Cyber-attack Automated Unconventional Sensor Environment (CAUSE), applies AI/ML-based models to develop novel, automated methods for event-based detection and prediction of cyber-attacks significantly earlier than existing approaches. Forecasting cyber-attack events with actionable details advances the state-of-the-art by enabling threat-specific cyber incident response and defense measures; Creation of Operationally Realistic 3D Environment (CORE3D), uses machine learning and deep learning techniques to develop methods for the construction of a fully automated high fidelity 3D model of the world using remote sensing data; Deep Intermodal Video Analytics (DIVA), leverages machine learning techniques to develop robust automatic activity detection in streaming video across multiple cameras; Finding Engineering-Linked Indicators (FELIX), uses AI for detection of engineering signatures across multiple biological organisms. The goal is to distinguish natural organisms from those that have been engineered; Functional Map of the World Challenge, developed algorithms that would quickly and accurately classify 63 classes of buildings and regions in satellite imagery. All the top participants used various forms of deep learning; Functional Genomic and Computational Assessment of Threats (Fun GCAT), develops AI/ML-based approaches to learn and classify genetic (e.g., DNA) sequence data by genetic taxonomy, sequence function, and threat potential; Mercury Challenge, asked challenge participants to make use of AI/ML approaches to forecast a variety of political events in the Middle East and North Africa region, such as non-violent civil unrest and military activity; Machine Intelligence from Cortical Networks (MICrONS), aims to revolutionize machine learning by reverse-engineering the algorithms of the brain. The program is expressly designed as a dialogue between data science and neuroscience; Machine Translation for English Retrieval of Information in Any Language (MATERIAL), develops machine learning methods to identify foreign language information from speech and text relevant to English queries, and providing evidence of relevance of the retrieved information in English in a meaningful way.

Microsoft is extending its global student competition program Imagine Cup to younger students with new Imagine Cup Junior competition. The new competition will target secondary school students between the ages of thirteen to eighteen globally. Microsoft says Imagine Cup Junior competition will help students learn about technology and build 21st century skills like communication, collaboration, critical thinking and creativity. The first edition will focus on Artificial Intelligence and introducing the students to Microsoft's AI for Good initiatives. Microsoft VP Education, Anthony Salcito says "we believe the future is in the hands of students around the world. Who are increasingly driven by a sense of purpose and want to have a positive impact on the world and their communities".

WASHINGTON – Confronted by persistent questions about his military action in the Middle East, President Donald Trump and his top officials offered a string of fresh explanations Friday, with Trump now contending Iranian militants had planned major attacks on four U.S. embassies. Just hours earlier, Secretary of State Mike Pompeo had said the U.S. didn't know when or where attacks might occur. Trump and other officials insisted anew that Iranian Gen. Qassem Soleimani had posed an imminent threat to the U.S., but they rebuffed repeated attempts to explain what they meant by "imminent." Trump, meanwhile, announced additional sanctions against Iran, which he had promised after a barrage of missiles fired by the Islamic State against American bases in Iraq earlier this week. Those Iranian missiles, which caused no casualties, were prompted by the U.S. drone strike that killed Gen. Qassem Soleimani last week in Baghdad.

"But as times change and we get to a place where we can deliver up on what I believe and the president believes is our right structure, with fewer resources dedicated to that mission, we will do so," he added. Mr. Pompeo and Treasury Secretary Steven T. Mnuchin also announced new sanctions on Iranian officials and on a few companies, including two in China, involved in the production and export of Iranian steel and other metals. The Trump administration had already imposed major sanctions on Iran's metals industry after Mr. Trump's withdrawal in 2018 from a landmark nuclear agreement with Iran, so analysts said the new sanctions would have little additional impact. Iraqi lawmakers voted on Sunday to expel United States forces after the American drone strike that killed 10 people in a two-car convoy -- Maj. The prime minister has not signed the bill yet, but had been criticizing the American troop presence in Iraq since a series of recent actions by the United States military.

Recently, a researcher showed that he could create Deepfake text with artificial intelligence that is so real that US government officials did not know it was computer-generated, and accepted it as legitimate public comment. He then did a Turing Test to see if humans trained on spotting natural language processing could tell the difference between bot and human text. They were right about 50% of the time – essentially as good as flipping a coin. While reading the academic paper, I thought to myself, "Could machine learning to do the same for international development?" We have so much nuance, arcane language, and peculiarities, I didn't think it was possible.

