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Lightweight Malicious Code Classification Method Based on Improved Squeeze Net

computers, Materials & Continua 2024年第1期78卷 551-567页

作者： Li Li Youran Kong Qing Zhang school of computer and control engineering Northeast Forestry UniversityHarbin150040China

With the growth of the Internet,more and more business is being done online,for example,online offices,online education and so *** this makes people’s lives more convenient,it also increases the risk of the network being attacked by malicious ***,it is important to identify malicious codes on computer systems ***,most of the existing malicious code detection methods have two problems:(1)The ability of the model to extract features is weak,resulting in poor model performance.(2)The large scale of model data leads to difficulties deploying on devices with limited ***,this paper proposes a lightweight malicious code identification model Lightweight Malicious Code Classification Method Based on Improved SqueezeNet(LCMISNet).In this paper,the MFire lightweight feature extraction module is constructed by proposing a feature slicing module and a multi-size depthwise separable convolution *** feature slicing module reduces the number of parameters by grouping *** multi-size depthwise separable convolution module reduces the number of parameters and enhances the feature extraction capability by replacing the standard convolution with depthwise separable convolution with different convolution kernel *** addition,this paper also proposes a feature splicing module to connect the MFire lightweight feature extraction module based on the feature reuse and constructs the lightweight model *** malicious code recognition accuracy of LCMISNet on the BIG 2015 dataset and the Malimg dataset reaches 98.90% and 99.58%,*** proves that LCMISNet has a powerful malicious code recognition *** addition,compared with other network models,LCMISNet has better performance,and a lower number of parameters and computations.

关键词： Lightweight neural network malicious code classification feature slicing feature splicing multi-size depthwise separable convolution

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Mobile Crowdsourcing Task Allocation Based on Dynamic Self-Attention GANs

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computers, Materials & Continua 2024年第4期79卷 607-622页

作者： Kai Wei Song Yu Qingxian Pan school of computer and control engineering Yantai UniversityYantai264005China school of computer and Information Science Southwest UniversityChongqing400715China

Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource *** traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenges when dealing with abnormal data flow nodes,leading to decreased allocation accuracy and *** address these issues,this study proposes a novel two-part invalid detection task allocation *** the first step,an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous *** to the baseline method,the model achieves an approximately 4%increase in the F1 value on the public *** the second step of the framework,task allocation modeling is performed using a twopart graph matching *** phase introduces a P-queue KM algorithm that implements a more efficient optimization *** allocation efficiency is improved by approximately 23.83%compared to the baseline *** results confirm the effectiveness of the proposed framework in detecting abnormal data nodes,enhancing allocation precision,and achieving efficient allocation.

关键词： Mobile crowdsourcing task allocation anomaly detection GAN attention mechanisms

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A GAN-EfficientNet-Based Traceability Method for Malicious Code Variant Families

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computers, Materials & Continua 2024年第7期80卷 801-818页

作者： Li Li Qing Zhang Youran Kong school of computer and control engineering Northeast Forestry UniversityHarbin150040China

Due to the diversity and unpredictability of changes in malicious code,studying the traceability of variant families remains *** this paper,we propose a GAN-EfficientNetV2-based method for tracing families of malicious code *** method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code *** method includes a lightweight classifier and a *** classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile,embedded,and other *** simulator utilizes an enhanced generative adversarial network to simulate different variants of malicious code and generates datasets to validate the model’s *** process helps identify model vulnerabilities and security risks,facilitating model enhancement and *** classifier achieves 98.61%and 97.59%accuracy on the MMCC dataset and Malevis dataset,*** simulator’s generated image of malicious code variants has an FID value of 155.44 and an IS value of 1.72±*** classifier’s accuracy for tracing the family of malicious code variants is as high as 90.29%,surpassing that of mainstream neural network *** meets the current demand for high generalization and anti-obfuscation abilities in malicious code classification models due to the rapid evolution of malicious code.

