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59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023

作者： Zhu, Qi Li, Wenchao Huang, Chao Chen, Xin Zhou, Weichao Wang, Yixuan Li, Jiajun Fu, Feisi Northwestern University Department of Electrical and Computer Engineering United States Boston University Department of Electrical and Computer Engineering United States University of Liverpool Department of Computer Science United Kingdom University of New Mexico Department of Computer Science United States

ISBN: (纸本)9798350328141

Neural networks are being applied to a wide range of tasks in autonomous systems, such as perception, prediction, planning, control, and general decision making. While they may improve system performance over traditional physical model-based methods, pressing concerns have been raised on the uncertain behaviors of neural networks under varying inputs, especially for safety-critical systems such as autonomous vehicles and robots. In this paper, we will discuss the challenges in ensuring the safety and robustness of neural network-enabled autonomous systems, and present our recent work in addressing these challenges. These include methods for certifying the robustness of neural networks, verifying the safety of neural network-controlled systems, designing these systems with safety assurance, and conducting safety-assured runtime adaptation. © 2023 IEEE.

关键词： Decision making

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Robust autonomous driving control using deep hybrid-learning network under rainy/snown conditions

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Multimedia Tools and Applications 2024年第41期83卷 89281-89295页

作者： Lee, Chao-Yang Khanum, Abida Sung, Tien-Wen Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical Engineering National Cheng Kung University No.1 University Road Tainan City701 Taiwan College of Computer Science and Mathematics Fujian University of Technology No3 Xueyuan Road Fuzhou350011 China

The study introduces a groundbreaking two-stage deep hybrid learning architecture, Robust Autonomous Driving Control (RADC), designed to address the formidable challenge of ensuring safe and efficient autonomous driving in adverse weather conditions, including heavy rain and snow, in complex scenarios. In the first stage, our proposal utilizes an encoder as a variational autoencoder (VAE) model. This encoder leverages the VAE to extract feature information from the perceptual data surrounding the environment. Moving on to the second stage, we build a decoder as an Inception-Bidirectional Long Short-Term Memory (IBL) model. This decoder combines the VAE latent features obtained in stage one with additional control vehicle information tasks, including steering, speed, etc. Our approach involves predicting driving behavior along a predetermined route, allowing autonomous vehicles to stay centered on the road, simulating diverse driving scenarios, and achieving significant reductions in route-following time. This framework utilizes deep hybrid learning methods and harnesses Nvidia GPU capabilities to evaluate the effectiveness of InceptionNet, ResNet-50, MobileNet, DenseNet, and VGG16 convolutional neural networks. It enhances vehicle route following with improved metrics such as reduced inference time, heightened accuracy, and minimized lane changes in training. The RADC demonstrates exceptional performance, notably achieving a mean square error of 0.0464, 0.0346, and a 6-millisecond inference time in challenging weather like hard rain and snow. The proposed model is validated through comprehensive experiments in the Airsim environment, showcasing notable interpretability, generalization, and robustness capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decision making

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Enhancing Cybersecurity: A Pipeline Approach for Efficient Machine Learning Intrusion Detection 14

Enhancing Cybersecurity: A Pipeline Approach for Efficient M...

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14th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Berei, Ethan Oun, Ahmed Khan, M. Ajmal Ohio Northern University Department of Electrical & Computer Engineering and Computer Science OH United States

ISBN: (纸本)9798350304138

The escalating commonness of severe cyberattacks on a global scale has advanced the critical need for integrating machine learning into cybersecurity practices. Seven models, trained on a reduced feature inSDN dataset, aim to detect malicious SDN traffic. Testing involves diverse traffic types generated with Pyshark and CICFlowMeter. The study achieves its goals by creating effective intrusion detection models, developing new test datasets, implementing the pipeline, and validating it through testing in the real-time practical network in the cybersecurity lab. The use of double-feature selection is pivotal. Overall, this research accelerates the deployment of supervised machine learning in intrusion detection, strengthening cybersecurity against evolving threats. © 2023 IEEE.

