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IoT devices have seen explosive growth, causing a data explosion, which makes it almost impossible to manage and monitor these networks. This has led to a demand for solutions providing real-time monitoring and detection of anomalies in cloud-based Internet of Things (IoT) networks. In cybersecurity, the term real-time monitoring denotes the ongoing analysis of data and network performance to detect potential problems. It allows for the identification of anomalies and possible weaknesses in the system. In contrast, anomaly detection is the process of finding deviations from what is considered normal for a system or data. Within cloud-based IoT networks, this might involve identifying abnormal traffic patterns or unusual activity from devices. To solve these challenges, we propose a cloud computing and machine learning-based solution. IoT devices generate a massive amount of data, which is processed in real-time and stored on the cloudbased infrastructure. Many machine learning algorithms analyze these data algorithms to identify anomalies or threats to security. This solution provides an active early warning detection of security breaches from the network management perspective, along with timely response in the case of abnormal behaviour. It will ultimately result in improved cloud-based networks for IoT devices with regard to reliability, security, and performance. This solution can be a key factor in driving the mass implementation of IoT in various sectors.

The advances in AI over the last decade has led to significantly better outcomes and improved experiences in consumer applications. Meanwhile, successful applications of AI in enterprises have been modest during this period. In this talk, I will provide an overview of the scope of Enterprise AI, how it is similar and different from AI for consumer applications, a few successful applications, open problems and challenges and the enormous opportunity to create better outcomes and transform the experiences for employees and customers of enterprises.

This article explores the merchant capitalist firm John Holt & Co. and its diverse business interests on the Spanish-controlled island of Fernando Po in the early twentieth century. We show that this well-capitalized firm struggled to make money in cocoa production due to market and labor volatility, but used its position to undercut competitors, exploit small farmers by offering financial services like high-interest credit and mortgages, and develop new profit lines such as providing packaging, shipping, and retail services to other planters. Holt & Co. enjoyed advantages over smaller competitors through its regional network and connections in Spain and Britain, which allowed it to more readily attract laborers and grow its influence and power. This case study provides new insights on how expatriate firms squeezed smaller partners and rivals during a tumultuous period in post-partition west-central Africa, and shows how this impacted Fernando Po’s workers and its economic development.

Natural Language Processing (NLP) models are found to exhibit discriminatory stereotypes across many social constructs, e.g. gender and race. In comparison to the progress made in reducing bias from static word embeddings, fairness in sentence-level text encoders received little consideration despite their wider applicability in contemporary NLP tasks. In this paper, we propose a debiasing method for pre-trained text encoders that both reduces social stereotypes, and inflicts next to no semantic damage. Unlike previous studies that directly manipulate the embeddings, we suggest to dive deeper into the operation of these encoders, and pay more attention to the way they pay attention to different social groups. We find that stereotypes are also encoded in the attention layer. Then, we work on model debiasing by redistributing the attention scores of a text encoder such that it forgets any preference to historically advantaged groups, and attends to all social classes with the same intensity. Our experiments confirm that reducing bias from attention effectively mitigates it from the model’s text representations.

Abstract Background: The objective of this study was to investigate factors associated with substance use disorder identification and follow-up rates among samples of members of a private health insurance plan. Methods: In an observational study, samples of claims data for 2017 for Commercial and Medicare members from a private health insurer were accessed and analyzed using descriptive statistics, decision tree analysis, and linear regression models. Results: Medicare members had higher rates of inclusion in a measure of substance use disorder than Commercial members, lower rates of initial short term follow-up, more opioid prescriptions from primary care provides, fewer prescriptions for opioid treatment, and higher rates of selected comorbid conditions. Mental health diagnoses and substance use disorder co-occurred frequently and to a greater extent in the Medicare sample. Among commercial members, there were primarily alcohol problems that increased with age, while opioid misuse at about 10% peaked in the mid-twenties. More males were included among all substance types. The overall rate for an initial short term follow-up visit indicating initiation of treatment was 30%. There were large differences in the follow-up rates across settings with a very low rate (4.6% for alcohol and 6.9% for opioid) in primary care settings. Conclusions: These results suggest that increased attention in primary care to young adult males and to older adults, may help to reduce substance use disorder rates, especially alcohol misuise.

Recounts the career and contributions of Florian Daniel.

One out of every six American women has been the victim of a sexual assault in their lifetime. However, the DNA casework backlog continues to increase outpacing the nation's capacity since DNA evidence processing in sexual assault casework remains a bottleneck due to laborious and time‐consuming differential extraction of victim's and perpetrator's cells. Additionally, a significant amount (60–90%) of male DNA evidence may be lost with existing procedures. Here, a microfluidic method is developed that selectively captures sperm using a unique oligosaccharide sequence (Sialyl‐LewisX), a major carbohydrate ligand for sperm‐egg binding. This method is validated with forensic mock samples dating back to 2003, resulting in 70–92% sperm capture efficiency and a 60–92% reduction in epithelial fraction. Captured sperm are then lysed on‐chip and sperm DNA is isolated. This method reduces assay‐time from 8 h to 80 min, providing an inexpensive alternative to current differential extraction techniques, accelerating identification of suspects and advancing public safety.