Recent use includes the assassination with VX of Kim Jong Nam, half-brother of North Korean leader Kim Jong Un, in Kuala Lumpur airport in February 2017 and the attempted murder of Sergei Skripal, a 66-year-old former Russian double agent, and his daughter with a Novichok nerve agent in March in England.

This track includes data-related tasks such as analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy, with special focus on social data on the web. Hence, the broader context of the track comprehends AI, web mining, information retrieval, natural language processing, and sentiment analysis. As the web rapidly evolves, web users are evolving with it. In an era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the social web to expand exponentially. The distillation of knowledge from such a large amount of unstructured information, however, is an extremely difficult task, as the contents of today’s web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction. The primary aim of this track is exploring the new frontiers of big data computing for opinion mining and sentiment analysis through machine learning techniques, knowledge-based systems, adaptive and transfer learning, in order to more efficiently retrieve and extract social information from the web.

A British computer expert who helped shut down the NHS'WannaCry' cyber attack has been charged in the US with creating banking malware. Marcus Hutchins, 23, has been charged with six counts of creating and distributing malware known as Kronos. Hutchins made a telephone call from jail hours after his arrest last August to an unidentified individual - which was recorded and filed by US prosecutors, according to court documents. He said he had written code as a youngster which was turned into malicious software that prosecutors say harvested banking details. According to court documents seen by The Washington Post, Hutchins said in the phone call: 'So I wrote code for a guy a while back who then incorporated it into a banking malware, so they have logs of that, and essentially they want to know my part of the banking operation or if I just sold the code on to some guy... once they found I sold the code to someone, they wanted me to give them his name, and I don't actually know anything about him.'

The curse of dimensionality in the realm of association rules is twofold. Firstly, we have the well known exponential increase in computational complexity with increasing item set size. Secondly, there is a \emph{related curse} concerned with the distribution of (spare) data itself in high dimension. The former problem is often coped with by projection, i.e., feature selection, whereas the best known strategy for the latter is avoidance. This work summarizes the first attempt to provide a computationally feasible method for measuring the extent of dimension curse present in a data set with respect to a particular class machine of learning procedures. This recent development enables the application of various other methods from geometric analysis to be investigated and applied in machine learning procedures in the presence of high dimension.

The overall team performance will obviously get much better as we click on at least two of those cylinders. When we get some of our guys back in the next week, we're confident our offense is going to perform better. It's incumbent upon us, with our bullpen, to get back to what we were doing last year. We're confident we have the guys down there to perform way better than we have.

Over the past years, distributed representations have proven effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey is focused on semantic representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their main limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and provides an analysis of five important aspects: interpretability, sense granularity, adaptability to different domains, compositionality and integration into downstream applications.

Ensuring flexible and efficient manufacturing of customized products in an increasing dynamic and turbulent environment without sacrificing cost effectiveness, product quality and on-time delivery has become a key issue for most industrial enterprises. A promising approach to cope with this challenge is the integration of cognitive capabilities in systems and processes with the aim of expanding the knowledge base used to perform managerial and operational tasks. In this work, a novel approach to real-time rescheduling is proposed in order to achieve sustainable improvements in flexibility and adaptability of production systems through the integration of artificial cognitive capabilities, involving perception, reasoning/learning and planning skills. Moreover, an industrial example is discussed where the SOAR cognitive architecture capabilities are integrated in a software prototype, showing that the approach enables the rescheduling system to respond to events in an autonomic way, and to acquire experience through intensive simulation while performing repair tasks.

You've probably been to a supermarket that printed coupons for you at checkout. Or listened to a playlist that your streaming service generated for you. Or gone shopping online and seen a list of products labeled "you might be interested in…." that did indeed contain some stuff that you were interested in. Recommendation engines take data about you, similar consumers, and available products, and use that to figure out what you might be interested in and therefore deliver those coupons, playlists, and suggestions. Recommendation engines can be extremely complex.

R9B's APCs are 24x7x365 security-as-a-service operations centers designed to deliver both managed detection and response services as well as threat hunting to a growing customer set. The ORION HUNT platform enables cyber defenders to stealthily maneuver in a client network to proactively search for adversaries that defeat passive and automated security products. Leveraging DarkLight will enable APC HUNT operations to find correlations and patterns within any size dataset, rapidly evaluating millions of events. The combination of DarkLight and APC operations delivers a new and powerful network security-based data analysis model. Combining the analytics engine with the ORION platform creates a dynamic, targeted collection and response mechanism.

This paper addresses the problem of learning the concept of "propagation" in the pretopology theoretical formalism. Our proposal is first to define the pseudo-closure operator (modeling the propagation concept) as a logical combination of neighborhoods. We show that learning such an operator lapses into the Multiple Instance (MI) framework, where the learning process is performed on bags of instances instead of individual instances. Though this framework is well suited for this task, its use for learning a pretopological space leads to a set of bags exponential in size. To overcome this issue we thus propose a learning method based on a low estimation of the bags covered by a concept under construction. As an experiment, percolation processes (forest fires typically) are simulated and the corresponding propagation models are learned based on a subset of observations. It reveals that the proposed MI approach is significantly more efficient on the task of propagation model recognition than existing methods.