bluuebunny/crossref_metadata_2025 huggingface dataset:
Viscous dark energy accretion activities : sonic speed, angular momentum and Mach number studies
Authors: Dutta Sandip , Biswas Promila , Biswas Ritabrata
Published: April 2021

In this present article, we study different accretion properties regarding viscous accretion of dark energy. Modified Chaplygin gas is chosen as the dark energy candidate. Viscosity is encountered with the help of Shakura–Sunyaev viscosity parameter. We study sonic speed vs radial distance curves. We compare between adiabatic and dark energy dominated cases and follow that sonic speed falls as we go nearer to the central gravitating object. As viscosity is imposed, a threshold drop in accretion sonic speed is followed. Average rate of fall in accretion sonic speed is increased with black hole’s spin. This is signifying that this kind of accretion is weakening the overall matter/energy infall. Specific angular momentum to Keplerian angular momentum ratio is found to fall as we go far from the black hole. Accretion Mach number turns high as we go towards the inner region and high wind Mach number is not allowed as we are going out. Combining, we conclude that the system weakens the feeding process of accretion.
PARAMETER ESTIMATION OF A SIMPLE, REALISTIC STOCHASTIC MODEL OF GASTRIC EMPTYING OF PELLETS UNDER FASTING CONDITIONS
Authors: SATIRACOO PAIROTE , DE GAETANO ANDREA
Published: April 2021

This study is aimed to develop a parameter estimation procedure for a stochastic model of gastric emptying of pellets under fasting conditions of healthy subjects that represent the irregular decrements of gastric contents after administration of pellets. An algorithm for the identification of experimental subjects into subgroups based on their gastric emptying rates has been proposed. The parameter estimation procedure was performed with observational data of gastric emptying profiles of 19 subjects from the existing literature. The identification algorithm was validated through comparison against the original results, showing that seven subjects indicated slow gastric emptying and 12 subjects indicated fast emptying. After excluding one subject with gastric stasis, the optimal results obtained from this study classified subjects into three subgroups: two subjects with slow, five subjects with intermediate and 11 subjects with fast gastric emptying. The proposed parameter estimation procedure of the stochastic model is relatively easy to implement and can be used to analyze real patients’ data. The algorithm for identification of the gastric emptying dynamics in experimental subjects could provide useful information for further investigations of long-term energy balance forecasting.
The diophantine equation x2 + paqb = yq
Authors: Godinho Hemar , Neumann Victor G. L.
Published: April 2021

In this paper, we consider the Diophantine equation in the title, where [Formula: see text] are distinct odd prime numbers and [Formula: see text] are natural numbers. We present many results given conditions for the existence of integers solutions for this equation, according to the values of [Formula: see text] and [Formula: see text]. Our methods are elementary in nature and are based upon the study of the primitive divisors of certain Lucas sequences as well as the factorization of certain polynomials.
The Real Economic Dimensions of Climate Change
Authors: Warner Koko , Zommers Zinta , Wreford Anita
Published: April 2021

The impacts of COVID-19 and efforts to stimulate recovery from the pandemic have highlighted the need for information about how disasters affect the real economy: temporal and spatial dynamics, cascading risks of disruption to employment, debt, trade, investments, bond markets, and real estate markets, among others. This commentary explores what information on the economic dimensions of climate change is needed to inform decisions about adapting to and effectively averting, minimizing, and addressing climate risks. We review the economic information presented in special reports from the IPCC AR6 cycle (SR1.5, SROCC, and SRCCL). We find that the information presented in these reports expands beyond costs of mitigation options, and potential negative GDP effects of climate impacts to include real economic dimensions in food production and land use (forestry and agriculture), coastal areas and fisheries, among others. This reflects an emerging literature which addresses a wider spectrum of economic and financial aspects relevant to climate change and national and regional priorities. Five emerging areas of work related to climate impacts on the real economy and on financial services provide essential additional information for decisions about efforts at all levels to achieve the objectives of the Paris Agreement and the overall objective of the UNFCCC Convention. Insights from economic analysis of the coronavirus pandemic—a sustained, complex disaster with global consequences across the real economy and financial services—can help highlight useful areas of research and discussion for policy makers considering climate impacts, vulnerabilities, and risks.
CONTRIBUTION OF AGROFORESTS IN ADAPTATION OF CLIMATE CHANGE EFFECTS: ESTIMATING THE PHYTODIVERSITY AND C SEQUESTRATION POTENTIAL IN 8-, 16- AND 24-YEAR-OLD OF PINUS SYLVESTRIS AGROFORESTS IN NORTHERN CAMEROON (TROPICAL AFRICA)
Authors: NOIHA NOUMI V. , KOUAM KAMNING P. , KAMDOUM DEMGUIA C. , ZAPFACK L.
Published: April 2021

