bluuebunny/crossref_metadata_2025 huggingface dataset:
Separate Online Networks During Life Transitions
Authors: Zhang Ben Zefeng , Liu Tianxiao , Corvite Shanley , Andalibi Nazanin , Haimson Oliver L.
Published: November 2022

Some life transitions can be difficult to discuss on social media, especially with networks of known ties, due to challenges such as stigmatization. Separate online networks can provide alternative spaces to discuss life transitions. To understand why and how people turn to separate networks, we interviewed 28 participants who had recently experienced life transitions. While prior research tends to focus on one life transition in isolation, this work examines social media sharing behaviors across a wide variety of life transitions. We describe how people often turn to separate networks during life transitions due to challenges faced in networks of known ties, yet encounter new challenges such as difficulty locating these networks. We describe support from waiting contributors and virtual friends. Finally, we provide insight into how online separate networks can be better designed through enhancing search functionality, promoting contribution, and providing context-sensitive templates for sharing in online spaces.
The Impact of Governance Bots on Sense of Virtual Community
Authors: Smith C. Estelle , Alam Irfanul , Tan Chenhao , Keegan Brian C. , Blanchard Anita L.
Published: November 2022

Bots are increasingly being used for governance-related purposes in online communities, yet no instrumentation exists for measuring how users assess their beneficial or detrimental impacts. In order to support future human-centered and community-based research, we developed a new scale called GOVernance Bots in Online communiTies (GOV-BOTs) across two rounds of surveys on Reddit (N=820). We applied rigorous psychometric criteria to demonstrate the validity of GOV-BOTs, which contains two subscales: bot governance (4 items) and bot tensions (3 items). Whereas humans have historically expected communities to be composed entirely of humans, the social participation of bots as non-human agents now raises fundamental questions about psychological, philosophical, and ethical implications. Addressing psychological impacts, our data show that perceptions of effective bot governance positively contribute to users' sense of virtual community (SOVC), whereas perceived bot tensions may only impact SOVC if users are more aware of bots. Finally, we show that users tend to experience the greatest SOVC across groups of subreddits, rather than individual subreddits, suggesting that future research should carefully re-consider uses and operationalizations of the term "community."
Understanding Emerging Obfuscation Technologies in Visual Description Services for Blind and Low Vision People
Authors: Alharbi Rahaf , Brewer Robin N. , Schoenebeck Sarita
Published: November 2022

Blind and low vision people use visual description services (VDS) to gain visual interpretation and build access in a world that privileges sight. Despite their many benefits, VDS have many harmful privacy and security implications. As a result, researchers are suggesting, exploring, and building obfuscation systems that detect and obscure private or sensitive materials. However, as obfuscation depends largely on sight to interpret outcomes, it is unknown whether Blind and low vision people would find such approaches useful. Our work aims to center the perspectives and opinions of Blind and low vision people on the potential of obfuscation to address privacy concerns in VDS. By reporting on interviews with 20 Blind and low vision people who use VDS, our findings reveal that popular research trends in obfuscation fail to capture the needs of Blind and low vision people. While obfuscation might be helpful in gaining more control, tensions around obfuscation misrecognition and confirmation are prominent. We turn to the framework of interdependence to unpack and understand obfuscation in VDS, enabling us to complicate privacy concerns, uncover the labor of Blind and low vision people, and emphasize the importance of safeguards. We provide design directions to move the trajectory of obfuscation research forward.
Understanding User Reliance on AI in Assisted Decision-Making
Authors: Cao Shiye , Huang Chien-Ming
Published: November 2022

Proper calibration of human reliance on AI is fundamental to achieving complementary performance in AI-assisted human decision-making. Most previous works focused on assessing user reliance, and more broadly trust, retrospectively, through user perceptions and task-based measures. In this work, we explore the relationship between eye gaze and reliance under varying task difficulties and AI performance levels in a spatial reasoning task. Our results show a strong positive correlation between percent gaze duration on the AI suggestion and user AI task agreement, as well as user perceived reliance. Moreover, user agency is preserved particularly when the task is easy and when AI performance is low or inconsistent. Our results also reveal nuanced differences between reliance and trust. We discuss the potential of using eye gaze to gauge human reliance on AI in real-time, enabling adaptive AI assistance for optimal human-AI team performance.
A Mixed-Methods Analysis of the Algorithm-Mediated Labor of Online Food Deliverers in China
Authors: Chen Zhilong , Lan Xiaochong , Piao Jinghua , Zhang Yunke , Li Yong
Published: November 2022

