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Motion Segmentation of Pedestrian Trajectories Using Angular Gaussian Mixture Model 23

Motion Segmentation of Pedestrian Trajectories Using Angular...

引用

5th World Symposium on Software engineering, WSSE 2023

作者： Al-Dhamari, Ahlam Hafeezallah, Adel Abu-Bakar, Syed Abd Rahman Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor81310 Malaysia Faculty of Computer Science and Engineering Hodeidah University Yemen Department of Electrical Engineering Taibah University Madinah Saudi Arabia

ISBN: (纸本)9798400708053

Motion segmentation and identification of structural segments from an ensemble of human trajectories continue to be a challenge. These processes entail distinguishing and categorizing distinct motion patterns manifested by pedestrians within a group, as well as recognizing conspicuous segments that exhibit their overall motion. Accurate motion segmentation is vital in many areas, particularly computer vision, automation, and human behavior investigation and monitoring. However, owing to the complexity and irregularity of human motion, this task demands sophisticated procedures. Addressing this challenge, the Angular Gaussian Mixture Model (AGMM) is proposed in this study for visual motion segmentation. The angular features of the pedestrian trajectories are incorporated into the GMM, allowing the overall proposed framework to accurately expose the similarity between the trajectories and fulfill motion segmentation. The experimental findings conducted on the CUHK benchmark demonstrate that the proposed framework outperforms various state-of-the-art methods. The findings signify the superior performance achieved by the proposed approach in effectively and accurately segmenting motion trajectories within different crowded scenarios. Statistical evaluation approaches, such as normalized mutual information index (NMI), purity, rand index (RI), F-measure, also known as the F1-score (F1), and accuracy (ACC), were utilized employing an immense number of real-world video clips. This study has effectively laid the foundation for an essential and initial stride towards achieving comprehensive, high-level crowd behavior analysis. © 2023 Copyright held by the owner/author(s).

关键词： Trajectories

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Attack Stealthiness and Detection of Multiagent Systems: A Zero-Sum Formulation 63

Attack Stealthiness and Detection of Multiagent Systems: A Z...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhu, Shiyong Wang, Miaomiao Chen, Jie Rantzer, Anders City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Lund University Department of Automatic Control Lund Sweden

ISBN: (纸本)9798350316339

Cybersecurity has in recent years emerged as a paramount concern in the design and operation of industrial systems and civil infrastructures, due mainly to their susceptibility to malicious cyber attacks which take advantage of the vulnerability of communication networks and IT devices. In this paper, we investigate such an attack and counter attack scenario by considering multiagent systems, a somewhat basic prototype of cyberphysical systems. We study false data injection attacks launched on the agent sensors, and possible defense of such attacks at the agent actuators. The primary issue under consideration is the stealthiness of the attacks, while steering a multiagent system away from its consensual state. We propose a metric to quantify the stealthiness, and formulate the stealthiness problem as one of zero-sum games. We solve the problem explicitly, which gives rise to a fundamental bound on the stealthiness achievable, and as well optimal attack and defense strategies that achieve the optimal stealthiness, both of which can be obtained in terms of certain augmented controllability Gramians associated with the agents. The stealthiness bound is seen to depend on agent dynamics and network characteristics including a measure of connectivity. © 2024 IEEE.

关键词： Cyber attacks

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Optimizing IoT Network Intrusion Detection: A Deep Learning Approach

Optimizing IoT Network Intrusion Detection: A Deep Learning ...

引用

Cloudification of the Internet of Things (CIoT)

作者： Khorshed Alam Md Fahad Monir Zoheb Hassan Md Tarek Habib Department of Computer Science & Engineering Independent University Bangladesh (IUB) Bangladesh Department of Electrical and Computer Engineering Virginia Tech USA Department of Electrical and Computer Engineering Université Laval Canada

