bluuebunny/crossref_metadata_2025 huggingface dataset:
A scalable galerkin multigrid method for real-time simulation of deformable objects
Authors: Xian Zangyueyang , Tong Xin , Liu Tiantian
Published: November 2019

We propose a simple yet efficient multigrid scheme to simulate high-resolution deformable objects in their full spaces at interactive frame rates. The point of departure of our method is the Galerkin projection which is simple to construct. However, a naïve Galerkin multigrid does not scale well for large and irregular grids because it trades-off matrix sparsity for smaller sized linear systems which eventually stops improving the performance. Given that observation, we design our special projection criterion which is based on skinning space coordinates with piecewise constant weights, to make our Galerkin multigrid method scale for high-resolution meshes without suffering from dense linear solves. The usage of skinning space coordinates enables us to reduce the resolution of grids more aggressively, and our piecewise constant weights further ensure us to always deal with reasonably-sparse linear solves. Our projection matrices also help us to manage multi-level linear systems efficiently. Therefore, our method can be applied to different optimization schemes such as Newton's method and Projective Dynamics, pushing the resolution of a real-time simulation to orders of magnitudes higher. Our final GPU implementation outperforms the other state-of-the-art GPU deformable body simulators, enabling us to simulate large deformable objects with hundred thousands of degrees of freedom in real-time.
Accelerating ADMM for efficient simulation and optimization
Authors: Zhang Juyong , Peng Yue , Ouyang Wenqing , Deng Bailin
Published: November 2019

The alternating direction method of multipliers (ADMM) is a popular approach for solving optimization problems that are potentially non-smooth and with hard constraints. It has been applied to various computer graphics applications, including physical simulation, geometry processing, and image processing. However, ADMM can take a long time to converge to a solution of high accuracy. Moreover, many computer graphics tasks involve non-convex optimization, and there is often no convergence guarantee for ADMM on such problems since it was originally designed for convex optimization. In this paper, we propose a method to speed up ADMM using Anderson acceleration, an established technique for accelerating fixed-point iterations. We show that in the general case, ADMM is a fixed-point iteration of the second primal variable and the dual variable, and Anderson acceleration can be directly applied. Additionally, when the problem has a separable target function and satisfies certain conditions, ADMM becomes a fixed-point iteration of only one variable, which further reduces the computational overhead of Anderson acceleration. Moreover, we analyze a particular non-convex problem structure that is common in computer graphics, and prove the convergence of ADMM on such problems under mild assumptions. We apply our acceleration technique on a variety of optimization problems in computer graphics, with notable improvement on their convergence speed.
SoftCon
Authors: Min Sehee , Won Jungdam , Lee Seunghwan , Park Jungnam , Lee Jehee
Published: November 2019

We present a novel and general framework for the design and control of underwater soft-bodied animals. The whole body of an animal consisting of soft tissues is modeled by tetrahedral and triangular FEM meshes. The contraction of muscles embedded in the soft tissues actuates the body and limbs to move. We present a novel muscle excitation model that mimics the anatomy of muscular hydrostats and their muscle excitation patterns. Our deep reinforcement learning algorithm equipped with the muscle excitation model successfully learned the control policy of soft-bodied animals, which can be physically simulated in real-time, controlled interactively, and resilient to external perturbations. We demonstrate the effectiveness of our approach with various simulated animals including octopuses, lampreys, starfishes, stingrays and cuttlefishes. They learn diverse behaviors such as swimming, grasping, and escaping from a bottle. We also implemented a simple user interface system that allows the user to easily create their creatures.
Learning body shape variation in physics-based characters
Authors: Won Jungdam , Lee Jehee
Published: November 2019

Recently, deep reinforcement learning (DRL) has attracted great attention in designing controllers for physics-based characters. Despite the recent success of DRL, the learned controller is viable for a single character. Changes in body size and proportions require learning controllers from scratch. In this paper, we present a new method of learning parametric controllers for body shape variation. A single parametric controller enables us to simulate and control various characters having different heights, weights, and body proportions. The users are allowed to create new characters through body shape parameters, and they can control the characters immediately. Our characters can also change their body shapes on the fly during simulation. The key to the success of our approach includes the adaptive sampling of body shapes that tackles the challenges in learning parametric controllers, which relies on the marginal value function that measures control capabilities of body shapes. We demonstrate parametric controllers for various physically simulated characters such as bipeds, quadrupeds, and underwater animals.
A differential theory of radiative transfer
Authors: Zhang Cheng , Wu Lifan , Zheng Changxi , Gkioulekas Ioannis , Ramamoorthi Ravi , Zhao Shuang
Published: November 2019

Physics-based differentiable rendering is the task of estimating the derivatives of radiometric measures with respect to scene parameters. The ability to compute these derivatives is necessary for enabling gradient-based optimization in a diverse array of applications: from solving analysis-by-synthesis problems to training machine learning pipelines incorporating forward rendering processes. Unfortunately, physics-based differentiable rendering remains challenging, due to the complex and typically nonlinear relation between pixel intensities and scene parameters.

