The naive Bayesian classifier(NBC) is a supervised machine learning algorithm having a simple model structure and good theoretical interpretability. However, the generalization performance of NBC is limited to a large...
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The naive Bayesian classifier(NBC) is a supervised machine learning algorithm having a simple model structure and good theoretical interpretability. However, the generalization performance of NBC is limited to a large extent by the assumption of attribute independence. To address this issue, this paper proposes a novel attribute grouping-based NBC(AG-NBC), which is a variant of the classical NBC trained with different attribute groups. AG-NBC first applies a novel effective objective function to automatically identify optimal dependent attribute groups(DAGs). Condition attributes in the same DAG are strongly dependent on the class attribute, whereas attributes in different DAGs are independent of one another. Then,for each DAG, a random vector functional link network with a SoftMax layer is trained to output posterior probabilities in the form of joint probability density estimation. The NBC is trained using the grouping attributes that correspond to the original condition attributes. Extensive experiments were conducted to validate the rationality, feasibility, and effectiveness of AG-NBC. Our findings showed that the attribute groups chosen for NBC can accurately represent attribute dependencies and reduce overlaps between different posterior probability densities. In addition, the comparative results with NBC, flexible NBC(FNBC), tree augmented Bayes network(TAN), gain ratio-based attribute weighted naive Bayes(GRAWNB), averaged one-dependence estimators(AODE), weighted AODE(WAODE), independent component analysis-based NBC(ICA-NBC), hidden naive Bayesian(HNB) classifier, and correlation-based feature weighting filter for naive Bayes(CFW) show that AG-NBC obtains statistically better testing accuracies, higher area under the receiver operating characteristic curves(AUCs), and fewer probability mean square errors(PMSEs) than other Bayesian classifiers. The experimental results demonstrate that AG-NBC is a valid and efficient approach for alleviating the attribute i
Linear regression model is one of the important learning models for classification tasks. However, the data from practical application inevitably contains some noise or is corrupted, which may lead to the decline of t...
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Label distribution learning(LDL) has shown advantages over traditional single-label learning(SLL) in many realworld applications, but its superiority has not been theoretically understood. In this paper, we attempt to...
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Label distribution learning(LDL) has shown advantages over traditional single-label learning(SLL) in many realworld applications, but its superiority has not been theoretically understood. In this paper, we attempt to explain why LDL generalizes better than SLL. Label distribution has rich supervision information such that an LDL method can still choose the sub-optimal label from label distribution even if it neglects the optimal one. In comparison, an SLL method has no information to choose from when it fails to predict the optimal label. The better generalization of LDL can be credited to the rich information of label distribution. We further establish the label distribution margin theory to prove this explanation; inspired by the theory,we put forward a novel LDL approach called LDL-LDML. In the experiments, the LDL baselines outperform the SLL ones, and LDL-LDML achieves competitive performance against existing LDL methods, which support our explanation and theories in this paper.
Software-defined networking (SDN) represents a paradigm shift in the management of networks, offering greater flexibility and centralized control. However, this centralized design presents distinct security issues. Th...
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Software-defined networking (SDN) represents a paradigm shift in the management of networks, offering greater flexibility and centralized control. However, this centralized design presents distinct security issues. The centralized controller becomes a prominent target for intruders, exposing the network to a wide range of risks, including direct attacks, unauthorized entry and manipulation of information, Denial-of-Service (DoS) attacks, and switch problems. Furthermore, present DDoS detection approaches in SDN have drawbacks due to their reliance on network topology, insufficient attack type coverage, obsolete datasets, and high hardware costs. This reliance on outdated data reduces adaptability to new attacks and slows detection. Therefore, in this research, we introduce a novel optimized deep learning-based approach for effective attack detection. The MASNet model is employed for feature extraction, identifying complex patterns in network traffic. Feature selection is refined using the Binary artificial Rabbit Optimizer Algorithm, focusing on the most critical attributes to enhance model accuracy. Attack detection is achieved through an ensemble of TaNet, and improved GhostNet termed the IGhostTaV2Net method, which work together to detect and categorize threats effectively. The hyperparameters of the ensemble approach are further optimized using the Satin Bowerbird Optimization (SBO) algorithm. Lastly, SDN’s dynamic capabilities are utilized to mitigate threats in real-time by rerouting traffic or blocking malicious connections, offering a robust and efficient solution for intrusion detection and response. This approach demonstrates high accuracy and effectiveness in managing network threats. Additionally, the outcomes highlight the efficacy of the suggested methodology by demonstrating exceptional accuracy of 99.82% in identifying and reducing these threats. The research makes a significant contribution to the current discussion on SDN environment security by pu
Background The annotation of fashion images is a significantly important task in the fashion industry as well as social media and ***,owing to the complexity and diversity of fashion images,this task entails multiple ...
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Background The annotation of fashion images is a significantly important task in the fashion industry as well as social media and ***,owing to the complexity and diversity of fashion images,this task entails multiple challenges,including the lack of fine-grained captions and confounders caused by dataset ***,confounders often cause models to learn spurious correlations,thereby reducing their generalization *** In this work,we propose the Deconfounded Fashion Image Captioning(DFIC)framework,which first uses multimodal retrieval to enrich the predicted captions of clothing,and then constructs a detailed causal graph using causal inference in the decoder to perform *** retrieval is used to obtain semantic words related to image features,which are input into the decoder as prompt words to enrich sentence *** the decoder,causal inference is applied to disentangle visual and semantic features while concurrently eliminating visual and language *** Overall,our method can not only effectively enrich the captions of target images,but also greatly reduce confounders caused by the *** verify the effectiveness of the proposed framework,the model was experimentally verified using the FACAD dataset.
Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distri...
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Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.
Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)*** paper explores LoRa as an alte...
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Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)*** paper explores LoRa as an alternative localization technology,leveraging its low power consumption,robust indoor penetration,and extensive coverage area,which render it highly suitable for diverse IoT *** comprehensively review several LoRa-based localization techniques,including time of arrival(ToA),time difference of arrival(TDoA),round trip time(RTT),received signal strength indicator(RSSI),and fingerprinting *** this review,we evaluate the strengths and limitations of each technique and investigate hybrid models to potentially improve positioning *** studies in smart cities,agriculture,and logistics exemplify the versatility of LoRa for indoor and outdoor *** findings demonstrate that LoRa technology not only overcomes the limitations of GPS regarding power consumption and coverage but also enhances the scalability and efficiency of IoT deployments in complex environments.
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have sh...
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Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views(i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named graph pooling contrast(GPS) to address these *** by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts.
Dear Editor,This letter focuses on the distributed cooperative regulation problem for a class of networked re-entrant manufacturing systems(RMSs). The networked system is structured with a three-tier architecture:the ...
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Dear Editor,This letter focuses on the distributed cooperative regulation problem for a class of networked re-entrant manufacturing systems(RMSs). The networked system is structured with a three-tier architecture:the production line, the manufacturing layer and the workshop layer. The dynamics of re-entrant production lines are governed by hyperbolic partial differential equations (PDEs) based on the law of mass conservation.
Online map construction is essential for autonomous robots to navigate in unknown environments. However, the presence of dynamic objects may introduce artifacts into the map, which can significantly degrade the perfor...
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