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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

ISBN: (纸本)9789819708338

The proceedings contain 165 papers. The special focus in this conference is on Algorithms and Architectures for Parallel processing. The topics include: MDCF: Multiple Dynamic Cuckoo Filters for LSM-Tree;a Game Theory Based Task Offloading Scheme for Maximizing Social Welfare in Edge Computing;research on Dos Attack Simulation and Detection in Low-Orbit Satellite network;malware Detection Method Based on Visualization;TOC: Joint Task Offloading and Computation Reuse in Vehicular Edge Computing;distributed Task Offloading for IoAV Using DDP-DQN;a Task Offloading and Resource Allocation Optimization Method in End-Edge-Cloud Orchestrated Computing;multi-agent Cooperative Intrusion Detection Based on Generative Data Augmentation;an Uncertainty-Aware Auction Mechanism for Federated Learning;Long Short-Term Deterministic Policy Gradient for Joint Optimization of Computational Offloading and Resource Allocation in MEC;query Optimization Mechanism for Blockchain-Based Efficient Data Traceability;research on the Evolution Path of network Hotspot Events Based on the Event Evolutionary Graph;Task Offloading in UAV-Assisted Vehicular Edge Computing networks;path Planning of Coastal Ships Based on Improved Hybrid A-Star;data Augmentation Method Based on Partial Noise Diffusion Strategy for One-Class Defect Detection Task;K Asynchronous Federated Learning with Cosine Similarity Based Aggregation on Non-IID Data;MPQUIC Transmission Control Strategy for SDN-Based Satellite network;an Energy Prediction Method for Energy Harvesting Wireless Sensor with Dynamically Adjusting Weight Factor;Bayesian Optimization for Auto-tuning Convolution neural network on GPU;persistent Sketch: A Memory-Efficient and Robust Algorithm for Finding Top-k Persistent Flows;faCa: Fast Aware and Competition-Avoided Balancing for Data Center network;Optimizing GNN Inference processing on Very Long Vector Processor;GDTM: Gaussian Differential Trust Mechanism for Optimal Recommender System;privacy-Enhanced Dyna

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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

ISBN: (纸本)9789819708611

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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

ISBN: (纸本)9789819708109

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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

ISBN: (纸本)9789819708079

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Application of neural networks in Data Communication Analysis 12

Application of Neural Networks in Data Communication Analysi...

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12th International Scientific conference on Communication and Information Technologies, KIT 2023

作者： Badidova, Bianca Turcanik, Michal Armed Forces Academy of General M. R.Štefánik Department of Informatics Liptovský Mikuláš Slovakia

ISBN: (纸本)9798350338393

The purpose of this paper is to analyze modern trends in the field of data communication analysis with a specific focus on identifying commonly used methodologies in the detection of unusual communication patterns. This paper provides a brief overview of the techniques employed in data communication analysis. We have examined particular techniques used for identifying abnormal communication patterns within network packets, associated with malicious attacks and their features. Given the information, we analyzed the utilization of artificial intelligence techniques for identifying unusual communication patterns, using feedforward neural network topologies. We proposed two types of topologies to address this problem - multilayer perceptron and convolutional neural network. The study includes the creation of unique datasets that contain both our generated data and openly available datasets, which are subsequently analyzed. The outcomes of this research include an assessment of the classification capabilities, solution complexity and processing time of multilayer perceptron and convolutional neural network topologies used in data communication analysis, based on the specific data flow and used computational system. © 2023 IEEE.

关键词： Multilayer neural networks

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Task-oriented Communication with Reliability-driven Retransmission Request 31

Task-oriented Communication with Reliability-driven Retransm...