We introduce a differential theory of radiative transfer, which shows how individual components of the radiative transfer equation (RTE) can be differentiated with respect to arbitrary differentiable changes of a scene. Our theory encompasses the same generality as the standard RTE, allowing differentiation while accurately handling a large range of light transport phenomena such as volumetric absorption and scattering, anisotropic phase functions, and heterogeneity. To numerically estimate the derivatives given by our theory, we introduce an unbiased Monte Carlo estimator supporting arbitrary surface and volumetric configurations. Our technique differentiates path contributions symbolically and uses additional boundary integrals to capture geometric discontinuities such as visibility changes.

We validate our method by comparing our derivative estimations to those generated using the finite-difference method. Furthermore, we use a few synthetic examples inspired by real-world applications in inverse rendering, non-line-of-sight (NLOS) and biomedical imaging, and design, to demonstrate the practical usefulness of our technique.
OpenSketch
Authors: Gryaditskaya Yulia , Sypesteyn Mark , Hoftijzer Jan Willem , Pont Sylvia , Durand Frédo , Bousseau Adrien
Published: November 2019

Product designers extensively use sketches to create and communicate 3D shapes and thus form an ideal audience for sketch-based modeling, non-photorealistic rendering and sketch filtering. However, sketching requires significant expertise and time, making design sketches a scarce resource for the research community. We introduce
OpenSketch
, a dataset of product design sketches aimed at offering a rich source of information for a variety of computer-aided design tasks.
OpenSketch
contains more than 400 sketches representing 12 man-made objects drawn by 7 to 15 product designers of varying expertise. We provided participants with front, side and top views of these objects, and instructed them to draw from two
novel
perspective viewpoints. This drawing task forces designers to
construct the shape
from their mental vision rather than directly copy what they see. They achieve this task by employing a variety of sketching techniques and methods not observed in prior datasets. Together with industrial design teachers, we distilled a taxonomy of line types and used it to label each stroke of the 214 sketches drawn from one of the two viewpoints. While some of these lines have long been known in computer graphics, others remain to be reproduced algorithmically or exploited for shape inference. In addition, we also asked participants to produce clean presentation drawings from each of their sketches, resulting in aligned pairs of drawings of different styles. Finally, we registered each sketch to its reference 3D model by annotating sparse correspondences. We provide an analysis of our annotated sketches, which reveals systematic drawing strategies over time and shapes, as well as a positive correlation between presence of construction lines and accuracy. Our sketches, in combination with provided annotations, form challenging benchmarks for existing algorithms as well as a great source of inspiration for future developments. We illustrate the versatility of our data by using it to test a 3D reconstruction deep network trained on synthetic drawings, as well as to train a filtering network to convert concept sketches into presentation drawings. We distribute our dataset under the Creative Commons CC0 license: https://ns.inria.fr/d3/OpenSketch.
A thermomechanical material point method for baking and cooking
Authors: Ding Mengyuan , Han Xuchen , Wang Stephanie , Gast Theodore F. , Teran Joseph M.
Published: November 2019

We present a Material Point Method for visual simulation of baking breads, cookies, pancakes and similar materials that consist of dough or batter (mixtures of water, flour, eggs, fat, sugar and leavening agents). We develop a novel thermomechanical model using mixture theory to resolve interactions between individual water, gas and dough species. Heat transfer with thermal expansion is used to model thermal variations in material properties. Water-based mass transfer is resolved through the porous mixture, gas represents carbon dioxide produced by leavening agents in the baking process and dough is modeled as a viscoelastoplastic solid to represent its varied and complex rheological properties. Water content in the mixture reduces during the baking process according to Fick's Law which contributes to drying and cracking of crust at the material boundary. Carbon dioxide gas produced by leavening agents during baking creates internal pressure that causes rising. The viscoelastoplastic model for the dough is temperature dependent and is used to model melting and solidification. We discretize the governing equations using a novel Material Point Method designed to track the solid phase of the mixture.
3D hodge decompositions of edge- and face-based vector fields
Authors: Zhao Rundong , Desbrun Mathieu , Wei Guo-Wei , Tong Yiying
Published: November 2019

