huashen218/convxai-cia-dataset huggingface dataset:
Title: Designing Tools and Interfaces for Ecological Restoration: An Investigation into the Opportunities and Constraints for Technological Interventions
Authors: family: Maheshwari
given: Aditi; family: Kumar Aggarwal
given: Abhay; family: Danielescu
given: Andreea
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: sustainability, environmental monitoring, human-computer-nature interaction, natural ecosystems, ecological restoration, precision agriculture

Abstract: As interest within the HCI community expands beyond urban settings, novel tools and devices are being developed to support more sustainable interactions with natural environments and inform conservation action. Yet little is known about the users of these devices, and how their requirements and priorities might affect the usability or operationalization of the devices in the real world. Using the ‘e-seed’, a biomimetic self-drilling interface as a ‘research probe’, we conducted a qualitative user study with 14 subject matter experts in areas like forestry and agriculture to understand the value and limits for devices and systems in ecological restoration and monitoring. We highlight unique challenges in existing ecological practices, opportunities for technological interventions, and the policies and economic constraints affecting the feasibility of such interventions. We present a set of critical design considerations for building and deploying novel devices in natural and semi-natural ecosystems and discuss implications for future research.
Title: RMS: Removing Barriers to Analyze the Availability and Surge Pricing of Ridesharing Services
Authors: family: Khan
given: Hassan Ali; family: Iqbal
given: Hassan; family: Shahzad
given: Muhammad; family: Jin
given: Guoliang
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Surge, COVID 19, Gig economy, Uber, Ridesharing services

Abstract: Ridesharing services do not make data of their availability (supply, utilization, idle time, and idle distance) and surge pricing publicly available. It limits the opportunities to study the spatiotemporal trends of the availability and surge pricing of these services. Only a few research studies conducted in North America analyzed these features for only Uber and Lyft. Despite the interesting observations, the results of prior works are not generalizable or reproducible because: i) the datasets collected in previous publications are spatiotemporally sensitive, i.e., previous works do not represent the current availability and surge pricing of ridesharing services in different parts of the world; and ii) the analyses presented in previous works are limited in scope (in terms of countries and ridesharing services they studied). Hence, prior works are not generally applicable to ridesharing services operating in different countries. This paper addresses the issue of ridesharing-data unavailability by presenting Ridesharing Measurement Suite (RMS). RMS removes the barrier of entry for analyzing the availability and surge pricing of ridesharing services for ridesharing users, researchers from various scientific domains, and regulators. RMS continuously collects the data of the availability and surge pricing of ridesharing services. It exposes real-time data of these services through i) graphical user interfaces and ii) public APIs to assist various stakeholders of these services and simplify the data collection and analysis process for future ridesharing research studies. To signify the utility of RMS, we deployed RMS to collect and analyze the availability and surge pricing data of 10 ridesharing services operating in nine countries for eight weeks in pre and during pandemic periods. Using the data collected and analyzed by RMS, we identify that previous articles miscalculated the utilization of ridesharing services as they did not count in the vehicles driving in multiple categories of the same service. We observe that during COVID-19, the supply of ridesharing services decreased by 54%, utilization of available vehicles increased by 6%, and a 5 × increase in the surge frequency of services. We also find that surge occurs in a small geographical region, and its intensity reduces by 50% in about 0.5 miles away from the location of a surge. We present several other interesting observations on ridesharing services’ availability and surge pricing.
Title: Outside Where? A Survey of Climates and Built Environments in Studies of HCI outdoors
Authors: family: Jones
given: Michael D; family: Von Feldt
given: Meredith; family: Andrus
given: Natalie
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Human-computer interaction, built environment, HCI outdoors

Abstract: We found significant gaps in the climates and built environments used as settings for studies of HCI outdoors. The experience of using a computer outdoors varies widely depending on location-specific factors such as weather and the availability of electricity. We surveyed 699 papers from CHI venues and found 101 studies involving a person and a computer interacting outdoors for which we could determine the study location. We categorized each study location by climate using the Köppen-Geiger scheme and by built environment using the Recreation Opportunity Spectrum. 91 of 101 studies took place in temperate or continental climates and 82 took place in urban settings. Emerging understanding of the ongoing impacts of climate change increases the importance of investigating HCI outdoors in a wider range of weather conditions. While some primitive natural settings have been preserved against development at great cost, we found no studies of HCI outdoors in those settings.
Title: Semantic Gap in Predicting Mental Wellbeing through Passive Sensing
Authors: family: Das Swain
given: Vedant; family: Chen
given: Victor; family: Mishra
given: Shrija; family: Mattingly
given: Stephen M.; family: Abowd
given: Gregory D.; family: De Choudhury
given: Munmun
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Activity Patterns, Social Media, Mental Wellbeing, Passive Sensing

