bluuebunny/crossref_metadata_2025 huggingface dataset:
On the Use of Multi-sensory Cues in Symmetric and Asymmetric Shared Collaborative Virtual Spaces
Authors: Jung Sungchul , Karki Nawam , Slutter Max , Lindeman Robert W.
Published: April 2021

Physical face-to-face collaboration with someone gives a higher-quality experience compared to mediated communication options, such as a phone- or video-based chat. Participants can share rich sensory cues to multiple human senses in a physical space. Also, the perceptual sensing of the surrounding environment including other peoples' reactions can influence human communication and emotion, and thus collaborative performance. Shared spaces in virtual environments provide degraded sensory experiences because most commercial virtual reality systems typically provide only visual and audio feedback. The impact of richer, multi-sensory feedback on joint decision-making tasks in VR is still an open area of research. Two independent studies exploring this topic are presented in this paper. We implemented a multi-sensory system that delivers vision, audio, tactile, and smell feedback, and we compared the system to a typical VR system. The scenario placed two users in a virtual theme-park safari ride with a number of non-player character (NPC) passengers to simulate realistic scenarios compared to the real-world and we varied the type and complexity of NPCs reactions to participants.

In Experiment 1, we provided both users with either multi-sensory or typical sensory feedback symmetrically as a between-subjects factor, and used NPC reaction type as a within-subjects factor. In Experiment 2, we provided sensory feedback asymmetrically to each user (i.e., one had multi-sensory cues and the other had typical sensory cues) as a between-subjects factor, and used NPC reaction type as a within-subjects factor. We found that the number of sensory channels and NPC reactions did not influence user perception significantly under either symmetric or asymmetric sensory feedback conditions. However, after accounting for individual personality traits (e.g., assertive, passive), as well as any existing relationship between the pairs, we found that increasing the number of sensory channels can significantly improve subjective responses.
Problematic Machine Behavior
Authors: Bandy Jack
Published: April 2021

While algorithm audits are growing rapidly in commonality and public importance, relatively little scholarly work has gone toward synthesizing prior work and strategizing future research in the area. This systematic literature review aims to do just that, following PRISMA guidelines in a review of over 500 English articles that yielded 62 algorithm audit studies. The studies are synthesized and organized primarily by behavior (discrimination, distortion, exploitation, and misjudgement), with codes also provided for domain (e.g. search, vision, advertising, etc.), organization (e.g. Google, Facebook, Amazon, etc.), and audit method (e.g. sock puppet, direct scrape, crowdsourcing, etc.). The review shows how previous audit studies have exposed public-facing algorithms exhibiting problematic behavior, such as search algorithms culpable of distortion and advertising algorithms culpable of discrimination. Based on the studies reviewed, it also suggests some behaviors (e.g. discrimination on the basis of intersectional identities), domains (e.g. advertising algorithms), methods (e.g. code auditing), and organizations (e.g. Twitter, TikTok, LinkedIn) that call for future audit attention. The paper concludes by offering the common ingredients of successful audits, and discussing algorithm auditing in the context of broader research working toward algorithmic justice.
Social-Emotional-Sensory Design Map for Affective Computing Informed by Neurodivergent Experiences
Authors: Zolyomi Annuska , Snyder Jaime
Published: April 2021

One of the grand challenges of artificial intelligence and affective computing is for technology to become emotionally-aware and thus, more human-like. Modeling human emotions is particularly complicated when we consider the lived experiences of people who are on the autism spectrum. To understand the emotional experiences of autistic adults and their attitudes towards common representations of emotions, we deployed a context study as the first phase of a Grounded Design research project. Based on community observations and interviews, this work contributes empirical evidence of how the emotional experiences of autistic adults are entangled with social interactions as well as the processing of sensory inputs. We learned that (1) the emotional experiences of autistic adults are embodied and co-constructed within the context of physical environments, social relationships, and technology use, and (2) conventional approaches to visually representing emotion in affective education and computing systems fail to accurately represent the experiences and perceptions of autistic adults. We contribute a social-emotional-sensory design map to guide designers in creating more diverse and nuanced affective computing interfaces that are enriched by accounting for neurodivergent users.
Auditing the Information Quality of News-Related Queries on the Alexa Voice Assistant
Authors: Dambanemuya Henry Kudzanai , Diakopoulos Nicholas
Published: April 2021

