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arXiv 2023年

作者： Tang, Chenhao Liu, Zhengliang Ma, Chong Wu, Zihao Li, Yiwei Liu, Wei Zhu, Dajiang Li, Quanzheng Li, Xiang Liu, Tianming Fan, Lei School of Cyber Science and Engineering Shanghai Jiao Tong University Shanghai200240 China School of Computing The University of Georgia Athens30602 United States School of Automation Northwestern Polytechnical University Xi’an710072 China Department of Radiation Oncology Mayo Clinic Phoenix85054 United States Department of Computer Science and Engineering The University of Texas at Arlington Arlington76019 United States Department of Radiology Massachusetts General Hospital Harvard Medical School Boston02114 United States

Privacy policies serve as the primary conduit through which online service providers inform users about their data collection and usage procedures. However, in a bid to be comprehensive and mitigate legal risks, these policy documents are often quite verbose. In practical use, users tend to click the Agree button directly rather than reading them carefully. This practice exposes users to risks of privacy leakage and legal issues. Recently, the advent of Large Language Models (LLM) such as ChatGPT and GPT-4 has opened new possibilities for text analysis, especially for lengthy documents like privacy policies. In this study, we investigate a privacy policy text analysis framework PolicyGPT based on the LLM. This framework was tested using two datasets. The first dataset comprises of privacy policies from 115 websites, which were meticulously annotated by legal experts, categorizing each segment into one of 10 classes. The second dataset consists of privacy policies from 304 popular mobile applications, with each sentence manually annotated and classified into one of another 10 categories. Under zero-shot learning conditions, PolicyGPT demonstrated robust performance. For the first dataset, it achieved an accuracy rate of 97%, while for the second dataset, it attained an 87% accuracy rate, surpassing that of the baseline machine learning and neural network models. © 2023, CC BY.

关键词： Zero-shot learning

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A novel method for ECG signal classification via one-dimensional convolutional neural network

A novel method for ECG signal classification via one-dimensi...

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作者： Hua, Xuan Han, Jungang Zhao, Chen Tang, Haipeng He, Zhuo Chen, Qinghui Tang, Shaojie Tang, Jinshan Zhou, Weihua College of Electronic Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing in Shaanxi Xi’an710121 China College of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China College of Computing Michigan Technological University HoughtonMI49931 United States School of Computing University of Southern Mississippi Long Beach39560 United States Department of Kinesiology and Integrative Physiology Michigan Technological University HoughtonMI49931 United States School of Automation Xi’an University of Posts and Telecommunications Xi’an710121 China

This paper develops an end-to-end ECG signal classification algorithm based on a novel segmentation strategy and 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals and alleviate the workload of physicians. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) is used for segmenting the original ECG data into segments to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments for diagnosis. The performance of the proposed end to end ECG signal classification algorithm was verified with the ECG signals from 48 records in the MIT-BIH arrhythmia database. When the heartbeat types were divided into the five classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, and paced beat, the classification accuracy, the area under the curve (AUC), the sensitivity, and the F1-score achieved by the proposed model were 0.9924, 0.9994, 0.99 and 0.99, respectively. When the heartbeat types were divided into six classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, paced beat and other beats, the beat classification accuracy, the AUC, the sensitivity, and the F1-score achieved by the model reached 0.9702, 0.9966, 0.97, and 0.97, respectively. When the heartbeat types were divided into five classes recommended by the Association for Advancement of Medical Instrumentation (AAMI), i.e., normal beat, supraventricular ectopic beat, ventricular ectopic beat, fusion beat, and unknown beat, the beat classification accuracy, the sensitivity, and the F1-score were 0.9745, 0.97, and 0.97, respectively. Experimental results show that the proposed method achieves better performance than the s

关键词： Electrocardiograms

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GSLB: The Graph Structure Learning Benchmark

arXiv

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arXiv 2023年

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023, CC BY.

