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2024 International Conference on Military Communication and Information systems, ICMCIS 2024

作者： Kloth, Sean Rettore, Paulo H.L. Zibner, Philipp Santos, Bruno P. Sevenich, Peter Institute of Computer Science 4 University of Bonn Bonn Germany Fraunhofer Fkie Department of Communication Systems Bonn Germany Federal University of Bahia Department of Computer Science Salvador Brazil

ISBN: (纸本)9798350373196

This study investigates the robustness of a Software-defined Networking (SDN) controller when confronted with a distributed Denial-of-Service (DDOS) attack in a tactical environment. A proactive defense mechanism is introduced to detect and respond to a flooding of 'packet-in' requests, triggering a response once the network features indicate an anomaly. The methodology consists of two components: the Cyber Defense Agent (CDA), consisting of monitoring, feature engineering, detection, and responses, and the Cyber Attack Agent (CAA), including the preparation, execution, and evaluation of the attack. The CDA monitors all the IP flows from the SDN controller and processes four main features such as the average number of 'packet-in' requests, the response time to these requests, the entropy of IP addresses and ports for source and destination, and 'packet-in' requests per switch to identify compromised switches. All the components were emulated and tested, collecting quantitative evidence to demonstrate the effectiveness of both agents. © 2024 IEEE.

关键词： Software defined networking

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Generalized Two-Point Visual Control Model of Human Steering for Accurate State Estimation1

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ASME Letters in Dynamic systems and Control 2025年第1期5卷 011004页

作者： Mai, Rene E. Sears, Katherine Roessling, Grace Julius, Agung Mishra, Sandipan Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Cognitive Science Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle's lateral state. © 2024 by ASME.

关键词： Man machine systems

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Identification of Age, Plumage, and Sex of Bird Species Using Pre-Trained Deep Convolutional Neural Network 2

Identification of Age, Plumage, and Sex of Bird Species Usin...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Banerjee, Sourik Gupta, Richa Pandian, J. Arun Saranya, S. Mariappan, L. Thanga Vellore Institute of Technology Department of Computer Application Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350306415

The research aims to identify the age, plumage, and sex of bird species using standard Pre-trained Deep Convolutional Neural Networks (Pre-DCNNs). The proposed work involves collecting various bird images, which are then used for training and validating the pre-trained DCNNs. The dataset contains 200 images of four bird species. The dataset was split into twelve labeled classes of images based on age, plumage, and sex. The standard image manipulation techniques such as cropping, flipping, mirroring, rotating, shearing, and skewing were used as image augmentation techniques to increase the size of the proposed dataset. The proposed data is preprocessed, including feature extraction and data cleaning, to ensure the performance of the Pre-Trained DCNNs. The Pre-Trained DCNNs were trained on 90% of the dataset to recognize patterns in bird images. The accuracy of the pre-trained DCNNs was evaluated using metrics such as classification accuracy. The research acknowledged the advantages and limitations of the standard Pre-Trained DCNNs on the age, plumage, and sex of birds using the images. Also, it compares the performance of the Pre-Trained DCNNs on the proposed bird species image dataset. © 2024 IEEE.

关键词： Bird Plumage Identification Bird Species Identification Deep Convolutional Neural Networks Image Augmentation Pre-trained networks

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Multi-layered Clustering for Context-aware Monitoring of District Heating Network

Multi-layered Clustering for Context-aware Monitoring of Dis...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Boeva, Veselka Abghari, Shahrooz Devagiri, Vishnu Manasa Brage, Jens Blekinge Institute of Technology Department of Computer Science Karlskrona Sweden Noda Intelligent Systems Ab Karlshamn Sweden

