huashen218/convxai-cia-dataset huggingface dataset:
Title: Examining the Use of Online Platforms for Employment: A Survey of U.S. Job Seekers
Authors: family: Dillahunt
given: Tawanna R; family: Israni
given: Aarti; family: Lu
given: Alex Jiahong; family: Cai
given: Mingzhi; family: Hsiao
given: Joey Chiao-Yin
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: job seekers, social media, Socio-demographic factors, internet, Employment

Abstract: Online employment resources are now as important as offline personal and professional networks, which have been pivotal in finding employment. However, it is unclear, which specific online resources are key to employment and how job seekers take advantage of them. Therefore, in an online survey of 768 job seekers, we investigated which online platforms, specific job search phases, behaviors, and job search strategies job seekers used in their job search, and which of these were associated with positive outcomes. We examined whether these results correlated with demographic factors and found differences in online platform use among income, gender, years of education, and race. Our results suggest that higher-income job seekers were more likely to use different strategies and more likely to get callbacks than lower-income job seekers. We raise new questions around demographics and technology and discuss the need for practitioners to design for a wider variety of job seekers.
Title: “Disadvantaged in the American-dominated Internet”: Sex, Work, and Technology
Authors: family: Barwulor
given: Catherine; family: McDonald
given: Allison; family: Hargittai
given: Eszter; family: Redmiles
given: Elissa M.
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: harassment, sex work, work, discrimination, technology, interview, sex, digital workplace, qualitative, marginalized population

Abstract: How do people in a precarious profession leverage technology to grow their business and improve their quality of life? Sex workers sit at the intersection of multiple marginalized identities and make up a sizeable workforce: the United Nations estimates that at least 42 million sex workers are conducting business across the globe. Yet, little research has examined how well technology fulfills sex workers’ business needs in the face of unique social, political, legal, and safety constraints. We present interviews with 29 sex workers in Germany and Switzerland where such work is legal, offering a first HCI perspective on this population’s use of technology. While our participants demonstrate savvy navigation of online spaces, sex workers encounter frustrating barriers due to an American-dominated internet that enforces puritan values globally. Our findings raise concerns about digital discrimination against sex workers and suggest concrete directions for the design of more inclusive technology.
Title: Ask Me or Tell Me? Enhancing the Effectiveness of Crowdsourced Design Feedback
Authors: family: Lekschas
given: Fritz; family: Ampanavos
given: Spyridon; family: Siangliulue
given: Pao; family: Pfister
given: Hanspeter; family: Gajos
given: Krzysztof Z.
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: sentiment, feedback framing, questioning, crowdsourced design feedback

Abstract: Crowdsourced design feedback systems are emerging resources for getting large amounts of feedback in a short period of time. Traditionally, the feedback comes in the form of a declarative statement, which often contains positive or negative sentiment. Prior research has shown that overly negative or positive sentiment can strongly influence the perceived usefulness and acceptance of feedback and, subsequently, lead to ineffective design revisions. To enhance the effectiveness of crowdsourced design feedback, we investigate a new approach for mitigating the effects of negative or positive feedback by combining open-ended and thought-provoking questions with declarative feedback statements. We conducted two user studies to assess the effects of question-based feedback on the sentiment and quality of design revisions in the context of graphic design. We found that crowdsourced question-based feedback contains more neutral sentiment than statement-based feedback. Moreover, we provide evidence that presenting feedback as questions followed by statements leads to better design revisions than question- or statement-based feedback alone.
Title: Human-Nature Relations in Urban Gardens: Explorations with Camera Traps
Authors: family: Vella
given: Kellie; family: Ploderer
given: Bernd; family: Brereton
given: Margot
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Urban Biodiversity, Citizen Sensing, Human Computer Interaction, Care, Cameras, Gardening, More-than-Human

Abstract: As cities grow, their people become increasingly distanced from nature except within private and public green spaces. Sensing technologies provide a means to harness curiosity about the animals living in these spaces, and possibly also connect interest to care. Yet little is known as to how people may use these technologies, or the implications for human-nature relations. To learn more, we gave commercial camera traps to ten adult participants to understand how they explored their gardens, what they wanted to learn, and what they did with this knowledge. We discovered trade-offs between control and care; the usefulness of different media and mystery; the temporalities of engaging in natural sensing practice; and a prevalence of sharing media within households. We discuss design for convivial cohabitation with the creatures in our garden. This research contributes to better human-nature relations through citizen sensing, as well as HCI for urban biodiversity conservation.
Title: Engaging with Nature Sounds & Citizen Science by Designing for Creative & Contextual Audio Encounters
Authors: family: L. Oliver
given: Jessica; family: Turkay
given: Selen; family: Brereton
given: Margot; family: M. Watson
given: David; family: Roe
given: Paul
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Creativity, Wildlife, Ludic Design, Games, Emotion, Birds, Nature, Games with a Purpose, Agent, Sound, Citizen Science, Participatory Design, Learning, Audio