关键词： Benchmarking

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Invisible Geolocation Signature Extraction From a Single Image

TechRxiv

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TechRxiv 2022年

作者： Choi, Jisoo Wong, Chau-Wai Hajj-Ahmad, Adi Wu, Min Ren, Yanpin The Department of Electrical and Computer Engineering The Forensic Sciences Cluster The Secure Computing Institute NC State University RaleighNC27695 United States Amazon SunnyvaleCA94089 United States The Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States The Department of Automation Tsinghua University Beijing100190 China

Geotagging images of interest are increasingly important to law enforcement, national security, and journalism. Today, many images do not carry location tags that are trustworthy and resilient to tampering;and landmark-based visual clues may not be readily present in every image, especially in those taken indoors. In this paper, we exploit an environmental signature from the power grid, the electric network frequency (ENF) signal, which can be inherently captured in a sensing stream at the time of recording and carries useful time–location information. Compared to the recent art of extracting ENF traces from audio and video recordings, it is very challenging to extract an ENF trace from a single image. We address this challenge by first mathematically examining the impact of the ENF embedding steps such as electricity to light conversion, scene geometry dilution of radiation, and image sensing. We then incorporate the verified parametric models of the physical embedding process into our proposed entropy minimization method. The optimized results of the entropy minimization are used for creating a two-level ENF presence–classification test for region-of-capturing localization. It identifies whether a single image has an ENF trace;if yes, whether it is at 50 or 60 Hz. We quantitatively study the relationship between the ENF strength and its detectability from a single image. This paper is the first comprehensive work to bring out a unique forensic capability of environmental traces that shed light on an image’s capturing location. © 2022, CC BY-SA.

关键词： Entropy

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Multi-objective Aerial IRS-assisted ISAC Optimization via Generative AI-enhanced Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Xie, Wenwen Sun, Geng Wang, Jiacheng Du, Hongyang Kang, Jiawen Huang, Kaibin Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong School of Automation Guangdong University of Technology Guangzhou510006 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Integrated sensing and communication (ISAC) has garnered substantial research interest owing to its pivotal role in advancing the development of next-generation (6G) wireless networks. However, achieving a performance balance between communication and sensing in the dual-function radar communication (DFRC)-based ISAC system remains a significant challenge. In this paper, an aerial intelligent reflecting surface (IRS)-assisted ISAC system is explored, where a base station (BS) supports dual-functional operations, enabling both data transmission for multiple users and sensing for a blocked target, with the channel quality enhanced by an IRS mounted on the unmanned aerial vehicle (UAV). Moreover, we formulate an integrated communication, sensing, and energy efficiency multi-objective optimization problem (CSEMOP), which aims to maximize the communication rate of the users and the echo rate of the target, while minimizing UAV propulsion energy consumption by jointly optimizing the BS beamforming matrix, IRS phase shifts, the flight velocity and angle of the UAV. Considering the non-convexity, trade-off, and dynamic nature of the formulated CSEMOP, we propose a generative diffusion model-based deep deterministic policy gradient (GDMDDPG) method to solve the problem. Specifically, the diffusion model is incorporated into the actor network of DDPG to improve the action quality, with noise perturbation mechanism for better exploration and recent prioritized experience replay (RPER) sampling mechanism for enhanced training efficiency. Simulation results indicate that the GDMDDPG method delivers superior performance compared to the existing methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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QoE Maximization for Multiple-UAV-Assisted Multi-Access Edge computing: An Online Joint Optimization Approach

