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5th China Conference on Intelligent Networked Things, CINT 2022

作者： Cai, Xin Xiao, Feng Wei, Bo State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Electrical Engineering Xinjiang University Urumqi830047 China

ISBN: (纸本)9789811989148

This paper presents a resilient event-triggered distributed algorithm for resource allocation of multi-agent systems under denial-of-service (DoS) attacks. A class of time-sequence-based and aperiodic DoS attacks exists in communication networks. A resilient event-triggered communication scheme is designed to determine when agents communicate with neighbors on the network in the presence of DoS attacks. An explicit condition in terms of the frequency and duration of DoS attacks is obtained to ensure the convergence of the designed resilient algorithm to the optimal allocation. Simulation results are given to validate the proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Denial-of-service attack

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VulPin: Finer-Grained Slicing for Pinpointing Vulnerability in Binary Programs: Precision in Vulnerability Detection: Advanced Slicing Techniques for Binary Program Analysis 7

VulPin: Finer-Grained Slicing for Pinpointing Vulnerability ...

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7th International Conference on computer Science and Artificial Intelligence, CSAI 2023

作者： Chukkol, Abdulrahman Hamman Adama Luo, Senlin Yunusa, Haruna Yusuf, Abdulganiyu Abdu Mohammed, Abulfatahi School of Information and Electronics Beijing Institute of Technology Beijing China Information System and Security and Countermeasures Experimental Center Beijing Institute of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science Beijing Institute of Technology Beijing China School of Management and Information Technology Modibbo Adama University Yola Nigeria

ISBN: (纸本)9798400708688

Identifying vulnerabilities in binary code with precision is a critical area of study in the field of software security. Most existing techniques fail to pinpoint the vulnerability's exact location, due to the extensive labelled data requirement and lack of targeted approach to improve on the problematic code. In this paper, we introduce VulPin, a system specifically designed to pinpoint vulnerabilities in binary code. This framework uses a hierarchical attention network that analyze program slices along with a vulnerability position matrix. Experimental outcomes indicate that our framework not only outperforms existing approaches but also pinpoints vulnerabilities more rapidly with a high Accuracy and a very low rate of false positives. © 2023 ACM.

关键词： Binary codes

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Solar Irradiance Forecasting using Gaussian Process Regression Model

Solar Irradiance Forecasting using Gaussian Process Regressi...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Nesrine Omri Jannet Jamii Majdi Mansouri Mohamed Faouzi Mimouni The Laboratory of Automatic Electrical System and Environment (LAS2E) National Engineering School of Monastir Tunisia Electrical and Computer Engineering Department Texas A&M University at Qatar Electrical Engineering Department National Engineering School at Monastir (ENIM) Tunisia

Photovoltaic power generation presents a significant problem for power system security due to their stochastic nature. Thus, photovoltaic energy forecasting plays a key role in addressing the challenges of grid stability. Then, accurate PV energy forecasting improve the output power and therefore reduce the need for reserve energy to balance energy. Also, it improves dispatch and planning power. In this paper, we study a short term forecasting of PV power using a Gaussian Process Regression (GPR) model. The meteorological conditions such as temperature is used as input, while solar irradiance, is used as output for GPR *** data set are illustrated from data NOAA'S Surface Radiation (SURFRAD) Network at Bondville station, located in the USA. A comparative study with other machine learning techniques is developed in terms of calculation time (CT), RMSE, MAE, and R 2 .

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Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS

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computer systems Science & engineering 2023年第5期45卷 1199-1210页

作者： Himanshu Sharma karthikeyan Rajagopal G.Gugapriya Rajneesh Pareek Arun Kumar HayaMesfer Alshahrani Mohamed K.Nour Hany Mahgoub Mohamed Mousa Anwer Mustafa Hilal Department of Computer Science&Engineering JECRC UniversityJaipurIndia Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia School of Electronics Engineering Vellore Institute of TechnologyChennaiIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multicarrier Waveform(MCW)has several advantages and plays a very important role in cellular *** generation(5G)MCW such as Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC)are thought to be important in 5G *** Peak to Average Power Ratio(PAPR)is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user *** paper presents a novel Divergence Selective Mapping(DSLM)and Divergence Partial Transmission Sequence(D-PTS)for 5G *** is seen that the proposed D-SLM and PTS lower PAPR with low computational *** work highlighted a combination of multi-data block partial transmit schemes along with tone *** this,an overlapping factor is used to determine the number of data blocks for every ***,considering only those data blocks that have minimum signal power,the use of DSLM and DPTS are required to eliminate the segment’s *** results reveal that the suggested hybrid technique proves to be better than the conventional PTS ***,the power saving performance of FBMC and NOMA is compared with the Orthogonal Frequency Division Multiplexing(OFDM)waveform.

