; family: Ye
given: Shunlong; family: Zhang
given: Jiang
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: scientific visualization, data presentation, interaction grammar

Abstract: Most of the existing scientific visualizations toward interpretive grammar aim to enhance customizability in either the computation stage or the rendering stage or both, while few approaches focus on the data presentation stage. Besides, most of these approaches leverage the existing components from the general-purpose programming languages (GPLs) instead of developing a standalone compiler, which pose a great challenge about learning curves for the domain experts who have limited knowledge about programming. In this paper, we propose IGScript, a novel script-based interaction grammar tool, to help build scientific data presentation animations for communication. We design a dual-space interface and a compiler which converts natural language-like grammar statements or scripts into a data story animation to make an interactive customization on script-driven data presentations, and then develop a code generator (decompiler) to translate the interactive data exploration animations back into script codes to achieve statement parameters. IGScript makes the presentation animations editable, e.g., it allows to cut, copy, paste, append, or even delete some animation clips. We demonstrate the usability, customizability, and flexibility of IGScript by a user study, four case studies conducted by using four types of commonly-used scientific data, and performance evaluations.
Title: Tactile Fixations: A Behavioral Marker on How People with Visual Impairments Explore Raised-line Graphics
Authors: family: Zhao
given: Kaixing; family: Bardot
given: Sandra; family: Serrano
given: Marcos; family: Simonnet
given: Mathieu; family: Oriola
given: Bernard; family: Jouffrais
given: Christophe
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Raised-line graphic, Visual impairment, Tactile Fixations

Abstract: Raised-line graphics are tactile documents made for people with visual impairments (VI). Their exploration relies on a complex two-handed behavior. To better understand the cognitive processes underlying this exploration, we proposed a new method based on “tactile fixations”. A tactile fixation occurs when a finger is stationary within a specific spatial and temporal window. It is known that stationary fingers play an active role when exploring tactile graphics, but they have never been defined or studied before. In this study, we first defined the concept of tactile fixation, then we conducted a behavioral study with ten participants with VI in order to assess the role of tactile fixations under different conditions. The results show that tactile fixations vary according to different factors such as the graphic type as well as the involved hand and the aim of the exploration.
Title: Tactile Compass: Enabling Visually Impaired People to Follow a Path with Continuous Directional Feedback
Authors: family: Liu
given: Guanhong; family: Yu
given: Tianyu; family: Yu
given: Chun; family: Xu
given: Haiqing; family: Xu
given: Shuchang; family: Yang
given: Ciyuan; family: Wang
given: Feng; family: Mi
given: Haipeng; family: Shi
given: Yuanchun
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: tactile directional feedback, path-following tasks, accessibility

Abstract: Accurate and effective directional feedback is crucial for an electronic traveling aid device that guides visually impaired people in walking through paths. This paper presents Tactile Compass, a hand-held device that provides continuous directional feedback with a rotatable needle pointing toward the planned direction. We conducted two lab studies to evaluate the effectiveness of the feedback solution. Results showed that, using Tactile Compass, participants could reach the target direction in place with a mean deviation of 3.03° and could smoothly navigate along paths of 60cm width, with a mean deviation from the centerline of 12.1cm. Subjective feedback showed that Tactile Compass was easy to learn and use.
Title: ThroughHand: 2D Tactile Interaction to Simultaneously Recognize and Touch Multiple Objects
Authors: family: Jung
given: Jingun; family: Son
given: Sunmin; family: Lee
given: Sangyoon; family: Kim
given: Yeonsu; family: Lee
given: Geehyuk
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Real-time interaction, Haptics, Accessibility, Shape-changing display, Visual impairments, Games

Abstract: Users with visual impairments find it difficult to enjoy real-time 2D interactive applications on the touchscreen. Touchscreen applications such as sports games often require simultaneous recognition of and interaction with multiple moving targets through vision. To mitigate this issue, we propose ThroughHand, a novel tactile interaction that enables users with visual impairments to interact with multiple dynamic objects in real time. We designed the ThroughHand interaction to utilize the potential of the human tactile sense that spatially registers both sides of the hand with respect to each other. ThroughHand allows interaction with multiple objects by enabling users to perceive the objects using the palm while providing a touch input space on the back of the same hand. A user study verified that ThroughHand enables users to locate stimuli on the palm with a margin of error of approximately 13 mm and effectively provides a real-time 2D interaction experience for users with visual impairments.
Title: Exploring Technology Design for Students with Vision Impairment in the Classroom and Remotely
Authors: family: Gadiraju
given: Vinitha; family: Doyle
given: Olwyn; family: Kane
given: Shaun
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: visually impaired, blind, classroom, children, teachers, Accessibility

Abstract: Teachers of the Visually Impaired (TVIs) teach academic and functional living skills simultaneously to prepare students with vision impairment to be successful and independent. Current educational tools primarily focus on academic instruction rather than this multifaceted approach needed for students. Our work aims to understand how technology can integrate behavioral skills, like independence, and support TVIs in their preferred teaching strategy. We observed elementary classrooms at a school for the blind for six weeks to study how educators design lessons and use technology to supplement their instruction in different subjects. After the observational study, we conducted remote interviews with educators to understand how technology can support students in building academic and behavioral skills in-person and remotely. Educators suggested incorporating audio feedback that motivates students to play and learn consistently, student progress tracking for parents and educators, and designing features that help students build independence and develop collaborative skills.
Title: Community Based Robot Design for Classrooms with Mixed Visual Abilities Children
Authors: family: Neto
given: Isabel; family: Nicolau
given: Hugo; family: Paiva
given: Ana
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: visual impairment, social robots, inclusion, participatory design