We present a compendium of Hodge decompositions of vector fields on tetrahedral meshes embedded in the 3D Euclidean space. After describing the foundations of the Hodge decomposition in the continuous setting, we describe how to implement a five-component orthogonal decomposition that generically splits, for a variety of boundary conditions, any given discrete vector field expressed as discrete differential forms into two potential fields, as well as three additional harmonic components that arise from the topology or boundary of the domain. The resulting decomposition is proper and mimetic, in the sense that the theoretical dualities on the kernel spaces of vector Laplacians valid in the continuous case (including correspondences to cohomology and homology groups) are exactly preserved in the discrete realm. Such a decomposition only involves simple linear algebra with symmetric matrices, and can thus serve as a basic computational tool for vector field analysis in graphics, electromagnetics, fluid dynamics and elasticity.
MIS compensation
Authors: Karlík Ondřej , Šik Martin , Vévoda Petr , Skřivan Tomáš , Křivánek Jaroslav
Published: November 2019

Multiple importance sampling (MIS) has become an indispensable tool in Monte Carlo rendering, widely accepted as a near-optimal solution for combining different sampling techniques. But an MIS combination, using the common balance or power heuristics, often results in an overly defensive estimator, leading to high variance. We show that by generalizing the MIS framework, variance can be substantially reduced. Specifically, we optimize one of the combined sampling techniques so as to decrease the overall variance of the resulting MIS estimator. We apply the approach to the computation of direct illumination due to an HDR environment map and to the computation of global illumination using a path guiding algorithm. The implementation can be as simple as subtracting a constant value from the tabulated sampling density done entirely in a preprocessing step. This produces a consistent noise reduction in all our tests with no negative influence on run time, no artifacts or bias, and no failure cases.
Deep face normalization
Authors: Nagano Koki , Luo Huiwen , Wang Zejian , Seo Jaewoo , Xing Jun , Hu Liwen , Wei Lingyu , Li Hao
Published: November 2019

From angling smiles to duck faces, all kinds of facial expressions can be seen in selfies, portraits, and Internet pictures. These photos are taken from various camera types, and under a vast range of angles and lighting conditions. We present a deep learning framework that can fully normalize unconstrained face images, i.e., remove perspective distortions, relight to an evenly lit environment, and predict a frontal and neutral face. Our method can produce a high resolution image while preserving important facial details and the likeness of the subject, along with the original background. We divide this ill-posed problem into three consecutive normalization steps, each using a different generative adversarial network that acts as an image generator. Perspective distortion removal is performed using a dense flow field predictor. A uniformly illuminated face is obtained using a lighting translation network, and the facial expression is neutralized using a generalized facial expression synthesis framework combined with a regression network based on deep features for facial recognition. We introduce new data representations for conditional inference, as well as training methods for supervised learning to ensure that different expressions of the same person can yield to not only a plausible but also a similar neutral face. We demonstrate our results on a wide range of challenging images collected in the wild. Key applications of our method range from robust image-based 3D avatar creation, portrait manipulation, to facial enhancement and reconstruction tasks for crime investigation. We also found through an extensive user study, that our normalization results can be hardly distinguished from ground truth ones if the person is not familiar.
Customizations and Expression Breakdowns in Ecosystems of Communication Apps
Authors: Griggio Carla F. , McGrenere Joanna , Mackay Wendy E.
Published: November 2019

The growing adoption of emojis, stickers and GIFs suggests a corresponding demand for rich, personalized expression in messaging apps. Some people customize apps to enable more personal forms of expression, yet we know little about how such customizations shape everyday communication. Since people increasingly communicate via multiple apps side-by-side, we are also interested in how customizing one app influences communication via other apps. We created a taxonomy of customization options based on interviews with 15 "extreme users" of communication apps. We found that participants tailored their apps to express their identities, organizational culture, and intimate bonds with others. They also experienced expression breakdowns: frustrations around barriers to transferring personal forms of expression across apps, which inspired inventive workarounds to maintain cross-app habits of expression, such as briefly switching apps to generate and export content for a particular conversation. We conclude with implications for personalized expression in ecosystems of communication apps.
"This Place Does What It Was Built For"
Authors: Frey Seth , Krafft P. M. , Keegan Brian C.
Published: November 2019

