We expect an increase in frequency and severity of cyber-attacks that comes along with the need of efficient security countermeasures. The process of attributing a cyber-attack helps in constructing efficient and targeted mitigative and preventive security measures. In this work, we propose an argumentation-based reasoner (ABR) that helps the analyst during the analysis of forensic evidence and the attribution process. Given the evidence collected from the cyber-attack, our reasoner helps the analyst to identify who performed the attack and suggests the analyst where to focus further analyses by giving hints of the missing evidence, or further investigation paths to follow. ABR is the first automatic reasoner that analyzes and attributes cyber-attacks by using technical and social evidence, as well as incomplete and conflicting information. ABR was tested on realistic cyber-attacks cases.

Recent formal approaches towards causality have made the concept ready for incorporation into the technical world. However, causality reasoning is computationally hard; and no general algorithmic approach exists that efficiently infers the causes for effects. Thus, checking causality in the context of complex, multi-agent, and distributed socio-technical systems is a significant challenge. Therefore, we conceptualize an intelligent and novel algorithmic approach towards checking causality in acyclic causal models with binary variables, utilizing the optimization power in the solvers of the Boolean Satisfiability Problem (SAT). We present two SAT encodings, and an empirical evaluation of their efficiency and scalability. We show that causality is computed efficiently in less than 5 seconds for models that consist of more than 4000 variables.

We propose a simple yet effective method to learn to segment new indoor scenes from an RGB-D sequence: State-of-the-art methods trained on one dataset, even as large as SUNRGB-D dataset, can perform poorly when applied to images that are not part of the dataset, because of the dataset bias, a common phenomenon in computer vision. To make semantic segmentation more useful in practice, we learn to segment new indoor scenes from sequences without manual annotations by exploiting geometric constraints and readily available training data from SUNRGB-D. As a result, we can then robustly segment new images of these scenes from color information only. To efficiently exploit geometric constraints for our purpose, we propose to cast these constraints as semi-supervised terms, which enforce the fact that the same class should be predicted for the projections of the same 3D location in different images. We show that this approach results in a simple yet very powerful method, which can annotate sequences of ScanNet and our own sequences using only annotations from SUNRGB-D.

Driverless cars are at least a decade away as the machines remain vulnerable to hacking, industry experts say. Further major problems are presented from the highly advanced technology to provide a car with the artificial intelligence required to drive as well as a human. The news comes after Tesla boss Elon Musk held his first Autonomy Day with investors this week, claiming he would have fully self-driving cars on the road by 2020. The UK government have also said they want self-driving cars within two years. The Tesla Model Y is unveiled at Tesla's design studio in Hawthorne on March 14, 2019 Tesla said their computer is low cost and low power, as well as'straightforward and simple.'

Line of Duty fans have asked Amazon's Alexa voice assistant about the identity of the mysterious'H' so many times that the devices are now quipping back. Videos have emerged online of fans asking Alexa to unmask the corrupt cop atthe centre of the show's plot but the device refuses, saying she's'too scared' or'can't be bothered'. The identity of'H' has plagued viewers throughout the series but the big reveal will identify the corrupt police officer at the top of the organised crime chain who has been pulling the strings. Social media user, Daniel Smith, was among those who could not bear to wait. he filmed himself asking his Alexa: 'Who is H?' The exasperated device replied: 'Honestly, I can't be bothered to talk about this anymore. 'Too many people are asking me who H is.' Gareth Evans, from Aberdare, Helen England, from North Tipperary, Teresa Rodmell, from Milton Keynes and Ollie Charles, from London, all asked the same question but got a more sinister response.

If you look under the hood of the internet, you'll find lots of gears churning along that make it all possible. For example, take a company like AT&T. They have to intimately understand what internet data are going where so that they can better accommodate different levels of usage. But it isn't practical to precisely monitor every packet of data, because companies simply don't have unlimited amounts of storage space. Because of this, tech companies use special algorithms to roughly estimate the amount of traffic heading to different IP addresses.

One of the problems in society that AI decision-making was meant to solve, was bias. After all, aren't computers less likely to have inherent views on, for example, race, gender, and sexuality? Well, that was true back in the days when, as a general rule, computers could only do what we told them. The rollout of machine learning, thanks to the explosion of Big Data, and the emergence of affordable computers with enough processing power to handle it have changed all that. In the old days, the term "garbage in, garbage out" concisely summed up the importance of high-quality data.

Bottom line: Enterprises are attaining double-digit improvements in forecast error rates, demand planning productivity, cost reductions and on-time shipments using machine learning today, revolutionizing supply chain management in the process. Machine learning algorithms and the models they're based on excel at finding anomalies, patterns and predictive insights in large data sets. Many supply chain challenges are time, cost and resource constraint-based, making machine learning an ideal technology to solve them. From Amazon's Kiva robotics relying on machine learning to improve accuracy, speed and scale to DHL relying on AI and machine learning to power their Predictive Network Management system that analyzes 58 different parameters of internal data to identify the top factors influencing shipment delays, machine learning is defining the next generation of supply chain management. Gartner predicts that by 2020, 95% of Supply Chain Planning (SCP) vendors will be relying on supervised and unsupervised machine learning in their solutions.

Few technologies this century have captured the imagination of so many technologists within the capital markets as artificial intelligence (AI). Thanks to a fertile combination of data, storage, and computing power, AI has emerged from its long winter and spread into nearly all areas of the capital markets, across the front, middle and back offices. While many vendors can claim AI capability--some of them dubiously--few have the bona fides of firms like Digital Reasoning. The Franklin, Tenn.-headquartered firm had its start in 2000, quickly winning defense contracts with the US Army, and in 2010, funding from the Central Intelligence Agency. It wasn't until around 2012, however, that Digital Reasoning entered into finance with a bang, signing up names such as Point72 and UBS.

Compositionality is a key strategy for addressing combinatorial complexity and the curse of dimensionality. Recent work has shown that compositional solutions can be learned and offer substantial gains across a variety of domains, including multi-task learning, language modeling, visual question answering, machine comprehension, and others. However, such models present unique challenges during training when both the module parameters and their composition must be learned jointly. In this paper, we identify several of these issues and analyze their underlying causes. Our discussion focuses on routing networks, a general approach to this problem, and examines empirically the interplay of these challenges and a variety of design decisions. In particular, we consider the effect of how the algorithm decides on module composition, how the algorithm updates the modules, and if the algorithm uses regularization.