The study aims at assessing the agrobiodiversity and carbon stocks by the pine agroforests in the Sudano-Guinean zone of Cameroon. Five [Formula: see text][Formula: see text]m sampling transects were established in each chronosequence, it was undertaken to assess the growth characteristics and biomass. Estimates of stocks of carbon in aboveground biomass, belowground biomass (BGB), total biomass (TB) and CO2 equivalent stock were incorporated in allometric equation based on nondestructive method. A total of 24 species from 23 genera and 17 families were inventoried. Annona senegalensis, Syzygium guineensis and Hymenocardia acida contributed the most to the importance value index (IVI). Density ranged between [Formula: see text]–[Formula: see text] stems/ha; basal area between [Formula: see text]–[Formula: see text][Formula: see text]m2/ha; Shannon index between [Formula: see text]–[Formula: see text] with the highest value for 8-year-old stands; Pielou’s evenness between [Formula: see text]–[Formula: see text] with the lowest value in 24-year-old stands. Aboveground biomass ranged between [Formula: see text]–[Formula: see text] Mg C/ha with the highest value in 16-year-old stands; belowground carbon from [Formula: see text] Mg C/ha to [Formula: see text] Mg C/ha and total carbon from [Formula: see text] Mg C/ha to [Formula: see text] Mg C/ha. The sequestration potential ranged from [Formula: see text] Mg CO[Formula: see text]/ha to [Formula: see text] Mg CO[Formula: see text]/ha. The sequestration rates were 84.77, 49.7 and 28.6 Mg CO[Formula: see text].ha[Formula: see text]yr[Formula: see text] in 8-, 16- and 24-year-old stands, respectively. Although our data reported that pine stands hosted a few number of species; they are true carbon sinks and useful to the REED[Formula: see text] community.
Transitioning Spiking Neural Network Simulators to Heterogeneous Hardware
Authors: Nguyen Quang Anh Pham , Andelfinger Philipp , Tan Wen Jun , Cai Wentong , Knoll Alois
Published: April 2021

Spiking neural networks (SNN) are among the most computationally intensive types of simulation models, with node counts on the order of up to 10
11
. Currently, there is intensive research into hardware platforms suitable to support large-scale SNN simulations, whereas several of the most widely used simulators still rely purely on the execution on CPUs. Enabling the execution of these established simulators on heterogeneous hardware allows new studies to exploit the many-core hardware prevalent in modern supercomputing environments, while still being able to reproduce and compare with results from a vast body of existing literature. In this article, we propose a transition approach for CPU-based SNN simulators to enable the execution on heterogeneous hardware (e.g., CPUs, GPUs, and FPGAs), with only limited modifications to an existing simulator code base and without changes to model code. Our approach relies on manual porting of a small number of core simulator functionalities as found in common SNN simulators, whereas the unmodified model code is analyzed and transformed automatically. We apply our approach to the well-known simulator NEST and make a version executable on heterogeneous hardware available to the community. Our measurements show that at full utilization, a single GPU achieves the performance of about 9 CPU cores. A CPU-GPU co-execution with load balancing is also demonstrated, which shows better performance compared to CPU-only or GPU-only execution. Finally, an analytical performance model is proposed to heuristically determine the optimal parameters to execute the heterogeneous NEST.
Security Threat Analyses and Attack Models for Approximate Computing Systems
Authors: Yellu Pruthvy , Buell Landon , Mark Miguel , Kinsy Michel A. , Xu Dongpeng , Yu Qiaoyan
Published: April 2021

Approximate computing (AC) represents a paradigm shift from conventional precise processing to inexact computation but still satisfying the system requirement on accuracy. The rapid progress on the development of diverse AC techniques allows us to apply approximate computing to many computation-intensive applications. However, the utilization of AC techniques could bring in new unique security threats to computing systems. This work does a survey on existing circuit-, architecture-, and compiler-level approximate mechanisms/algorithms, with special emphasis on potential security vulnerabilities. Qualitative and quantitative analyses are performed to assess the impact of the new security threats on AC systems. Moreover, this work proposes four unique visionary attack models, which systematically cover the attacks that build covert channels, compensate approximation errors, terminate normal error resilience mechanisms, and propagate additional errors. To thwart those attacks, this work further offers the guideline of countermeasure designs. Several case studies are provided to illustrate the implementation of the suggested countermeasures.
Efficient One-pass Synthesis for Digital Microfluidic Biochips
Authors: Mohammadzadeh Naser , Wille Robert , Keszocze Oliver
Published: April 2021