In recent years, China has witnessed the proliferation and success of the online food delivery industry, an emerging type of the gig economy. Online food deliverers who deliver the food from restaurants to customers play a critical role in enabling this industry. Mediated by algorithms and coupled with interactions with multiple stakeholders, this emerging kind of labor has been taken by millions of people. In this paper, we present a mixed-methods analysis to investigate this labor of online food deliverers and uncover how the mediation of algorithms shapes it. Combining large-scale quantitative data-driven investigations of 100,000 deliverers' behavioral data with in-depth qualitative interviews with 15 online food deliverers, we demonstrate their working activities, identify how algorithms mediate their delivery procedures, and reveal how they perceive their relationships with different stakeholders as a result of their algorithm-mediated labor. Our findings provide important implications for enabling better experiences and more humanized labor of deliverers as well as workers in gig economies of similar kinds.
Accessible Design is Mediated by Job Support Structures and Knowledge Gained Through Design Career Pathways
Authors: Andrew Sarah , Tigwell Garreth W.
Published: November 2022

Digital designers often do not make their work accessible (e.g., websites failing criteria set by the W3C's Web Content Accessibility Guidelines), and accessible design research discusses many solutions to address this problem (e.g., teaching accessibility within university design and technical courses). However, prior research in this area typically does not acknowledge whether recommendations and resources to support accessible design are suitable for all digital designers due to different training pathways and job support structures (e.g., large-company vs. rural and self-employed designers or designers who learned their skills outside of formal education settings). We interviewed 20 digital designers from rural and urban areas, as well as working from home and remotely, to understand the challenges they experience in making accessible content within the context of their workplace. We find that job support structures mediate the effectiveness of current accessible design recommendations and resources, and we suggest how to improve accessible design support to meet the needs of under-resourced designers.
Are We Healthier Together? Two Strategies for Supporting Macronutrient Assessment Skills and How the Crowd Can Help (or Not)
Authors: Harmon Sarah M. , Heitkemper Elizabeth M. , Mamykina Lena , Hwang Maria L.
Published: November 2022

Learning macronutrient assessment skills can support improved health outcomes and overall wellbeing. We conducted two Mechanical Turk studies to investigate how users might benefit from the crowd's input in macronutrient assessment education. We first determined whether the wisdom of the crowd alone would provide users with enough insight to arrive at accurate macronutrient estimates. Next, we tested two methods of teaching macronutrient assessment skills (Comparison and Decomposition) and analyzed their effectiveness. Results from these studies indicate that while the crowd alone may not be sufficient to support this type of education, users may yet benefit from access to community-generated photos and labels while they use either the Comparison or Decomposition strategy.
Beyond the Pandemic and Privacy Concerns: Perceived Benefit and Expected Use of Pandemic-Tracking Apps in India
Authors: Seberger John S. , Patil Sameer
Published: November 2022

Pandemic-tracking apps could be a viable pandemic-mitigation technique, although their efficacy has been weakened by limited adoption in many places. Therefore, it is important to investigate how the perceptions of such apps are formed in different regions, which may help explain the differences in adoption. We replicated prior work on the adoption of pandemic-tracking apps in the United States with participants from India (n = 236). We identified that the perceptions of pandemic-tracking apps are connected to social orientation, familiarity with health-related technology, and demographics. We found that the perceptions and expected use were uncorrelated with privacy concerns, suggesting that privacy may not necessarily be the most suitable lens for studying the adoption of pandemic-tracking apps in India. Based on the findings, we make several recommendations for future pandemic-preparedness campaigns and identify the need to continue the trend toward contextualizing privacy-centered research with privacy-adjacent individual factors across multiple regions.
The Algorithmic Crystal: Conceptualizing the Self through Algorithmic Personalization on TikTok
Authors: Lee Angela Y. , Mieczkowski Hannah , Ellison Nicole B. , Hancock Jeffrey T.
Published: November 2022

This research examines how TikTok users conceptualize and engage with personalized algorithms on the TikTok platform. Using qualitative methods, we analyzed 24 interviews with TikTok users to explore how algorithmic personalization processes inform people's understanding of their identities as well as shape their orientation to others. Building on insights from our qualitative data and previous scholarship on algorithms and identity, we propose a novel conceptual model to understand how people think about and interact with personalized algorithmic systems. Drawing on the metaphor of crystals and their properties, the algorithmic crystal framework is an analytic frame that captures user understandings of how personalized algorithms (1) interact with user identity by reflecting user self-concepts that are both multifaceted and dynamic and (2) shape perspectives on others encountered through the algorithm, by orienting users to recognize parts of themselves refracted in other users and to experience ephemeral, diffracted connections with groups of similar others. We describe how the algorithmic crystal framework can extend theory and inform new lines of research around the implications of algorithms in self-concept development and social life.
You Are Not Alone
Authors: Choi Ryuhaerang , Yun Chanwoo , Cho Hyunsung , Hong Hwajung , Lee Uichin , Lee Sung-Ju
Published: November 2022

