Although deep neural networks have shown promising performances on various tasks, even achieving human-level performance on some, they are shown to be susceptible to incorrect predictions even with imperceptibly small perturbations to an input. There exists a large number of previous works which proposed to defend against such adversarial attacks either by robust inference or detection of adversarial inputs. Yet, most of them cannot effectively defend against whitebox attacks where an adversary has a knowledge of the model and defense. More importantly, they do not provide a convincing reason why the generated adversarial inputs successfully fool the target models. To address these shortcomings of the existing approaches, we hypothesize that the adversarial inputs are tied to latent features that are susceptible to adversarial perturbation, which we call vulnerable features. Then based on this intuition, we propose a minimax game formulation to disentangle the latent features of each instance into robust and vulnerable ones, using variational autoencoders with two latent spaces. We thoroughly validate our model for both blackbox and whitebox attacks on MNIST, Fashion MNIST5, and Cat & Dog datasets, whose results show that the adversarial inputs cannot bypass our detector without changing its semantics, in which case the attack has failed.

Ensuring compliance with various laws and regulations is of utmost priority for financial institutions. Traditional methods in this area have been shown to be inefficient. Manual processing does not scale well. Automated efforts are hindered due to the lack of formalization of domain knowledge and problems of integrating such knowledge into software systems. In this work we propose an approach to tackle these issues by encoding them into software contracts using a Controlled Natural Language. In particular, we encode a portion of the Money Market Statistical Reporting (MMSR) regulations into contracts specified by the clojure.spec framework. We show how various features of a contract framework, in particular clojure.spec, can help to tackle issues that occur when dealing with compliance: validation with explanations and test data generation. We benchmark our proposed solution and show that this approach can effectively solve compliance issues in this particular use case.

Though Deep Neural Networks (DNN) show excellent performance across various computer vision tasks, several works show their vulnerability to adversarial samples, i.e., image samples with imperceptible noise engineered to manipulate the network's prediction. Adversarial sample generation methods range from simple to complex optimization techniques. Majority of these methods generate adversaries through optimization objectives that are tied to the pre-softmax or softmax output of the network. In this work we, (i) show the drawbacks of such attacks, (ii) propose two new evaluation metrics: Old Label New Rank (OLNR) and New Label Old Rank (NLOR) in order to quantify the extent of damage made by an attack, and (iii) propose a new adversarial attack FDA: Feature Disruptive Attack, to address the drawbacks of existing attacks. FDA works by generating image perturbation that disrupt features at each layer of the network and causes deep-features to be highly corrupt. This allows FDA adversaries to severely reduce the performance of deep networks. W e experimentally validate that FDA generates stronger adversaries than other state-of-the-art methods for image classification, even in the presence of various defense measures. More importantly, we show that FDA disrupts feature-representation based tasks even without access to the task-specific network or methodology.

BEIRUT – Unknown warplanes targeted overnight an arms depot and posts of Iranian-backed militias in eastern Syria, near the Iraqi border, killing at least 18 fighters, Syrian opposition activists said Monday. The strikes come amid rising tensions in the Middle East and the crisis between Iran and the U.S. in the wake of the collapsing nuclear deal between Tehran and world powers. An official with an Iranian-backed militia in Iraq blamed Israel for the airstrikes that hit in the eastern Syrian town of Boukamal. There was no immediate comment from Israel. Israeli Prime Minister Benjamin Netanyahu said last month that Iran has no immunity anywhere and that the Israeli military "will act -- and currently are acting -- against them."

In recent years, the increase in accessible, large-scale computing power and storage has ushered in a new dawn for artificial intelligence (AI), with the technology appearing to finally catch up with the existing algorithms to bring machine learning (ML) to realisation. In this article, we discuss some of the key applications of ML that have shown success in clinical research. Moreover, we consider how machine learning is impacting on clinical trials, utilising decades of structured clinical trial data alongside real-world data (RWD) and other valuable data sources to support clinical trial design, execution and analysis. Combining computational skills and drug development experience, data science teams can support the pharma and biotech industry to generate business value through the application of machine learning. For ML algorithms to be successful they require large, quality data sets for their application.

Kleyman brings more than 15 years of experience to his role as Executive Vice President of Digital Solutions at Switch. Using the latest innovations such as AI, machine learning, blockchain, DevOps, cloud, and advanced technologies, Kleyman delivers solutions to customers that help them achieve their business goals and remain competitive in their market. An active member in the technology industry, he was ranked number 16 globally in the Onalytica study that reviewed the top 100 most influential individuals in the cloud landscape; and number 4 in another Onalytica study that reviewed the industry's top Data Security Experts. His published and referenced work can be found on ITPro Today, Data Center Knowledge, InformationWeek, Network Computing, AFCOM, TechTarget, Dark Reading, Forbes, CBS Interactive, Slashdot, and more.

The U.S. National Institute of Standards and Technology (NIST) is developing a plan for federal engagement in the development of AI technical standards and related tools. This is in response to President Trump's Executive Order on Maintaining American Leadership in Artificial Intelligence.

If enemy cruise missiles, helicopter gunfire and even fighter-jet launched bombs close in on Navy surface ships at sea, service commanders could employ a range of time-sensitive layered defenses to include interceptor missiles, deck-mounted guns, electronic warfare tactics and even lasers. Navy preparations for this kind of scenario include the use of radar, long-range sensors and coordinated surveillance with surface, undersea and air assets - all seemingly operated for rapid response-enabled destruction of incoming enemy fire. Virtually all of these contingencies rely upon an often overlooked area of maritime warfare -- information warfare. Targeting data for pretty much any defensive weapons system would need to precede or inform fire control systems and certain kinds of sensor-weapons fusion. For this reason, the Navy is revving up its focus on training a new generation of information warriors to surge into future decades, hopefully, armed with the technical skills needed to counter enemy attacks today and 20 years from now.

A wide range of experts representing academics, private sector, disaster managers, nongovernmental organizations (NGOs), and United Nations agencies gathered recently to examine how artificial intelligence (AI) and big data can be used in disaster risk reduction to save lives. "As humanitarians and disaster managers, we don't want to be only spectators in this big data industry but want to learn from this industry and shape its work for our needs," said Ms. Adelina Kamal, Executive Director of the ASEAN Coordination Centre for Humanitarian Assistance (AHA Centre). She added, "Having a data intelligence system will allow the AHA Centre to quickly analyze data and transform it to sharp, accurate, and reliable information, increasing scale and solidarity for One ASEAN One Response." The ASEAN Workshop on Disaster Reporting and Big Data for Disaster Management held in Jakarta, Indonesia, March 18-19, organized by the AHA Centre, was attended by about one hundred participants, including 30 representatives from the association's national disaster management organizations. AHA Centre is responsible for coordinating humanitarian response and risk reduction for ASEAN's ten member states.

The contest between China and America, the world's two superpowers, has many dimensions, from skirmishes over steel quotas to squabbles over student visas. Both countries are investing large sums in militarised artificial intelligence (ai), from autonomous robots to software that gives generals rapid tactical advice in the heat of battle. China frets that America has an edge thanks to the breakthroughs of Western companies, such as their successes in sophisticated strategy games. America fears that China's autocrats have free access to copious data and can enlist local tech firms on national service. Neither side wants to fall behind.