Prime Minister Shinzo Abe and his Maltese counterpart Joseph Muscat agreed Wednesday to closely cooperate in maintaining maritime order based on the rule of law. In a news conference after their meeting in Tokyo, Abe said the two leaders confirmed the importance of the issue. Malta is the home of the International Maritime Law Institute aimed at training specialists in the field. Abe said they also agreed to work together in resolving outstanding issues surrounding North Korea, including Japanese nationals abducted by Pyongyang in the 1970s and 1980s. On the economic front, Muscat, the first Maltese prime minister to visit Japan in 28 years, expressed hope for an early implementation of a free trade agreement Japan and the European Union signed last month.

When Charles Givre, lead data scientist at Deutsche Bank, teaches security teams about the benefits of applying security data science techniques, he often focuses on a common malware tactic: domain-generation algorithms. Used by malicious programs to establish contact with a command-and-control server, domain-generation algorithms, or DGAs, create a list of domain names as potential contact points using pseudo-random algorithms. The domains change often -- usually daily -- and can look random or use random words. For humans, finding a single computer's call to a random domain is a difficult problem. Yet data analysis can quickly call out the anomalous communications.

Deep learning video analysis has increasingly become a trend in security. With hardware advances and an overwhelming amount of data, their future growth potential is not to be ignored. That was one of the main points discussed by Memoori in its recent blog post titled "Will AI Video Analytics Finally Add'Real Intelligence' to Video Surveillance?." Needless to say, artificial intelligence (AI) and deep learning have garnered the attention of security vendors and users alike. The technology learns different objects, behavior or character traits and can detect abnormalities and irregularities with more precision and accuracy than video content analysis technologies employed in the past. "Video analytics has eaten a few free lunches over the last 15 years. Whilst it has certainly added some value to video installations, there has been much debate about exactly'how intelligent' the technology really is and whether it provides satisfactory ROI. But in 2018, there is now a growing belief that video analytics could finally move beyond what has been achieved through conventional rule-based systems," the post said.

It's a timeless manufacturing goal: to produce high quality products at a minimum cost. Factory 4.0 is already demonstrating its value by enabling manufacturers to reach this goal more successfully than ever, and one of the core technologies driving this new wave of ultra-automation is Industrial AI and Machine Learning. Data has become a valuable resource, and it's cheaper than ever to capture and store. Through the use of artificial intelligence, specifically Machine Learning, manufacturers can use data to significantly impact their bottom line by greatly improving efficiency, employee safety, and product quality. Maintenance represents a significant part of any manufacturing operation's expenses.

Foreign Minister Taro Kono agreed Wednesday with his German counterpart to promote free trade amid a rising protectionist tide, while supporting a rules-based international order. During talks in Tokyo, Kono and German Foreign Minister Heiko Maas stressed the importance of closer economic ties just days after the signing of a free trade agreement between Japan and the European Union. "The free, open and rules-based international order faces a serious challenge," Kono said during a joint press briefing with Maas. "Closer cooperation between Japan and Germany, (countries) that share the same values such as democracy, and lead Asia and Europe … is taking on greater importance than ever." The signing earlier this month of the free trade deal, which covers about a third of the world's economy, has been seen as symbolic of the concerted effort to counter the increasingly protectionist steps taken by U.S. President Donald Trump.

We propose a Multi-vAlue Rule Set (MRS) model for in-hospital predicting patient mortality. Compared to rule sets built from single-valued rules, MRS adopts a more generalized form of association rules that allows multiple values in a condition. Rules of this form are more concise than classical single-valued rules in capturing and describing patterns in data. Our formulation also pursues a higher efficiency of feature utilization, which reduces possible cost in data collection and storage. We propose a Bayesian framework for formulating a MRS model and propose an efficient inference method for learning a maximum \emph{a posteriori}, incorporating theoretically grounded bounds to iteratively reduce the search space and improve the search efficiency. Experiments show that our model was able to achieve better performance than baseline method including the current system used by the hospital.

Following the recent successful examples of large technology companies, many modern enterprises seek to build knowledge graphs to provide a unified view of corporate knowledge and to draw deep insights using machine learning and logical reasoning. There is currently a perceived disconnect between the traditional approaches for data science, typically based on machine learning and statistical modelling, and systems for reasoning with domain knowledge. In this paper we present a state-of-the-art Knowledge Graph Management System, Vadalog, which delivers highly expressive and efficient logical reasoning and provides seamless integration with modern data science toolkits, such as the Jupyter platform. We demonstrate how to use Vadalog to perform traditional data wrangling tasks, as well as complex logical and probabilistic reasoning. We argue that this is a significant step forward towards combining machine learning and reasoning in data science.

Over the past years, there has been a resurgence of Datalog-based systems in the database community as well as in industry. In this context, it has been recognized that to handle the complex knowl\-edge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quantification. Yet, Datalog-based reasoning in the presence of existential quantification is in general undecidable. Many efforts have been made to define decidable fragments. Warded Datalog+/- is a very promising one, as it captures PTIME complexity while allowing ontological reasoning. Yet so far, no implementation of Warded Datalog+/- was available. In this paper we present the Vadalog system, a Datalog-based system for performing complex logic reasoning tasks, such as those required in advanced knowledge graphs. The Vadalog system is Oxford's contribution to the VADA research programme, a joint effort of the universities of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the main contribution of this paper, we illustrate the first implementation of Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive termination control strategy. We also provide a comprehensive experimental evaluation.

It seems everyone is talking about artificial intelligence and machine learning, especially within the fraud prevention sphere. But despite all the buzz, it's not always clear how these intelligent elements actually help curb fraud rates. First things first: though they are often used interchangeably, artificial intelligence (AI) and machine learning (ML) are not the same thing. AI refers to machines that are able to carry out tasks in a human way, while machine learning is a component of AI that involves giving a machine access to large amounts of data and allowing it to learn for itself and solve problems based on that data and patterns the machine recognizes. The concepts of Artificial Intelligence and machine learning have been around since the 1950s. However, only recently have they become a reality for businesses due to advanced developments in the field and newfound affordability.

Today, many companies are entrusting their top business-critical operations and decisions to artificial intelligence. Rather than traditional, rule-based programming, users now have the ability to provide machine data, define outcomes, and let it create its own algorithms and provide recommendations to the business. For instance, an auto insurance company can feed a machine a library of photos of previous totaled cars with data on their make, model and payout. The system can then be "trained" to review future incidents, determine if a car is totaled, and give a recommended payout amount. This streamlines the review process, which is both a positive for the company and customer.