bluuebunny/crossref_metadata_2025 huggingface dataset:
PuzzleMe: Leveraging Peer Assessment for In-Class Programming Exercises
Authors: Wang April Yi , Chen Yan , Chung John Joon Young , Brooks Christopher , Oney Steve
Published: October 2021

Peer assessment, as a form of collaborative learning, can engage students in active learning and improve their learning gains. However, current teaching platforms and programming environments provide little support to integrate peer assessment for in-class programming exercises. We identified challenges in conducting such exercises and adopting peer assessment through formative interviews with instructors of introductory programming courses. To address these challenges, we introduce PuzzleMe, a tool to help Computer Science instructors to conduct engaging in-class programming exercises. PuzzleMe leverages peer assessment to support a collaboration model where students provide timely feedback on their peers' work. We propose two assessment techniques tailored to in-class programming exercises: live peer testing and live peer code review. Live peer testing can improve students' code robustness by allowing them to create and share lightweight tests with peers. Live peer code review can improve code understanding by intelligently grouping students to maximize meaningful code reviews. A two-week deployment study revealed that PuzzleMe encourages students to write useful test cases, identify code problems, correct misunderstandings, and learn a diverse set of problem-solving approaches from peers.
Algorithmic Risk Assessments Can Alter Human Decision-Making Processes in High-Stakes Government Contexts
Authors: Green Ben , Chen Yiling
Published: October 2021

Governments are increasingly turning to algorithmic risk assessments when making important decisions, such as whether to release criminal defendants before trial. Policymakers assert that providing public servants with algorithmic advice will improve human risk predictions and thereby lead to better (e.g., fairer) decisions. Yet because many policy decisions require balancing risk-reduction with competing goals, improving the accuracy of predictions may not necessarily improve the quality of decisions. If risk assessments make people more attentive to reducing risk at the expense of other values, these algorithms would diminish the implementation of public policy even as they lead to more accurate predictions. Through an experiment with 2,140 lay participants simulating two high-stakes government contexts, we provide the first direct evidence that risk assessments can systematically alter how people factor risk into their decisions. These shifts counteracted the potential benefits of improved prediction accuracy. In the pretrial setting of our experiment, the risk assessment made participants more sensitive to increases in perceived risk; this shift increased the racial disparity in pretrial detention by 1.9%. In the government loans setting of our experiment, the risk assessment made participants more risk-averse; this shift reduced government aid by 8.3%. These results demonstrate the potential limits and harms of attempts to improve public policy by incorporating predictive algorithms into multifaceted policy decisions. If these observed behaviors occur in practice, presenting risk assessments to public servants would generate unexpected and unjust shifts in public policy without being subject to democratic deliberation or oversight.
What Makes Tweetorials Tick: How Experts Communicate Complex Topics on Twitter
Authors: Gero Katy Ilonka , Liu Vivian , Huang Sarah , Lee Jennifer , Chilton Lydia B.
Published: October 2021

People are increasingly getting information and news from social media. On Twitter we are seeing the emergence of "tweetorials" -- long, explanatory Twitter threads written by experts. In this work we study tweetorials as a form of science writing. While scientists have begun to champion the importance of Twitter as a science communication medium, few have studied how people are successfully using this medium to communicate complex and nuanced ideas. To understand how tweetorials work, we curated a collection of 46 clear and engaging tweetorials from multiple domains. We analyzed these tweetorials for the writing techniques that they employ, and found that while tweetorials use many traditional science writing techniques, they also use more subjective language, actively build credibility, and incorporate media in unique ways. In addition, we report on a workshop we ran to aid science PhD students in writing tweetorials, and find that while providing common tweetorial techniques improves their writing, the students still struggle to balance their scientific sensibilities with the informal tone associated with tweetorials. We discuss the implications of using informal and subjective language in science communication, as well as how technology can support scientists in writing tweetorials.
The Everyday HCI of Uber Drivers in India: A Developing Country Perspective
Authors: Sehrawat Ujjwal , sawhney namit , Yeleswarapu Tejaswini , Rangaswamy Nimmi
Published: October 2021

Uber drivers in India are witnessing a slow emergence of everyday work formalization predominantly through their engagement with the Uber platform. If a vast segment of the informal employment sector in a country resembles gig work, can companies like Uber bring organizational ability and wage regularity to employment? Despite Uber's economic model being challenged on several labor fronts in scholarship from the global North, we argue everyday interactions with the Uber platform are ushering organized work practices and improved financial stability for drivers who formerly hail from the informal employment sector in India. The everyday driving for Uber is filtered through a conceptual and practical work model drivers gain with due experience of Uber's platform features. Our study uncovers the relationship between the controlling demands of the Uber platform and ensuing driver work adaptations. We present findings from a qualitative investigation of Uber ride-hailing services impacting drivers to 1. optimize earnings 2. link work effort to wages 3. converge towards platform compliance. We posit engaged and persistent interactions with the Uber platform bring structure and formality to the profession of driving for Indian Uber drivers.
Discovering and Validating AI Errors With Crowdsourced Failure Reports
Authors: Cabrera Ángel Alexander , Druck Abraham J. , Hong Jason I. , Perer Adam
Published: October 2021