Abstract: Wildlife calls are the best witnesses to the health of ecosystems, if only we know how to listen to them. Efforts to understand and inform restoration of healthy ecosystems with environmental audio recordings languish from insufficient tools to learn and identify sounds in recordings. To address this problem, we designed and playtested the Bristle Whistle Challenge prototype with ten players. We explored how to design delightful interactions with audio for gaining awareness of nature sounds and supporting wildlife conservation through citizen science. We found that rather than presenting audio alone, it was necessary to connect sounds to other senses and experiences in creative ways to impart meaning and enhance engagement. We offer recommendations to design creative and contextual interactions with media to build awareness of nature's wonders. We call for greater efforts in interaction design to engage people with nature, which is the key to turning around our environmental crisis.
Title: Exploring a Makeup Support System for Transgender Passing based on Automatic Gender Recognition
Authors: family: Chong
given: Toby; family: Maudet
given: Nolwenn; family: Harima
given: Katsuki; family: Igarashi
given: Takeo
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: gender, transgender, machine learning, Automatic Gender Recognition, inclusivity

Abstract: How to handle gender with machine learning is a controversial topic. A growing critical body of research brought attention to the numerous issues transgender communities face with the adoption of current automatic gender recognition (AGR) systems. In contrast, we explore how such technologies could potentially be appropriated to support transgender practices and needs, especially in non-Western contexts like Japan. We designed a virtual makeup probe to assist transgender individuals with passing, that is to be perceived as the gender they identify as. To understand how such an application might support expressing transgender individuals gender identity or not, we interviewed 15 individuals in Tokyo and found that in the right context and under strict conditions, AGR based systems could assist transgender passing.
Title: Karamad: A Voice-based Crowdsourcing Platform for Underserved Populations
Authors: family: Randhawa
given: Shan M; family: Ahmad
given: Tallal; family: Chen
given: Jay; family: Raza
given: Agha Ali
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: HCI4D, Interactive Voice Response, telephonic surveys, IVR, cellphone., telephone, crowdsourcing, Pakistan, underserved, speech, low-literate, mobile phone, voice, income, ICT4D, crowd work

Abstract: Crowdsourcing enables the completion of large-scale and hard-to-automate tasks, while allowing people to earn money. However, 3.6 billion people – a workforce comprising 46.4% of the world population – who could benefit most from this source of income lack the access and literacy to use computers, smartphones, and the internet. In this paper we present Karamad, a voice-based crowdsourcing platform that allows workers in low-resource regions to complete crowd work using low-end phones and receive payments as mobile airtime balance. We explore the usefulness, scalability, and sustainability of Karamad in Pakistan through a 6-month deployment. Without any advertising, training, or airtime subsidies, Karamad organically engaged 725 workers who completed 3,939 tasks (involving 43,006 components) including translations, dataset generation, and surveys on demographics, accessibility, disability, health, employment, and literacy. Collectively, the workers produced a valuable service market for potential customers and included female, unemployed, non-literate, and blind users.
Title: Hardhats and Bungaloos: Comparing Crowdsourced Design Feedback with Peer Design Feedback in the Classroom
Authors: family: Oppenlaender
given: Jonas; family: Kuosmanen
given: Elina; family: Lucero
given: Andrés; family: Hosio
given: Simo
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: design feedback, crowdsourcing, crowd feedback system, classroom study, peer review

Abstract: Feedback is an important aspect of design education, and crowdsourcing has emerged as a convenient way to obtain feedback at scale. In this paper, we investigate how crowdsourced design feedback compares to peer design feedback within a design-oriented HCI class and across two metrics: perceived quality and perceived fairness. We also examine the perceived monetary value of crowdsourced feedback, which provides an interesting contrast to the typical requester-centric view of the value of labor on crowdsourcing platforms. Our results reveal that the students (N = 106) perceived the crowdsourced design feedback as inferior to peer design feedback in multiple ways. However, they also identified various positive aspects of the online crowds that peers cannot provide. We discuss the meaning of the findings and provide suggestions for teachers in HCI and other researchers interested in crowd feedback systems on using crowds as a potential complement to peers.
Title: Breakdowns and Breakthroughs: Observing Musicians’ Responses to the COVID-19 Pandemic
Authors: family: Cai
given: Carrie J; family: Carney
given: Michelle; family: Zada
given: Nida; family: Terry
given: Michael
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: COVID-19, remote collaboration, creativity support tools, life disruption

