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2023 International Conference on Applied Mathematics and Digital Simulation, AMDS 2023

作者： Chen, Ao Chen, Tianding He, Hong Wang, Hui Fan, Yimei Yu, Xin School of Physics and Information Engineering Minnan Normal University Fujian Province China School of Computing and Information Science Fuzhou Institute of Technology Fujian Province China School of Data and Computer Science Xiamen Institute of Technology Fujian Province China

In response to the limitations of existing unmanned surface vehicles in complex marine environments, where they often fail to meet the requirements for extended endurance, broad operational range, offshore missions, and prolonged operational durations, an autonomous Unmanned Surface Vehicle (NUSV) has been designed and developed. The NUSV is propelled by wind, solar, and wave energy sources. Concerning wind energy, a micro wind energy storage mechanism based on a spiral spring has been designed to enable power generation in low-wind conditions. A wave energy transmission device, complete with a dynamic model, has been devised for wave energy. This device converts torque generated from wave action in different directions into a unified torque, providing continuous power to drive the generator. Solar energy is harnessed using Maximum Power Point Tracking (MPPT) algorithms to ensure optimal operation of the solar panels. The electrical energy generated from these three natural sources is stored using Pulse Width Modulation (PWM) and DC-DC Boost converters. A programmable logic controller manages and allocates this stored electrical energy for the NUSV's equipment, including underwater propulsion systems, surface cameras, laser radar, GPS, and other electronic devices. This integration of wind, solar, and wave energy sources enables the NUSV to meet power demands, allowing for long-duration operations at sea, extended offshore missions, wide operational ranges, and prolonged mission durations. © Published under licence by IOP Publishing Ltd.

关键词： DC-DC converters

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An Innovative Secure and Privacy-Preserving Federated Learning Based Hybrid Deep Learning Model for Intrusion Detection in Internet-Enabled Wireless Sensor Networks

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Jena, Soumya Ranjan Rahman, Mohammad Zia Ur Sinha, Deepak K. kumar, P. Rajendra Vimal, Vrince Singh, Kamred Udham Syamsundararao, Thalakola Kumar, J.N.V.R. Swarup J, Balajee School of Computing and Artificial Intelligence NIMS University Jaipur Rajasthan India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist A.P. India University Bengaluru NRI Institute of Technology Agiripalli India Computer Science and Engineering Graphic Era Hill University Dehradun India School of Computing Graphic Era Hill University Dehradun India Department of CSE-Data Science KKR and KSR Institute of Technology and Sciences Guntur Andhra Pradesh India Visakhapatnam India

Cyberspace faces numerous security challenges, necessitating advanced research in intrusion detection systems (IDS) to mitigate vulnerabilities. Wireless Sensor Networks (WSNs) connected to the Internet are particularly vulnerable, requiring robust protection mechanisms. Traditional IDS struggle with identifying unknown attacks and maintaining data privacy, especially in WSNs. This study proposes a novel approach integrating Stacked Convolutional Neural Networks (SCNN), Bidirectional Long Short Term Memory (Bi-LSTM), and the African Vulture Optimization Algorithm (AVOA) within a framework of Federated Learning (FL). The integrated model, SCNN-Bi-LSTM-AVOA-FL, aims to enhance intrusion detection efficacy while preserving data privacy. A tailored AVOA optimization method fine-tunes SCNN-Bi-LSTM hyperparameters, leveraging specialized datasets (WSN-DS, CIC-IDS-2017, and WSN-BFSF) for attack detection and categorization. Evaluations compare variants with and without FL techniques (proposed-1 and proposed-2) across metrics such as accuracy, precision, recall, and F1-Score. Results demonstrate significant improvements in prediction performance, validating the efficacy of the proposed approach in enhancing IDS capabilities for WSNs. This research contributes a comprehensive framework for addressing security challenges in WSNs through advanced machine learning and optimization techniques. IEEE

