When it comes to sensitive data leak, time is of the essence. It doesn't take long for a leak to turn into a data breach. A few weeks ago, Comparitech's security research team set up a honeypot simulating a database on an ElasticSearch instance, and put fake user data inside of it. The first attack came less than 9 hours after deployment. In order to beat attackers, you can either compete on equal grounds and use an internet-of-things search engine like Shodan.io or BinaryEdge, via a combination of random manual searches and Python scripts.

Aside from staying alive and healthy, the biggest concern most people have during the pandemic is the future of their jobs. Unemployment in the U.S. has skyrocketed, from 5.8 million in February 2020 to 16.3 million in July 2020, according to the U.S. Bureau of Labor Statistics. But it's not only the lost jobs that are reshaping work in the wake of COVID-19; the nature of many of the remaining jobs has changed, as remote work becomes the norm. And in the midst of it all, automation has become potentially a threat to some workers and a salvation to others. In our upcoming special issue, titled "Automation and jobs in the new normal," we examine this tension and explore the good, bad, and unknown of how automation could affect jobs in the immediate and near future.

Health screenings might become part of the touchless airport experience, too. Most people have seen images of passengers getting their temps taken with handheld thermometer wands at gates or security checkpoints. But increasingly, airports are opting for (or testing out) walk-through thermal-screening cameras, which operate by detecting heat emanating from a person's body and then estimating its core temperature. The idea with both devices is to detect people with fevers who might be infected with COVID-19. Airlines have asked the U.S. government for temperature screenings at airports to keep passengers safer and make them more confident about flying.

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The taxation of multi-national companies is a complex field, since it is influenced by the legislation of several states. Laws in different states may have unforeseen interaction effects, which can be exploited by allowing multinational companies to minimize taxes, a concept known as tax planning. In this paper, we present a knowledge graph of multinational companies and their relationships, comprising almost 1.5M business entities. We show that commonly known tax planning strategies can be formulated as subgraph queries to that graph, which allows for identifying companies using certain strategies. Moreover, we demonstrate that we can identify anomalies in the graph which hint at potential tax planning strategies, and we show how to enhance those analyses by incorporating information from Wikidata using federated queries.

In this work, we attempt to solve the integer-weight knapsack problem using the D-Wave 2000Q adiabatic quantum computer. The knapsack problem is a well-known NP-complete problem in computer science, with applications in economics, business, finance, etc. We attempt to solve a number of small knapsack problems whose optimal solutions are known; we find that adiabatic quantum optimization fails to produce solutions corresponding to optimal filling of the knapsack in all problem instances. We compare results obtained on the quantum hardware to the classical simulated annealing algorithm and two solvers employing a hybrid branch-and-bound algorithm. The simulated annealing algorithm also fails to produce the optimal filling of the knapsack, though solutions obtained by simulated and quantum annealing are no more similar to each other than to the correct solution. We discuss potential causes for this observed failure of adiabatic quantum optimization.

A method to predict time-series using multiple deep learners and a Bayesian network is proposed. In this study, the input explanatory variables are Bayesian network nodes that are associated with learners. Training data are divided using K-means clustering, and multiple deep learners are trained depending on the cluster. A Bayesian network is used to determine which deep learner is in charge of predicting a time-series. We determine a threshold value and select learners with a posterior probability equal to or greater than the threshold value, which could facilitate more robust prediction. The proposed method is applied to financial time-series data, and the predicted results for the Nikkei 225 index are demonstrated.

Bike sharing systems (BSS) have been a popular traveling service for years and are used worldwide. It is attractive for cities and users who wants to promote healthier lifestyles; to reduce air pollution and greenhouse gas emission as well as improve traffic. One major challenge to docked bike sharing system is redistributing bikes and balancing dock stations. Some studies propose models that can help forecasting bike usage; strategies for rebalancing bike distribution; establish patterns or how to identify patterns. Other studies propose to extend the approach by including weather data. This study aims to extend upon these proposals and opportunities to explore how and in what magnitude weather impacts bike usage. Bike usage data and weather data are gathered for the city of Washington D.C. and are analyzed using k-means clustering algorithm. K-means managed to identify three clusters that correspond to bike usage depending on weather conditions. The results show that the weather impact on bike usage was noticeable between clusters. It showed that temperature followed by precipitation weighted the most, out of five weather variables.

The COVID-19 pandemic is not just a health crisis, but a socio-economic crisis as well. The global economy is projected to decline sharply this year, owing to the disruptions in global markets and value chains. The pandemic-triggered global economic recession will likely be the deepest one in advanced economies since World War II and the first output contraction in emerging and developing economies in at least the past six decades, according to the World Bank's latest Global Economic Prospects report. COVID-19-related confinement measures such as nationwide lockdowns, travel bans, border closures, and social distancing have impacted every individual and organization, regardless of its size, in one way or the other. Overall, the crisis has changed the way we socialize, work, learn, and perform basic day-to-day activities.

Disruptive technologies drive doctrinal and operational changes for modern militaries. Particularly in the last 120 years, industrial and technological advancements have revolutionized warfare. In the last century, the United States military has developed a military machine that is centered on a guiding command-and-control principle: centralized control, decentralized execution. However, the next decade will bring significant advancements in autonomous decision-making and artificial intelligence, chauffeuring in resilient, distributed command and control and dislodging the human operator from mission command. While artificial intelligence and autonomous systems are already broadly employed in health, transportation and digital services, true autonomy in military systems is still in development.