Banking Trojans, botnets are primary drivers of financially-motivated cybercrime. In this paper, we first analyzed how an APT-based banking botnet works step by step through the whole lifecycle. Specifically, we present a multi-stage system that detects malicious banking botnet activities which potentially target the organizations. The system leverages Cyber Data Lake as well as multiple artificial intelligence techniques at different stages. The evaluation results using public datasets showed that Deep Learning based detections were highly successful compared with baseline models. The proposed detections are partially in production on Cyber Data Lake within the organization, and we are continuing to work with internal security teams on further operational challenges.

We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabilistic logic programming language ProbLog can be adapted for the new language. We theoretically and experimentally demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic) programming, and (iv) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.

The inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace. Knowledge and query authoring using natural language, especially controlled natural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced the KALM system (Knowledge Authoring Logic Machine) that supports knowledge authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introduces KALM-QA (KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, called MetaQA, which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark. It is under consideration for acceptance in TPLP.

The evolution and development of events have their own basic principles, which make events happen sequentially. Therefore, the discovery of such evolutionary patterns among events are of great value for event prediction, decision-making and scenario design of dialog systems. However, conventional knowledge graph mainly focuses on the entities and their relations, which neglects the real world events. In this paper, we present a novel type of knowledge base - Event Logic Graph (ELG), which can reveal evolutionary patterns and development logics of real world events. Specifically, ELG is a directed cyclic graph, whose nodes are events, and edges stand for the sequential, causal or hypernym-hyponym (is-a) relations between events. We constructed two domain ELG: financial domain ELG, which consists of more than 1.5 million of event nodes and more than 1.8 million of directed edges, and travel domain ELG, which consists of about 30 thousand of event nodes and more than 234 thousand of directed edges. Experimental results show that ELG is effective for the task of script event prediction.

The developer of a popular app which transforms users' faces to predict how they will look as older people has insisted they are not accessing users' photographs without permission. FaceApp, which was launched by a Russian developer in 2017, uses artificial intelligence allowing people to see how they would look with different hair colour, eye colour or as a different gender. The app has topped download charts again this week, after users homed in on its ageing filter, which has since been used by dozens of celebrities and prominent figures to picture how they will supposedly look in several decades' time. This surge of interest has in turn created concerns that FaceApp is systematically harvesting users' images. People who upload their image to the app transfer the picture to a server controlled by the developer, with the photograph processing done remotely, rather than on their phone. These concerns have been heightened by growing awareness of online privacy issues in recent years and the fact that the developer is based in Russia, where many high-profile online misinformation campaigns have been based, in addition to a loosely-phrased privacy policy.

Learning new skills can make older people's brains three decades younger in just six weeks, according to a new study. Taking up three new tasks at the same time boosts mental power and protects against Alzheimer's disease, scientists have found. These skills may range from language lessons to using an iPad, photography, writing music or painting. Taking up three new skills, such as language lessons or learning how to use an iPad, at the same time can make older people's brains three decades younger in just six weeks (file photo) The course workload would be similar to an undergraduate's and adds to growing evidence that dementia is avoidable through lifestyle changes. After less than two months, those in their 80s increased their cognitive abilities to levels similar to those seen in someone in their 50s.

A daredevil retired pilot has been captured on camera performing loops, rolls and a dramatic dive while flying the'world's smallest' twin-jet aircraft. Bob Grimstead, 70, flew at an altitude of 5,000ft (1,524m) in the diminutive plane which has been described as a'bubble car with wings'. At just 13ft (4m) long, 4ft (1.2m) wide and weighing a mere 180lbs, Mr Grimstead, from West Sussex, was able to reach speeds of 140mph (225kmh). The former British Airways airline pilot used to fly 400 tonne jumbo jets and said he had no fear taking to the skies in the micro plane and said it was'superb fun'. Bob Grimstead, 70, (pictured) flew the diminutive jet at 5,000ft (1,524m).

SONAL SHAH: It's also about how do we make data more useful for people to use and to solve problems in their communities? TANYA OTT: Okay, that is a big job. Who is this superhuman who fills it? TANYA OTT: We'll tell you, in a moment. But first, let me say, you're listening to the Press Room, where we talk about some of the biggest issues facing businesses today. I'm Tanya Ott and joining me today are Bill Eggers … I am the executive director and a professor of practice at Georgetown University's Beeck Center. TANYA OTT: Bill and Sonal are coauthors of The CDO Playbook – a guide for Chief Data Officers. For the last decade, government has been focused on making data more open and easily [accessible] to the public.

In another sign that smart technology is transforming the farming industry, engineers at the University of Cambridge have developed a robot that uses machine learning to pick lettuce. The robot, dubbed "Vegebot", has been designed to first identify iceberg lettuce and then decide if it is healthy and ready to be picked, the university said Monday. If this is the case, it will then cut the lettuce without damaging it. The Vegebot was first trained to identify and pick the delicate crop in a laboratory and has now undertaken successful tests in a range of field conditions. The university added that while the prototype device was neither as fast or efficient as a human, it showed how robots could be used in agriculture on a wider scale.

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