Abstract: When modeling passive data to infer individual mental wellbeing, a common source of ground truth is self-reports. But these tend to represent the psychological facet of mental states, which might not align with the physiological facet of that state. Our paper demonstrates that when what people “feel” differs from what people “say they feel”, we witness a semantic gap that limits predictions. We show that predicting mental wellbeing with passive data (offline sensors or online social media) is related to how the ground-truth is measured (objective arousal or self-report). Features with psycho-social signals (e.g., language) were better at predicting self-reported anxiety and stress. Conversely, features with behavioral signals (e.g., sleep), were better at predicting stressful arousal. Regardless of the source of ground truth, integrating both signals boosted prediction. To reduce the semantic gap, we provide recommendations to evaluate ground truth measures and adopt parsimonious sensing.
Title: Round Numbers Can Sharpen Cognition
Authors: family: Nguyen
given: Huy Anh; family: Hofman
given: Jake M; family: Goldstein
given: Daniel G
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: number representation, estimation, experimentation, cognitive effect, recall, measurement, round numbers

Abstract: Scientists and journalists strive to report numbers with high precision to keep readers well-informed. Our work investigates whether this practice can backfire due to the cognitive costs of processing multi-digit precise numbers. In a pre-registered randomized experiment, we presented readers with several news stories containing numbers in either precise or round versions. We then measured their ability to approximately recall these numbers and make estimates based on what they read. Our results revealed a counter-intuitive effect where reading round numbers helped people better approximate the precise values, while seeing precise numbers made them worse. We also conducted two surveys to elicit individual preferences for the ideal degree of rounding for numbers spanning seven orders of magnitude in various contexts. From the surveys, we found that people tended to prefer more precision when the rounding options contained only digits (e.g., ”2,500,000”) than when they contained modifier terms (e.g., ”2.5 million”). We conclude with a discussion of how these findings can be leveraged to enhance numeracy in digital content consumption.
Title: Probability Weighting in Interactive Decisions: Evidence for Overuse of Bad Assistance, Underuse of Good Assistance
Authors: family: Cockburn
given: Andy; family: Quinn
given: Philip; family: Gutwin
given: Carl; family: Chen
given: Zhe; family: Suwanaposee
given: Pang
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: prospect theory, overuse, probability weighting, underuse., Interface suggestions

Abstract: The effective use of assistive interfaces (i.e. those that offer suggestions or reform the user’s input to match inferred intentions) depends on users making good decisions about whether and when to engage or ignore assistive features. However, prior work from economics and psychology shows systematic decision-making biases in which people overreact to low probability events and underreact to high probability events – modelled using a probability weighting function. We examine the theoretical implications of this probability weighting for interaction, including its suggestion that users will overuse inaccurate interface assistance and underuse accurate assistance. We then conduct a new analysis of data from a previously published study, quantifying the degree of bias users exhibited, and demonstrating conformance with these predictions. We discuss implications for design, including strategies that could be used to mitigate the deleterious effects of the observed biases.
Title: Multifractal Mice: Operationalising Dimensions of Readiness-to-hand via a Feature of Hand Movement
Authors: family: Bennett
given: Dan; family: Roudaut
given: Anne; family: Metatla
given: Oussama
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: engagement, cognitive science, complex systems, phenomenology, readiness to hand, embodiment, user experience

Abstract: The philosophical construct readiness-to-hand describes focused, intuitive, tool use, and has been linked to tool-embodiment and immersion. The construct has been influential in HCI and design for decades, but researchers currently lack appropriate measures and tools to investigate it empirically. To support such empirical work we investigate the possibility of operationalising readiness-to-hand in measurements of multfractality in movement, building on recent work in cognitive science. We conduct two experiments (N=44, N=30) investigating multifractality in mouse movements during a computer game, replicating prior results and contributing new findings. Our results show that multifractality correlates with dimensions associated with readiness-to-hand, including skill and task-engagement, during tool breakdown, task learning and normal play. We describe future possibilities for the application of these methods in HCI, supporting such work by sharing scripts and data (https://osf.io/2hm9u/), and introducing a new data-driven approach to parameter selection.
Title: Investigating the Tradeoffs of Everyday Text-Entry Collection Methods
Authors: family: Rodrigues
given: André; family: Nicolau
given: Hugo; family: Santos
given: André; family: Branco
given: Diogo; family: Rainey
given: Jay; family: Verweij
given: David; family: Smeddinck
given: Jan David; family: Montague
given: Kyle; family: Guerreiro
given: Tiago
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: user experience, touch behaviours, trade-offs, in-the-wild, text-entry, data collection, performance