Whether we recognize it or not, the Internet is rife with exciting and original institutional forms that are transforming social organization on and offline. Governing these Internet platforms and other digital institutions has posed a challenge for engineers and managers, many of whom have little exposure to the relevant history or theory of institutional design. The dominant guiding practices for the design of digital institutions to date in human-computer interaction, computer-supported cooperative work, and the tech industry at large have been an incentive-focused behavioral engineering paradigm encompassing atheoretical approaches such as emulation, A/B-testing, engagement maximization, and piecemeal issue-driven engineering. One institutional analysis framework that has been useful in the study of traditional institutions comes from scholars of natural resource management, particularly that community of economists, anthropologists, and environmental and political scientists focused around the work of Elinor Ostrom, known collectively as the "Ostrom Workshop." A key finding from this community that has yet to be broadly incorporated into the design of many digital institutions is the importance of including participatory change mechanisms in what is called a "constitutional layer" of institutional design. The institutional rules that compose a constitutional layer facilitate stakeholder participation in the ongoing process of institutional design change. We explore to what extent consideration of constitutional layers is met or could be better met in three varied cases of digital institutions: cryptocurrencies, cannabis informatics, and amateur Minecraft server governance. Examining such highly varied cases allows us to demonstrate the broad relevance of constitutional layers in many different types of digital institutions.
StressMon
Authors: Zakaria Camellia , Balan Rajesh , Lee Youngki
Published: November 2019

Stress and depression are a common affliction in all walks of life. When left unmanaged, stress can inhibit productivity or cause depression. Depression can occur independently of stress. There has been a sharp rise in mobile health initiatives to monitor stress and depression. However, these initiatives usually require users to install dedicated apps or multiple sensors, making such solutions hard to scale. Moreover, they emphasise sensing individual factors and overlook social interactions, which plays a significant role in influencing stress and depression while being a part of a social system. We present StressMon, a stress and depression detection system that leverages single-attribute location data, passively sensed from the WiFi infrastructure. Using the location data, it extracts a detailed set of movement, and physical group interaction pattern features without requiring explicit user actions or software installation on client devices. These features are used in two different machine learning models to detect stress and depression. To validate StressMon, we conducted three different longitudinal studies at a university with different groups of students, totalling up to 108 participants. Our evaluation demonstrated StressMon detecting severely stressed students with a 96.01% True Positive Rate (TPR), an 80.76% True Negative Rate (TNR), and a 0.97 area under the ROC curve (AUC) score (a score of 1 indicates a perfect binary classifier) using a 6-day prediction window. In addition, StressMon was able to detect depression at 91.21% TPR, 66.71% TNR, and 0.88 AUC using a 15-day window. We end by discussing how StressMon can expand CSCW research, especially in areas involving collaborative practices for mental health management.
How Data Scientists Use Computational Notebooks for Real-Time Collaboration
Authors: Wang April Yi , Mittal Anant , Brooks Christopher , Oney Steve
Published: November 2019

Effective collaboration in data science can leverage domain expertise from each team member and thus improve the quality and efficiency of the work. Computational notebooks give data scientists a convenient interactive solution for sharing and keeping track of the data exploration process through a combination of code, narrative text, visualizations, and other rich media. In this paper, we report how synchronous editing in computational notebooks changes the way data scientists work together compared to working on individual notebooks. We first conducted a formative survey with 195 data scientists to understand their past experience with collaboration in the context of data science. Next, we carried out an observational study of 24 data scientists working in pairs remotely to solve a typical data science predictive modeling problem, working on either notebooks supported by synchronous groupware or individual notebooks in a collaborative setting. The study showed that working on the synchronous notebooks improves collaboration by creating a shared context, encouraging more exploration, and reducing communication costs. However, the current synchronous editing features may lead to unbalanced participation and activity interference without strategic coordination. The synchronous notebooks may also amplify the tension between quick exploration and clear explanations. Building on these findings, we propose several design implications aimed at better supporting collaborative editing in computational notebooks, and thus improving efficiency in teamwork among data scientists.
The Principles and Limits of Algorithm-in-the-Loop Decision Making
Authors: Green Ben , Chen Yiling
Published: November 2019

The rise of machine learning has fundamentally altered decision making: rather than being made solely by people, many important decisions are now made through an "algorithm-in-the-loop'' process where machine learning models inform people. Yet insufficient research has considered how the interactions between people and models actually influence human decisions. Society lacks both clear normative principles regarding how people should collaborate with algorithms as well as robust empirical evidence about how people do collaborate with algorithms. Given research suggesting that people struggle to interpret machine learning models and to incorporate them into their decisions---sometimes leading these models to produce unexpected outcomes---it is essential to consider how different ways of presenting models and structuring human-algorithm interactions affect the quality and type of decisions made. This paper contributes to such research in two ways. First, we posited three principles as essential to ethical and responsible algorithm-in-the-loop decision making. Second, through a controlled experimental study on Amazon Mechanical Turk, we evaluated whether people satisfy these principles when making predictions with the aid of a risk assessment. We studied human predictions in two contexts (pretrial release and financial lending) and under several conditions for risk assessment presentation and structure. Although these conditions did influence participant behaviors and in some cases improved performance, only one desideratum was consistently satisfied. Under all conditions, our study participants 1) were unable to effectively evaluate the accuracy of their own or the risk assessment's predictions, 2) did not calibrate their reliance on the risk assessment based on the risk assessment's performance, and 3) exhibited bias in their interactions with the risk assessment. These results highlight the urgent need to expand our analyses of algorithmic decision making aids beyond evaluating the models themselves to investigating the full sociotechnical contexts in which people and algorithms interact.
The Coerciveness of the Primary Key
Authors: Bopp Chris , Benjamin Lehn M. , Voida Amy
Published: November 2019