Abstract: This paper describes results from a study examining how musicians have been affected by the restrictive environments imposed by the COVID-19 pandemic, and the corresponding technological implications. Through a survey of 29 musicians and additional interviews with 7 professional improvisation musicians, we observe the challenges musicians face in 1) finding the technological infrastructure and shared spaces necessary for dynamic, remote creative collaborations, 2) producing the mindset, free time, and sources of inspiration necessary for initiating creativity during times of stress, and 3) maintaining (and potentially expanding) the social connections and creative community vital to creative practice. We also report on how some musicians have creatively leveraged the radical changes in their lives to drive new artistic practices and styles of music. Collectively, these data illustrate the resilience of certain existing remote collaboration and creative tools, while suggesting ways other digital tools could more flexibly accommodate new use cases. The data also suggest the value of tools and services that help people creatively cope with radical changes to their lives and livelihoods.
Title: CakeVR: A Social Virtual Reality (VR) Tool for Co-designing Cakes
Authors: family: Mei
given: Yanni; family: Li
given: Jie; family: de Ridder
given: Huib; family: Cesar
given: Pablo
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Co-design, Remote Collaboration, Cake Design, Social Virtual Reality

Abstract: Cake customization services allow clients to collaboratively personalize cakes with pastry chefs. However, remote (e.g., email) and in-person co-design sessions are prone to miscommunication, due to natural restrictions in visualizing cake size, decoration, and celebration context. This paper presents the design, implementation, and expert evaluation of a social VR application (CakeVR) that allows a client to remotely co-design cakes with a pastry chef, through real-time realistic 3D visualizations. Drawing on expert semi-structured interviews (4 clients, 5 pastry chefs), we distill and incorporate 8 design requirements into our CakeVR prototype. We evaluate CakeVR with 10 experts (6 clients, 4 pastry chefs) using cognitive walkthroughs, and find that it supports ideation and decision making through intuitive size manipulation, color/flavor selection, decoration design, and custom celebration theme fitting. Our findings provide recommendations for enabling co-design in social VR and highlight CakeVR’s potential to transform product design communication through remote interactive and immersive co-design.
Title: Adapting User Interfaces with Model-based Reinforcement Learning
Authors: family: Todi
given: Kashyap; family: Bailly
given: Gilles; family: Leiva
given: Luis; family: Oulasvirta
given: Antti
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Monte Carlo Tree Search, Reinforcement Learning, Adaptive User Interfaces, Predictive Models

Abstract: Adapting an interface requires taking into account both the positive and negative effects that changes may have on the user. A carelessly picked adaptation may impose high costs to the user – for example, due to surprise or relearning effort – or “trap” the process to a suboptimal design immaturely. However, effects on users are hard to predict as they depend on factors that are latent and evolve over the course of interaction. We propose a novel approach for adaptive user interfaces that yields a conservative adaptation policy: It finds beneficial changes when there are such and avoids changes when there are none. Our model-based reinforcement learning method plans sequences of adaptations and consults predictive HCI models to estimate their effects. We present empirical and simulation results from the case of adaptive menus, showing that the method outperforms both a non-adaptive and a frequency-based policy.
Title: VisiFit: Structuring Iterative Improvement for Novice Designers
Authors: family: Chilton
given: Lydia B; family: Ozmen
given: Ecenaz Jen; family: Ross
given: Sam H; family: Liu
given: Vivian
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Design tools, Iterative design, Computational design

Abstract: Visual blends are an advanced graphic design technique to seamlessly integrate two objects into one. Existing tools help novices create prototypes of blends, but it is unclear how they would improve them to be higher fidelity. To help novices, we aim to add structure to the iterative improvement process. We introduce a method for improving prototypes that uses secondary design dimensions to explore a structured design space. This method is grounded in the cognitive principles of human visual object recognition. We present VisiFit – a computational design system that uses this method to enable novice graphic designers to improve blends with computationally generated options they can select, adjust, and chain together. Our evaluation shows novices can substantially improve 76% of blends in under 4 minutes. We discuss how the method can be generalized to other blending problems, and how computational tools can support novices by enabling them to explore a structured design space quickly and efficiently.
Title: Investigating the Homogenization of Web Design: A Mixed-Methods Approach
Authors: family: Goree
given: Samuel; family: Doosti
given: Bardia; family: Crandall
given: David; family: Su
given: Norman Makoto
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: computational social science, web design, interviews, historical analysis, design homogenization

