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共 8519 条记录，以下是71-80 订阅

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QuChain: On the Security and Privacy of Peer-to-Peer System using Blind Quantum Computation 23

QuChain: On the Security and Privacy of Peer-to-Peer System ...

引用

IEEE 23rd international conference on Nanotechnology (NANO)

作者： Hafiz, Muhammad Waseem Deebak, B. D. Majeed, Abdul Hwang, Seong Oun Gachon Univ Dept Comp Engn Seongnam 13120 South Korea Gachon Univ Dept IT Convergence Engn Seongnam 13120 South Korea

ISBN: (纸本)9798350333466

Quantum threats to blockchain technology have become a pressing concern since the advent of quantum computing has the potential to undermine system security and trust. These advances might jeopardize the cryptographic techniques that are currently employed to protect and verify authenticity in blockchain systems. This article discusses the significance of quantum computing as a threat to blockchain systems, as well as the potential of adopting blind quantum computation to mitigate threats. The tremendous blind quantum computation approach for blockchain technology enables the classical processing of encrypted data to protect records in a quantum-resistant manner while disclosing no information about the data itself.

关键词： Blockchain

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Energy-Efficient Client Sampling for Federated Learning in Heterogeneous Mobile Edge computing Networks 59

Energy-Efficient Client Sampling for Federated Learning in H...

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59th Annual IEEE international conference on Communications (IEEE ICC)

作者： Tang, Jian Li, Xiuhua Li, Hui Xiong, Min Wang, Xiaofei Leung, Victor C. M. Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China Tianjin Univ Coll Intelligence & Comp TKLAN Tianjin Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

ISBN: (纸本)9781728190549

To address network congestion and data privacy concerns, federated learning (FL) that combines multiple clients and a parameter server has been widely used in mobile edge computing (MEC) networks to process the abundant data generated by mobile clients. However, the existing client sampling methods do not adequately consider the data heterogeneity and system heterogeneity. Parameter server selects inappropriate clients to participate in the FL training process. This inevitably leads to slower convergence of the global model and higher energy consumption. In this paper, we design a client sampling model with the goal of selecting suitable clients to improve the energy efficiency of FL in heterogeneous MEC networks. Then we propose an energy-efficient client sampling strategy by quantifying the communication capability, computation capability and data quality of clients. Based on the quantization results, clients are assigned with a corresponding sampled probability. Simulation results show that our proposed strategy can effectively accelerate the convergence of the global model and reduce the energy consumption compared with the baseline schemes.

关键词： Federated learning

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Energy Consumption Minimization of multi-UAV Assisted Mobile Edge computing in SAGIN

Energy Consumption Minimization of multi-UAV Assisted Mobile...

引用

IEEE/CIC international conference on Communications in China (ICCC)

作者： Zhou, Qian Yao, Yuanyuan Liu, Qi Huang, Sai Yue, Xinwei Beijing Informat Sci & Technol Univ Key Lab Informat & Commun Syst Minist Informat Ind Beijing 100101 Peoples R China Beijing Informat Sci & Technol Univ Key Lab Modern Measurement Control Technol Minist Educ Beijing 100101 Peoples R China Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China

ISBN: (纸本)9798350377675;9798350377682

Space-air-ground integrated networks (SAGIN) is one of the new network architectures in the 6th Generation Mobile Communication technology (6G), which can provide higher speed and lower latency network access and coverage for ground user terminals. Under the premise of comprehensive connection, it provides solutions for service problems such as information acquisition, transmission, and processing. Given the remote geographical environment and the limited computing capability of users, SAGIN networks can provide reliable computing offload services within their coverage area by employing mobile edge computing servers on satellites and UAVs. In terms of the energy consumtion minimization problem, A Joint optimizing of offloading decision, task allocation, computing frequency and UAV deployment (JODFQ) algorithm is proposed, to decompose the original non-convex problem into three sub-problems that are iteratively solved alternately, while meeting the maximum tolerable latency of underlying tasks. Simulation results have verified the convergence and feasibility of the proposed algorithm. Compared with algorithms that use local-satellite, full-satellite and fixed UAV position schemes, the JODFQ algorithm effectively reduces the total system energy consumption. The research findings serve as a valuable reference for achieving low energy consumption and high efficiency SAGIN systems.

