Computer Science

Anxiety disorders are the most common class of psychiatric problems affecting both children and adults. However, tools to effectively monitor and manage anxiety are lacking, and comparatively limited research has been applied to addressing the unique challenges around anxiety. Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods, allowing for real-time mental health surveillance and disease management. This paper presents eWellness, an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual's device in a continuous and passive manner. We report on an initial pilot study tracking ten people over the course of a month that showed a nearly 76% success rate at predicting daily anxiety and depression levels based solely on the passively monitored features.

The use of machine learning systems in processing job applications has made the process agile and efficient, but at the same time it has created problems in terms of equality, reliability and transparency. In this paper we explain some of the uses of ML in job selection processes in the United States, and we present some the racial and sexual biases that have been detected. There are both practical and legal obstacles that impede the detection and analysis of these biases. It is also unclear how to approach algorithmic discrimination from a legal point of view. A possible analytical tool is provided by the American doctrine of Disparate Impact, but we show some of its limitations and problems when adapted to other legal systems, such as Colombian law. To conclude, we offer some desiderata that any legal analysis of algorithmic discrimination should provide.

The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used. Meanwhile, artificial intelligence (AI), especially machine- and deep-learning (ML/DL) methods, have been increasingly used across many stages of the drug development process. Advancements in AI have also provided new strategies to analyze large, multidimensional RWD. Thus, we conducted a rapid review of articles from the past 20 years, to provide an overview of the drug development studies that use both AI and RWD. We found that the most popular applications were adverse event detection, trial recruitment, and drug repurposing. Here, we also discuss current research gaps and future opportunities.

Early detection is key for treating those diagnosed with specific learning disorder, which includes problems with spelling, grammar, punctuation, clarity and organization of written expression. Intervening early can prevent potential negative consequences from this disorder. Deep convolutional neural networks (CNNs) perform better than human beings in many visual tasks such as making a medical diagnosis from visual data. The purpose of this study was to evaluate the ability of a deep CNN to detect students with a diagnosis of specific learning disorder from their handwriting. The MobileNetV2 deep CNN architecture was used by applying transfer learning. The model was trained using a data set of 497 images of handwriting samples from students with a diagnosis of specific learning disorder, as well as those without this diagnosis. The detection of a specific learning disorder yielded on the validation set a mean area under the receiver operating characteristics curve of 0.89. This is a novel attempt to detect students with the diagnosis of specific learning disorder using deep learning. Such a system as was built for this study, may potentially provide fast initial screening of students who may meet the criteria for a diagnosis of specific learning disorder.

Researchers have suggested that "the Manosphere," a conglomerate of men-centered online communities, may serve as a gateway to far right movements. In that context, this paper quantitatively studies the migratory patterns between a variety of groups within the Manosphere and the Alt-right, a loosely connected far right movement that has been particularly active in mainstream social networks. Our analysis leverages over 300 million comments spread through Reddit (in 115 subreddits) and YouTube (in 526 channels) to investigate whether the audiences of channels and subreddits associated with these communities have converged between 2006 and 2018. In addition to subreddits related to the communities of interest, we also collect data on counterparts: other groups of users which we use for comparison (e.g., for YouTube we use a set of media channels). Besides measuring the similarity in the commenting user bases of these communities, we perform a migration study, calculating to which extent users in the Manosphere gradually engage with Alt-right content. Our results suggest that there is a large overlap between the user bases of the Alt-right and of the Manosphere and that members of the Manosphere have a bigger chance to engage with far right content than carefully chosen counterparts. However, our analysis also shows that migration and user base overlap varies substantially across different platforms and within the Manosphere. Members of some communities (e.g., Men's Rights Activists) gradually engage with the Alt-right significantly more than counterparts on both Reddit and YouTube, whereas for other communities, this engagement happens mostly on Reddit (e.g., Pick Up Artists). Overall, our work paints a nuanced picture of the pipeline between the Manosphere and the Alt-right, which may inform platforms' policies and moderation decisions regarding these communities.