ISBN: (数字)9798350356717

ISBN: (纸本)9798350356724

Network Intrusion Detection System (NIDS) serves as a essential component in data protection by monitoring computer networks for threats that can bypass conventional defenses such as malware and hackers. Deep learning (DL) techniques provide a promising approach for analyzing raw IoT network data to identify subtle patterns indicative of intrusion attempts. This study addresses a crucial research gap by developing a Deep Convolutional Neural Network (DCNN) model specifically designed for the efficient detection of stealthy and polymorphic variants while reducing false positives. Utilizing the NF-ToN-IoT dataset, the proposed model achieves outstanding performance metrics on test data, with an accuracy of 0.9923, precision of 0.9925, recall of 0.9979, and F1 score of 0.9952. To comprehensively evaluate the robustness of the model, a multi-dataset validation strategy is employed. The model is retrained and assessed on established benchmark datasets on IoT Networks, including NF-UNSW-NB15, NF-UNSW-NB15-v2 and NF-BoTIoT, demonstrating exceptional performance. Furthermore, the significance of the contribution is validated by comparing the proposed model against previously established architectures such as CNN+BiLSTM, DNN, GRU+RNN, and CNN+LSTM using the NF-ToN-IoT dataset. The proposed model consistently outperforms these prior models, highlighting its efficacy and advancements in the field. Additionally, an ablation study is conducted to analyze the individual components of the Deep CNN model, providing insights into their contributions towards detecting malware traffic and offering guidance for optimizing future NIDS models in the cybersecurity domain. Making our work available open-source on https://***/codewithkhurshed/IDSIUB can enhance its accessibility and promote future research opportunities in Network Intrusion Detection.

关键词： Deep learning Analytical models Network intrusion detection computer architecture Transformers Malware Robustness Convolutional neural networks Monitoring Software development management

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CLPiDA: A Contrastive Learning Approach for Predicting Potential PiRNA-Disease Associations

CLPiDA: A Contrastive Learning Approach for Predicting Poten...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hu, Xiaowen Zhang, Yuanpeng Zhang, Jiaxuan Deng, Lei Central South University School of Computer Science and Engineering Changsha China Xinjiang University School of Software Urumqi China University of California Department of Electrical and Computer Engineering San DiegoCA United States

ISBN: (纸本)9798350337488

Piwi-interacting RNAs (piRNAs) function as critical regulators, safeguarding genome stability through mechanisms like transposable element repression and gene stability maintenance, while also being associated with various disease pathways. Developing computationally efficient methods to predict piRNA-disease associations is vital for enhancing disease-specific drug discovery while managing costs. In this study, we present CLPiDA, a novel method for predicting potential piRNA-disease associations. CLPiDA begins by computing gaussian kernel similarities for piRNA-piRNA and disease-disease pairs to establish initial embeddings for piRNAs and diseases. Subsequently, it employs a parameter-sharing online and target network, along with data augmentation techniques, to create a contrastive learning framework. This facilitates the generation of embeddings for piRNAs and diseases using piRNA-disease association pairs. Furthermore, CLPiDA employs a cross-prediction approach to determine association scores for specific piRNAs and diseases. Notably, CLPiDA introduces a novel approach by excluding negative samples, thereby avoiding the introduction of false negatives and enhancing its reliability. Comparative experiments validate CLPiDA's superiority in terms of performance over existing methods. Case studies underscore CLPiDA's efficacy as a valuable tool for predicting piRNA-disease associations, providing valuable insights for biological experiments. The data and source code for CLPiDA are available at https://***/altriavin/CLPiDA. © 2023 IEEE.

关键词： contrastive learning graph convolutional network piRNA-disease association

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Internet of Things Oriented Human Body Enabled Communication Protocol Design for Health Monitoring Services

Internet of Things Oriented Human Body Enabled Communication...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Krishnamoorthy, P. Vasanthi, R. Pavithra, K.N. Jagadeesh, K. Vijayaraj, T. Arumugam, Deepak Sasi Institute of Technology and Engineering Department of Computer Science and Engineering Andhra Pradesh Tadepalligudem India St. Joseph's Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R. M. D. Engineering College Department of Science and Humanities Tamil Nadu Chennai India Siddharth Institute of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Puttur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Simats Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