AI systems can fail to learn important behaviors, leading to real-world issues like safety concerns and biases. Discovering these systematic failures often requires significant developer attention, from hypothesizing potential edge cases to collecting evidence and validating patterns. To scale and streamline this process, we introduce crowdsourced failure reports, end-user descriptions of how or why a model failed, and show how developers can use them to detect AI errors. We also design and implement Deblinder, a visual analytics system for synthesizing failure reports that developers can use to discover and validate systematic failures. In semi-structured interviews and think-aloud studies with 10 AI practitioners, we explore the affordances of the Deblinder system and the applicability of failure reports in real-world settings. Lastly, we show how collecting additional data from the groups identified by developers can improve model performance.
The Flaky Accretions of Infrastructure: Sociotechnical Systems, Citizenship, and the Water Supply
Authors: Joshi Tejaswini , Bardzell Jeffrey , Bardzell Shaowen
Published: October 2021

The convergence of networked digital infrastructures and built environments have given rise to the "urban user", a conflation of "the user" and "the resident" or "the citizen". The urban user and the city infrastructures are mutually constituted phenomena formed through the interactions between them. In this research, we contribute an ethnographic study that focuses on the everyday interactions between the urban user and water infrastructures in Pune, India. Using Nikhil Anand's concept of "hydraulic citizenship" to analyze our ethnographic data, we showcase the mutually constitutive process of infrastructuring and subjectivization of the "citizen", bringing attention to the ad hoc, heterogeneously constituted water infrastructures in Pune that aspire to be "smart" even before becoming functional infrastructures. In doing so, we hope to expand possible research trajectories within smart city research agendas by decoupling it from Western assumptions and also by linking them to an interactional account of the everyday relationships among residents, infrastructures, and municipalities.
The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies
Authors: Fogliato Riccardo , Chouldechova Alexandra , Lipton Zachary
Published: October 2021

As algorithmic risk assessment instruments (RAIs) are increasingly adopted to assist decision makers, their predictive performance and potential to promote inequity have come under scrutiny. However, while most studies examine these tools in isolation, researchers have come to recognize that assessing their impact requires understanding the behavior of their human interactants. In this paper, building off of several recent crowdsourcing works focused on criminal justice, we conduct a vignette study in which laypersons are tasked with predicting future re-arrests. Our key findings are as follows: (1) Participants often predict that an offender will be rearrested even when they deem the likelihood of re-arrest to be well below 50%; (2) Participants do not anchor on the RAI's predictions; (3) The time spent on the survey varies widely across participants and most cases are assessed in less than 10 seconds; (4) Judicial decisions, unlike participants' predictions, depend in part on factors that are orthogonal to the likelihood of re-arrest. These results highlight the influence of several crucial but often overlooked design decisions and concerns around generalizability when constructing crowdsourcing studies to analyze the impacts of RAI
"How advertiser-friendly is my video?": YouTuber's Socioeconomic Interactions with Algorithmic Content Moderation
Authors: Ma Renkai , Kou Yubo
Published: October 2021

To manage user-generated harmful video content, YouTube relies on AI algorithms (e.g., machine learning) in content moderation and follows a retributive justice logic to punish convicted YouTubers through demonetization, a penalty that limits or deprives them of advertisements (ads), reducing their future ad income. Moderation research is burgeoning in CSCW, but relatively little attention has been paid to the socioeconomic implications of YouTube's algorithmic moderation. Drawing from the lens of algorithmic labor, we describe how algorithmic moderation shapes YouTubers' labor conditions through algorithmic opacity and precarity. YouTubers coped with such challenges from algorithmic moderation by sharing and applying practical knowledge they learned about moderation algorithms. By analyzing video content creation as algorithmic labor, we unpack the socioeconomic implications of algorithmic moderation and point to necessary post-punishment support as a form of restorative justice. Lastly, we put forward design considerations for algorithmic moderation systems.
Everyday Algorithm Auditing: Understanding the Power of Everyday Users in Surfacing Harmful Algorithmic Behaviors
Authors: Shen Hong , DeVos Alicia , Eslami Motahhare , Holstein Kenneth
Published: October 2021

