Healthcare professionals (HCPs) face increasing occupational stress and burnout. Supporting HCPs’ need for relatedness is fundamental to their psychological wellbeing and resilience. However, how technologies could support HCPs’ relatedness in the workplace remains less explored. This study incorporated semi-structured interviews (n = 15) and co-design workshops (n = 21) with HCPs working in the UK National Health Service (NHS), to explore their current practices and preferences for workplace relatedness support, and how technology could be utilized to benefit relatedness. Qualitative analysis yielded a four-layer model of HCPs’ relatedness need, which includes Informal Interactions, Camaraderie and Bond, Community and Organizational Care, and Shared Identity. Workshops generated eight design concepts (e.g., Playful Encounter, Collocated Action, and Memories and Stories) that operationalize the four relatedness need layers. We conclude by highlighting the theoretical relevance, practical design implications, and the necessity to strengthen relatedness support for HCPs in the era of digitalization and artificial intelligence.

While significant progress has been made in specifying neural networks capable of representing uncertainty, deep networks still often suffer from overconfidence and misaligned predictive distributions. Existing approaches for measuring this misalignment are primarily developed under the framework of calibration, with common metrics such as Expected Calibration Error (ECE). However, calibration can only provide a strictly marginal assessment of probabilistic alignment. Consequently, calibration metrics such as ECE are distribution-wise measures and cannot diagnose the point-wise reliability of individual inputs, which is important for real-world decision-making. We propose a stronger condition, which we term conditional congruence, for assessing probabilistic fit. We also introduce a metric, Conditional Congruence Error (CCE), that uses conditional kernel mean embeddings to estimate the distance, at any point, between the learned predictive distribution and the empirical, conditional distribution in a dataset. We perform several high dimensional regression tasks and show that CCE exhibits four critical properties: correctness, monotonicity, reliability, and robustness.

The origins of the cybersecurity field were highly practice-oriented, often from an individual Community of Practice (CoP), such as the military. In many cases, emergent cybersecurity academic programs relied on these practitioners as adjunct faculty to transfer and impart knowledge to students. However, this approach proved insufficient to develop an overall academic discipline of cybersecurity with effective educational programs to meet the growing and changing need for cybersecurity professionals. To address this gap, the authors designed and developed a Doctor of Science (D.Sc.) program focusing on applied research with knowledge conversion from multiple individual CoPs and a strong workforce academia interchange. The program leverages Nonaka's knowledge conversion model and Wenger's communities of practice (CoP) theory to catalyze innovation and cultivate an integrated community of cybersecurity practices. Doctoral students conduct in-depth research in a specific cybersecurity area of their choice, developing cybersecurity products, frameworks, tools, and solutions for their CoP and often for a wider audience. Faculty gain knowledge of advancements in the current practices of cybersecurity through integrating individual CoPs that they can then use in developing their cybersecurity academic programs at the undergraduate and graduate levels. The chosen research method for this work-in-progress paper is a qualitative thematic analysis through open-ended questionnaires. The research sample will consist of doctoral students currently in the program or recently graduated and faculty teaching the courses in the D.Sc. program. The study aims to explore the knowledge interchange and learning experiences within the CoP regarding research scholarship, dissertation development, and cybersecurity innovation. The insights and lessons learned from this program can serve as a model for other academic institutions seeking to enhance the practicality and innovation within their cybersecurity programs while contributing to developing a much-needed, more robust, and secure cyber ecosystem.

This Innovative Practice Full Paper presents a case study on constructing and implementing a connected program in the Doctor of Science (D.Sc.) in Cybersecurity at Marymount University. We adapted a connected curriculum framework to this professional doctorate program based on two overarching pillars: andragogy theory and a multidisciplinary perspective. We use an andragogical instructional methodology that follows a learner-centered teaching philosophy to promote professional and adult learner engagement. Furthermore, the D.Sc. program takes a multidisciplinary approach to bridge and integrate domain-specific silos such as technology and its evolution, risk management, legal compliance, human factors, machine learning, business impact, and more. This integration helps enhance students' knowledge in multiple disciplinary specializations, focusing on problem-solving skills across the various domains important in cybersecurity. Building on these two pillars, our connected program model integrates five dimensions of connectivity to connect 1) academic learning and research with the workplace, 2) research activities and research-related curriculum over time, 3) various disciplines related to real-world cybersecurity challenges, 4) students with each other, across phases and with alumni, and 5) students with research and researchers across multiple domains. This paper's primary contribution is to demonstrate an innovative andrological and connected approach to tackle the ever-changing cybersecurity threats by cultivating the next generation of cybersecurity leaders with both advanced technical and refined management skills. We further showcase the potential of externalizing this framework in other settings and discuss future research work.