Digital microfluidics biochips are a promising emerging technology that provides fluidic experimental capabilities on a chip (i.e., following the
lab-on-a-chip
paradigm). However, the design of such biochips still constitutes a challenging task that is usually tackled by multiple individual design steps, such as binding, scheduling, placement, and routing. Performing these steps consecutively may lead to design gaps and infeasible results. To address these shortcomings, the concept of one-pass design for digital microfluidics biochips has recently been proposed—a holistic approach avoiding the design gaps by considering the whole synthesis process as large. But implementations of this concept available thus far suffer from either high computational effort or costly results. In this article, we present an efficient one-pass solution that is runtime efficient (i.e., rarely needing more than a second to successfully synthesize a design) while, at the same time, producing better results than previously published heuristic approaches. Experimental results confirm the benefits of the proposed solution and allow for realizing really large assays composed of thousands of operations in reasonable runtime.
The Language of Situational Empathy
Authors: Zhou Ke , Aiello Luca Maria , Scepanovic Sanja , Quercia Daniele , Konrath Sara
Published: April 2021

Empathy is the tendency to understand and share others' thoughts and feelings. Literature in psychology has shown through surveys potential beneficial implications of empathy. Prior psychology literature showed that a particular type of empathy called "situational empathy" --- an immediate empathic response to a triggering situation (e.g., a distressing situation) --- is reflected in the language people use in response to the situation. However, this has not so far been properly measured at scale. In this work, we collected 4k textual reactions (and corresponding situational empathy labels) to different stories. Driven by theoretical concepts, we developed computational models to predict situational empathy from text and, in so doing, we built and made available a list of empathy-related words. When applied to Reddit posts and movie transcripts, our models produced results that matched prior theoretical findings, offering evidence of external validity and suggesting its applicability to unstructured data. The capability of measuring proxies for empathy at scale might benefit a variety of areas such as social media, digital healthcare, and workplace well-being.
Exploring Lightweight Interventions at Posting Time to Reduce the Sharing of Misinformation on Social Media
Authors: Jahanbakhsh Farnaz , Zhang Amy X. , Berinsky Adam J. , Pennycook Gordon , Rand David G. , Karger David R.
Published: April 2021

When users on social media share content without considering its veracity, they may unwittingly be spreading misinformation. In this work, we investigate the design of lightweight interventions that nudge users to assess the accuracy of information as they share it. Such assessment may deter users from posting misinformation in the first place, and their assessments may also provide useful guidance to friends aiming to assess those posts themselves.

In support of lightweight assessment, we first develop a taxonomy of the reasons why people believe a news claim is or is not true; this taxonomy yields a checklist that can be used at posting time. We conduct evaluations to demonstrate that the checklist is an accurate and comprehensive encapsulation of people's free-response rationales.

In a second experiment, we study the effects of three behavioral nudges---1) checkboxes indicating whether headings are accurate, 2) tagging reasons (from our taxonomy) that a post is accurate via a checklist and 3) providing free-text rationales for why a headline is or is not accurate---on people's intention of sharing the headline on social media. From an experiment with 1668 participants, we find that both providing accuracy assessment and rationale reduce the sharing of false content. They also reduce the sharing of true content, but to a lesser degree that yields an overall decrease in the fraction of shared content that is false.

Our findings have implications for designing social media and news sharing platforms that draw from richer signals of content credibility contributed by users. In addition, our validated taxonomy can be used by platforms and researchers as a way to gather rationales in an easier fashion than free-response.
Designing Sharing Economy Platforms through a 'Solidarity HCI' lens
Authors: Ntouros Vasilis , Kouki Hara , Vlachokyriakos Vasilis
Published: April 2021

Despite sharing economy's promise of a novel, inclusive and community building socio-techno-economic system, sharing economy has been indicted, among others for profiteering from previously private and occasionally non-monetized activities, for turning the activity of sharing into an individualistic and impersonal one, for reproducing stereotypes and creating precarious jobs. In the epicenter of such critiques are the 'big' and 'limelight-ed' platform-firms, such as AirBnB and Uber and the digital infrastructures they employ. To the best of our knowledge, the majority of related research focuses on sharing economy platforms of this ilk. In response, acknowledging that the problem is not the agency of the digital in the activity of sharing per se, but that the wrong people set the terms, design and benefit from this mediation, we find it timely to explore the existence of community-driven sharing economy initiatives and explore how they use the digital to support their ends. As a result, in this paper we report from our engagement with a ride-sharing initiative, called 'Share the ride ;)' that has operated within a Facebook group since 2009 and is the most popular ride-sharing 'platform' in Greece. Extrapolating from our findings and while adopting a 'Solidarity HCI' approach, a call to design for 'human' rather than market needs, we participate in the 'sharing discourse' by providing design implications for the development of sharing economy platforms which can favor community building, participation, self-organization and the nurturing of a generative sharing ideology. To this end, we suggest the development of malleable sharing economy platforms and of mechanisms that can support the development of relational trust(s) and enduring social connections. Finally, we underscore that in order to favor the establishment of such relations, those platforms should employ architectures which esteem pluralism and self-affirmation.
Plan Early, Revise More
Authors: Krishna Kumaran Sneha R. , Yin Yue , Bailey Brian P.
Published: April 2021