A growing body of literature has proposed formal approaches to audit algorithmic systems for biased and harmful behaviors. While formal auditing approaches have been greatly impactful, they often suffer major blindspots, with critical issues surfacing only in the context of everyday use once systems are deployed. Recent years have seen many cases in which everyday users of algorithmic systems detect and raise awareness about harmful behaviors that they encounter in the course of their everyday interactions with these systems. However, to date little academic attention has been granted to these bottom-up, user-driven auditing processes. In this paper, we propose and explore the concept of everyday algorithm auditing, a process in which users detect, understand, and interrogate problematic machine behaviors via their day-to-day interactions with algorithmic systems. We argue that everyday users are powerful in surfacing problematic machine behaviors that may elude detection via more centrally-organized forms of auditing, regardless of users' knowledge about the underlying algorithms. We analyze several real-world cases of everyday algorithm auditing, drawing lessons from these cases for the design of future platforms and tools that facilitate such auditing behaviors. Finally, we discuss work that lies ahead, toward bridging the gaps between formal auditing approaches and the organic auditing behaviors that emerge in everyday use of algorithmic systems.
Designing Technologies to Support Parent-Child Relationships: A Review of Current Findings and Suggestions for Future Directions
Authors: Shin Ji Youn , Rheu Minjin , Huh-Yoo Jina , Peng Wei
Published: October 2021

Diverse fields, including CSCW, Communication, and Human Development studies, have investigated how technologies can better support parent-child relationships. While these studies are scattered across literature, little effort has been made to synthesize the findings. We conducted a review of studies that examined the factors associated with parent-child relationships that are mediated by technologies. Specifically, we synthesized previous studies based on children's age groups and different family contexts, including cohabitation. From a total of 12,942 search results from two databases, and 32 results from the hand-searching process, we conducted a full-text review of 190 articles and identified 19 suitable studies. An additional search during the revision cycle resulted in 6 more full-text reviews and 1 additional study being included in the data analysis. We analyzed challenges and facilitators in designing CSCW systems supporting parent-child relationships for families living together or apart and families with children of different developmental stages. Findings showed two common challenges, which should be addressed in technology designed to support parent-child relationships: discrepancies in expected communication between parents and child(ren) and the complex emotions of parents toward parenting caused by their busy schedules. Challenges specific to families who are living apart included consequences from being physically distant and having limited access to communication resources. The following factors commonly helped facilitate parent-child relationships: (1) reciprocity norms of the family (2) reinforcement of transparency, affection, and trust, (3) a physical proxy of each other through an object or interface design, (4) accessibility, the sophistication level of technology, and communication resources, (5) enjoyable, age-appropriate shared content among parents and children, and (6) situational awareness and routine as ways to increase parent-child relationships. Media richness and synchronicity in system design and privacy preservation without interruption facilitated parent-child relationships of families living apart. Based on the findings, we discuss opportunities for technological innovation for physically co-located families and the importance of considering children's age and developmental stages in designing technology for parent-child relationships.
Quality Control in Crowdsourcing based on Fine-Grained Behavioral Features
Authors: Pei Weiping , Yang Zhiju , Chen Monchu , Yue Chuan
Published: October 2021

Crowdsourcing is popular for large-scale data collection and labeling, but a major challenge is on detecting low-quality submissions. Recent studies have demonstrated that behavioral features of workers are highly correlated with data quality and can be useful in quality control. However, these studies primarily leveraged coarsely extracted behavioral features, and did not further explore quality control at the fine-grained level, i.e., the annotation unit level. In this paper, we investigate the feasibility and benefits of using fine-grained behavioral features, which are the behavioral features finely extracted from a worker's individual interactions with each single unit in a subtask, for quality control in crowdsourcing. We design and implement a framework named Fine-grained Behavior-based Quality Control (FBQC) that specifically extracts fine-grained behavioral features to provide three quality control mechanisms: (1) quality prediction for objective tasks, (2) suspicious behavior detection for subjective tasks, and (3) unsupervised worker categorization. Using the FBQC framework, we conduct two real-world crowdsourcing experiments and demonstrate that using fine-grained behavioral features is feasible and beneficial in all three quality control mechanisms. Our work provides clues and implications for helping job requesters or crowdsourcing platforms to further achieve better quality control.
Narratives + Diagrams: An Integrated Approach for Externalizing and Sharing People's Causal Beliefs
Authors: Yen Chi-Hsien (Eric) , Cheng Haocong , Yen Grace Yu-Chun , Bailey Brian P. , Huang Yun
Published: October 2021