People experience various stressful events in their daily lives. Receiving social support, especially from peers who went through a similar experience, helps individuals cope with such stress. We propose StressTrendmeter, a mobile application that targets college students for anonymously sharing the source of stress via the form of hashtags, viewing stress topics based on trends, and providing social support through the empathy button and hashtag-based chat. We deployed StressTrendmeter to 222 students from two universities for five weeks. With hashtags and trending features, students found StressTrendmeter (i)helpful to spontaneously yet concisely articulate their stress topics and (ii) easy to browse through and become aware of issues around the campus. Our study reveals that social sharing with StressTrendmeter brought awareness, resonance, and accountability as students empathized and expressed support. Based on our study, we share design implications for social support systems with community awareness.
Cone of Vision as a Behavioural Cue for VR Collaboration
Authors: Bovo Riccardo , Giunchi Daniele , Alebri Muna , Steed Anthony , Costanza Enrico , Heinis Thomas
Published: November 2022

Mutual awareness of visual attention is essential for collaborative work. In the field of collaborative virtual environments (CVE), it has been proposed to use Field-of-View (FoV) frustum visualisations as a cue to support mutual awareness during collaboration. Recent studies on FoV frustum visualisations focus on asymmetric collaboration with AR/VR hardware setups and 3D reconstructed environments. In contrast, we focus on the general-purpose CVEs (i.e., VR shared offices), whose popularity is increasing due to the availability of low-cost headsets, and the restrictions imposed by the pandemic. In these CVEs collaboration roles are symmetrical, and the same 2D content available on desktop computers is displayed on 2D surfaces in a 3D space (VR screens). We prototyped one such CVE to evaluate FoV frustrum visualisation within this collaboration scenario. We also implement a FoV visualisation generated from an average fixation map (AFM), therefore directly generated by users' gaze behaviour which we call Cone of Vision (CoV). Our approach to displaying the frustum visualisations is tailored for 2D surfaces in 3D space and allows for self-awareness of this visual cue. We evaluate CoV in the context of a general exploratory data analysis (EDA) with 10 pairs of participants. Our findings indicate that CoV is beneficial during shifts between independent and collaborative work and supports collaborative progression across the visualisation. Self-perception of the CoV improves visual attention coupling, reduces the number of times users watch the collaborator's avatars and offers a consistent representation of the shared reality.
Dealing with Digital Service Closure
Authors: Gould Sandy J. J. , Wiseman Sarah
Published: November 2022

People integrate digital services into their day-to-day lives, often with the assumption that they will always be available. What happens when these services close down? The introduction of services might be carefully planned, but their closure may not benefit from the same degree of consideration. A more developed understanding of the effects of closures might make it possible to minimize negative consequences for users. This paper builds on sustainability, digital memories, and collaborative-work research through an empirical investigation of service closure. Fifty-five participants completed a questionnaire that solicited experiences of service closure and attitudes toward prospective closure. Through a qualitative analysis of participant responses, we synthesized six themes that reflected the practical and emotional effects of service closure on people: disempowerment, disconnection, loss of capability, trust, time and effort, and notice periods. We make suggestions for ways that service features related to these themes might be managed during closure, but also identify less tractable challenges: as part of this investigation, we introduce and develop the concept of service patinas to describe the important but entirely service-bound data that contextualize digital artefacts.
Deradicalizing YouTube: Characterization, Detection, and Personalization of Religiously Intolerant Arabic Videos
Authors: Albadi Nuha , Kurdi Maram , Mishra Shivakant
Published: November 2022

Growing evidence suggests that YouTube's recommendation algorithm plays a role in online radicalization via surfacing extreme content. Radical Islamist groups, in particular, have been profiting from the global appeal of YouTube to disseminate hate and jihadist propaganda. In this quantitative, data-driven study, we investigate the prevalence of religiously intolerant Arabic YouTube videos, the tendency of the platform to recommend such videos, and how these recommendations are affected by demographics and watch history. Based on our deep learning classifier developed to detect hateful videos and a large-scale dataset of over 350K videos, we find that Arabic videos targeting religious minorities are particularly prevalent in search results (30%) and first-level recommendations (21%), and that 15% of overall captured recommendations point to hateful videos. Our personalized audit experiments suggest that gender and religious identity can substantially affect the extent of exposure to hateful content. Our results contribute vital insights into the phenomenon of online radicalization and facilitate curbing online harmful content.
End-User Audits: A System Empowering Communities to Lead Large-Scale Investigations of Harmful Algorithmic Behavior
Authors: Lam Michelle S. , Gordon Mitchell L. , Metaxa Danaë , Hancock Jeffrey T. , Landay James A. , Bernstein Michael S.
Published: November 2022

