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SSRN 2022年

作者： Geng, Haijun Meng, Zhuo Zhang, Han Shi, Xingang Wang, Zhiliang Yin, Xia Chen, Meng Guo, Yingya The School of Automation and Software Engineering Shanxi University School of Computer and Information Technology China The Institute for Network Sciences and Cyberspace Tsinghua University China The Department of Computer Science and Technology Tsinghua University China The School of Big Data and Software Engineering Chongqing University China The College of Computer and Data Science Fuzhou University China

To support critical applications or guarantee a high quality of service, Internet Service Providers (ISPs) generally deploy a technique called routing protection algorithm for local fast rerouting. Alternate next hops are stored alongside with the default next hops in a router’s forwarding table, and can be immediately activated to invoke a loop free repair path in face of link or node failures, or used for multipath transmission. However, the existing routing protection methods either cannot protect all possible single-failure scenarios in the network or needs the assistance of an auxiliary address. In response to the above problems, the present study proposes a single-failure routing protection algorithm based on hop by hop routing (SPA). The algorithm has the following three advantages: (1) supports hop-by-hop forwarding, (2) supports incremental deployment, and (3) can protect all possible singlefailure scenarios in the network as long as network keep connected. Extensive simulations show that, compared to the state-of-the-art schemes which are adopted by the industry, SPA not only can handle arbitrary single-failure situations in the network but also has a small path stretch. These advantages make SPA a good candidate for practical solution for IP fast reroute when network failure occurs. © 2022, The Authors. All rights reserved.

关键词： Internet service providers

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Deep Learning based Multilabel Hateful Speech Text Comments Recognition and Classification Model for Resource Scarce Ethiopian Language: The case of Afaan Oromo

Deep Learning based Multilabel Hateful Speech Text Comments ...

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Current Development in engineering and Technology (CCET), IEEE International Conference on

作者： Naol Bakala Defersha Jemal Abawajy Kula Kekeba Software Engineering Department College of Electrical and Mechanical Engineering Centre of Excellence for HPC & Big Data Analytics Addis Ababa Science and Technology Ethiopia Faculty of Science Engineering and Built Environment School of Information Technology Deakin University Geelong VIC Australia Department of Software Engineering Technology Centre of Excellence for College of Electrical and Mechanical Engineering Addis Ababa Science and Technology Ethiopia

In response to the phenomenon of hate speech on popular social media such as Facebook, a number of researchers have investigated and developed different automated techniques in order to detect and moderate hate speech. Reliable hate speech detection tools ranging from traditional machine learning models to deep neural network models have been developed and deployed for resource rich languages such as English, French and Arabic. On the Other hand, the topic of building automated techniques for hate speech detection in Afaan Oromo is still the subject of very limited studies. In this study, we have used pretrained word embedding approaches such as word2vec and fast text, and assessed their effectiveness for hateful speech recognition and classification in Afaan Oromo text comments on Facebook. By using the proposed model, the effectiveness of deep learning algorithms like CNN and LSTM was also assessed. Moreover, the significance of one of the recent topic modeling approaches, i.e., BERTopic, has been investigated to in extracting topics that can help the categorization of Afaan Oromo hate speech. Afaan Oromo Multilabel Hateful Speech Detection and dataset (AOMLHSDD), which contains 30728 text comments, was created after we employed the BERTopic to extract relevant topics. Accordingly, Then, we have conducted different experiments to test the effectiveness of CNN and LSTM models by using word2vec and fast text. The result of our experiments has shown showed that LSTM has outperformed the CNN approach by achieving the accuracy of 98.32%.

关键词： Deep learning Voice activity detection Social networking (online) Text recognition Hate speech Speech recognition data models

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Fractional discrete neural networks with variable order: solvability, finite time stability and synchronization

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The European Physical Journal Special Topics 2024年 1-14页

作者： Hioual, Amel Alomari, Saleh Al-Tarawneh, Hassan Ouannas, Adel Grassi, Giuseppe Laboratory of Dynamical Systems and Control University of Oum El-Bouaghi Oum El Bouaghi Algeria Faculty of Science and Information Technology Software Engineering Jadara University Irbid Jordan Department of Data Sciences and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Department of Mathematics and Computer Science University of Oum EL-Bouaghi Oum El Bouaghi Algeria Dipartimento Ingegneria Innovazione Universita del Salento Lecce Italy