Causal knowledge is of interest in many areas, such as statistics and machine learning, as it allows people and algorithms to predict outcomes and make data-driven decisions. Researchers in CSCW have proposed tools and workflows to externalize causal knowledge or beliefs from a group of people; however, most of the generated causal diagrams lack a deeper understanding of the causal mechanisms or could not capture diverse beliefs. By integrating narratives with causal diagrams, we implemented an interactive system that allows users to 1) write narratives to rationalize their perceived causal relationships, 2) visualize their causal models using directed diagrams, and 3) review and utilize others' causal diagrams and narratives. We conducted a user study (N=20) to learn how participants leveraged this integrated approach to externalize their perceived causal models for a given application context. Our results showed that the approach implemented in our tool enabled the externalization of users' causal beliefs (e.g., how and why a causal relationship might occur), allowed blind spots of individuals' causal reasoning to be revealed (e.g., learning new ideas from peers), and inspired their causal reasoning (e.g., revising or adding new causal relationships). We also identified the individual differences in people's causal beliefs and observed the impacts of showing others' causal models when one is building his/her causal diagram and narratives. This work provides practical design implications for developing collaborative tools that facilitate capturing and sharing causal beliefs.
Case Studies on the Motivation and Performance of Contributors Who Verify and Maintain In-Flux Tabular Datasets
Authors: Wallace Shaun , Papoutsaki Alexandra , Tan Neilly H. , Guo Hua , Huang Jeff
Published: October 2021

The life cycle of a peer-produced dataset follows the phases of growth, maturity, and decline. Paying crowdworkers is a proven method to collect and organize information into structured tables. However, these tabular representations may contain inaccuracies due to errors or data changing over time. Thus, the maturation phase of a dataset can benefit from the additional human examination. One method to improve accuracy is to recruit additional paid crowdworkers to verify and correct errors. An alternative method relies on unpaid contributors, collectively editing the dataset during regular use. We describe two case studies to examine different strategies for human verification and maintenance of in-flux tabular datasets. The first case study examines traditional micro-task verification strategies with paid crowdworkers, while the second examines long-term maintenance strategies with unpaid contributions from non-crowdworkers. Two paid verification strategies that produced more accurate corrections at a lower cost per accurate correction were redundant data collection followed by final verification from a trusted crowdworker and allowing crowdworkers to review any data freely. In the unpaid maintenance strategies, contributors provided more accurate corrections when asked to review data matching their interests. This research identifies considerations and future approaches to collectively improving information accuracy and longevity of tabular information.
#TeamTrees: Investigating How YouTubers Participate in a Social Media Campaign
Authors: Niu Shuo , Mai Cat , McKim Katherine G. , McCrickard Scott
Published: October 2021

YouTube is not only a platform for content creators to share videos but also a virtual venue for hosting community activities, such as social media campaigns (SMCs). SMCs for public awareness is a growing and reoccurring phenomenon on YouTube, during which content creators make videos to engage their audience and raise awareness of global challenges. However, how the unique celebrity culture on YouTube affects collective actions is an underexplored area. This work examines an SMC on YouTube, #TeamTrees, initiated by a YouTube celebrity and sought to raise people's awareness of tree-planting and climate change. The authors annotated and analyzed 992 #TeamTrees videos to explore how YouTube celebrities, professionals, and amateurs in different channel topics diagnose problems, present solutions, and motivate actions. This study also looks into whether platform identities and framing activities affect campaign reach and engagement. Results suggest that #TeamTrees reached creators who are generally not active in social issues. The participating YouTubers were likely to motivate the viewers to donate and join celebrities' and community's actions, but less involved in examining the environmental problems. Celebrities' videos dominated the campaign's influence. Amateurs' videos had a higher engagement level, although they need more support to frame campaign activities. Based on these findings, we discuss design implications for video-sharing platforms to support future SMCs.
Disproportionate Removals and Differing Content Moderation Experiences for Conservative, Transgender, and Black Social Media Users: Marginalization and Moderation Gray Areas
Authors: Haimson Oliver L. , Delmonaco Daniel , Nie Peipei , Wegner Andrea
Published: October 2021

Social media sites use content moderation to attempt to cultivate safe spaces with accurate information for their users. However, content moderation decisions may not be applied equally for all types of users, and may lead to disproportionate censorship related to people's genders, races, or political orientations. We conducted a mixed methods study involving qualitative and quantitative analysis of survey data to understand which types of social media users have content and accounts removed more frequently than others, what types of content and accounts are removed, and how content removed may differ between groups. We found that three groups of social media users in our dataset experienced content and account removals more often than others: political conservatives, transgender people, and Black people. However, the types of content removed from each group varied substantially. Conservative participants' removed content included content that was offensive or allegedly so, misinformation, Covid-related, adult, or hate speech. Transgender participants' content was often removed as adult despite following site guidelines, critical of a dominant group (e.g., men, white people), or specifically related to transgender or queer issues. Black participants' removed content was frequently related to racial justice or racism. More broadly, conservative participants' removals often involved harmful content removed according to site guidelines to create safe spaces with accurate information, while transgender and Black participants' removals often involved content related to expressing their marginalized identities that was removed despite following site policies or fell into content moderation gray areas. We discuss potential ways forward to make content moderation more equitable for marginalized social media users, such as embracing and designing specifically for content moderation gray areas.
Characterizing Social Imaginaries and Self-Disclosures of Dissonance in Online Conspiracy Discussion Communities
Authors: Phadke Shruti , Samory Mattia , Mitra Tanushree
Published: October 2021

