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Classification of IoT Device Communication Through Machine Learning Techniques 1

1st Eai International Conference on Forthcoming Networks and Sustainability in the IoT Era, FoNeS 2020

作者： Ahmad, Sheraz Prasad, K. N. R. Surya Vara Ullah, Zaib Mostarda, Leonardo Al-Turjman, Fadi Computer Science Division University of Camerino Camerino62032 Italy Department of Electrical and Computer Engineering University of British Columbia Columbia Canada Artificial Intelligence Department Near East University Nicosia Mersin 10 Turkey Research Center for AI and IoT Near East University Nicosia Mersin 10 Turkey

ISBN: (数字)9783030694319

ISBN: (纸本)9783030694302

The Internet of Things (IoT) also called the Internet of Everything is a system of smart interconnected devices. The smart devices are uniquely identifiable over the network and perform autonomous data communication over the network with or without human-to-computer interaction. These devices have a high level of diversity, heterogeneity, and operates with various computational capabilities. It is highly necessary to develop a framework that allows to classify the devices into different categories from effective management, security, and privacy perspectives. Various solutions such as network traffic analysis, network protocols analysis, etc. have been developed to solve the problem of device classification. The signal of a device is an important feature that could be utilized to classify various network devices. We propose a framework to identify network devices based on their signal analysis. We have developed a training data set, by collecting signals from various Wi-Fi and Bluetooth devices in a specific geographic area. A machine learning-based model is proposed for the prediction of network device classification (e.g., a Wi-Fi or Bluetooth device) with 100% accuracy. Furthermore, clustering techniques are applied to the acquired signals to predict the total number of active Wi-Fi devices in a given region. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Internet of things

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Improving efficiency of the path optimization method for a gauge theory

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Physical Review D 2023年第3期107卷 034509-034509页

作者： Yusuke Namekawa Kouji Kashiwa Hidefumi Matsuda Akira Ohnishi Hayato Takase Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima 730-0053 Japan Department of Physics Faculty of Science Kyoto University Kyoto 606-8502 Japan Yukawa Institute for Theoretical Physics Kyoto University Kyoto 606-8502 Japan Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka 811-0295 Japan Department of Physics and Center for Field Theory and Particle Physics Fudan University Shanghai 200433 China

We investigate efficiency of a gauge-covariant neural network and an approximation of the Jacobian in optimizing the complexified integration path toward evading the sign problem in lattice field theories. For the construction of the complexified integration path, we employ the path optimization method. The two-dimensional U(1) gauge theory with the complex gauge-coupling constant is used as a laboratory to evaluate the efficiency. It is found that the gauge-covariant neural network, which is composed of the Stout-like smearing, can enhance the average phase factor, as the gauge-invariant input does. For the approximation of the Jacobian, we test the most drastic case in which we perfectly drop the Jacobian during the learning process. It reduces the numerical cost of the Jacobian calculation from O(N3) to O(1), where N means the number of degrees of freedom of the theory. The path optimization using this Jacobian approximation still enhances the average phase factor at expense of a slight increase of the statistical error.

关键词： Lattice gauge theory Machine learning

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Jamming Mitigation in JRC Systems via Deep Reinforcement Learning and Backscatter-supported Intelligent Deception Strategy 6

Jamming Mitigation in JRC Systems via Deep Reinforcement Lea...

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6th IEEE International Conference on computer and Communication Systems, ICCCS 2021

作者： Ismail, Lotfi Niyato, Dusit Sun, Sumei Hoang, Dinh Thai Kim, Dong In Liang, Ying-Chang School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute for Infocomm Research Astar Singapore Singapore School of Electrical and Data Engineering University of Technology Sydney SydneyNSW Australia Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Center for Intelligent Networking and Communications University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9780738126043

In this paper, we develop a framework to optimize the trade-off between radar sensing and data transmission in Joint Radar-Communication (JRC) systems under smart and reactive jamming attacks. First, we propose a novel JRC design architecture that uses backscatter technology and deception strategy to leverage jamming attacks for a JRC system. The deception strategy is used to predict the jammer's action and adopt appropriate counterattack instantaneously, while backscatter technology is used to transmit data on the jamming signals. To deal with the jamming strategy uncertainty (e.g., jamming capability), we then develop a deep reinforcement learning algorithm to quickly find the optimal defense policy for the JRC system. Our in-depth investigation reveals that the proposed design not only significantly undermines the jamming attacks, but also utilises jamming signals to improve the system performances. Compared with conventional anti-jamming methods, our proposed design significantly improves data throughput while maintaining a satisfactory radar sensing performance in dynamic environments. Moreover, the proposed deep reinforcement learning based solution can converge four times faster than a conventional deep Q- Learning based solution. © 2021 IEEE.