Research in the field of dynamic behaviors in neural networks with variable-order differences is currently a thriving area, marked by various significant discoveries. However, when it comes to discrete-time neural networks featuring fractional variable-order nonlocal and nonsingular kernels, there has been limited exploration. This paper stands as one of the initial contributions to this subject, focusing primarily on the topics of stability and synchronization in finite-time within discrete neural networks. The research employs the nabla ABC variable-order difference operator, with a primary approach involving the investigation of a novel Gronwall inequality using the Atangana-Baleanu difference variable-order sum operator. This analysis leads to the development of a uniqueness theorem and a criterion for the stability in finite-time of variable-order discrete neural networks. Furthermore, the requirements stemming from this type of stability and the novel Gronwall inequality serve as the foundation for establishing the conditions necessary for achieving finite-time synchronization in these networks, employing a specific control using state feedback method. Finally, the study utilizes numerical solutions to validate the obtained results.

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A Skeletal Sequence-Based Method for Assessing Motor Coordination in Children 6th

A Skeletal Sequence-Based Method for Assessing Motor Coordin...

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6th International Conference on Innovative Computing, IC 2023

作者： Pei, Zitong Song, Wenai Zhao, Nanbing Chen, Zhiyu Cui, Wenbo Lei, Yi Chen, Yanjie Wang, Qing School of Software Engineering North University of China Taiyuan038507 China Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Children’s Health Care Centre Beijing Children’s Hospital Capital Medical University Beijing100045 China Department of Automation Tsinghua University Beijing100084 China Pharmacovigilance Research Center for Information Technology and Data Science Cross-Strait Tsinghua Research Institute Xiamen361015 China

ISBN: (纸本)9789819922864

Children’s motor coordination is an important component of physical fitness test for young children. The development of children’s motor coordination occurs throughout children’s motor development, and it is not only limited by the maturity of children’s neurodevelopment, but also plays an important role in promoting children’s neurodevelopment. This study proposes an automatic assessment method based on deep learning to improve assessment efficiency and reduce costs. The method combines human posture estimation, similarity calculation and time series feature extraction for the assessment of children’s movements. The results showed that the accuracy rate and redundancy rate of the fine action coin toss keyframes finding algorithm are 89.8% and 7.5%;the accuracy rate of the dynamic action standing long jump keyframes finding algorithm is 74.3%;the accuracy rate, precision rate and recall rate of the fine action coin toss assessment algorithm are 74.4%, 72.5% and 90.0%;the accuracy rate, precision rate and recall rate of the static action single-leg balance assessment algorithm are 87.1%, 73.5% and 87.1%;the accuracy rate, precision rate and recall rate of the dynamic action standing long jump assessment algorithm are 71.6%, 73.5% and 71.4%;the results of the three actions generally matched the expert assessment results. The method provides a good auxiliary tool for determining the motor development level of young children, and provides a good technical support for achieving the goal of "promoting the early comprehensive development of young children through actions". © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Improving Routing Decision Algorithm for RPL Networks

Improving Routing Decision Algorithm for RPL Networks

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International Conference on Information Technology (ICIT)

作者： Abdullah Al Zaqebah Omar Almomani Malek Almomani Adeeb Alsaaidah Ahmad Adel Abu-Shareha Ahmad Althunibat Department of Computer Science The World IslamicSciences and Education University Amman Jordan Department of Information System and Networks The World Islamic Sciences and Education University Amman Jordan Software Engineering The World Islamic Sciences and Education University Amman Jordan Department of Networks and Information Security Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Zaytoonah University of Jordan Amman Jordan

Low Power Networks are spreading worldwide, seeking to enable small devices to join wireless networks. This requires a routing mechanism that makes it possible and seamless. This research aims to create a Low Power and Lossy Networks (RPL) routing protocol that balances power usage and node dependability. The main emphasis of the algorithm presented here is the choice made by network nodes (Zigbees) regarding which path to deliver the message along. Results for this routing technique seem promising. Utilizing it increased packet transfer reliability while enforcing power conservation. Furthermore, a 10% reduction in the network's overall power consumption was made.

