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.

Many distributed machine learning frameworks have recently been built to speed up the large-scale data learning process. However, most distributed machine learning used in these frameworks still uses an offline algorithm model which cannot cope with the data stream problems. In fact, large-scale data are mostly generated by the non-stationary data stream where its pattern evolves over time. To address this problem, we propose a novel Evolving Large-scale Data Stream Analytics framework based on a Scalable Parsimonious Network based on Fuzzy Inference System (Scalable PANFIS), where the PANFIS evolving algorithm is distributed over the worker nodes in the cloud to learn large-scale data stream. Scalable PANFIS framework incorporates the active learning (AL) strategy and two model fusion methods. The AL accelerates the distributed learning process to generate an initial evolving large-scale data stream model (initial model), whereas the two model fusion methods aggregate an initial model to generate the final model. The final model represents the update of current large-scale data knowledge which can be used to infer future data. Extensive experiments on this framework are validated by measuring the accuracy and running time of four combinations of Scalable PANFIS and other Spark-based built in algorithms. The results indicate that Scalable PANFIS with AL improves the training time to be almost two times faster than Scalable PANFIS without AL. The results also show both rule merging and the voting mechanisms yield similar accuracy in general among Scalable PANFIS algorithms and they are generally better than Spark-based algorithms. In terms of running time, the Scalable PANFIS training time outperforms all Spark-based algorithms when classifying numerous benchmark datasets.

Developers know a whole lot about the machine learning (ML) systems that they produce and manage, that is a given. But, there's a demand for non-developers to have a higher level understanding of the kinds of systems. Expert systems and artificial neural networks would be the classical two important classes. With the advancements in computing functionality, softwares capacities, algorithm complexity and analytical algorithm could be said to have combined both of them. This article is a summary of the three different types of systems.

The user uses the control panel (A) to specify the detail information to visualize (e.g., level of detail, rule filters). The rule-based explanatory representation is visualized as a matrix (B), where each row represents a rule, and each column is a feature used in the rules. The user can also filter the data or use a customized input in the data filter (C) and navigate the filtered dataset in the data table (D). Abstract--With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large number of potential but neglected users are the domain experts with little knowledge of machine learning but are expected to work with machine learning systems. In this paper, we present an interactive visualization technique to help users with little ...

As networks become more software-driven, they generate vastly greater amounts of data, which provides some challenges: adhering to compliance and customer privacy guidelines, while harvesting the massive amounts of data--it is physically impossible for humans to tackle the sheer volume that is created. But the vast amounts of data also provide an opportunity for businesses: leveraging analytics and machine learning to gather insights that can help network management move from reactive to proactive to assurance. This doesn't just mean a massive shift in technology because the human element won't simply go away. Instead, by combining human intellect and creativity with the computing power AI offers, innovative design and management techniques will be developed to build self-improving intelligent algorithms. The algorithms allow networks to operate in a way that far outweighs networks of the past.