Suppose aliens existed, and imagine that some of them had been watching our planet for its entire four and a half billion years. What would they have seen? Over most of that vast timespan, Earth's appearance altered slowly and gradually. Continents drifted; ice cover waxed and waned; successive species emerged, evolved, with many of them becoming extinct. But in just a tiny sliver of Earth's history--the last hundred centuries--the patterns of vegetation altered much faster than before. This signaled the start of agriculture--and later urbanization. The changes accelerated as the human population increased. Then came even faster changes.

Given the increasing complexity of threats in smart cities, the changing environment, and the weakness of traditional security systems, which in most cases fail to detect serious threats such as zero-day attacks, the need for alternative more active and more effective security methods keeps increasing. Such approaches are the adoption of intelligent solutions to prevent, detect and deal with threats or anomalies under the conditions and the operating parameters of the infrastructure in question. This research paper introduces the development of an intelligent Threat Defense system, employing Blockchain Federated Learning, which seeks to fully upgrade the way passive intelligent systems operate, aiming at implementing an Advanced Adaptive Cooperative Learning (AACL) mechanism for smart cities networks. The AACL is based on the most advanced methods of computational intelligence while ensuring privacy and anonymity for participants and stakeholders. The proposed framework combines Federated Learning for the distributed and continuously validated learning of the tracing algorithms. Learning is achieved through encrypted smart contracts within the blockchain technology, for unambiguous validation and control of the process. The aim of the proposed Framework is to intelligently classify smart cities networks traffic derived from Industrial IoT (IIoT) by Deep Content Inspection (DCI) methods, in order to identify anomalies that are usually due to Advanced Persistent Threat (APT) attacks.

Analyzing multi-source data, which are multiple views of data on the same subjects, has become increasingly common in molecular biomedical research. Recent methods have sought to uncover underlying structure and relationships within and/or between the data sources, and other methods have sought to build a predictive model for an outcome using all sources. However, existing methods that do both are presently limited because they either (1) only consider data structure shared by all datasets while ignoring structures unique to each source, or (2) they extract underlying structures first without consideration to the outcome. We propose a method called supervised joint and individual variation explained (sJIVE) that can simultaneously (1) identify shared (joint) and source-specific (individual) underlying structure and (2) build a linear prediction model for an outcome using these structures. These two components are weighted to compromise between explaining variation in the multi-source data and in the outcome. Simulations show sJIVE to outperform existing methods when large amounts of noise are present in the multi-source data. An application to data from the COPDGene study reveals gene expression and proteomic patterns that are predictive of lung function. Functions to perform sJIVE are included in the R.JIVE package, available online at http://github.com/lockEF/r.jive .

Current work in explainable reinforcement learning generally produces policies in the form of a decision tree over the state space. Such policies can be used for formal safety verification, agent behavior prediction, and manual inspection of important features. However, existing approaches fit a decision tree after training or use a custom learning procedure which is not compatible with new learning techniques, such as those which use neural networks. To address this limitation, we propose a novel Markov Decision Process (MDP) type for learning decision tree policies: Iterative Bounding MDPs (IBMDPs). An IBMDP is constructed around a base MDP so each IBMDP policy is guaranteed to correspond to a decision tree policy for the base MDP when using a method-agnostic masking procedure. Because of this decision tree equivalence, any function approximator can be used during training, including a neural network, while yielding a decision tree policy for the base MDP. We present the required masking procedure as well as a modified value update step which allows IBMDPs to be solved using existing algorithms. We apply this procedure to produce IBMDP variants of recent reinforcement learning methods. We empirically show the benefits of our approach by solving IBMDPs to produce decision tree policies for the base MDPs.