The premise of a primary key is simple enough: every record or row in a table should have some number or string that can uniquely identify it. Primary keys are essential for linking data spread across database tables, and for looking up and retrieving data from specific records. Yet for an identifier that seems so straightforward and uncontroversial, we find myriad ways that this unassuming bit of infrastructure has an outsized influence in human services work. Through case studies of the organizational networks of two nonprofit human services organizations, we find that different stakeholders use variants of identifiers to support work practices that are far more complex and social than the linking of tables or the lookup of data. Yet we also find that the low-level technical properties of the primary key are often coercive, forcing end-users to work on the infrastructure's terms-influencing the nature and order of the work, creating new forms of work, and influencing the tenor of the relationships among stakeholders. The technical abstractions of the underlying infrastructure, then, start to become the de facto public policy. We conclude by offering design provocations for better supporting identification across a variety of contexts.
A Forensic Qualitative Analysis of Contributions to Wikipedia from Anonymity Seeking Users
Authors: Champion Kaylea , McDonald Nora , Bankes Stephanie , Zhang Joseph , Greenstadt Rachel , Forte Andrea , Hill Benjamin Mako
Published: November 2019

By choice or by necessity, some contributors to commons-based peer production sites use privacy-protecting services to remain anonymous. As anonymity seekers, users of the Tor network have been cast both as ill-intentioned vandals and as vulnerable populations concerned with their privacy. In this study, we use a dataset drawn from a corpus of Tor edits to Wikipedia to uncover the character of Tor users' contributions. We build in-depth narrative descriptions of Tor users' actions and conduct a thematic analysis that places their editing activity into seven broad groups. We find that although their use of a privacy-protecting service marks them as unusual within Wikipedia, the character of many Tor users' contributions is in line with the expectations and norms of Wikipedia. However, our themes point to several important places where lack of trust promotes disorder, and to contributions where risks to contributors, service providers, and communities are unaligned.
Privacy Perceptions and Designs of Bystanders in Smart Homes
Authors: Yao Yaxing , Basdeo Justin Reed , Mcdonough Oriana Rosata , Wang Yang
Published: November 2019

As the Internet of Things (IoT) devices make their ways into people's homes, traditional dwellings are turning into smart homes. While prior empirical studies have examined people's privacy concerns of smart homes and their desired ways of mitigating these concerns, the focus was primarily on the end users or device owners. Our research investigated the privacy perceptions and design ideas of smart home bystanders, i.e., people who are not the owners nor the primary users of smart home devices but can potentially be involved in the device usage, such as other family members or guests. We conducted focus groups and co-design activities with eighteen participants. We identified three impacting factors of bystanders' privacy perceptions (e.g., perceived norms) and a number of design factors to mitigate their privacy concerns (e.g., asking for device control). We highlighted bystanders' needs for privacy and controls, as well as the tension of privacy expectations between the owners/users and the bystanders in smart homes. We discussed how future designs can better support and balance the privacy needs of different stakeholders in smart homes.
Circadian Rhythms and Physiological Synchrony
Authors: Jun Eunice , McDuff Daniel , Czerwinski Mary
Published: November 2019

Circadian rhythms determine daily sleep cycles, mood, and cognition. Depending on an individual's circadian preference, or chronotype (i.e.,"early birds" and "night owls"), the rhythms shift earlier or later in the day. Early birds experience circadian arousal peaks earlier in the morning than night owls. Prior work has shown that individuals are more effective at analytic tasks during their peak arousal times but are more creative during their off-peak times. We investigate if these findings hold true for small groups. We find that time of day and a group's majority chronotype impact performance on analytic and creative tasks. Physiological synchrony among group members positively predicts group satisfaction. Specifically, homogeneous groups perform worse on all tasks regardless of time of day, but they achieve greater physiological synchrony and feel more satisfied as a group. Based on these findings, we present and advocate for a temporal dimension of group diversity.
Hateful People or Hateful Bots?
Authors: Albadi Nuha , Kurdi Maram , Mishra Shivakant
Published: November 2019

