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.

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.

The large thermal capacity of buildings enables heating, ventilating, and air-conditioning (HVAC) systems to be exploited as demand response (DR) resources. Optimal DR of HVAC units is challenging, particularly for multi-zone buildings, because this requires detailed physics-based models of zonal temperature variations for HVAC system operation and building thermal conditions. This paper proposes a new strategy for optimal DR of an HVAC system in a multi-zone building, based on supervised learning (SL). Artificial neural networks (ANNs) are trained with data obtained under normal building operating conditions. The ANNs are replicated using piecewise linear equations, which are explicitly integrated into an optimal scheduling problem for price-based DR. The optimization problem is solved for various electricity prices and building thermal conditions. The solutions are further used to train a deep neural network (DNN) to directly determine the optimal DR schedule, referred to here as supervised-learning-aided meta-prediction (SLAMP). Case studies are performed using three different methods: explicit ANN replication (EAR), SLAMP, and physics-based modeling. The case study results verify the effectiveness of the proposed SL-based strategy, in terms of both practical applicability and computational time, while also ensuring the thermal comfort of occupants and cost-effective operation of the HVAC system.

As neural networks have begun performing increasingly critical tasks for society, ranging from driving cars to identifying candidates for drug development, the value of their ability to perform uncertainty quantification (UQ) in their predictions has risen commensurately. Permanent dropout, a popular method for neural network UQ, involves injecting stochasticity into the inference phase of the model and creating many predictions for each of the test data. This shifts the computational and energy burden of deep neural networks from the training phase to the inference phase. Recent work has demonstrated near-lossless conversion of classical deep neural networks to their spiking counterparts. We use these results to demonstrate the feasibility of conducting the inference phase with permanent dropout on spiking neural networks, mitigating the technique's computational and energy burden, which is essential for its use at scale or on edge platforms. We demonstrate the proposed approach via the Nengo spiking neural simulator on a combination drug therapy dataset for cancer treatment, where UQ is critical. Our results indicate that the spiking approximation gives a predictive distribution practically indistinguishable from that given by the classical network.

Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks. Unfortunately, the high cost of generating strong adversarial examples makes standard adversarial training impractical on large-scale problems like ImageNet. We present an algorithm that eliminates the overhead cost of generating adversarial examples by recycling the gradient information computed when updating model parameters. Our "free" adversarial training algorithm achieves state-of-the-art robustness on CIFAR-10 and CIFAR-100 datasets at negligible additional cost compared to natural training, and can be 7 to 30 times faster than other strong adversarial training methods. Using a single workstation with 4 P100 GPUs and 2 days of runtime, we can train a robust model for the large-scale ImageNet classification task that maintains 40% accuracy against PGD attacks.

The cyberspace and the development of new technologies, especially intelligent systems using artificial intelligence, present enormous challenges to computer professionals, data scientists, managers and policy makers. There is a need to address professional responsibility, ethical, legal, societal, and policy issues. This paper presents problems and issues relevant to computer professionals and decision makers and suggests a curriculum for a course on ethics, law and policy. Such a course will create awareness of the ethics issues involved in building and using software and artificial intelligence.

This week, I interviewed Yuval Noah Harari, the author of three best-selling books about the history and future of our species, and Fei-Fei Li, one of the pioneers in the field of artificial intelligence. The event was hosted by the Stanford Center for Ethics and Society, the Stanford Institute for Human-Centered Artificial Intelligence, and the Stanford Humanities Center. A transcript of the event follows, and a video is posted below. Nicholas Thompson: Thank you, Stanford, for inviting us all here. I want this conversation to have three parts: First, lay out where we are; then talk about some of the choices we have to make now; and last, talk about some advice for all the wonderful people in the hall. Yuval, the last time we talked, you said many, many brilliant things, but one that stuck out was a line where you said, "We are not just in a technological crisis. We are in a philosophical crisis." So explain what you meant and explain how it ties to AI. Let's get going with a note of ...

Artificial intelligence can work wonders, but often it works in mysterious ways. Machine learning is based on the principle that a software program can analyze a huge set of data and fine-tune its algorithms to detect patterns and come up with solutions that humans may miss. That's how Google DeepMind's Alpha Go AI agent learned to play the ancient game of Go (and other games) well enough to beat expert players. But if programmers and users can't figure out how AI algorithms came up with their results, that black-box behavior can be a cause for concern. It may become impossible to judge whether AI agents have picked up unjustified biases or racial profiling from their data sets.

If you live in southwest Virginia, don't be surprised at the sight of a drone winging its way on another airborne delivery run over your neighborhood soon. Wing, the drone delivery service spun off from Alphabet's Google, hopes to start flights to homes and businesses in the Blacksburg and Christiansburg areas by the end of the year now that it has the blessings of the Federal Aviation Administration. The FAA announced earlier this week that it had approved Wing as the first air carrier certified for drone delivery. In receiving the certification, Wing beat Amazon to the punch despite all the attention that the online merchandise giant has drawn over its interest in deliveries by air. Both companies, along with others, have been racing to develop drones as a more cost-effective way of delivering small, high-value orders, like medicine.

Mobile and ubiquitous sensing of urban air quality (AQ) has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. A necessary and value-added step towards data-driven sustainable urban management is fine-granular AQ inference, which estimates grid-level pollutant concentrations at every instance of time using AQ data collected from fixed-location and mobile sensors. We present the Mobile Air Pollution Sensing (MAPS) framework, which consists of data preprocessing, urban feature extraction, and AQ inference. This is applied to a case study in Beijing (3,025 square km, 19 June - 16 July 2018), where PM2.5 concentrations measured by 28 fixed monitoring stations and 15 vehicles are fused to infer hourly PM2.5 concentrations in 3,025 1km-by-1km grids. Two machine learning structures, namely Deep Feature Spatial-Temporal Tree (DFeaST-Tree) and Deep Feature Spatial-Temporal Network (DFeaST-Net), are proposed to infer PM2.5 concentrations supported by 62 types of urban data that encompass geography, land use, traffic, public, and meteorology. This allows us to infer fine-granular PM2.5 concentrations based on sparse AQ measurements (less than 5% coverage) with good accuracy (SMAPE<15%, R-square>0.9), while accounting for the regional transport of air pollutants outside the study area. In-depth discussions are provided on the heterogeneity of fixed and mobile data sources, spatial coverage of mobile sensing, and importance of urban features for inferring PM2.5 concentrations.