Because algorithm audits are conducted by technical experts, audits are necessarily limited to the hypotheses that experts think to test. End users hold the promise to expand this purview, as they inhabit spaces and witness algorithmic impacts that auditors do not. In pursuit of this goal, we propose end-user audits-system-scale audits led by non-technical users-and present an approach that scaffolds end users in hypothesis generation, evidence identification, and results communication. Today, performing a system-scale audit requires substantial user effort to label thousands of system outputs, so we introduce a collaborative filtering technique that leverages the algorithmic system's own disaggregated training data to project from a small number of end user labels onto the full test set. Our end-user auditing tool, IndieLabel, employs these predicted labels so that users can rapidly explore where their opinions diverge from the algorithmic system's outputs. By highlighting topic areas where the system is under-performing for the user and surfacing sets of likely error cases, the tool guides the user in authoring an audit report. In an evaluation of end-user audits on a popular comment toxicity model with 17 non-technical participants, participants both replicated issues that formal audits had previously identified and also raised previously underreported issues such as under-flagging on veiled forms of hate that perpetuate stigma and over-flagging of slurs that have been reclaimed by marginalized communities.
Leveraging Structured Trusted-Peer Assessments to Combat Misinformation
Authors: Jahanbakhsh Farnaz , Zhang Amy X. , Karger David R.
Published: November 2022

Platform operators have devoted significant effort to combating misinformation on behalf of their users. Users are also stakeholders in this battle, but their efforts to combat misinformation go unsupported by the platforms. In this work, we consider three new user affordances that give social media users greater power in their fight against misinformation: (1) the ability to provide structured accuracy assessments of posts, (2) user-specified indication of trust in other users, and (3) and user configuration of social feed filters according to assessed accuracy. To understand the potential of these designs, we conducted a need-finding survey of 192 people who share and discuss news on social media, finding that many already act to limit or combat misinformation, albeit by repurposing existing platform affordances that lack customized structure for information assessment. We then conducted a field study of a prototype social media platform that implements these user affordances as structured inputs to directly impact how and whether posts are shown. The study involved 14 participants who used the platform for a week to share news while collectively assessing their accuracy. We report on users' perception and use of these affordances. We also provide design implications for platforms and researchers based on our empirical observations.
Modeling Motivational Interviewing Strategies on an Online Peer-to-Peer Counseling Platform
Authors: Shah Raj Sanjay , Holt Faye , Hayati Shirley Anugrah , Agarwal Aastha , Wang Yi-Chia , Kraut Robert E. , Yang Diyi
Published: November 2022

Millions of people participate in online peer-to-peer support sessions, yet there has been little prior research on systematic psychology-based evaluations of fine-grained peer-counselor behavior in relation to client satisfaction. This paper seeks to bridge this gap by mapping peer-counselor chat-messages to motivational interviewing (MI) techniques. We annotate 14,797 utterances from 734 chat conversations using 17 MI techniques and introduce four new interviewing codes such as ''chit-chat'' and ''inappropriate'' to account for the unique conversational patterns observed on online platforms. We automate the process of labeling peer-counselor responses to MI techniques by fine-tuning large domain-specific language models and then use these automated measures to investigate the behavior of the peer counselors via correlational studies. Specifically, we study the impact of MI techniques on the conversation ratings to investigate the techniques that predict clients' satisfaction with their counseling sessions. When counselors use techniques such as reflection and affirmation, clients are more satisfied. Examining volunteer counselors' change in usage of techniques suggest that counselors learn to use more introduction and open questions as they gain experience. This work provides a deeper understanding of the use of motivational interviewing techniques on peer-to-peer counselor platforms and sheds light on how to build better training programs for volunteer counselors on online platforms.
Our Browser Extension Lets Readers Change the Headlines on News Articles, and You Won't Believe What They Did!
Authors: Jahanbakhsh Farnaz , Zhang Amy X. , Karahalios Karrie , Karger David R.
Published: November 2022

Headlines play a critical role in how users perceive articles. But many headline publishers craft headlines in ways that either attract clicks in an attempt to earn ad revenue, or misinform users or manipulate their opinions for malicious intents. Such headlines can do harm since many users simply skim and share headlines without reading the articles in full. We present an exploratory browser extension that empowers users to suggest headlines they deem better for news articles. Users can view headlines suggested by other users that they follow as they browse websites. We conducted a study of 27 users who used the extension for one week to read news and suggest headlines. We found that users saw value in the tool and used it to change headlines that they found in need of improvement. We characterize the changes that people make to headlines if enabled. We also report on a followup study we conducted with 312 participants to evaluate headlines suggested by the tool. The purpose of the study was to examine whether headlines suggested by untrained users could be preferred over original headlines by professional editors. We found that a substantial number of the suggested headlines were indeed preferred. Our work explores the designs for, and opportunities and consequences of, empowering news consumers by giving them control over the content curation process.
Platform-Mediated Food Delivery Work
Authors: Kusk Kalle , Nouwens Midas
Published: November 2022