Arabic Twitter space is crawling with bots that fuel political feuds, spread misinformation, and proliferate sectarian rhetoric. While efforts have long existed to analyze and detect English bots, Arabic bot detection and characterization remains largely understudied. In this work, we contribute new insights into the role of bots in spreading religious hatred on Arabic Twitter and introduce a novel regression model that can accurately identify Arabic language bots. Our assessment shows that existing tools that are highly accurate in detecting English bots don't perform as well on Arabic bots. We identify the possible reasons for this poor performance, perform a thorough analysis of linguistic, content, behavioral and network features, and report on the most informative features that distinguish Arabic bots from humans as well as the differences between Arabic and English bots. Our results mark an important step toward understanding the behavior of malicious bots on Arabic Twitter and pave the way for a more effective Arabic bot detection tools.
"When you can do it, why can't I?": Racial and Socioeconomic Differences in Family Technology Use and Non-Use
Authors: Garg Radhika , Sengupta Subhasree
Published: November 2019

There is racial diversity as well as economic inequality in the United States (U.S.). To gain a nuanced understanding of how households from different socio economic and racial backgrounds integrate technology into their lives, we conducted a diary study with 22 parents who were Asian Indian (the fastest-growing immigrant population in U.S.) and 18 who were White American (the largest racial group in U.S.) parents from the working and middle classes. The participants logged in-situ instances of using smart phones and speaker use by, with, and around children for 8 weeks, and were interviewed once every four weeks (two times in total). Our findings reveal differences and similarities in parents' attitudes and practices of using or not using these devices around and with children, in parental restrictions of children's use of technology, and children's daily use patterns. The paper concludes with a discussions of the implications of our findings and suggestions for future design improvements in smart phones and speakers.
How Emotional and Contextual Annotations Involve in Sensemaking Processes of Foreign Language Social Media Posts
Authors: Lim Hajin , Cosley Dan , Fussell Susan R.
Published: November 2019

The goal of this paper is to investigate how computational tools to annotate communication can support multilingual sense-making on social media. We conducted a field study of SenseTrans, a browser extension that uses sentiment analysis and named entity extraction techniques to annotate Facebook posts with emotional and contextual information. Interviews with 18 participants who used SenseTrans in their Facebook newsfeed for two weeks suggest that the annotations often supported sensemaking by providing additional information they could use to get a quick gist of the posts or to supplement their own interpretations. Participants varied in the extent to which they were motivated to evaluate the credibility of and form mental models of how the annotations were generated, which shaped how they utilized the annotations for sensemaking. Our findings demonstrate the value of designing to support cross-lingual communication and inform design implications for intelligent tools that support communication and sensemaking.
'Routine Infrastructuring' as 'Building Everyday Resilience with Technology'
Authors: Semaan Bryan
Published: November 2019

Getting a divorce. Being diagnosed with a disease. Going through a relationship breakup. Living through a natural disaster. All of these events are often life disrupting and debilitating. While some disruptive events are short-lived, some can be a routine part of everyday life. This leads to the question of how people who experience prolonged disruption in their lives build resilience---that is, how do they manage and overcome such events? To explore this question, this paper utilizes a case study approach to explore the use, creation, and re-appropriation of technology across three prolonged disruptions-the Second Gulf War in Iraq, veteran transitions, and the coming out experiences of LGBTQ-identifying people. Using a conceptual frame that brings together routine dynamics and infrastructuring, we find that engaging in routine infrastructuring practices generated resilience in people's daily lives---a phenomenon we dub 'routine infrastructuring' as 'building everyday resilience with technology.' We then theorize properties of infrastructure and infrastructuring practice that enable resiliency, and conclude with how infrastructuring is a form of care work that is oriented towards individuals, communities, and society.
"I feel like only half a man"
Authors: Patel Dilisha , Blandford Ann , Warner Mark , Shawe Jill , Stephenson Judith
Published: November 2019

Infertility can place a significant burden on couples and individuals when trying to conceive. Approximately 20-30% of all cases of infertility are due to male-related factors. Whatever the cause of difficulty in conceiving, little is known about how men find support when dealing with fertility issues, or when or how online resources are being used. This paper reports on a qualitative study of anonymous online posts (N=603) from forums related to fertility that are used by men. We analysed this data using thematic analysis to understand how men are using online forums as a resource when experiencing fertility issues. We found that online forums play a valued role in facilitating connections between men experiencing an often stigmatised condition. These forums offer men accessible and private spaces which allow for more open discussion, helping them to make sense of their situation. We discuss our findings in relation to Genuis and Bronstein's model of finding a "new normal" and present our elaborated model of finding a "new normal" in the context of experiencing fertility problems.
Safe Enough to Share
Authors: Lazar Amanda , Dixon Emma E.
Published: November 2019