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31st IEEE International conference on network Protocols (ICNP)

作者： Ding, Ziheng Yang, Qianqian Zhang, Zhaoyang Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China

ISBN: (纸本)9798350303223

The advanced Deep Learning (DL) techniques have enabled the development of semantic communication systems by their remarkable information processing and end -to -end optimization capabilities. However, the lack of performance guarantee of these DL -based methods also brings concerns on the reliability of semantic communication systems. To address this issue, we propose a semantic communication scheme with reliability -driven retransmission requests in order to improve the transmission efficiency and guarantee the reliability of the inference result at the same time. In particular, the transmitter first sends a basic amount of the information, and the receiver infers with this information, and assesses the reliability of the results. If the derived reliability is below a given threshold, a retransmission request is sent to the sender for more information to be transmitted. We exploit the entropy of the output logits to quantify the reliability of the classification results. More specifically, lower entropy corresponds to higher confidence, indicating higher reliability. Experimental results validate the effectiveness of the proposed scheme in terms of improving transmission efficiency and computational efficiency while maintaining the reliability of the inference results.

关键词： Semantic communication entropy regularization ARQ neural network

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Fault Identification in Honda Scoopy 110CC Continuous Variable Transmission Using Backpropagation neural networks 12

Fault Identification in Honda Scoopy 110CC Continuous Variab...

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12th IEEE International conference on Communication, networks and Satellite, COMNETSAT 2023

作者： Santoso, Rizky Dwi Pradana, Zein Hanni Wibisono, Gunawan Institute Technology Telkom Electrical Engineering Central Java Purwokerto Indonesia Institute Technology Telkom Telecommunications Engineering Central Java Purwokerto Indonesia

ISBN: (纸本)9798350341102

Intensive research in the field of signal processing has driven remarkable advancements in communication technology, particularly in the realm of voice recognition. Voice recognition concepts find application across various domains, with one such application being sound recognition within the context of Continuous Variable Transmission (CVT) for 110cc motor *** study aims to identify potential issues in CVT systems by employing artificial neural networks using Learning Predictive Coding (LPC), Mel Frequency Cepstral Coefficient (MFCC), the Artificial neural network (ANN) Backpropagation method to classify distinct sounds emanating from Honda Scoopy 110cc motor scooters. The dataset used comprises 100 CVT engine sound recordings, equally distributed between 50 samples of normal engine sounds and 50 samples of damaged engine *** research findings reveal the highest level of accuracy achieved with order 16 and 16 hidden neurons, resulting in a testing accuracy of 81.3%, a validation accuracy of 100.00%, and a testing accuracy of 90%. This data strongly supports the effectiveness of the backpropagation artificial neural network method for precise CVT issue identification. © 2023 IEEE.

关键词： Backpropagation

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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

ISBN: (纸本)9789819707973

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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

ISBN: (纸本)9789819708581

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network Traffic Anomaly Detection Method Based on Improved Clustering Analysis

Network Traffic Anomaly Detection Method Based on Improved C...

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2022 International conference on Signal processing, Computer networks, and Communications, SPCNC 2022

作者： Xu, Chao Liu, Ying Zhang, Wenjie Meng, Meng Teng, Ziyi Tian, Yunjia State Grid Liaoning Electric Power Supply Co. LTD. Information & Telecommunication Branch Shenyang110000 China

ISBN: (纸本)9781510664616

With the surge in the amount of data transmitted on the network, intelligent learning and other technologies have emerged to solve the problem of anomaly detection of streaming data in large data. For network security issues, based on the extraction of network traffic characteristics, network traffic classification or clustering is an important technical means to discover network faults and network attacks. In this paper, a distributed detection framework for detecting anomalous behaviors of encrypted network traffic is proposed. The intelligent router is adopted to obtain the encrypted network traffic monitoring stub, and then the neural network codec is used to adaptively learn the characteristics of the encrypted traffic and identify the abnormal behavior of the encrypted protocol traffic. A wide coverage traffic pattern extraction algorithm based on the network state sequence is designed to obtain the traffic patterns that represent the network conditions of the data center. Finally, the simulation test verifies that the model has frequent traffic patterns with priority. The performance of the network anomaly detection model is better than other detection methods, which improves the accuracy of detection and has a better recognition effect. © 2023 SPIE.

关键词： network security

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