Smart speakers are becoming increasingly ubiquitous in society and are now used for satisfying a variety of information needs, from asking about the weather or traffic to accessing the latest breaking news information. Their growing use for news and information consumption presents new questions related to the quality, source diversity, and comprehensiveness of the news-related information they convey. These questions have significant implications for voice assistant technologies acting as algorithmic information intermediaries, but systematic information quality audits have not yet been undertaken. To address this gap, we develop a methodological approach for evaluating information quality in voice assistants for news-related queries. We demonstrate the approach on the Amazon Alexa voice assistant, first characterising Alexa's performance in terms of response relevance, accuracy, and timeliness, and then further elaborating analyses of information quality based on query phrasing, news category, and information provenance. We discuss the implications of our findings for future audits of information quality on voice assistants and for the consumption of news information via such algorithmic intermediaries more broadly.
A Design Exploration of Health-Related Community Displays
Authors: Gooch Daniel , Price Blaine A. , Klis-Davies Anna , Webb Julie
Published: April 2021

The global population is ageing, leading to shifts in healthcare needs. It is well established that increased physical activity can improve the health and wellbeing of many older adults. However, motivation remains a prime concern. We report findings from a series of focus groups where we explored the concept of using community displays to promote physical activity to a local neighborhood. In doing so, we contribute both an understanding of the design space for community displays, as well as a discussion of the implications of our work for the broader CSCW community. We conclude that our work demonstrates the potential for developing community displays for increasing physical activity amongst older adults.
The Complementary Nature of Perceived and Actual Time Spent Online in Measuring Digital Well-being
Authors: Mok Lillio , Anderson Ashton
Published: April 2021

As online platforms become ubiquitous, there is growing concern that their use can potentially lead to negative outcomes in users' personal lives, such as disrupted sleep and impacted social relationships. A central question in the literature studying these problematic effects is whether they are associated with the amount of time users spend on online platforms. This is often addressed by either analyzing self-reported measures of time spent online, which are generally inaccurate, or using objective metrics derived from server logs or tracking software. Nonetheless, how the two types of time measures comparatively relate to problematic effects -- whether they complement or are redundant with each other in predicting problematicity -- remains unknown. Additionally, transparent research into this question is hindered by the literature's focus on closed platforms with inaccessible data, as well as selective analytical decisions that may lead to reproducibility issues.

In this work, we investigate how both self-reported and data-derived metrics of time spent relate to potentially problematic effects arising from the use of an open, non-profit online chess platform. These effects include disruptions to sleep, relationships, school and work performance, and self-control. To this end, we distributed a gamified survey to players and linked their responses with publicly-available game logs. We find problematic effects to be associated with both self-reported and data-derived usage measures to similar degrees. However, analytical models incorporating both self-reported and actual time explain problematic effects significantly more effectively than models with either type of measure alone. Furthermore, these results persist across thousands of possible analytical decisions when using a robust and transparent statistical framework. This suggests that the two methods of measuring time spent measure contain distinct, complementary information about problematic usage outcomes and should be used in conjunction with each other.
AI-Assisted Human Labeling
Authors: Ashktorab Zahra , Desmond Michael , Andres Josh , Muller Michael , Joshi Narendra Nath , Brachman Michelle , Sharma Aabhas , Brimijoin Kristina , Pan Qian , Wolf Christine T. , Duesterwald Evelyn , Dugan Casey , Geyer Werner , Reimer Darrell
Published: April 2021

Human labeling of training data is often a time-consuming, expensive part of machine learning. In this paper, we study "batch labeling", an AI-assisted UX paradigm, that aids data labelers by allowing a single labeling action to apply to multiple records. We ran a large scale study on Mechanical Turk with 156 participants to investigate labeler-AI-batching system interaction. We investigate the efficacy of the system when compared to a single-item labeling interface (i.e., labeling one record at-a-time), and evaluate the impact of batch labeling on accuracy and time. We further investigate the impact of AI algorithm quality and its effects on the labelers' overreliance, as well as potential mechanisms for mitigating it. Our work offers implications for the design of batch labeling systems and for work practices focusing on labeler-AI-batching system interaction.
Vartalaap
Authors: Adhikary Rishiraj , Patel Zeel B. , Srivastava Tanmay , Batra Nipun , Singh Mayank , Bhatia Udit , Guttikunda Sarath
Published: April 2021

Air pollution is a global challenge for cities across the globe. Understanding the public perception of air pollution can help policymakers engage better with the public and appropriately introduce policies. Accurate public perception can also help people to identify the health risks of air pollution and act accordingly. Unfortunately, current techniques for determining perception are not scalable: it involves surveying few hundred people with questionnaire-based surveys. Using the advances in natural language processing (NLP), we propose a more scalable solution called Vartalaap to gauge public perception of air pollution via the microblogging social network Twitter. We curated a dataset of more than 1.2M tweets discussing Delhi-specific air pollution. We find that (unfortunately) the public is supportive of unproven mitigation strategies to reduce pollution, thus risking their health due to a false sense of security. We also find that air quality is a year-long problem, but the discussions are not proportional to the level of pollution and spike up when pollution is more visible. The information required by Vartalaap is publicly available and, as such, it can be immediately applied to study different societal issues across the world.
Participatory Design for Intergenerational Culture Exchange in Immigrant Families
Authors: Liaqat Amna , Axtell Benett , Munteanu Cosmin
Published: April 2021