关键词： Federated learning

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Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study [Extended Abstract] 40

Matching Knowledge Graphs in Entity Embedding Spaces: An Exp...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zeng, Weixin Zhao, Xiang Tan, Zhen Tang, Jiuyang Cheng, Xueqi National University of Defense Technology Laboratory for Big Data and Decision China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China Institute of Computing Technology CAS China

ISBN: (纸本)9798350317152

Entity alignment (EA) identifies equivalent entities that locate in different knowledge graphs (KGs), and has attracted growing research interests over the last few years with the advancement of KG embedding techniques. Although a pile of embedding-based EA frameworks have been developed, they mainly focus on improving the performance of entity representation learning, while largely overlook the subsequent stage that matches KGs in entity embedding spaces. Nevertheless, accurately matching entities based on learned entity representations is crucial to the overall alignment performance, as it coordinates individual alignment decisions and determines the global matching result. Hence, it is essential to understand how well existing solutions for matching KGs in entity embedding spaces perform on present benchmarks, as well as their strengths and weaknesses. To this end, in this article we provide a comprehensive survey and evaluation of matching algorithms for KGs in entity embedding spaces in terms of effectiveness and efficiency on both classic settings and new scenarios that better mirror real-life challenges. Based on in-depth analysis, we provide useful insights into the design trade-offs and good paradigms of existing works, and suggest promising directions for future development. © 2024 IEEE.

关键词： Embeddings

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OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models 62

OOP: Object-Oriented Programming Evaluation Benchmark for La...

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Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Wang, Shuai Ding, Liang Shen, Li Luo, Yong Du, Bo Tao, Dacheng Institute of Artificial Intelligence School of Computer Science Wuhan University China Hubei Luojia Laboratory Wuhan China The University of Sydney Australia School of Cyber Science and Teachnology Sun Yat-sen University China College of Computing & Data Science Nanyang Technology University Singapore

ISBN: (纸本)9798891760998

Advancing automated programming necessitates robust and comprehensive code generation benchmarks, yet current evaluation frameworks largely neglect object-oriented programming (OOP) in favour of functional programming (FP), e.g., HumanEval and MBPP. To address this, (1) our study introduces a pioneering OOP-focused benchmark, featuring 431 Python programs that encompass essential OOP concepts and features like classes and encapsulation methods. (2) We propose a novel evaluation metric, pass@o, tailored for OOP, enhancing traditional pass@k metric. (3) Our evaluation of 23 leading large language models (LLMs), including both general and code-specialized models, reveals three key insights: 1) pass@o offers a more relevant and comprehensive assessment for OOP code generation;2) Despite excelling in FP, code-specialized LLMs like WizardCoder lag in OOP compared to models like ChatGPT;3) The poor performance of all advanced LLMs on our OOP benchmark highlights a critical need for improvements in this field. Our benchmark and scripts are publicly released at: https://***/alphadl/OOP-eval. © 2024 Association for Computational Linguistics.

关键词： Object oriented programming

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SoVAR: Build Generalizable Scenarios from Accident Reports for Autonomous Driving Testing 24

SoVAR: Build Generalizable Scenarios from Accident Reports f...

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39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024

作者： Guo, An Zhou, Yuan Tian, Haoxiang Fang, Chunrong Sun, Yunjian Sun, Weisong Gao, Xinyu Luu, Anh Tuan Liu, Yang Chen, Zhenyu State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798400712487

