Russia has started tactical fighter jet exercises over the Baltic Sea with the goal of testing readiness to perform combat and other special operations, the country's defence ministry has said, a day after Moscow said its jets had scrambled to intercept United Kingdom military planes over the Black Sea. "The main goal of the exercise is to test the readiness of the flight crew to perform combat and special tasks as intended," Russia's defence ministry said on Tuesday. "The crews of the Su-27 [fighter jets] of the Baltic Fleet fired from airborne weapons at cruise missiles and mock enemy aircraft," the ministry announced on the Telegram messaging channel, adding that as well as improving skills, Russian fighter pilots are on "round-the-clock combat duty" guarding the air space of Russia's Kaliningrad exclave. Wedged between Poland and Lithuania on the Baltic coast, Kaliningrad is Moscow's westernmost state and was part of Germany until the end of World War II. Given to the Soviet Union at the Potsdam Conference in 1945, the enclave has roughly 1 million residents – mainly Russians but also a small number of Ukrainians, Poles and Lithuanians.

Chinese Premier Li Qiang has called for more "communication and exchange" to avoid misunderstanding in his remarks at the opening of this year's'Summer Davos' in Tianjin, the first in-person event in three years following the COVID-19 pandemic. The three-day summit, which got under way on Tuesday, is hosted by the World Economic Forum but will focus heavily on China's place in the world and concerns about how the global economy can move forward in an increasingly fractious world, according to the agenda. Li told delegates it was time to support globalisation and deeper economic cooperation. "In the West, some people are hyping up what is called'cutting reliance and de-risking'," Li said. "These two concepts… are a false proposition, because the development of economic globalisation is such that the world economy has become a common entity in which you and I are both intermingled. The economies of many countries are blended with each other, rely on each other, make accomplishments because of one another and develop together. This is actually a good thing, not a bad thing."

A transnational inquiry has been launched to determine accountability for the deaths of five passengers aboard the OceanGate Expeditions submersible that imploded during a descent to the wreckage of the Titanic in the North Atlantic, the United States Coast Guard announced Sunday. Maritime agencies from Canada, France and Britain are joining an investigation that will be led by the Coast Guard, Capt. Jason Neubauer said during a news conference at Coast Guard Base Boston. Neubauer said the priority of the investigation, known as a Marine Board of Investigation, or MBI, "is to recover items from the seafloor." Neubauer said investigators will also determine "the cause of this marine casualty" and establish accountability.

Cybersecurity solutions have shown promising performance when detecting ransomware samples that use fixed algorithms and encryption rates. However, due to the current explosion of Artificial Intelligence (AI), sooner than later, ransomware (and malware in general) will incorporate AI techniques to intelligently and dynamically adapt its encryption behavior to be undetected. It might result in ineffective and obsolete cybersecurity solutions, but the literature lacks AI-powered ransomware to verify it. Thus, this work proposes RansomAI, a Reinforcement Learning-based framework that can be integrated into existing ransomware samples to adapt their encryption behavior and stay stealthy while encrypting files. RansomAI presents an agent that learns the best encryption algorithm, rate, and duration that minimizes its detection (using a reward mechanism and a fingerprinting intelligent detection system) while maximizing its damage function. The proposed framework was validated in a ransomware, Ransomware-PoC, that infected a Raspberry Pi 4, acting as a crowdsensor. A pool of experiments with Deep Q-Learning and Isolation Forest (deployed on the agent and detection system, respectively) has demonstrated that RansomAI evades the detection of Ransomware-PoC affecting the Raspberry Pi 4 in a few minutes with >90% accuracy.

The data article presents the large bilingual parallel corpus of low-resourced language pair Sanskrit-Hindi, named SAHAAYAK 2023. The corpus contains total of 1.5M sentence pairs between Sanskrit and Hindi. To make the universal usability of the corpus and to make it balanced, data from multiple domain has been incorporated into the corpus that includes, News, Daily conversations, Politics, History, Sport, and Ancient Indian Literature. The multifaceted approach has been adapted to make a sizable multi-domain corpus of low-resourced languages like Sanskrit. Our development approach is spanned from creating a small hand-crafted dataset to applying a wide range of mining, cleaning, and verification. We have used the three-fold process of mining: mining from machine-readable sources, mining from non-machine readable sources, and collation from existing corpora sources. Post mining, the dedicated pipeline for normalization, alignment, and corpus cleaning is developed and applied to the corpus to make it ready to use on machine translation algorithms.

Review-Based Recommender Systems (RBRS) have attracted increasing research interest due to their ability to alleviate well-known cold-start problems. RBRS utilizes reviews to construct the user and items representations. However, in this paper, we argue that such a reliance on reviews may instead expose systems to the risk of being shilled. To explore this possibility, in this paper, we propose the first generation-based model for shilling attacks against RBRSs. Specifically, we learn a fake review generator through reinforcement learning, which maliciously promotes items by forcing prediction shifts after adding generated reviews to the system. By introducing the auxiliary rewards to increase text fluency and diversity with the aid of pre-trained language models and aspect predictors, the generated reviews can be effective for shilling with high fidelity. Experimental results demonstrate that the proposed framework can successfully attack three different kinds of RBRSs on the Amazon corpus with three domains and Yelp corpus. Furthermore, human studies also show that the generated reviews are fluent and informative. Finally, equipped with Attack Review Generators (ARGs), RBRSs with adversarial training are much more robust to malicious reviews.

