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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yuan, Jingyang Luo, Xiao Qin, Yifang Zhao, Yusheng Ju, Wei Zhang, Ming Peking University National Key Laboratory for Multimedia Information Processing Beijing China Peking University School of Computer Science Beijing China University of California Department of Computer Science Los Angeles United States

ISBN: (纸本)9781728163277

Node classification on graphs is a significant task with a wide range of applications, including social analysis and anomaly detection. Even though graph neural networks (GNNs) have produced promising results on this task, current techniques often presume that label information of nodes is accurate, which may not be the case in real-world applications. To tackle this issue, we investigate the problem of learning on graphs with label noise and develop a novel approach dubbed Consistent Graph Neural Network (CGNN) to solve it. Specifically, we employ graph contrastive learning as a regularization term, which promotes two views of augmented nodes to have consistent representations. Since this regularization term cannot utilize label information, it can enhance the robustness of node representations to label noise. Moreover, to detect noisy labels on the graph, we present a sample selection technique based on the homophily assumption, which identifies noisy nodes by measuring the consistency between the labels with their neighbors. Finally, we purify these confident noisy labels to permit efficient semantic graph learning. Extensive experiments on three well-known benchmark datasets demonstrate the superiority of our CGNN over competing approaches. © 2023 IEEE.

关键词： Contrastive Learning Graph Neural Networks Label Noise

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Cooperative Multi-Agent Deep Reinforcement Learning for Adaptive Decentralized Emergency Voltage Control

Cooperative Multi-Agent Deep Reinforcement Learning for Adap...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Zhang, Ying Yue, Meng Montana State University Department of Electrical and Computer Engineering BozemanMT United States Brookhaven National Laboratory Interdisciplinary Science Department UptonNY United States

ISBN: (纸本)9798350313604

Under voltage load shedding (UVLS) for power grid emergency control builds the last defensive perimeter to prevent cascade outages and blackouts in case of contingencies. This letter proposes a novel cooperative multi-Agent deep reinforcement learning (MADRL)-based UVLS algorithm in an adaptive decentralized way. With well-designed input signals reflecting the voltage deviation, newly structured neural networks are developed as intelligent agents to obtain control actions and their probabilities to accommodate high uncertainties in volatile power system operations. Moreover, the interaction among the agents for coordinated control is implemented and refined by a state-of-The-Art attention mechanism, which helps agents concentratively learn effective interacted information. The proposed method realizes decentralized coordinated control, adapting to extremely high uncertainties. Case studies on an IEEE benchmark system indicate the superior performance of the proposed algorithm. © 2024 IEEE.

关键词： Reinforcement learning

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Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse

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Digital Communications and Networks 2022年第4期8卷 498-507页

作者： Yihong Yang Sheng Ding Yuwen Liu Shunmei Meng Xiaoxiao Chi Rui Ma Chao Yan School of Computer Science Qufu Normal UniversityRizhao276826China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguangWeifang262700China Department of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210000China General Education Department Shandong First Medical University(Shandong Academy Of Medical Sciences)Taian271000China

Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things(IoT)technology,which can monitor the environmental information in real-time and employ the information to contribute to intelligent *** the process,anomaly detection for wireless sensor data plays an important ***,the traditional anomaly detection algorithms originally designed for anomaly detection in static data do not properly consider the inherent characteristics of the data stream produced by wireless sensors such as infiniteness,correlations,and concept drift,which may pose a considerable challenge to anomaly detection based on data stream and lead to low detection accuracy and ***,the data stream is usually generated quickly,which means that the data stream is infinite and ***,any traditional off-line anomaly detection algorithm that attempts to store the whole dataset or to scan the dataset multiple times for anomaly detection will run out of memory ***,there exist correlations among different data streams,and traditional algorithms hardly consider these ***,the underlying data generation process or distribution may change over ***,traditional anomaly detection algorithms with no model update will lose their *** these issues,a novel method(called DLSHiForest)based on Locality-Sensitive Hashing and the time window technique is proposed to solve these problems while achieving accurate and efficient *** experiments are executed using a real-world agricultural greenhouse dataset to demonstrate the feasibility of our *** results show that our proposal is practical for addressing the challenges of traditional anomaly detection while ensuring accuracy and efficiency.

关键词： Anomaly detection Data stream DLSHiForest Smart greenhouse Edge computing

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Accurate diagnosis of non-Hodgkin lymphoma on whole-slide images using deep learning 9

Accurate diagnosis of non-Hodgkin lymphoma on whole-slide im...

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9th IEEE International Conference on sciences of Electronics, Technologies of Information and Telecommunications, SETIT 2022

作者： Khelil, Hathem El Moumene Zerari, Abd Djerou, Leila Algeria University of Biskra LISIA Laboratory Computer Science Department Algeria

ISBN: (数字)9781728184425

ISBN: (纸本)9781728184425

Digital Pathology is the technique of digitizing histology slides to create high-resolution images. One of the important applications for digital pathology is tissue level classification, such as the identification of the three most common kinds of non-Hodgkin lymphomas;Mantle Cell Lymphoma, Follicular Lymphoma and Chronic Lymphocytic Leukemia, which pose a significant problem for pathologists due to their inherent complexity. In this research, deep learning ideas are combined with an improvement of the CNN algorithm to propose a Non-Hodgkin Lymphoma model which is able, effectively, to categorize these subtypes of non-Hodgkin lymphomas. The proposed model was trained using NIA-curated dataset images and achieved a classification accuracy of 98.7%, about 0.6% better than current classification strategies for non-Hodgkin's lymphomas based on deep learning. © 2022 IEEE.

