Responsible Artificial Intelligence (RAI) is a combination of ethics associated with the usage of artificial intelligence aligned with the common and standard frameworks. This survey paper extensively discusses the global and national standards, applications of RAI, current technology and ongoing projects using RAI, and possible challenges in implementing and designing RAI in the industries and projects based on AI. Currently, ethical standards and implementation of RAI are decoupled which caters each industry to follow their own standards to use AI ethically. Many global firms and government organizations are taking necessary initiatives to design a common and standard framework. Social pressure and unethical way of using AI forces the RAI design rather than implementation.

Language is not neutral; it frames understanding, structures power, and shapes governance. This paper argues that misnomers like cybersecurity and artificial intelligence (AI) are more than semantic quirks; they carry significant governance risks by obscuring human agency, inflating expectations, and distorting accountability. Drawing on lessons from cybersecurity's linguistic pitfalls, such as the 'weakest link' narrative, this paper highlights how AI discourse is falling into similar traps with metaphors like 'alignment,' 'black box,' and 'hallucination.' These terms embed adversarial, mystifying, or overly technical assumptions into governance structures. In response, the paper advocates for a language-first approach to AI governance: one that interrogates dominant metaphors, foregrounds human roles, and co-develops a lexicon that is precise, inclusive, and reflexive. This paper contends that linguistic reform is not peripheral to governance but central to the construction of transparent, equitable, and anticipatory regulatory frameworks.

Do Large Language Models (LLMs) hold positions that conflict with your country's values? Occasionally they do! However, existing works primarily focus on ethical reviews, failing to capture the diversity of national values, which encompass broader policy, legal, and moral considerations. Furthermore, current benchmarks that rely on spectrum tests using manually designed questionnaires are not easily scalable. To address these limitations, we introduce NaVAB, a comprehensive benchmark to evaluate the alignment of LLMs with the values of five major nations: China, the United States, the United Kingdom, France, and Germany. NaVAB implements a national value extraction pipeline to efficiently construct value assessment datasets. Specifically, we propose a modeling procedure with instruction tagging to process raw data sources, a screening process to filter value-related topics and a generation process with a Conflict Reduction mechanism to filter non-conflicting values.We conduct extensive experiments on various LLMs across countries, and the results provide insights into assisting in the identification of misaligned scenarios. Moreover, we demonstrate that NaVAB can be combined with alignment techniques to effectively reduce value concerns by aligning LLMs' values with the target country.

Wikipedia is powered by MediaWiki, a free and open-source software that is also the infrastructure for many other wiki-based online encyclopedias. These include the recently launched website Ruwiki, which has copied and modified the original Russian Wikipedia content to conform to Russian law. To identify practices and narratives that could be associated with different forms of knowledge manipulation, this article presents an in-depth analysis of this Russian Wikipedia fork. We propose a methodology to characterize the main changes with respect to the original version. The foundation of this study is a comprehensive comparative analysis of more than 1.9M articles from Russian Wikipedia and its fork. Using meta-information and geographical, temporal, categorical, and textual features, we explore the changes made by Ruwiki editors. Furthermore, we present a classification of the main topics of knowledge manipulation in this fork, including a numerical estimation of their scope. This research not only sheds light on significant changes within Ruwiki, but also provides a methodology that could be applied to analyze other Wikipedia forks and similar collaborative projects.

A BSTRACT In the era of data expansion, ensuring data privacy has become increasingly critical, posing significant challenges to traditional AI-based applications. In addition, the increasing adoption of IoT devices has introduced significant cybersecurity challenges, making traditional Network Intrusion Detection Systems (NIDS) less effective against evolving threats, and privacy concerns and regulatory restrictions limit their deployment. Federated Learning (FL) has emerged as a promising solution, allowing decentralized model training while maintaining data privacy to solve these issues. However, despite implementing privacy-preserving technologies, FL systems remain vulnerable to adversarial attacks. Furthermore, data distribution among clients is not heterogeneous in the FL scenario. We propose WeiDetect, a two-phase, server-side defense mechanism for FL-based NIDS that detects malicious participants to address these challenges. In the first phase, local models are evaluated using a validation dataset to generate validation scores. These scores are then analyzed using a Weibull distribution, identifying and removing malicious models. We conducted experiments to evaluate the effectiveness of our approach in diverse attack settings. Our evaluation included two popular datasets, CIC-Darknet2020 and CSE-CIC-IDS2018, tested under non-IID data distributions. Our findings highlight that WeiDetect outperforms state-of-the-art defense approaches, improving higher target class recall up to 70% and enhancing the global model's F1 score by 1% to 14%. K eywords Federated learning Poisoning attacks Network intrusion detection systems Non-independent and identically distributed data Weibull distribution 1 Introduction The rapid advancement of the Internet has created a highly interconnected world. The adoption of IoT for connectivity has increased significantly, leading to security vulnerabilities due to the inherent nature of IoT devices and systems. According to [1], it was emphasized that IoT devices are expected to reach 55.7 billion by 2025; the increasing volume of data generated by these devices also opens the door to cyber attackers. This further signifies the critical role of the Network Intrusion Detection System (NIDS), which detects suspicious activities and improves the security of the IoT network ecosystem. The NIDS employs signature, behavior, or specification-based approaches to identify network anomalies and protect the system from unauthorized use or access [2]. However, these approaches have become less efficient in recognizing unknown attacks, rendering them incapable of detecting new or evolving threats. The paper [3, 4] highlight that Machine Learning (ML) based NIDSs are efficient alternatives that identify normal and abnormal traffic patterns in IoT Corresponding author: vinod.puthuvath@unipd.it Although these ML models have been widely employed in various solutions to enable dynamic and adaptive IDS in IoT environments.

