A BSTRACT Recent advances in deep learning theory have evoked the study of generalizabil-ity across different local minima of deep neural networks (DNNs). While current work focused on either discovering properties of good local minima or developing regularization techniques to induce good local minima, no approach exists that can tackle both problems. We achieve these two goals successfully in a unified manner. Specifically, based on the Fisher information we propose a metric both strongly indicative of generalizability of local minima and effectively applied as a practical regularizer. We provide theoretical analysis including a generalization bound and empirically demonstrate the success of our approach in both capturing and improving the generalizability of DNNs. Experiments are performed on CIFAR-10 and CIFAR-100 for various network architectures. 1 I NTRODUCTION Recently, there has been a surge in the interest of acquiring a theoretical understanding over deep neural network's behavior. Breakthroughs have been made in characterizing the optimization process, showing that learning algorithms such as stochastic gradient descent (SGD) tend to end up in one of the many local minima which have close-to-zero training loss (Choromanska et al., 2015; Dauphin et al., 2014; Kawaguchi, 2016; Nguyen & Hein, 2018; Du et al., 2018). It is, therefore, natural to ask two closely related questions: (a) What kind of local minima can generalize better? To our knowledge, existing work focused only on one of the two questions. For the "what" question, various definitions of "flatness/sharpness" have been introduced and analyzed (Keskar et al., 2017; Neyshabur et al., 2018; 2017; Wu et al., 2017; Liang et al., 2017).

--Wide spread of satirical news in online communities is an ongoing trend. The nature of satires are so inherently ambiguous that sometimes it's too hard even for humans to understand whether it's actually satire or not. So, research interest has grown in this field. The purpose of this research is to detect Bangla satirical news spread in online news portals as well as social media. In this paper we propose a hybrid technique for extracting feature from text documents combining Word2V ecand TF-IDF. Using our proposed feature extraction technique, with standard CNN architecture we could detect whether a Bangla text document is satire or not with an accuracy of more than 96%. Satires can be considered as a literary form which involves a delicate balance between criticism and humor.

Gartner has unveiled its biggest predictions for IT organisations and users for 2020 and beyond. These predictions analyse how technology is changing society and the expectations of users. "Technology is changing the notion of what it means to be human," said Daryl Plummer, VP and Fellow at Gartner. "CIOs in end-user organizations must understand the effects of the change and reset expectations for what technology means." "This year's predictions help us move beyond thinking about mere notions of technology adoption and draw us more deeply into issues surrounding what it means to be human in the digital world," said Plummer.

Math and Statistics for Data Science are essential because these disciples form the basic foundation of all the Machine Learning Algorithms. In fact, Mathematics is behind everything around us, from shapes, patterns, and colors, to the count of petals in a flower. Mathematics is embedded in each and every aspect of our lives. Although having a good understanding of programming languages, Machine Learning algorithms and following a data-driven approach is necessary to become a Data Scientist, Data Science isn't all about these fields. In this blog post, you will understand the importance of Math and Statistics for Data Science and how they can be used to build Machine Learning models.

The statistical study, the report outlines the Global Artificial Intelligence in Education System Industry including production, cost/profit, supply-demand, and import-export. The total market is further bifurcated into a company, by country, and by various segmentation for the competitive landscape study.

Key Questions Answered in this Report: • What are the trends in the global military artificial intelligence and cybernetics across different regions? Global Military Artificial Intelligence Market Forecast, 2019-2024 The Global Military Artificial Intelligence Market report projects the market to grow at a significant CAGR of 18.66% on the basis of value during the forecast period from 2019 to 2024. North America dominated the global military artificial intelligence market with a share of 48.23% in 2019. North America, including the major countries such as the U.S., is the most prominent region for the military artificial intelligence market. In North America, the U.S. acquired a major market share in 2019 due to the major deployment of counter measures in defense sector in the country.

Dr. Claude Shannon, one of the pioneers of the field of artificial intelligence, with an electronic ... [ ] mouse designed to navigate its way around a maze after only one'training' run. A schism lies at the heart of the field of artificial intelligence. Since its inception, the field has been defined by an intellectual tug-of-war between two opposing philosophies: connectionism and symbolism. These two camps have deeply divergent visions as to how to "solve" intelligence, with differing research agendas and sometimes bitter relations. Today, connectionism dominates the world of AI. The emergence of deep learning, which is a quintessentially connectionist technique, has driven the worldwide explosion in AI activity and funding over the past decade.

Dr. Claude Shannon, one of the pioneers of the field of artificial intelligence, with an electronic ... [ ] mouse designed to navigate its way around a maze after only one'training' run. A schism lies at the heart of the field of artificial intelligence. Since its inception, the field has been defined by an intellectual tug-of-war between two opposing philosophies: connectionism and symbolism. These two camps have deeply divergent visions as to how to "solve" intelligence, with differing research agendas and sometimes bitter relations. Today, connectionism dominates the world of AI. The emergence of deep learning, which is a quintessentially connectionist technique, has driven the worldwide explosion in AI activity and funding over the past decade.

Ontology Design Patterns (ODPs) have become an established and recognised practice for guaranteeing good quality ontology engineering. There are several ODP repositories where ODPs are shared as well as ontology design methodologies recommending their reuse. Performing rigorous testing is recommended as well for supporting ontology maintenance and validating the resulting resource against its motivating requirements. Nevertheless, it is less than straightforward to find guidelines on how to apply such methodologies for developing domain-specific knowledge graphs. ArCo is the knowledge graph of Italian Cultural Heritage and has been developed by using eXtreme Design (XD), an ODP- and test-driven methodology. During its development, XD has been adapted to the need of the CH domain e.g. gathering requirements from an open, diverse community of consumers, a new ODP has been defined and many have been specialised to address specific CH requirements. This paper presents ArCo and describes how to apply XD to the development and validation of a CH knowledge graph, also detailing the (intellectual) process implemented for matching the encountered modelling problems to ODPs. Relevant contributions also include a novel web tool for supporting unit-testing of knowledge graphs, a rigorous evaluation of ArCo, and a discussion of methodological lessons learned during ArCo development.

Internet censorship, basically, is a very effective strategy used by dictatorial governments to limit access to information available online for controlling freedom of expression and prevent rebellion and discord. Countries at the forefront of adopting Internet censorship, as per the findings of the 2019 Freedom House report, are India and China as these are declared to be the worst abusers of digital freedom. Conversely, the US, Brazil, Sudan, and Kazakhstan are the countries where Internet freedom has considerably declined recently. When a country curbs Internet freedom, activists need to find ways to evade it. However, they may not need to manually search for it now that "Geneva" is here. The term is a shorter version of Genetic Evasion.