Abstract: Visually impaired children (VI) face challenges in collaborative learning in classrooms. Robots have the potential to support inclusive classroom experiences by leveraging their physicality, bespoke social behaviors, sensors, and multimodal feedback. However, the design of social robots for mixed-visual abilities classrooms remains mostly unexplored. This paper presents a four-month-long community-based design process where we engaged with a school community. We provide insights into the barriers experienced by children and how social robots can address them. We also report on a participatory design activity with mixed-visual abilities children, highlighting the expected roles, attitudes, and physical characteristics of robots. Findings contextualize social robots within inclusive classroom settings as a holistic solution that can interact anywhere when needed and suggest a broader view of inclusion beyond disability. These include children’s personality traits, technology access, and mastery of school subjects. We finish by providing reflections on the community-based design process.
Title: LightWrite: Teach Handwriting to The Visually Impaired with A Smartphone
Authors: family: Wu
given: Zihan; family: Yu
given: Chun; family: Xu
given: Xuhai; family: Wei
given: Tong; family: Zou
given: Tianyuan; family: Wang
given: Ruolin; family: Shi
given: Yuanchun
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Handwriting Learning, Accessibility, Blind and Visually-Impaired Users

Abstract: Learning to write is challenging for blind and low vision (BLV) people because of the lack of visual feedback. Regardless of the drastic advancement of digital technology, handwriting is still an essential part of daily life. Although tools designed for teaching BLV to write exist, many are expensive and require the help of sighted teachers. We propose LightWrite, a low-cost, easy-to-access smartphone application that uses voice-based descriptive instruction and feedback to teach BLV users to write English lowercase letters and Arabian digits in a specifically designed font. A two-stage study with 15 BLV users with little prior writing knowledge shows that LightWrite can successfully teach users to learn handwriting characters in an average of 1.09 minutes for each letter. After initial training and 20-minute daily practice for 5 days, participants were able to write an average of 19.9 out of 26 letters that are recognizable by sighted raters.
Title: LineChaser: A Smartphone-Based Navigation System for Blind People to Stand in Lines
Authors: family: Kuribayashi
given: Masaki; family: Kayukawa
given: Seita; family: Takagi
given: Hironobu; family: Asakawa
given: Chieko; family: Morishima
given: Shigeo
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: visual impairment, orientation and mobility, pedestrian detection, line detection

Abstract: Standing in line is one of the most common social behaviors in public spaces but can be challenging for blind people. We propose an assistive system named LineChaser, which navigates a blind user to the end of a line and continuously reports the distance and direction to the last person in the line so that they can be followed. LineChaser uses the RGB camera in a smartphone to detect nearby pedestrians, and the built-in infrared depth sensor to estimate their position. Via pedestrian position estimations, LineChaser determines whether nearby pedestrians are standing in line, and uses audio and vibration signals to notify the user when they should start/stop moving forward. In this way, users can stay correctly positioned while maintaining social distance. We have conducted a usability study with 12 blind participants. LineChaser allowed blind participants to successfully navigate lines, significantly increasing their confidence in standing in lines.
Title: Smartphone Usage by Expert Blind Users
Authors: family: Jain
given: Mohit; family: Diwakar
given: Nirmalendu; family: Swaminathan
given: Manohar
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: TalkBack, touchscreen, expert users., usage pattern, smartphone, blind users, text entry, security, accessibility, app usage, gestures, vision impairment, TTS

Abstract: People with vision impairments access smartphones with the help of screen reader apps such as TalkBack for Android and VoiceOver for iPhone. Prior research has mostly focused on understanding touchscreen phone adoption and typing performance of novice blind users by logging their real-world smartphone usage. Understanding smartphone usage pattern and practices of expert users can help in developing tools and tutorials for transitioning novice and intermediate users to expert users. In this work, we logged smartphone usage data of eight expert Android smartphone users with visual impairments for four weeks, and then interviewed them. This paper presents a detailed analysis that uncovered novel usage patterns, such as extensive usage of directional gestures, reliance on voice and external keyboard for text input, and repurposed explore by touch for single-tap. We conclude with design recommendations to inform the future of mobile accessibility, including hardware guidelines and rethinking accessible software design.
Title: Examining Visual Semantic Understanding in Blind and Low-Vision Technology Users
Authors: family: Potluri
given: Venkatesh; family: Grindeland
given: Tadashi E; family: Froehlich
given: Jon E.; family: Mankoff
given: Jennifer
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Accessibility, blind and low-vision creators, visual design

Abstract: Visual semantics provide spatial information like size, shape, and position, which are necessary to understand and efficiently use interfaces and documents. Yet little is known about whether blind and low-vision (BLV) technology users want to interact with visual affordances, and, if so, for which task scenarios. In this work, through semi-structured and task-based interviews, we explore preferences, interest levels, and use of visual semantics among BLV technology users across two device platforms (smartphones and laptops), and information seeking and interactions common in apps and web browsing. Findings show that participants could benefit from access to visual semantics for collaboration, navigation, and design. To learn this information, our participants used trial and error, sighted assistance, and features in existing screen reading technology like touch exploration. Finally, we found that missing information and inconsistent screen reader representations of user interfaces hinder learning. We discuss potential applications and future work to equip BLV users with necessary information to engage with visual semantics.