The development of the Internet of Things(IoT)has brought great convenience to ***,some information security problems such as privacy leakage are caused by communicating with risky *** is a challenge to choose reliabl...
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The development of the Internet of Things(IoT)has brought great convenience to ***,some information security problems such as privacy leakage are caused by communicating with risky *** is a challenge to choose reliable users with which to interact in the ***,trust plays a crucial role in the IoT because trust may avoid some *** usually choose reliable users with high trust to maximize their own interests based on reinforcement ***,trust propagation is time-consuming,and trust changes with the interaction process in social *** track the dynamic changes in trust values,a dynamic trust inference algorithm named Dynamic Double DQN Trust(Dy-DDQNTrust)is proposed to predict the indirect trust values of two users without direct contact with each *** proposed algorithm simulates the interactions among users by double ***,CurrentNet and TargetNet networks are used to select users for *** users with high trust are chosen to interact in future ***,the trust value is updated dynamically until a reliable trust path is found according to the result of the ***,the trust value between indirect users is inferred by aggregating the opinions from multiple users through a Modified Collaborative Filtering Averagebased Similarity(SMCFAvg)aggregation *** are carried out on the FilmTrust and the Epinions *** with TidalTrust,MoleTrust,DDQNTrust,DyTrust and Dynamic Weighted Heuristic trust path Search algorithm(DWHS),our dynamic trust inference algorithm has higher prediction accuracy and better scalability.
Image clustering has received significant attention due to the growing importance of image *** have explored Riemannian manifold clustering,which is capable of capturing the non-linear shapes found in real-world ***,t...
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Image clustering has received significant attention due to the growing importance of image *** have explored Riemannian manifold clustering,which is capable of capturing the non-linear shapes found in real-world ***,the complexity of image data poses substantial challenges for modelling and feature *** methods such as covariance matrices and linear subspace have shown promise in image modelling,and they are still in their early stages and suffer from certain ***,these include the uncertainty of representing data using only one Riemannian manifold,limited feature extraction capacity of single kernel functions,and resulting incomplete data representation and *** overcome these limitations,the authors propose a novel approach called join multiple Riemannian manifold representation and multi-kernel non-redundancy for image clustering(MRMNR-MKC).It combines covariance matrices with linear subspace to represent data and applies multiple kernel functions to map the non-linear structural data into a reproducing kernel Hilbert space,enabling linear model analysis for image ***,the authors use matrix-induced regularisation to improve the clustering kernel selection process by reducing redundancy and assigning lower weights to identical ***,the authors also conducted numerous experiments to evaluate the performance of our approach,confirming its superiority to state-of-the-art methods on three benchmark datasets.
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 b...
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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.
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 maliciou...
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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.
With the development of modern science and economy, congestions and accidents are brought by increasing traffics. And to improve efficiency, traffic signal based control is usually used as an effective model to allevi...
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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 challenge...
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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.
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-ti...
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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.
Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection b...
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Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of controlengineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.
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-memrist...
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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.
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 th...
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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.
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