Distributed Gibbs Sampling and LDA Modelling for Large Scale Big Data Management on PySpark

Karras, Christos; Karras, Aristeidis; Tsolis, Dimitrios; Giotopoulos, Konstantinos C.; Sioutas, Spyros

doi:10.1109/seeda-cecnsm57760.2022.9932990

Cited by 8 publications

(1 citation statement)

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“…Towards the final analysis, the collection and management of large amounts of data have become an essential component in the development of technology related to AI. In particular, the fields of automated machine learning, clustering, Gibbs sampling, and data structures [110][111][112][113] have emerged in recent days due to their robustness. Particularly in managing big data on AVs, there is a vital part in the pipeline by which autonomous vehicles consume and process a vast amount and variety of data, which is essential for improving safety, security, efficiency, and the overall user experience.…”

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Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions

Giannaros,

Karras,

Theodorakopoulos

et al. 2023

JCP

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Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophisticated technologies, including sensors, cameras, GPS, radar, light imaging detection and ranging (LiDAR), and advanced computing systems. These components work in concert to accurately perceive the vehicle’s environment, ensuring the capacity to make optimal decisions in real-time. At the heart of AV functionality lies the ability to facilitate intercommunication between vehicles and with critical road infrastructure—a characteristic that, while central to their efficacy, also renders them susceptible to cyber threats. The potential infiltration of these communication channels poses a severe threat, enabling the possibility of personal information theft or the introduction of malicious software that could compromise vehicle safety. This paper offers a comprehensive exploration of the current state of AV technology, particularly examining the intersection of autonomous vehicles and emotional intelligence. We delve into an extensive analysis of recent research on safety lapses and security vulnerabilities in autonomous vehicles, placing specific emphasis on the different types of cyber attacks to which they are susceptible. We further explore the various security solutions that have been proposed and implemented to address these threats. The discussion not only provides an overview of the existing challenges but also presents a pathway toward future research directions. This includes potential advancements in the AV field, the continued refinement of safety measures, and the development of more robust, resilient security mechanisms. Ultimately, this paper seeks to contribute to a deeper understanding of the safety and security landscape of autonomous vehicles, fostering discourse on the intricate balance between technological advancement and security in this rapidly evolving field.

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confidence: 99%