关键词： 5G PAPR D-SLM D-PTS

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Local-to-Global Self-Supervised Representation Learning for Diabetic Retinopathy Grading

arXiv

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arXiv 2024年

作者： Hajighasemlou, Mostafa Sheikhaei, Samad Soltanian-Zadeh, Hamid School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran14399 Iran Advancom Lab. School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran14395 Iran Medical Image Analysis Laboratory Departments of Radiology and Research Administration Henry Ford Health System DetroitMI48202 United States

Artificial intelligence algorithms have demonstrated their image classification and segmentation ability in the past decade. However, artificial intelligence algorithms perform less for actual clinical data than those used for simulations. This research aims to present a novel hybrid learning model using self-supervised learning and knowledge distillation, which can achieve sufficient generalization and robustness. The self-attention mechanism and tokens employed in ViT, besides the local-to-global learning approach used in the hybrid model, enable the proposed algorithm to extract a high-dimensional and high-quality feature space from images. To demonstrate the proposed neural network's capability in classifying and extracting feature spaces from medical images, we use it on a dataset of Diabetic Retinopathy images, specifically the EyePACS dataset. This dataset is more complex structurally and challenging regarding damaged areas than other medical images. For the first time in this study, self-supervised learning and knowledge distillation are used to classify this dataset. In our algorithm, for the first time among all self-supervised learning and knowledge distillation models, the test dataset is 50% larger than the training dataset. Unlike many studies, we have not removed any images from the dataset. Finally, our algorithm achieved an accuracy of 79.1±1% in the linear classifier and 74.36% in the k-NN algorithm for multiclass classification. Compared to a similar state-of-the-art model, our results achieved higher accuracy and more effective representation spaces. © 2024, CC BY.

关键词： Self-supervised learning

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First wetland mapping at 10-m spatial resolution in South America using multi-source and multi-feature remote sensing data

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Science China Earth Sciences 2024年第10期67卷 3252-3269页

作者： Weiwei SUN Gang YANG Yuling HUANG Dehua MAO Ke HUANG Lin ZHU Xiangchao MENG Tian FENG Chao CHEN Yong GE Department of Geography and Spatial Information Techniques Ningbo UniversityNingbo 315211China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences&Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Natural Resources and Planning Bureau Shanghai Pudong New DistrictShanghai 200100China Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China School of Geography Science and Geomatics Engineering Suzhou University of Science and TechnologySuzhou 215009China

Wetland degradation has been accelerating in recent years globally. Accurate information on the geographic distribution and categories of wetlands is essential for their conservation and management. Despite being the world′s fourth largest continent, South America has limited research on wetland mapping, and there is currently no available map that provides comprehensive information on wetland distribution and categories in the region. To address this issue, we used Sentinel-1, Sentinel-2 and SRTM data, developed a sample collection method and a wetland mapping method with a collection of multi-source features such as optical features, polarization features and shape features for South American wetlands. We produced a 10-m resolution wetland map based on the Google Earth Engine(GEE) platform. Our Level-1 wetland cover map accurately captured six wetland sub-categories with an overall accuracy of 96.24% and a kappa coefficient of 0.8649, while our Level-2 water cover map included five sub-categories with an overall accuracy of 97.23% and a kappa coefficient of0.9368. The results show that the total area of existing wetlands in South America is approximately 1,737,000 km~2, which is6.8% of the total land area. Among the ten wetland categories, shallow sea had the largest area(960,527.4 km~2), while aquaculture ponds had the smallest area 1513.6 km~2. Swamp had the second largest area(306,240.1 km~2). Brazil, Argentina,Venezuela, Bolivia, and Colombia were found to have the largest wetland areas, with Brazil and Colombia having the most diverse wetland categories. This product can serve as baseline data for subsequent monitoring, management, and conservation of South American wetlands.