The rapid advancement of Internet of Things (IoT) technology has transformed healthcare by enabling remote patient monitoring, chronic disease management, and preventive care solutions. Human Body Communication (HBC) stands out as a promising integration within this evolution, utilizing the body's electrical conductivity for seamless data transmission between wearable health devices and healthcare systems. This direct connection ensures secure, real-time monitoring without traditional connectivity constraints. Proposing a protocol design framework, this abstract aims to optimize communication between IoT and HBC technologies to enhance health monitoring services and the framework accommodates a variety of sensors crucial for comprehensive health assessment, including temperature, EEG, ECG, heart rate, pressure, blood glucose, oxygen saturation, respiratory rate, and blood pressure sensors. By integrating IoT and HBC, healthcare enters a new era characterized by improved accessibility, efficiency, and personalized care delivery. This convergence fosters proactive healthcare, empowering individuals to manage their well-being while enabling healthcare providers to deliver timely interventions for better patient outcomes. © 2024 IEEE.

关键词： Internet of things

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Version Age-Optimal Cached Status Updates in a Gossiping Network with Energy Harvesting Sensor 21

Version Age-Optimal Cached Status Updates in a Gossiping Net...

引用

21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Delfani, Erfan Pappas, Nikolaos Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Computer and Information Science Linköping University Linköping Sweden

ISBN: (纸本)9783903176553

In this work, we consider a real-time IoT monitoring system in which an energy harvesting sensor with a finite-size battery measures a physical process and transmits the status updates to an aggregator. The aggregator, equipped with caching capabilities, can serve the external requests of a destination network with either a stored update or a fresh update from the sensor. We assume that the destination network act as a gossiping network in which the update packets are forwarded among the nodes in a randomized setting. We utilize the Markov Decision Process framework to model and optimize the network’s average Version Age of Information (AoI) and obtain the optimal policy at the aggregator. The structure of the optimal policy is analytically demonstrated and numerically verified. Numerical results highlight the effect of the system parameters on the average Version AoI. The simulations reveal the superior performance of the optimal policy compared to a set of baseline policies. © 2023 IFIP.

关键词： Energy harvesting

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PINN with Memory: A Novel Methodology for State of Charge Estimation of Lithium-ion Batteries Under Dynamic Load Profile

PINN with Memory: A Novel Methodology for State of Charge Es...

引用

2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Kharal, Ali Yousaf Naqvi, Ijaz Haider Arshad, Naveed Department of Electrical Engineering Lahore University of Management Sciences Lahore Pakistan Department of Computer Science Lahore University of Management Sciences Lahore Pakistan

ISBN: (纸本)9798350396782

Batteries are the enabling force behind electric vehicles (EVs), which require energy storage for operation and mobility. The battery packs are complex systems, and their operation must be regulated within the specified limits to ensure safety and reliable operation. The battery management system (BMS) of EVs continuously estimates the state of charge (SOC) and the state of health (SOH) of the cells, enabling available energy, power, and range calculations. The SOC depends on various factors, but one critical factor that influences the current SOC is the load influenced by the driving conditions. SOC estimation techniques can be broadly categorized as model-based or data-driven, with each approach offering distinct advantages and disadvantages. While data-driven techniques can yield accurate estimations for the data they are trained on, they often encounter challenges such as overfitting and limited generalizability due to their detachment from the physical model. This paper developed a hybrid data-driven and model-based technique to estimate the SOC of a cell. The proposed model uses the equivalent circuit model with memory in a physics-informed neural network to accurately predict the SOC of a cell when exposed to a Worldwide Harmonized Light Vehicles Test Procedure (WLTP) class 3 drive profile. The proposed model achieves a mean absolute error and the mean squared error of 0.7240% and 0.0106%, respectively, outperforming the base LSTM models. © 2023 IEEE.

关键词： BMS Li-ion batteries LSTM Physics-informed Neural Network State of Charge estimation

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Research on dangerous flight weather prediction based on machine learning 4

Research on dangerous flight weather prediction based on mac...