Autonomous driving systems (ADSs) have undergone remarkable development and are increasingly employed in safety-critical applications. However, recently reported data on fatal accidents involving ADSs suggests that the desired level of safety has not yet been fully achieved. Consequently, there is a growing need for more comprehensive and targeted testing approaches to ensure safe driving. Scenarios from real-world accident reports provide valuable resources for ADS testing, including critical scenarios and high-quality seeds. However, existing scenario reconstruction methods from accident reports often exhibit limited accuracy in information extraction. Moreover, due to the diversity and complexity of road environments, matching current accident information with the simulation map data for reconstruction poses significant *** this paper, we design and implement SoVAR, a tool for automatically generating road-generalizable scenarios from accident reports. SoVAR utilizes well-designed prompts with linguistic patterns to guide the large language model (LLM) in extracting accident information from textual data. Subsequently, it formulates and solves accident-related constraints in conjunction with the extracted accident information to generate accident trajectories. Finally, SoVAR reconstructs accident scenarios on various map structures and converts them into test scenarios to evaluate its capability to detect defects in industrial ADSs. We experiment with SoVAR, using the accident reports from the National Highway Traffic Safety Administration's (NHTSA) database to generate test scenarios for the industrial-grade ADS Apollo. The experimental findings demonstrate that SoVAR can effectively generate generalized accident scenarios across different road structures. Furthermore, the results confirm that SoVAR identified 5 distinct safety violation types that contributed to the crash of Baidu Apollo. © 2024 Copyright is held by the owner/author(s). Publication ri

关键词： Highway administration

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Playing to the Strengths of High- and Low-Resolution Cues for Ultra-High Resolution Image Segmentation

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IEEE Robotics and Automation Letters 2025年第8期10卷 7787-7794页

作者： Qi Li Jiexin Luo Chunxiao Chen Jiaxin Cai Wenjie Yang Yuanlong Yu Shengfeng He Wenxi Liu College of Computer and Data Science Fuzhou University Fuzhou China School of Computing and Information Systems Singapore Management University Singapore

In ultra-high resolution image segmentation task for robotic platforms like AAVs and autonomous vehicles, existing paradigms process a downsampled input image through a deep network and the original high-resolution image through a shallow network, then fusing their features for final segmentation. Although these features are designed to be complementary, they often contain redundant or even conflicting semantic information, which leads to blurred edge contours, particularly for small objects. This is especially detrimental to robotics applications requiring precise spatial awareness. To address this challenge, we propose a novel paradigm that disentangles the task into two independent subtasks concerning high- and low-resolution inputs, leveraging high-resolution features exclusively to capture low-level structured details and low-resolution features for extracting semantics. Specifically, for the high-resolution input, we propose a region-pixel association experts scheme that partitions the image into multiple regions. For the low-resolution input, we assign compact semantic tokens to the partitioned regions. Additionally, we incorporate a high-resolution local perception scheme with an efficient field-enriched local context module to enhance small object recognition in case of incorrect semantic assignment. Extensive experiments demonstrate the state-of-the-art performance of our method and validate the effectiveness of each designed component.

关键词： Semantics Image resolution Feature extraction data mining Image edge detection Training Semantic segmentation Autonomous vehicles Accuracy Spatial resolution

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Part-Based Convolutional Neural Network and Dual Interactive Wasserstein Generative Adversarial Networks for Land Mark Detection and Localization of Autonomous Robots in Outdoor Environment Note: Sub-titles are not captured in Xplore and should not be used 1

Part-Based Convolutional Neural Network and Dual Interactive...

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1st International Conference on Computational science and Technology, ICCST 2022

作者： Sindhu, S. Saravanan, M. Srm Institute of Science and Technology Faculty of School of Computing Department of Data Science and Business Systems Kattankulathur India Srm Institute of Science and Technology Faculty of School of Computing Department of Networking and Communications Kattankulathur India