Online discussion platforms provide a forum to strengthen and propagate belief in misinformed conspiracy theories. Yet, they also offer avenues for conspiracy theorists to express their doubts and experiences of cognitive dissonance. Such expressions of dissonance may shed light on who abandons misguided beliefs and under what circumstances. This paper characterizes self-disclosures of dissonance about QAnon-a conspiracy theory initiated by a mysterious leader "Q" and popularized by their followers ?anons"-in conspiratorial subreddits. To understand what dissonance and disbelief mean within conspiracy communities, we first characterize their social imaginaries-a broad understanding of how people collectively imagine their social existence. Focusing on 2K posts from two image boards, 4chan and 8chan, and 1.2 M comments and posts from 12 subreddits dedicated to QAnon, we adopt a mixed-methods approach to uncover the symbolic language representing the movement,expectations,practices,heroes and foes of the QAnon community. We use these social imaginaries to create a computational framework for distinguishing belief and dissonance from general discussion about QAnon, surfacing in the 1.2M comments. We investigate the dissonant comments to characterize the dissonance expressed along QAnon social imaginaries. Further, analyzing user engagement with QAnon conspiracy subreddits, we find that self-disclosures of dissonance correlate with a significant decrease in user contributions and ultimately with their departure from the community. Our work offers a systematic framework for uncovering the dimensions and coded language related to QAnon social imaginaries and can serve as a toolbox for studying other conspiracy theories across different platforms. We also contribute a computational framework for identifying dissonance self-disclosures and measuring the changes in user engagement surrounding dissonance. Our work provide insights into designing dissonance based interventions that can potentially dissuade conspiracists from engaging in online conspiracy discussion communities.
Putting Tools in Their Place: The Role of Time and Perspective in Human-AI Collaboration for Qualitative Analysis
Authors: Feuston Jessica L. , Brubaker Jed R.
Published: October 2021

Large datasets or 'big data' corpora are typically the domain of quantitative scholars, who work with computational tools to derive numerical and descriptive insights. However, recent work asks how computational tools and other technologies, such as AI, can support qualitative scholars in developing deep and complex insights from large amounts of data. Addressing this question, Jiang et al. found that qualitative scholars are generally opposed to incorporating AI in their practices of data analysis. In this paper, we provide nuance to these earlier findings, showing that the stage of qualitative analysis matters for how scholars believe AI can and should be used. Through interviews with 15 CSCW and HCI qualitative researchers, we explore how AI can be included throughout different stages of qualitative analysis. We find that qualitative scholars are amenable to working with AI in diverse ways, such as for data exploration and coding, as long as it assists rather than automates their analytic work practice. Based on our analysis, we discuss how incorporating AI into qualitative research can shift some analytic practices, and how designing for human-AI collaboration in qualitative analysis necessitates considering tradeoffs in scale, abstraction, and task delegation.
Physicality As an Anchor for Coordination: Examining Collocated Collaboration in Physical and Mobile Augmented Reality Settings
Authors: Poretski Lev Poretski , Lanir Joel , Margalit Ram , Arazy Ofer
Published: October 2021

Design and co-creation activities around 3D artifacts often require close collocated coordination between multiple users. Augmented reality (AR) technology can support collocated work enabling users to flexibly work with digital objects while still being able to use the physical space for coordination. With most of current research focusing on remote AR collaboration, less is known about collocated collaboration in AR, particularly in relation to interpersonal dynamics between the collocated collaborators. Our study aims at understanding how shared augmented reality facilitated by mobile devices (mobile augmented reality or MAR) affects collocated users' coordination. We compare the coordination behaviors that emerged in a MAR setting with those in a comparable fully physical setting by simulating the same task -of the shared physical dimension for participants' ability to coordinate in the context of collaborative co-creation. Namely, participants working in a fully physical setting were better able to leverage the work artifact itself for their coordination needs, working in a mode that we term artifact-oriented coordination. Conversely, participants collaborating around an AR artifact leveraged the shared physical workspace for their coordination needs, working in what we refer to as space-oriented coordination. We discuss implications for a AR-based collaboration and propose directions for designers of AR tools.
ChamberBreaker: Mitigating the Echo Chamber Effect and Supporting Information Hygiene through a Gamified Inoculation System
Authors: Jeon Youngseung , Kim Bogoan , Xiong Aiping , LEE DONGWON , Han Kyungsik
Published: October 2021