关键词： space air ground integrated networks mobile edge computing unmanned aerial vehicle successive convex approximation

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Comparative Analysis of Machine Learning Algorithms for Anomaly Detection 9

Comparative Analysis of Machine Learning Algorithms for Anom...

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Siddhesh, T.S. Rajagopal, Shinu M. Bhaskaran, Sreebha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In the rapidly evolving digital landscape network security and efficiency are important especially with the increasingly widespread network anomalies that can cause severe performance degradation. Using dataset, Data preprocessing is performed. which includes removing missing values and standardizing attributes. A comparative analysis is been done among the models to determine the finest among them. The algorithm taken for comparing is Isolation Forest and it is compared with Random Forest, Decision Tree, K-mean and Logistic Regression. And the metrics for the comparison are accuracy, precision, recall, F1 score, training time and testing time. © 2024 IEEE.

关键词： Anomaly detection

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Data to Diagnosis: Harnessing STRING Insights to Unravel the Biomarkers of Alzheimer's 15

Data to Diagnosis: Harnessing STRING Insights to Unravel the...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rajeev, Anagha Jose, Bijoy A. Johnson, Sinoy Koyyappurath, Sayuj Cochin University of Science and Technology Department of Computer Science Kochi India Cochin University of Science and Technology Department of Biotechnology Kochi India

ISBN: (纸本)9798350370249

Alzheimer's disease, shrouded in complexity, poses a challenge to predictive modeling due to extensive missing data in gene and protein interaction analyses. Identifying pivotal 'hub genes' crucial for cellular networks becomes elusive, hampering effective disease understanding. Machine learning models, essential for accurate predictions, exacerbated by diverse disease manifestations. The challenge intensifies with fragmented, inconsistent data sources laden with gaps and format disparities. Our research introduces the 'Hub Gene convergence Schema,' a novel approach that systematically identifies pivotal hub genes, significantly enhancing predictive accuracy for Alzheimer's disease, enabling early diagnosis and personalized treatment. The intended 'Hub Gene convergence Schema,' a diagram integral to our project, further enhances our approach. The ultimate goal is adaptive models facilitating early diagnosis, personalized treatment, and a profound comprehension of Alzheimer's, marking a crucial step in our relentless pursuit of combating this formidable adversary. © 2024 IEEE.

关键词： Alzheimer's disease Hub genes interactome Predictive modeling

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Research on Fuzzy Variable Step Size LMS Adaptive Filtering Algorithm Based on Convex Combination 2

Research on Fuzzy Variable Step Size LMS Adaptive Filtering ...

引用

2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Li, Wenjun Wu, Caiyun Li, Xinze Li, Na Shenyang Ligong University School of Equipment Engineering Shenyang China Beijing University of Technology Machinery and Energy Engineering Institute Beijing China

ISBN: (纸本)9798350368888

This study investigates the convergence speed and mean square error capability of the Least Mean Square (LMS) adaptive filtering algorithm, presenting a novel approach termed the Fuzzy Variable Learning Rate LMS algorithm based on convex combination. Firstly, in order to enhancing convergence speed, an enhanced LMS algorithm employing variable learning rate based on sine functions, denoted as the Improved Variable Step Size LMS Algorithm based on Sine Function (γ-SLMS), is introduced. Building upon this, to mitigate the steady-state error, the Fuzzy Variable Learning Rate LMS Algorithm based on Mamdani Model (FMS-LMS) is proposed. Finally, utilizing a convex combination framework, the Fuzzy Variable Learning Rate LMS Adaptive Filtering Algorithm Based on Convex Combination (cFMS-LMS) is formulated. Simulation results indicate that the cFMS-LMS algorithm surpasses traditional LMS algorithms in both convergence speed and mean square error. Furthermore, it shows lower computational complexity compared to LMS algorithms utilizing Sugeno fuzzy inference. © 2024 IEEE.

