22 research outputs found
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The criminalisation of identity theft in Saudi Arabia: balancing security and privacy to mitigate data crimes
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis thesis offers a critical analysis of Saudi Arabia‘s cybercrime enforcement framework. Emphasis is placed on identity theft. It identifies and maps key concepts and debates in this fast-paced area of legal reform and considers the many ways in which identity theft related offences challenge or undermine traditional theories of Saudi criminal law. It shows that identity theft related offences often involve the use of techniques which are not necessarily defined or regulated as criminal offences under the relevant laws of Saudi Arabia. The Kingdom of Saudi Arabia operates within an Islamic legal system and many aspects of Islamic Sharia (law) continue to influence elements of the Kingdom‘s criminal law, policy and procedure. However, many aspects of Sharia law remain ill-suited to computer crime. In 2007, Saudi Arabia passed and began implementing dedicated cybercrime legislation, but it is shown that this law fails to explicitly and effectively criminalise identity theft and other preparatory offences. Identity theft is by nature a global phenomenon which transcends international policy debates on data privacy and security. Saudi Arabia‘s limited participation in the most important international frameworks on cybercrime is a major impediment to the effectiveness of the investigation and prosecution of identity theft in the Kingdom. It is argued that Saudi Arabia‘s rate of criminal prosecution would be improved by greater cooperation at the international level. Legislative reform, though essential, will not be effective to combat identity theft, if it is not enforced and supplemented with adequate extra-territorial data protection laws and, crucially, industry-led security solutions. The latter should be enacted through cooperation with other states on information sharing initiatives and solutions which can be used to identify and detect threats
Circumferential Labral Reconstruction Using the Knotless Pull-Through Technique—Surgical Technique
Advanced Sensor Systems for Robotics and Autonomous Vehicles
In robotic and autonomous vehicle applications, sensor systems play a critical role. Machine learning (ML), data science, artificial intelligence (AI), and the internet of things (IoT) are all advancing, which opens up new possibilities for autonomous vehicles. For vehicle control, traffic monitoring, and traffic management applications, the integration of robotics, IoT, and AI is a very powerful combination. For effective robotic and vehicle control, robot sensor devices require an advanced sensor system. As a result, the AI-based system seeks the attention of the researcher to make the best use of sensor data for various robotic applications while conserving energy. The efficient collection of the data from sensors is a significant difficulty that AI technologies can effectively address. The data consistency method can also be used for time-constraint data collection applications. The present chapter discusses three important methods to improve the quality of service (QoS) and quality of experience (QoE) parameters of the robotic and autonomous vehicle applications. The first one is consistency-guaranteed and collision-resistant approach that can be used by the advanced sensor devices for the data aggregation and the removal of the redundant data. The second one is aggregation aware AI-based methods to improve the lifetime of the robotic devices and the last one is dividing the sensors devices based on continuous and event-monitoring robotic application and usage of the application-specific protocol to deal with the corresponding data. In addition the present chapter also discusses the role of sensor systems for various applications