Receiving feedback on preliminary work allows content creators to gain insight and improve outcomes. However, a lack of commitment for gathering feedback and experiencing evaluation apprehension can delay feedback seeking. In this paper, we operationalize goal setting theory for planning feedback goals and test the effects on feedback seeking and revision. In an online experiment, participants (N=245) wrote an initial story after planning feedback goals (or not), submitted the story for feedback at a time of their choice, and revised the story based on feedback received. Participants anticipated feedback from a supervisor or peer to induce different levels of evaluation apprehension. We found that participants who planned proximal feedback goals sought feedback when their stories were less developed and revised the stories more after receiving feedback compared to when participants planned distant goals. Additionally, participants who anticipated feedback from a supervisor, regardless of goal planning, improved the quality of their stories the most. We did not find that goal setting or the provider's role affected evaluation apprehension. Our findings indicate that content creators should be guided to plan proximal feedback goals to encourage sharing of early drafts of creative work and receive feedback from someone in a position of higher perceived power to foster the most revision and improvement on those drafts.
An Image of Society
Authors: Metaxa Danaë , Gan Michelle A. , Goh Su , Hancock Jeff , Landay James A.
Published: April 2021

Algorithmically-mediated content is both a product and producer of dominant social narratives, and it has the potential to impact users' beliefs and behaviors. We present two studies on the content and impact of gender and racial representation in image search results for common occupations. In Study 1, we compare 2020 workforce gender and racial composition to that reflected in image search. We find evidence of underrepresentation on both dimensions: women are underrepresented in search at a rate of 42% women for a field with 50% women; people of color are underrepresented with 16% in search compared to an occupation with 22% people of color (the latter being proportional to the U.S. workforce). We also compare our gender representation data with that collected in 2015 by Kay et al., finding little improvement in the last half-decade. In Study 2, we study people's impressions of occupations and sense of belonging in a given field when shown search results with different proportions of women and people of color. We find that both axes of representation as well as people's own racial and gender identities impact their experience of image search results. We conclude by emphasizing the need for designers and auditors of algorithms to consider the disparate impacts of algorithmic content on users of marginalized identities.
Understanding the Technological Practices and Needs of Music Therapists
Authors: Baglione Anna N. , Clemens Michael Paul , Maestre Juan F. , Min Aehong , Dahl Luke , Shih Patrick C.
Published: April 2021

Music therapists provide critical, evidence-based care to a diverse range of clients. However, despite their active role in empowering individuals affected by disability, stigma, grief, and trauma, music therapists remain understudied by the HCI community. We present the results of a mixed methods study of 10 interviewees and 20 survey respondents in the U.S., all of whom are practicing music therapists. Our results show that music therapists engage in technology-aided practices such as making personalized connections with clients, assisting in identity formation, encouraging musicking (music-making), and preserving legacies. Results also show that music therapists face key challenges such as environmental, societal, and financial constraints, including high workload, lack of awareness of the value of music therapy among the general community, and limited access to secure technologies for remote client care. In light of these challenges, we present a set of design implications for creating future technologies for music therapists. This work diverges from previous studies on music therapy technologies, which focus largely on interventions with music therapy clients, by highlighting the often-neglected perspectives from music therapists.
Leveling Up Teamwork in Esports
Authors: Musick Geoff , Zhang Rui , McNeese Nathan J. , Freeman Guo , Hridi Anurata Prabha
Published: April 2021

A large body of research has underscored the importance of the cognitive process of team cognition and its relation to team performance. However, little research has focused on applying such an important teamwork process to computer-mediated collaboration within a fast-paced virtual environment. In this paper, we use esports as a research platform to address this limitation due to its fast-paced nature and its heavy reliance on teamwork. We report the experience and perceptions of 20 players with regard to their descriptions of team cognition within esports. We found that esports players relied on their game experience and understanding of role interdependencies in order to develop team cognition with strangers. We also found that experienced teams utilized a mutual understanding of teammate skills and personalities in order to predict responses and limit the verbal communication required to make quick team decisions. We contribute to CSCW by extending the cognitive understanding of computer-mediated collaboration and by advancing research on team cognition and how it can occur within a fast-paced virtual environment.
Construction of Shared Situational Awareness in Traffic Management
Authors: Jones Ridley , Beach Michael W. , McClure Haughey Melinda , Sutherland Will , Lee Charlotte P.
Published: April 2021

