Motivated by applications in web caches and content delivery in peer-to-peer networks, we consider the non-metric data placement problem and develop distributed algorithms for computing or approximating its optimal so...
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Healthcare is among the industries that are very interested in blockchain technology due to its potential. Blockchain and the interplanetary file system are emerging technologies that include distributed fault toleran...
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ISBN:
(纸本)9798331540661;9798331540678
Healthcare is among the industries that are very interested in blockchain technology due to its potential. Blockchain and the interplanetary file system are emerging technologies that include distributed fault tolerance, decentralization, flexible security features, and effective data management. By providing a decentralized solution, the blockchain and Interplanetary file system integration addresses the issues of data security, integrity, and accessibility in healthcare systems. The healthcare industry electronically maintains medical data, including prescriptions, diagnostic results, and personal patient information. When there is a risk of a data breach or loss, many healthcare institutions store patient data utilizing centralized models and third-party applications. The current centralized system has a 75% accuracy rate. To guarantee the integrity and security of medical data, the suggested system would demonstrate the qualities of blockchain technology, including immutability, transparency, and decentralization. The suggested system's methodology stores and hashes data using Ethereum smart contracts and consensus mechanisms. Approaches based on consensus algorithms are used to combine data upload and storage authentication and validation. Following data mining, the uploaded data will be verified and saved in the Interplanetary File System with unique content identifiers. Only those who have registered are able to access the saved data. The risk of unwanted tampering or data breaches is decreased because all transactions pertaining to the storage and access of data are recorded in an unchangeable and transparent manner. The suggested distributed and decentralized system has a 90% accuracy rate. Moreover, the decentralized nature of blockchain eliminates the necessity for a central authority, so reducing the likelihood of a single point of failure and enhancing data resilience.
The development and automation of Quality of Service (QoS) networks requires efficient algorithms for dynamic resource allocation. The main goal of these algorithms is to provide services that meet the QoS requirement...
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ISBN:
(数字)9781665485982
ISBN:
(纸本)9781665485982
The development and automation of Quality of Service (QoS) networks requires efficient algorithms for dynamic resource allocation. The main goal of these algorithms is to provide services that meet the QoS requirements of individual users while ensuring efficient use of network resources. Cloud Radio access Network (C-RAN) is a future direction in wireless communications to implement cellular radio access subsystems in current 4G, 5G and next generation networks. In the C-RAN architecture, the baseband units (BBUs) reside in a group of virtual base stations connected to the radio remote controllers (RRHs) through a high-bandwidth, low-latency front-haul network. The C-RAN architecture offers significant advantages in terms of centralized resource pooling, network flexibility, and cost savings. In this work, we demonstrate a heterogeneous C-RAN network that implements a dynamic resource allocation algorithm that enables optimal resource utilization in mobile communication networks. The C-RAN network is implemented with OpenStack and uses Docker containers to switch between LTE and GSM systems while the algorithm computes the optimal resources for the highest achievable throughput.
Precise evaluation of crop canopy characteristics requires optical remote sensing (RS) equipped with solid procedures. Despite much research on the subject, algorithm performance employing RS still needs improvement. ...
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Large language models (LLMs) often contain misleading content, emphasizing the need to align them with human values to ensure secure AI systems. Reinforcement learning from human feedback (RLHF) has been employed to a...
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ISBN:
(纸本)1577358872
Large language models (LLMs) often contain misleading content, emphasizing the need to align them with human values to ensure secure AI systems. Reinforcement learning from human feedback (RLHF) has been employed to achieve this alignment. However, it encompasses two main drawbacks: (1) RLHF exhibits complexity, instability, and sensitivity to hyperparameters in contrast to SFT. (2) Despite massive trial-and -error, multiple sampling is reduced to pairwise contrast, thus lacking contrasts from a macro perspective. In this paper, we propose Preference Ranking Optimization (PRO) as an efficient SFT algorithm to directly fine-tune LLMs for human alignment. PRO extends the pair-wise contrast to accommodate preference rankings of any length. By iteratively contrasting candidates, PRO instructs the LLM to prioritize the best response while progressively ranking the rest responses. In this manner, PRO effectively transforms human alignment into aligning the probability ranking of n responses generated by LLM with the preference ranking of humans towards these responses. Experiments have shown that PRO outperforms baseline algorithms, achieving comparable results to ChatGPT and human responses through automatic based, reward -based, GPT-4, and human evaluations.
The digital era has brought a surge in the amount of data generated, increasing the need for data security across individuals, organizations, and governments. Protecting sensitive information from unauthorized access ...
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Due to the expansion of multimedia data types and accessible bandwidth, there is an increasing demand for video retrieval systems as consumers move away from text-based retrieval systems and towards content-based retr...
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As the video streaming traffic in mobile networks is increasing, improving the content delivery process becomes crucial, e.g., by utilizing edge computing support. At an edge node, we can deploy adaptive bitrate (ABR)...
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ISBN:
(纸本)9783030983550;9783030983543
As the video streaming traffic in mobile networks is increasing, improving the content delivery process becomes crucial, e.g., by utilizing edge computing support. At an edge node, we can deploy adaptive bitrate (ABR) algorithms with a better understanding of network behavior and access to radio and player metrics. In this work, we present ECAS-ML, Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming with Machine Learning. ECAS-ML focuses on managing the tradeoff among bitrate, segment switches and stalls to achieve a higher quality of experience (QoE). For that purpose, we use machine learning techniques to analyze radio throughput traces and predict the best parameters of our algorithm to achieve better performance. The results show that ECAS-ML outperforms other client-based and edge-based ABR algorithms.
This research project aims to improve the quality of created Syllabus by maintaining format consistency and addressing the need for competent course coordinators. To accomplish this, the study employs a range of metho...
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Image encryption is a fundamental component of modern data security that guarantees the integrity, privacy, and confidentiality of sensitive visual content. This paper provides a thorough examination of image encrypti...
Image encryption is a fundamental component of modern data security that guarantees the integrity, privacy, and confidentiality of sensitive visual content. This paper provides a thorough examination of image encryption, comparing and contrasting different encryption techniques and their benefits and limitations as well as real-world uses. In order to strengthen image data security against unwanted access and manipulation, we first offer some basic understanding of image encryption. The suitability of symmetric, asymmetric, and hybrid encryption techniques in various contexts is examined. We also explore the assessment criteria used to evaluate encryption algorithms, highlighting the significance of using suitable measures to precisely gauge security and effectiveness. We also discuss common issues with image encryption, like complicated key management and computational complexity. The review also explores future directions that picture encryption might take, including multi-media encryption techniques, resistance to cryptanalysis, and quantum image encryption. We also highlight how important image encryption is in a variety of fields, such as finance, healthcare, journalism, intellectual property protection, and military operations. We will conduct a thorough analysis of current cryptography schemes and multimedia encryption algorithms to provide a comprehensive overview of the current security landscape tailored to digital multimedia technology. The findings from this survey will enhance our understanding of the efficacy and dependability of secure multimedia encryption schemes, ultimately aiding in the development of more efficient and robust encryption methods for the future. This article aims to summarize and assess numerous algorithms using different methodologies based on multiple characteristics such as MSE, PSNR, NC, BER, and so on.
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