Fox News Flash top headlines for Dec. 7 are here. Check out what's clicking on Foxnews.com The U.S. military believes the unarmed drone that went missing over the Libyan capital last month was actually shot down by Russian air defenses. The U.S. Africa Command is demanding the return of the aircraft's wreckage, which had been part of an operation conducted in Libya to assess the area's security and monitor for violent extremist activity. The command didn't give a reason for the drone loss after the Nov. 21 incident, but they had been investigating, Reuters reported.

Evolving technology, best practice and landmark evidence in glaucoma care were reviewed by an international expert faculty in session presentations and debates during the 11th Moorfields International Glaucoma Symposium 2019. The authors were meeting chairs and provide an overview of symposium proceedings. Hans Lemij, Rotterdam Eye Hospital, the Netherlands, discussed glaucoma optical coherence tomography (OCT) imaging and automated segmentation issues, noting several common image artefacts. Paul Foster highlighted research by the UK Biobank Eye and Vision Consortium related to cognitive function and the expanding use of OCT imaging in dementia and neurodegeneration research. Findings show that a thinner retinal nerve fibre layer (RNFL) is associated with worse cognitive function in individuals without known neurodegenerative disease, as well as a greater likelihood of future cognitive decline [1]. The Rotterdam Study also revealed an association of retinal neurodegeneration on OCT with an increased risk of dementia, including Alzheimer's disease [2].

Our NodeXL #ddj mapping from November 25 to December 1 finds The New York Times profiling Bellingcat and its use of OSINT techniques; the International Consortium of Investigative Journalists and Stanford University collaborating to employ artificial intelligence to solve a journalistic problem; and the Science Communication Lab creating a beautiful interactive scientific poster to explore the world's oceans. Open source journalism might just be the best antidote to spin: the transparency of its authors showing their work during each step of the investigative process helps earn readers' trust. The New York Times profiles Bellingcat, an investigative news site that uses open source techniques. The collaborative Implant Files investigation exposed the lax regulation of the $400 billion medical device industry worldwide. But when the International Consortium of Investigative Journalists wanted to know if women suffered disproportionately from faulty medical devices, it hit a data roadblock. The journalists then turned to artificial intelligence to help their reporting.

A large share of countries around the world are now using Chinese AI surveillance technology, including facial recognition technology, in full or in part. This is according to a report by Carnegie Endowment for International Peace. Many countries are combining Chinese tech with U.S.-made surveillance tech, among them the U.S. and China themselves, but also India, Australia, Brazil and several European countries. Many countries in Latin America, South-East Asia, Africa and the Middle East are relying on Chinese technology alone after participating in the Belt and Road initiative, as is Japan, the only developed country to do so. China is not only a prominent user of AI-powered surveillance and facial recognition but also a big producer and exporter of the technology.

Consistent in their quest to spearhead innovative, groundbreaking events, Eventus International is hosting the first ever AI In Gaming 2020 summit in Dubai on 26 and 27 February at Crowne Plaza Dubai. Joining a lineup of top international industry experts, is Andrew Pearson, founder and MD of Intelligencia Limited, who will be speaking at AI In Gaming 2020. Andrew Pearson was born in Pakistan, grew up in Singapore and was educated in England and America. With a degree in psychology from UCLA, Pearson has had a varied career in IT, marketing, mobile technology, social media and entertainment.In 2011, Pearson relocated to Hong Kong to open Qualex Asia Limited, bringing its parent company's experience into the ASEAN region. Pearson is the Managing Director of Intelligencia Limited, a leading implementer of BI, CI, data warehousing, data modeling, predictive analytics, data visualisation, digital marketing, mobile, social media and cloud solutions for the gaming, finance, telco, hospitality and retail industries.

Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power gri ds, and transportation systems. Similar to other information systems, a significant threat to indust rial control systems is the attack from cyberspace--the offensive maneuvers launched by "anon ymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information. Owing to the importance of industrial control systems, and the possibly devastating consequences of being attacked, significant endeavors have been attempted to secure industrial control systems from cyberattacks. Among them are intrusio n detection systems that serve as the first line of defense by monitoring and reporting potenti ally malicious activities. Classical machine-learning-based intrusion detection methods usua lly generate prediction models by learning modest-sized training samples all at once. Such approac h is not always applicable to industrial control systems, as industrial control systems must proces s continuous control commands with limited computational resources in a nonstop way. To satisf y such requirements, we propose using online learning to learn prediction models from the control ling data stream. W e introduce several state-of-the-art online learning algorithms categorical ly, and illustrate their efficacies on two typically used testbeds--power system and gas pipeline. Fur ther, we explore a new cost-sensitive online learning algorithm to solve the class-imbalance pro blem that is pervasive in industrial intrusion detection systems. Our experimental results ind icate that the proposed algorithm can achieve an overall improvement in the detection rate of cybe rattacks in industrial control systems. Modern industrial control systems are microprocessor-equ ipped devices and associated communication networks used to monitor and operate physica l equipment in the industrial environment.

