Mobility is the biggest issue for blind individuals. In order to move around, they must rely on others. Therefore, we are creating a navigational aid for blind people, to help the blind people in the world from variou...
详细信息
In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. Wit...
详细信息
The widespread adoption of cloud storage enables users to remotely access resources through a self-service model. Utilizing pay-per-use storage services provided by cloud service providers (CSPs) requires users to com...
详细信息
ISBN:
(数字)9798350366440
ISBN:
(纸本)9798350366457
The widespread adoption of cloud storage enables users to remotely access resources through a self-service model. Utilizing pay-per-use storage services provided by cloud service providers (CSPs) requires users to commit financially to their resources. This paper introduces a Secure Cloud Storage (SCS) framework, offering a secure architecture for cloud storage using a consortium blockchain network to address trust issues. This framework substitutes the third-party auditor with peers of a consortium blockchain network, which handles the role of data storage and verification. Storage space is divided into uncommitted and committed segments. Uncommitted storage is used for storing unverified documents, while committed storage is reserved for documents that have been validated through a consensus mechanism. In contrast, committed storage is des-ignated for the storage of committed documents. Documents validated by a consensus threshold of peer nodes are moved from uncommitted to committed storage. The implementation of the SCS framework is conducted using Hyperledger Fabric, a modular blockchain platform optimized for permissioned networks. The security analysis demonstrates that SCS effectively protects cloud storage against attacks, including unauthorized access attacks, data integrity attacks, and malicious server attacks, while maintaining data integrity and auditability. The performance evaluation shows that document upload and retrieval times, block acceptance, execution times, and latency are all improved compared to state-of-the-art cloud storage techniques.
By implementing a successful and effective Electronic-Know Your Customer (e-KYC) infrastructure, conventional defects and expenses may be greatly reduced. Strong authentication procedures are used by existing e-KYC so...
详细信息
By implementing a successful and effective Electronic-Know Your Customer (e-KYC) infrastructure, conventional defects and expenses may be greatly reduced. Strong authentication procedures are used by existing e-KYC solutions to guarantee safety and confidentiality. However, these technologies have shortcomings like as key administration expenses and dependency on encryption. We suggest a revolutionary strategy that integrates blockchain technology, Web 3.0, and quantum computing to get around these restrictions. In this research, we show how current e-KYC processes could possibly be improved, making them faster and more reliable, by integrating Web 3.0 with quantum computing. Our solution makes it simple to quickly verify several consumers, increasing productivity. We provide improved safety for online requests, transcending the weaknesses inherent in traditional encryption techniques by utilizing quantum computing technology and applying the concept of quantum encryption key methodology. We contribute to the development of safe and private solutions by addressing the shortcomings of the present e-KYC systems. The results of our research show how Web 3.0 and quantum computer technology have the power to completely alter the e-KYC environment.
Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and...
详细信息
A set of orthogonal multipartite quantum states are called (distinguishability-based) genuinely nonlocal if they are locally indistinguishable across any bipartition of the subsystems. In this work, we consider the pr...
详细信息
Precision medicine in cancer treatment increasingly relies on advanced radiotherapies, such as proton beam radiotherapy, to enhance efficacy of the treatment. When the proton beam in this treatment interacts with pati...
详细信息
In the context of addressing the problem of people who do not undergo a diagnosis of heart failure due to pulmonary conditions on time, a solution to this problem would allow early preventive detection to avoid the de...
详细信息
ISBN:
(纸本)9798350398977
In the context of addressing the problem of people who do not undergo a diagnosis of heart failure due to pulmonary conditions on time, a solution to this problem would allow early preventive detection to avoid the development of severe disease efficiently. Our approach employs the use of medical data retrieved from the user to determine and predict whether there is a likelihood of a potential condition. To solve this problem, according to a users medical measurement history, a deep learning model can be implemented to determine a preventive diagnosis or otherwise to follow up on an already detected condition. By posing the problem as a classification task, it can be taken advantage of a deep learning model focused on heart failure or pulmonary conditions to make a preliminary diagnosis and determine if there are signs of any symptomatology.
In this study, machine learning (ML) techniques are employed to predict used car prices. Several features are used to calculate the price of used cars, but in this paper, we find efficient ways to find the most precis...
详细信息
Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation ...
详细信息
Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a exible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.
暂无评论