SAE Level 4 Automated Driving Systems (ADSs) are deployed on public roads, including Waymo's Rider-Only (RO) ride-hailing service (without a driver behind the steering wheel). The objective of this study was to perform a retrospective safety assessment of Waymo's RO crash rate compared to human benchmarks, including disaggregated by crash type. Eleven crash type groups were identified from commonly relied upon crash typologies that are derived from human crash databases. Human benchmarks were aligned to the same vehicle types, road types, and locations as where the Waymo Driver operated. Waymo crashes were extracted from the NHTSA Standing General Order (SGO). RO mileage was provided by the company via a public website. Any-injury-reported, Airbag Deployment, and Suspected Serious Injury+ crash outcomes were examined because they represented previously established, safety-relevant benchmarks where statistical testing could be performed at the current mileage. Data was examined over 56.7 million RO miles through the end of January 2025, resulting in a statistically significant lower crashed vehicle rate for all crashes compared to the benchmarks in Any-Injury-Reported and Airbag Deployment, and Suspected Serious Injury+ crashes. Of the crash types, V2V Intersection crash events represented the largest total crash reduction, with a 96% reduction in Any-injury-reported (87%-99% CI) and a 91% reduction in Airbag Deployment (76%-98% CI) events. Cyclist, Motorcycle, Pedestrian, Secondary Crash, and Single Vehicle crashes were also statistically reduced for the Any-Injury-Reported outcome. There was no statistically significant disbenefit found in any of the 11 crash type groups. This study represents the first retrospective safety assessment of an RO ADS that made statistical conclusions about more serious crash outcomes and analyzed crash rates on a crash type basis.

Securing the Future of IVR: AI-Driven Innovation with Agile Security, Data Regulation, and Ethical AI Integration Khushbu Mehboob Shaikh T echnical Lead, Principal T echnical Account Manager Twilio Inc. Irving, Texas, United States ORCID: 0009-0000-8681-5830 Georgios Giannakopoulos Principal Engineer, Independent Researcher The Hague, The Netherlands ORCID: 0000-0002-3707-3276 Abstract --The rapid digitalization of communication systems has elevated Interactive V oice Response (IVR) technologies to become critical interfaces for customer engagement. With Artificial Intelligence (AI) now driving these platforms, ensuring secure, compliant, and ethically designed development practices is more imperative than ever . AI-powered IVRs leverage Natural Language Processing (NLP) and Machine Learning (ML) to personalize interactions, automate service delivery, and optimize user experiences. However, these innovations expose systems to heightened risks, including data privacy breaches, AI decision opacity, and model security vulnerabilities. We propose a practical governance framework that embeds agile security principles, compliance with global data legislation, and user-centric ethics. Emphasizing privacy-by-design, adaptive risk modeling, and transparency, the paper argues that ethical AI integration is not a feature but a strategic imperative. Through this multidimensional lens, we highlight how modern IVRs can transition from communication tools to intelligent, secure, and accountable digital frontlinesresilient against emerging threats and aligned with societal expectations. I NTRODUCTION Interactive V oice Response (IVR) systems have long served as essential digital entry points in customer service operations, enabling organizations to automate call handling, reduce wait times, and streamline user interactions [1].

This paper presents our simulation of cyber-attacks and detection strategies on the traffic control system in Daytona Beach, FL. using Raspberry Pi virtual machines and the OPNSense firewall, along with traffic dynamics from SUMO and exploitation via the Metasploit framework. We try to answer the research questions: are we able to identify cyber attacks by only analyzing traffic flow patterns. In this research, the cyber attacks are focused particularly when lights are randomly turned all green or red at busy intersections by adversarial attackers. Despite challenges stemming from imbalanced data and overlapping traffic patterns, our best model shows 85\% accuracy when detecting intrusions purely using traffic flow statistics. Key indicators for successful detection included occupancy, jam length, and halting durations.

The increasing automation of traffic management systems has made them prime targets for cyberattacks, disrupting urban mobility and public safety. Traditional network-layer defenses are often inaccessible to transportation agencies, necessitating a machine learning-based approach that relies solely on traffic flow data. In this study, we simulate cyberattacks in a semi-realistic environment, using a virtualized traffic network to analyze disruption patterns. We develop a deep learning-based anomaly detection system, demonstrating that Longest Stop Duration and Total Jam Distance are key indicators of compromised signals. To enhance interpretability, we apply Explainable AI (XAI) techniques, identifying critical decision factors and diagnosing misclassification errors. Our analysis reveals two primary challenges: transitional data inconsistencies, where mislabeled recovery-phase traffic misleads the model, and model limitations, where stealth attacks in low-traffic conditions evade detection. This work enhances AI-driven traffic security, improving both detection accuracy and trustworthiness in smart transportation systems.