Taiwan's consumer electronics providers have begun exploring new market segments in the face of slowing global economic growth, decreased replacement demand, persistent concerns over the U.S.-China trade war, and stiff competition from China. The 2019 edition of IFA, one of the world's largest consumer electronics exhibitions, which was held in Berlin in September, provided a glimpse into the progress some of these companies are making. Acer, known primarily for its consumer notebooks and tablets, showed off a few new additions to its line of gaming-oriented computing products designed to please some of the world's most demanding gamers. The company's Predator brand, launched in 2016, includes gaming notebooks, desktops, and displays that boast ultra-low response times and ultra-high resolution. They also contain extra-efficient cooling technology to facilitate long hours of gaming despite the energy-gobbling graphics.

Every year, our team of futurists, analysts, and consultants at Frost & Sullivan's Transformational Healthcare Group comes together to brainstorm and predict the themes, technologies, and global forces that will define the next 12 to 18 months for the healthcare industry. We also retrospect how we did each year, and each year we are becoming more accurate in the predictions we make. For the 2019 predictions that were released in November 2018, six out of eight predictions realized as anticipated, while the two remaining predictions have not panned out exactly the way we thought. The new vision for healthcare for 2020 and beyond will not just focus on access, quality, and affordability but also on predictive, preventive, and outcome-based care models promoting social and financial inclusion. As we are on the verge of entering a new decade of change globally, 2020 will be a reality check for long-pending national healthcare policies and regulatory reforms that must reinvigorate future strategies.

Convolutional neural network (CNN)-based feature learning has become state of the art, since given sufficient training data, CNN can significantly outperform traditional methods for various classification tasks. However, feature learning becomes more difficult if some training labels are noisy. With traditional regularization techniques, CNN often overfits to the noisy training labels, resulting in sub-par classification performance. In this paper, we propose a robust binary classifier, based on CNNs, to learn deep metric functions, which are then used to construct an optimal underlying graph structure used to clean noisy labels via graph Laplacian regularization (GLR). GLR is posed as a convex maximum a posteriori (MAP) problem solved via convex quadratic programming (QP). To penalize samples around the decision boundary, we propose two regularized loss functions for semi-supervised learning. The binary classification experiments on three datasets, varying in number and type of features, demonstrate that given a noisy training dataset, our proposed networks outperform several state-of-the-art classifiers, including label-noise robust support vector machine, CNNs with three different robust loss functions, model-based GLR, and dynamic graph CNN classifiers.

Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important. In this paper, we propose Bayesian quantized networks (BQNs), quantized neural networks (QNNs) for which we learn a posterior distribution over their discrete parameters. We provide a set of efficient algorithms for learning and prediction in BQNs without the need to sample from their parameters or activations, which not only allows for differentiable learning in QNNs, but also reduces the variance in gradients. We demonstrate BQNs achieve both lower predictive errors and better-calibrated uncertainties than E-QNN (with less than 20% of the negative log-likelihood). A Bayesian approach to deep learning considers the network's parameters to be random variables and seeks to infer their posterior distribution given the training data. Models trained this way, called Bayesian neural networks (BNNs) (Wang & Y eung, 2016), in principle have well-calibrated uncertainties when they make predictions, which is important in scenarios such as active learning and reinforcement learning (Gal, 2016). Furthermore, the posterior distribution over the model parameters provides valuable information for evaluation and compression of neural networks. There are three main challenges in using BNNs: (1) Intractable posterior: Computing and storing the exact posterior distribution over the network weights is intractable due to the complexity and high-dimensionality of deep networks. These challenges are typically addressed either by making simplifying assumptions about the distributions of the parameters and activations, or by using sampling-based approaches, which are expensive and unreliable (likely to overestimate the uncertainties in predictions). Our goal is to propose a sampling-free method which uses probabilistic propagation to deterministically learn BNNs. A seemingly unrelated area of deep learning research is that of quantized neural networks (QNNs), which offer advantages of computational and memory efficiency compared to continuous-valued models.