huashen218/convxai-cia-dataset huggingface dataset:
Title: NBSearch: Semantic Search and Visual Exploration of Computational Notebooks
Authors: family: Li
given: Xingjun; family: Wang
given: Yuanxin; family: Wang
given: Hong; family: Wang
given: Yang; family: Zhao
given: Jian
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: document and text analysis., computational notebooks, search result visualization, Semantic code search

Abstract: Code search is an important and frequent activity for developers using computational notebooks (e.g., Jupyter). The flexibility of notebooks brings challenges for effective code search, where classic search interfaces for traditional software code may be limited. In this paper, we propose, NBSearch, a novel system that supports semantic code search in notebook collections and interactive visual exploration of search results. NBSearch leverages advanced machine learning models to enable natural language search queries and intuitive visualizations to present complicated intra- and inter-notebook relationships in the returned results. We developed NBSearch through an iterative participatory design process with two experts from a large software company. We evaluated the models with a series of experiments and the whole system with a controlled user study. The results indicate the feasibility of our analytical pipeline and the effectiveness of NBSearch to support code search in large notebook collections.
Title: Glinda: Supporting Data Science with Live Programming, GUIs and a Domain-specific Language
Authors: family: DeLine
given: Robert A
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: live programming, data science, domain-specific language, exploratory programming

Abstract: Researchers have explored several avenues to mitigate data scientists’ frustrations with computational notebooks, including: (1) live programming, to keep notebook results consistent and up to date; (2) supplementing scripting with graphical user interfaces (GUIs), to improve ease of use; and (3) providing domain-specific languages (DSLs), to raise a script’s level of abstraction. This paper introduces Glinda, which combines these three approaches by providing a live programming experience, with interactive results, for a domain-specific language for data science. The language’s compiler uses an open-ended set of “recipes” to execute steps in the user’s data science workflow. Each recipe is intended to combine the expressiveness of a written notation with the ease-of-use of a GUI. Live programming provides immediate feedback to a user’s input, whether in the form of program edits or GUI gestures. In a qualitative evaluation with 12 professional data scientists, participants highly rated the live programming and interactive results. They found the language productive and sufficiently expressive and suggested opportunities to extend it.
Title: UMLAUT: Debugging Deep Learning Programs using Program Structure and Model Behavior
Authors: family: Schoop
given: Eldon; family: Huang
given: Forrest; family: Hartmann
given: Bjoern
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: End-User ML, ML Debugging, ML Development

Abstract: Training deep neural networks can generate non-descriptive error messages or produce unusual output without any explicit errors at all. While experts rely on tacit knowledge to apply debugging strategies, non-experts lack the experience required to interpret model output and correct Deep Learning (DL) programs. In this work, we identify DL debugging heuristics and strategies used by experts, andIn this work, we categorize the types of errors novices run into when writing ML code, and map them onto opportunities where tools could help. We use them to guide the design of Umlaut. Umlaut checks DL program structure and model behavior against these heuristics; provides human-readable error messages to users; and annotates erroneous model output to facilitate error correction. Umlaut links code, model output, and tutorial-driven error messages in a single interface. We evaluated Umlaut in a study with 15 participants to determine its effectiveness in helping developers find and fix errors in their DL programs. Participants using Umlaut found and fixed significantly more bugs and were able to implement fixes for more bugs compared to a baseline condition.
Title: TweakIt: Supporting End-User Programmers Who Transmogrify Code
Authors: family: Lau
given: Sam; family: Srinivasa Ragavan
given: Sruti Srinivasa; family: Milne
given: Ken; family: Barik
given: Titus; family: Sarkar
given: Advait
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: data analysts, live programming, data workflows, opportunistic code reuse, End-user programming

Abstract: End-user programmers opportunistically copy-and-paste code snippets from colleagues or the web to accomplish their tasks. Unfortunately, these snippets often don’t work verbatim, so these people—who are non-specialists in the programming language—make guesses and tweak the code to understand and apply it successfully. To support their desired workflow and facilitate tweaking and understanding, we built a prototype tool, TweakIt, that provides users with a familiar live interaction to help them understand, introspect, and reify how different code snippets would transform their data. Through a usability study with 14 data analysts, participants found the tool to be useful to understand the function of otherwise unfamiliar code, to increase their confidence about what the code does, to identify relevant parts of code specific to their task, and to proactively explore and evaluate code. Overall, our participants were enthusiastic about incorporating TweakIt in their own day-to-day work.
Title: Understanding Trigger-Action Programs Through Novel Visualizations of Program Differences
Authors: family: Zhao
given: Valerie; family: Zhang
given: Lefan; family: Wang
given: Bo; family: Littman
given: Michael L.; family: Lu
given: Shan; family: Ur
given: Blase
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: trigger-action programs, end-user programming, diffs, debugging

