The Journal Gazette reports that federal Judge Jon E. DiGuilio in South Bend issued a permanent injunction Monday sought by Grace College and Seminary. The ruling stops the enforcement of a portion of the law related to providing contraception, abortion-inducing drugs and sterilization through student and employee health insurance plans.

Our state of mind is based on experiences and what other people tell us. This may result in conflicting information, uncertainty, and alternative facts. We present a robot that models relativity of knowledge and perception within social interaction following principles of the theory of mind. We utilized vision and speech capabilities on a Pepper robot to build an interaction model that stores the interpretations of perceptions and conversations in combination with provenance on its sources. The robot learns directly from what people tell it, possibly in relation to its perception. We demonstrate how the robot's communication is driven by hunger to acquire more knowledge from and on people and objects, to resolve uncertainties and conflicts, and to share awareness of the per- ceived environment. Likewise, the robot can make reference to the world and its knowledge about the world and the encounters with people that yielded this knowledge.

GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings Marek Sikora a,b,, Łukasz Wróbel a,b,, Adam Gudyś a, a Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland b Institute of Innovative Technologies, EMAG, Leopolda 31, 40-189 Katowice, PolandAbstract This article presents GuideR, a user-guided rule induction algorithm, which overcomes the largest limitation of the existing methods---the lack of the possibility to introduce user's preferences or domain knowledge to the rule learning process. Automatic selection of attributes and attribute ranges often leads to the situation in which resulting rules do not contain interesting information. We propose an induction algorithm which takes into account user's requirements. Our method uses the sequential covering approach and is suitable for classification, regression, and survival analysis problems. The effectiveness of the algorithm in all these tasks has been verified experimentally, confirming guided rule induction to be a powerful data analysis tool. Introduction Sequential covering rule induction algorithms can be used for both, predictive and descriptive purposes [1, 2, 3, 4]. In spite of the development of increasingly sophisticated versions of those algorithms [5, 6], the main principle remains unchanged and involves two phases: rule growing and rule pruning. In the latter, some of these conditions are removed. In comparison to other machine learning methods, rule sets obtained by sequential covering algorithm, also known as separate-and-conquer strategy (SnC), are characterized by good predictive as well as descriptive capabilities. Taking into consideration only the former, superior results can often be obtained using other methods, e.g. However, data models obtained this way are much less comprehensible than rule sets. In the case of rule learning for descriptive purposes, the algorithms of association rule induction [12, 13, 14] or subgroup discovery [15, 6], are applied. The former leads to a very large number of rules which must then be limited by filtering according to rule interestingness measures [16, 17, 18]. Nevertheless, rule sets obtained by subgroup discovery are characterized by worse predictive abilities than those generated by the standard sequential covering approach. Therefore, if creating a prediction system with comprehensible data model is the main objective, the application of sequential covering rule induction algorithms provides the most sensible solution.

LONDON – For many U.S. allies, Secretary of Defense Jim Mattis is the last of the Trump administration's so-called grown-ups in the room. So at Asia's main annual security forum he got a warm reception for his firm defense of the rules-based order the U.S. helped to build after World War II. Increasingly, though, Mattis' reassurance is not enough. The U.S. -- as much as China -- is seen as a threat to that system, undermining the very solutions the retired Marine Corps general offered to counter Beijing's rule breaking in the South China Sea. On Sunday, tiny Singapore, one of the United States' most like-minded partners in the region, drew a direct equivalence between the U.S. and China.

There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. These explanations are important to ensure algorithmic fairness, identify potential bias/problems in the training data, and to ensure that the algorithms perform as expected. However, explanations produced by these systems is neither standardized nor systematically assessed. In an effort to create best practices and identify open challenges, we provide our definition of explainability and show how it can be used to classify existing literature. We discuss why current approaches to explanatory methods especially for deep neural networks are insufficient. Finally, based on our survey, we conclude with suggested future research directions for explanatory artificial intelligence.

UK Prime Minister Theresa May has said she is disappointed by the US's "unjustified decision" to slap tariffs on EU steel and aluminium. The tariffs of 25% on steel and 10% on aluminium, which affect the EU, Canada and Mexico, came into effect on Friday. All three are planning retaliatory moves. Mrs May said the EU and UK should be exempted and would work together to "protect and safeguard our workers and industries". UK Steel said the tariffs, which apply to a wide range of steel and aluminium products such as sheets, plates, bars, pipes and "semi-finished" products, will damage not only the UK steel sector but also the US economy.

"Both prime ministers further agreed to India's proposal for continuous and institutionalized naval engagements in their shared maritime space, including the establishment of maritime exercises with like-minded regional partners," the Singapore Defence Ministry said in a statement.

SINGAPORE – India's Prime Minister Narendra Modi has renewed calls for a maritime agreement that would make international waters more secure amid increasingly tense territorial disputes in the South China Sea. After meeting Singapore's Prime Minister Lee Hsien Loong on Friday, Modi says they reiterated their commitment to a rules-based order for maritime security and called for an open, fair and transparent agreement. China is pitted against smaller neighbors in multiple disputes over islands, coral reefs and lagoons in waters crucial to global trade. Beijing has rejected accusations it's militarizing the area and has been working with the 10 members of the Association of Southeast Asian Nations to reach a code of conduct to avoid frictions. Modi will give a keynote address on Friday at the Shangri-La Dialogue, an annual security conference.

Artificial intelligence (AI) is hot. Billions of dollars in venture capital have been invested in AI firms, including firms that focus on solving supply chain problems. Machine learning (ML), a subset of AI, is particularly hot. Interestingly, while AI is not a new technology in supply chain management, so much more data is becoming available for analysis, that we're seeing a new focus on using these techniques to improve supply chain applications, including warehouse technologies. Any device that perceives its environment and takes actions that maximize its chance of success toward some goal is using artificial intelligence in some manner.

We'd like to offer you a complimentary copy of Gartner's Market Guide for Data Preparation. Tamr, the leader in enterprise data unification, has again been recognized as a Representative Vendor in Gartner's Market Guide for Data Preparation. "Data and analytics leaders are struggling due to their over-reliance on IT-centric tools for finding, cataloging and transforming relevant data and making it accessible to the growing number of distributed users in the enterprise both inside and outside of centralized data and analytics teams. Due to this, organizations report that they spend more than 60% of their time in data preparation, leaving little time for actual analysis." Our view of the underlying cause of the problem is that the deterministic, rules-based approaches inherent in traditional approaches to data integration can't keep pace with the number of sources that must be unified to generate big breakthroughs.