CSCW holds a valuable set of notions and tools for future granular inquiry into the work practice on food delivery platforms and pertinent questions from the field of food delivery resonate with research agendas within CSCW. This argument is based on a synthetic literature review that identifies characteristics of food delivery work and connects them to previous scholarship within CSCW. With few exceptions, the food delivery workers in the papers included are typically young, male and of immigrant background. The work practice the papers describe typically relies on algorithmic management, flexible scheduling, pay-per-delivery, and while food delivery workers work solitarily, workers repeatedly organize in both formal and informal groups. Finally, the review shows that while these characteristics are common, the local environment where the work is done has a significant impact on the workers' practice, resulting in meaningful differences between places and platforms that should be considered. The discussion brings forth salient issues and outlines two alleys of future work.
Reimagining the Mobile Phone: Investigating Speculative Approaches to Design in Human-Computer Interaction for Development (HCI4D)
Authors: Wyche Susan
Published: November 2022

Design has traditionally been used to solve problems in the field of Human-Computer Interaction for Development (HCI4D). Mobile phone applications intended to address health and education challenges in low and middle-income countries (LMICs) are prominent examples of these efforts. In this paper, I argue that design-especially, speculative approaches-can play a different role in HCI4D. Within HCI, speculative design has proven to be useful for raising critical questions. Here, I present a collection of nine speculative design concepts that are intended to raise questions about mobile phones; in particular, how they are designed and what they are used for. These concepts are inspired by my +10 years of studying mobile phone use in Kenya. In the discussion, I elaborate on how speculative design approaches can benefit HCI4D by prompting questions about whether the field is solving the right problems. I also discuss how speculative design is useful for drawing attention to other problems and topics of study.
Towards Creative Version Control
Authors: Sterman Sarah , Nicholas Molly Jane , Paulos Eric
Published: November 2022

Version control systems are powerful tools for managing history information and shaping personal and collaborative processes. While many complex tools exist for software engineering, and basic functionality for capturing versions is often found in collaborative applications such as text editors and design layout tools, these systems are not attuned to the needs and behaviors of creative practitioners within those domains, and fail to support creative practitioners in many others. Through 18 semi-structured interviews across diverse domains of creativity, we investigate how creative practitioners use version histories in their process. With the familiar paradigms and features of software version control as an organizing structure, we discuss how these creative practitioners embrace, challenge, and complicate uses of version histories in four ways: using versions as a palette of materials, providing confidence and freedom to explore, leveraging low-fidelity version capture, and reflecting on and reusing versions across long time scales. We discuss how the themes present across this wide range of mediums and domains can provide insight into future designs and uses of version control systems to support creative process.
Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata
Authors: Heger Amy K. , Marquis Liz B. , Vorvoreanu Mihaela , Wallach Hanna , Wortman Vaughan Jennifer
Published: November 2022

Data is central to the development and evaluation of machine learning (ML) models. However, the use of problematic or inappropriate datasets can result in harms when the resulting models are deployed. To encourage responsible AI practice through more deliberate reflection on datasets and transparency around the processes by which they are created, researchers and practitioners have begun to advocate for increased data documentation and have proposed several data documentation frameworks. However, there is little research on whether these data documentation frameworks meet the needs of ML practitioners, who both create and consume datasets. To address this gap, we set out to understand ML practitioners' data documentation perceptions, needs, challenges, and desiderata, with the ultimate goal of deriving design requirements that can inform future data documentation frameworks. We conducted a series of semi-structured interviews with 14 ML practitioners at a single large, international technology company. We had them answer a list of questions taken from datasheets for datasets~\citegebru2018datasheets. Our findings show that current approaches to data documentation are largely ad hoc and myopic in nature. Participants expressed needs for data documentation frameworks to be adaptable to their contexts, integrated into their existing tools and workflows, and automated wherever possible. Despite the fact that data documentation frameworks are often motivated from the perspective of responsible AI, participants did not make the connection between the questions that they were asked to answer and their responsible AI implications. In addition, participants often had difficulties prioritizing the needs of dataset consumers and providing information that someone unfamiliar with their datasets might need to know. Based on these findings, we derive seven design requirements for future data documentation frameworks such as more actionable guidance on how the characteristics of datasets might result in harms and how these harms might be mitigated, more explicit prompts for reflection, automated adaptation to different contexts, and integration into ML practitioners' existing tools and workflows.
Understanding User Perceptions of Response Delays in Crowd-Powered Conversational Systems
Authors: Abbas Tahir , Gadiraju Ujwal , Khan Vassilis-Javed , Markopoulos Panos
Published: November 2022

Crowd-powered conversational systems (CPCS) are gaining considerable attention for their potential utility in a variety of application domains, for which automated conversational interfaces are still too limited. CPCS currently suffer from long response delays, which hampers their potential as conversational partners. The majority of prior work in this area has focused on demonstrating the feasibility of the approach and improving performance, while evaluation studies have primarily focused on response latency and ways to reduce it. Relatively little is currently known about how response delays in a CPCS can affect user experience. While the importance of reducing response latency is widely recognized in the broader field of human-computer interaction, little attention has been paid to how response quality, response delay, conversational context, and the complexity of the task affect how users experience the conversation, and how they perceive waiting for responses in particular. We conducted a between-subjects experiment (N = 478), to examine the influence of these four factors on the overall waiting experience of users. Results show that users 1) evaluated the waiting experience more negatively when the response delay was longer than 8 seconds, 2) underestimated the elapsed time but experienced more frustration in tasks with high complexity, 3) underestimated the elapsed time and experienced less frustration with high quality bot's utterances, 4) judged response delays to be slightly longer, and experienced more frustration in an emotion-centric CPCS compared to a task-centric CPCS. Our insights can inform the design of future CPCSs with regards to defining performance requirements and anticipating their potential impact on the user experience they can facilitate.
Warm Solutions: Centering Nurse Contributions in Medical Making
Authors: Lakshmi Udaya , Arriaga Rosa I.
Published: November 2022