Because of the increasingly negative impacts of the echo chamber effect, such as the dissemination of fake news and political polarization occurring in social networking services (SNSs), considerable efforts are being made to mitigate this effect. Prior HCI studies have presented the development of user interfaces to display information that reflects various standpoints, with the aim of nudging people to consume information in a more objective fashion. However, these efforts still lack the ability to highlight the characteristics, generation processes, and negative effects of echo chambers, so they may not be effective in helping people become sufficiently aware of the echo chamber effect and those who are already in an echo chamber. In this paper, we present ChamberBreaker (CB), which has been designed to help increase a player's awareness of and preemptively respond to an echo chamber effect based on psychological concepts: inoculation, heuristics for judging, and gamification. Through a user study with 882 participants (control group: 446, experimental group: 436), we demonstrated the feasibility of our game-based methodology to support the awareness of the echo chamber effect and the importance of maintaining diverse perspectives when consuming information. Our findings highlight the externalization of psychological standpoints in mitigating an echo chamber effect and suggest design implications for system development---the consideration of demographics, playing time, and the connection to fake news recognition---for digital literacy education. You can play CB at http://tiny.cc/chamberbreaker (The game only works with Chrome.)
Who is the Expert? Reconciling Algorithm Aversion and Algorithm Appreciation in AI-Supported Decision Making
Authors: Hou Yoyo Tsung-Yu , Jung Malte F.
Published: October 2021

The increased use of algorithms to support decision making raises questions about whether people prefer algorithmic or human input when making decisions. Two streams of research on algorithm aversion and algorithm appreciation have yielded contradicting results. Our work attempts to reconcile these contradictory findings by focusing on the framings of humans and algorithms as a mechanism. In three decision making experiments, we created an algorithm appreciation result (Experiment 1) as well as an algorithm aversion result (Experiment 2) by manipulating only the description of the human agent and the algorithmic agent, and we demonstrated how different choices of framings can lead to inconsistent outcomes in previous studies (Experiment 3). We also showed that these results were mediated by the agent's perceived competence, i.e., expert power. The results provide insights into the divergence of the algorithm aversion and algorithm appreciation literature. We hope to shift the attention from these two contradicting phenomena to how we can better design the framing of algorithms. We also call the attention of the community to the theory of power sources, as it is a systemic framework that can open up new possibilities for designing algorithmic decision support systems.
Thing Constellation Visualizer: Exploring Emergent Relationships of Everyday Objects
Authors: Huang Yi-Ching (Janet) , Cheng Yu-Ting , Liang Rung-Huei , Hsu Jane Yung-jen , Chen Lin-Lin
Published: October 2021

Designing future IoT ecosystems requires new approaches and perspectives to understand everyday practices. While researchers recognize the importance of understanding social aspects of everyday objects, limited studies have explored the possibilities of combining data-driven patterns with human interpretations to investigate emergent relationships among objects. This work presents Thing Constellation Visualizer (thingCV), a novel interactive tool for visualizing the social network of objects based on their co-occurrence as computed from a large collection of photos. ThingCV enables perspective-changing design explorations over the network of objects with scalable links. Two exploratory workshops were conducted to investigate how designers navigate and make sense of a network of objects through thingCV. The results of eight participants showed that designers were actively engaged in identifying interesting objects and their associated clusters of related objects. The designers projected social qualities onto the identified objects and their communities. Furthermore, the designers changed their perspectives to revisit familiar contexts and to generate new insights through the exploration process. This work contributes a novel approach to combining data-driven models with designerly interpretations of thing constellation towards More-Than Human-Centred Design of IoT ecosystems.
Legibility and the Legacy of Racialized Dispossession in Digital Agriculture
Authors: Liu Jen , Sengers Phoebe
Published: October 2021

This paper examines the causes and consequences of legibility as an organizing principle in the design of digital agriculture (DA) systems in the United States. Legibility refers to systems of governance that use simplified understandings of a situation to control and direct action upon it. Legibility in digital agriculture systems occurs at the confluence of two traditions of legibility: the data-driven model common in the design of digital systems, and tactics for the control of nature and labor that have developed in the United States since the foundation of the colonies. Our argument draws from (1) a historical analysis of broader patterns of agricultural technology and racialized land dispossession in what is now the United States and (2) empirical fieldwork that examines the adoption and maintenance of digital agriculture systems in rural New York State. We describe the role that legibility historically has played in the development of agricultural systems in the US, and their consequences for who is able to farm and how. This history raises the questions: What is made legible to whom? In that process, what becomes illegible? While legibility promises transparent and environmentally beneficial control, in our fieldwork we find that the demands of legibility are also restructuring the physical landscape, creating additional invisible labor, producing systems that are brittle to real-world conditions on farms, and creating opaque systems that block people from adapting to their circumstances. In reading our fieldwork together with the historical case, we demonstrate the pressures that are shaping the stakes, subject, and objects of legibility in agricultural technology. As more data-driven systems are used for environmental contexts, the CSCW community needs to extend its ways to understand how data-driven systems impact land, labor, and resources.
A Humanoid Robot’s Effortful Adaptation Boosts Partners’ Commitment to an Interactive Teaching Task
Authors: Vignolo Alessia , Powell Henry , Rea Francesco , Sciutti Alessandra , Mcellin Luke , Michael John
Published: October 2021

