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arXiv 2024年

作者： Wang, Tianfu Shen, Li Fan, Qilin Xu, Tong Liu, Tongliang Xiong, Hui Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science University of Science and Technology of China Hefei230027 China The School of Big Data and Software Engineering Chongqing University ChinaScience Chongqing401331 China JD Explore Academy The UBTECH Sydney Artificial Intelligence Centre The School of Information Technologies Faculty of Engineering and Information Technologies The University of Sydney DarlingtonNSW2008 Australia Thrust of Artificial Intelligence Nansha Guangdong Guangzhou511400 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

As an essential resource management problem in network virtualization, virtual network embedding (VNE) aims to allocate the finite resources of physical network to sequentially arriving virtual network requests (VNRs) with different resource demands. Since this is an NP-hard combinatorial optimization problem, many efforts have been made to provide viable solutions. However, most existing approaches have either ignored the admission control of VNRs, which has a potential impact on long-term performances, or not fully exploited the temporal and topological features of the physical network and VNRs. In this paper, we propose a deep Hierarchical Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for VNE, named HRL-ACRA. Specifically, the whole VNE process is decomposed into an upper-level policy for deciding whether to admit the arriving VNR or not and a lower-level policy for allocating resources of the physical network to meet the requirement of VNR through the HRL approach. Considering the proximal policy optimization as the basic training algorithm, we also adopt the average reward method to address the infinite horizon problem of the upper-level agent and design a customized multi-objective intrinsic reward to alleviate the sparse reward issue of the lower-level agent. Moreover, we develop a deep feature-aware graph neural network to capture the features of VNR and physical network and exploit a sequence-to-sequence model to generate embedding actions iteratively. Finally, extensive experiments are conducted in various settings, and show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue. Our code is available at https://***/GeminiLight/hrl-acra. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Extracting and Classifying 3D Medical Image Texture Characteristics using GLCM and LBP-Based Techniques

Extracting and Classifying 3D Medical Image Texture Characte...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Srinath R. Sharmila N. Raghavendran M. C. Babu K. Ramanan R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Date Science RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Sciences Chennai Institute of Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Recent advances in 3D sensor technology have advanced 3D imaging systems. This sparks interest in 3D picture feature extraction and categorization. According to the literature, textural content is essential to visuals. In this case, we extract texture characteristics from three-dimensional photos to improve feature differentiation. Category of volumetric data with texture is possible using this method. We employ feature vectors from Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix methods to achieve this. The visual pattern and volumetric data contrast, homogeneity, and local anisotropy are described. The proposed approach was tested using a publicly accessible dataset of modified volumetric textured pictures. Generative Adversarial Networks (GAN) and recurrent Neural Networks (RNN) are classified. Our solution outperforms manually constructed techniques for extracting texture characteristics from 3D and 2D data and standard deep-learning networks. Even with few photographs per class, the recommended method improves class distinction and yields good results. Show exceptional accuracy. Integrating the Gray Level Co-occurrence Matrix (GLCM) with Local Binary Patterns (LBP) improved classification performance. The GLCM approach, which captures spatial relationships, and the LBP method, which depicts local texture, was advantageous. This hybrid method improved feature extraction and classification, notably for complex 3D medical pictures with subtle texture changes.

关键词： Deep learning Visualization Three-dimensional displays Machine learning algorithms Recurrent neural networks Liver Feature extraction Generative adversarial networks Classification algorithms Tumors

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Application of Neural Networks for Network Traffic Monitoring and Analysis

Application of Neural Networks for Network Traffic Monitorin...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Oleksandr Kushnerov Pierre Murr Serhii Herasymov Stanislav Milevskyi Marharyta Melnyk Sergii Golovashych Department of Economic Cybernetics Sumy State University Sumy Ukraine Computer Engineering Department International University of Science and Technology in Kuwait Kuwait Kuwait Cybersecurity department National Technical University “Kharkiv polytechnic institute” Kharkiv Ukraine Department of Cyber Security and information protection SET University Kyiv Ukraine Department of Software Engineering and Management Intelligent Technologies National Technical University “Kharkiv polytechnic institute” Kharkiv Ukraine

ISBN: (数字)9798350354423

ISBN: (纸本)9798350354430

Monitoring and analyzing network traffic are critically important tasks in the context of increasing cyber threats. Traditional data processing methods often struggle to handle the large volumes and complexity of modern network traffic, necessitating the use of advanced technologies such as neural networks and machine learning. This article demonstrates the application of neural networks for monitoring and analyzing network traffic, highlighting their ability to learn from large datasets to detect complex patterns and anomalies. Using TensorFlow, we developed a neural network model based on an autoencoder that effectively identifies and classifies anomalies in network traffic. The model's effectiveness was validated using a synthetic dataset from Kaggle, showing high accuracy on both training and testing data. Our approach significantly improves the efficiency and accuracy of threat detection compared to traditional methods, making it a valuable tool for enhancing cybersecurity.