Abstract: Visual design provides the backdrop to most of our interactions over the Internet, but has not received as much analytical attention as textual content. Combining computational with qualitative approaches, we investigate the growing concern that visual design of the World Wide Web has homogenized over the past decade. By applying computer vision techniques to a large dataset of representative websites images from 2003–2019, we show that designs have become significantly more similar since 2007, especially for page layouts where the average distance between sites decreased by over 30%. Synthesizing interviews from 11 experienced web design professionals with our computational analyses, we discuss causes of this homogenization including overlap in source code and libraries, color scheme standardization, and support for mobile devices. Our results seek to motivate future discussion of the factors that influence designers and their implications on the future trajectory of web design.
Title: FashionQ: An AI-Driven Creativity Support Tool for Facilitating Ideation in Fashion Design
Authors: family: Jeon
given: Youngseung; family: Jin
given: Seungwan; family: Shih
given: Patrick C.; family: Han
given: Kyungsik
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: ideation process, artificial intelligence utilization, cognitive operation, fashion design, creativity support tool

Abstract: Recent research on creativity support tools (CST) adopts artificial intelligence (AI) that leverages big data and computational capabilities to facilitate creative work. Our work aims to articulate the role of AI in supporting creativity with a case study of an AI-based CST tool in fashion design based on theoretical groundings. We developed AI models by externalizing three cognitive operations (extending, constraining, and blending) that are associated with divergent and convergent thinking. We present FashionQ, an AI-based CST that has three interactive visualization tools (StyleQ, TrendQ, and MergeQ). Through interviews and a user study with 20 fashion design professionals (10 participants for the interviews and 10 for the user study), we demonstrate the effectiveness of FashionQ on facilitating divergent and convergent thinking and identify opportunities and challenges of incorporating AI in the ideation process. Our findings highlight the role and use of AI in each cognitive operation based on professionals’ expertise and suggest future implications of AI-based CST development.
Title: Vinci: An Intelligent Graphic Design System for Generating Advertising Posters
Authors: family: Guo
given: Shunan; family: Jin
given: Zhuochen; family: Sun
given: Fuling; family: Li
given: Jingwen; family: Li
given: Zhaorui; family: Shi
given: Yang; family: Cao
given: Nan
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: design tool, deep generative networks, Graphic design

Abstract: Advertising posters are a commonly used form of information presentation to promote a product. Producing advertising posters often takes much time and effort of designers when confronted with abundant choices of design elements and layouts. This paper presents Vinci, an intelligent system that supports the automatic generation of advertising posters. Given the user-specified product image and taglines, Vinci uses a deep generative model to match the product image with a set of design elements and layouts for generating an aesthetic poster. The system also integrates online editing-feedback that supports users in editing the posters and updating the generated results with their design preference. Through a series of user studies and a Turing test, we found that Vinci can generate posters as good as human designers and that the online editing-feedback improves the efficiency in poster modification.
Title: Screen2Vec: Semantic Embedding of GUI Screens and GUI Components
Authors: family: Li
given: Toby Jia-Jun; family: Popowski
given: Lindsay; family: Mitchell
given: Tom; family: Myers
given: Brad A
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: GUI embedding, interaction mining, screen semantics

Abstract: Representing the semantics of GUI screens and components is crucial to data-driven computational methods for modeling user-GUI interactions and mining GUI designs. Existing GUI semantic representations are limited to encoding either the textual content, the visual design and layout patterns, or the app contexts. Many representation techniques also require significant manual data annotation efforts. This paper presents Screen2Vec, a new self-supervised technique for generating representations in embedding vectors of GUI screens and components that encode all of the above GUI features without requiring manual annotation using the context of user interaction traces. Screen2Vec is inspired by the word embedding method Word2Vec, but uses a new two-layer pipeline informed by the structure of GUIs and interaction traces and incorporates screen- and app-specific metadata. Through several sample downstream tasks, we demonstrate Screen2Vec’s key useful properties: representing between-screen similarity through nearest neighbors, composability, and capability to represent user tasks.
Title: “Warm Bodies”: A Post-Processing Technique for Animating Dynamic Blood Flow on Photos and Avatars
Authors: family: McDuff
given: Daniel; family: Nowara
given: Ewa M.
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: embodied agents, blood flow, perfusion, photoplethysmography

Abstract: What breathes life into an embodied agent or avatar? While body motions such as facial expressions, speech and gestures have been well studied, relatively little attention has been applied to subtle changes due to underlying physiology. We argue that subtle pulse signals are important for creating more lifelike and less disconcerting avatars. We propose a method for animating blood flow patterns, based on a data-driven physiological model that can be used to directly augment the appearance of synthetic avatars and photo-realistic faces. While the changes are difficult for participants to “see”, they significantly more frequently select faces with blood flow as more anthropomorphic and animated than faces without blood flow. Furthermore, by manipulating the frequency of the heart rate in the underlying signal we can change the perceived arousal of the character.