The construct of "situational awareness" (SA) has a rich and productive history within both academic literature and practice. Situational awareness as a technical term has its earliest roots in formative human factors research in service of military and flight applications. However, its value as a construct in other domains, particularly those having to do with rapid sensemaking in safety-sensitive conditions, has led to a broader applied and theoretical interest over the past few decades. As a discipline, CSCW has been relatively less engaged with this concept, but has empirical and theoretical tools that will be valuable to its study. To bring CSCW more fully into the conversation, we present a description of how operators in a city department of transportation's transportation management center (TMC) develop and maintain situational awareness for themselves and the key recipients of their critical information outputs. We identify some of the schemas operators must develop in order to effectively construct situational awareness and dynamically articulate common fields of work, and the social collaborative practices they engage in to support that awareness. Implications for design and further research are proposed.
Wikipedia Beyond the English Language Edition
Authors: Bipat Taryn , Alimohammadi Negin , Yu Yihan , McDonald David W. , Zachry Mark
Published: April 2021

Do models of collaboration among contributors of Wikipedia generalize beyond the larger, western editions of the encyclopedia? In this study, we expanded upon the known collaborative mechanisms on the English Wikipedia and demonstrated that the collaboration model is best captured through the interplay of these mechanisms. We annotated talk page conversations for types of power plays or vies for control over edits that are made to articles, to understand how policy and power play mechanisms in editors' discussions account for behavior in English (EN), Farsi (FA), and Chinese (ZH) language editions of Wikipedia. Our findings show that the same power plays used in EN exist in both FA and ZH but the frequency of their usage differs across the editions. These variations suggest that editors in different language communities value contrasting types of policies to compete for power while discussing and editing articles. Our study contributes to a deeper understanding of how collaboration models developed from a western perspective translate to non-western languages.
Understanding the Education of Children with Autism in Bangladesh
Authors: Hridi Anurata Prabha , Saha Anik , Sinha Anik , Abeer Ifti Azad , Ahmed Nova , Ahmed Shameem
Published: April 2021

This paper aims to understand the educational settings of children with autism (CWA) in the specialized schools of Bangladesh from their parents' perspective. Sparse research in the same context to understand teachers' perceptions has been found. Though parents and teachers are an integral part of these children's educational experience, limited research has been conducted to deeply understand and facilitate parent-teacher relationships to offer CWA an improved learning experience in Bangladesh. Our in-depth qualitative study on urban parents (N=10) showed current challenges and opportunities relating to the schools designed for CWA. The work takes a closer look at the existing concerns and confirms that parent-teacher communication in autism schools here is a complex one because of other interferences (e.g., social, and familial stigma, lack of transparency from administrators, insufficient qualified teachers) as felt by the parents. Nevertheless, the remarkable coverage of cellular networks and increasing usage of mobile phones in Bangladesh places us in a unique position to expect positive changes by employing ICT, which we believe will open avenues for future researchers and guide them in implementing robust technology to support an improved learning environment for the CWA and their families.
Lost in Co-curation
Authors: Park So Yeon , Lee Sang Won
Published: April 2021

Online tools enable users to co-create artifacts remotely. However, creative collaborations can also occur for the social process of collaboration itself, for which measures of success and engagement expectations can be more ambiguous, and individuals' dedication and social dynamics more important. Co-curating music in collaborative playlists (CPs) is one example of creative collaboration that encompasses both roles, and can therefore have more subtleties within its interactions. We conducted two studies using online surveys to understand perceived comfort with and hesitation toward the social dynamics embedded in CPs. Differences in collaborators' ownership perceptions toward CPs and their comfort in interacting with these CPs emerged. We also found a varying desire for situated communication, dependent upon the action taken and perceived ownership (of both a CP and the songs contained), with more users expecting greater comfort when a communication channel exists. From these results, we present four design considerations for more positive and engaging experiences in creative online co-curation.
A Transformer-based Framework for Neutralizing and Reversing the Political Polarity of News Articles
Authors: Liu Ruibo , Jia Chenyan , Vosoughi Soroush
Published: April 2021