We study the problem of combinatorial multi-armed bandits (CMAB) under strategic manipulations of rewards, where each arm can modify the emitted reward signals for its own interest. Our setting elaborates a more realistic model of adaptive arms that imposes relaxed assumptions compared to adversarial corruptions and adversarial attacks. Algorithms designed under strategic arms gain robustness in real applications while avoiding being overcautious and hampering the performance. We bridge the gap between strategic manipulations and adversarial attacks by investigating the optimal colluding strategy among arms under the MAB problem. We then propose a strategic variant of the combinatorial UCB algorithm, which has a regret of at most $O(m\log T + m B_{max})$ under strategic manipulations, where $T$ is the time horizon, $m$ is the number of arms, and $B_{max}$ is the maximum budget. We further provide lower bounds on the strategic budgets for attackers to incur certain regret of the bandit algorithm. Extensive experiments corroborate our theoretical findings on robustness and regret bounds, in a variety of regimes of manipulation budgets.

Demand for some of Nvidia's chips has been so hot that it has outpaced the company's ability to increase production, adding to chip-supply shortages riling the semiconductor industry. Nvidia's newest graphics cards were a holiday sensation, Chief Financial Officer Colette Kress said during an earnings call. She added that some inventories are likely to remain low in the first quarter even as Nvidia increases supply. "Throughout our supply chain, stronger demand globally has limited the availability of capacity and components," Ms. Kress said. President Biden on Wednesday signed an executive order directing a broad review of supply chains for semiconductors and other critical materials.

GCHQ's director has said artificial intelligence software could have a profound impact on the way it operates, from spotting otherwise missed clues to thwart terror plots to better identifying the sources of fake news and computer viruses. Jeremy Fleming's remarks came as the spy agency prepared to publish a rare paper on Thursday defending its use of machine-learning technology to placate critics concerned about its bulk surveillance activities. "AI, like so many technologies, offers great promise for society, prosperity and security. Its impact on GCHQ is equally profound," he said. "While this unprecedented technological evolution comes with great opportunity, it also poses significant ethical challenges for all of society, including GCHQ." AI is considered controversial because it relies on computer algorithms to make decisions based on patterns found in data.

Thousands of incarcerated people in Arizona have been kept behind bars by a software glitch, according to a report by KJZZ broadcast Monday. Anonymous whistleblowers from the Arizona Department of Corrections whistleblowers leaked details about the situation to the Phoenix NPR member station. Arizona has the fifth highest imprisonment rate in the country, and its incarcerated people are mostly nonviolent drug offenders. In 2019, the state Legislature passed a law aiming to change that by providing a way for nonviolent criminals to secure early release. For every seven days spent in a GED or substance abuse treatment program, an incarcerated person can shave three days off a sentence.

Scientists are developing a'digital twin' of Earth to predict future events caused by climate change to help world leaders better prepare. Developed by European scientists and ETH Zurich, researchers say the machine learning algorithm will develop and test simulations leading up to 2050. The virtual model will also predict all processes'as realistically as possible,' including the influence of humans on water, food and energy management and the processes in the physical Earth system. Scientists say the digital twin will provide an accurate representation of the past, present and future changes of our real world. The virtual planet is part of a ten-year program by the European Union called Destination Earth that is designed to push Europe to achieve net carbon neutrality by 2050.

In a February 19 speech at the Munich Security Conference, delivered virtually from the White House, President Joe Biden declared, "We must shape the rules that will govern the advance of technologies and the norms of behavior in cyberspace, artificial intelligence, biotechnology, so they are used to lift people up, not used to pin them down." A few weeks earlier, during an address at the State Department's Truman Building, the president said, "Diplomacy is back at the center of our foreign policy." The Trump administration's undermining of years of work on internet diplomacy makes technology an ever more vital (and challenging) element of renewed US engagement abroad. Digital issues are no longer extricable from "traditional" foreign policy issues across trade, human rights, and security. And as the new White House starts to navigate these waters, one idea in particular has become a sort of bumper sticker for an overarching strategy: Unite democracies on technology. As the Chinese and Russian governments become more technologically assertive and undermine human rights, and as democracies grapple with how to appropriately implement rules and regulations for the likes of artificial intelligence systems, this work is essential.