Technology has been a major disruptor in the way banking was done just a few years ago. While the pandemic accelerated the adoption of technology across industries and sectors, our dependence on these advancements has been magnified to a great extent. For example, the recent Annual Report 2020-21 of Reserve Bank of India shows, the total digital transaction volume in 2020-21 stood at 4,371 crores, as against 3,412 crores in 2019-20, attesting to the resilience of the digital payment system in the face of the pandemic. As these technological advancements continue to disrupt the traditional ways of banking, we see a whole new spectrum of newer and faster banking solutions. Online deposits, mobile wallets, e-bill payments, and so on have fundamentally become a norm for how financial transactions are carried out nowadays.

US regulators filed a lawsuit Thursday to block the $40-billion merger of graphics chip star Nvidia with mobile chip technology powerhouse Arm Ltd, fearing it would undermine competition. The move comes as US President Joe Biden strives to ramp up domestic chip production to ease American industry's reliance on imports. "The proposed vertical deal would give one of the largest chip companies control over the computing technology and designs that rival firms rely on to develop their own competing chips," the Federal Trade Commission said in a release, calling chips "critical infrastructure." The world faces a global shortage of semiconductors, choking production of a wide range of products including automobiles, sending new and used car prices surging. The FTC echoed concerns expressed about the merger by regulators in the United Kingdom, who recently ordered an in-depth probe of the take-over.

China is pursuing what its leaders call a "first-mover advantage" in artificial intelligence (AI), facilitated by a state-backed plan to achieve breakthroughs by modeling human cognition. While not unique to China, the research warrants concern since it raises the bar on AI safety, leverages ongoing U.S. research, and exposes U.S. deficiencies in tracking foreign technological threats. The article begins with a review of the statutory basis for China's AI-brain program, examines related scholarship, and analyzes the supporting science. China's advantages are discussed along with the implications of this brain-inspired research. Recommendations to address our concerns are offered in conclusion. All claims are based on primary Chinese data.1 Analysts familiar with China's technical development programs understand that in China things happen by plan, and that China is not reticent about announcing these plans. On July 8, 2017 China's State Council released its "New Generation AI Development Plan"2 to advance Chinese artificial intelligence in three stages, at the end of which, in 2030, China would lead the world in AI theory, technology, and applications.3

In 2015, Elon Musk, Bill Gates and the late Stephen Hawking were rather incongruously nominated for the 2015 Luddite Award -- an honor bestowed by the Information Technology & Information Foundation for "The Worst of the Year's Worst Innovation Killers." Musk, Gates and Hawking -- along with many others -- had expressed growing concerns over the potential risks of naïve and irresponsible developments in artificial intelligence, commonly referred to as AI. In spite of their collective technological optimism, the speed of recent advances had them running scared. Six years on, the debate over the potential risks of AI, and how to ensure its ethical and responsible development and use, is fiercer than ever -- so much so that the White House has just committed to developing a "bill of rights" to guard against the inappropriate use of AI and similarly powerful tech. Yet, as with many technology trends, the challenges and opportunities AI presents are more complex than they may at first seem.

The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph -- nodes represent hits, while edges represent possible track segments -- and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called $\texttt{hls4ml}$, for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments.

Machine learning models are known to be susceptible to adversarial attacks which can cause misclassification by introducing small but well designed perturbations. In this paper, we consider a classical hypothesis testing problem in order to develop fundamental insight into defending against such adversarial perturbations. We interpret an adversarial perturbation as a nuisance parameter, and propose a defense based on applying the generalized likelihood ratio test (GLRT) to the resulting composite hypothesis testing problem, jointly estimating the class of interest and the adversarial perturbation. While the GLRT approach is applicable to general multi-class hypothesis testing, we first evaluate it for binary hypothesis testing in white Gaussian noise under $\ell_{\infty}$ norm-bounded adversarial perturbations, for which a known minimax defense optimizing for the worst-case attack provides a benchmark. We derive the worst-case attack for the GLRT defense, and show that its asymptotic performance (as the dimension of the data increases) approaches that of the minimax defense. For non-asymptotic regimes, we show via simulations that the GLRT defense is competitive with the minimax approach under the worst-case attack, while yielding a better robustness-accuracy tradeoff under weaker attacks. We also illustrate the GLRT approach for a multi-class hypothesis testing problem, for which a minimax strategy is not known, evaluating its performance under both noise-agnostic and noise-aware adversarial settings, by providing a method to find optimal noise-aware attacks, and heuristics to find noise-agnostic attacks that are close to optimal in the high SNR regime.