Language and cultural barriers critically threaten the social relationships between grandparents and grandchildren in immigrant families. Cultural exchange activities, like shared storytelling, can foster these crucial connections. However, existing barriers make these seemingly routine interactions challenging for families to navigate. The resulting intergenerational drift places grandparents at high risk of sustained social isolation from their families. Past works have presented technology-mediated supports for grandparent-grandchild social interactions in non-immigrant families and have found that these interventions do foster stronger connections in both physically close and distant multigenerational families. We explore how to support the specific needs of immigrant families through Magic Thing participatory design workshops with grandchildren and grandparents together in order to reveal the social interactions that would support their cultural exchange. We use the Magic Thing to move the standard dialogic grandparent-grandchild relationship into a trialogic one, creating space for comfortable social connection and storytelling through the shared creation of the design. We find that technology-mediated support of intergenerational immigrant cultural exchange must be designed for this trialogic process, consider the role of expressing values as a form of meta-commentary on a story, and shift the perspective on existing "barriers" to consider how they might foster further engagement.
"We Would Never Write That Down"
Authors: Petersen Anette C. M. , Christensen Lars Rune , Harper Richard , Hildebrandt Thomas
Published: April 2021

This paper draws attention to new complexities of deploying artificial intelligence (AI) to sensitive contexts, such as welfare allocation. AI is increasingly used in public administration with the promise of improving decision-making through predictive modelling. To accurately predict, it needs all the agreed criteria used as part of decisions, formal and informal. This paper empirically explores the informal classifications used by caseworkers to make unemployed welfare seekers 'fit' into the formal categories applied in a Danish job centre. Our findings show that these classifications are documentable, and hence traceable to AI. However, to the caseworkers, they are at odds with the stable explanations assumed by any bureaucratic recording system as they involve negotiated and situated judgments of people's character. Thus, for moral reasons, caseworkers find them ill-suited for formal representation and predictive purposes and choose not to write them down. As a result, although classification work is crucial to the job centre's activities, AI is denuded of the real-world (and real work) character of decision-making in this context. This is an important finding for CSCW as it is not only about whether AI can 'do' decision-making in particular contexts, as previous research has argued. This paper shows that problems may also be caused by people's unwillingness to provide data to systems. It is the purpose of this paper to present the empirical results of this research, followed by a discussion of implications for AI-supported practice and research.
Exploring the Role of Artifacts in Collective Dance Re-staging
Authors: Rivière Jean-Philippe , Fdili Alaoui Sarah , Caramiaux Baptiste , Mackay Wendy E.
Published: April 2021

Preparing a new dance performance involves more than learning individual steps. We are interested in understanding how dancers collaborate as they rehearse a new dance piece, with a particular emphasis on how they use physical and digital artifacts to support this process. We conducted a 12-month longitudinal observational study with a dance company that re-staged a dance piece, taken from the contemporary repertoire and unknown to the dancers. The study focused on the role that artifacts play in shaping the learning of a dance piece. We showed how dancers produced an ecology of artifacts with the aim of analyzing the choreographic ideas behind the dance and sharing them with other learners. We showed that sharing these artifacts was challenging because they are idiosyncratic and embody their creator perspective and vocabulary. We then illustrated how dancers overcome this challenge by compiling artifacts and distributing the learning task among the group in order to create a common knowledge of the piece which improves the learning process. We conclude with design opportunities for technologies supporting long-term dance learning processes.
Search with Discretion
Authors: Iqbal Modassir , Shilton Katie , Sayed Mahmoud F. , Oard Douglas , Rivera Jonah Lynn , Cox William
Published: April 2021

This paper describes and assesses the value sensitive design (VSD) of a test collection: data used to train and evaluate a machine learning system for information retrieval. The project used the VSD framework and methods to design a test collection annotated for discretion. We conducted qualitative stakeholder interviews to develop values personas, which guided annotation of a collection of corporate emails for contextual notions of sensitivity. Both qualitative and quantitative evaluations of the method reveal that the values personas concretely shaped annotators' sensitivity judgments, and analysis of the test collection itself demonstrates that the sensitivity annotations have utility for identifying features that may correlate with email sensitivity. Values personas for training data annotation expand the toolkit of methods for value-sensitive machine learning.
Hello? Is There Anybody in There?
Authors: Li Xuyang , Bahursettiwar Antara , Kogan Marina
Published: April 2021