Making medical devices in healthcare settings engages practitioners in organizational innovation. Nurses improvise physical workarounds at the bedside in response to patient needs. Yet nurse-led problem-solving is rarely centralized in an emerging innovation ecosystem through medical making. We interviewed medical makers in six healthcare makerspaces to understand factors for nurse inclusion in the medical making ecosystem. Findings from 16 multi-stakeholder interviews with 6 facilitators and 10 nurses in the USA, reveal insights into nurse-led problem-solving with and without the use of physical prototyping (making) in formal innovation spaces with maker technologies. We report how a nurse's capacity for making is practice-driven to address in-patient discomfort, repair their own practice, and update standardized workflows. Most nurses iterate on low-tech solutions facing barriers to formal collaboration when they attempt to scale up. Their technical capabilities extend from innovation-centered resources (e.g., lab spaces, technologies), often with complete reliance on facilitators who have limited authority in the medical system. We contribute to themes around practice-based innovation, participation in technology design, and articulation work for collaborative innovation. From nurse makers' experiences, we discuss how nurse participation can be supported in healthcare technology design.
Explainable Goal-driven Agents and Robots - A Comprehensive Review
Authors: Sado Fatai , Loo Chu Kiong , Liew Wei Shiung , Kerzel Matthias , Wermter Stefan
Published: November 2022

Recent applications of autonomous agents and robots have brought attention to crucial trust-related challenges associated with the current generation ofartificial intelligence (AI)systems. AI systems based on the connectionist deep learning neural network approach lack capabilities of explaining their decisions and actions to others, despite their great successes. Without symbolic interpretation capabilities, they are ‘black boxes’, which renders their choices or actions opaque, making it difficult to trust them in safety-critical applications. The recent stance on the explainability of AI systems has witnessed several approaches toeXplainable Artificial Intelligence (XAI); however, most of the studies have focused on data-driven XAI systems applied in computational sciences. Studies addressing the increasingly pervasive goal-driven agents and robots are sparse at this point in time. This paper reviews approaches on explainable goal-driven intelligent agents and robots, focusing on techniques for explaining and communicating agents’ perceptual functions (e.g., senses, vision) and cognitive reasoning (e.g., beliefs, desires, intentions, plans, and goals) with humans in the loop. The review highlights key strategies that emphasize transparency, understandability, and continual learning for explainability. Finally, the paper presents requirements for explainability and suggests a road map for the possible realization of effective goal-driven explainable agents and robots.
Keywords-enhanced Deep Reinforcement Learning Model for Travel Recommendation
Authors: Chen Lei , Cao Jie , Liang Weichao , Wu Jia , Ye Qiaolin
Published: November 2022

Tourism is an important industry and a popular entertainment activity involving billions of visitors per annum. One challenging problem tourists face is identifying satisfactory products from vast tourism information. Most of travel recommendation methods regard the recommendation procedure as a static process and only focus on immediate rewards. Meanwhile, they often infer user intensions from click behaviors and ignore the informative keywords of the clicked products. To this end, in this article, we present a Keywords-enhanced Deep Reinforcement Learning model (KDRL) framework. Specifically, we formalize travel recommendation as a Markov Decision Process and implement it upon the Actor–Critic framework. It integrates keyword information into the reinforcement learning–(RL) based recommendation framework by devising novel state representation and reward function and learns the travel recommendation and keywords generation simultaneously. To the best of our knowledge, this is the first time that keywords are explicitly discussed and used in RL-based travel recommendations. Extensive experiments are performed on the real-world datasets and the results clearly show the superior performance of KDRL compared with the baseline methods.
Structural Optimization of Al-Doped Li[Ni0.90Co0.05Mn0.05]O2 Cathode for Li-Ion Batteries
Authors: Sun H. Hohyun , Pastel Glenn R. , Zhang Sheng S. , Tran Dat T. , Allen Jan L.
Published: November 2022