关键词： Wiener filtering

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Comparative Analysis of Legal Outcome Prediction with Detailed and Summarized Text 9

Comparative Analysis of Legal Outcome Prediction with Detail...

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Varshini, Surisetty Hima Varma, Gottimukkala Sarayu Prabhakar, Priyanka Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350394474

Legal Outcome Prediction(LOP) is the process of predicting the possible outcome of a court case based on the contents in the case *** aim of this work is to perform a comparative analysis to assess the effectiveness of popular language models for outcome prediction with the case file as well as its summary *** models of BERT, LegalBERT, DistilBERT and RoBERTa are enhanced with transfer learning on the legal data files and the models are evaluated with accuracy and F-score. It is observed that RoBERTa performs best in LOP for both complete case file text as well as for summary text. © 2024 IEEE.

关键词： Forecasting

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An Experimental Analysis of RL based Compiler Optimization Techniques using Compiler GYM 9

An Experimental Analysis of RL based Compiler Optimization T...

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Nikith, Boda Venkata Reeha, Shaik Reddy, G. Mani Prakash Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350394474

Compiler optimization significantly impacts algorithm performance, whether it may be graph-based, parallel computing workloads and more. Understanding how these algorithms' performance varies under different optimization scenarios is crucial. This study explores diverse Reinforcement Learning (RL) algorithms for code optimization in Compiler Gym, including Actor-Critic, Random Walk, Tabular Q, and a Brute Force approach. Three benchmark datasets, Dijkstra, CRC32, and npbv0 from Compiler Gym libraries, and are evaluated using execution time and maximum reward metrics. This study reveals that the npbv0 dataset consistently outperforms others across all RL algorithms, demonstrating its compatibility with RL-based compiler optimization. This research highlights the role of compiler optimization in algorithm performance, emphasizing the potential of RL techniques in diverse workloads, with npbv0 excelling as a benchmark dataset. © 2024 IEEE.

关键词： Reinforcement learning

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Intensive Tasks Offloading Decision Algorithms in Satellite Edge computing Systems 22

Intensive Tasks Offloading Decision Algorithms in Satellite ...

引用

22nd international conference on Optical Communications and Networks, ICOCN 2024

作者： Wang, Yanxiang Chai, Furong Zhang, Qi Li, Yuanfeng Sun, Meng Sun, Dandan Tian, Feng Wang, Fu Wang, Yongjun Tian, Qinghua Yang, Leijing Beijing100876 China BUPT Beijing Key Laboratory of Space-ground Interconnection and Convergence Beijing100876 China National Univ. of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha410073 China

To address the offloading allocation problem for intensive tasks in Satellite Edge computing (SEC) systems, a Sequence-to-Sequence (Seq2Seq) and Deep Reinforcement Learning (DRL) based decision generation algorithm, e... 详细信息

ISBN: (纸本)9798350367652

关键词： Computation offloading

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An enhanced dung beetle optimizer for cloud task schedule 4

An enhanced dung beetle optimizer for cloud task schedule

引用

4rth international conference on Applied Mathematics, Modelling, and Intelligent computing, CAMMIC 2024

作者： Xie, Rongxiang Li, Shaobo Wu, Fengbin Wu, Libang Xiong, Xuan State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

ISBN: (纸本)9781510681637

Aiming to address the shortcomings of Dung Beetle Optimizer (DBO) in the areas of accuracy and convergence speed in dealing with cloud computing task scheduling issues, this paper suggests an advanced Dung Beetle Optimizer (DGTDBO)based cloud computing task scheduling method by combining the differential strategy and the perturbation strategy. Among them, the differential strategy strengthens the global exploration performance of the method, while the perturbation strategy improves the capability of the algorithm to escape from the local optimal trap. Through validation on different sizes of cloud computing systems, it is confirmed that the suggested DGTDBO can enhance the solution accuracy and speed of the algorithm effectively. It is considered as a promising cloud scheduling method. © 2024 SPIE.

关键词： Cloud computing

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