As social media has become more present in people's day-to-day lives, many turn to these platforms in natural disasters to keep abreast of the ever-evolving crisis situation. Facing the increasing amount of crisis-related information available on the social media platforms, users tend to focus on and reach out to authoritative sources---individuals or organizations that provide authoritative and credible crisis-related information due to their official status or position. As they provide high-quality information, response from the authoritative sources can be especially valuable to social media users directly affected in natural disasters. In this study, we aim to extend the reach of credible information during crisis, and direct the attention of authoritative users to the affected users who need their help. Specifically, we investigate what factors differentiate the tweets by regular users that receive responses from authoritative accounts from those that do not. We find that regular users' popularity and official accounts' level of busyness do not seem to affect the likelihood of tweets receiving a response. We thus explore the linguistic features of the tweets' content. Topic modeling and sentiment analysis results suggest that these linguistic aspects of the tweets may affect the response rate from authoritative sources. Our findings suggest crisis-related policy implications, as well as design implications for social media platforms where such exchanges take place, which can potentially increase the reach of credible information in a crisis and help those affected obtain the safety-critical information they need.
Open Collaborative Writing
Authors: Pe-Than Ei Pa Pa , Dabbish Laura , Herbsleb James
Published: April 2021

Work of all kinds increasingly takes place via networked digital environments that support diverse modes of collaboration. Previous work has investigated how collaborative writing takes place in shared and open workspaces. This study investigates how the "fork-and-pull'' model of distributed version management, now widely utilized in software development, supports coordination and public contribution in collaborative writing. We employ a case study approach to understand coordination around written artifacts in an open pull-based environment. Through interviews and archival analysis of two open text projects: a mathematics textbook on homotopy type theory (HoTT) and open source policies by 18F, a branch of federal agency, we reconstruct how text artifacts were created. We find that in both cases an intensive multi-channel communication among a small core preceded an explicit release action in the form of public communication to solicit broader contribution. Our analysis reveals a dichotomy between two modes of collaboration: a perfecting mode of crafting a public written document versus an evolution mode. In perfecting, authors used the fork-and-pull model to manage contributions refining the text to a more correct version. In evolution, authors used the fork-and-pull model to use a written artifact as a template and starting point, customizing it to their relevant scope. Based on our results, we consider how we might design systems and policies to support pull-based coordination around written artifacts and the forms it takes.
"Facebook Promotes More Harassment"
Authors: Nova Fayika Farhat , DeVito Michael Ann , Saha Pratyasha , Rashid Kazi Shohanur , Roy Turzo Shashwata , Afrin Sadia , Guha Shion
Published: April 2021

Social interaction across multiple online platforms is a challenge for gender and sexual minorities (GSM) due to the stigmatization they face, which increases the complexity of their self-presentation decisions. These online interactions and identity disclosures can be more complicated for GSM in non-Western contexts due to consequentially different audiences and perceived affordances by the users, and limited baseline understanding of the conflation of these two with local norms and the opportunities they practically represent. Using focus group discussions and semi-structured interviews, we engaged with 61 Hijra individuals from Bangladesh, a severely stigmatized GSM from south Asia, to understand their overall online participation and disclosure behaviors through the lens of personal social media ecosystems. We find that along with platform audiences, affordances, and norms, participant skill/knowledge, and cultural influences also impact navigation through multiple platforms, resulting in differential benefits from privacy features. This impacts how Hijra perceive online spaces, and shape their self-presentation and disclosure behaviors over time. Content Warning: This paper discusses graphic contents (e.g. rape and sexual harassment) related to Hijra.
Getting Your Facebook Affairs in Order
Authors: Gach Katie Z. , Brubaker Jed R.
Published: April 2021

Legacy Contact is a Facebook feature that allows account holders to choose someone to manage their profile after they have died. This study evaluates the Legacy Contact setup process, and asks how users form and communicate expectations about what an active post-mortem manager's responsibilities would be. We conducted 30 semi-structured interviews with adult Facebook account holders in the US who either configured their own settings or were chosen for future delegation by someone else. We found that account holders chose the Facebook Friend they felt closest to, and that selection was often reciprocated. Both account holders and legacy contacts felt confident that they had communicated (or could communicate) well about the legacy contact's responsibilities, but their expectations did not align with the actual functionality of the Legacy Contact system. We argue that the misalignment of user expectations and system functionality indicates a failed setup process. Our findings suggest that post-mortem management systems require a setup process that is fundamentally different from the quick-clickthrough standards of everyday interaction design. The ramifications of a post-mortem manager's expectations not being met during a time of grief, points to the urgent need for a setup process that prompts thoughtfulness and deliberation, and forms accurate expectations for account holders and future post-mortem managers alike.
What Can Analytics for Teamwork Proxemics Reveal About Positioning Dynamics In Clinical Simulations?
Authors: Fernandez-Nieto Gloria , Martinez-Maldonado Roberto , Echeverria Vanessa , Kitto Kirsty , An Pengcheng , Buckingham Shum Simon
Published: April 2021

