International Conference on Cloud Computing [Services Society]

This paper examines the potential of integrating blockchain technology, encrypted Non-Fungible Tokens (NFTs), and the InterPlanetary File System (IPFS) to advance knowledge management systems, particularly within academic settings. We propose an architecture that addresses the limitations of traditional knowledge management systems by enhancing data security, copyright protection, and accessibility. The system leverages blockchain’s immutable and transparent properties, IPFS’s decentralized storage capabilities, and the unique identification features of NFTs secured with six encryption algorithms—RSA, RC4, DES, ChaCha20, Blowfish, and AES. The study involves deploying smart contracts on four Ethereum Virtual Machine (EVM)-compatible platforms—Binance Smart Chain, Polygon, Fantom, and Celo—to assess each platform’s effectiveness in managing encrypted NFTs and to identify the optimal encryption method for operational efficiency. Our findings aim to demonstrate how these technologies can potentially enhance the robustness and efficiency of knowledge management in academic environments.

The rise of generative artificial intelligence, especially large language models (LLMs), is subversively changing the legal industry. This paper explores how prompt engineering can be leveraged to control LLMs for advanced legal knowledge generation, moving beyond simple information retrieval to the creation of new legal insights. It discusses how LLMs, through cross-domain knowledge integration and counterfactual reasoning, can generate novel legal arguments. The paper introduces various prompt engineering techniques, from basic commands to advanced, structured instructions embedding legal frameworks like the “legal syllogism”. A case study on contract risk analysis demonstrates how the quality of prompts directly impacts the quality of LLM-generated output. Finally, the paper addresses the significant challenges and ethical dilemmas, including model “hallucinations”, inherent data bias, and professional responsibility in the age of AI. It advocates for a human-centric, collaborative model where legal professionals act as responsible operators, guiding AI to enhance legal practice while upholding fairness and justice.

This research paper presents a new method for tackling resource allocation challenges in multi-Fog and Cloud systems with varying demands, leveraging reinforcement learning. Our approach is structured in two phases: first, we identify the optimal Fog node for resource allocation, and in the second phase, reinforcement learning is applied to determine the best long-term strategy for whether the selected Fog node should handle tasks locally or offload them to the Cloud. To improve convergence speed, we integrate a Genetic Algorithm (GA) into the reinforcement learning process. The goal is to maximize Fog resource utilization while considering the number of resource blocks and each request’s maximum delay tolerance. Experimental evaluations confirm the effectiveness of our method in enhancing resource allocation within Fog computing environments. We benchmark our approach against pure Reinforcement Learning (RL) approach in dynamic resource allocation scenarios, with results showing that the combined approach outperforms others in speed, resource utilization, and load balancing.

This paper examines the integration of blockchain technology, smart contracts, RSA-encrypted Non-Fungible Tokens (NFTs), and the InterPlanetary File System (IPFS) to address critical challenges in managing voting systems. The approach aims to enhance transparency, ensure the security of voting records, and improve operational efficiency through the adoption of these technologies. Key features of blockchain such as immutability, decentralization, and cryptographic security are leveraged to mitigate risks associated with vote tampering and fraud while enhancing accountability through verifiable transactions. The system’s design emphasizes cross-platform compatibility with various Ethereum Virtual Machine (EVM)-supported blockchain platforms, enabling flexibility and scalability. The paper details the system’s implementation, evaluates its performance, and discusses the financial implications of its deployment, aiming to present a comprehensive view of how these technologies can improve the integrity and transparency of electoral processes.

Artificial intelligence (AI) technologies have enhanced operational efficiency and personalized services in news through content generation, distribution, and user behavior analysis. However, they introduce privacy risks like unauthorized data collection across the data lifecycle. Meanwhile, the industry faces dual challenges from technological implementation complexities and lagging legal-ethical frameworks. This survey systematically reviews AI-driven privacy protection in news, outlining application scenarios, privacy risks, and technical, legal-ethical solutions (e.g., federated learning, differential privacy). By synthesizing existing research contributions and identifying gaps, this work proposes future research directions in technical optimization, legal framework enhancement, and social ethics construction, providing a comprehensive reference for the field's advancement.