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distributed and Decentralized Edge Caching in 5G networks Using Non-Volatile Memory systems 42

Distributed and Decentralized Edge Caching in 5G Networks Us...

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42nd IEEE international Conference on distributed Computing systems (ICDCS)

作者： Zeng, Yiming Huang, Yaodong Liu, Zhenhua Liu, Ji SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA SUNY Stony Brook Dept Appl Math & Stat Stony Brook NY 11794 USA Shenzhen Univ Coll Comp & Software Engn Shenzhen Peoples R China

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Edge caching is an effective way to reduce congestion and latency in 5G networks. Non-volatile memory (NVM) devices are developing fast, with the potential of fast access, and higher endurance versus traditional storage devices, to further boost mobile data offloading efficiency in 5G networks. This paper studies how to effectively use the two-layer storage system (NVM-enhanced) in 5G edge caching. We first model an edge caching optimization problem with NVM storage devices included and develop a parallel distributed algorithm with guaranteed convergence in joint caching and routing decisions. A fully decentralized algorithm for scenarios without any coordination is further developed which also guarantees the convergence. Real-world trace-driven simulations and experiments over a small-scale system demonstrate that NVM significantly boosts the performance of edge caching and the proposed algorithms outperform the existing ones.

关键词： edge caching non-volatile memory distributed and decentralized algorithms 5G networks

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8th international symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024

8th International Symposium on Cyber Security, Cryptology, a...

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8th international symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024

ISBN: (纸本)9783031769337

The proceedings contain 27 papers. The special focus in this conference is on Cyber Security, Cryptology, and Machine Learning. The topics include: Robust Secure Aggregation for Co-located IoT Devices with Corruption Localization;spoofing-Robust Speaker Verification Based on Time-Domain Embedding;provable Imbalanced Point Clustering;Mezzo TLS 1.3 Protocol, Suitable for Transmitting Already-Encrypted Data: – Short Paper –;LLMSecCode: Evaluating Large Language Models for Secure Coding;distributed Verifiable Random Function with Compact Proof;fiat-Shamir in the Wild;predicting the Degradation Rate of Technical systems at Early Stages of Development;reminisce for Securing Private-Keys in Public;minimally Intrusive Access Management to Content Delivery networks Based on Performance Models and Access Patterns;on the Effects of Neural Network-Based Output Prediction Attacks on the Design of Symmetric-Key Ciphers;entanglement-Based Mutual Quantum Distance Bounding;evaluation of Posits for Spectral Analysis Using a Software-Defined Dataflow Architecture;access Policy Prediction via User Behavior;a Probabilistic Model for Rounding Errors: A New Look at the Table-Maker’s Dilemma;on the Overflow and p-adic Theory Applied to Homomorphic Encryption;Beneath the Cream: Unveiling Relevant Information Points from CrimeBB with Its Ground Truth Labels;cybersecurity Enhancement for Wireless networks Using Aerial Reconfigurable Intelligent Surfaces;KNN+X;challenges in Timed-Cryptography: A Position Paper (Short Paper);Post Quantum Lightweight OWF Candidates: Based on Theoretically Secure Primitives: Xors, Error Detection Codes, Permutations, Polynomials, Interaction, and Nesting (Short Version);super-Teaching in Machine Learning;A Lattice Attack Against a Family of RSA-Like Cryptosystems;on the Security Related Properties of Randomly Constructed Combinatorial Structures;HBSS+: Simple Hash-Based Stateless Signatures Revisited: (Preliminary Version, Short Paper).

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DECISION: Distributing OpenVX Applications on CPUs, GPUs and FPGAs using OpenCL 36

DECISION: Distributing OpenVX Applications on CPUs, GPUs and...

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36th IEEE international Parallel and distributed Processing symposium (IEEE IPDPS)

作者： Kalms, Lester Haering, Tim Goehringer, Diana Tech Univ Dresden Chair Adapt Dynam Syst Dresden Germany

ISBN: (纸本)9781665497473

The demand for computer vision systems and algorithms is steadily increasing. However, users often have to deal with different or new languages, architectures and tools. Furthermore, there is often no linkage between vendors, defined standards, or model-based modularization to connect everything. We propose a modularized framework for distributing applications on heterogeneous systems consisting of CPUs, GPUs, and FPGAs. The user builds an OpenVX-compliant application without knowledge of the underlying hardware. The middleend automatically schedules and maps the nodes to the available OpenCL devices. Benefits of FPGA acceleration, such as pipelining and running multiple nodes in parallel, are taken into account. The backend generates a program including memory management, synchronization mechanisms and data transfers, even between vendors. This is executed in our parallelized OpenCL based runtime system with minimal overhead. We achieved speedups of 1.63 for a heterogeneous schedule in comparison to a single GPU design when limiting the FPGA resources. Without this limitation a speedup of 13.39 is achieved for the same application.

