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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Zhang, Hongbin Bai, Yikang Zhao, Dongmei Liu, Bin Xu, Ying Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang China Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China

ISBN: (纸本)9798350327380

It is a challenge to monitor the network security state in a large-scale network environment because the selection of situation assessment indicators is overrun by massive observational information. To address this issue, this paper proposes a network security situational assessment model based on a reduced indicator system. The model categorises the characteristics of network assets and threats into endogenous situational factors and exogenous situational factors. By applying the multi-label extremely random tree algorithm to select the importance of network features, a reduced indicator system with independence and strong comprehensiveness is established. Additionally, the weights of the reduced indicator system are calculated using an improved analytical hierarchy process, which yields the evaluation results for network security situations. The experimental results show that the model improves the accuracy and timeliness of the situation assessment model, and the method can reflect the network situation in real time. © 2023 IEEE.

关键词： Network security

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Using Rough Sets to Improve the High-dimensional data Anomaly Detection Method Based on Extended Isolation Forest

Using Rough Sets to Improve the High-dimensional Data Anomal...

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International Conference on Computer Supported Cooperative Work in Design

作者： Hanlin Liu Jiantao Zhou Hua Li College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods.

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based knowledge Graph Reasoning Architecture

A Graph Sequence Generator and Multi-head Self-attention Mec...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yuejia Wu Jian-Tao Zhou College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.

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Low-Dose CT Reconstruction Using dataset-Free Learning

SSRN

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SSRN 2024年

作者： Wang, Feng Wang, Renfang Yang, Bo Qiu, Hong College of Big Data and Software Engineering Zhejiang Wanli University Zhejiang Ningbo315100 China Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province School of Earth Sciences Zhejiang University Zhejiang Hangzhou310027 China

Low-Dose computer tomography (LDCT) is an ideal alternative to reduce radiation risk in clinical applications. Although supervised-deep-learning-based reconstruction methods have demonstrated superior performance compared to conventional model-driven reconstruction algorithms, they require collecting massive pairs of low-dose and norm-dose CT images for neural network training, which limits their practical application in LDCT imaging. In this paper, we propose an unsupervised and training data-free learning reconstruction method for LDCT imaging that avoids the requirement for training data. The proposed method is a post-processing technique that aims to enhance the initial low-quality reconstruction results, and it reconstructs the high-quality images by neural work training that minimizes the l-norm distance between the CT measurements and their corresponding simulated sinogram data, as well as the total variation (TV) value of the reconstructed image. Moreover, the proposed method does not require to set the weights for both the data fidelity term and the plenty term. Experimental results on the AAPM challenge data and LoDoPab-CT data demonstrate that the proposed method is able to effectively suppress the noise and preserve the tiny structures. And these results also shows the proposed method's low computational cost and rapid convergence. The source code is available at https://***/linfengyu77/IRLDCT. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Improvement in oxidation resistance of Cantor alloy through microstructure tailoring

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China Foundry 2022年第6期19卷 503-510页

作者： Bao-lin Pang Man Wang Yue-gang Shen Xiao-li Xi Zuo-ren Nie Collaborative Innovation Center of Capital Resource-Recycling Material Technology Faculty of Materials and ManufacturingBeijing University of TechnologyBeijing 100124China Key Laboratory of Advanced Functional Materials Ministry of EducationBeijing University of TechnologyBeijing 100124China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of TechnologyBeijing 100124China

A method of improving the oxidation resistance of Cantor alloy through microstructure tailoring was *** with distinctive microstructures were achieved by different annealing treatments on the cold-rolled Cantor *** test was then carried out on the various annealed samples at 800°C for 24 h in *** oxidation behavior was evaluated in terms of oxidation rate,surface and cross-section microstructure *** stratified oxide layers including outer Mn_(2)O_(3) and inner Cr_(2)O_(3) were observed in all the annealed samples,the thickness of Mn_(2)O_(3) and Cr_(2)O_(3) layers was ***,the samples exhibited different oxidation *** results indicate that the oxidation resistance of various annealed samples is closely related with grain size as well as twin density.

关键词： Cantor alloy annealing temperature grain size twins oxidation behavior

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A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for Efficient Gridless Line Spectrum Estimation With Applications in Tomographic SAR Imaging

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for...

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作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou Jiangsu215123 China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing100190 China

This article presents a novel efficient method for gridless line spectrum estimation problems with single snapshot and sparse signals, namely the gradient descent least-squares (GDLS) method. Conventional single-snapshot (a.k.a. single-measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the range and/or azimuth resolution or adopt the sparsity constraint and utilize the compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus, it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least-squares (LSs) estimation problem and solves the corresponding objective function via a gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, and performance analysis for evenly distributed antenna array cases are discussed in this article. Numerical simulations show that the proposed GDLS algorithm outperforms the state-of-the-art methods, for example, CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experimental data. Results show the great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. © 1980-2012 IEEE.

