High-Risk Artificial Intelligence Systems under the European Union’s Artificial Intelligence Act: Systemic Flaws and Practical Challenges

The European Union’s (EU) Artificial Intelligence Act (EU AI Act) has adopted a risk-based approach to artificial intelligence (AI) regulation, where AI systems are subjected to different regulatory standards depending on the seriousness of the risk they pose to public interest. High-risk AI systems, the largest category, are subject to strict regulatory requirements imposed throughout their life cycle, ranging from comprehensive conformity assessment to human rights impact assessment and risk management systems. However, the EU AI Act’s high-risk classification system has two systemic fundamental flaws that undermine its ability to strike a fair balance between the risks of various uses of AI technologies and their societal benefits. First, it defines high-risk AI systems through hyper-technical enumeration, potentially excluding certain AI systems from the high-risk category, even if they pose significant risks to public interest. The Act grants the European Commission the power to revise the high-risk category by adding new AI use cases to the list, if they pose similar or greater risks as the existing ones. But the Commission’s power to revise the list does not adequately address the potential loopholes to be created by the restrictive method of defining high-risk AI systems. Second, due to its failure to consider the specific contexts in which AI technologies are used, the EU AI Act could impose disproportionate regulatory burdens on providers and deployers by improperly classifying their AI use cases as high-risk. By using practical examples based on assessment of several real-world use cases of AI technologies conducted in July 2023 during the St. Gallen University First Grand Challenge on the EU AI Act, this paper argues that the EU AI Act requires revision to adequately regulate AI technologies. The paper proposes a solution to address the EU AI Act’s shortcomings, based on the way the law defines high-risk in the context of data protection impact assessment

Practical Power and Rate Control for WiFi

While there has been extensive theoretical work on sophisticated joint resource allocation algorithms for wireless networks, their applicability to WiFi (IEEE 802.11) networks is very limited. One of the main reasons is the limitations in changing MAC parameters in current driver implementations. To this end, in this work, we developed a general cross-layer communication interface in the Linux kernel between the IEEE 802.11 PHY and MAC to enable per packet TPC. Based on this implementation, we realize an decentralized rate-power controller (Minstrel-Piano). Our initial evaluation shows that Minstrel-Piano is able to significantly decrease the power levels while maintaining the same link performance. These results are encouraging for a better interference management and consequently, better resource allocation in WiFi networks

An improved random bit-stuffing technique with a modified RSA algorithm for resisting attacks in information security (RBMRSA)

The recent innovations in network application and the internet have made data and network security the major role in data communication system development. Cryptography is one of the outstanding and powerful tools for ensuring data and network security. In cryptography, randomization of encrypted data increases the security level as well as the Computational Complexity of cryptographic algorithms involved. This research study provides encryption algorithms that bring confidentiality and integrity based on two algorithms. The encryption algorithms include a well-known RSA algorithm (1024 key length) with an enhanced bit insertion algorithm to enhance the security of RSA against different attacks. The security classical RSA has depreciated irrespective of the size of the key length due to the development in computing technology and hacking system. Due to these lapses, we have tried to improve on the contribution of the paper by enhancing the security of RSA against different attacks and also increasing diffusion degree without increasing the key length. The security analysis of the study was compared with classical RSA of 1024 key length using mathematical evaluation proofs, the experimental results generated were compared with classical RSA of 1024 key length using avalanche effect in (%) and computational complexity as performance evaluation metrics. The results show that RBMRSA is better than classical RSA in terms of security but at the cost of execution time.publishedVersio

Self-optimization of coverage and capacity based on a fuzzy neural network with cooperative reinforcement learning

Self-organization is a key concept in long-term evolution (LTE) systems to reduce capital and operational expenditures (CAPEX and OPEX). Self-optimization of coverage and capacity, which allows the system to periodically and automatically adjust the key radio frequency (RF) parameters through intelligent algorithms, is one of the most important tasks in the context of self-organizing networks (SON). In this paper, we propose self-optimization of antenna tilt and power using a fuzzy neural network optimization based on reinforcement learning (RL-FNN). In our approach, a central control mechanism enables cooperation-based learning by allowing distributed SON entities to share their optimization experience, represented as the parameters of learning method. Specifically, SON entities use cooperative Q-learning and reinforced back-propagation method to acquire and adjust their optimization experience. To evaluate the coverage and capacity performance of RL-FNN, we analyze cell-edge performance and cell-center performance indicators jointly across neighboring cells and specifically consider the difference in load distribution in a given region. The simulation results show that RL-FNN performs significantly better than the best fixed configuration proposed in the literature. Furthermore, this is achieved with significantly lower energy consumption. Finally, since each self-optimization round completes in less than a minute, RL-FNN can meet the need of practical applications of self-optimization in a dynamic environment