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Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

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Computers, Materials & Continua 2024年第3期78卷 3469-3487页

作者： Sureka Sivavelu Venkatesh Palanisamy School of Computer Science engineering and information systems Vellore Institute of TechnologyVellore632014India

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two sta

关键词： Software defect prediction feature selection nonparametric statistical Torgerson-Gower scaling technique quadratic censored regressive convolution deep neural network softstep activation function nelder-mead method

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2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

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Computer Modeling in engineering & Sciences 2024年第8期140卷 2035-2051页

作者： Sandeep Dasari Rajesh Kaluri School of Computer Science engineering and information systems Vellore Institute of TechnologyVellore632014India

The increasing data pool in finance sectors forces machine learning(ML)to step into new *** data has significant financial implications and is *** users data from several organizations for various banking services may result in various intrusions and privacy *** a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global ***,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of *** address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global *** improves the privacy of the local *** analysis used the credit card and Canadian institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)***,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and *** experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

关键词： Federated learning FedAvg FedProx flower framework privacy preservation financial sectors

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A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

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Building Simulation 2024年第3期17卷 441-457页

作者： Mohammad Kaosain Akbar Manar Amayri Nizar Bouguila Concordia institute for information systems engineering Concordia UniversityMontrealCanada

Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial *** plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level *** enables informed decision-making,energy optimization,and cost ***,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world *** learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled *** initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy *** is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption *** paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled *** two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared *** superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the p

关键词： semi-supervised learning non-intrusive load monitoring middle-point thresholding deep learning TCN LSTM

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A survey of decision making in adversarial games

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Science China(information Sciences) 2024年第4期67卷 85-112页

作者： Xiuxian LI Min MENG Yiguang HONG Jie CHEN College of Electronics and information engineering Tongji University Shanghai Research institute for Intelligent Autonomous systems

In many practical applications, such as poker, chess, drug interdiction, cybersecurity, and national defense, players often have adversarial stances, i.e., the selfish actions of each player inevitably or intentionally inflict loss or wreak havoc on other players. Therefore, adversarial games are important in realworld applications. However, only special adversarial games, such as Bayesian games, are reviewed in the literature. In this respect, this study aims to provide a systematic survey of three main game models widely employed in adversarial games, i.e., zero-sum normal-form and extensive-form games, Stackelberg(security) games, and zero-sum differential games, from an array of perspectives, including basic knowledge of game models,(approximate) equilibrium concepts, problem classifications, research frontiers,(approximate) optimal strategy-seeking techniques, prevailing algorithms, and practical applications. Finally, promising future research directions are also discussed for relevant adversarial games.

关键词： adversarial games zero-sum games Stackelberg games differential games Nash equilibrium correlated equilibrium regret

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An adaptive 3D reconstruction method for asymmetric dual-angle multispectral stereo imaging system on UAV platform

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Science China(information Sciences) 2024年第8期67卷 285-299页

作者： Chen WANG Xian LI Yanfeng GU Zixu WANG School of Electronics and information engineering Harbin Institute of Technology Beijing institute of Control and Electronic technology

A multispectral imaging system often cannot capture 3D spatial information owing to hardware limitations, which diminishes the effectiveness across various domains. To address this problem, we have developed a multispectral stereo imaging system along with an adaptive 3D reconstruction algorithm. Unlike existing unmanned aerial vehicle stereo imaging systems, our multispectral stereo imaging system uses two multispectral cameras with asymmetric spectral bands positioned at different angles. This design enables the acquisition of a higher number of bands and lateral spatial information while maintaining a lightweight structure. This system introduces challenges such as large geometric distortions and intensity differences between multiple bands. To accurately recover 3D spatial information, we propose an adaptive 3D reconstruction method. This method employs a position and orientation system-assisted projection transformation and a normalized threshold adjustment strategy. Finally, mutual information is used to reconstruct the multispectral images densely, effectively addressing nonlinear differences and generating a comprehensive multispectral point cloud. Our stereo system was used for two real data collections in different regions, and the efficacy of the proposed 3D reconstruction method was validated by comparing it with existing methods and commercial software.