Aluminum is a well-known dopant element as it is used in Li[Ni0.80Co0.15Al0.05]O2 but its optimized usage in Li[Ni
x
Co
y
Mn
z
]O2 cathodes with x ≥ 0.90 has not been explored in depth. Here, we survey various Al-dopant concentrations (0.5, 1, 2, 3, and 5 mol%) to investigate the effect of Al-doping of highly Ni-rich layered Li[Ni0.90Co0.05Mn0.05]O2 cathode with respect to the electrochemical and structural properties. It is shown that Al is effective in enhancing the electrochemical performance of the Li[Ni0.90Co0.05Mn0.05]O2 cathode at higher than 1 mol% concentrations but becomes detrimental at 3 mol% or greater concentrations. In particular, Li[Ni0.90Co0.05Mn0.05]O2 with 2 mol% Al-doping delivers the most optimal electrochemical performance with 93% capacity retention over 100 cycles at 0.5 C within the 2.7–4.3 V voltage range as compared to the 85% retention of Li[Ni0.90Co0.05Mn0.05]O2 under the same conditions vs Li0/Li+ in half cells. Analyses of the ex situ and in situ X-ray diffraction patterns, cell impedances over 100 cycles, and cross-sectional electrode images reveal that appropriate amounts of Al in the layered structure alleviate the extent of unit cell volume changes during charge, but excessive Al-dopant addition leads to the formation of γ-LiAlO2 byproducts that consume available Li to significantly impair the efficacy of the cathode.
Phospholipase Cε insufficiency causes ascending aortic aneurysm and dissection
Authors: Atchison Douglas K. , O’Connor Christopher L. , Converso-Baran Kimber , Bergin Ingrid L. , Zhang Hongyu , Wang Yu , Hartman John R. , Ju Wenjun , Smrcka Alan V. , Ganesh Santhi K. , Bitzer Markus
Published: November 2022

We describe a novel phenotype by which PLCε deficiency predisposes to aortic valve insufficiency and ascending aortic aneurysm, dissection, and sudden death in the setting of ANG II-mediated hypertension. We demonstrate PLCE1 variants in patients with type A aortic dissection and aortic insufficiency, suggesting that PLCE1 may also play a role in human aortic disease. This finding is of very high significance because it has not been previously demonstrated that PLCε directly mediates aortic dissection.
Renal TLR-7/TNF-α pathway as a potential female-specific mechanism in the pathogenesis of autoimmune-induced hypertension
Authors: Chaudhari Sarika , D’Souza Bradley M. , Morales Jessica Y. , Young-Stubbs Cassandra M. , Shimoura Caroline G. , Ma Rong , Mathis Keisa W.
Published: November 2022

There is a high prevalence of hypertension in male and female SLE; however, male SLE mice are hypertensive without renal involvement. The development of hypertension in female SLE mice is renocentric and strongly associated with injurious renal mechanisms like the TLR-7→TNF-α pathway. This clear difference in the pathogenesis among the sexes could have a significant impact on how we treat patients with hypertension with underlying chronic autoimmune/inflammatory diseases.
Uncovering the Key Targets and Therapeutic Mechanisms of Qizhen Capsule in Gastric Cancer through Network Pharmacology and Bioinformatic Analyses
Authors: Zhou Wanmei , Yu Xuefei , Zhang Ziwei , Wang Xiao , Nie Chengdong , Zhang Guang , Chen Ning , Zheng Wei
Published: November 2022

Objective. This study is aimed at screening out effective active compounds of Qizhen capsule (QZC) and exploring the underlying mechanisms against gastric cancer (GACA) by combining both bioinformatic analysis and experimental approaches. Weighted gene coexpression network analysis (WGCNA), network pharmacology, molecular docking simulation, survival analysis, and data-based differential gene and protein expression analysis were employed to predict QZC’s potential targets and explore the underlying mechanisms. Subsequently, multiple experiments, including cell viability, apoptosis, and protein expression analyses, were conducted to validate the bioinformatics-predicted therapeutic targets. The results indicated that luteolin, rutin, quercetin, and kaempferol were vital active compounds, and TP53, MAPK1, and AKT1 were key targets. Molecular docking simulation showed that the four abovementioned active compounds had high binding affinities to the three main targets. Enrichment analysis showed that vital active compounds exerted therapeutic effects on GACA through regulating the TP53 pathway, MAPK pathway, and PI3K/AKT pathway. Furthermore, data-based gene expression analysis revealed that TP53 and JUN genes were not only differentially expressed between normal and GACA tissues but also correlated with clinical stages. In parallel, in vitro experimental results suggested that QZC exerted therapeutic effects on GACA by decreasing IC50 values, downregulating AKT expression, upregulating TP53 and MAPK expression, and increasing apoptosis of SGC-7901 cells. This study highlights the potential candidate biomarkers, therapeutic targets, and basic mechanisms of QZC in treating GACA, providing a foundation for new drug development, target mining, and related animal studies in GACA.
Characterization of the Immune Microenvironmental Landscape of Lung Squamous Cell Carcinoma with Immune Cell Infiltration
Authors: Chen Chunji , Tang Dongfang , Gu Chang , Wang Bin , Yao Yuanshan , Wang Rui , Zhang Huibiao , Gao Wen
Published: November 2022