arXiv

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arXiv 2024年

作者： He, Long Sun, Geng Sun, Zemin Wu, Qingqing Kang, Jiawen Niyato, Dusit Han, Zhu Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The School of Automation Guangdong University of Technology Guangzhou510006 China The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446701 Korea Republic of The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In disaster scenarios, conventional terrestrial multi-access edge computing (MEC) paradigms, which rely on fixed infrastructure, may become unavailable due to infrastructure damage. With high-probability line-of-sight (LoS) communication, flexible mobility, and low cost, unmanned aerial vehicle (UAV)-assisted MEC is emerging as a new promising paradigm to provide edge computing services for ground user devices (UDs) in disaster-stricken areas. However, the limited battery capacity, computing resources, and spectrum resources also pose serious challenges for UAV-assisted MEC, which can potentially shorten the service time of UAVs and degrade the quality of experience (QoE) of UDs without an effective control approach. To this end, in this work, we first present a hierarchical architecture of multiple-UAV-assisted MEC networks that enables the coordinated provision of edge computing services by multiple UAVs. Then, we formulate a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) to maximize the QoE of UDs while considering the energy consumption constraints of UAVs. Since the problem is proven to be a future-dependent and NP-hard problem, we propose a novel online joint task offloading, resource allocation, and UAV trajectory planning approach (OJTRTA) to solve the problem. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results provide empirical evidence supporting the superior system performance of the proposed OJTRTA in comparison to alternative approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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SAMAUG: POINT PROMPT AUGMENTATION FOR SEGMENT ANYTHING MODEL

arXiv

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arXiv 2023年

作者： Dai, Haixing Ma, Chong Yan, Zhiling Liu, Zhengliang Shi, Enze Li, Yiwei Shu, Peng Wei, Xiaozheng Zhao, Lin Wu, Zihao Zeng, Fang Zhu, Dajiang Liu, Wei Li, Quanzheng Sun, Lichao Zhang, Shu Liu, Tianming Li, Xiang The School of Computing University of Georgia AthensGA United States The School of Automation Northwestern Polytechnical University Xi'An China The Department of Computer Science and Engineering Lehigh University BethlehemPA United States The Department of Radiology Massachusetts General Hospital Harvard Medical School BostonMA United States The Department of Computer Science and Engineering University of Texas ArlingtonTX United States The Department of Radiation Oncology Mayo Clinic United States The School of Computer Science Northwestern Polytechnical University Xi'An China

This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. SAMAug generates augmented point prompts to provide more information about the user's intention to SAM. Starting with an initial point prompt, SAM produces an initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts. By incorporating these extra points, SAM can generate augmented segmentation masks based on both the augmented point prompts and the initial prompt, resulting in improved segmentation performance. We conducted evaluations using four different point augmentation strategies: random sampling, sampling based on maximum difference entropy, maximum distance, and saliency. Experiment results on the COCO, Fundus, COVID QU-Ex, and ISIC2018 datasets show that SAMAug can boost SAM's segmentation results, especially using the maximum distance and saliency. SAMAug demonstrates the potential of visual prompt augmentation for computer vision. Codes of SAMAug are available at ***/yhydhx/SAMAug. © 2023, CC BY.

关键词： Image segmentation

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Enhancing EEG-Based Cross-Subject Emotion Recognition via Adaptive Source Joint Domain Adaptation

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IEEE Transactions on Affective computing 2024年

作者： Liu, Ke Luo, Xin Zhu, Wenrui Yu, Zhuliang Yu, Hong Xiao, Bin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Shanghai Jiao Tong University School of Medicine Department of Neurology Songjjiang Hospital and Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

EEG emotion recognition is crucial in both human-machine interaction and healthcare. However, recognizing emotions across different subjects remains challenging due to individual variability. While existing multi-source domain adaptation methods have been utilized for cross-subject EEG emotion decoding, they often struggle with irrelevant or weakly relevant source domains, leading to negative transfer. Additionally, variations within subdomains are often neglected in these studies. We propose a joint domain adaptation method, Adaptive Source Joint Domain Adaptation (ASJDA) to address these issues. ASJDA utilizes an unsupervised adaptive source selection strategy to select a subset of source domains by evaluating the Jensen-Shannon divergence between the source and target domains, choosing those most relevant to the target. Subsequently, it implements joint domain adaptation with these chosen sources at both the domain and category subdomain levels. Our proposed method outperforms existing state-of-the-art methods, achieving cross-subject accuracies of 96.81% in SEED, 89.69% in SEED-IV, and 69.31% in DEAP. This work significantly advances the state of the art in EEG emotion recognition by effectively addressing the challenges of cross-subject variability. The source code for ASJDA is available at https://***/Pam098/ASJDA. © 2010-2012 IEEE.

