WASHINGTON – The Russian Ministry of Defense said it formed a department dedicated to developing weapons that use artificial intelligence. The announcement was made Aug. 17 by Alexander Osadchu, the head of Russia's military's innovative development department, at the Army 2022 summit, according to the state-owned news agency TASS. "In order to intensify work on the use of artificial intelligence technologies in the interests of creating models of weapons for military and special equipment, a department for the development of artificial intelligence technologies has been created," he said in a statement. The program could serve to counter the Pentagon's top AI authority, the newly formed Chief Digital and Artificial Intelligence Office, which is tasked with expanding the use of AI throughout U.S. the military. The CDAO launched in February and reached full operational capability in June.

Tech trends have more future impacts on the lives of tech students than everyone else, which is why they need to know all the major ones. This article will expose you to the top tech trends and how they change our lives. So, if you are a tech student or are intrigued by technology, you should spend a few minutes checking out the trends we have listed below. Extended reality is a trend in technology that tries to mimic the natural world and even break beyond the limits of the real world. Extended reality has already found its way into our smart devices as augmented reality (AR).

Machine learning (ML) is one of the hottest and most lucrative tech trends. According to a survey on the state of AI conducted by McKinsey in 2021, 67 percent of companies that adopted AI-related technologies saw increases in revenue. Increased adoption of ML technology has given rise to some of the best machine learning startups, all of which are leading the digital transformation in the 21st Century. These machine learning startup companies are located around the globe, including San Francisco, Santa Clara, San Jose, San Mateo, Redwood City, and the rest of Silicon Valley, as well as places like London and Tel Aviv. This article will explore exciting startups in the private sector and public sector, looking at their innovative ideas, funding, and expected growth.

Quick Heal Technologies, a leader in the cybersecurity space, has collaborated with RevBits to address the advanced protection need for an on-premise infrastructure of government organisations. The partnership will help Quick Heal enhance its Seqrite product portfolio while enabling RevBits to expand its market presence in India. The association also aims to help government organizations that prefer to run their critical systems on-premise rather than in the cloud, thereby ensuring that they operate safely and seamlessly without the fear of cyberattacks. Dr. Sanjay Katkar, Joint MD & CTO, Quick Heal Technologies Ltd, said, "We are delighted to partner with a leading cybersecurity solution provider like RevBits, well-known for its unique three-phase detection method that can protect businesses even from the most sophisticated attacks. It also complements our existing suite of Seqrite security solutions and will help us further strengthen our Seqrite product portfolio to offer the best cybersecurity solutions to our customers. At Quick Heal, we strive to innovate ourselves in sync with the ever-evolving threat landscape and devise solutions that exactly fit this purpose. We will continue collaborating with more such players to augment our products and services, cementing a leadership presence in the market today, tomorrow and beyond."

Accenture Federal Services delivers a range of innovative, tech-enabled services for the U.S. Federal Government to address the complex, sensitive challenges of national security and intelligence missions. Refer a qualified candidate and earn up to $10K. Accenture Federal Services is seeking a data engineer who is excited about using their technical skills to have a positive impact on national security. We are a cadre of information management, data science, and technology experts dedicated to revolutionizing the way we "do data", while upholding proper protection, accessibility, and release mandates. The objective of our efforts is to accelerate the development and application of data science capabilities – including artificial intelligence and machine learning – to the customer's core missions.

Datasets are essential to apply AI algorithms to Cyber Physical System (CPS) Security. Due to scarcity of real CPS datasets, researchers elected to generate their own datasets using either real or virtualized testbeds. However, unlike other AI domains, a CPS is a complex system with many interfaces that determine its behavior. A dataset that comprises merely a collection of sensor measurements and network traffic may not be sufficient to develop resilient AI defensive or offensive agents. In this paper, we study the \emph{elements} of CPS security datasets required to capture the system behavior and interactions, and propose a dataset architecture that has the potential to enhance the performance of AI algorithms in securing cyber physical systems. The framework includes dataset elements, attack representation, and required dataset features. We compare existing datasets to the proposed architecture to identify the current limitations and discuss the future of CPS dataset generation using testbeds.

