Enhancing Network Security Management in Water Systems using FM-based Attack Attribution Aleksandar Avdalovi c, Joseph Khoury, Ahmad Taha, Elias Bou-Harb Division of Computer Science and Engineering, Louisiana State University, USA Civil and Environmental Engineering, V anderbilt University, USA Abstract --Water systems are vital components of modern infrastructure, yet they are increasingly susceptible to sophisticated cyber attacks with potentially dire consequences on public health and safety. While state-of-the-art machine learning techniques effectively detect anomalies, contemporary model-agnostic attack attribution methods using LIME, SHAP, and LEMNA are deemed impractical for large-scale, interdependent water systems. This is due to the intricate interconnectivity and dynamic interactions that define these complex environments. Such methods primarily emphasize individual feature importance while falling short of addressing the crucial sensor-actuator interactions in water systems, which limits their effectiveness in identifying root cause attacks. T o this end, we propose a novel model-agnostic Factorization Machines (FM)-based approach that capitalizes on water system sensor-actuator interactions to provide granular explanations and attributions for cyber attacks. For instance, an anomaly in an actuator pump activity can be attributed to a top root cause attack candidates, a list of water pressure sensors, which is derived from the underlying linear and quadratic effects captured by our approach. In multi-feature cyber attack scenarios involving intricate sensor-actuator interactions, our FM-based attack attribution method effectively ranks attack root causes, achieving approximately 20% average improvement over SHAP and LEMNA. Additionally, our approach maintains strong performance in single-feature attack scenarios, demonstrating versatility across different types of cyber attacks. Notably, our approach maintains a low computational overhead equating to an O(n) time complexity, making it suitable for real-time applications in critical water system infrastructure. Our work underscores the importance of modeling feature interactions in water systems, offering a robust tool for operators to diagnose and mitigate root cause attacks more effectively. I NTRODUCTION W ATER systems at the physical layer comprise critical components such as flow and pressure sensors, and actuators, which are monitored and controlled by cyber layer systems to ensure a safe and reliable water supply for both communities and industries.

Large language models have been used to simulate human society using multi-agent systems. Most current social simulation research emphasizes interactive behaviors in fixed environments, ignoring information opacity, relationship variability and diffusion diversity. In this paper, we study the dynamics of information diffusion in 12 asymmetric open environments defined by information content and distribution mechanisms. We first present a general framework to capture the features of information diffusion. Then, we designed a dynamic attention mechanism to help agents allocate attention to different information, addressing the limitations of LLM-based attention. Agents start by responding to external information stimuli within a five-agent group, increasing group size and forming information circles while developing relationships and sharing information. Additionally, we observe the emergence of information cocoons, the evolution of information gaps, and the accumulation of social capital, which are closely linked to psychological, sociological, and communication theories.

Telegram is one of the most used instant messaging apps worldwide. Some of its success lies in providing high privacy protection and social network features like the channels -- virtual rooms in which only the admins can post and broadcast messages to all its subscribers. However, these same features contributed to the emergence of borderline activities and, as is common with Online Social Networks, the heavy presence of fake accounts. Telegram started to address these issues by introducing the verified and scam marks for the channels. Unfortunately, the problem is far from being solved. In this work, we perform a large-scale analysis of Telegram by collecting 35,382 different channels and over 130,000,000 messages. We study the channels that Telegram marks as verified or scam, highlighting analogies and differences. Then, we move to the unmarked channels. Here, we find some of the infamous activities also present on privacy-preserving services of the Dark Web, such as carding, sharing of illegal adult and copyright protected content. In addition, we identify and analyze two other types of channels: the clones and the fakes. Clones are channels that publish the exact content of another channel to gain subscribers and promote services. Instead, fakes are channels that attempt to impersonate celebrities or well-known services. Fakes are hard to identify even by the most advanced users. To detect the fake channels automatically, we propose a machine learning model that is able to identify them with an accuracy of 86%. Lastly, we study Sabmyk, a conspiracy theory that exploited fakes and clones to spread quickly on the platform reaching over 1,000,000 users.

Post Millennial senior editor Andy Ngo unpacks what led to the arrests of members of an apparent transgender vegan cult on'The Ingraham Angle.' The apparent head of a radical transgender cult linked to six killings, including a U.S. Border Patrol agent, told a Maryland judge last week, "I haven't done anything wrong" while pleading for access to vegan food behind bars. "I might starve to death if you cannot answer me," Jack Amadeus LaSota, 34, who goes by "Ziz," told Judge Erich Bean during a bail hearing in Allegany County District Court in Maryland on Feb. 18, according to audio obtained by the San Francisco Chronicle. "I need the jail to be ordered for me to have a vegan diet. It's more important than whatever this hearing is."