Effective teamwork is critical to improve patient outcomes in healthcare. However, achieving this capabilityrequires that pre-service nurses develop the spatial abilities they will require in their clinical placements, suchas: learning when to remain close to the patient and to other team members; positioning themselves correctlyat the right time; and deciding on specific team formations (e.g. face-to-face or side-by-side) to enable effectiveinteraction or avoid disrupting clinical procedures. However, positioning dynamics are ephemeral and caneasily become occluded by the multiple tasks nurses have to accomplish. Digital traces automatically capturedby indoor positioning sensors can be used to address this problem for the purpose of improving nurses' reflection, learning and professional development. This paper presents; i) a qualitative study that illustrateshow to elicit spatial behaviours from educators' pedagogical expectations, and ii) a modelling approachthat transforms nurses' low-level position traces into higher-order proxemics constructs, informed by sucheducatos' expectations, in the context of simulation-based teamwork training. To illustrate our modellingapproach, we conducted an in-the-wild study with 55 undergraduate students and five educators from whompositioning traces were captured in eleven authentic nursing education classes. Low-levelx-ydata was usedto model three proxemic constructs: i) co-presence in interactional spaces, ii) socio-spatial formations (i.e.f-formations), and ii) presence in spaces of interest. Through a number of vignettes, we illustrate how indoorpositioning analytics can be used to address questions that educators and researchers have about teamwork inhealthcare simulation settings.
Cat and Mouse Game
Authors: Veeraraghavan Rajesh
Published: April 2021

This article uses findings from a field study of the world's largest guaranteed employment scheme (NREGA) in India to understand how digital technology mediates work relations and power dynamics within a bureaucracy. In this initiative, upper-level bureaucrats in the south Indian state of Andhra Pradesh built a digital network to remove local discretion at the "last mile" of an implementation of NREGA. I show how digital infrastructure affords actors at both the first and last mile opportunities to modify software to control as well as subvert certain practices. This article refers to this dialectic phenomenon as "governance by patching" and defines it as a socio-technical instantiation of a top-down process that focuses on small changes, iterative, and political process for positive change. Governance by patching is, therefore, neither a purely technical process nor an exclusively administrative one. Rather, it refers to the ability to fix unanticipated problems that arise in the implementation of governance programs by altering the socio-technical systems. The struggle for power continues, but on the new digital terrain.
Resisting Racism in Tech Design
Authors: Rankin Yolanda A. , Henderson Kallayah K.
Published: April 2021

Speech-based interfaces that feature conversational agents have a diverse population of users, including children. While recent HCI research has investigated child-agent interactions in the context of speech-based interfaces with rare mention of sample populations' racial makeup, scant research has examined how racially minoritized groups, in particular Black youth, interact with such technologies. Subsequently, issues of race and racism continue to be largely neglected within the HCI community, contributing to technology that marginalizes Black people. Leveraging Black feminist epistemology as a critical framework, we engage Black students in the ideation process of conversational agents as speech-based interfaces, drawing attention to how race, gender, and class influence their design choices. Our findings reveal that Black students prefer custom-designed conversational agents that are modeled after Black women, possessing knowledge of Black culture and serving as life coaches to help them achieve their goals. We also confirm that the current design of speech-based interfaces perpetuates racism, reinforcing whiteness as the norm.
A Two Phases Self-healing Framework for Service-oriented Systems
Authors: Alhosban Amal , Malik Zaki , Hashmi Khayyam , Medjahed Brahim , Al-Ababneh Hassan
Published: April 2021

Service-Oriented Architectures (SOA) enable the automatic creation of business applications from independently developed and deployed Web services. As Web services are inherently
a priori
unknown, how to deliver reliable Web services compositions is a significant and challenging problem. Services involved in an SOA often do not operate under a single processing environment and need to communicate using different protocols over a network. Under such conditions, designing a fault management system that is both efficient and extensible is a challenging task. In this article, we propose SFSS, a self-healing framework for SOA fault management. SFSS is predicting, identifying, and solving faults in SOAs. In SFSS, we identified a set of high-level exception handling strategies based on the QoS performances of different component services and the preferences articled by the service consumers. Multiple recovery plans are generated and evaluated according to the performance of the selected component services, and then we execute the best recovery plan. We assess the overall user dependence (i.e., the service is independent of other services) using the generated plan and the available invocation information of the component services. Due to the experiment results, the given technique enhances the service selection quality by choosing the services that have the highest score and betters the overall system performance. The experiment results indicate the applicability of SFSS and show improved performance in comparison to similar approaches.
Utilizing Web Trackers for Sybil Defense
Authors: Flores Marcel , Kahn Andrew , Warrior Marc , Mislove Alan , Kuzmanovic Aleksandar
Published: April 2021

