Massive connection and high data rates are the main requirements for next-generation networks. The next-generation networks can meet their primary requirements by using a combination of reconfigurable intelligent surf...
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This paper explores using convolutional neural networks (CNNs) for unsupervised image segmentation. The method enhances pixel labeling accuracy through superpixel and propagates back strategies. Leveraging CNN’s feat...
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Alzheimer’s disease (AD) is a brain degenerative disease, so the Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) of cerebral images are effective data in detecting the onset of the disease. In...
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Open-vocabulary object detection (OVD) models are considered to be Large Multi-modal Models (LMM), due to their extensive training data and a large number of parameters. Mainstream OVD models prioritize object coarse-...
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Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least t...
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Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.
Traffic encryption techniques facilitate cyberattackers to hide their presence and activities. Traffic classification is an important method to prevent network threats. However, due to the tremendous traffic volume an...
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Traffic encryption techniques facilitate cyberattackers to hide their presence and activities. Traffic classification is an important method to prevent network threats. However, due to the tremendous traffic volume and limitations of computing, most existing traffic classification techniques are inapplicable to the high-speed network environment. In this paper, we propose a High-speed Encrypted Traffic Classification(HETC) method containing two stages. First, to efficiently detect whether traffic is encrypted, HETC focuses on randomly sampled short flows and extracts aggregation entropies with chi-square test features to measure the different patterns of the byte composition and distribution between encrypted and unencrypted flows. Second, HETC introduces binary features upon the previous features and performs fine-grained traffic classification by combining these payload features with a Random Forest model. The experimental results show that HETC can achieve a 94% F-measure in detecting encrypted flows and a 85%–93% F-measure in classifying fine-grained flows for a 1-KB flow-length dataset, outperforming the state-of-the-art comparison methods. Meanwhile, HETC does not need to wait for the end of the flow and can extract mass computing features. The average time for HETC to process each flow is only 2 or 16 ms, which is lower than the flow duration in most cases, making it a good candidate for high-speed traffic classification.
Advanced Driver Assistance Systems (ADAS) are designed to prevent collisions, identify the condition of drivers while operating vehicles, and provide additional information to enhance drivers' awareness of potenti...
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This work is to carryout a research if a lead acid battery built with a Supercapacitor (Hybrid Battery) will give a better life cycle in applications where the charge/discharge currents are very high and in the order ...
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In social data analytics,Virtual Community(VC)detection is a primary challenge in discovering user relationships and enhancing social *** formation is used for personal interaction between *** the usual methods didn’...
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In social data analytics,Virtual Community(VC)detection is a primary challenge in discovering user relationships and enhancing social *** formation is used for personal interaction between *** the usual methods didn’t find the Suspicious Behaviour(SB)needed to make a *** Generalized Jaccard Suspicious Behavior Similarity-based Recurrent Deep Neural Network Classification and Ranking(GJSBS-RDNNCR)Model addresses these *** GJSBS-RDNNCR model comprises four layers for VC formation in Social Networks(SN).In the GJSBS-RDNNCR model,the SN is given as an input at the input *** that,the User’s Behaviors(UB)are extracted in the first Hidden Layer(HL),and the Generalized Jaccard Similarity coefficient calculates the similarity value at the second HL based on the *** the third HL,the similarity values are examined,and SB tendency is classified using the Activation Function(AF)in the Output Layer(OL).Finally,the ranking process is performed with classified users in SN and their *** analysis is performed with metrics such as Classification Accuracy(CA),Time Complexity(TC),and False Positive Rate(FPR).The experimental setup consid-ers 250 tweet users from the dataset to identify the SBs of users.
In a multiprocessor system, the task scheduling function is a vital performance to minimize many issues. A multiprocessor system is applicable for the different multimedia applications with massive-sized task sets. Ho...
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