ISBN: (纸本)9798350362480

In this study, we propose to explore multi-layered clustering to provide a context-aware data analytics tool for monitoring the network behavior of subsystems, such as a district heating (DH) network. Multi-layer clustering, in contrast to multi-view clustering, does not assume conditional independence of layers. The main idea of our approach is based on the integration of clustering models produced by considering different perspectives that capture information about the monitored subsystems' operational behavior or performance as well as their contextual environment. The initial clustering layer can reflect a static context, which is important for the subsystems' performance. It will be used as a base on which clustering models produced with respect to other analyzed operational characteristics and contexts will be layered. This will facilitate analysis and comparison of the subsystems' behavior in two comparable time periods and, eventually, identification of deviations that need attention. The proposed approach is evaluated and validated in a use case from the DH domain. The multi-layered clustering is applied and demonstrated to be robust for continuous context-aware analysis of the performance of a network of DH substations. © 2024 IEEE.

关键词： context-aware analysis district heating hypergraph visualization multi-layer clustering shared nearest neighbor similarity

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Posture Prediction in Response to Seat Angle Change with Smart Chair

Posture Prediction in Response to Seat Angle Change with Sma...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Esumi, Tsubasa Takemura, Noriko Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Department of Artificial Intelligence Fukuoka Japan

ISBN: (纸本)9781665410205

Long-term desk work with lousy posture causes physical disabilities. To solve this problem, we have developed a smart chair with a controllable seat angle in our previous research. In order to control the seat angle and guide the user to the correct posture using this smart chair, it is necessary to investigate how the posture changes in response to the seat angle change. In this study, we first constructed a dataset by capturing posture data from 30 subjects while changing the seat angle of the smart chair. This dataset trained a model based on a graph neural network to predict posture in response to seat angle changes. We compared the model's prediction performance in the evaluation experiment depending on the training data and the body part. © 2024 IEEE.

关键词： Graph neural networks

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Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining 4

Video Anomaly Detection Using Pre-Trained Deep Convolutional...

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4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Kumar, Nampally Vijay Srimukhi, Vaddadi Lakshmi Sai Siruvoru, Vahini Ashritha, Guda Ruthvini, Merugu Sheshu, Thugapindi Shiva Department of Computer Science and Business Systems B V Raju Institute of Technology Narsapur Medak502313 India

ISBN: (纸本)9798331528614

Monitoring of CCTV is readily accessible today, but software is unable to accurately detect aberrant behaviour, such as shoplifting or sudden running, which is referred to as an anomaly. The existing techniques trained the model to recognize abnormal behaviours by using all normal images, but they were strongly dependent on a huge number of training pictures. The author of this work uses three distinct pretrained CNN models/algorithms, dubbed anomaly detection, object detection, and tracking, to get around the problems. The pre-trained models exhibit high efficiency in accurately extracting features, which are subsequently retrained using the De-noising Auto Encoder model (DAE). After DAE has trained on normal images, it will alert users with red bounding boxes if it detects any aberrant frames in the movie. The UCSD pedestrian dataset, which is used in this proposed work to train the model, shows people walking, which is considered normal behaviour. If, however, a person arrives on a bicycle or a tree falls, the model detects this as aberrant behaviour, also known as anomaly detection. The distinction between normal and pathological behaviour, known as spectral and temporal behaviour—spectral denoting place, and temporal denoting TIME—is extracted through the application of context mining. In order to carry out this experiment, we used the UCSD images dataset, which is taken from movies. ©2024 IEEE.

关键词： Deep neural networks

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Corrections to "Reed Solomon Codes Against Adversarial Insertions and Deletions"

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IEEE Transactions on Information Theory 2025年第4期71卷 3237-3238页

作者： Con, Roni Shpilka, Amir Tamo, Itzhak Technion – Israel Institute of Technology Department of Computer Science Haifa3200003 Israel Tel Aviv University Blavatnik School of Computer Science Tel Aviv6997801 Israel Tel Aviv University Department of Electrical Engineering-Systems Tel Aviv6997801 Israel