We tested the hypothesis that, if a robot apparently invests effort in teaching a new skill to a human participant, the human participant will reciprocate by investing more effort in teaching the robot a new skill, too. To this end, we devised a scenario in which the iCub and a human participant alternated in teaching each other new skills. In the
Adaptive condition
of the
robot teaching phase
, the iCub slowed down its movements when repeating a demonstration for the human learner, whereas in the
Unadaptive condition
it sped the movements up when repeating the demonstration. In a subsequent
participant teaching phase
, human participants were asked to give the iCub a demonstration, and then to repeat it if the iCub had not understood. We predicted that in the
Adaptive condition
, participants would reciprocate the iCub’s adaptivity by investing more effort to slow down their movements and to increase segmentation when repeating their demonstration. The results showed that this was true when participants experienced the
Adaptive condition
after the
Unadaptive condition
and not when the order was inverted, indicating that participants were particularly sensitive to the changes in the iCub’s level of commitment over the course of the experiment.
The Effectiveness of Dynamically Processed Incremental Descriptions in Human Robot Interaction
Authors: Wallbridge Christopher D. , Smith Alex , Giuliani Manuel , Melhuish Chris , Belpaeme Tony , Lemaignan Séverin
Published: October 2021

We explore the effectiveness of a dynamically processed incremental referring description system using under-specified ambiguous descriptions that are then built upon using linguistic repair statements, which we refer to as a dynamic system. We build a dynamically processed incremental referring description generation system that is able to provide contextual navigational statements to describe an object in a potential real-world situation of nuclear waste sorting and maintenance. In a study of 31 participants, we test the dynamic system in a case where a user is remote operating a robot to sort nuclear waste, with the robot assisting them in identifying the correct barrels to be removed. We compare these against a static non-ambiguous description given in the same scenario. As well as looking at efficiency with time and distance measurements, we also look at user preference. Results show that our dynamic system was a much more efficient method—taking only 62% of the time on average—for finding the correct barrel. Participants also favoured our dynamic system.
New Approaches to Glioblastoma
Authors: Khasraw Mustafa , Fujita Yoko , Lee-Chang Catalina , Balyasnikova Irina V. , Najem Hinda , Heimberger Amy B.
Published: October 2021

Faced with unique immunobiology and marked heterogeneity, treatment strategies for glioblastoma require therapeutic approaches that diverge from conventional oncological strategies. The selection and prioritization of targeted and immunotherapeutic strategies will need to carefully consider these features and companion biomarkers developed alongside treatment strategies to identify the appropriate patient populations. Novel clinical trial strategies that interrogate the tumor microenvironment for drug penetration and target engagement will inform go/no-go later-stage clinical studies. Innovative trial designs and analyses are needed to move effective agents toward regulatory approvals more rapidly.
Social Norms Matter: University Students’ Misbehaviors in the Metro Carriage
Authors: Zhang Dong , Baig Farrukh , Mao Song-Kai , Xu Hong-Feng , Jin Hui , Li Li
Published: October 2021

Metro travelers’ travel experience is highly influenced by fellow passengers’ misbehaviors such as eating or littering in the carriage and sound blaster, which are common in the metro carriage. Although operators have implemented various regulations to reduce misbehavior, little theoretical research has investigated such behavior motivators to provide targeted guidelines for specific passenger segments. To this end, this study explores how demographic and perceived social norms of university students affect their misbehaviors, i.e., eating in the carriage, public display of affection, sound blaster, cross-legged sitting, leaning against the pole, and littering, in the metro carriage of Shanghai, China. With the structural equation model, it is revealed that both injunctive and descriptive norms impose significant impacts on passengers’ inappropriate behaviors, with the effect of the former generally to a greater degree. Gender heterogeneity in passenger misbehavior is also observed, where males significantly perform better in eating in the carriage and cross-legged sitting. These findings may decode the underlying motivation of passenger misbehaviors and provide guidelines for effective intervention with targeted policy design and implementation.
RDMMFET: Representation of Dense Multimodality Fusion Encoder Based on Transformer
Authors: Zhang Xu , Han DeZhi , Chang Chin-Chen
Published: October 2021

