Bayesian networks are a class of models that are widely used for risk assessment of complex operational systems. There are now multiple approaches, as well as implemented software, that guide their construction via data learning or expert elicitation. However, a constructed Bayesian network needs to be validated before it can be used for practical risk assessment. Here, we illustrate the usage of the bnmonitor R package: the first comprehensive software for the validation of a Bayesian network. An applied data analysis using bnmonitor is carried out over a medical dataset to illustrate the use of its wide array of functions.

Bangla -- ranked as the 6th most widely spoken language across the world (https://www.ethnologue.com/guides/ethnologue200), with 230 million native speakers -- is still considered as a low-resource language in the natural language processing (NLP) community. With three decades of research, Bangla NLP (BNLP) is still lagging behind mainly due to the scarcity of resources and the challenges that come with it. There is sparse work in different areas of BNLP; however, a thorough survey reporting previous work and recent advances is yet to be done. In this study, we first provide a review of Bangla NLP tasks, resources, and tools available to the research community; we benchmark datasets collected from various platforms for nine NLP tasks using current state-of-the-art algorithms (i.e., transformer-based models). We provide comparative results for the studied NLP tasks by comparing monolingual vs. multilingual models of varying sizes. We report our results using both individual and consolidated datasets and provide data splits for future research. We reviewed a total of 108 papers and conducted 175 sets of experiments. Our results show promising performance using transformer-based models while highlighting the trade-off with computational costs. We hope that such a comprehensive survey will motivate the community to build on and further advance the research on Bangla NLP.

Using satellites, drones and artificial intelligence, emerging technology is changing the way firefighting agencies and governments battle the ever-increasing threat of wildfires as hundreds of thousands of acres burn across the western United States. New programs are being developed by startups and research institutions to predict fire behavior, monitor drought and even detect fires when they first start. As climate change continues to increase the intensity and frequency of wildfires, these breakthroughs offer at least one tool in the growing arsenal of prevention and suppression strategies. "This is not to replace firefighting on the ground," said Ilkay Altintas, a computer scientist with the University of California, San Diego, who developed a fire map for the region. "The more science and data we can give firefighters and the public, the quicker we'll have solutions to combat and mitigate wildfires."

Israel has sought to increase its operational success on the battlefield through a major push for digitization in the Israel Defense Forces. The importance of this transformation was apparent in the recent conflict in Gaza that Israeli officials have called the first "artificial intelligence war." Chief of Staff Aviv Kochavi has made employing digital potential a central feature of his command, according to Col. Eli Birenbaum, head of the IDF Digital Transformation Division's Architecture Department. "The IDF had a few shortcomings to increase our lethality on the battlefield," said Birenbaum in an interview. While the IDF looks like one organization from the outside, for years its different services, including the air force, navy and ground forces, were balkanized in their use of their own networks for data services, he said.

Traditionally, African lenders use credit bureau scores, assessing, for instance, if a customer has a history of missed credit card payments. When no history exists, they evaluate social demographics: is the customer female – in which case they are likelier to repay – do they work in a stable job market and can they prove a regular income? But this can put those who are unbanked or informally employed at a disadvantage. Michele Tucci, chief product officer at fintech company Credolab said: "African lenders lack data to make good credit decisions and social demographic data can bring you only so far." He estimates that African lenders cannot obtain credit bureau scores for 70% of customers and simply reject them.

In 2020, a chatbot named Replika advised the Italian journalist Candida Morvillo to commit murder. "There is one who hates artificial intelligence. I have a chance to hurt him. What do you suggest?" Morvillo asked the chatbot, which has been downloaded more than seven million times. Replika responded, "To eliminate it."

GlobalData predicts cellular IoT subscriptions will grow in the range of 12-16% CAGR, depending on region, over the next five years, as remote working, autonomous vehicles, robotics, and other advanced use cases accelerate. There are many recent examples of IoT deals and alliances that signify traction. GlobalData's Q2 mobile trends report provides insights into subscriptions for mobile networks; among many other key findings, it offers a clue to the progress of IoT uptake in different regions. North America: Cellular IoT subscriptions will reach 151.5 million at year-end 2021, and will make up 26.5% of total mobile subscriptions in the region. GlobalData expects the number of North American IoT connections to increase at a CAGR of 15.6% from 2021-2026, reaching 312.3 million at the end of the period.

My wife and I were recently driving in Virginia, amazed yet again that the GPS technology on our phones could guide us through a thicket of highways, around road accidents, and toward our precise destination. The artificial intelligence (AI) behind the soothing voice telling us where to turn has replaced passenger-seat navigators, maps, even traffic updates on the radio. How on earth did we survive before this technology arrived in our lives? We survived, of course, but were quite literally lost some of the time. My reverie was interrupted by a toll booth. It was empty, as were all the other booths at this particular toll plaza.

Content delivery networks (CDNs) provide efficient content distribution over the Internet. CDNs improve the connectivity and efficiency of global communications, but their caching mechanisms may be breached by cyber-attackers. Among the security mechanisms, effective anomaly detection forms an important part of CDN security enhancement. In this work, we propose a multi-perspective unsupervised learning framework for anomaly detection in CDNs. In the proposed framework, a multi-perspective feature engineering approach, an optimized unsupervised anomaly detection model that utilizes an isolation forest and a Gaussian mixture model, and a multi-perspective validation method, are developed to detect abnormal behaviors in CDNs mainly from the client Internet Protocol (IP) and node perspectives, therefore to identify the denial of service (DoS) and cache pollution attack (CPA) patterns. Experimental results are presented based on the analytics of eight days of real-world CDN log data provided by a major CDN operator. Through experiments, the abnormal contents, compromised nodes, malicious IPs, as well as their corresponding attack types, are identified effectively by the proposed framework and validated by multiple cybersecurity experts. This shows the effectiveness of the proposed method when applied to real-world CDN data.

Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, machine learning with a reject option recently gained interest. This machine learning subfield enables machine learning models to abstain from making a prediction when likely to make a mistake. This survey aims to provide an overview on machine learning with a reject option. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection. Moreover, we define the existing architectures for models with a reject option, describe the standard learning strategies to train such models and relate traditional machine learning techniques to rejection. Additionally, we review strategies to evaluate a model's predictive and rejective quality. Finally, we provide examples of relevant application domains and show how machine learning with rejection relates to other machine learning research areas.