Abstract: Trigger-action programming (if-this-then-that rules) empowers non-technical users to automate services and smart devices. As a user’s set of trigger-action programs evolves, the user must reason about behavior differences between similar programs, such as between an original program and several modification candidates, to select programs that meet their goals. To facilitate this process, we co-designed user interfaces and underlying algorithms to highlight differences between trigger-action programs. Our novel approaches leverage formal methods to efficiently identify and visualize differences in program outcomes or abstract properties. We also implemented a traditional interface that shows only syntax differences in the rules themselves. In a between-subjects online experiment with 107 participants, the novel interfaces better enabled participants to select trigger-action programs matching intended goals in complex, yet realistic, situations that proved very difficult when using traditional interfaces showing syntax differences.
Title: Visualizing Examples of Deep Neural Networks at Scale
Authors: family: Yan
given: Litao; family: Glassman
given: Elena L.; family: Zhang
given: Tianyi
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: deep neural networks, code examples, Visualization

Abstract: Many programmers want to use deep learning due to its superior accuracy in many challenging domains. Yet our formative study with ten programmers indicated that, when constructing their own deep neural networks (DNNs), they often had a difficult time choosing appropriate model structures and hyperparameter values. This paper presents ExampleNet—a novel interactive visualization system for exploring common and uncommon design choices in a large collection of open-source DNN projects. ExampleNet provides a holistic view of the distribution over model structures and hyperparameter settings in the corpus of DNNs, so users can easily filter the corpus down to projects tackling similar tasks and compare design choices made by others. We evaluated ExampleNet in a within-subjects study with sixteen participants. Compared with the control condition (i.e., online search), participants using ExampleNet were able to inspect more online examples, make more data-driven design decisions, and make fewer design mistakes.
Title: What We Can Learn From Visual Artists About Software Development
Authors: family: Li
given: Jingyi; family: Hashim
given: Sonia; family: Jacobs
given: Jennifer
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: visual art, software development, creativity support tools

Abstract: This paper explores software’s role in visual art production by examining how artists use and develop software. We conducted interviews with professional artists who were collaborating with software developers, learning software development, and building and maintaining software. We found artists were motivated to learn software development for intellectual growth and access to technical communities. Artists valued efficient workflows through skilled manual execution and personal software development, but avoided high-level forms of software automation. Artists identified conflicts between their priorities and those of professional developers and computational art communities, which influenced how they used computational aesthetics in their work. These findings contribute to efforts in systems engineering research to integrate end-user programming and creativity support across software and physical media, suggesting opportunities for artists as collaborators. Artists’ experiences writing software can guide technical implementations of domain-specific representations, and their experiences in interdisciplinary production can aid inclusive community building around computational tools.
Title: Spacewalker: Rapid UI Design Exploration Using Lightweight Markup Enhancement and Crowd Genetic Programming
Authors: family: Zhong
given: Mingyuan; family: Li
given: Gang; family: Li
given: Yang
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: genetic programming, Markup language, crowdsourcing, design search, tools

Abstract: User interface design is a complex task that involves designers examining a wide range of options. We present Spacewalker, a tool that allows designers to rapidly search a large design space for an optimal web UI with integrated support. Designers first annotate each attribute they want to explore in a typical HTML page, using a simple markup extension we designed. Spacewalker then parses the annotated HTML specification, and intelligently generates and distributes various configurations of the web UI to crowd workers for evaluation. We enhanced a genetic algorithm to accommodate crowd worker responses from pairwise comparison of UI designs, which is crucial for obtaining reliable feedback. Based on our experiments, Spacewalker allows designers to effectively search a large design space of a UI, using the language they are familiar with, and improve their design rapidly at a minimal cost.
Title: ReverseORC: Reverse Engineering of Resizable User Interface Layouts with OR-Constraints
Authors: family: Jiang
given: Yue; family: Stuerzlinger
given: Wolfgang; family: Lutteroth
given: Christof
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: resizable user interface, adaptive user interface, constraint-based layout, ORC Layout, reverse engineering