关键词： Deep learning

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Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation Via Decoupling Optimization

SSRN

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SSRN 2023年

作者： Li, Mengtian Lin, Shaohui Wang, Zihan Shen, Yunhang Zhang, Baochang Ma, Lizhuang Visual Media Intelligent Processing Research Group Department of Film and Television Engineering Shanghai University Shanghai200072 China School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Youtu Lab Tencent China

Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding. However, the existing SSL-based methods suffer from severe training bias, mainly due to class imbalance and long-tail distributions of the point cloud data. As a result, they lead to a biased prediction for the tail class segmentation. In this paper, we introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively. In particular, we first employ two-round pseudo-label generation to select unlabeled points across head-to-tail classes. We further introduce multi-class imbalanced focus loss to adaptively pay more attention to feature learning across head-to-tail classes. We fix the backbone parameters after feature learning and retrain the classifier using ground-truth points to update its parameters. Ex-✩Fully documented templates are available in the elsarticle package on CTAN. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Adaptive Bandwidth Optimization Techniques for Underwater Fiber Optic Networks

Adaptive Bandwidth Optimization Techniques for Underwater Fi...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Sunil D. Kale Bhavuk Samrat Varalakshmi S Mohit Tiwari Vaishali Singh Umamaheswari. B Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Faculty of Engineering and Technology Center for Management Studies Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

Underwater fiber optic networks provide hyperlinks that permit communiqué among offshore structures, deep-sea bases, and oil rigs. Despite the growing need for better facts costs, the constrained to be had bandwidth in such networks stays a major undertaking for their efficient use. To overcome this task, a variety of adaptive bandwidth optimization techniques were proposed in current years. A number of those strategies involve dynamic modulation and coding, whilst other techniques use adaptive scheduling algorithms. All tactics rely upon dynamic replace of bandwidth allocation to make the only use of the underlying fiber hyperlink. Within the case of dynamic modulation and coding, modifications are made to the physical layer configuration of the hyperlink to conform to converting conditions on the hyperlink. On the other hand, adaptive scheduling strategies use course optimization algorithms to select the maximum green route for records to tour, re-allocating bandwidth as had to accommodate heavy loads. Subsequently, new techniques such as EQUINOX are also being explored to further optimize bandwidth usage on underwater fiber optic networks by means of combining elements of each dynamic modulation and coding and adaptive scheduling.

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Weapon Detection Using Faster R-CNN Inception-V2 for a CCTV Surveillance System 25

Weapon Detection Using Faster R-CNN Inception-V2 for a CCTV ...

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25th International computer science and engineering Conference, ICSEC 2021

作者： Hnoohom, Narit Chotivatunyu, Pitchaya Maitrichit, Nagorn Sornlertlamvanich, Virach Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand Faculty of Data Science Tokyo Japan University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665411974

Thailand has faced unrest in recent years, as have other countries around the world. The continuation of present trends means a tendency for an increase in both crimes against people and property. Nowadays, CCTV technology is widely used as surveillance and monitoring tools to help keep people safe. However, most of them still rely primarily on police personnel to inspect the displays. A weapon detection system can reduce the screen-reading workload of police officers with a limited workforce. The integration of weapon detection with CCTV cameras has a role to play in solving the problem. To develop the weapon detection system, the datasets used in this research were collected from 2 public datasets: ARMAS Weapon detection dataset and IMFDB Weapon detection system. The object detection method was used from TensorFlow Object Detection API using 1) SSD MobileNet-V1, 2) EfficientDet-D0 and 3) Faster R-CNN Inception Resnet-V2. For all experimental results, the object detection model is the Faster R-CNN Inception V2 using dataset 1, ARMAS Weapon detection dataset, with the highest mAP of 0.540 with the Average Precision with 0.5 IoU and 0.75 IoU at 0.793 and 0.627, respectively. © 2021 IEEE.

