Contrastive learning has achieved significant progress in the field of self-supervised skeleton-based action recognition. However, existing methods often apply strong augmentations directly to skeleton data, which can...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Contrastive learning has achieved significant progress in the field of self-supervised skeleton-based action recognition. However, existing methods often apply strong augmentations directly to skeleton data, which can distort or even lose the semantic of the skeletons. Additionally, most methods focus on unified feature extraction through spatiotemporal modeling, leading to spatiotemporal entanglement that hinders model’s interpretability and degrades performance. To address these issues, we propose a Progressive Augmentation and SpatioTemporal Decoupling contrastive learning model (PASTD). Specifically, we propose progressive augmentation to enhance the model’s generalization capability, which generates multiple distinct positive sample pairs through multiple branches and uses inter-branch constraints to maintain consistent motion patterns. Moreover, we propose spatiotemporal decoupling module to separate the feature’s spatial and temporal information, using a dual-path self-attention module combined with an intra-branch cross-domain learning strategy to facilitate information exchange between domains. Extensive experiments on the four public datasets NTU-RGB+D (60&120) and PKU-MMD (I&II) demonstrate the effectiveness of these components. Moreover, PASTD outperforms state-of-the-art methods across various evaluation metrics.
Neutral atom (NA) quantum systems are emerging as a leading platform for quantum computation, offering superior or competitive qubit count and gate fidelity compared to superconducting circuits and ion traps. However,...
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Despite the growing research and development of botnet detection tools, an ever-increasing spread of botnets and their victims is being witnessed. Due to the frequent adaptation of botnets to evolving responses offere...
Despite the growing research and development of botnet detection tools, an ever-increasing spread of botnets and their victims is being witnessed. Due to the frequent adaptation of botnets to evolving responses offered by host-based and network-based detection mechanisms, traditional methods are found to lack adequate defense against botnet threats. In this regard, the suggestion is made to employ flow-based detection methods and conduct behavioral analysis of network traffic. To enhance the performance of these approaches, this paper proposes utilizing a hybrid deep learning method that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods. CNN efficiently extracts spatial features from network traffic, such as patterns in flow characteristics, while LSTM captures temporal dependencies critical to detecting sequential patterns in botnet behaviors. Experimental results reveal the effectiveness of the proposed CNN-LSTM method in classifying botnet traffic. In comparison with the results obtained by the leading method on the identical dataset, the proposed approach showcased noteworthy enhancements, including a 0.61% increase in precision, a 0.03% augmentation in accuracy, a 0.42% enhancement in the recall, a 0.51% improvement in the F1-score, and a 0.10% reduction in the false-positive rate. Moreover, the utilization of the CNN-LSTM framework exhibited robust overall performance and notable expeditiousness in the realm of botnet traffic identification. Additionally, we conducted an evaluation concerning the impact of three widely recognized adversarial attacks on the Information Security Centre of Excellence dataset and the Information Security and Object Technology dataset. The findings underscored the proposed method’s propensity for delivering a promising performance in the face of these adversarial challenges.
The development of compact and efficient devices has been made possible by the growth of Very Large Scale Integration (VLSI) technologies, which has transformed modern electronics. However, there are cautions regardin...
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Balancing the consistency of style and the integrity of content is the main challenge in arbitrary style transfer domain. Currently, local style details can be effectively captured by attention mechanism but easily pr...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Balancing the consistency of style and the integrity of content is the main challenge in arbitrary style transfer domain. Currently, local style details can be effectively captured by attention mechanism but easily produce distorted style patterns and inconsistent content structure. In this paper, we propose a Content Affinity Preserving Arbitrary Style Transfer (CAPAST) framework to ensure style features can be stably integrated into the content structure. Considering the local feature learning ability of CNN and the global feature representation advantage of transformer, a dual encoder is proposed to capture local and global features of images with the combination between transformer and CNN. In addition, a channel and spatially aligned attention (CSAA) is introduced to generate high-quality results by stably fusing style features and content features. In experiments, we demonstrated the superior performance of our method in preventing content structure distortion and maintaining consistency between style and content. Codes are available at https://***/miaopashi-zxy/CAPAST.
The quick advancement of healthcare systems necessitates robust and efficient network security keys to defend sensitive patient records and guarantee uninterrupted service delivery. The current IDS has many challenges...