People often prefer to consume news with similar political predispositions and access like-minded news articles, which aggravates polarized clusters known as "echo chamber". To mitigate this phenomenon, we propose a computer-aided solution to help combat extreme political polarization. Specifically, we present a framework for reversing or neutralizing the political polarity of news headlines and articles. The framework leverages the attention mechanism of a Transformer-based language model to first identify polar sentences, and then either flip the polarity to the neutral or to the opposite through a GAN network. Tested on the same benchmark dataset, our framework achieves a 3%-10% improvement on the flipping/neutralizing success rate of headlines compared with the current state-of-the-art model. Adding to prior literature, our framework not only flips the polarity of headlines but also extends the task of polarity flipping to full-length articles. Human evaluation results show that our model successfully neutralizes or reverses the polarity of news without reducing readability. We release a large annotated dataset that includes both news headlines and full-length articles with polarity labels and meta-data to be used for future research. Our framework has a potential to be used by social scientists, content creators and content consumers in the real world.
Detection of Social Identification in Workgroups from a Passively-sensed WiFi Infrastructure
Authors: Zakaria Camellia , Lee Youngki , Balan Rajesh
Published: April 2021

Social identification: how much individuals psychologically associate themselves with a group has been posited as an essential construct to measure individual and group dynamics. Studies have shown that individuals who identify very differently from their workgroup provide critical cues to the lack of social support or work overloads. However, measuring identification is typically achieved through time-consuming and privacy-invasive surveys. We hypothesize that the extremities in-group norm affects individuals' behaviors, thus more likely to give rise to negative appraisals. As a more convenient and less-invasive technique, we propose a method to predict individuals who are increasingly different in identifying themselves with their working peers using mobility data passively sensed from the WiFi infrastructure. To test our hypothesis, we collected WiFi data of 62 college students over a whole semester. Students provided regular self-reports on their identification towards a workgroup as ground truth. We analyze the contrasts between groups' mobility patterns and build a classification model to determine students who identify very differently from their workgroup. The classifier achieves approximately 80% True Positive Rate (TPR), 73% True negative rate (TNR), and 78% Accuracy (ACC). Such a mechanism can help distinguish students who are more likely to struggle with negative workgroup appraisals and enable interventions to improve their overall team experience.
The Work of Workplace Disclosure
Authors: Ganesh Kausalya , Lazar Amanda
Published: April 2021

Health disclosure at work is complicated for people with invisible chronic conditions. Due to the lack of visible symptoms, invisible conditions affect the work life of people in ways that are not obvious to others. This study examines how people disclose and conceal their conditions in the workplace and opens the design space for this topic. In the first phase, we analyzed posts on two subreddit forums, r/migraine and r/fibromyalgia, and found a range of strategies that individuals use to disclose or conceal their conditions. In the second phase, we created five technological design concepts based on these strategies that were shown to eight people with migraines or fibromyalgia in semi-structured interviews. Based on these phases, we contribute understandings of disclosure and concealment of invisible conditions in the workplace for future research, such as potential areas for intervention ranging from individual to societal level efforts, as well as the potential and limitations of relying on empathy from others.
Beyond the Command
Authors: Wagman Kelly B. , Parks Lisa
Published: April 2021

Machines, from artificially intelligent digital assistants to embodied robots, are becoming more pervasive in everyday life. Drawing on feminist science and technology studies (STS) perspectives, we demonstrate how machine designers are not just crafting neutral objects, but relationships between machines and humans that are entangled in human social issues such as gender and power dynamics. Thus, in order to create a more ethical and just future, the dominant assumptions currently underpinning the design of these human-machine relations must be challenged and reoriented toward relations of justice and inclusivity. This paper contributes the "social machine" as a model for technology designers who seek to recognize the importance, diversity and complexity of the social in their work, and to engage with the agential power of machines. In our model, the social machine is imagined as a potentially equitable relationship partner that has agency and as an "other" that is distinct from, yet related to, humans, objects, and animals. We critically examine and contrast our model with tendencies in robotics that consider robots as tools, human companions, animals or creatures, and/or slaves. In doing so, we demonstrate ingrained dominant assumptions about human-machine relations and reveal the challenges of radical thinking in the social machine design space. Finally, we present two design challenges based on non-anthropomorphic figuration and mutuality, and call for experimentation, unlearning dominant tendencies, and reimagining of sociotechnical futures.
Promoting Self-Efficacy Through an Effective Human-Powered Nonvisual Smartphone Task Assistant
Authors: Rodrigues André , Santos André R.B. , Montague Kyle , Guerreiro Tiago
Published: April 2021

Accessibility assessments typically focus on determining a binary measurement of task performance success/failure; and often neglect to acknowledge the nuances of those interactions. Although a large population of blind people find smartphone interactions possible, many experiences take a significant toll and can have a lasting negative impact on the individual and their willingness to step out of technological comfort zones. There is a need to assist and support individuals with the adoption and learning process of new tasks to mitigate these negative experiences. We contribute with a human-powered nonvisual task assistant for smartphones to provide pervasive assistance. We argue, in addition to success, one must carefully consider promoting and evaluating factors such as self-efficacy and the belief in one's own abilities to control and learn to use technology. In this paper, we show effective assistant positively affects self-efficacy when performing new tasks with smartphones, affects perceptions of accessibility and enables systemic task-based learning.
Design Ethics in Practice - Points of Departure
Authors: Lindberg Sharon , Karlström Petter , Männikkö Barbutiu Sirkku
Published: April 2021