Despite huge advancements and progress in the world of global health over the past decades, many middle and low-income countries are still falling behind, unable to reach their sustainable development goals. This, in turn, is creating an urgency to prioritize wellbeing, and AI holds enormous promise in transforming the provision of healthcare in resource strained environments. The Artificial Intelligence in Global Health report, funded by the USAID's Center for Innovation and Impact, Rockefeller Foundation, and the Bill & Melinda Gates Foundation, outlined 27 cases of AI in global healthcare, and the massive potential it holds for drastically improving health in LEDC's. The use of AI was split into four key areas - population health, patient and front line health worker virtual assistants, and physician clinical decision support. Not only does the report provide solutions that could improve the access, quality, and effectiveness of global healthcare, but it also takes into account the current maturity of AI systems and the feasibility of these solutions.

WASHINGTON – U.S. President Donald Trump on Thursday said he supported expanding the North American Treaty Organization to include Middle Eastern nations, as the United States seeks to limit its troop footprint globally. "I think that NATO should be expanded and we should include the Middle East, absolutely," Trump told reporters at the White House. Trump proposed increased NATO involvement in the Middle East on Wednesday, when he addressed the Iranian strikes against U.S. troops in Iraq, carried out in retaliation for a U.S. drone strike that killed a top Iranian military commander. The military leader, Qassem Soleimani, played a major role in the fight against Islamic State militants in the region. Trump said Islamic State presented an international problem that other countries should help address. "We can come home, largely come home and use NATO," Trump said.

WASHINGTON – The U.S. House of Representatives voted Thursday to limit President Donald Trump's authority to strike Iran, a mostly symbolic move Democrats say defends Congress's constitutional powers but Republicans say endangers national security. The resolution was adopted on a 224-194 vote, as tensions in the Middle East remain high after a U.S. drone strike killed Iranian general Qassem Soleimani. Iran retaliated with missiles striking an Iraqi base used by U.S. troops late Tuesday, without casualties, leaving uncertainty about future hostilities. With Thursday's resolution, the House is saying that Congress should be consulted before the conflict with Iran escalates. The Senate now can either consider the House-passed measure or move forward with a different version introduced by Sen. Tim Kaine, a Democrat from Virginia.

In this research, an extensive literature review was performed on the recent developments of the ambulance routing problem (ARP) and ambulance location problem (ALP). Both are respective modifications of the vehicle routing problem (VRP) and maximum covering problem (MCP), with modifications to objective functions and constraints. Although alike, a key distinction is emergency service systems (EMS) are considered critical and the optimization of these has become all the more important as a result. Similar to their parent problems, these are NP-hard and must resort to approximations if the space size is too large. Much of the current work has simply been on modifying existing systems through simulation to achieve a more acceptable result. There has been attempts towards using meta-heuristics, though practical experimentation is lacking when compared to VRP or MCP. The contributions of this work are a comprehensive survey of current methodologies, summarized models, and suggested future improvements.

The entropy models have been recently adopted in many studies to evaluate the distribution of the shear stress in circular channels. However, the uncertainty in their predictions and their reliability remains an open question. We present a novel method to evaluate the uncertainty of four popular entropy models, including Shannon, Shannon-Power Low (PL), Tsallis, and Renyi, in shear stress estimation in circular channels. The Bayesian Monte-Carlo (BMC) uncertainty method is simplified considering a 95% Confidence Bound (CB). We developed a new statistic index called as FREEopt-based OCB (FOCB) using the statistical indices Forecasting Range of Error Estimation (FREE) and the percentage of observed data in the CB (Nin), which integrates their combined effect. The Shannon and Shannon PL entropies had close values of the FOCB equal to 8.781 and 9.808, respectively, had the highest certainty in the calculation of shear stress values in circular channels followed by traditional uniform flow shear stress and Tsallis models with close values of 14.491 and 14.895, respectively. However, Renyi entropy with much higher values of FOCB equal to 57.726 has less certainty in the estimation of shear stress than other models. Using the presented results in this study, the amount of confidence in entropy methods in the calculation of shear stress to design and implement different types of open channels and their stability is determined.