We introduce a novel method for gravitational wave detection that combines: 1) hybrid dilated convolution neural networks to accurately model both short-and long-range temporal sequential information of gravitational wave signals; and 2) graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a detector network. These spatiotemporal-graph AI models are tested for signal detection of gravitational waves emitted by quasi-circular, non-spinning and quasi-circular, spinning, non-precessing binary black hole mergers. For the latter case, we needed a dataset of 1.2 million modeled waveforms to densely sample this signal manifold. Thus, we reduced time-to-solution by training several AI models in the Polaris supercomputer at the Argonne Leadership Supercomputing Facility within 1.7 hours by distributing the training over 256 NVIDIA A100 GPUs, achieving optimal classification performance. This approach also exhibits strong scaling up to 512 NVIDIA A100 GPUs. We then created ensembles of AI models to process data from a three detector network, namely, the advanced LIGO Hanford and Livingston detectors, and the advanced Virgo detector. An ensemble of 2 AI models achieves state-of-the-art performance for signal detection, and reports seven misclassifications per decade of searched data, whereas an ensemble of 4 AI models achieves optimal performance for signal detection with two misclassifications for every decade of searched data. Finally, when we distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, our AI ensemble is capable of processing a decade of gravitational wave data from a three detector network within 3.5 hours, i.e., 2.5 10

The detection of hate speech in political discourse is a critical issue, and this becomes even more challenging in low-resource languages. To address this issue, we introduce a new dataset named IEHate, which contains 11,457 manually annotated Hindi tweets related to the Indian Assembly Election Campaign from November 1, 2021, to March 9, 2022. We performed a detailed analysis of the dataset, focusing on the prevalence of hate speech in political communication and the different forms of hateful language used. Additionally, we benchmark the dataset using a range of machine learning, deep learning, and transformer-based algorithms. Our experiments reveal that the performance of these models can be further improved, highlighting the need for more advanced techniques for hate speech detection in low-resource languages. In particular, the relatively higher score of human evaluation over algorithms emphasizes the importance of utilizing both human and automated approaches for effective hate speech moderation. Our IEHate dataset can serve as a valuable resource for researchers and practitioners working on developing and evaluating hate speech detection techniques in low-resource languages. Overall, our work underscores the importance of addressing the challenges of identifying and mitigating hate speech in political discourse, particularly in the context of low-resource languages. The dataset and resources for this work are made available at https://github.com/Farhan-jafri/Indian-Election.

On December 7, 2020, Ghanaians participated in the polls to determine their president for the next four years. To gain insights from this presidential election, we conducted stance analysis (which is not always equivalent to sentiment analysis) to understand how Twitter, a popular social media platform, reflected the opinions of its users regarding the two main presidential candidates. We collected a total of 99,356 tweets using the Twitter API (Tweepy) and manually annotated 3,090 tweets into three classes: Against, Neutral, and Support. We then performed preprocessing on the tweets. The resulting dataset was evaluated using two lexicon-based approaches, VADER and TextBlob, as well as five supervised machine learning-based approaches: Support Vector Machine (SVM), Logistic Regression (LR), Multinomial Na\"ive Bayes (MNB), Stochastic Gradient Descent (SGD), and Random Forest (RF), based on metrics such as accuracy, precision, recall, and F1-score. The best performance was achieved by Logistic Regression with an accuracy of 71.13%. We utilized Logistic Regression to classify all the extracted tweets and subsequently conducted an analysis and discussion of the results. For access to our data and code, please visit: https://github.com/ShesterG/Stance-Detection-Ghana-2020-Elections.git

Conventional Congestion Control (CC) algorithms,such as TCP Cubic, struggle in tactical environments as they misinterpret packet loss and fluctuating network performance as congestion symptoms. Recent efforts, including our own MARLIN, have explored the use of Reinforcement Learning (RL) for CC, but they often fall short of generalization, particularly in competitive, unstable, and unforeseen scenarios. To address these challenges, this paper proposes an RL framework that leverages an accurate and parallelizable emulation environment to reenact the conditions of a tactical network. We also introduce refined RL formulation and performance evaluation methods tailored for agents operating in such intricate scenarios. We evaluate our RL learning framework by training a MARLIN agent in conditions replicating a bottleneck link transition between a Satellite Communication (SATCOM) and an UHF Wide Band (UHF) radio link. Finally, we compared its performance in file transfer tasks against Transmission Control Protocol (TCP) Cubic and the default strategy implemented in the Mockets tactical communication middleware. The results demonstrate that the MARLIN RL agent outperforms both TCP and Mockets under different perspectives and highlight the effectiveness of specialized RL solutions in optimizing CC for tactical network environments.