关键词： Classification (of information)

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

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IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

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Worst-Case Integrity Attacks and Resilient State Estimation With Partially Secured Measurements

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IEEE Transactions on Control of Network Systems 2025年第2期12卷 1769-1779页

作者： Zhou, Jing Shang, Jun Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China

This paper examines the problem of optimal deception attacks against state estimation with partially secured measurements, where smart sensors transmit innovation sequences to the remote end for information fusion. Due to resource limitations or defensive countermeasures, the adversary can only modify data packets transmitted through unreliable channels. Meanwhile, the attack should be synthesized with sophistication to deceive an anomaly detector. To investigate the vulnerabilities of such estimation systems without feedback links and enhance security performance, the optimal attack policy is derived by formulating and explicitly solving a convex optimization problem, with the goal of maximizing the sum of estimation errors. Subsequently, a novel attack detection and resilient state estimation algorithm is proposed to ensure an acceptable level of estimation accuracy. The theoretical performance metrics, including false alarm rates for the proposed detector, are provided. Finally, the effectiveness of the results is confirmed through numerical examples. © 2014 IEEE.

关键词： Optimal detection

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Stereo Conversion with Disparity-Aware Warping, Compositing and Inpainting

Stereo Conversion with Disparity-Aware Warping, Compositing ...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Lukas Mehl Andrés Bruhn Markus Gross Christopher Schroers Institute for Visualization and Interactive Systems University of Stuttgart Department of Computer Science Computer Graphics Lab ETH Zurich DisneyResearch|Studios Switzerland

Despite of exciting advances in image-based rendering and novel view synthesis, it is still challenging to achieve high-resolution results that can reach production-level quality when applying such methods to the task of stereo conversion. At the same time, only very few dedicated stereo conversion approaches exist, which also fall short in terms of the required quality. Hence, in this paper, we present a novel method for high-resolution 2D-to-3D conversion. It is fully differentiable in all of its stages and performs disparity-informed warping, consistent foreground-background compositing, and background-aware inpainting. To enable temporal consistency in the resulting video, we propose a strategy to integrate information from additional video frames. Extensive ablation studies validate our design choices, leading to a fully automatic model that outperforms existing approaches by a large margin (49-70% LPIPS error reduction). Finally, inspired from current practices in manual stereo conversion, we introduce optional interactive tools into our model, which allow to steer the conversion process and make it significantly more applicable for 3D film production.

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Morocco's COVID-19 instance as a potential epidemiological scenario 3

Morocco's COVID-19 instance as a potential epidemiological s...

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3rd International Conference on Innovative Research in Applied science, Engineering and Technology, IRASET 2023

作者： Chouit, El Mehdi Rachdi, Mohamed Bellafkih, Mostafa Raouyane, Brahim Raiss Laboratory Department of Mathematics and Computer Science Rabat Morocco Ben m'Sik Ensad Hassan Ii University Tim Laboratory Faculty of Sciences Casablanca Morocco Ain Chock University Hassan Ii Faculty of Sciences Department of Mathematics and Computer Science Casablanca Morocco

ISBN: (纸本)9798350398366

Machine learning is widely used in all industries. In order to speed up decision-making on the most likely course of action, machine learning have shown their effectiveness in analyzing perioperative effects. In several technological sectors, ML models have been utilized for a while to specify and rank dangerous threat variables. To handle forecasting problems, several different prediction approaches are regularly used. The work shows how ML models can forecast the frequency of COVID-19 instances in the future, which is now considered to be a serious threat to civilisation. In this study, we carried out a comparison analysis of two widely used machine learning models: SVM and linear regression. Each model projects three variables: the cumulative count of confirmed cases, the cumulative count of fatalities, and the cumulative count of recoveries over the next 30 days. The study's conclusions demonstrate that applying these tactics to the current COVID-19 pandemic scenario is a viable choice. To boost accuracy, we used two ML models. The results of the experiment indicate that when it comes to COVID-19 prediction, Linear Regression yields the best results, while SVM yields the worst. © 2023 IEEE.

关键词： COVID-19

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User story clustering in agile development:a framework and an empirical study

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Frontiers of computer science 2023年第6期17卷 43-59页

作者： Bo YANG Xiuyin MA Chunhui WANG Haoran GUO Huai LIU Zhi JIN School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry Oriented Intelligent Information Processing National Forestry and Grassland AdministrationBeijing 100083China School of Information Science and Technology North China University of TechnologyBeijing 100144China College of Computer Science and Technology Inner Mongolia Normal UniversityHohhot 010020China Department of Computer Science and Software Engineering Swinburne University of TechnologyHawthorn VIC 3122Australia Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of EducationBeijing 100871China Institute of Software School of Computer SciencePeking UniversityBeijing 100871China

Agile development aims at rapidly developing software while embracing the continuous evolution of user requirements along the whole development *** stories are the primary means of requirements collection and elicitation in the agile development.A project can involve a large amount of user stories,which should be clustered into different groups based on their functionality’s similarity for systematic requirements analysis,effective mapping to developed features,and efficient ***,the current user story clustering is mainly conducted in a manual manner,which is time-consuming and subjective to human *** this paper,we propose a novel approach for clustering the user stories automatically on the basis of natural language ***,the sentence patterns of each component in a user story are first analysed and determined such that the critical structure in the representative tasks can be automatically extracted based on the user story *** similarity of user stories is calculated,which can be used to generate the connected graph as the basis of automatic user story *** evaluate the approach based on thirteen datasets,compared against ten baseline *** results show that our clustering approach has higher accuracy,recall rate and F1-score than these *** is demonstrated that the proposed approach can significantly improve the efficacy of user story clustering and thus enhance the overall performance of agile *** study also highlights promising research directions for more accurate requirements elicitation.

关键词： user story agile development user story mapping clustering

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