关键词： Spectrum analysis

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MAQMC:Multi-Agent Deep Q-Network for Multi-Zone Residential HVAC Control

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Computer Modeling in engineering & Sciences 2023年第9期136卷 2759-2785页

作者： Zhengkai Ding Qiming Fu Jianping Chen You Lu Hongjie Wu Nengwei Fang Bin Xing School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China School of Architecture and Urban Planning Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation Center Co. Ltd.Chongqing400707China

The optimization of multi-zone residential heating,ventilation,and air conditioning(HVAC)control is not an easy task due to its complex dynamic thermal model and the uncertainty of occupant-driven cooling *** reinforcement learning(DRL)methods have recently been proposed to address the HVAC control ***,the application of single-agent DRL formulti-zone residential HVAC controlmay lead to non-convergence or slow *** this paper,we propose MAQMC(Multi-Agent deep Q-network for multi-zone residential HVAC Control)to address this challenge with the goal of minimizing energy consumption while maintaining occupants’thermal *** is divided into MAQMC2(MAQMC with two agents:one agent controls the temperature of each zone,and the other agent controls the humidity of each zone)and MAQMC3(MAQMC with three agents:three agents control the temperature and humidity of three zones,respectively).The experimental results showthatMAQMC3 can reduce energy consumption by 6.27%andMAQMC2 by 3.73%compared with the fixed point;compared with the rule-based,MAQMC3 andMAQMC2 respectively can reduce 61.89%and 59.07%comfort *** addition,experiments with different regional weather data demonstrate that the well-trained MAQMC RL agents have the robustness and adaptability to unknown environments.

关键词： Deep reinforcement learning multi-zone residential HVAC multi-agent energy conservation comfort

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Characterizing and Detecting Gas-Inefficient Patterns in Smart Contracts

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Journal of Computer Science & Technology 2022年第1期37卷 67-82页

作者： Que-Ping Kong Zi-Yan Wang Yuan Huang Xiang-Ping Chen Xiao-Cong Zhou Zi-Bin Zheng Gang Huang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China School of Software Engineering Sun Yat-sen UniversityZhuhai 519082China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion School of Communication and DesignSun Yat-sen UniversityGuangzhou 510006China Peking University Shenzhen Graduate School Shenzhen 518000China

Ethereum blockchain is a new internetware with tens of millions of smart contracts running on *** from general programs,smart contracts are decentralized,tamper-resistant and permanently ***,to avoid resource abuse,Ethereum charges users for deploying and invoking smart contracts according to the size of contract and the operations executed by *** is necessary to optimize smart contracts to save ***,since developers are not familiar with the operating environment of smart contracts(i.e.,Ethereum virtual machine)or do not pay attention to resource consumption during development,there are many optimization opportunities for smart *** fill this gap,this paper defines six gas-inefficient patterns from more than 25,000 posts and proposes an optimization approach at the source code level to let users know clearly where the contract is *** evaluate the prevalence and economic benefits of gas-inefficient patterns,this paper conducts an empirical study on more than 160,000 real smart *** promising experimental results demonstrate that 52.75%of contracts contain at least one gas-inefficient pattern proposed in this *** these patterns are removed from the contract,at least 0.30 can be saved per contract.

关键词： smart contract anti-pattern detection optimization empirical study

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Research on Freshwater Fish Recognition Based on Improved Random Forest

Research on Freshwater Fish Recognition Based on Improved Ra...

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2023 International Conference on Intelligent Management and Software engineering, IMSE 2023

作者： Zhang, Xiaorui Zhang, Longqing Chen, Hongming Chen, Qian Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China Guangdong University of Science and Technology Dongguan523083 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798350394146

In this paper, the performance of models based on decision trees and improved random forest is explored for the fish classification problem. Through comparative experiments, the differences in performance in terms of accuracy between these two models are studied. The experiments utilize fish datasets from different ecological environments and geographic regions, containing rich morphological feature information. Firstly, we construct a fish classification model based on decision trees and train and test it on the dataset. This model demonstrates a decent performance in terms of accuracy, achieving a classification accuracy of 79.16%. However, to further enhance classification performance, we introduce the random forest model, which integrates predictions from multiple decision trees. Through cross-validation and parameter tuning, an improved random forest classifier is built. The experimental results illustrate a significant improvement in fish classification tasks with the improved random forest model. Its accuracy reaches 91.88%, showing a 7.5-percentage-point increase compared to the random forest model. Furthermore, we test the generalization performance of the model and find that the improved random forest model also exhibits good classification ability on unseen samples. In summary, this research, by contrasting and analyzing the performance of decision tree models and the improved random forest model, validates the effectiveness and superiority of the random forest in the fish classification problem. This provides essential insights for fields such as ecological environment monitoring and fish resource management, while also offering inspiration for further exploration of the application of other machine learning methods to similar problems. ©2023 IEEE.

关键词： Decision trees

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Construction of generic core loss prediction model based on APSO algorithm

Construction of generic core loss prediction model based on ...

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data Science and Information Technology (DSIT), International Conference on

作者： Xianyi Fu Boyang Diao Zhong Yang Bin Liu Cheng Zhu National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

Aiming at the current problem of insufficient core loss prediction accuracy of magnetic components under complex operating conditions, this paper firstly constructs the temperature-dependent Steinmetz equation TDSE by introducing the temperature factor on the basis of the original Steinmetz equation, and further constructs the TDSE m and TDSE a models by multiplication and addition, and verifies experimentally that the temperature is more The TDSE models are further constructed by multiplication and addition. Then the TDMSE model is constructed on the basis of the TDSE model to adapt to different waveform excitations. In order to improve the applicability of the model, the iTDMSE core loss prediction model is constructed on the basis of the TDMSE model by introducing dummy variables to introduce the material and excitation waveform categories into the model, and finally the iTDMSE core loss prediction model is constructed that can span different material types and operating environment conditions. Finally, the APSO algorithm is used to find the optimal combination of variable parameters, and the experiments show that the model guarantees the high accuracy of core loss prediction on the basis of strong generalization.

关键词： Adaptation models Analytical models Accuracy Temperature Core loss Predictive models Prediction algorithms Mathematical models data models Power electronics

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