Lehmann and Magidor's rational closure is acknowledged as a landmark in the field of non-monotonic logics and it has also been re-formulated in the context of Description Logics (DLs). We show here how to model a rational form of entailment for expressive DLs, such as SROIQ, providing a novel reasoning procedure that compiles a non-monotone DL knowledge base into a description logic program (dl-program).

Gradual pattern mining allows for extraction of attribute correlations through gradual rules such as: "the more X, the more Y". Such correlations are useful in identifying and isolating relationships among the attributes that may not be obvious through quick scans on a data set. For instance, a researcher may apply gradual pattern mining to determine which attributes of a data set exhibit unfamiliar correlations in order to isolate them for deeper exploration or analysis. In this work, we propose an ant colony optimization technique which uses a popular probabilistic approach that mimics the behavior biological ants as they search for the shortest path to find food in order to solve combinatorial problems. In our second contribution, we extend an existing gradual pattern mining technique to allow for extraction of gradual patterns together with an approximated temporal lag between the affected gradual item sets. Such a pattern is referred to as a fuzzy-temporal gradual pattern and it may take the form: "the more X, the more Y, almost 3 months later". In our third contribution, we propose a data crossing model that allows for integration of mostly gradual pattern mining algorithm implementations into a Cloud platform. This contribution is motivated by the proliferation of IoT applications in almost every area of our society and this comes with provision of large-scale time-series data from different sources.

Africa has over 2000 languages, but these languages are not well-represented in the existing Natural Language Processing ecosystem. One challenge is the lack of useful African language datasets that we can use to solve different social and economic problems. In this article, I have compiled a list of African language datasets from across the web. You can use these datasets in various NLP tasks such as text classification, named entity recognition, machine translation, sentiment analysis, speech recognition, and topic modeling. I've made this collection of datasets public to give you an opportunity to use your skills and help solve different challenges.

… are coming to grips with the opportunities of emerging technologies such as Artificial Intelligence (AI), robotics, and the Internet of Things. These and …

Global Artificial Intelligence in Medical Imaging Market report identifies key players operating in the market and comprehensively analyzes their market rankings and core competencies. The report describes major factors (drivers, restraints, opportunities, and challenges) influencing the growth of the market and submarkets. It enhances the decision-making process by understanding the strategies that underpins commercial interest with respect to products, segmentation, and industry verticals. This marketing report helps develop/modify business expansion plans by using substantial growth offering developed and emerging markets. The worldwide report gives strategic recommendations in key business sections in light of the market estimations.

If you walk down the street shouting out the names of every object you see -- garbage truck! But if you go through an obstacle course, and you show them how to navigate a series of challenges to get to the end unscathed, they would. Most machine learning algorithms are shouting names in the street. They perform perceptive tasks that a person can do in under a second. But another kind of AI -- deep reinforcement learning -- is strategic.

"A period that had been broadly described as engagement has come to an end," Kurt Campbell, the Indo-Pacific Coordinator at the United States (US) National Security Council, told a virtual audience in May on the subject of US-China relations. "The dominant paradigm is going to be competition." On several occasions, Campbell has highlighted that one of the major arenas of this competition will concern technology. This is increasingly reflected in US national security structures. Today, there is both a senior director and coordinator for technology and national security at the White House; the National Economic Council has briefed the Cabinet on supply chain resilience; and the focus of Department of Defense policy reviews have been on emerging military technologies. The subject of intensifying technology competition is also making its way into new US avenues for cooperation with partners, including with India.

AI used to be an idea of a super-intelligent computer doing god-knows-what on a site in Silicon Valley. AI applications are now present in our day-to-day lives. With rapid development of innovative technological solutions, Artificial intelligence (AI) has now emerged as a key variant on how data is analysed to inform decisions at corporate and individual levels. AI is the simulation of human intelligence processes by machines, especially computer systems. Today's top AI firms are leaders in global business development, as artificial intelligence has grown in importance in the commercial world.

The U.S. military has launched two drone strikes against Taliban positions in northern Afghanistan, a U.S. defense official told Fox News on Friday. The strikes came hours before Afghan President Ashraf Ghani is set to meet President Biden at the White House Friday afternoon. An unknown number of Taliban fighters were killed in Baghlan and Kunduz provinces, where the strikes took place. Taliban fighters react to a speech by their senior leader in the Shindand district of Herat province, Afghanistan, in 2016. Three more districts fell to the Taliban overnight.

Brooklyn, New York, June 23, 2021 (GLOBE NEWSWIRE) -- According to a new market research report published by Global Market Estimates, the Global Artificial Intelligence in Cement Production Market is projected to grow at a CAGR value of 28.5% during the forecast period [2021 to 2026]. The rising need for automation, as this industry is yet to transform into digitalization, increasing need to the rising cost of manual procedures, and rising need from the end-user industry for high-quality cement will help the AI in cement production market to grow rapidly. Browse 151 Market Data Tables and 111 Figures spread through 181 Pages and in-depth TOC on "Global Artificial Intelligence in Cement Production Market - Forecast to 2026"