Visual question answering (VQA) is the natural language question-answering of visual images. The model of VQA needs to make corresponding answers according to specific questions based on understanding images, the most important of which is to understand the relationship between images and language. Therefore, this paper proposes a new model, Representation of Dense Multimodality Fusion Encoder Based on Transformer, for short, RDMMFET, which can learn the related knowledge between vision and language. The RDMMFET model consists of three parts: dense language encoder, image encoder, and multimodality fusion encoder. In addition, we designed three types of pretraining tasks: masked language model, masked image model, and multimodality fusion task. These pretraining tasks can help to understand the fine-grained alignment between text and image regions. Simulation results on the VQA v2.0 data set show that the RDMMFET model can work better than the previous model. Finally, we conducted detailed ablation studies on the RDMMFET model and provided the results of attention visualization, which proves that the RDMMFET model can significantly improve the effect of VQA.
The Effects of Sesame Consumption on Glycemic Control in Adults: A Systematic Review and Meta-Analysis of Randomized Clinical Trial
Authors: Yargholi Alireza , Najafi Mohammad Hasan , Zareian Mohammad Ali , Hawkins Jessie , Shirbeigi Laila , Ayati Mohammad Hossein
Published: October 2021

Objectives. In recent years, diabetes has become a global health problem that creates a tremendous economic burden for many countries. Clinical trials evaluating the hypoglycemic effects of sesame consumption have produced conflicting results. This systematic review and meta-analysis was conducted to evaluate the effectiveness of sesame as a popular natural herb on glycemic indices in adults. Methods. The search for related articles in PubMed, Scopus, Google Scholar, and Cochrane library was conducted through May 2021. Results were reported as weighted mean differences (WMD) with 95% confidence intervals (CI) using a random-effects model. Results. A total of 605 studies were identified through online searching, and a total of eight RCTs representing 382 participants were included in this study. The meta-analyses revealed that sesame consumption significantly decreases serum fasting blood sugar (FBS): (WMD: −28.23 mg/dl; 95% CI (−39.16, −17.13), I2 = 97.6%; 95% CI (96, 98)), and hemoglobin A1c (HbA1c): (WMD: −1.00%; 95% CI (−1.11, −0.88), I2 = 0%; 95% CI (0, 79)) as compared to the control group. Conclusion. This study provides evidence of the hypoglycemic effects of sesame consumption, particularly in diabetic patients. Additional RCTs on sesame and its preparations should be conducted in different populations to increase generalizability.
Multiple Positive Solutions for a Class of Boundary Value Problem of Fractionalp,q-Difference Equations underp,q-Integral Boundary Conditions
Authors: Liu Yongyang , Liu Yansheng
Published: October 2021

This paper is mainly concerned with a class of fractionalp,q-difference equations underp,q-integral boundary conditions. Multiple positive solutions are established by using the topological degree theory and Krein–Rutman theorem. Finally, two examples are worked out to illustrate the main results.
Bacterial Etiology of Bloodstream Infections and Antimicrobial Resistance Patterns from a Tertiary Care Hospital in Malé, Maldives
Authors: Maharath Aishath , Ahmed Mariyam Shabeena
Published: October 2021

Background. Bloodstream infections pose a significant health problem worldwide and is a major cause of morbidity and mortality in many countries. It is important to have country-specific data for major pathogens causing bloodstream infections, in light of emerging resistance patterns of common bacterial isolates. Due to the scarcity of reports in this area, the aim of this study was to identify bacterial pathogens causing bloodstream infections among the study population. Methods. A retrospective analysis of blood culture samples received at the Department of Laboratory Medicine, Indira Gandhi Memorial Hospital, Malé, Maldives, was performed for reports between January 2016 and December 2017. Results. Out of the 471 culture-positive samples, 278 (59%) were males and 193 (41%) were females. Amongst the culture-positive samples, 338 (71.8%) Gram-positive organisms were isolated and 133 (28.2%) Gram-negative organisms were isolated. Coagulase-negative Staphylococcus (CoNS) was the most frequently isolated blood-borne bacterial pathogen in this study, accounting for 53.6% and 50.9% of the isolates in 2016 and 2017, respectively. Other frequently isolated pathogens included Staphylococcus aureus (15.9% and 10.3%), Klebsiella spp. (10.5% and 16.4%), and Escherichia coli (7.1% and 10.8%). Coagulase-negative Staphylococcus (CoNS) revealed high percentage of resistance among the tested antimicrobials, ampicillin, cephalexin, cefotaxime, and gentamicin. Over the two years, a significant difference between the percentage resistance among paediatric and adult patients was observed for coagulase-negative Staphylococcus (CoNS) isolate resistance to ampicillin

p
≤
0.001

, cephalexin

p
≤
0.001

, cefotaxime

p
≤
0.001

, gentamicin

p
=
0.008

, and cotrimoxazole (SXT)

p
≤
0.001

. When comparing the significant antimicrobial resistance trends, it can be seen that Enterobacteriaceae isolates also demonstrated high resistance to ampicillin and gentamicin as well as second- and third-generation cephalosporins. Conclusions. This study highlights the major bacterial pathogens involved in bloodstream infections in the healthcare setting of Malé, Maldives, and antibiotic resistance patterns. The results indicate that further characterization of bacteremia and its resistance patterns is needed to combat bloodstream infections.