Abstract: Typing on mobile devices is a common and complex task. The act of typing itself thereby encodes rich information, such as the typing method, the context it is performed in, and individual traits of the person typing. Researchers are increasingly using a selection or combination of experience sampling and passive sensing methods in real-world settings to examine typing behaviours. However, there is limited understanding of the effects these methods have on measures of input speed, typing behaviours, compliance, perceived trust and privacy. In this paper, we investigate the tradeoffs of everyday data collection methods. We contribute empirical results from a four-week field study (N=26). Here, participants contributed by transcribing, composing, passively having sentences analyzed and reflecting on their contributions. We present a tradeoff analysis of these data collection methods, discuss their impact on text-entry applications, and contribute a flexible research platform for in the wild text-entry studies.
Title: FlatMagic: Improving Flat Colorization through AI-driven Design for Digital Comic Professionals
Authors: family: Yan
given: Chuan; family: Chung
given: John Joon Young; family: Kiheon
given: Yoon; family: Gingold
given: Yotam; family: Adar
given: Eytan; family: Hong
given: Sungsoo Ray
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Human-AI collaboration, digital comic colorization, Intermediate Representation, system for professionals, automation and control

Abstract: Creating digital comics involves multiple stages, some creative and some menial. For example, coloring a comic requires a labor-intensive stage known as ‘flatting,’ or masking segments of continuous color, as well as creative shading, lighting, and stylization stages. The use of AI can automate the colorization process, but early efforts have revealed limitations—technical and UX—to full automation. Via a formative study of professionals, we identify flatting as a bottleneck and key target of opportunity for human-guided AI-driven automation. Based on this insight, we built FlatMagic, an interactive, AI-driven flat colorization support tool for Photoshop. Our user studies found that using FlatMagic significantly reduced professionals’ real and perceived effort versus their current practice. While participants effectively used FlatMagic, we also identified potential constraints in interactions with AI and partially automated workflows. We reflect on implications for comic-focused tools and the benefits and pitfalls of intermediate representations and partial automation in designing human-AI collaboration tools for professionals.
Title: Supercharging Trial-and-Error for Learning Complex Software Applications
Authors: family: Masson
given: Damien; family: Vermeulen
given: Jo; family: Fitzmaurice
given: George; family: Matejka
given: Justin
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: conceptual model, trial-and-error, learning by exploration, software learning, technique, design space

Abstract: Despite an abundance of carefully-crafted tutorials, trial-and-error remains many people’s preferred way to learn complex software. Yet, approaches to facilitate trial-and-error (such as tooltips) have evolved very little since the 1980s. While existing mechanisms work well for simple software, they scale poorly to large feature-rich applications. In this paper, we explore new techniques to support trial-and-error in complex applications. We identify key benefits and challenges of trial-and-error, and introduce a framework with a conceptual model and design space. Using this framework, we developed three techniques: ToolTrack to keep track of trial-and-error progress; ToolTrip to go beyond trial-and-error of single commands by highlighting related commands that are frequently used together; and ToolTaste to quickly and safely try commands. We demonstrate how these techniques facilitate trial-and-error, as illustrated through a proof-of-concept implementation in the CAD software Fusion 360. We conclude by discussing possible scenarios and outline directions for future research on trial-and-error.
Title: Paper Trail: An Immersive Authoring System for Augmented Reality Instructional Experiences
Authors: family: Rajaram
given: Shwetha; family: Nebeling
given: Michael
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: STEM., augmented reality, educational technology, immersive paper

Abstract: Prior work has demonstrated augmented reality’s benefits to education, but current tools are difficult to integrate with traditional instructional methods. We present Paper Trail, an immersive authoring system designed to explore how to enable instructors to create AR educational experiences, leaving paper at the core of the interaction and enhancing it with various forms of digital media, animations for dynamic illustrations, and clipping masks to guide learning. To inform the system design, we developed five scenarios exploring the benefits that hand-held and head-worn AR can bring to STEM instruction and developed a design space of AR interactions enhancing paper based on these scenarios and prior work. Using the example of an AR physics handout, we assessed the system’s potential with PhD-level instructors and its usability with XR design experts. In an elicitation study with high-school teachers, we study how Paper Trail could be used and extended to enable flexible use cases across various domains. We discuss benefits of immersive paper for supporting diverse student needs and challenges for making effective use of AR for learning.
Title: A Layered Authoring Tool for Stylized 3D animations
Authors: family: Ma
given: Jiaju; family: Wei
given: Li-Yi; family: Kazi
given: Rubaiat Habib
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: 3D, animation, stylization