Oscar Wilde's quip, "Life imitates art far more than art imitates life", needs updating: replace "art" with "AI". The Amazon page for Alexander C Karp and Nicholas W Zapiska's new book, The Technological Republic: Hard Power, Soft Belief and the Future of the West, also lists: a "workbook" containing "key takeaways" from the volume; a second volume on how the Karp/Zapiska tome "can help you navigate life"; and a third offering another "workbook" comprising a "Master Plan for Navigating Digital Age and the Future of Society". It is conceivable that these parasitical works were written by humans, but I wouldn't bet on it. The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link.

The Trump administration recently announced that it would be laying off approximately 6,700 workers at the Internal Revenue Service, about 8% of the people employed by the agency. Tens of thousands of federal employees at other agencies are also losing their jobs. The timing could not be worse. Millions of returns will need to be processed. Questions will need to be answered.

At a recent artificial intelligence global summit, Chinese Vice Premier Zhang Guoqing encouraged other countries to embrace accessibility to Chinese artificial intelligence technology, such as the DeepSeek chatbot, in their domestic markets. Zhang claimed China's goal was to share achievements among nations and build "a community with a shared future for mankind" while safeguarding security. The United States must not fall for yet another trick by China. DeepSeek is a dangerous weapon that is almost certainly part of China's Unrestricted Warfare Doctrine. The concept of "Unrestricted Warfare" was created by two People's Liberation Army officers, Qiao Liang and Wang Xiangsui, in 1999.

Are governments and Hollywood films secretly pumping people's minds full of messages which push obedience, alcohol addiction, and disseminate'woke' theories? It's long been known that world governments are fascinated by mind control, with groups like the Central Intelligence Agency (CIA) allegedly conducting sinister experiments on the public. In the 1950s and 60s, the CIA's infamous MKUltra program recruited civilians, mental patients, and drug addicts in an effort to reprogram minds. However, some believe social media has given world governments and entertainment giants new tools to control minds. This includes mind control expert Jason Christoff.

The rapid development of artificial intelligence (AI) has significantly transformed human-computer interactions, making it essential to establish robust design standards to ensure effective, ethical, and human-centered AI (HCAI) solutions. Standards serve as the foundation for the adoption of new technologies, and human-AI interaction (HAII) standards are critical to supporting the industrialization of AI technology by following an HCAI approach. These design standards aim to provide clear principles, requirements, and guidelines for designing, developing, deploying, and using AI systems, enhancing the user experience and performance of AI systems. Despite their importance, the creation and adoption of HCAI-based interaction design standards face challenges, including the absence of universal frameworks, the inherent complexity of HAII, and the ethical dilemmas that arise in such systems. This chapter provides a comparative analysis of HAII versus traditional human-computer interaction (HCI) and outlines guiding principles for HCAI-based design. It explores international, regional, national, and industry standards related to HAII design from an HCAI perspective and reviews design guidelines released by leading companies such as Microsoft, Google, and Apple. Additionally, the chapter highlights tools available for implementing HAII standards and presents case studies of human-centered interaction design for AI systems in diverse fields, including healthcare, autonomous vehicles, and customer service. It further examines key challenges in developing HAII standards and suggests future directions for the field. Emphasizing the importance of ongoing collaboration between AI designers, developers, and experts in human factors and HCI, this chapter stresses the need to advance HCAI-based interaction design standards to ensure human-centered AI solutions across various domains.

Parallel corpora play an important role in training machine translation (MT) models, particularly for low-resource languages where high-quality bilingual data is scarce. This review provides a comprehensive overview of available parallel corpora for Indic languages, which span diverse linguistic families, scripts, and regional variations. We categorize these corpora into text-to-text, code-switched, and various categories of multimodal datasets, highlighting their significance in the development of robust multilingual MT systems. Beyond resource enumeration, we critically examine the challenges faced in corpus creation, including linguistic diversity, script variation, data scarcity, and the prevalence of informal textual content.We also discuss and evaluate these corpora in various terms such as alignment quality and domain representativeness. Furthermore, we address open challenges such as data imbalance across Indic languages, the trade-off between quality and quantity, and the impact of noisy, informal, and dialectal data on MT performance. Finally, we outline future directions, including leveraging cross-lingual transfer learning, expanding multilingual datasets, and integrating multimodal resources to enhance translation quality. To the best of our knowledge, this paper presents the first comprehensive review of parallel corpora specifically tailored for low-resource Indic languages in the context of machine translation.