关键词： Malicious code variant traceability feature reuse lightweight neural networks code visualization attention mechanism

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Hybrid tri-memristor hyperchaotic map and application in Wasserstein Generative Adversarial Nets

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Science China(Technological Sciences) 2024年第6期67卷 1855-1865页

作者： GU Yang BAO Han YU XiHong HUA ZhongYun BAO BoCheng XU Quan school of Microelectronics and control engineering Changzhou UniversityChangzhou213159China school of computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Inspired by basic circuit connection methods,memristors can also be utilized in the construction of complex discrete chaotic *** investigate the dynamical effects of hybrid memristors,we propose two hybrid tri-memristor hyperchaotic(HTMH)mapping structures based on the hybrid parallel/cascade and cascade/parallel operations,*** the HTMH mapping structure with hybrid parallel/cascade operation as an example,this map possesses a spatial invariant set whose stability is closely related to the initial states of the *** distributions and bifurcation behaviours dependent on the control parameters are explored with numerical ***,the memristor initial offset-boosting mechanism is theoretically demonstrated,and memristor initial offset-boosting behaviours are numerically *** results clarify that the HTMH map can exhibit hyperchaotic behaviours and extreme multistability with homogeneous coexisting infinite *** addition,an FPGA hardware platform is fabricated to implement the HTMH map and generate pseudorandom numbers(PRNs)with high ***,the generated PRNs can be applied in Wasserstein generative adversarial nets(WGANs)to enhance training stability and generation capability.

关键词： memristor hybrid tri-memristor map spatial invariant set hyperchaos extreme multistability generative adversarial nets

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An Online Q-Learning Method for Linear-Quadratic Nonzero-Sum Stochastic Differential Games with Completely Unknown Dynamics

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Journal of Systems Science & Complexity 2024年第5期37卷 1907-1922页

作者： ZHANG Bao-Qiang WANG Bing-Chang CAO Ying school of control Science and engineering Shandong University

In this paper, the authors design a reinforcement learning algorithm to solve the adaptive linear-quadratic stochastic n-players non-zero sum differential game with completely unknown dynamics. For each player, a critic network is used to estimate the Q-function, and an actor network is used to estimate the control input. A model-free online Q-learning algorithm is obtained for solving this kind of problems. It is proved that under some mild conditions the system state and weight estimation errors can be uniformly ultimately bounded. A simulation with five players is given to verify the effectiveness of the algorithm.

关键词： Actor-critic algorithm model-free adaptive control nonzero-sum stochastic game reinforcement learning

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Value iteration algorithm for continuous-time linear quadratic stochastic optimal control problems

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Science China(Information Sciences) 2024年第2期67卷 170-180页

作者： Guangchen WANG Heng ZHANG school of control Science and engineering Shandong University

In this study, we investigate a continuous-time infinite-horizon linear quadratic stochastic optimal control problem with multiplicative noise in control and state variables. Using the techniques of stochastic stability, exact observability, and stochastic approximation, a value iteration algorithm is developed to solve the corresponding generalized algebraic Riccati equation. Unlike the existing policy iteration algorithm, this algorithm does not rely on an initial stabilizing control. Further, this algorithm can also be used to compute policy evaluation steps that arise in the policy iteration algorithm. Herein, a simulation example is provided to validate the obtained results.