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Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning

arXiv

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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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FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation

arXiv

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arXiv 2024年

作者： Wang, Tianfu Fan, Qilin Wang, Chao Yang, Long Ding, Leilei Yuan, Nicholas Jing Xiong, Hui School of Computer Science and Technology University of Science and Technology of China China School of Big Data and Software Engineering Chongqing University China Guangzhou HKUST Fok Ying Tung Research Institute China School of Artificial Intelligence Peking University China Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong

Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a FLexible And Generalizable RL framework for VNE, named FlagVNE. Specifically, we design a bidirectional action-based Markov decision process model that enables the joint selection of virtual and physical nodes, thus improving the exploration flexibility of solution space. To tackle the expansive and dynamic action space, we design a hierarchical decoder to generate adaptive action probability distributions and ensure high training efficiency. Furthermore, to overcome the generalization issue for varying VNR sizes, we propose a meta-RL-based training method with a curriculum scheduling strategy, facilitating specialized policy training for each VNR size. Finally, extensive experimental results show the effectiveness of FlagVNE across multiple key metrics. Our code is available at GitHub1 Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Symmetrical Supervision with Transformer for Few-shot Medical Image Segmentation

Symmetrical Supervision with Transformer for Few-shot Medica...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yao Niu Zhiming Luo Sheng Lian Lei Li Shaozi Li Haixin Song Department of Artificial Intelligence Xiamen University Fujian China College of Computer and Data Science Fuzhou University Fujian China Department of Software Engineering Xiamen University Fujian China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Few-shot learning can potentially learn the target knowledge in extremely few data regimes. Existing few-shot medical image segmentation methods fail to consider the global anatomy correlation between the support and query sets. They generally adopt a weak one-way information transmission that can not fully explore the knowledge to segment query data. To address this problem, we propose a novel Symmetrical Supervision network based on traditional two-branch methods. We raise two main contributions: (1) The Symmetrical Supervision Mechanism is leveraged to strengthen the supervision of network training; (2) A transformer-based Global Feature Alignment module is introduced to increase the global consistency between the two branches. Experimental results on two challenging datasets (abdominal segmentation dataset CHAOS and cardiac segmentation dataset MS-CMRSeg) show a remarkable performance compared to other comparing methods.

关键词： Training Chaos Image segmentation Correlation Biological system modeling Information processing Transformers

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things

A Secured and Optimized Broadcast Authentication Scheme for ...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Syed Waleed Riaz Fazlullah Khan Insaf Ullah Bandar Alshawi Ryan Alturki Mohammad Wedyan Computer Science Department Abdul Wali Khan University Mardan KPK Pakistan Faculty of Science and Engineering School of Computer Science University of Nottingham Ningbo China Ningbo Zhejiang China Institute for Analytics and Data Science University of Essex Colchester UK Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Sciences Faculty of Information Technology and Computer Sciences Yarmouk University (YU) Irbid Jordan

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes.

关键词： Performance evaluation Computational modeling Authentication Surgery Internet of Medical Things Public key cryptography Real-time systems Information management Monitoring Read only memory

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software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms

arXiv

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arXiv 2023年

作者： Akter, Mst Shapna Faruk, Md Jobair Hossain Anjum, Nafisa Masum, Mohammad Shahriar, Hossain Rahman, Akond Wu, Fan Cuzzocrea, Alfredo Department of Computer Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department of Applied Data Science San Jose State University United States Department of Computer Science and Software Engineering Auburn University United States Department of Computer Science Tuskegee University United States iDEA Lab University of Calabria Rende Italy

The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Learning approaches are proven in detecting and preventing software security vulnerabilities. Besides, emerging quantum machine learning can be promising in addressing SSC attacks. Considering the distinction between traditional and quantum machine learning, performance could be varies based on the proportions of the experimenting dataset. In this paper, we conduct a comparative analysis between quantum neural networks (QNN) and conventional neural networks (NN) with a software supply chain attack dataset known as ClaMP. Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively. We evaluated the performance of both models with different proportions of the ClaMP dataset to identify the f1 score, recall, precision, and accuracy. We also measure the execution time to check the efficiency of both models. The demonstration result indicates that execution time for QNN is slower than NN with a higher percentage of datasets. Due to recent advancements in QNN, a large level of experiments shall be carried out to understand both models accurately in our future research. © 2023, CC BY.

关键词： Supply chains

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