关键词： Training Accuracy Neural networks Telecommunication traffic Machine learning Data models Threat assessment computer security Monitoring Testing

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AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification

arXiv

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arXiv 2022年

作者： Yang, Suorong Li, Jinqiao Zhao, Jian Shen, Furao State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China The State Key Laboratory for Novel Software Technology School of Electronic Science and Engineering Nanjing University Nanjing210023 China

Data augmentation is a widely used technique for enhancing the generalization ability of convolutional neural networks (CNNs) in image classification tasks. Occlusion is a critical factor that affects on the generalization ability of image classification models. In order to generate new samples, existing data augmentation methods based on information deletion simulate occluded samples by randomly removing some areas in the images. However, those methods cannot delete areas of the images according to their structural features of the images. To solve those problems, we propose a novel data augmentation method, AdvMask, for image classification tasks. Instead of randomly removing areas in the images, AdvMask obtains the key points that have the greatest influence on the classification results via an end-to-end sparse adversarial attack module. Therefore, we can find the most sensitive points of the classification results without considering the diversity of various image appearance and shapes of the object of interest. In addition, a data augmentation module is employed to generate structured masks based on the key points, thus forcing the CNN classification models to seek other relevant content when the most discriminative content is hidden. AdvMask can effectively improve the performance of classification models in the testing process. The experimental results on various datasets and CNN models verify that the proposed method outperforms other previous data augmentation methods in image classification tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification

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Performance assessment and multiobjective optimization of a biomass waste-fired gasification combined cycle for emission reduction

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Chemosphere 2023年第1期334卷 138980页

作者： Hai, Tao Alshahri, Abdullah H. Mohammed, Amin Salih Sharma, Aman Almujibah, Hamad R. Mohammed Metwally, Ahmed Sayed Ullah, Mirzat School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China School of Electronics and Information Engineering Ankang University Ankang China Universiti Teknologi MARA Shah Alam40450 Malaysia Department of Civil Engineering College of Engineering Taif University P.O. Box 11099 Taif City21974 Saudi Arabia Department of Software and Informatics Engineering College of Engineering Salahaddin University-Erbil Kurdistan Region Iraq Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Institute of Engineering and Technology GLA University UP Mathura281406 India Department of Mathematics College of Science King Saud University Riyadh11451 Saudi Arabia Graduate School of Economics and Management Ural Federal University Yekaterinburg620002 Russia

The use of renewable fuels leads to reduction in the use of fossil fuels and environmental pollutants. In this study, the design and analysis of a CCPP based on the use of syngas produced from biomass is discussed. The studied system includes a gasifier system to produce syngas, an external combustion gas turbine and a steam cycle to recover waste heat from combustion gases. Design variables include syngas temperature, syngas moisture content, CPR, TIT, HRSG operating pressure, and PPTD. The effect of design variables on performance components such as power generation, exergy efficiency and total cost rate of the system is investigated. Also, through multi-objective optimization, the optimal design of the system is done. Finally, it is observed that at the final decisioned optimal point, the produced power is 13.4 MW, the exergy efficiency is 17.2%, and the TCR is 118.8 $/h. © 2023

关键词： Gasification

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On the Correctness of Speculative Consensus

arXiv

引用

arXiv 2022年

作者： Hellings, Jelle Rahnama, Sajjad Gupta, Suyash Sadoghi, Mohammad Department of Computing and Software McMaster University HamiltonON Canada Exploratory Systems Lab Department of Computer Science University of California Davis DavisCA United States RISELab Department of Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA United States

The introduction of Bitcoin fueled the development of blockchain-based resilient data management systems that are resilient against failures, enable federated data management, and can support data provenance. The key factor determining the performance of such resilient data management systems is the consensus protocol used by the system to replicate client transactions among all participants. Unfortunately, existing high-throughput consensus protocols are costly and impose significant latencies on transaction processing, which rules out their usage in responsive high-performance data management systems. In this work, we improve on this situation by introducing the Proof-of-Execution consensus protocol (PoE), a consensus protocol designed for high-performance low-latency resilient data management. PoE introduces speculative execution, which minimizes latencies by starting execution before consensus is reached, and PoE introduces proof-of-executions to guarantee successful execution to clients. Furthermore, PoE introduces a single-round check-commit protocol to reduce the overall communication costs of consensus. Hence, we believe that PoE is a promising step towards flexible general-purpose low-latency resilient data management systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Information management

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Backpropagation on Dynamical Networks

arXiv

引用

arXiv 2022年

作者： Tan, Eugene Corrêa, Dèbora Stemler, Thomas Small, Michael The Department of Mathematics & Statistics The University of Western Australia CrawleyWA6009 Australia The Department of Computer Science & Software Engineering The University of Western Australia Crawley WA6009 Australia