关键词： Object detection

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Anisotropic Exchange Spin Model to Investigate the Curie Temperature Dispersion of Finite-Size L10-FePt Magnetic Nanoparticles

arXiv

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arXiv 2025年

作者： Ochiai, Kohei Tsuyama, Tomoyuki Shimizu, Sumera Zhang, Lei Watanabe, Jin Kudo, Fumito Zhu, Jian-Gang Okuno, Yoshishige Resonac Corporation Research Center for Computational Science and Informatics 8 Ebisu-cho Kanagawa-ku Kanagawa. Yokohama221-8517 Japan Resonac Hard Disk Corporation Research & Development Center 5-1 Yawatakaigan-dori Chiba Ichihara290-0067 Japan Data Storage Systems Center Carnegie Mellon University PittsburghPA15213 United States Electrical and Computer Engineering Department Carnegie Mellon University PittsburghPA15213 United States Materials Science and Engineering Department Carnegie Mellon University PittsburghPA15213 United States

We developed an anisotropic spin model that accounts for magnetic anisotropy and evaluated the Curie temperature (Tc) dispersion due to finite size effects in L10-FePt nanoparticles. In heat-assisted magnetic recording (HAMR) media, a next-generation magnetic recording technology, high-density recording is achieved by locally heating L10-FePt nanoparticles near their Tc and rapidly cooling them. However, variations in Tc caused by differences in particle size and shape can compromise recording stability and areal density capacity, making the control of Tc dispersion critical. In this study, we constructed atomistic LLG models to explicitly incorporate the spin exchange anisotropy of L10-FePt, based on parameters determined by first-principles calculations. Using this model, we evaluated the impact of particle size on Tc dispersion. As a result, (1) the Tc dispersion critical to the performance of HAMR can be reproduced, whereas it was previously underestimated by isotropic models and (2) approximately 70% of the experimentally observed Tc dispersion can be attributed to particle size effects. This research highlights the role of exchange anisotropy in amplifying finite-size effects and underscores the importance of size control in HAMR media. Copyright © 2025, The Authors. All rights reserved.

关键词： Curie temperature

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Neuromodulated Meta-Learning

arXiv

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arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence 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 Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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SeaConvNeXt:A Lightweight Two-Branch Network Architecture for Efficient Prediction of Specific IHC Proteins and Antigens on Hematoxylin and Eosin(H&E)Images

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Big data Mining and Analytics 2024年第4期7卷 1212-1236页

作者： Yuli Chen Guoping Chen Guoying Shi Yao Zhou Jiayang Bai Germán Corredor Cheng Lu Xiujuan Lei School of Computer Science Shaanxi Normal UniversityXi’an 710119China Department of Pathology Shaanxi Provincial Hospital of Traditional Chinese MedicineXi’an 710003China Wallace H.Coulter Department of Biomedical Engineering Emory University and Georgia Institute of TechnologyAtlanta 30322GAUSAand also with Joseph Maxwell Cleland Atlanta VA Medical CenterDecatur 30033GAUSA Department of Radiology and Medical Research Institute Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhou 510080Chinaand also with Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and ApplicationGuangzhou 510080China

Immunohistochemistry(IHC)is a vital technique for detecting specific proteins and antigens in tissue sections using antibodies,aiding in the analysis of tumor growth and ***,IHC is costly and time-consuming,making it challenging to implement on a large *** address this issue,we introduce a method that enables virtual IHC staining directly on Hematoxylin and Eosin(H&E)***,we have developed a novel registration technique,called Bi-stage Registration based on density Clustering(BiReC),to enhance the registration efficiency between H&E and IHC *** method involves automatically generating numerous Regions Of Interest(ROI)labels on the H&E image for model training,with the labels being determined by the intensity of IHC ***,we propose a novel two-branch network architecture,called SeaConvNeXt,which integrates a lightweight Squeeze-Enhanced Axial(SEA)attention mechanism to efficiently extract and fuse multi-level local and global features from H&E images for direct prediction of specific proteins and *** SeaConvNeXt consists of a ConvNeXt branch and a global fusion *** ConvNeXt branch extracts multi-level local features at four stages,while the global fusion branch,including an SEA Transformer module and three global blocks,is designed for global feature extraction and multiple feature *** experiments demonstrate that SeaConvNeXt outperforms current state-of-the-art methods on two public datasets with corresponding IHC and H&E images,achieving an AUC of 90.7%on the HER2SC dataset and 82.5%on the CRC *** results suggest that SeaConvNeXt has great potential for predicting virtual IHC staining on H&E images.

关键词： Immunohistochemistry(IHC) Bi-stage Registration based on density Clustering(BiReC) automatic label generation SeaConvNeXt attention mechanism multi-level local and global features virtual IHC staning prediction

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