关键词： OpenVX OpenCL Framework Scheduling Mapping computer Vision FPGA GPU CPU

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Application-Based Traffic Classification by Employing Machine Learning Models on Software-Defined Networking 1

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international Conference on computer Science, Electronics and Industrial Engineering, CSEI 2023

作者： Nuñez-Agurto, Daniel Fuertes, Walter Marrone, Luis Pisco, Lesly Ordoñez, Xiomara Rodriguez, German Department of Computer Science Universidad de las Fuerzas Armadas - ESPE P.O. Box 17-15-231B Av. General Rumiñahui S/N Sangolquí Ecuador Faculty of Computer Science Universidad Nacional de La Plata La Plata1900 Argentina

ISBN: (数字)9783031709814

ISBN: (纸本)9783031709807

The exponential growth of new applications and services on the Internet demands increased bandwidth allocation and quality of service. However, traditional networks need help in addressing this issue. Software-defined networking has emerged as a promising solution because it provides a global view of network traffic by separating the control plane from the data plane. This separation offers the necessary flexibility to incorporate new functionalities, including the integration of machine learning, which has become essential for optimizing and effectively managing networks. Therefore, this article aims to define a model for application classification using machine learning techniques to enhance bandwidth allocation and quality of service in Software-Defined networks. We employ the Sample, Explore, Modify, Model, and Assess (SEMMA) methodology to develop the classifier and train four machine learning models, Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and K-nearest neighbor (KNN), using multiple feature sets from a public data set. The results obtained demonstrate that with 15 features, the highest level of accuracy is achieved, with the following model accuracies: RF (99.99%), DT (99.89%), KNN (99.92%), and SVM (92.06%). These results underscore the remarkable effectiveness of the RF model in application classification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Quality of service

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Efficient Implementation of a Fully Analog Neural Network on a Reconfigurable Platform 66

Efficient Implementation of a Fully Analog Neural Network on...

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2023 IEEE 66th international Midwest symposium on Circuits and systems, MWSCAS 2023

作者： Ige, Afolabi Hasler, Jennifer School of Eletrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9798350302103

This paper investigates the potential of floating gate field-effect transistors (FETs) as primitives for subthreshold computation in analog neural networks. By leveraging the inherent properties of these transistors, we demonstrate their suitability for constructing neural network activation functions, such as sigmoid and rectified linear units (ReLUs), as well as winner-take-all (WTA) circuits for softmax activation. Our end-to-end analog implementation successfully classifies the concentric circles problem, illustrating the advantages of maintaining an analog signal chain throughout the process. © 2023 IEEE.

关键词： Analog computing Analog neural networks Computing-in-memory

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Continuous Complex Relational Community Detection on Multi-layer networks 21

Continuous Complex Relational Community Detection on Multi-l...

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21st IEEE international symposium on Parallel and distributed Processing with Applications, 13th IEEE international Conference on Big Data and Cloud Computing, 16th IEEE international Conference on Social Computing and Networking and 13th international Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zong, Chuanyu Cai, Ziyi Li, Wenju Qiu, Tao Li, Jiajia Xia, Xiufeng Shenyang Aerospace University School of Computer Science Shenyang China Shenyang Aircraft Corporation Shenyang China

ISBN: (纸本)9798350329223

Detecting communities in multi-layer heterogeneous information networks (HINs) is an important problem in graph analysis such as social network analysis and genome element analysis. Relational community detection on single-layer networks cannot reflect the actual complex requirements of users who find the desired communities from HINs, the reason is that single-layer networks can only reflect the relational communities in the current context. To overcome the limitations of relational communities on single-layer networks, we propose the problem of Continuous Complex Relational Community Detection (CCRCD) on multi-layer networks, which aims to find a maximum complex relational community that satisfies relational and temporal constraints on multi-layer networks. CCRCD allows users to specify more fine-grained connection requirements by limiting the number of meta-paths between vertices in communities and the duration time of communities. In this case, the problem of detecting continuous complex relational communities is defined over multi-layer networks in this paper. To solve this problem, we explore two exact algorithms based on the idea of sliding window and multi-branch detection, respectively. We also propose a heuristic algorithm to solve the CCRCD problem. Finally, we conduct comprehensive experimental studies on four real HINs to demonstrate that our algorithms can effectively find continuous complex relational communities on multi-layer networks. © 2023 IEEE.

关键词： Heuristic algorithms

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Enhanced Alzheimer's Disease Detection Using Deep Neural networks with Spatial Feature Enhancement 17

Enhanced Alzheimer's Disease Detection Using Deep Neural Net...