ISBN: (纸本)9781665476553

Robot localization is a fundamental competency required by an autonomous robot because the robot's location knowledge is an essential precursor to making decisions about future actions. Accurate localization of robots or autonomous vehicles is the most important requirement for autonomous applications. In this manuscript, a part-based convolutional neural network and dual interactive Wasserstein generative adversarial networks for landmark detection and localization of autonomous robots in an outdoor environment are proposed (P-CNN-DIWGAN-LMD-LZ). This research contains two phases landmark detection phase and the localization phase. In the landmark detection phase, the part-based convolutional neural network (P-CNN) is proposed to detect landmarks in capturing images. This landmark detection process creates 3 categories of responses for every detected landmarks instance: bounding box, label, and score. The bounding box has positioning with the sizing of detected landmarks at the input imagery. Label implicates detected landmark's class name. A score signifies an abjectness score that scales bounding box membership for landmarks or background classes. In the localization phase, a dual interactive Wasserstein generative adversarial network (DIWGAN) is proposed to determine robot location coordinates. Finally, the proposed method attains high robot localization recall at high accuracy in the real-world environment. Here, the outdoor robot localization dataset is taken from the KITTI dataset. The proposed method is implemented in Python;its performance is estimated under certain performance metrics, like mean absolute error (MAE), cosine proximity (CP), and accuracy. The performance of the proposed method shows higher accuracy compared with existing approaches, like DQP-ODA-LMD-LZ, TDL-LMD-LZ, and 3D-RISS-MLEVD- LMD-LZ. © 2022 IEEE.

关键词： Generative adversarial networks

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Boosting Robustness in Automatic Modulation Recognition for Wireless Communications

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1635-1648页

作者： Zhao, Yuhang Wang, Yajie Zhang, Chuan Li, Chunhai Xiong, Zehui Zhu, Liehuang Niyato, Dusit Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Guilin University of Electronic Technology Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin541004 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Nanyang Technological University College of Computing and Data Science 639798 Singapore

In the radio frequency field, deep neural networks have been widely used for automatic modulation recognition tasks due to their superior accuracy. However, it has been shown that these models are susceptible to adversarial examples, which are the kinds of carefully crafted perturbations that can lead to model misclassification and raise security issues in applications. To solve this problem, we propose an Ultra-Fusion Adversarial Training method, which combines adversarial training and ensemble learning to enable the model robustness to withstand different attack strengths. We explore the number and distribution of ensembled attacks and introduce a Fermi-function-like distribution to optimally balance the performance of different attack strengths. Additionally, we investigate the effect of the signal-to-noise ratio (SNR) interval on the model accuracy and robustness, suggesting the effective SNR interval for training. Considering the demand for practical application scenarios of modulation recognition, we propose a comprehensive robustness metric based on weighted integral to evaluate the robustness of the trained models. Numerical experiments demonstrate that our method improves the model's robustness by 31.89% against white-box attacks, and achieves up to an 80.54% improvement in black-box scenarios. These results show that our method has the ability to resiliently resist potential attacks of various strengths and can be applied to spectrum application scenarios with high-security requirements. © 2015 IEEE.

关键词： Signal modulation

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A Unified Framework Integrating Knowledge and data for Collaborative Root Cause Identification 5

A Unified Framework Integrating Knowledge and Data for Colla...

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5th International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Yu, Jiefei Cao, Zicheng He, Siyi Gu, Zuyi Xu, Yingcheng Zhong, Kai Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui China School of Big Data and Statistics Anhui University Anhui China

ISBN: (纸本)9798331529192

Capturing the root cause and propagation path of the fault is critical to ensuring the safety and efficiency of industrial processes, especially those that inadequately utilize process knowledge and data. To address this issue, a unified framework integrating knowledge and data for collaborative root cause identification is proposed. First, the knowledge causal graph (KCG) is constructed using expert knowledge and industrial flow charts, providing a preliminary reference for subsequent causality analysis. Next, by replacing the traditional vector autoregression (VAR) model in Granger Causality (GC) with the gated recurrent unit (GRU), a more reliable causal relationship between variables is obtained. Additionally, a causality fusion propagation path identification method (CF-PPI) is designed to identify the root cause and propagation path of the fault, so that the obtained fault propagation path has less redundancy and higher accuracy. Finally, the method is validated using data from the ASHRAE RP-1043 centrifugal chiller. © 2024 IEEE.

关键词： Knowledge graph

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