Background. Increasing evidence supports that immune cell infiltration (ICI) patterns play a key role in the tumor progression of lung squamous cell carcinoma (LUSC). However, to date, the immune infiltration picture of LUSC has not been elucidated. Method. TCGA was used to download multiomics data from LUSC samples. At the same time, we included two datasets on lung squamous cell carcinoma, GSE17710 and GSE157010. To reveal the landscape of tumor immune microenvironment (TIME), the ESTIMATE algorithm, ssGSEA approach, and CIBERSORT analysis are used. To quantify the ICI pattern in a single tumor, consistent clustering is used to determine the LUSC subtype based on the ICI pattern, and principal component analysis (PCA) is used to obtain the ICI score. The prognostic value of the Kaplan-Meier curves is confirmed. GSEA (Gene Set Enrichment Analysis) was used to perform functional annotation. To investigate the immunotherapeutic effects of the ICI score, the immunophenotyping score (IPS) is used. Finally, analyze the mutation data with the “maftools” R package. Results. We identified four different immune infiltration patterns with different prognosis and biological characteristics in 792 LUSC samples. The identification of ICI patterns in individual tumors developed under ICI-related characteristic genes based on the ICI score helps to analyze the biological process, clinical results, immune cell infiltration, immunotherapy effects, and genetic variation. Immune failure is indicated by a high ICI score subtype marked by immunosuppression. Patients with low ICI scores have an abundance of efficient immune cells, which corresponds to the immunological activation phenotype and may have therapeutic benefits. The immunophenotypic score was used as a surrogate indicator of immunotherapy results, and samples with low ICI scores obtained significantly higher immunophenotypic scores. Finally, the relationship between the ICI score and tumor mutation burden (TMB) was proven. Conclusion. This study fully clarified the indispensable role of the ICI model in the complexity and diversity of TIME. The quantitative identification of ICI patterns in a single tumor will help draw the picture of TIME and further optimize precision immunotherapy.
Aqueous Humor Apolipoprotein Concentration and Severity of Diabetic Retinopathy in Type 2 Diabetes
Authors: Saucedo Lucia , Pfister Isabel B. , Schild Christin , Zandi Souska , Garweg Justus G.
Published: November 2022

An imbalance of plasma apolipoproteins has been linked to diabetic retinopathy (DR); however, there is scarce information regarding their presence in the aqueous humor (AH) and their role in DR. Here, we aimed at analysing the relationship between apolipoprotein concentrations in human AH and the severity of DR. Concentrations of apolipoproteins were measured retrospectively in patients with type 2 diabetes mellitus (T2DM) without DR (

n
=
23

), with mild to moderate nonproliferative DR (NPDR) (

n
=
13

), and advanced NPDR/proliferative DR (PDR) (

n
=
14

) using a multiplex immunoassay. Compared to the non-apparent DR group, the concentrations of seven apolipoproteins were elevated in advanced NPDR/PDR (Apo AI 5.8-fold, Apo AII 4.5-fold, Apo CI 3.3-fold, Apo CIII 6.8-fold, Apo D 3.3-fold, Apo E 2.4-fold, and Apo H 6.6-fold). No significant differences were observed in apolipoprotein concentrations between patients with non-apparent DR and healthy controls (

n
=
17

). In conclusion, the AH concentrations of apolipoproteins AI, AII, CI, CIII, D, E, and H increased in advancing stages of DR, suggesting their role in the pathogenesis of DR, which deserves further examination.
Analysis of Induced Current on Power Transmission Lines for the Reradiation Interference in Medium Wave Based on Characteristic Modes
Authors: Tang Bo , Yang Xiaofeng , Zhang Jiangong , Zhao Zhibin , Wang Feng , Gan Zheyuan
Published: November 2022

The reradiation interference from power transmission lines (PTL) on the adjacent wireless stations is directly caused by the induced current on metal parts, which means the interference could be suppressed by reducing the induced current. In order to effectively analyze the induced current of PTL, a method for analyzing induced current on PTL based on characteristic modes is proposed combining MoM with intrinsic modes. This method breaks through the traditional antenna resonance theory proposed by IEEE and could effectively avoid model equivalent defects and frequency limitations of traditional methods. Firstly, a generalized characteristic equation about the characteristic mode currents of PTL is constructed, and the numerical solutions of the characteristic mode currents are obtained by the idea of a discrete solution. Then, through the weighted orthogonal relationship between different characteristic mode currents, the expansion coefficients of the characteristic mode currents are obtained; finally, combined with the Poynting theorem, the physical meaning of the expansion coefficients and their related quantities are explained from the perspective of energy and the analysis of the induced current is realized from the physical level. The results of the example analysis show that the error between the peak frequencies of the induced current calculated by the method in this paper and the peak frequencies calculated by MoM does not exceed 2.46% and the method in this paper can well explain the disappearance of the “double wavelength loop resonance frequency” under the excitation of a vertically polarized plane wave.