Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system ***, due to the model's inherent uncertainty, rigorous vali...
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Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system ***, due to the model's inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata,which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model ***, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units(PDMUs), and a reverse breadth-first search(BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.
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
The counterflow burner is a combustion device used for research on *** utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitates the opti...
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The counterflow burner is a combustion device used for research on *** utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitates the optimization of the combustion process and enhances combustion *** existing deep convolutional models,InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and *** has garnered significant attention for its computational efficiency,remarkable model accuracy,and exceptional feature extraction ***,since this model still has limitations in the combustion state recognition task,we propose a Triple-Scale Multi-Stage InceptionNeXt(TSMS-InceptionNeXt)combustion state recognitionmethod based on feature extraction ***,to address the InceptionNeXt model’s limited ability to capture dynamic features in flame images,we introduce Triplet Attention,which applies attention to the width,height,and Red Green Blue(RGB)dimensions of the flame images to enhance its ability to model dynamic ***,to address the issue of key information loss in the Inception deep convolution layers,we propose a Similarity-based Feature Concentration(SimC)mechanism to enhance the model’s capability to concentrate on critical ***,to address the insufficient receptive field of the model,we propose a Multi-Scale Dilated Channel Parallel Integration(MDCPI)mechanism to enhance the model’s ability to extract multi-scale contextual ***,to address the issue of the model’s Multi-Layer Perceptron Head(MlpHead)neglecting channel interactions,we propose a Channel Shuffle-Guided Channel-Spatial Attention(ShuffleCS)mechanism,which integrates information from different channels to further enhance the representational power of the input *** validate the effectiveness of the method,experiments are conducted on the counterflow burner flame visible light image datase
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.
Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal **...
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Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal *** at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks,an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5(YOLOv5)is ***,we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network(Real-ESRGAN)by changing the U-shaped network(U-Net)to Attention U-Net,to preprocess the images;secondly,we propose a new Focus module that introduces the Marr operator,which can provide more boundary information for the network;again,because Complete Intersection over Union(CIOU)cannot accommodate target borders that are increasing and decreasing,replace CIOU with Extended Intersection over Union(EIOU),while the loss function is changed to Focal and Efficient IOU(Focal-EIOU)due to the different difficulty of sample *** the homemade dataset,the precision of our method is 94%,the recall is 70.8%,and the map@.5 is 83.6%,which is an improvement of 1.3%in precision,9.7%in recall,and 7%in map@.5 over the original *** algorithm can meet the needs of electrolysis tank pole plate abnormal temperature detection,which can lay a technical foundation for improving production efficiency and reducing production waste.
Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of softwareengineering theo...
Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of softwareengineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.
Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance b...
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Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.
Understanding and quantifying the capabilities of foundation models, particularly in text-to-image(T2I) generation, is crucial for verifying their alignment with human expectations and practical requirements. However,...
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Understanding and quantifying the capabilities of foundation models, particularly in text-to-image(T2I) generation, is crucial for verifying their alignment with human expectations and practical requirements. However, evaluating T2I foundation models presents significant challenges due to the complex, multi-dimensional psychological factors that influence human preferences for generated images. In this work, we propose MindScore, a multi-view framework for assessing the generation capacity of T2I models through the lens of human preference. Specifically, MindScore decomposes the evaluation into four complementary modules that align with human cognitive processing of images: matching, faithfulness, quality,and realness. The matching module quantifies the semantic alignment between generated images and prompt text, while the faithfulness module measures how accurately the images reflect specific prompt details. Furthermore, we incorporate quality and realness modules to capture deeper psychological preferences, recognizing that unpleasant or distorted images often trigger adverse human responses. Extensive experiments on three T2I datasets with human preference annotations clearly validate the superiority of our proposed MindScore over various state-of-the-art baselines. Our case studies further reveal that MindScore offers valuable insights into T2I generation from a human-centric perspective.
In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some we...
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In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).
Let P be a set of points in the plane and let T be a maximum-weight spanning tree of P. For an edge (p, q), let Dpq be the diametral disk induced by (p, q), i.e., the disk having the segment pq as its diameter. Let DT...
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