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17th IEEE international symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Hassan, Najmul Miah, Abu Saleh Musa Okuyama, Yuichi Shin, Jungpil The University of Aizu Computer Science and Engineering Aizuwakamatsu Japan

ISBN: (纸本)9798331530471

Alzheimer's Disease (AD) presents a pressing global health concern, significantly affecting the elderly. As the predominant form of dementia, AD progressively damages brain cells, resulting in significant neurological impairment. Developing an automated system for detecting AD is critical, prompting researchers to explore deep learning (DL) methods, which have demonstrated efficacy in diverse fields, including medical image analysis. Researchers are still facing challenges to achieving high-performance accuracy in AD work due to the intricacies involved in training hierarchical convolutional neural networks (CNNs). Initially, we applied an initial convolutional block comprising a convolutional layer and a spatial feature enhancement module, along with MaxPooling and Dropout layers. Subsequently, we implemented three intermediate convolutional blocks to capture complex relational features, followed by a final convolutional block. The final stage involves a DL-based classification block to generate probabilistic information, and we also experimented with machine learning (ML) algorithms such as Support Vector Machines (SVM) and Random Forests (RF). Our extensive experiments on the Kaggle binary class AD dataset demonstrated the efficacy of our model, achieving an impressive accuracy of 99.02%. The strengths of our proposed model lie in its ability to effectively capture and enhance spatial features through residual learning and multiple convolutional stages, leading to superior performance in AD detection compared to existing approaches. © 2024 IEEE.

关键词： Support vector machines

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RGB Image-Based Hand Pose Estimation: A Survey on Deep Learning Perspective 20

RGB Image-Based Hand Pose Estimation: A Survey on Deep Learn...

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20th CSI international symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Farjadi, Seyed Amirhossein Akbarzadeh-T, M.-R. Ghiasi-Shirazi, Kamaledin Ferdowsi University of Mashhad Center of Excellence on Soft Computing and Intelligent Information Processing Department of Computer Engineering Mashhad Iran Ferdowsi University of Mashhad Department of Computer Engineering Mashhad Iran

ISBN: (纸本)9798350383942

The pervasive integration of modern artificial intelligence into daily life necessitates robust human-computer interaction, underscored by advancements in computer technology. As the primary human tool, hand position, and orientation are vital for applications like virtual reality, with hand pose estimation playing a pivotal role. Despite the challenges inherent in RGB image-based estimation, the coalescence of extensive datasets, neural networks, and heightened computational capabilities has catalyzed the utilization of deep learning in visual tasks, particularly advancing hand pose estimation. This review exclusively centers on methodologies of RGB image-based approaches within the realm of hand pose estimation. The paper explores recent advancements in this category, providing a comprehensive analysis of outcomes and charting future directions. Organized around discussions on challenges, advancements, and future directions, this review aims to contribute to the ongoing discourse in the dynamic RGB image-based hand pose estimation field. By addressing unique challenges specific to this method, the paper offers insights that contribute to developing precise and robust hand pose estimation systems, impacting the broader landscape of computer vision and human-machine interaction. © 2024 IEEE.

关键词： computer vision

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A recommendation algorithm based on adaptive privacy protection 9

A recommendation algorithm based on adaptive privacy protect...

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9th international symposium on Advances in Electrical, Electronics, and computer Engineering, ISAEECE 2024

作者： Song, Yiyang Li, Xiaoyong Yuan, Kaiguo Kong, Weiyu Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781510683303

Differential privacy, as a provable privacy protection framework, has been widely applied in recommendation systems. However, the integration of existing collaborative filtering algorithms with differential privacy typically encounters two issues. Firstly, algorithms often use the same privacy budget for data from different time periods, introducing unnecessary noise and reducing performance. Secondly, when using gradient descent to solve collaborative filtering models, the allocation of privacy budgets is relatively simple, usually evenly distributed across each iteration. This allocation requires a predefined number of iterations. If the number is too small, the algorithm may terminate before reaching the optimal solution;if too large, each budget per iteration may be too small, leading to excessive noise. To address these issues, an adaptive privacy budget recommendation algorithm is proposed. Firstly, a dynamic time window-based privacy budget calculation algorithm is utilized to provide varying levels of privacy protection for rating data from different time periods, thus avoiding unnecessary noise. Then, an adaptive privacy budget gradient descent strategy is employed to solve the collaborative filtering model, which dynamically allocates privacy budgets based on the noise gradient of each round. This algorithm is compared with existing differential privacy-based recommendation algorithms on the Movielens-1M dataset, demonstrating superior predictive accuracy and lower sensitivity to privacy protection. © 2024 SPIE.

关键词： Differential privacy

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Securing Mobile Adhoc Network with Quantum Key Distribution: An Experimental Analysis 4

Securing Mobile Adhoc Network with Quantum Key Distribution:...

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4th international Conference on Sustainable Expert systems, ICSES 2024

作者： Thapa, Sneha Bandyopadhyay, Anjan Gourisaria, Mahendra Kumar Singh, Satyendr Patra, Sudhansu Shekhar School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India Bml Munjal University Computer Science & Engineering Department Gurugram India

ISBN: (纸本)9798331540364

Mobile adhoc networks (MANETs) are characterized by their dynamic and decentralized nature, making them inherently vulnerable to security threats. This paper proposes a novel security framework that integrates Quantum Key Distribution (QKD) with error correction, privacy amplification, and distributed key management to enhance the security of MANETs. The experimental analysis was conducted using simulations to evaluate the effectiveness of the proposed framework across various node densities and mobility patterns. Our results demonstrate improved security in key exchanges but also highlight trade-offs in network performance metrics such as throughput and latency. © 2024 IEEE.

关键词： Mobile security

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