The recent advent of connected and automated vehicles (CAVs) is expected to transform the transportation system. CAV technologies are being developed rapidly and they are foreseen to penetrate the market at a rapid pace. On the other hand, work zones (WZs) have become common areas on highway systems as a result of the increasing construction and maintenance activities. The near future will therefore bring the coexistence of CAVs and WZs which makes their interaction inevitable. WZs expose all vehicles to a sudden and complex geometric change in the roadway environment, something that may challenge many of CAV navigation capabilities. WZs however also impose a space contraction resulting in adverse traffic impacts, something that legitimately calls for benefiting from the highly efficient CAV functions. CAVs should be able to reliably traverse WZ geometry and WZs should benefit from CAV intelligent functions. This paper reviews the state-of-the-art and the key concepts, opportunities, and challenges of deploying CAV systems at WZs. The reviewed subjects include traffic performance and behaviour, technologies and infrastructure, and regulatory considerations. Eighteen CAV mobility, safety, and environmental concepts and functions were distributed over the WZ area which was subdivided into five segments: further upstream, approach area, queuing area, WZ activity, and termination area. In addition, among other topics reviewed and discussed are detection of WZ features, smart traffic control devices, various technologies at connected WZs, cross-border harmonization, liability, insurance, and privacy. The paper also provides a research agenda with a list of research needs supported by experts rating and inputs. The paper aims to provide a bird eye view, but with necessary details that can benefit researchers, practitioners, and transportation agencies.

Since the education sector is associated with highly dynamic business environments which are controlled and maintained by information systems, recent technological advancements and the increasing pace of adopting artificial intelligence (AI) technologies constitute a need to identify and analyze the issues regarding their implementation in education sector. However, a study of the contemporary literature reveled that relatively little research has been undertaken in this area. To fill this void, we have identified the benefits and challenges of implementing artificial intelligence in the education sector, preceded by a short discussion on the concepts of AI and its evolution over time. Moreover, we have also reviewed modern AI technologies for learners and educators, currently available on the software market, evaluating their usefulness. Last but not least, we have developed a strategy implementation model, described by a five-stage, generic process, along with the corresponding configuration guide. To verify and validate their design, we separately developed three implementation strategies for three different higher education organizations. We believe that the obtained results will contribute to better understanding the specificities of AI systems, services and tools, and afterwards pave a smooth way in their implementation.

Corruption continues to be one of the biggest societal challenges of our time. New hope is placed in Artificial Intelligence (AI) to serve as an unbiased anti-corruption agent. Ever more available (open) government data paired with unprecedented performance of such algorithms render AI the next frontier in anti-corruption. Summarizing existing efforts to use AI-based anti-corruption tools (AI-ACT), we introduce a conceptual framework to advance research and policy. It outlines why AI presents a unique tool for top-down and bottom-up anti-corruption approaches. For both approaches, we outline in detail how AI-ACT present different potentials and pitfalls for (a) input data, (b) algorithmic design, and (c) institutional implementation. Finally, we venture a look into the future and flesh out key questions that need to be addressed to develop AI-ACT while considering citizens' views, hence putting "society in the loop".

In-person instruction for professional development or other types of workplace training provides a social environment and immediate feedback mechanisms that typically ensure all participants are successful. Online, self-paced instruction lacks these mechanisms and relies on the motivation and persistence of each individual learner, often resulting in low completion rates. In this study, we studied the effect of introducing enabling tools and live feedback into an online learning experience on learner performance in the course, persistence in the course, and election to complete supplemental readings and assignments. The findings from our experiments show positive correlations with strong statistical significance between live interactions and all performance measures studied. Research funded by the National Science Foundation, award number #1843391.

Today, literature skills are necessary. Engineering and other technical professions are not an exception from this requirement. Traditionally, technical reading and writing have been framed with a limited scope, containing documentation, specifications, standards, and related text types. Nowadays, however, the scope covers also other text types, including legal, policy, and related documents. Given this motivation, this paper evaluates the readability of 201 legislations and related policy documents in the European Union (EU). The digital single market (DSM) provides the context and five classical readability indices the methods. The empirical results indicate that (i) generally a Ph.D. level education is required to comprehend the DSM laws and policy documents. Although (ii) the results vary across the five indices used, (iii) readability has slightly improved over time.