Peer production communities create valuable content such as software, encyclopedia articles, and map data. As part of the creation process, these communities define production standards for their content, e.g., semantic and syntactic requirements. We carried out a study in OpenStreetMap to investigate the role of that community's standards for geographic metadata. We found that most applied metadata was consistent with the community's standards; however, we also found that the standards identified many opportunities for applying metadata that were not achieved. In addition, when we situated the standards in the context of OpenStreetMap's data model, we found a significant amount of ambiguity; the syntax allowed only one value, but everyday meaning -- and the standards themselves -- called for multiple values. Our results suggest significant opportunities for OpenStreetMap to produce additional valuable open source content to power applications.

Bots have been important to peer production's success. Wikipedia, OpenStreetMap, and Wikidata all have taken advantage of automation to perform work at a rate and scale exceeding that of human contributors. Understanding the ways in which humans and bots behave in these communities is an important topic, and one that relies on accurate bot recognition. Yet, in many cases, bot activities are not explicitly flagged and could be mistaken for human contributions. We develop a machine classifier to detect previously unidentified bots using implicit behavioral and other informal editing characteristics. We show that this method yields a high level of fitness under both formal evaluation (PR-AUC: 0.845, ROC-AUC: 0.985) and a qualitative analysis of "anonymous" contributor edit sessions. We also show that, in some cases, unflagged bot activities can significantly misrepresent human behavior in analyses. Our model has the potential to support future research and community patrolling activities.

In addition to encyclopedia articles and software, peer production communities produce structured data, e.g., Wikidata and OpenStreetMap's metadata. Structured data from peer production communities has become increasingly important due to its use by computational applications, such as CartoCSS, MapBox, and Wikipedia infoboxes. However, this structured data is usable by applications only if it follows standards. We did an interview study focused on OpenStreetMap's knowledge production processes to investigate how -- and how successfully -- this community creates and applies its data standards. Our study revealed a fundamental tension between the need to produce structured data in a standardized way and OpenStreetMap's tradition of contributor freedom. We extracted six themes that manifested this tension and three overarching concepts, correctness, community, and code, which help make sense of and synthesize the themes. We also offered suggestions for improving OpenStreetMap's knowledge production processes, including new data models, sociotechnical tools, and community practices (e.g. stronger leadership).

As the third most popular social network among millennials, Snapchat is well known for its picture and video messaging system that deletes content after it is viewed. However, the Stories feature of Snapchat offers a different perspective of ephemeral content sharing, with pictures and videos that are available for friends to watch an unlimited number of times for 24 hours. We conduct-ed an in-depth qualitative investigation by interviewing 18 participants and reviewing 14 days of their Stories posts. We identify five themes focused on how participants perceive and use the Stories feature, and apply a Goffmanesque metaphor to our analysis. We relate the Stories medium to other research on self-presentation and identity curation in social media.

Wikipedia-based studies and systems frequently assume that no two articles describe the same concept. However, in this paper, we show that this article-as-concept assumption is problematic due to editors' tendency to split articles into parent articles and sub-articles when articles get too long for readers (e.g. "Portland, Oregon" and "History of Portland, Oregon" in the English Wikipedia). In this paper, we present evidence that this issue can have significant impacts on Wikipedia-based studies and systems and introduce the sub-article matching problem. The goal of the sub-article matching problem is to automatically connect sub-articles to parent articles to help Wikipedia-based studies and systems retrieve complete information about a concept. We then describe the first system to address the sub-article matching problem. We show that, using a diverse feature set and standard machine learning techniques, our system can achieve good performance on most of our ground truth datasets, significantly outperforming baseline approaches.

Wikipedia articles about places, OpenStreetMap features, and other forms of peer-produced content have become critical sources of geographic knowledge for humans and intelligent technologies. In this paper, we explore the effectiveness of the peer production model across the rural/urban divide, a divide that has been shown to be an important factor in many online social systems. We find that in both Wikipedia and OpenStreetMap, peer-produced content about rural areas is of systematically lower quality, is less likely to have been produced by contributors who focus on the local area, and is more likely to have been generated by automated software agents (i.e. "bots"). We then codify the systemic challenges inherent to characterizing rural phenomena through peer production and discuss potential solutions.