The purpose of this note is to correct an error made by Con et al. (2023), specifically in the proof of Theorem 9. Here we correct the proof but as a consequence we get a slightly weaker result. In Theorem9, we claimed that for integers k and n such that k q RS code that can decode from n - 2k + 1 insdel errors where (Formula presented). Here we prove the following. Theorem 1: For integers n and k q RS-code, where (Formula presented) is a prime power, that can decode from n - 2k + 1 adversarial insdel errors. Note that the exponent of n is 4k - 3 whereas in Theorem 9 it is 4k - 4. For constant dimensional codes, the field size is of order O(n4k-3) , and in particular, for k = 2 the field size is of order O(n5) . © 1963-2012 IEEE.

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Machine Learning-Based Prediction of COVID-19: A Robust Approach for Early Diagnosis and Treatment 5th

Machine Learning-Based Prediction of COVID-19: A Robust Appr...

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5th International Conference on Trends in Computational and Cognitive Engineering, TCCE 2023

作者： Johora, Fatema Tuj Mahfuja, Israt Binte Masuqur Rahman, A.N.M. Rahman, Md Mosfikur Rahman, Md Sadekur Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Daffodil Bangladesh Department of Information Systems Kobe Institute of Computing Kobe Japan

ISBN: (纸本)9789819719228

The worldwide health catastrophe sparked by the COVID-19 epidemic continues, emphasizing the need for novel solutions in prediction, early diagnosis, and treatment. While vaccine development has progressed, the virus’s eradication remains questionable. This research presents a strong machine learning-driven approach to addressing this complicated problem. Rapid viral transmission and the lack of a single cure hamper illness diagnosis and treatment, particularly in highly populated developing countries like Bangladesh. Against this context, machine learning and artificial intelligence have emerged as critical instruments for the improvement of health care and medical research. This research investigates several machine learning algorithms and approaches that show potential for COVID-19 prediction. Among these is an algorithm that assesses individual vulnerability and environmental variables, allowing for self-diagnosis and early intervention, reducing the strain on healthcare institutions and government resources. This study demonstrates how machine learning may improve forecast accuracy and provide proactive treatment in pandemic management. Advanced techniques, such as K-nearest neighbor, Decision Tree, Random Forest, AdaBoost, XGBoost, Stochastic Gradient Descent, Linear SVC, Perceptron, Naive Bayes, Support Vector Machines, Logistic Regression, and Discriminant Analysis, are essential for achieving the best results. Finally, this research emphasizes the need for novel responses to global health challenges. Healthcare systems may improve their forecasting capacities and early intervention techniques by leveraging machine learning. This paper highlights technology’s revolutionary impact in redefining healthcare paradigms and encouraging resilience in the face of enormous difficulties. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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A Trust-based Framework for the Detection and Avoidance of Blackhole Nodes in Opportunistic Networks 9

A Trust-based Framework for the Detection and Avoidance of B...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Kundu, Protyasha Saha, Swarnadeep Kundu, Anindita Barai, Smritikona Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350383201

An Opportunistic Network (OppNet), as opposed to a ubiquitous centralized network, relies on sporadic and opportunistic encounters between nodes to facilitate communication. The uncertainty about the node's nature presents challenges when nodes with malicious intents are present in the network. One category of malicious nodes includes blackhole nodes, which receive messages but drop them intentionally. In this paper, we introduce a novel trust function designed to detect blackhole nodes within the network. Our function has been widely tested and simulated to validate and increase the efficacy of transmission of messages in the network. We have performed a comparative analysis with various protocols (PROPHET, Epidemic and Spray And Wait) on several parameters such as Delivery Ratio, Overhead Ratio and Number of Messages dropped. The results demonstrate that our trust function is able to correctly detect and avoid blackhole nodes. We observed significant improvement in delivery ratio in Epidemic Routing (47%), Prophet (41%), and Spray And Wait Routing (17%) protocols when adjusting blackhole nodes while keeping the total network nodes constant. © 2023 IEEE.

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