Attackers use malicious software, known as malware, to steal sensitive data, damage network infrastructure, and hold information for ransom. One of the top priorities for computer security tools is to detect malware and prevent or minimize its impact on both corporate and personal networks. Traditionally, signature-based methods have been used to detect files previously identified as malicious with near perfect precision, but potentially miss newer malware samples. With the advent of self-modifying malware and the rapid increase in novel threats, signature-based methods are insufficient on their own. By generalizing patterns of known benign/malicious training examples, machine learning (ML) exhibits the capability to quickly and accurately classify novel file samples in many research studies [19]. Moreover, ML-based malware research has made the transition from the subject of myriad research efforts to a current mainstay of commercial-off-the-shelf (COTS) malware detectors. Yet, few practical evaluations of COTS ML-based technologies have been conducted. Turning from the academic literature to market reports from commercial companies can provide (for a fee) useful information, specifically, end-user feedback, itemization of all technologies in the antivirus/endpoint detection and response marketplace [17], and even statistics showing the efficacy of the detectors on malware tests [4, 40].

Abstract--Real world data often exhibits a long-tailed and open-ended (i.e. with unseen classes) distribution. A practical recognition system must balance between majority (head) and minority (tail) classes, generalize across the distribution, and acknowledge novelty upon the instances of unseen classes (open classes). We define Open Long-Tailed Recognition++ (OLTR++) as learning from such naturally distributed data and optimizing for the classification accuracy over a balanced test set which includes both known and open classes. OLTR++ handles imbalanced classification, few-shot learning, open-set recognition, and active learning in one integrated algorithm, whereas existing classification approaches often focus only on one or two aspects and deliver poorly over the entire spectrum. The key challenges are: 1) how to share visual knowledge between head and tail classes, 2) how to reduce confusion between tail and open classes, and 3) how to actively explore open classes with learned knowledge. Our algorithm, OLTR++, maps images to a feature space such that visual concepts can relate to each other through a memory association mechanism and a learned metric (dynamic meta-embedding) that both respects the closed world classification of seen classes and acknowledges the novelty of open classes. Additionally, we propose an active learning scheme based on visual memory, which learns to recognize open classes in a data-efficient manner for future expansions. On three large-scale open long-tailed datasets we curated from ImageNet (object-centric), Places (scene-centric), and MS1M (face-centric) data, as well as three standard benchmarks (CIFAR-10-LT, CIFAR-100-LT, and iNaturalist-18), our approach, as a unified framework, consistently demonstrates competitive performance. Notably, our approach also shows strong potential for the active exploration of open classes and the fairness analysis of minority groups.

Unsupervised summarization methods have achieved remarkable results by incorporating representations from pre-trained language models. However, existing methods fail to consider efficiency and effectiveness at the same time when the input document is extremely long. To tackle this problem, in this paper, we proposed an efficient Coarse-to-Fine Facet-Aware Ranking (C2F-FAR) framework for unsupervised long document summarization, which is based on the semantic block. The semantic block refers to continuous sentences in the document that describe the same facet. Specifically, we address this problem by converting the one-step ranking method into the hierarchical multi-granularity two-stage ranking. In the coarse-level stage, we propose a new segment algorithm to split the document into facet-aware semantic blocks and then filter insignificant blocks. In the fine-level stage, we select salient sentences in each block and then extract the final summary from selected sentences. We evaluate our framework on four long document summarization datasets: Gov-Report, BillSum, arXiv, and PubMed. Our C2F-FAR can achieve new state-of-the-art unsupervised summarization results on Gov-Report and BillSum. In addition, our method speeds up 4-28 times more than previous methods.\footnote{\url{https://github.com/xnliang98/c2f-far}}

Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, pro-environmental action, political engagement, and even participation in violent protests. Various computational methods in Natural Language Processing (NLP) have been used to detect moral sentiment from textual data, but in order to achieve better performances in such subjective tasks, large sets of hand-annotated training data are needed. Previous corpora annotated for moral sentiment have proven valuable, and have generated new insights both within NLP and across the social sciences, but have been limited to Twitter. To facilitate improving our understanding of the role of moral rhetoric, we present the Moral Foundations Reddit Corpus, a collection of 16,123 Reddit comments that have been curated from 12 distinct subreddits, hand-annotated by at least three trained annotators for 8 categories of moral sentiment (i.e., Care, Proportionality, Equality, Purity, Authority, Loyalty, Thin Morality, Implicit/Explicit Morality) based on the updated Moral Foundations Theory (MFT) framework. We use a range of methodologies to provide baseline moral-sentiment classification results for this new corpus, e.g., cross-domain classification and knowledge transfer.