关键词： adaptive source selection cross-subject domain adaptation EEG emotion recognition

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Non-Contact Condition Monitoring of Electrical Drive

Non-Contact Condition Monitoring of Electrical Drive

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2020 Ural Symposium on Biomedical engineering, Radioelectronics and Information Technology, USBEREIT 2020

作者： Topolsky, Dmitry Topolskaya, Irina Mitin, Vyacheslav Topolsky, Nikita Yumagulov, Nikolay Gonenko, Tatyana Theoretical Basics of Electrical Engineering Chelyabinsk Russia Department of Electronic Computing Machines Chelyabinsk Russia Planar Department of Engineering Chelyabinsk Russia Department for Education in Physics and Mathematics Nizhnevartovsk Russia Omsk State Technical University Automation and Robotics Omsk Russia

ISBN: (数字)9781728131658

ISBN: (纸本)9781728131658

Smart manufacturing involves a continuous reduction in operating costs. One of the actively developing areas of development of intelligent control systems is the condition monitoring of equipment. Condition monitoring helps not only to prevent the sudden failure of equipment components, but also to determine in advance their actual life, which helps to develop an appropriate plan for equipment shutdowns and repair works. The development of equipment monitoring technologies requires new solutions. In the presented article, non-contact technology for conditional monitoring of an intelligent electric drive is proposed. The technology is based on monitoring of forces transmitted by rotating parts of electric drives by calculating a torque of electric motors. An initial data for calculating a torque are the measured values of currents, voltages and rotational speeds in an adjustable electric drive, as well as own parameters of electric motors. Control and monitoring of equipment is carried out by using a universal supervisory control and data acquisition system. During the study, a hardware and software implementation of the new approach to the condition monitoring of electric drives was proposed. Results of modeling the response of a condition monitoring system to a change of torque in a structure of an electric drive at critical infrastructure are presented. © 2020 IEEE.

关键词： Induction motors

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Deep Learning for Multi-Class Electrical Load Detection in Smart X-Energy Systems on Edge computing Devices

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Expert Systems with Applications 2025年

作者： Eduardo L. Pereira Wanderley C. Celeste Leonardo J. Silvestre Jussara F. Fardin Helder R.O. Rocha Department of Industrial Automation Federal Institute of Espirito Santo Brazil Department of Computing and Electronics Federal University of Espirito Santo Brazil Department of Electrical Engineering Federal University of Espirito Santo Brazil

Smart Energies are systems aimed at energy efficiency through intelligent use. One of the main challenges of Smart X-Energy (Grids, Industries 4.0, Buildings, and Homes) is identifying electro-electronic loads, as this allows energy consumption to be detailed and understood more precisely. This information allows users to make decisions based on technical and economic criteria. This work presents a novel solution to the challenge of identifying similar and dissimilar electrical and electronic loads for intelligent electrical systems with a large number of classes (64 and 256 groupings of electrical and electronic equipment). This study uses a deep learning methodology, which allows dealing with a large amount of raw data, eliminating the need for pre-processing the load signature data. Furthermore, this article compares the classic Random Forest tool with the Convolutional Neural Network (CNN) based tool. The simulation results show that the CNN provides 99.58 % and 98.23 % of accuracy for 64 and 256 classes respectively, against 99.09 % and 93.00 % for Randdom Forest. In addition to these comparisons, a broader analysis is conducted by evaluating the performance of Extreme Gradient Boosting (XGBoost) and the deep learning method Multiscale Patch Time Series Transformers, providing a comprehensive assessment of state-of-the-art techniques for the classification task. Another contribution of this study is the analysis of how varying sampling rates and bit resolutions affect model accuracy. This investigation examined a range of values considering a minimum resolution of 10 bits and a sampling rate of 12,495 samples per second. Memory usage assessments revealed that the proposed models could be effectively embedded on the BeagleBone Black Rev. C platform with a 12-bit resolution and a sampling rate of 12,495 samples per second. At this setting, the models consistently achieved an accuracy exceeding 95 %, making them suitable for deployment on Edge computing Devices.

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