关键词： Pipelines

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Fast Geometric Learning of MIMO Signal Detection over Grassmannian Manifolds

Fast Geometric Learning of MIMO Signal Detection over Grassm...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Shelim, Rashed Saad, Walid Ramakrishnan, Naren Virginia Tech Department of Electrical and Computer Engineering United States Virginia Tech Department of Computer Science ArlingtonVA22203 United States

ISBN: (纸本)9798350351255

Domain or statistical distribution shifts are a key staple of the wireless communication channel, because of the dynamics of the environment. Deep learning (DL) models for detecting multiple-input multiple-output (MIMO) signals in dynamic communication require large training samples (in the order of hundreds of thousands to millions) and online retraining to adapt to domain shift. Some dynamic networks, such as vehicular networks, cannot tolerate the waiting time associated with gathering a large number of training samples or online fine-tuning which incurs significant end-to-end delay. In this paper, a novel classification technique based on the concept of geodesic flow kernel (GFK) is proposed for MIMO signal detection. In particular, received MIMO signals are first represented as points on Grassmannian manifolds by formulating basis of subspaces spanned by the rows vectors of the received signal. Then, the domain shift is modeled using a geodesic flow kernel integrating the subspaces that lie on the geodesic to characterize changes in geometric and statistical properties of the received signals. The kernel derives low-dimensional representations of the received signals over the Grassman manifolds that are invariant to domain shift and is used in a geometric support vector machine (G-SVM) algorithm for MIMO signal detection in an unsupervised manner. Simulation results reveal that the proposed method achieves promising performance against the existing baselines like OAMPnet and MMNet with only 1,200 training samples and without online retraining. © 2024 IEEE.

关键词： Support vector machines

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Exploring the Maze: A Comparative Study of Path Finding Algorithms for PAC-Man Game 21

Exploring the Maze: A Comparative Study of Path Finding Algo...

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21st International Learning and Technology Conference, L and T 2024

作者： Salem, Nema Haneya, Hala Balbaid, Hanin Asrar, Manal Effat University Electrical and Computer Engineering Department Jeddah Saudi Arabia ECoE Effat University Computer Science Department Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Artificial Intelligence (AI) has become an integral part of our lives, finding applications across various industries. Search algorithms play a crucial role in AI. This paper focuses on the comparison of different search algorithms within the context of path-planning in the UC Berkeley's PAC-Man's game. The algorithms under consideration include Depth-First Search (DFS), Breadth-First Search (BFS), Uniform Cost Search (UCS), Iterative Deepening Depth First Search (IDDFS), and A⇤ Search. The objective is to identify the most effective algorithm in terms of path-finding performance. The study's findings reveal that the A⇤ search algorithm outperforms the others in terms of score, cost, and node expansion, making it the most suitable choice for finding the shortest path in the PAC-Man's game. © 2024 IEEE.

关键词： Iterative methods

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Intelligent ADHD Detection Using Explainable Multimodal Fusion Model 4

Intelligent ADHD Detection Using Explainable Multimodal Fusi...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Tsai, Ho-Jung Wu, Hsiao-Kuang Chen, Chun-Chuan Yeh, Chen-Hsin Chu, Tzu-Yueh Yeh, Shih-Ching National Central University Department of Computer Science and Information Engineering Taiwan National Central University Department of Biomedical Sciences and Engineering Taiwan Hunan University College of Computer Science and Electrical Engineering China

ISBN: (纸本)9798350363586

Deep learning has been proved to diagnose Attention Deficit/Hyperactivity Disorder (ADHD) accurately, but it has raised concerns about trustworthiness because of the lack of explainability. Fortunately, the development of explainable artificial intelligence (XAI) offers a solution to this problem. In this study, we employed a VR-based GO/NOGO task with distractions, capturing participants' eye movement, head movement, and electroencephalography (EEG) data. We used the collected data to train an explainable multimodal fusion model. Besides classifying between ADHD and normal children, the proposed model also generates explanation heatmaps. The heatmaps provide the importance of specific variables and timestamps in the EEG data to help us analyze the patterns captured by the model. According to our observations, the model identified specific time intervals that related to specific event-related potentials (ERPs) components. The heatmaps also demonstrate that the impacts of distractions vary between not only the GO and NOGO events but also ADHD and normal children. © 2024 IEEE.

关键词： Eye movements

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Computationally efficient reductions between some statistical models 36

Computationally efficient reductions between some statistica...