Abstract: Reverse engineering (RE) of user interfaces (UIs) plays an important role in software evolution. However, the large diversity of UI technologies and the need for UIs to be resizable make this challenging. We propose ReverseORC, a novel RE approach able to discover diverse layout types and their dynamic resizing behaviours independently of their implementation, and to specify them by using OR constraints. Unlike previous RE approaches, ReverseORC infers flexible layout constraint specifications by sampling UIs at different sizes and analyzing the differences between them. It can create specifications that replicate even some non-standard layout managers with complex dynamic layout behaviours. We demonstrate that ReverseORC works across different platforms with very different layout approaches, e.g., for GUIs as well as for the Web. Furthermore, it can be used to detect and fix problems in legacy UIs, extend UIs with enhanced layout behaviours, and support the creation of flexible UI layouts.
Title: GestureMap: Supporting Visual Analytics and Quantitative Analysis of Motion Elicitation Data by Learning 2D Embeddings
Authors: family: Dang
given: Hai; family: Buschek
given: Daniel
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: deep learning, visual analytics, Gesture elicitation, dimensionality reduction

Abstract: This paper presents GestureMap, a visual analytics tool for gesture elicitation which directly visualises the space of gestures. Concretely, a Variational Autoencoder embeds gestures recorded as 3D skeletons on an interactive 2D map. GestureMap further integrates three computational capabilities to connect exploration to quantitative measures: Leveraging DTW Barycenter Averaging (DBA), we compute average gestures to 1) represent gesture groups at a glance; 2) compute a new consensus measure (variance around average gesture); and 3) cluster gestures with k-means. We evaluate GestureMap and its concepts with eight experts and an in-depth analysis of published data. Our findings show how GestureMap facilitates exploring large datasets and helps researchers to gain a visual understanding of elicited gesture spaces. It further opens new directions, such as comparing elicitations across studies. We discuss implications for elicitation studies and research, and opportunities to extend our approach to additional tasks in gesture elicitation.
Title: Styling Words: A Simple and Natural Way to Increase Variability in Training Data Collection for Gesture Recognition
Authors: family: Kang
given: Woojin; family: Jung
given: In-Taek; family: Lee
given: DaeHo; family: Hong
given: Jin-Hyuk
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Gesture recognition, Data collection, Styling words, Machine learning, Human gesture variability

Abstract: Due to advances in deep learning, gestures have become a more common tool for human-computer interaction. When implementing a large amount of training data, deep learning models show remarkable performance in gesture recognition. Since it is expensive and time consuming to collect gesture data from people, we are often confronted with a practicality issue when managing the quantity and quality of training data. It is a well-known fact that increasing training data variability can help to improve the generalization performance of machine learning models. Thus, we directly intervene in the collection of gesture data to increase human gesture variability by adding some words (called styling words) into the data collection instructions, e.g., giving the instruction "perform gesture #1 faster" as opposed to "perform gesture #1." Through an in-depth analysis of gesture features and video-based gesture recognition, we have confirmed the advantageous use of styling words in gesture training data collection.
Title: UISketch: A Large-Scale Dataset of UI Element Sketches
Authors: family: Sermuga Pandian
given: Vinoth Pandian; family: Suleri
given: Sarah; family: Jarke
given: Prof. Dr. Matthias
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Deep Neural Networks, Dataset, Sketch recognition, Sketch dataset, Wireframes, Low-fidelity prototypes, UI element sketches, Sketching interfaces

Abstract: This paper contributes the first large-scale dataset of 17,979 hand-drawn sketches of 21 UI element categories collected from 967 participants, including UI/UX designers, front-end developers, HCI, and CS grad students, from 10 different countries. We performed a perceptual study with this dataset and found out that UI/UX designers can recognize the UI element sketches with ~96% accuracy. To compare human performance against computational recognition methods, we trained the state-of-the-art DNN-based image classification models to recognize the UI elements sketches. This study revealed that the ResNet-152 model outperforms other classification networks and detects unknown UI element sketches with 91.77% accuracy (chance is 4.76%). We have open-sourced the entire dataset of UI element sketches to the community intending to pave the way for further research in utilizing AI to assist the conversion of lo-fi UI sketches to higher fidelities.
Title: Improving Viewing Experiences of First-Person Shooter Gameplays with Automatically-Generated Motion Effects
Authors: family: Yun
given: Gyeore; family: Lee
given: Hyoseung; family: Han
given: Sangyoon; family: Choi
given: Seungmoon
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: gameplay, automatic generation, motion effect, viewing experience, game