User tracking has become ubiquitous practice on the Web, allowing services to recommend behaviorally targeted content to users. In this article, we design Alibi, a system that utilizes such readily available personalized content, generated by recommendation engines in real time, as a means to tame Sybil attacks. In particular, by using ads and other tracker-generated recommendations as implicit user “certificates,” Alibi is capable of creating meta-profiles that allow for rapid and inexpensive validation of users’ uniqueness, thereby enabling an Internet-wide Sybil defense service.

We demonstrate the feasibility of such a system, exploring the aggregate behavior of recommendation engines on the Web and demonstrating the richness of the meta-profile space defined by such inputs. We further explore the fundamental properties of such meta-profiles, i.e., their construction, uniqueness, persistence, and resilience to attacks. By conducting a user study, we show that the user meta-profiles are robust and show important scaling effects. We demonstrate that utilizing even a moderate number of popular Web sites empowers Alibi to tame large-scale Sybil attacks.
Performance Evaluation of Intel Optane Memory for Managed Workloads
Authors: Akram Shoaib
Published: April 2021

Intel Optane memory offers non-volatility, byte addressability, and high capacity. It suits managed workloads that prefer large main memory heaps. We investigate Optane as the main memory for managed (Java) workloads, focusing on performance scalability. As the workload (core count) increases, we note Optane’s performance relative to DRAM. A few workloads incur a slight slowdown on Optane memory, which helps conserve limited DRAM capacity. Unfortunately, other workloads scale poorly beyond a few core counts.

This article investigates scaling bottlenecks for Java workloads on Optane memory, analyzing the application, runtime, and microarchitectural interactions. Poorly scaling workloads allocate objects rapidly and access objects in Optane memory frequently. These characteristics slow down the mutator and substantially slow down garbage collection (GC). At the microarchitecture level, load, store, and instruction miss penalties rise. To regain performance, we partition heaps across DRAM and Optane memory, a hybrid that scales considerably better than Optane alone. We exploit state-of-the-art GC approaches to partition heaps. Unfortunately, existing GC approaches needlessly waste DRAM capacity because they ignore runtime behavior.

This article also introduces performance impact-guided memory allocation (PIMA) for hybrid memories. PIMA maximizes Optane utilization, allocating in DRAM only if it improves performance. It estimates the performance impact of allocating heaps in either memory type by sampling. We target PIMA at graph analytics workloads, offering a novel performance estimation method and detailed evaluation. PIMA identifies workload phases that benefit from DRAM with high (94.33%) accuracy, incurring only a 2% sampling overhead. PIMA operates stand-alone or combines with prior approaches to offer new performance versus DRAM capacity trade-offs. This work opens up Optane memory to a real-life role as the main memory for Java workloads.
Reduction Mechanism of Eu3+in Ca2+xLa8−x(SiO4)6O2–0.5x: Eu2+Phosphors
Authors: Luo ChaoFei , Liu Ke , Ji WeiWei , Li Zhan , Yin Liangjun , Hao LuYuan , Xu Xin , Agathopoulos Simeon
Published: April 2021

A series of apatite phosphors with a general formula of Ca2+xLa8−x(SiO4)6O2–0.5x: Eu2+were successfully synthesized by a conventional solid-state method. The increasing substitution of Ca2+for La3+ions in the crystal favors the reduction from Eu3+to Eu2+ions, resulting in phosphors with a strong green emission at 510 nm. XANES results reveal that when the value of x increases, the amount of Eu2+also increases. Rietveld refinement shows that the produced materials belong to the hexagonal crystal system, with a space group of P6/3 m (176). Based on the Rietveld refinement, EPR, and XANES results (from Rietveld refinement, EPR, and XANES analyses), it is suggested that the non-equivalent (i.e. lowering of the valence from +3 to +2) substitution of Ca2+for La3+leads to a decrease of the number of O2−ions in the crystal and, thus, favors the reduction of Eu3+to Eu2+.
Defining comparative physiology: results from an online survey and systematic review
Authors: Basile A. J. , Kirkton S. D. , Hedrick M. S. , Carey H. V. , Sweazea K. L.
Published: April 2021