CSCW research is increasingly interested in the ways that people use technology to discuss health and disability online. In addition to studying how people share information and seek and provide emotional support, a growing area of interest is health activism. In this paper, we analyze how a project centered around sharing "real and raw" experiences with dementia provides a safe platform for people to share their authentic experiences. These accounts counter predominant depictions of dementia and push back on tokenistic involvement of people with this condition. In a study involving observations and interviews with members of this project, we find that people with dementia must negotiate several goals which at times compete with each other: sharing a "real and raw" look at dementia, changing attitudes, showcasing a polished presentation, and inhabiting a safe space. The paper concludes with a discussion of future directions for CSCW on configuring a space for dialogue on sensitive topics, health activism, and sharing online with dementia.
How Do People Change Their Technology Use in Protest?
Authors: Li Hanlin , Vincent Nicholas , Tsai Janice , Kaye Jofish , Hecht Brent
Published: November 2019

Researchers and the media have become increasingly interested in protest users, or people who change (protest use) or stop (protest non-use) their use of a company's products because of the company's values and/or actions. Past work has extensively engaged with the phenomenon of technology non-use but has not focused on non-use (nor changed use) in the context of protest. With recent research highlighting the potential for protest users to exert leverage against technology companies, it is important for technology stakeholders to understand the prevalence of protest users, their motivations, and the specific tactics they currently use. In this paper, we report the results of two surveys (n = 463 and n = 398) of representative samples of American web users that examine if, how, and why people have engaged in protest use and protest non-use of the products of five major technology companies. We find that protest use and protest non-use are relatively common, with 30% of respondents in 2019 reporting they were protesting at least one major tech company. Furthermore, we identify that protest users' most common motivations were (1) concerns about business models that profit from user data and (2) privacy; and the most common tactics were (1) stopping use and (2) leveraging ad blockers. We also identify common challenges and roadblocks faced by active and potential protest users, which include (1) losing social connections and (2) the lack of alternative products. Our results highlight the growing importance of protest users in the technology ecosystem and the need for further social computing research into this phenomenon. We also provide concrete design implications for existing and future technologies to support or account for protest use and protest non-use.
4X: A Hybrid Approach for Scaffolding Data Collection and Interest in Low-Effort Participatory Sensing
Authors: Garg Kapil , Kim Yongsung , Gergle Darren , Zhang Haoqi
Published: November 2019

Participatory sensing systems in which people actively participate in the data collection process must account for both the needs of data contributors and the data collection goals. Existing approaches tend to emphasize one or the other, with opportunistic and directed approaches making opposing tradeoffs between providing convenient opportunities for contributors and collecting high-fidelity data. This paper explores a new, hybrid approach, in which collected data-even if low-fidelity initially-can provide useful information to data contributors and inspire further contributions. We realize this approach with 4X, a multi-stage data collection framework that first collects data opportunistically by requesting contributions at specific locations along users' routes and then uses collected data to direct users to locations of interest to make additional contributions that build data fidelity and coverage. To study the efficacy of 4X, we implemented 4X into LES, an application for collecting information about campus locations and events. Results from two field deployments (N = 95, N = 18) show that the 4X framework created 34% more opportunities for contributing data without increasing disruption, and yielded 49% more data by directing users to locations of interest. Our results demonstrate the value and potential of multi-stage, dynamic data collection processes that draw on multiple sources of motivation for data, and how they can be used to better meet data collection goals as data becomes available while avoiding unnecessary disruption.
How Trainees Use the Information from Telepointers in Remote Instruction
Authors: Semsar Azin , McGowan Hannah , Feng Yuanyuan , Zahiri H. Reza , Park Adrian , Kleinsmith Andrea , Mentis Helena
Published: November 2019

Researchers have shown both performance drawbacks and benefits of using telepointers or similar display overlay-technologies in remote instruction; however, there is not a clear understanding of why there are these performance effects. This poses a challenge in knowing how and when to successfully use or design telepointing technologies in remote instruction. A better understanding is needed with the rise of remote workers in a wide array of industries from oil rig repair to surgery, and the proliferation of heads-up displays or telecommunications devices to support these future work practices. In this study, we explore how the information conveyed through a telepointer is taken up and acted upon by surgical trainees in a laparoscopic surgical telementoring setting. We collected audio and video data of 12 surgical trainees who performed standard laparoscopic surgical tasks on a physical model under the guidance of a surgical trainer. We investigated both action and talk to determine how the telepointer-based information was used. Our findings reveal three main challenges in using the instructional information conveyed through the telepointer including the trainees' tendency of attending to the telepointer instruction as the primary source of information. We argue that the found challenges are socio-technical in nature and require a redesign of the mentoring context as well as the technological tools.
GroundTruth: Augmenting Expert Image Geolocation with Crowdsourcing and Shared Representations
Authors: Venkatagiri Sukrit , Thebault-Spieker Jacob , Kohler Rachel , Purviance John , Mansur Rifat Sabbir , Luther Kurt
Published: November 2019