Benchmark datasets play a central role in the organization of machine learning research. They coordinate researchers around shared research problems and serve as a measure of progress towards shared goals. Despite the foundational role of benchmarking practices in this field, relatively little attention has been paid to the dynamics of benchmark dataset use and reuse, within or across machine learning subcommunities. In this paper, we dig into these dynamics. We study how dataset usage patterns differ across machine learning subcommunities and across time from 2015-2020. We find increasing concentration on fewer and fewer datasets within task communities, significant adoption of datasets from other tasks, and concentration across the field on datasets that have been introduced by researchers situated within a small number of elite institutions. Our results have implications for scientific evaluation, AI ethics, and equity/access within the field.

Sparse events, such as malign attacks in real-time network traffic, have caused big organisations an immense hike in revenue loss. This is due to the excessive growth of the network and its exposure to a plethora of people. The standard methods used to detect intrusions are not promising and have significant failure to identify new malware. Moreover, the challenges in handling high volume data with sparsity, high false positives, fewer detection rates in minor class, training time and feature engineering of the dimensionality of data has promoted deep learning to take over the task with less time and great results. The existing system needs improvement in solving real-time network traffic issues along with feature engineering. Our proposed work overcomes these challenges by giving promising results using deep-stacked autoencoders in two stages. The two-stage deep learning combines with shallow learning using the random forest for classification in the second stage. This made the model get well with the latest Canadian Institute for Cybersecurity - Intrusion Detection System 2017 (CICIDS-2017) dataset. Zero false positives with admirable detection accuracy were achieved.

A strong cyber defense system should be able to detect, monitor, and promptly leverage defence mechanisms to the cyber threats including evolving and intelligent attacks Hou et al. [2020], Brundage et al. [2018], Jang-Jaccard and Nepal [2014], Camp et al. [2019]. However, traditional defensive techniques cannot avoid the novel and evolving attacks which can leverage AI technology to plan and launch various attacks. AI-powered attacks can be categorized based on AI-aided and AI-embedded attacks. AI-aided attacks are those that leverage AI to launch the attacks effectively. In this type, the intelligent attackers use AI techniques. However, in AI-embedded attacks, the threats are weaponized by AI themselves such as Deep locker Stoecklin [2018] while in the AI-aided attacks, the attackers could launch various AI-based techniques to detect and recognize the target network, vulnerabilities, and valuable targets Kaloudi and Li [2020]. In fact, they utilize various AI techniques as a tool for various purposes. In Kaloudi and Li [2020], the authors investigated the AI-powered cyber attacks and mapped them onto a proposed framework with new threats including the classification of several aspects of threats that use AI during the cyber-attack life cycle.

Wikidata is a frequently updated, community-driven, and multilingual knowledge graph. Hence, Wikidata is an attractive basis for Entity Linking, which is evident by the recent increase in published papers. This survey focuses on four subjects: (1) Which Wikidata Entity Linking datasets exist, how widely used are they and how are they constructed? (2) Do the characteristics of Wikidata matter for the design of Entity Linking datasets and if so, how? (3) How do current Entity Linking approaches exploit the specific characteristics of Wikidata? (4) Which Wikidata characteristics are unexploited by existing Entity Linking approaches? This survey reveals that current Wikidata-specific Entity Linking datasets do not differ in their annotation scheme from schemes for other knowledge graphs like DBpedia. Thus, the potential for multilingual and time-dependent datasets, naturally suited for Wikidata, is not lifted. Furthermore, we show that most Entity Linking approaches use Wikidata in the same way as any other knowledge graph missing the chance to leverage Wikidata-specific characteristics to increase quality. Almost all approaches employ specific properties like labels and sometimes descriptions but ignore characteristics such as the hyper-relational structure. Hence, there is still room for improvement, for example, by including hyper-relational graph embeddings or type information. Many approaches also include information from Wikipedia, which is easily combinable with Wikidata and provides valuable textual information, which Wikidata lacks.