This thesis employs a hybrid CNN-Transformer architecture, alongside a detailed anthropological framework, to investigate potential historical connections between the visual morphology of the Indus Valley script and pictographic systems of the Tibetan-Yi Corridor. Through an ensemble methodology of three target scripts across 15 independently trained models, we demonstrate that Tibetan-Yi Corridor scripts exhibit approximately six-fold higher visual similarity to the Indus script (0.635) than to the Bronze Age Proto-Cuneiform (0.102) or Proto-Elamite (0.078). Contrary to expectations, when measured through direct script-to-script embedding comparisons, the Indus script maps closer to Tibetan-Yi Corridor scripts with a mean cosine similarity of 0.930 (CI: [0.917, 0.942]) than to contemporaneous West Asian signaries, which recorded mean similarities of 0.887 (CI: [0.863, 0.911]) and 0.855 (CI: [0.818, 0.891]). Across dimensionality reduction and clustering methods, the Indus script consistently clusters closest to Tibetan-Yi Corridor scripts. These computational findings align with observed pictorial parallels in numeral systems, gender markers, and iconographic elements. Archaeological evidence of contact networks along the ancient Shu-Shendu road, coinciding with the Indus Civilization's decline, provides a plausible transmission pathway. While alternate explanations cannot be ruled out, the specificity and consistency of similarities suggest more complex cultural transmission networks between South and East Asia than previously recognized.

President Donald Trump is turning his attention to the U.S. shipbuilding industry, which is leagues behind its near-peer competitor China, and recently signed an executive order designed to reinvigorate it. Trump's April 10 order instructs agencies to develop a Maritime Action Plan and orders the U.S. trade representative to compile a list of recommendations to address China's "anticompetitive actions within the shipbuilding industry," among other things. Additionally, the executive order instructs a series of assessments regarding how the government could bolster financial support through the Defense Production Act, the Department of Defense Office of Strategic Capital, a new Maritime Security Trust Fund, investment from shipbuilders from allied countries and other grant programs. But simply throwing money at the shipbuilding industry won't solve the problem, according to Bryan Clark, director of the Hudson Institute think tank's Center for Defense Concepts and Technology. "It is unlikely that just putting more money into U.S. shipbuilding – even with foreign technical assistance – will make U.S. commercial shipbuilders competitive with experienced and highly-subsidized shipyards in China, Korea, or Japan," Clark said in a Monday email to Fox News Digital.

A day before Easter, Russian President Vladimir Putin announced a temporary ceasefire for the Christian holiday. Like other Russian promises, this one was broken too. Ukrainian media reported Russian drone attacks, shelling and firefights across the front lines. Ukrainian civilians were also targeted. This ceasefire that wasn't came on the tail of another one: a 30-day ceasefire that was supposed to cover energy infrastructure.

A troubling trend has emerged in schools across the United States, with young students falling victim to the increasing use of artificial intelligence (AI)-powered "nudify" apps that have the power to create fake pornography of classmates. "Nudify" is an umbrella term referring to a plethora of widely available apps and websites that allow users to alter photos of full-dressed individuals and virtually undress them. Some apps can create nude images with just a headshot of the victim. Don Austin, the superintendent of the Palo Alto Unified School District, told Fox News Digital that this type of online harassment can be more relentless compared to traditional in-person bullying. "It used to be that a bully had to come over and push you. Palo Alto is not a community where people are going to come push anybody into a locker. But it's not immune from online bullying," Austin said.

Drones could be used for NHS-related missions in remote areas, inspecting offshore wind turbines and supplying oil rigs by 2026 as part of a new regulatory regime in the UK. David Willetts, the head of a new government unit helping to deploy new technologies in Britain, said there were obvious situations where drones could be used if the changes go ahead next year. Ministers announced plans this month to allow drones to fly long distances without their operators seeing them. Drones cannot be flown "beyond visual line of sight" under current regulations, making their use for lengthy journeys impossible. In an interview with the Guardian, Lord Willetts, chair of the Regulatory Innovation Office (RIO), said the changes could come as soon as 2026, but that they would apply in "atypical" aviation environments at first, which would mean remote areas and over open water. Referring to the NHS, Willetts said there was potentially a huge market for drone operators.