关键词： 3D reconstruction feature extraction multispectral images stereo imaging system unmanned aerial vehicle

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Robustness of interdependent networks with weak dependency links and reinforced nodes

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Science China(information Sciences) 2024年第2期67卷 329-330页

作者： Qian LI Hongtao YU Weitao HAN Ruiyang HUANG Shaomei LI Jianpeng ZHANG institute of information technology PLA Strategic Support Force Information Engineering University

Interdependent networks are more vulnerable than single,non-interacting networks under cascading failure [1–3]. Percolation theory provides a quantifiable theoretical framework to quantify and evaluate the robustness of networked systems. Most existing models [1–3] on the robustness of interdependent networks mainly focused on the giant connected component(GCC) to explore the structural robustness from the macro perspective,

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Gas sensor array based on carbon-based thin-film transistor for selective detection of indoor harmful gases

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Rare Metals 2024年第9期43卷 4401-4411页

作者： Can Liu Yu Sun Jia-Yi Guo Xiu-Lei Li Lu Tao Jin-Yong Hu Jue-Xian Cao Ping-Hua Tang Yong Zhang School of Electrical and information engineering Hunan Institute of Engineering Physics and Optoelectronics&Hunan institute of Advanced Sensing and information technology Xiangtan University

The identification of indoor harmful gases is imperative due to their significant threats to human health and *** achieve accurate identification,an effective strategy of constructing a sensor array combined with the pattern recognition algorithm is ***-based thin-film transistors are selected as the sensor array unit,with semiconductor carbon nanotubes （CNTs） within the TFT channels modified with different metals （Au,Cu and Ti） for selective responses to NH3,H2S and HCHO,*** accurate gas species identification,an identification mode that combines linear discriminant analysis algorithms and logistic regression classifier is *** test results demonstrate that by preprocessing the sensor array’s sensing data with the LDA algorithm and subsequently employing the LR classifier for identification,a 100% recognition rate can be achieved for three target gases （NH3,H2S and HCHO）.This work provides significant guidance for future applications of chip-level gas sensors in the realms of the Internet of Things and Artificial Intelligence.

关键词： Carbon-based thin-film transistors Gas sensor arrays Linear discriminant analysis Multi-gas detection Semiconductor carbon nanotubes

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A framework of force of information influence and application for C4KISR system

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Journal of systems engineering and Electronics 2024年第2期35卷 430-443页

作者： MAO Shaojie DIAO Lianwang SUN Yu WANG Heng YI Kan XU Xin MAO Xiaobin ZHANG Kecheng SHENG Long Science and technology on information systems engineering Laboratory The 28th Research Institute of China Electronics Technology Group CorporationNanjing 210007China Unit 94860 of the PLA Nanjing 210008China

The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.

关键词： information warfare command control communications computer kill intelligence surveillance reconnaissance(C4KISR)system information circulation force of information influence information entropy

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A projected Newton algorithm based on chemically allowed interval for chemical equilibrium computations

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Frontiers of Chemical Science and engineering 2024年第3期18卷 25-37页

作者： Hongbin Lu Shaohui Tao Xiaoyan Sun Li Xia Shuguang Xiang institute of Process systems engineering College of Chemical EngineeringQingdao University of Science and TechnologyQingdao266042China

The chemical equilibrium equations utilized in reactive transport modeling are complex and nonlinear,and are typically solved using the Newton-Raphson *** this algorithm is known for its quadratic convergence near the solution,it is less effective far from the solution,especially for ill-conditioned *** such cases,the algorithm may fail to converge or require excessive *** address these limitations,a projected Newton method is introduced to incorporate the concept of *** method constrains the Newton step by utilizing a chemically allowed interval that generates feasible descending ***,we utilize the positive continuous fraction method as a preconditioning technique,providing reliable initial values for solving the *** numerical results are compared with those derived using the regular Newton-Raphson method,the Newton-Raphson method based on chemically allowed interval updating rules,and the bounded variable least squares method in six different test *** numerical results highlight the robustness and efficacy of the proposed algorithm.

关键词： chemical equilibrium reactive transport modeling numerical methods projected Newton method

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Network traffic classification:Techniques,datasets,and challenges

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Digital Communications and Networks 2024年第3期10卷 676-692页

作者： Ahmad Azab Mahmoud Khasawneh Saed Alrabaee Kim-Kwang Raymond Choo Maysa Sarsour College of information technology and systems Victorian Institute of TechnologyAustralia College of engineering Al Ain UniversityAbu DhabiUnited Arab Emirates information systems and Security College of ITUnited Arab Emirates UniversityAl Ain15551United Arab Emirates Department of information systems and Cyber Security University of Texas at San AntonioSan AntonioTX78260USA School of Photovoltaic and Renewable Energy engineering University of New South WalesSydneyNSW2052Australia

In network traffic classification,it is important to understand the correlation between network traffic and its causal application,protocol,or service group,for example,in facilitating lawful interception,ensuring the quality of service,preventing application choke points,and facilitating malicious behavior *** this paper,we review existing network classification techniques,such as port-based identification and those based on deep packet inspection,statistical features in conjunction with machine learning,and deep learning *** also explain the implementations,advantages,and limitations associated with these *** review also extends to publicly available datasets used in the ***,we discuss existing and emerging challenges,as well as future research directions.

关键词： Network classification Machine learning Deep learning Deep packet inspection Traffic monitoring

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