Recent years have seen an increased interest in ethically responsible design of technology in society at large as well as among design practitioners. While design ethics is a well-established research area, less research has enquired into how design practitioners understand ethics. Through semi-structured interviews we have explored practitioners' approaches to ethics in design. Our findings show noticing, reflecting and reacting as three ways that ethical matters are approached. This ties into dynamic and participatory views on ethics, rather than viewing ethics as a set of formal frameworks and deductive reasoning practices. Practitioners need approaches that tie in to existing work practices. Our results provide practice-led suggestions and points of departure of when and how it might be suitable to infuse design ethics into design practice.
Speaking from Experience
Authors: Rincón Cami , Keyes Os , Cath Corinne
Published: April 2021

Voice-Activated Artificial Intelligence (VAI) is increasingly ubiquitous, whether appearing as context-specific conversational assistants or more personalised and generalised personal assistants such as Alexa or Siri. CSCW and other fields have regularly studied the (positive and negative) social consequences of VAI design and deployment. One particular focus has been questions of gender, and the implications that the (often-feminine) gendering of VAIs has for societal norms and user experiences. Studies into this have largely elided transgender (trans) existences; the few exceptions to this operate largely from an external and predetermined idea of trans and/or non-binary user needs, centered on representation. In this study, we undertook a series of qualitative interviews with trans and/or non-binary users of VAIs to explore their experiences and needs. Our results show that these needs are far more than simply improvements to representation, and that users raise substantial concerns around the underlying framing of gender by even well-intentioned developers, the privacy and safety implications of ubiquitous VAI, and the motivations of the vast for-profit companies that deploy much of this technology. We provide both immediate recommendations for designers and researchers seeking to create trans-inclusive VAIs, and wider, critical proposals for how we as researchers go about assessing technological systems and appropriate points of intervention.
How Not to Measure Social Network Privacy
Authors: Li Yao , Ghaiumy Anaraky Reza , Knijnenburg Bart
Published: April 2021

Privacy has been conceptualized as a multi-dimensional construct in prior research. However, most multi-dimensional conceptualizations were developed based on populations from western countries. It remains an open question whether the underlying dimensions of privacy stays consistent in non-western countries. Through a series of factor analyses on two survey datasets, we compare the dimensions of privacy concern, information disclosure, general disclosiveness, and privacy management strategies among social network users in the US, China and South Korea. We find significant cross-country differences in the dimensions of these privacy-related concepts, indicating that the fundamental understanding of these concepts varies substantially across these countries. We discuss possible explanations of these cross-country differences and make methodological suggestions for future work.
Protect and Project
Authors: Tally Anne Clara , Kim Yu Ra , Boustani Katreen , Nippert-Eng Christena
Published: April 2021

Video game players face a fundamental challenge in managing their competing desires for both privacy and publicity, for being both apart from, and a part of, the communities in which they play. In this paper, we argue that "gamertags" are important tools for protecting gamers' privacy as well as creative outlets for expressing meaningful aspects of identity. Based on 30 semi-structured interviews focused on players' usernames, we find through the pseudonyms under which they play, gamers both hide identifying information such as their offline names and addresses while bringing attention to information that is deeply meaningful to them, such as their family nickname or favorite music. By deemphasizing some parts of their identity and by emphasizing others, players not only shape how they are perceived by other gamers, but they also attempt to preclude accidental disclosure of more identifying information. We argue that gamertag practices thus constitute an important form of boundary work through which gamers actively seek to draw lines between their offline and multiple online worlds in the ways that they wish. We argue that gamers use these names to both protect and project aspects of their identities--at times even seeking protection through projection--as a way of addressing their competing desires to both conceal and reveal different aspects of their identities. As boundary work, players' efforts to carefully protect personally-identifying information and intentionally project personally meaningful information to their communities help them better manage their online identities, relationships with others, and overall data privacy.
Supporting Collaborative Sequencing of Small Groups through Visual Awareness
Authors: Kim Tae Soo , Goyal Nitesh , Kim Jeongyeon , Kim Juho , Hong Sungsoo Ray
Published: April 2021