Abstract: In recent times, millions of people enjoy watching video gameplays at an eSports stadium or home. We seek a method that improves gameplay spectator or viewer experiences by presenting multisensory stimuli. Using a motion chair, we provide the motion effects automatically generated from the audiovisual stream to the viewers watching a first-person shooter (FPS) gameplay. The motion effects express the game character’s movement and gunfire action. We describe algorithms for the computation of such motion effects developed using computer vision techniques and deep learning. By a user study, we demonstrate that our method of providing motion effects significantly improves the viewing experiences of FPS gameplay. The contributions of this paper are with the motion synthesis algorithms integrated for FPS games and the empirical evidence for the benefits of experiencing multisensory gameplays.
Title: Understanding Tilt in Esports: A Study on Young League of Legends Players
Authors: family: Wu
given: Minerva; family: Lee
given: Je Seok; family: Steinkuehler
given: Constance
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: tilt, social-emotional learning, esports, stress

Abstract: Tilt as a gaming term is associated with frustration, rage, and deterioration of gameplay ability. In this exploratory study, we surveyed 95 esports players in a high school esports league on their definitions of tilt, triggers for tilting, responses to tilt, and perception of its malleability. We found that players are tilted most commonly by their own teammates rather than opponents, with their most negative tilt responses reserved for themselves. The majority surveyed believe that they can change how easily they are tilted and believing so was found to lead players to choose more positive responses to tilt. In contrast, perceiving tilt as malleable was found to increase the probability that participants respond with positive strategies. Implications for efforts to improve esports culture and community are discussed.
Title: Virtual Reality Esports - Understanding Competitive Players’ Perceptions of Location Based VR Esports
Authors: family: Turkay
given: Selen; family: Formosa
given: Jessica; family: Cuthbert
given: Robert; family: Adinolf
given: Sonam; family: Brown
given: Ross Andrew
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Player Experiences, Virtual Reality Esports, Qualitative Study

Abstract: Competitive VR gaming has emerged as a new trend in recent years, due to the availability of consumer grade VR technologies and the rise of esports as a billion dollar industry. Despite the considerable attention to competitive VR gaming, there is a lack of research on attitudes and experiences that players have with these games. In this qualitative study with a pre-post interview design, we recruited eight competitive Counter-Strike: Global Offensive players from a university esports club. We aimed to understand their attitudes towards VR esports and their experiences playing a representative location based VR esports game. Findings showed that players had visceral and positive affective experiences in the game, such as how players map physical movements to the game. These findings can help design future competitive VR esports, while also further contributing to HCI as the first exploration on player experiences with VR esports, laying groundwork for future studies.
Title: “Why Are You Playing Games? You Are a Girl!”: Exploring Gender Biases in Esports
Authors: family: Madden
given: Daniel; family: Liu
given: Yuxuan; family: Yu
given: Haowei; family: Sonbudak
given: Mustafa Feyyaz; family: Troiano
given: Giovanni M; family: Harteveld
given: Casper
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: qualitative studies, equity, competitive gaming, gender, Esports, interviews, inclusivity, gender biases

Abstract: Esports are rapidly growing within academia. In HCI, earlier work explored how problematic behaviors emerging from gender biases (e.g., toxicity) negatively impact female participation in esports. Here, we further explore gender biases in esports by interviewing 19 self-identified female and male professional gamers and event organizers. We inquire our interviewees about personal experiences with gender biases in esports and their perspective on how these biases impact participation, inclusivity, and career prospects. Our interviewees see gender biases in esports as a consequence of stereotypical gender roles in gaming tout-court (e.g., girls do not like violence, boys are competitive by nature). The rationale for separating male and female in esports, however, seems rooted in the need for female gamers to create role-models and grow in self-confidence. We scrutinize the considerations emerging from our interviews under a Feminist HCI lens and discuss how HCI research can help design equitable environments in esports.
Title: “Constant Pressure of Having to Perform”: Exploring Player Health Concerns in Esports
Authors: family: Madden
given: Daniel; family: Harteveld
given: Casper
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Physical Health, Esports, Mental Health, Wellness, Video Games

Abstract: Esports is a rapidly growing industry, generating interest from research disciplines including marketing, social sciences, and human-computer interaction. Despite its continued growth, there is a lack of studies surrounding the health of esports players. Previous work on the subject is limited, as research has only recently begun to explore the potential factors affecting physical and psychological wellness. Using an exploratory mixed-methods approach, a series of semi-structured interviews (n = 10) and an online survey (n = 68) were used to identify the biggest health concerns among esports players. The results demonstrate a better understanding of issues regarding physical and psychological wellness according to the players. Moving forward, we suggest the HCI community adapts mindfulness, ergonomics, and social-emotional learning as methods for supporting player’s health concerns.