August Krogh’s 1929 principle is referenced as the cornerstone of comparative physiology (CP). However, there are diverse views as to what type of research falls under the CP approach. This study had three aims: 1) determine how CP is defined through an online survey (OS) of physiologists and a systematic review (SR), 2) put forth an updated definition of CP by summarizing OS and SR results, and 3) outline the numerous CP research approaches. Professional physiology societies ( n = 54) were invited to share the OS with their members, and a SR was conducted, which yielded 197 and 70 definitions, respectively. The three most common words in descending order in the OS definitions were “different,” “animals,” and “species” and in the SR definitions, “animals,” “species,” and “organisms.” The three most prevalent themes from the OS and SR definitions were comparing/differences/diversity across species (78% and 51%, respectively), response to the environment/ecology (28% and 43%, respectively), and included evolution or adaptation (24% and 60%, respectively). Ten research approaches were identified, which include broad comparison (i.e., many species generalization), specific comparison (e.g., 2 species; for traits that are different, exaggerated, extreme, missing, or not induced), or comparison while considering evolution (i.e., evolutionary physiology), ecology (i.e., ecophysiology), or human physiology/medicine. Only 5% and 33% of OS and SR definitions described or mentioned Krogh’s principle. In conclusion, CP can best be defined as a compilation of research approaches that utilize different types of comparisons to elucidate physiological mechanisms and not simply comparing physiologies as the name implies.
Clinical and Paraclinical Screening for Celiac Disease in Children with Intractable Epilepsy
Authors: Ghazizadeh Esslami Golnaz , Allahverdi Bahar , Badv Reza Shervin , Heidari Morteza , Khosroshahi Nahid , Shabani-Mirzaee Hosein , Eftekhari Kambiz
Published: April 2021

Background. Celiac disease is the inflammatory entropy caused by hypersensitivity to gluten, which occurs in susceptible individuals. Some studies have suggested a link between celiac disease and epilepsy in children. Our aim was to screen for clinical and paraclinical features of celiac disease in children with intractable epilepsy. Methods. This was a cross-sectional study. Children aged 2 to 18 years with refractory epilepsy that referred to the pediatric neurology clinic within one year (2018–2019) were enrolled. Demographic and clinical characteristics of patients, especially clinical manifestations of celiac disease, were recorded in a questionnaire. A venous blood sample was sent to determine the total IgA, anti-tTG (IgA), and anti-endomysial antibody (IgA). Endoscopy was performed in cases where the celiac serological test was positive. Results. Seventy children with idiopathic drug-resistant epilepsy (44 boys) were evaluated. The height-for-age index was 49.2% and the weight-for-age index was 38.2% less than normal. Constipation (48.6%), anorexia (25.7%), and abdominal pain (21.4%) were the most common gastrointestinal symptoms. Celiac serological tests were negative in all children. Therefore, endoscopy and bowel biopsy were not performed in any case. Conclusion. Celiac disease was not found in any patient with intractable epilepsy. Gastrointestinal symptoms and growth disorders in this group may be related to the underlying disease or medications and not to celiac disease.
The Differences of Population Birth Defects in Epidemiology Analysis between the Rural and Urban Areas of Hunan Province in China, 2014–2018
Authors: Xiong Lili , Chen Qiongying , Wang Aihua , Kong Fanjuan , Xie Donghua , Xie Zhiqun
Published: April 2021

Objectives. To compare the differences of epidemiology analysis in population birth defects (BDs) between the rural and urban areas of Hunan Province in China. Methods. The data of population‐based BDs in Liuyang county (rural) and Shifeng district (urban) in Hunan Province for 2014–2018 were analyzed. BD prevalence rates, percentage change, and annual percentage change (APC) by sex and age were calculated to evaluate time trends. Risk factors associated with BDs were assessed using simple and multiple logistic regression analyses. Results. The BD prevalence rate per 10,000 perinatal infants (PIs) was 220.54 (95% CI: 211.26‐230.13) in Liuyang and 181.14 (95% CI: 161.18‐202.87) in Shifeng. Significant decreasing trends in BD prevalence rates were noted in the female PIs (APC = −9.31, P = 0.044) and the total BD prevalence rate in Shifeng (APC = −14.14, P = 0.039). Risk factors for BDs were as follows: rural area, male PIs, PIs with gestational age < 37 weeks, PIs with birth weight < 2500 g, and migrant pregnancies. Conclusions. We should focus on rural areas, reduce the prevalence of premature and low birth weight infants, and provide maternal healthcare services for migrant pregnancies for BD prevention from the perspective of population‐based BD surveillance.
An Improved Artificial Neural Network Model for Effective Diabetes Prediction
Authors: Bukhari Muhammad Mazhar , Alkhamees Bader Fahad , Hussain Saddam , Gumaei Abdu , Assiri Adel , Ullah Syed Sajid
Published: April 2021

Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks (ANNs) in the medical discipline emerged for data‐driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system (DSS), and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network (ABP‐SCGNN) algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes (PID) dataset using accuracy and mean squared error (MSE) as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP‐SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes.
Wind‐Induced Vibration Control of High‐Rise Structures Using Compound Damping Cables
Authors: Yu Jianda , Duan Zhibo , Zhang Xiangqi , Peng Jian
Published: April 2021

Based on the vibration reduction mechanism of compound damping cables, this study focuses on the wind‐induced vibration control of high‐rise structures with additional mass at the top. The differential equation of motion of the system under the action of the composite damping cable is established, and the analytical solution of the additional damping ratio of the structure is deduced, which is verified by model tests. The vibration response of the structure under the action of simple harmonic vortex excitation and randomly fluctuating wind loads is studied, and the effect of different viscous coefficients of the dampers in the composite damping cable and different installation heights of the damping cable on the vibration control is analyzed. The results show that a small vortex excitation force will cause large vibrations of low‐dampened towering structures, and the structure will undergo buffeting under the action of wind load pulse force. The damping cable can greatly reduce the amplitude of structural vibration. The root means square of structural vibration displacement varies with damping. The viscosity coefficient of the device and the installation height of the main cable of the damping cable are greatly reduced.
A Lightweight Deep Learning‐Based Pneumonia Detection Approach for Energy‐Efficient Medical Systems
Authors: Almaslukh Bandar
Published: April 2021

Early detection of pneumonia disease can increase the survival rate of lung patients. Chest X‐ray (CXR) images are the primarily means of detecting and diagnosing pneumonia. Detecting pneumonia from CXR images by a trained radiologist is a challenging task. It needs an automatic computer‐aided diagnostic system to improve the accuracy of diagnosis. Developing a lightweight automatic pneumonia detection approach for energy‐efficient medical systems plays an important role in improving the quality of healthcare with reduced costs and speedier response. Recent works have proposed to develop automated detection models using deep learning (DL) methods. However, the efficiency and effectiveness of these models need to be improved because they depend on the values of the models’ hyperparameters. Choosing suitable hyperparameter values is a critical task for constructing a lightweight and accurate model. In this paper, a lightweight DL approach is proposed using a pretrained DenseNet‐121‐based feature extraction method and a deep neural network‐ (DNN‐) based method with a random search fine‐tuning technique. The DenseNet‐121 model is selected due to its ability to provide the best representation of lung features. The use of random search makes the tuning process faster and improves the efficiency and accuracy of the DNN model. An extensive set of experiments are conducted on a public dataset of CXR images using a set of evaluation metrics. The experiments show that the approach achieved 98.90% accuracy with an increase of 0.47% compared to the latest approach on the same dataset. Moreover, the experimental results demonstrate the approach that the average execution time for detection is very low, confirming its suitability for energy‐efficient medical systems.
Glycemic Control Status and Long-Term Clinical Outcomes in Diabetic Chronic Total Occlusion Patients: An Observational Study
Authors: Zhang Xuehui , Nie Maoxiao , Chen Xue , Liang Zhe , Zhao Quanming
Published: April 2021

Background. Whether good glycemic control can result in clinical benefits for diabetic chronic total occlusion (CTO) patients is still a matter of debate. Methods. We studied 1029 diabetic CTO patients. Based on one-year glycosylated hemoglobin A (HbA1c) levels, we assigned the patients into 2 groups: HbA1c<7% group (n = 448) and HbA1c ≥ 7% group (n = 581). We further subdivided the patients into the successful CTO revascularization (CTO-SR) and nonsuccessful CTO revascularization (CTO-NSR) groups. Kaplan–Meier analysis and Cox regression before and after propensity score matching were used to compare major adverse cardiovascular events (MACE) and other endpoints. Results. There were no significant differences between the groups in terms of most endpoints in the overall patients. After propensity score-matched analysis, patients with HbA1c < 7.0 tended to be superior in terms of MACE, which was mainly attributed to repeat revascularization but the other endpoints. Furthermore, the benefit of the HbA1c < 7 group was more prominent among patients with CTO-NSR in terms of MACE, repeat revascularization, and target vessel revascularization (TVR); and the improvement of the HbAc1 < 7 group was more prominent among patients without chronic heart failure (CHF) (

P
=
0.027

). Conclusions. HbA1c < 7.0 was associated with a reduced incidence of MACE, which was mainly attributed to a reduction in repeat revascularization. Good glycemic control can improve diabetic CTO patients’ clinical prognosis, especially in CTO-NSR patients.