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The quick advancement of healthcare systems necessitates robust and efficient network security keys to defend sensitive patient records and guarantee uninterrupted service delivery. The current IDS has many challenges, such as a high false positive rate, poor accuracy of detection, slow response to threats, and inability to scale well. This paper proposes an efficient and real-time intrusion detection system (IDS) using advanced machine learning techniques within a software-defined networking (SDN) framework specifically tailored for healthcare systems. The proposed architecture implements a Machine Learning (ML) model that combines the SVM and KNN to better identify malicious activities. Full sets of detection and mitigation capabilities are implemented to address different types of traffic in the network with the least interference. Through the different evaluation measures, the efficiency of the proposed model is assured. Network performance is determined by success rate queries, packet losses in each domain path, and the CPU being used by the system. Responsiveness is measured through delay metrics grounded on end-to-end delay, hop-to-hop packet delay, latency rate, and propagation delay. Moreover, model accuracy fidelity is reviewed via precision assessment, alpha (α) affecting the accuracy of the model, and confusion matrix with different techniques with the proposed hybrid SVM-KNN model. Last of all, a comparison of the security of the models in question strengthens the argument in favor of the proposed model. More specifically, flow and network topology diagrams are included to show how integration may be accomplished in linkage or merger with existing healthcare networks. The results also present a 30% overall advancement in detection and mitigation by presenting the hybrid SVM-KNN model to overcome other traditional models. This proposed model shows significant improvements not less than 20-30% improvement in CPU use, 30-50% reduction in end-to-end delay,
In a large-scale distributed machine learning system, coded computing has attracted wide-spread attention since it can effectively alleviate the impact of stragglers. However, several emerging problems greatly limit t...
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The research topic of Path planning is extremely challenging area of concentration within the field of mobile robots. However, path planning algorithms for mobile robot tasks are contingent upon the environment and it...
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ISBN:
(数字)9798331509262
ISBN:
(纸本)9798331509279
The research topic of Path planning is extremely challenging area of concentration within the field of mobile robots. However, path planning algorithms for mobile robot tasks are contingent upon the environment and its level of complexity. This paper analyzes four distinct path planning methods for simulated indoor environment. The proposed algorithms include conventional Ant Colony methods, Ant System (AS), Ant Colony System (ACS), as well as standard methods Dijkstra and AStar (A*). We analyzed and examined these algorithms by employing various metrics/maps with complexity. The results indicate that the traditional path planning algorithm Dijkstra and A* approaches surpass the other ant colony techniques in terms of both computation time and path distance.
The usage of various drugs has increased significantly in recent years, leading to a higher possibility of drug-drug interactions (DDIs). The concurrent usage of multiple drugs can result in potentially hazardous inte...
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ISBN:
(数字)9798331509934
ISBN:
(纸本)9798331509941
The usage of various drugs has increased significantly in recent years, leading to a higher possibility of drug-drug interactions (DDIs). The concurrent usage of multiple drugs can result in potentially hazardous interactions, making it crucial to foresee DDIs to prevent adverse effects and enhance patient safety. Traditional DDI prediction methods often require extensive examinations, which can be time consuming and resource intensive. As a result, automatic DDI prediction methods have gained attention in the literature, offering clinicians support for making more accurate decisions and designing effective treatment plans. Despite progress in DDI prediction studies, substantial challenges remain in the field. Our study addresses these challenges by proposing a deep learning-based model leveraging drug features. Specifically, this study introduces an enhanced multi-head self-attention transformer-based method, which incorporates pharmacological features to achieve improved performance. The proposed method consists of two primary stages: feature extraction and model design. To evaluate the efficacy of the proposed method, performance evaluation procedures -Accuracy (ACC), Precision (PRE), Recall (REC), and F -Score-are utilized. Comparative experiments are conducted with several state-of-the-art methods on a data set specifically created for this study. Out of all, the proposed method achieves mean values of ACC, PRE, REC, and F-Score as 87.49%, 87.19%, 82.76%, and 84.56 %, respectively, surpassing the performance of other methods. The results unequivocally demonstrate the effectiveness and superiority of the proposed method in predicting DDIs.
Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent s...
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ISBN:
(数字)9798331533366
ISBN:
(纸本)9798331533373
Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation Systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.
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