Dynamical networks are versatile models that describe a variety of behaviours such as synchronisation and feedback. However, applying these models in real systems is difficult as prior information of the connectivity structure or local dynamics is often unknown and must be inferred from observations of network states. Additionally, the influence of coupling interactions further complicates the isolation of local node dynamics. Given the architectural similarities between dynamical networks and recurrent neural network (RNNs), we propose a network inference method based on the backpropagation through time algorithm used to train RNNs. This method aims to simultaneously infer both the connectivity structure and local node dynamics from node state observations. An approximation of local node dynamics is first constructed using a neural network. This is alternated with an adapted BPTT algorithm to regress corresponding network weights by minimising prediction errors of the network based on the previously constructed local models until convergence. This method was found to be successful in identifying the connectivity structure for coupled networks of chaotic oscillators. Freerun prediction performance with the resulting local models and weights was comparable to the true system with noisy initial conditions. The method is also extended to asymmetric negative coupling. © 2022, CC BY.

关键词： Backpropagation

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A Novel Approach for DDoS Attack Detection Using Big Data and Machine Learning

A Novel Approach for DDoS Attack Detection Using Big Data an...

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2021 International Conference on Smart Systems and Advanced Computing, SysCom 2021

作者： Gaurav, Akshat Zhou, Zhili Chui, Kwok Tai Colace, Francesco Chaurasia, Priyanka Hsu, Ching-Hsien Ronin Institute MontclairNJ07043 United States School Engineering Research Center of Digital Forensics Ministry of Education School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Hong Kong University of Salerno Italy Ulster University Magee campus Londonderry United Kingdom Department of Computer Science and Information Engineering Asia University Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Taiwan Department of Medical Research China Medical University Hospital China Medical University Taiwan

Due to the developemt in the latest digital technologies, internet service use has surged recently. In order for these online businesses to succeed, they must be able to consistently and effectively supply their services. As a result of the DDoS assault, online sources are impacted in terms of both their availability and their computational capacity. DDoS attacks are useful for cyber-attackers since there is no effective techniqque for the identification of them. In recent years, researchers have been experimenting with duffernet latest techniques like machine learning (ML) approaches to see whether they can build effective methods for detecting DDoS assaults. Machine learning and big data are used to identify DDoS assaults in this research paper. © 2021 Copyright for this paper by its authors.

关键词： Machine learning

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Exploiting Renewable Energy and UPS Systems to Reduce Power Consumption in Data Centers

Research Square

引用

Research Square 2021年

作者： Peng, Xiaopu Bhattacharya, Tathagata Cao, Ting Mao, Jianzhou Tekreeti, Taha Qin, Xiao Department of Computer Science and Software Engineering Auburn University AuburnAL United States

To develop environmental friendly and energy-efficient data centers, it is prudent to leverage on-site renewable sources like solar and wind. Data centers deploy distributed UPS systems to improve efficiency, scalability, and reliability of UPS systems, thereby handling the intermittent nature of renewable energy. We propose a renewable-energy manager called REDUX to (1) offer a smart way of managing energy supply of data centers powered by grid and renewable energy and (2) maintain a desirable balance between energy cost and system performance. To achieve this overarching objective, REDUX judiciously orchestrates distribute UPS devices (i.e., recharge or discharge) to allocate energy resources when (1) grid price is at low or high states or (2) renewable energy generation is at a low or fluctuate level. REDUX not only guarantees the stable operation of daily workload conditions, but also cuts back the energy cost of data centers by improving power resource utilization. Compared with the existing strategies, REDUX demonstrates a prominent capability of mitigating average peak workload and boosting renewable-energy utilization. © 2021, CC BY.

关键词： Renewable energy resources

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Low-Power Random Access for Timely Status Update: Packet-based or Connection-based?

arXiv

引用

arXiv 2022年

作者： Chan, Tse-Tin Feng, Jian Pan, Haoyuan The Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

This paper studies low-power random access protocols for timely status update systems with information freshness requirements, measured by age of information (AoI). In an extensive network, a fundamental challenge is scheduling a large number of transmitters to access the wireless channel in a way that achieves low network-wide AoI while consuming minimal power. Conventional packet-based random access protocols involve transmitters contending for the channel by sending their entire data packets. When the packet duration is long, the time wasted due to packet collisions can be significant. In contrast, connection-based random access protocols establish connections with the receiver before transmitting data packets. From an information freshness perspective, there should be conditions that favor one approach over the other. We present a comparative study of the average AoI of packet-based and connection-based random access protocols. Specifically, we consider frame slotted Aloha (FSA) as a representative of packet-based random access and design a request-then-access (RTA) protocol for connection-based random access. Our analyses indicate that the choice between packet-based or connection-based protocols depends mainly on the payload size of update packets and the transmit power budget. In particular, RTA saves power and significantly reduces AoI, especially when the payload size is large. Overall, our investigation offers insights into the practical design of random access protocols for low-power timely status update systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Transmitters

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