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36th International Conference on Algorithmic Learning Theory, ALT 2025

作者： Lou, Mengqi Bresler, Guy Pananjady, Ashwin School of Industrial and Systems Engineering Georgia Tech United States Department of Electrical Engineering and Computer Science MIT United States Schools of Industrial and Systems Engineering and Electrical and Computer Engineering Georgia Tech United States

We study the problem of approximately transforming a sample from a source statistical model to a sample from a target statistical model without knowing the parameters of the source model, and construct several computationally efficient such reductions between canonical statistical experiments. In particular, we provide computationally efficient procedures that approximately reduce uniform, Erlang, and Laplace location models to general target families. We illustrate our methodology by establishing nonasymptotic reductions between some canonical high-dimensional problems, spanning mixtures of experts, phase retrieval, and signal denoising. Notably, the reductions are structure-preserving and can accommodate missing data. We also point to a possible application in transforming one differentially private mechanism to another. © 2025 M. Lou, G. Bresler & A. Pananjady.

关键词： Differential privacy

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Early Detection of Cyber-Physical Attacks on Electric Vehicles Fast Charging Stations Using Wavelets and Deep Learning

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 220-231页

作者： Abu-Nassar, Ahmad M. Morsi, Walid G. Electrical Computer and Software Engineering Department Faculty of Engineering and Applied Science OshawaONL1G 0C5 Canada

Transportation electrification plays an important role in the operation of the smart grid through the integration of the electric vehicle fast charging stations (EVFCSs), which allows the electric vehicles to provide regulation services to the grid through the vehicle-to-grid (V2G) concept. However, such an integration makes smart grid assets prone to cyber vulnerability threats. In this paper, a cyber-physical attack detection approach is developed to early detect such attacks. The proposed approach combines the continuous wavelet transform (CWT) and the convolution neural network (CNN) to provide an effective detection technique. The proposed detection approach has undergone rigorous testing that considered 420 realistic operational scenarios. Unlike in previous work, the proposed detection approach was found to be effective in automatically learning the salient features from the data as well as identifying the frequency bands that hold such features and using them in the classification process. Furthermore, this work investigated the cyber-attack detection accuracy using different time resolutions of smart meters. The results have shown that the proposed approach effectively detects cyber-physical attacks with an accuracy of 99.76% and a low computational time of 1.8 seconds. © 2024 IEEE.

关键词： Feature extraction

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Mean-field Chaos Diffusion Models 41

Mean-field Chaos Diffusion Models

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41st International Conference on Machine Learning, ICML 2024

作者： Park, Sungwoo Kim, Dongjun Alaa, Ahmed M. Department of Electrical Engineering and Computer Sciences UC Berkeley United States Department of Computer Science Stanford United States UCSF United States

In this paper, we introduce a new class of score-based generative models (SGMs) designed to handle high-cardinality data distributions by leveraging concepts from mean-field theory. We present mean-field chaos diffusion models (MF-CDMs), which address the curse of dimensionality inherent in high-cardinality data by utilizing the propagation of chaos property of interacting particles. By treating high-cardinality data as a large stochastic system of interacting particles, we develop a novel score-matching method for infinite-dimensional chaotic particle systems and propose an approximation scheme that employs a subdivision strategy for efficient training. Our theoretical and empirical results demonstrate the scalability and effectiveness of MF-CDMs for managing large high-cardinality data structures, such as 3D point clouds. Copyright 2024 by the author(s)

关键词： Stochastic systems

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Blockchain in Project Management for Information Security, Transparency and Accountability

Blockchain in Project Management for Information Security, T...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Rahman, Tahmid Noor Abedin Khan, Mohammad Fasiul Bin Shahed, Labib Kafi, Abdulla Hil BRAC University Department of Electrical and Electronic Engineering Dhaka Bangladesh BRAC University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331510756

Blockchain technology, first developed for Bitcoin, offers transformative potential for project management. We present ChainManager, a conceptual blockchain-based project management platform developed with Flutter. Built on Ethereum, the blockchain network is deployed using OCI (Oracle Cloud Infrastructure), offering adaptability to varying project needs. This platform for managing projects makes use of smart contracts and immutable activity tracking in order to ensure the safety of activities such as monitoring work hours and controlling access. According to our findings, blockchain technology has the capability to solve long-standing problems, such as maintaining the integrity of data and gaining the trust of stakeholders. On the other hand, limitations such as scalability and high energy consumption continue to exist, which calls for additional study and innovation. Although there are obstacles to overcome, the purpose of this study is to investigate how blockchain technology can revolutionize project management by securing project data, automating processes, enhancing overall operational efficiency, and guaranteeing responsibility to stakeholders. © 2025 IEEE.

关键词： Ethereum

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