Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Iran claimed responsibility Sunday for a missile barrage that struck near a sprawling U.S. consulate complex in the northern Iraqi city of Irbil, saying it was retaliation for an Israeli strike in Syria that killed two members of its Revolutionary Guard earlier this week. No injuries were reported in Sunday's attack on the city of Irbil, which marked a significant escalation between the U.S. and Iran. Hostility between the longtime foes has often played out in Iraq, whose government is allied with both countries.

Researchers at The University of Texas at Dallas have used automated machine learning in a new way to forecast state violence in Africa, and they expect the technology to have even wider predictive applications. Dr. Vito D'Orazio, associate professor of political science in the School of Economic, Political and Policy Sciences, and his team created the dynamic forecasting model as part of a competition sponsored by the Violence Early-Warning System (ViEWS) project at Uppsala University's Department of Peace and Conflict Research. The research was subsequently published online Jan. 15 in the journal International Interactions. The ViEWS contest challenged competitors to forecast -- up to six months out -- the change in the number of fatalities in a country or region stemming from state-based violence, which is armed conflict in which at least one party is a government. Forecasts from the UT Dallas team were so accurate on the subnational level -- consisting of randomly gridded map areas that don't take countries' borders into account -- that they won that part of the competition for predictive accuracy and split the win for originality.

Consider an alternative history for the war in Ukraine. Intrepid Ukrainian Army units mount an effort to pick off Russian supply convoys. But rather than rely on sporadic air cover, the Russian convoys travel under a blanket of cheap drones. The armed drones carry relatively simple artificial intelligence (AI) that can identify human forms and target them with missiles. The tactic claims many innocent civilians, as the drones kill nearly anyone close enough to the convoys to threaten them with anti-tank weapons.

We present a new machine learning based bed-occupancy detection system that uses the accelerometer signal captured by a bed-attached consumer smartphone. Automatic bed-occupancy detection is necessary for automatic long-term cough monitoring, since the time which the monitored patient occupies the bed is required to accurately calculate a cough rate. Accelerometer measurements are more cost effective and less intrusive than alternatives such as video monitoring or pressure sensors. A 249-hour dataset of manually-labelled acceleration signals gathered from seven patients undergoing treatment for tuberculosis (TB) was compiled for experimentation. These signals are characterised by brief activity bursts interspersed with long periods of little or no activity, even when the bed is occupied. To process them effectively, we propose an architecture consisting of three interconnected components. An occupancy-change detector locates instances at which bed occupancy is likely to have changed, an occupancy-interval detector classifies periods between detected occupancy changes and an occupancy-state detector corrects falsely-identified occupancy changes. Using long short-term memory (LSTM) networks, this architecture was demonstrated to achieve an AUC of 0.94. When integrated into a complete cough monitoring system, the daily cough rate of a patient undergoing TB treatment was determined over a period of 14 days. As the colony forming unit (CFU) counts decreased and the time to positivity (TPP) increased, the measured cough rate decreased, indicating effective TB treatment. This provides a first indication that automatic cough monitoring based on bed-mounted accelerometer measurements may present a non-invasive, non-intrusive and cost-effective means of monitoring long-term recovery of TB patients.

Nvidia is a technology company that designs graphics processing units (GPUs) for the gaming and professional markets, as well as system on chip units (SoCs) for the mobile computing and automotive market. With the company's intelligent video analytics (IVA), AI-powered inventory management, and customer & store analytics, organizations are able to deliver better customer experiences. NVIDIA accelerated data science solution with RAPIDS enables enterprises to tap into GPU-accelerated machine learning (ML) with faster model iteration, better prediction accuracy, and lowest data science total cost of ownership (TCO). With its subsidiaries and strong distribution network, the company has its presence across North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa. The major subsidiaries of the company include PGI Compilers & Tools, Icera, Uli Electronics Inc, ModViz, Inc., and others.

Change.org is searching for a Data Engineer to help in the development of our next-generation ML platform to support all of our internal machine learning operational needs at large scale. We're a social impact business (a public benefit company), and the largest tech platform focused on civic action in the world with 80m monthly users, 50,000 campaigns launched on the site every month, 150 staff, and a new revenue model that has grown by 500% in 2 years. We're growing quickly, and our users win campaigns for change once every hour. From strengthening hate crime legislation in South Africa; fighting corruption in Indonesia, Italy, and Brazil; to fighting violence against women in India. We are looking for a Data Engineer who has a passion to learn and build ML workflow orchestration & distributed data processing at scale.

Global Artificial Intelligence market report provides an in-depth overview of Product Specification, technology, product type and production analysis considering major factors such as Revenue, Cost, Gross and Gross Margin. The current trends will determine what options will be for strategical investment. The Artificial Intelligence Market report also provides an in-depth survey of key players in the market which is based on the various objectives of an organization such as profiling, the product outline, the quantity of production, required raw material, and the financial health of the organization. This Artificial Intelligence market Report cover strategic profiling of key players in the market, comprehensively analyzing their core competencies, and drawing a competitive landscape for the market. At present, the market is developing its presence and some of the Global Artificial Intelligence Market key players Involved in the study are Advanced Micro Devices, Inc., Aicure, LLC, Arm, Ltd., Atomwise, Inc., AYASDI AI LLC, Baidu Inc., ClariFI, Inc., Cyrcadia Health, Enlitic, Inc., H2O.ai, Research study is to define Market Sizes of various segments & countries in previous years and to forecast the values to the next 5-8 years.

Ukraine's most sophisticated attack drone is about as stealthy as a crop duster: slow, low-flying and completely defenseless. So when the Russian invasion began, many experts expected the few drones that the Ukrainian forces managed to get off the ground would be shot down in hours. But more than two weeks into the conflict, Ukraine's drones -- Turkish-made Bayraktar TB2 models that buzz along at about half the speed of a Cessna – are not only still flying; they also shoot guided missiles at Russian missile launchers, tanks and supply trains, according to Pentagon officials. The drones have become a sort of lumbering canary in the war's coal mine, a sign of the astonishing resiliency of the Ukrainian defense forces and the larger problems that the Russians have encountered. "The performance of the Russian military has been shocking," said David Deptula, a retired three-star Air Force general who planned the U.S. air campaigns in Afghanistan in 2001 and the Persian Gulf in 1991.

During Russia's war on Ukraine, video footage has circulated on the internet showing the Turkish combat drone Bayraktar TB2 successfully striking the Russian army. But as so often during heightened conflicts, it is hard to distinguish between factual events and misinformation – some videos of the drone attacks have already been exposed as the latter. Given the chaotic events on the ground, it is almost impossible to assess how often and how successfully Ukraine has utilised its Turkish drones so far, Mauro Gilli, senior researcher in military technology and international security at ETH Zurich, told Al Jazeera. "There have been some video footages allegedly showing the employment of the TB2. Of course, information at this point is fragmented, and it needs to be taken with caution. "We do know that Ukraine bought some TB2 over the past years and that Turkey and Ukraine signed an agreement for the production within Ukrainian borders of the TB2 – but, as far as I know, production had not started yet.

Corruption is not only the issue of third-world countries but also is equally problematic for the developed ones. In this context, white-collar crimes like corruption, and other financial frauds make the countries' economies fragile and pathetic. In order to control this, there are some institutions, like Transparency international. However, the question is whether this institution has defeated corruption at the global level or not. Moreover, is this the only way to control corruption?