关键词： stochastic systems optimal control linear quadratic stochastic problem generalized algebraic Riccati equation value iteration algorithm

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Global adaptive output-feedback tracking with prescribed performance for uncertain nonlinear systems

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Science China(Information Sciences) 2024年第5期67卷 150-164页

作者： Yuan WANG Yungang LIU school of control Science and engineering Shandong University

At present, one typical control strategy for guaranteeing transient and steady-state performance is funnel control and prescribed performance control. The strategy features completely discarding the system nonlinearities, even if they are completely known and available. Such an intrinsic feature requires the controller to produce a larger control effect to eliminate the negative impact caused by the high nonlinearities, leading to a conservative controller. In this paper, we fully take advantage of known information on nonlinearities in control design, instead of completely discarding it as done in funnel control. Particularly, we leverage adaptive techniques(i.e., high-gain dynamic compensation) to deal with unknown system nonlinearities. Meanwhile, we integrate the tools of output feedback, tracking control, and the performance *** such, an adaptive output-feedback scheme is developed for global tracking with spatiotemporal performance specifications: arbitrarily given tracking accuracy and accuracy-ensured time. A simulation example supports the developed approach.

关键词： uncertain nonlinear systems output feedback prescribed performance unknown control direction dynamic high gain global tracking

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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN school of computer Science Shaanxi Normal University Faculty of computer Science and control engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Proteins RNA Neural networks Encoding Robustness Decoding Convolutional neural networks

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An efficient schedulability analysis based on worst-case interference time for real-time systems

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Science China(Information Sciences) 2024年第09期67卷 88-104页

作者： Hongbiao LIU Mengfei YANG Lei QIAO Xi CHEN Jian GONG school of computer Science and Technology Xidian University China Academy of Space Technology Beijing Institute of control engineering

Real-time systems are widely implemented in the Internet of Things(IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-time systems, we proposed an exact Boolean analysis based on interference(EBAI) for schedulability analysis in real-time systems. EBAI is based on worst-case interference time(WCIT), which considers both the release jitter and blocking time of the task. We improved the efficiency of the three existing tests and provided a comprehensive summary of related research results in the field. Abundant experiments were conducted to compare EBAI with other related results. Our evaluation showed that in certain cases, the runtime gain achieved using our analysis method may exceed 73% compared to the stateof-the-art schedulability test. Furthermore, the benefits obtained from our tests grew with the number of tasks, reaching a level suitable for practical application. EBAI is oriented to the five-tuple real-time task model with stronger expression ability and possesses a low runtime overhead. These characteristics make it applicable in various real-time systems such as spacecraft, autonomous vehicles, industrial robots, and traffic command systems.

关键词： five-tuple real-time task model real-time system spacecraft Internet of Things exact schedulability analysis worst-case interference time

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A calibration and compensation method for an industrial robot with high accuracy harmonic reducers

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Science China(Technological Sciences) 2024年第3期67卷 725-739页

作者： ZHAO YiYang LIU Xun CAO JiaBin LIU JinYu XU Yan YAN WeiXin school of Mechanical engineering Shanghai Jiao Tong UniversityShanghai 200240China College of computer and control engineering Northeast Forestry UniversityHarbin 150040China

Industrial serial robots need high stiffness to keep absolute pose accuracy and meet the requirements in practical applications. However, the weak stiffness feature of robot joints and the payloads affected on robot end-effector, which will also increase the pose error of robot. Especially, the existing calibration methods often consider under no-payload condition without discussing the payload state. In this paper, we report a new industrial serial robot composed by a new harmonic reducer: Model-Y, based on high accuracy and high stiffness, and a kinematic parameter calibration algorithm which is based on a harmonic reducer forcedeformation model. To decrease the accuracy effects of payload, an iterative calibration method for kinematic parameters with payload situation was proposed. Simulation and experiments are conducted to verify the effectiveness of the proposed calibration method using the self-developed industrial serial robot. The results show a remarkably improved accuracy in absolute position and orientation with the robot's payload range. The position mean error has 70% decreased to 0.1 mm and the orientation mean error diminished to less than 0.01° after calibration with compensation. Additionally, online linear and circular tests are carried out to evaluate the position error of the robot during large-scale spatial and low-speed continuous movement. The accuracy is consistent with the previous calibration results, indicating the effectiveness and advantages of the proposed strategy in this article.

关键词： industrial serial robot Model-Y harmonic calibration stiffness model online compensation

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