Expert investigators bring advanced skills and deep experience to analyze visual evidence, but they face limits on their time and attention. In contrast, crowds of novices can be highly scalable and parallelizable, but lack expertise. In this paper, we introduce the concept of shared representations for crowd--augmented expert work, focusing on the complex sensemaking task of image geolocation performed by professional journalists and human rights investigators. We built GroundTruth, an online system that uses three shared representations-a diagram, grid, and heatmap-to allow experts to work with crowds in real time to geolocate images. Our mixed-methods evaluation with 11 experts and 567 crowd workers found that GroundTruth helped experts geolocate images, and revealed challenges and success strategies for expert-crowd interaction. We also discuss designing shared representations for visual search, sensemaking, and beyond.
How to Communicate when Submitting Patches
Authors: Tan Xin , Zhou Minghui
Published: November 2019

Communication when submitting patches (CSP) is critical in software development, because ineffective communication wastes the time of developers and reviewers, and is even harmful to future product release and maintenance. In distributed software development, CSP is usually in the form of computer-mediated communication (CMC), which is a crucial topic concerned by the CSCW community. However, what to say and how to say in communication including CSP has been rarely studied. To bridge this knowledge gap and provide relevant guidance for developers to conduct CSP, in this study, we conducted an empirical study on the Linux kernel. We found four themes involving 17 expression elements that characterize what to express when submitting patches and three themes of contextual factors that determine how these elements are applied. Considering both expression elements and context, combined with an online survey, we obtained 17 practices for communication. Among them, four practices, such as "provide sufficient reasons" and "provide a trade-off" are the most important but difficult practices, for which we provide specific instructions. We also found that the "individual factors" plays a special role in communication, which may lead to potential problems even in accepted patches. Based on these findings, we discuss the recommendations for different practitioners, including patch submitters, reviewers, and tool designers, and the implications for open source software (OSS) communities and the CSCW researchers.
Passively-sensed Behavioral Correlates of Discrimination Events in College Students
Authors: Sefidgar Yasaman S. , Seo Woosuk , Kuehn Kevin S. , Althoff Tim , Browning Anne , Riskin Eve , Nurius Paula S. , Dey Anind K. , Mankoff Jennifer
Published: November 2019

A deep understanding of how discrimination impacts psychological health and well-being of students could allow us to better protect individuals at risk and support those who encounter discrimination. While the link between discrimination and diminished psychological and physical well-being is well established, existing research largely focuses on chronic discrimination and long-term outcomes. A better understanding of the short-term behavioral correlates of discrimination events could help us to concretely quantify such experiences, which in turn could support policy and intervention design. In this paper we specifically examine, for the first time, what behaviors change and in what ways in relation to discrimination. We use actively-reported and passively-measured markers of health and well-being in a sample of 209 first-year college students over the course of two academic quarters. We examine changes in indicators of psychological state in relation to reports of unfair treatment in terms of five categories of behaviors: physical activity, phone usage, social interaction, mobility, and sleep. We find that students who encounter unfair treatment become more physically active, interact more with their phone in the morning, make more calls in the evening, and spend more time in bed on the day of the event. Some of these patterns continue the next day. Our results further our understanding of the impact of discrimination and can inform intervention work.
"The cavalry ain't coming in to save us"
Authors: Dickinson Jessa , Díaz Mark , Le Dantec Christopher A. , Erete Sheena
Published: November 2019

Cities are increasingly integrating sensing and information and communication technologies to improve municipal services, civic engagement, and quality of life for residents. Although these civic technologies have the potential to affect economic, social, and environmental factors, there has been less focus on residents of lower income communities' involvement in civic technology design. Based on two public forums held in underserved communities, we describe residents' perceptions of civic technologies in their communities and challenges that limit the technologies' viability. We found that residents viewed civic technology as a tool that should strengthen existing community assets by providing an avenue to connect assets and build upon them. We describe how an asset-based approach can move us toward designing civic technology that develops stronger relationships among community-led initiatives, and between the community and local government--- rather than a data-driven approach to civic tech that focuses on transactions between residents and city services.