关键词： Wetland mapping Google Earth Engine Sentinel imagery South America

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Bayesian hypergame approach to equilibrium stability and robustness in moving target defense 63

Bayesian hypergame approach to equilibrium stability and rob...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Hanzheng Cheng, Zhaoyang Chen, Guanpu Johansson, Karl Henrik Shanghai Research Institute for Autonomous Intelligent Systems Tongji University Shanghai China University of Chinese Academy of Science Academy of Mathematics and System Science Chinese Academy of Science Beijing China Kth Royal Institute of Technology and the Digital Futures Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350316339

We investigate the equilibrium stability and robustness in a class of moving target defense problems, in which players have both incomplete information and asymmetric cognition. We first establish a Bayesian Stackelberg game model for incomplete information and then employ a hypergame reformulation to address asymmetric cognition. With the core concept of the hyper Bayesian Nash equilibrium (HBNE), a condition for achieving both the strategic and cognitive stability in equilibria can be realized by solving linear equations. Moreover, to deal with players' underlying perturbed knowledge, we study the equilibrium robustness by presenting a condition of robust HBNE under the given configuration. Experiments evaluate our theoretical results. © 2024 IEEE.

关键词： Hierarchical systems

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Multitask Learning for Improved Late Mechanical Activation Detection of Heart from Cine Dense MRI

Multitask Learning for Improved Late Mechanical Activation D...

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IEEE International Symposium on Biomedical Imaging

作者： Jiarui Xing Shuo Wang Kenneth C. Bilchick Frederick H. Epstein Amit R. Patel Miaomiao Zhang Department of Electrical and Computer Engineering University of Virginia USA School of Medicine University of Virginia Health System USA Department of Biomedical Engineering University of Virginia Health System USA Department of Computer Science University of Virginia USA

The selection of an optimal pacing site, which is ideally scar-free and late activated, is critical to the response of cardiac resynchronization therapy (CRT). Despite the success of current approaches formulating the detection of such late mechanical activation (LMA) regions as a problem of activation time regression, their accuracy remains unsatisfactory, particularly in cases where myocardial scar exists. To address this issue, this paper introduces a multi-task deep learning framework that simultaneously estimates LMA amount and classify the scar-free LMA regions based on cine displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI). With a newly introduced auxiliary LMA region classification sub-network, our proposed model shows more robustness to the complex pattern cause by myocardial scar, significantly eliminates their negative effects in LMA detection, and in turn improves the performance of scar classification. To evaluate the effectiveness of our method, we tests our model on real cardiac MR images and compare the predicted LMA with the state-of-the-art approaches. It shows that our approach achieves substantially increased accuracy. In addition, we employ the gradient-weighted class activation mapping (Grad-CAM) to visualize the feature maps learned by all methods. Experimental results suggest that our proposed model better recognizes the LMA region pattern.

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Investigation of Traffic Radar Coverage Efficiency under Different Placement Strategies

Investigation of Traffic Radar Coverage Efficiency under Dif...

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International Conference on Internet of Things 2024, ICIoT 2024

作者： Yao, Shun Zhou, Jin Chieng, David Kwong, Chiew Foong Lee, Boon Giin Li, Jianqiang Chen, Yusen Next-Generation Internet of Everything Laboratory Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Computer Science University of Nottingham Ningbo China Ningbo China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Zhejiang Communication Investment Group Co. Ltd. Zhejiang China

This paper presents a comprehensive investigation of traffic radar coverage efficiency under different placement strategies in collaboration with Zhejiang Communications Investment Group Company Limited (CICO). The overall goal is to maximise coverage while minimising blind spots and the number of radars, given the constraints of the road. The NSGA-II multi-objective optimisation algorithm is found to be capable of generating a set of optimal solutions for the proposed radar placement configurations. When aiming for 100% coverage, the results indicate that a multi-antenna radar configuration requires the lowest radar density. Overall, this study provides valuable insights to transportation infrastructure owners in determining the best radar placement strategies for road traffic monitoring. © The Institution of engineering & Technology 2024.

关键词： Multiobjective optimization

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Improved Transmission Constraint Screening for Market Operations

Improved Transmission Constraint Screening for Market Operat...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Lotfi, Hossein Ghaljehei, Mohammad Marwali, Muhammad New York Independent System Operator Department of Energy Market Operations RensselaerNY United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ United States

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

This paper proposes two potential transmission constraint screening processes (TCSPs) for consideration with respect to the energy market administered by the New York Independent system Operator, Inc. (NYISO). The proposed TCSPs are intended to dynamically prevent duplicate and redundant transmission shortages from being priced by the graduated transmission shortage mechanism employed in the NYISO-administered markets, which is often referred to as a 'graduated transmission demand curve' (GTDC or TDC). The goal is to create mechanisms to avoid unnecessarily high locational marginal prices (LMPs) that can result from multiple-counting of the shortage price of duplicate/redundant transmission constraints or the application of multiple TDC-imposed shortage costs on a single facility. The proposed TCSP approaches are tested on historical power market data that show more meaningful price signals and reflect accurate system conditions. © 2022 IEEE.

关键词： Costs

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