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2024 4th International Conference on computer, Remote Sensing and Aerospace, CRSA 2024

作者： Liu, Haoxing Xie, Renjie Qin, Haoshen Li, Yizhou Flight Department Shanghai Jixiang Airlines Co. Ltd. Shanghai201315 China Department of Computer & Information Science & Engineering Herbert Wertheim College of Engineering University of Florida Gainesville32611 United States Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland44106 United States

With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve the early warning capability of flight dangerous weather and ensure the safe flight of aircraft is the primary task of aviation meteorological services. In this work, support vector machine (SVM) models are used to predict hazardous flight weather, especially for meteorological conditions with high uncertainty such as storms and turbulence. SVM is a supervised learning method that distinguishes between different classes of data by finding optimal decision boundaries in a high-dimensional space. To meet the needs of this study, we chose the radial basis function (RBF) as the kernel function, which helps to deal with nonlinear problems and enables the model to better capture complex meteorological data structures. During the model training phase, we used historical meteorological observations from multiple weather stations, including temperature, humidity, wind speed, wind direction, and other meteorological indicators closely related to flight safety. From this data, the SVM model learns how to distinguish between normal and dangerous flight weather conditions. © Published under licence by IOP Publishing Ltd.

关键词： Radial basis function networks

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Adversarial Learning Aided Malicious Network Traffic Detection Based on EDE Framework

Adversarial Learning Aided Malicious Network Traffic Detecti...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Zhang, Lanping Wang, Yu Ohtsuki, Tomoaki Adebisi, Bamidele Wu, Hsiao-Chun Gui, Guan Njupt College of Telecommunications and Information Engineering Nanjing China Keio University Department of Information and Computer Science Yokohama Japan Manchester Metropolitan University Faculty of Science and Engineering Manchester United Kingdom Louisiana State University School of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798350345384

With the increasing number of new malicious software and network attacks, anomalous network traffic detection technology needs to continuously identify unknown attacks in order to adapt to complex network environments. In this paper, we propose a novel adversarial learning aided malicious network traffic detection (AMD) method based on encoder-decoder-encoder (EDE) framework. The proposed AMD method detects unknown anomalies in a single type of semi-supervised form and converts network traffic data into pictures for later processing. First, an anomalous network traffic detection model consisting of two encoders, a decoder and a discriminator is introduced. Second, by using the visibility of normal samples and the unrecognizability of unknown anomaly samples, the difference between the two samples is enlarged to facilitate effective sample detection. Finally, experiments show that the proposed model detects anomalies in targeted space and is robust to complex and noisy network environments. At the same time, compared to traditional machine learning methods, AMD's shows performance improvement of up to 40%. © 2023 IEEE.

关键词： Anomaly detection

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COVID-19 Disease Prediction System Using Genetic Algorithm (Case Study: Nigeria)

COVID-19 Disease Prediction System Using Genetic Algorithm (...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Atanda, Oladayo G. Adebiyi, Marion O. Omolaja, Oladunjoye O. Akindeji, Kayode Adebiyi, Ayodele A. Landmark University Department of Computer Science Omu-Aran Nigeria SDG 3: Good Health and Well-Being Nigeria Durban University of Technology Department of Electrical Power Engineering Durban South Africa Landmark University Department of Computer Sciences Omu-Aran Nigeria

ISBN: (纸本)9798350358155

This research paper presents a COVID-19 disease prediction system designed specifically for Nigeria. The system combines the Genetic Algorithm (GA) approach with Support Vector Regression (SVR) models to improve the accuracy and efficiency of COVID-19 prediction models. The main objective is to enable early detection and effective management of COVID-19 cases. By integrating GA and SVR, the system optimizes feature selection and utilizes advanced regression techniques to forecast the likelihood of COVID-19 infection more accurately. The study utilizes a diverse dataset comprising COVID-19 cases reported in Nigeria. The dataset includes variables such as age, gender, comorbidities, symptoms, and geographic location. The effectiveness of the genetic algorithm-based prediction system is initially evaluated through rigorous assessment and comparison. Then, the SVR model is introduced as an alternative approach, and its performance is evaluated based on accuracy, sensitivity, and specificity. The results show that the genetic algorithm-based system achieves an impressive 100 percent accuracy in predicting the likelihood of COVID-19 infection. Upon integrating the SVR model, the accuracy slightly decreases to 97 percent. However, the SVR model demonstrates strong performance and exhibits promising potential for accurate prediction and early detection of COVID-19 cases. In conclusion, the COVID-19 disease prediction system developed using a combination of genetic algorithms and SVR models is a valuable tool for accurately predicting the likelihood of COVID-19 infection in Nigeria. © 2024 IEEE.

关键词： COVID-19

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