Collaborative Sequencing (CoSeq) is the process by which a group collaboratively constructs a sequence. CoSeq is ubiquitous, occurring across diverse situations like trip planning, course scheduling, or book writing. Building a consensus on a sequence is desirable to groups. However, accomplishing this requires groups to dedicate significant effort to comprehensively discuss preferences and resolve conflicts. Furthermore, as numerous decisions must be assessed to construct a sequence, this challenge can be exacerbated in CoSeq. However, little research has aimed to effectively support consensus building in CoSeq. As a first step to systematically understand and support consensus building in CoSeq, we conducted a formative study to gain insights into how visual awareness may facilitate the holistic recognition of preferences and the resolution of conflicts within a group. From the study, we identified design requirements to support consensus building and designed a novel visual awareness technique for CoSeq. We instantiated this design in a collaborative travel itinerary planning system, Twine, and conducted a summative study to evaluate its effects. We found that visual awareness could decrease the effort of communicating preferences by 21%, and participants' comments suggest that it also encouraged group members to behave more cooperatively when building a consensus. We discuss future research directions to further explore the needs and challenges in this unique context and to advance the development of support for CoSeq tasks.
To Trust or to Think
Authors: Buçinca Zana , Malaya Maja Barbara , Gajos Krzysztof Z.
Published: April 2021

People supported by AI-powered decision support tools frequently overrely on the AI: they accept an AI's suggestion even when that suggestion is wrong. Adding explanations to the AI decisions does not appear to reduce the overreliance and some studies suggest that it might even increase it. Informed by the dual-process theory of cognition, we posit that people rarely engage analytically with each individual AI recommendation and explanation, and instead develop general heuristics about whether and when to follow the AI suggestions. Building on prior research on medical decision-making, we designed three cognitive forcing interventions to compel people to engage more thoughtfully with the AI-generated explanations. We conducted an experiment (N=199), in which we compared our three cognitive forcing designs to two simple explainable AI approaches and to a no-AI baseline. The results demonstrate that cognitive forcing significantly reduced overreliance compared to the simple explainable AI approaches. However, there was a trade-off: people assigned the least favorable subjective ratings to the designs that reduced the overreliance the most. To audit our work for intervention-generated inequalities, we investigated whether our interventions benefited equally people with different levels of Need for Cognition (i.e., motivation to engage in effortful mental activities). Our results show that, on average, cognitive forcing interventions benefited participants higher in Need for Cognition more. Our research suggests that human cognitive motivation moderates the effectiveness of explainable AI solutions.
Phenotyping Neurodegeneration in Human iPSCs
Authors: Li Jonathan , Fraenkel Ernest
Published: April 2021

Induced pluripotent stem cell (iPSC) technology holds promise for modeling neurodegenerative diseases. Traditional approaches for disease modeling using animal and cellular models require knowledge of disease mutations. However, many patients with neurodegenerative diseases do not have a known genetic cause. iPSCs offer a way to generate patient-specific models and study pathways of dysfunction in an in vitro setting in order to understand the causes and subtypes of neurodegeneration. Furthermore, iPSC-based models can be used to search for candidate therapeutics using high-throughput screening. Here we review how iPSC-based models are currently being used to further our understanding of neurodegenerative diseases, as well as discuss their challenges and future directions.
Governance by Data
Authors: Johns Fleur
Published: April 2021

Law and social science scholars have long elucidated ways of governing built around state governance of populations and subjects. Yet many are now grappling with the growing prevalence of practices of governance that depart, to varying degrees, from received models. The profusion of digital data, and the deployment of machine learning in its analysis, are redirecting states’ and international organizations’ attention away from the governance of populations as such and toward the amassing, analysis, and mobilization of hybrid data repositories and real-time data flows for governance. Much of this work does not depend on state data sources or on conventional statistical models. The subjectivities nurtured by these techniques of governance are frequently not those of choosing individuals. Digital objects and mediators are increasingly prevalent at all scales. This article surveys how scholars are beginning to understand the nascent political technologies associated with this shift toward governance by data.
Key Insights and Research Prospects at the Dawn of the Population Genomics Era for Verticillium dahliae
Authors: Chen Jie-Yin , Klosterman Steven J. , Hu Xiao-Ping , Dai Xiao-Feng , Subbarao Krishna V.
Published: April 2021

The genomics era has ushered in exciting possibilities to examine the genetic bases that undergird the characteristic features of Verticillium dahliae and other plant pathogens. In this review, we provide historical perspectives on some of the salient biological characteristics of V. dahliae, including its morphology, microsclerotia formation, host range, disease symptoms, vascular niche, reproduction, and population structure. The kaleidoscopic population structure of this pathogen is summarized, including different races of the pathogen, defoliating and nondefoliating phenotypes, vegetative compatibility groupings, and clonal populations. Where possible, we place the characteristic differences in the context of comparative and functional genomics analyses that have offered insights into population divergence within V. dahliae and the related species.Current challenges are highlighted along with some suggested future population genomics studies that will contribute to advancing our understanding of the population divergence in V. dahliae.