In recent years, numerous large-scale cyberattacks have exploited Internet of Things (IoT) devices, a phenomenon that is expected to escalate with the continuing proliferation of IoT technology. Despite considerable efforts in attack detection, intrusion detection systems remain mostly reactive, responding to specific patterns or observed anomalies. This work proposes a proactive approach to anticipate and mitigate malicious activities before they cause damage. This paper proposes a novel network intrusion prediction framework that combines Large Language Models (LLMs) with Long Short Term Memory (LSTM) networks. The framework incorporates two LLMs in a feedback loop: a fine-tuned Generative Pre-trained Transformer (GPT) model for predicting network traffic and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) for evaluating the predicted traffic. The LSTM classifier model then identifies malicious packets among these predictions. Our framework, evaluated on the CICIoT2023 IoT attack dataset, demonstrates a significant improvement in predictive capabilities, achieving an overall accuracy of 98%, offering a robust solution to IoT cybersecurity challenges.

Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive parallelism of a supercomputer by using Artificial Intelligence (AI) to learn from and adapt a workflow as it executes. Colmena allows scientists to define how their application should respond to events (e.g., task completion) as a series of cooperative agents. In this paper, we describe the design of Colmena, the challenges we overcame while deploying applications on exascale systems, and the science workflows we have enhanced through interweaving AI. The scaling challenges we discuss include developing steering strategies that maximize node utilization, introducing data fabrics that reduce communication overhead of data-intensive tasks, and implementing workflow tasks that cache costly operations between invocations. These innovations coupled with a variety of application patterns accessible through our agent-based steering model have enabled science advances in chemistry, biophysics, and materials science using different types of AI. Our vision is that Colmena will spur creative solutions that harness AI across many domains of scientific computing.

The United Kingdom has provided Ukraine with robotic "war dogs" that have started assisting troops on the battlefield and terrifying Russian troops who see them, according to reports. "The robot dog demonstrated its capabilities in delivering a range of critical equipment, showcasing its potential as an invaluable asset to military units," manufacturer Brit Alliance said of the units. "The robot dog exhibited exceptional mobility and agility, crucial for traversing complex and hostile environments," the company added. "Whether navigating through debris, climbing over obstacles, or moving stealthily across open ground, the robot dog has proven itself capable of maintaining a high level of operational effectiveness." The British second-generation Brit Alliance Dog (BAD2) has taken to the battlefield, utilizing remote-sensing technology and a thermal-infrared camera to navigate the tricky landscape and perform a wide range of wartime tasks, such as delivering equipment or reconnaissance.

When the federal government closed Castle Air Force Base in Merced County in the 1990s, the dilapidated buildings and vast expanse of aging tarmac left behind seemed more like a liability than an opportunity. But by 2018, the old runways that once carried B-52 bombers had found a new and unexpected customer: Google, which was testing its experimental self-driving vehicles there, far from the prying eyes of Silicon Valley. At the urging of then-state Assemblyman Adam Gray, California gave Merced County 6.5 million that year to expand the self-driving testing program at the old base. A few years later, Gray invested there, too. In 2022, a company in which Gray is a minority owner bought four apartment buildings on the former base from Merced County, according to a Times review of business filings, property records and Gray's financial disclosures.

Why are national politicians like Nancy Pelosi lining up alongside artificial intelligence companies to oppose safety regulations on this new industry proposed in California's state legislature? Subscribe to Slate Plus to access ad-free listening to the whole What Next family and all your favorite Slate podcasts. Subscribe today on Apple Podcasts by clicking "Try Free" at the top of our show page. Sign up now at slate.com/whatnextplus to get access wherever you listen.

President Volodymyr Zelenskyy touted a newly developed Ukrainian "drone missile" on Saturday that he said would take the war back to Russia and scornfully derided Russia's Vladimir Putin as a "sick old man from Red Square." As Ukraine marked 33 years of post-Soviet independence, Zelenskyy said the new weapon, Palianytsia, was faster and more powerful than the domestically made drones that Kyiv has so far used to fight back against Russia, striking its oil refineries and military airfields. "Our enemy will ... know what the Ukrainian way for retaliation is. Worthy, symmetrical, long-ranged," he said.

ESG Rating Disagreement and Corporate Total Factor Productivity:Inference and Prediction Zhanli Li ESG rating disagreement can lead to a decline in corporate total factor productivity When faced with ESG rating disagreement, reactive green innovation by enterprises does not lead to improvements in total factor productivity. Abstract This paper explores the relationship between ESG rating disagreement and total factor productivity (TFP) based on data from Chinese domestic ESG rating agencies and financial data of A-share listed companies in China from 2015 to 2022. On one hand, the empirical results show that ESG rating disagreement reduces corporate TFP, a conclusion that is validated through multiple robustness tests. The mechanism analysis reveals an interaction effect between green innovation and ESG rating disagreement. Specifically, in firms without ESG rating disagreement, green innovation promotes the improvement of TFP; however, in firms with disagreement, although ESG rating disagreement may drive green innovation, this does not lead to an increase in TFP. The heterogeneity analysis indicates that this effect is more pronounced in non-state-owned, asset-intensive, and lowpollution enterprises.

Annotation of political discourse is resource-intensive, but recent developments in NLP promise to automate complex annotation tasks. Fine-tuned transformer-based models outperform human annotators in some annotation tasks, but they require large manually annotated training datasets. In our contribution, we explore to which degree a manually annotated dataset can be automatically replicated with today's NLP methods, using unsupervised machine learning and zero- and few-shot learning.

This study presents a novel application of Graph Neural Networks (GNNs) to optimize dealership network planning for a luxury car manufacturer in the U.S. By conducting a comprehensive literature review on dealership location determinants, the study identifies 65 county-level explanatory variables, augmented by two additional measures of regional interconnectedness derived from social and mobility data. An ablation study involving 34 variable combinations and ten state-of-the-art GNN operators reveals key insights into the predictive power of various variables, particularly highlighting the significance of competition, demographic factors, and mobility patterns in influencing dealership location decisions. The analysis pinpoints seven specific counties as promising targets for network expansion. This research not only illustrates the effectiveness of GNNs in solving complex geospatial decision-making problems but also provides actionable recommendations and valuable methodological insights for industry practitioners.

Speech deepfake detection has recently gained significant attention within the multimedia forensics community. Related issues have also been explored, such as the identification of partially fake signals, i.e., tracks that include both real and fake speech segments. However, generating high-quality spliced audio is not as straightforward as it may appear. Spliced signals are typically created through basic signal concatenation. This process could introduce noticeable artifacts that can make the generated data easier to detect. We analyze spliced audio tracks resulting from signal concatenation, investigate their artifacts and assess whether such artifacts introduce any bias in existing datasets. Our findings reveal that by analyzing splicing artifacts, we can achieve a detection EER of 6.16% and 7.36% on PartialSpoof and HAD datasets, respectively, without needing to train any detector. These results underscore the complexities of generating reliable spliced audio data and lead to discussions that can help improve future research in this area.