Abstract: Guided by the 12 principles of animation, stylization is a core 2D animation feature but has been utilized mainly by experienced animators. Although there are tools for stylizing 2D animations, creating stylized 3D animations remains a challenging problem due to the additional spatial dimension and the need for responsive actions like contact and collision. We propose a system that helps users create stylized casual 3D animations. A layered authoring interface is employed to balance between ease of use and expressiveness. Our surface level UI is a timeline sequencer that lets users add preset stylization effects such as squash and stretch and follow through to plain motions. Users can adjust spatial and temporal parameters to fine-tune these stylizations. These edits are propagated to our node-graph-based second level UI, in which the users can create custom stylizations after they are comfortable with the surface level UI. Our system also enables the stylization of interactions among multiple objects like force, energy, and collision. A pilot user study has shown that our fluid layered UI design allows for both ease of use and expressiveness better than existing tools.
Title: Design Guidelines for Prompt Engineering Text-to-Image Generative Models
Authors: family: Liu
given: Vivian; family: Chilton
given: Lydia B
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: design guidelines, prompt engineering., text-to-image, computational creativity, multimodal generative models, AI co-creation

Abstract: Text-to-image generative models are a new and powerful way to generate visual artwork. However, the open-ended nature of text as interaction is double-edged; while users can input anything and have access to an infinite range of generations, they also must engage in brute-force trial and error with the text prompt when the result quality is poor. We conduct a study exploring what prompt keywords and model hyperparameters can help produce coherent outputs. In particular, we study prompts structured to include subject and style keywords and investigate success and failure modes of these prompts. Our evaluation of 5493 generations over the course of five experiments spans 51 abstract and concrete subjects as well as 51 abstract and figurative styles. From this evaluation, we present design guidelines that can help people produce better outcomes from text-to-image generative models.
Title: AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts
Authors: family: Wu
given: Tongshuang; family: Terry
given: Michael; family: Cai
given: Carrie Jun
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Human-AI Interaction, Natural Language Processing, Large Language Models

Abstract: Although large language models (LLMs) have demonstrated impressive potential on simple tasks, their breadth of scope, lack of transparency, and insufficient controllability can make them less effective when assisting humans on more complex tasks. In response, we introduce the concept of Chaining LLM steps together, where the output of one step becomes the input for the next, thus aggregating the gains per step. We first define a set of LLM primitive operations useful for Chain construction, then present an interactive system where users can modify these Chains, along with their intermediate results, in a modular way. In a 20-person user study, we found that Chaining not only improved the quality of task outcomes, but also significantly enhanced system transparency, controllability, and sense of collaboration. Additionally, we saw that users developed new ways of interacting with LLMs through Chains: they leveraged sub-tasks to calibrate model expectations, compared and contrasted alternative strategies by observing parallel downstream effects, and debugged unexpected model outputs by “unit-testing” sub-components of a Chain. In two case studies, we further explore how LLM Chains may be used in future applications.
Title: Discovering the Syntax and Strategies of Natural Language Programming with Generative Language Models
Authors: family: Jiang
given: Ellen; family: Toh
given: Edwin; family: Molina
given: Alejandra; family: Olson
given: Kristen; family: Kayacik
given: Claire; family: Donsbach
given: Aaron; family: Cai
given: Carrie J; family: Terry
given: Michael
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: generative language models, code synthesis, prompt programming

Abstract: In this paper, we present a natural language code synthesis tool, GenLine, backed by 1) a large generative language model and 2) a set of task-specific prompts that create or change code. To understand the user experience of natural language code synthesis with these new types of models, we conducted a user study in which participants applied GenLine to two programming tasks. Our results indicate that while natural language code synthesis can sometimes provide a magical experience, participants still faced challenges. In particular, participants felt that they needed to learn the model’s “syntax,” despite their input being natural language. Participants also struggled to form an accurate mental model of the types of requests the model can reliably translate and developed a set of strategies to debug model input. From these findings, we discuss design implications for future natural language code synthesis tools built using large generative language models.