1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves ...
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1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves ***,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency *** is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.
With the recent technological developments,massive vehicular ad hoc networks(VANETs)have been established,enabling numerous vehicles and their respective Road Side Unit(RSU)components to communicate with *** best way ...
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With the recent technological developments,massive vehicular ad hoc networks(VANETs)have been established,enabling numerous vehicles and their respective Road Side Unit(RSU)components to communicate with *** best way to enhance traffic flow for vehicles and traffic management departments is to share thedata they *** needs to be more protection for the VANET *** effective and safe methodof outsourcing is suggested,which reduces computation costs by achieving data security using a homomorphicmapping based on the conjugate operation of *** research proposes a VANET-based data outsourcingsystem to fix the *** keep data outsourcing secure,the suggested model takes cryptography models *** will keep the generated keys for the purpose of vehicle *** controlling and overseeingthe outsourced data while preserving privacy,the suggested approach considers the Trusted Certified Auditor(TCA).Using the secret key,TCA can identify the genuine identity of VANETs when harmful messages *** proposed model develops a TCA-based unique static vehicle labeling system using cryptography(TCA-USVLC)for secure data outsourcing and privacy preservation in *** proposed model calculatesthe trust of vehicles in 16 ms for an average of 180 vehicles and achieves 98.6%accuracy for data encryption toprovide *** proposedmodel achieved 98.5%accuracy in data outsourcing and 98.6%accuracy in privacypreservation in fog-enabled *** curve cryptography models can be applied in the future for betterencryption and decryption rates with lightweight cryptography operations.
In the field of object detection for remote sensing images, especially in applications such as environmental monitoring and urban planning, significant progress has been made. This paper addresses the common challenge...
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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.
The paper proposed a secured and efficient data aggregation mechanism leveraging the edge computing paradigm and homomorphic data encryption technique. The paper used a unique combination of Paillier cryptosystem and ...
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This study presents a sustainable and effective approach for obtaining land uses and land covers (LULC) details utilizing remote sensing images. The modified Convolutional Neural Network (CNN), Inception-Resnet V2, is...
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The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software w...
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The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two sta
The Information Retrieval system aims to discover relevant documents and display them as query responses. However, the ever-changing nature of user queries poses a substantial research problem in defining the necessar...
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The Information Retrieval system aims to discover relevant documents and display them as query responses. However, the ever-changing nature of user queries poses a substantial research problem in defining the necessary data to respond accurately. The Major intention for this study is for enhance the retrieval of relevant information in response to user queries. The aim to develop an advanced IR system that adapts to changing user requirements. By introducing WMO_DBN, we seek to improve the efficiency and accuracy of information retrieval, catering to both general and specific user searches. The proposed methodology comprises three important steps: pre-processing, feature choice, and categorization. Initially, unstructured data subject to pre-processing to transform it into a structured format. Subsequently, relevant features are selected to optimize the retrieval process. The final step involves the utilization of WMO_DBN, a novel deep learning model designed for information retrieval based on the query data. Additionally, similarity calculation is employed to improve the effectiveness for the network training model. The investigational evaluation for the suggested model was conducted, and its performance is measured regarding the metrics of recall, precision, accuracy, and F1 score, the present discourse concerns their significance within the academic realm. The results prove the superiority of WMO_DBN in retrieving relevant information compared to traditional approaches. This research introduces novel method for addressing the challenges in information retrieval with the integration of WMO_DBN. By applying pre-processing, feature selection, and a deep belief neural network, the proposed system achieves more accurate and efficient retrieval of relevant information. The study contributes to the advancement of information retrieval systems and emphasizes the importance of adapting to users' evolving search queries. The success of WMO_DBN in retrieving relevant inform
With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapi...
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With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of *** technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the *** the traditional blockchain,data is stored in a Merkle *** data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based ***,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of *** solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC ***,this paper uses PVC instead of the Merkle tree to store big data generated by *** can improve the efficiency of traditional VC in the process of commitment and ***,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of *** mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.
Deep features can exhibit superior retrieval performance than low-level features. However, low-level features (e.g. colour and orientation) can be extracted by generally imitating the human visual perceptual system. C...
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