The recent growth in the use of Large Language Models has made them vulnerable to sophisticated adversarial assaults, manipulative prompts, and encoded malicious inputs. Existing countermeasures frequently necessitate retraining models, which is computationally costly and impracticable for deployment. Without the need for retraining or fine-tuning, this study presents a unique defense paradigm that allows LLMs to recognize, filter, and defend against adversarial or malicious inputs on their own. There are two main parts to the suggested framework: (1) A prompt filtering module that uses sophisticated Natural Language Processing (NLP) techniques, including zero-shot classification, keyword analysis, and encoded content detection (e.g. base64, hexadecimal, URL encoding), to detect, decode, and classify harmful inputs; and (2) A summarization module that processes and summarizes adversarial research literature to give the LLM context-aware defense knowledge. This approach strengthens LLMs' resistance to adversarial exploitation by fusing text extraction, summarization, and harmful prompt analysis. According to experimental results, this integrated technique has a 98.71% success rate in identifying harmful patterns, manipulative language structures, and encoded prompts. By employing a modest amount of adversarial research literature as context, the methodology also allows the model to react correctly to harmful inputs with a larger percentage of jailbreak resistance and refusal rate. While maintaining the quality of LLM responses, the framework dramatically increases LLM's resistance to hostile misuse, demonstrating its efficacy as a quick and easy substitute for time-consuming, retraining-based defenses.

Telegram has become a major space for political discourse and alternative media. However, its lack of moderation allows misinformation, extremism, and toxicity to spread. While prior research focused on these particular phenomena or topics, these have mostly been examined separately, and a broader understanding of the Telegram ecosystem is still missing. In this work, we fill this gap by conducting a large-scale analysis of the Italian Telegram sphere, leveraging a dataset of 186 million messages from 13,151 chats collected in 2023. Using network analysis, Large Language Models, and toxicity detection tools, we examine how different thematic communities form, align ideologically, and engage in harmful discourse within the Italian cultural context. Results show strong thematic and ideological homophily. We also identify mixed ideological communities where far-left and far-right rhetoric coexist on particular geopolitical issues. Beyond political analysis, we find that toxicity, rather than being isolated in a few extreme chats, appears widely normalized within highly toxic communities. Moreover, we find that Italian discourse primarily targets Black people, Jews, and gay individuals independently of the topic. Finally, we uncover common trend of intra-national hostility, where Italians often attack other Italians, reflecting regional and intra-regional cultural conflicts that can be traced back to old historical divisions. This study provides the first large-scale mapping of the Italian Telegram ecosystem, offering insights into ideological interactions, toxicity, and identity-targets of hate and contributing to research on online toxicity across different cultural and linguistic contexts on Telegram.

The US government is expanding its surveillance of social media to monitor millions of visitors and immigrants – and its embrace of more data analytics and artificial intelligence tools could increase scrutiny of US citizens as well. "It is nearly – if not entirely – impossible for the government to focus only on non-citizens and not look at anyone else's social media," says Rachel Levinson-Waldman at the Brennan Center for Justice, a public policy non-profit…

Treasury Secretary Scott Bessent kicked off Michael Milken's annual financial bash in Beverly Hills by doubling down on President Trump's economic policy of trade reform, tax cuts and deregulation -- promising the "America First" agenda would be "the blueprint for a more abundant world." The former hedge fund manager, in a brief speech Monday that opened the Milken Institute Global Conference, said that all three elements of the policy must be taken together in order to be understood. "They are interlocking parts of an engine designed to drive long-term investment in the American economy," he said, in remarks at the Beverly Hilton. "Tariffs are engineered to encourage companies like yours to invest directly in the United States. Hire your workers here, build your factories here, make your products here. You'll be glad you did, not only because we have the most productive work force in the world, but because we will soon have the most favorable tax and regulatory environment as well," he said.

Russia has reported that it repelled a drone attack on Moscow as the capital city prepares to host a major military parade with foreign leaders in attendance. Russia's air defence systems intercepted "four drones flying towards Moscow", Mayor Sergei Sobyanin said on Monday. The attack appears intended to unsettle Moscow's preparations for events marking the end of the Great Patriotic War, commonly known as World War II elsewhere, on May 9. President Vladimir Putin has called for a 72-hour ceasefire to mark the occasion starting on May 8. However, Ukraine has demanded instead a 30-day truce aimed at agreeing to a permanent ceasefire in the conflict that began when Russia invaded in February 2022. Sobyanin said in a post on Telegram that there were no reports of injuries or damage.

Russian forces repelled four drones flying towards Moscow, the capital's mayor, Sergei Sobyanin, said in a post on Telegram. There were no initial reports of damage or casualties, Sobyanin said, adding that emergency services were working at the scene. Ukrainian forces attacked a factory in Russia's Bryansk region, destroying much of the plant, Governor Alexander Bogomaz said on Telegram. There were no casualties, Bogomaz said. Russian forces destroyed 13 Ukrainian drones overnight over Russia's Rostov, Belgorod and Bryansk regions, Moscow's Ministry of Defence said on Sunday.