Payment Channel Network(PCN)provides the off-chain settlement of *** is one of the most promising solutions to solve the scalability issue of the *** routing techniques in PCN have been ***,both incentive attack and p...
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Payment Channel Network(PCN)provides the off-chain settlement of *** is one of the most promising solutions to solve the scalability issue of the *** routing techniques in PCN have been ***,both incentive attack and privacy protection have not been considered in existing *** this paper,we present an auction-based system model for PCN routing using the Laplace differential privacy *** formulate the cost optimization problem to minimize the path cost under the constraints of the Hashed Time-Lock Contract(HTLC)tolerance and the channel *** propose an approximation algorithm to find the top K shortest paths constrained by the HTLC tolerance and the channel capacity,i.e.,top K-restricted shortest ***,we design the probability comparison function to find the path with the largest probability of having the lowest path cost among the top K-restricted shortest paths as the final ***,we apply the binary search to calculate the transaction fee of each *** both theoretical analysis and extensive simulations,we demonstrate that the proposed routing mechanism can guarantee the truthfulness and individual rationality with the probabilities of 1/2 and 1/4,*** can also ensure the differential privacy of the *** experiments on the real-world datasets demonstrate that the privacy leakage of the proposed mechanism is 73.21%lower than that of the unified privacy protection mechanism with only 13.2%more path cost compared with the algorithm without privacy protection on average.
The secured access is studied in this paper for the network of the image remote *** sensor in this network encounters the information security when uploading information of the images wirelessly from the sensor to the...
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The secured access is studied in this paper for the network of the image remote *** sensor in this network encounters the information security when uploading information of the images wirelessly from the sensor to the central collection *** order to enhance the sensing quality for the remote uploading,the passive reflection surface technique is *** one eavesdropper that exists nearby this sensor is keeping on accessing the same networks,he may receive the same image from this *** goal in this paper is to improve the SNR of legitimate collection unit while cut down the SNR of the eavesdropper as much as possible by adaptively adjust the uploading power from this sensor to enhance the security of the remote sensing *** order to achieve this goal,the secured energy efficiency performance is theoretically analyzed with respect to the number of the passive reflection elements by calculating the instantaneous performance over the channel fading *** on this theoretical result,the secured access is formulated as a mathematical optimization problem by adjusting the sensor uploading power as the unknown variables with the objective of the energy efficiency maximization while satisfying any required maximum data rate of the eavesdropper ***,the analytical expression is theoretically derived for the optimum uploading *** simulations verify the design approach.
Wireless power transmission has been widely used to replenish energy for wireless sensor networks, where the energy consumption rate of sensor nodes is usually time varying and indefinite. However, few works have inve...
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At present, deep learning technologies have been widely used in the field of natural language process, such as text summarization. In CQA, the answer summary could help users get a complete answer quickly. There are s...
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With the continuous development of artificial intelligence technology, machine learning in distributed network systems, such as IoVflntemet of Vehicles), will inevitably lead to privacy leakage. At present, there are ...
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Syslog records critical information of network when the system is running, and has been used to help practitioners carry out various network maintenance and operation activities. Because of abundance of syslog, automa...
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For large-scale multitask wireless sensor networks (LSM-WSNs), the traditional data collection mode could suffer low energy-efficiency on data transmission, since the large-scale multitask scenarios could result in mu...
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Few-shot text classification involves transferring knowledge from a limited dataset to perform classification tasks in unseen domains. Existing metric-based meta-learning models, such as prototypical networks, have sh...
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The rapid growth of mobile applications,the popularity of the Android system and its openness have attracted many hackers and even criminals,who are creating lots of Android ***,the current methods of Android malware ...
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The rapid growth of mobile applications,the popularity of the Android system and its openness have attracted many hackers and even criminals,who are creating lots of Android ***,the current methods of Android malware detection need a lot of time in the feature engineering ***,these models have the defects of low detection rate,high complexity,and poor practicability,*** analyze the Android malware samples,and the distribution of malware and benign software in application programming interface(API)calls,permissions,and other *** classify the software’s threat levels based on the correlation of ***,we propose deep neural networks and convolutional neural networks with ensemble learning(DCEL),a new classifier fusion model for Android malware ***,DCEL preprocesses the malware data to remove redundant data,and converts the one-dimensional data into a two-dimensional gray ***,the ensemble learning approach is used to combine the deep neural network with the convolutional neural network,and the final classification results are obtained by voting on the prediction of each single *** based on the Drebin and Malgenome datasets show that compared with current state-of-art models,the proposed DCEL has a higher detection rate,higher recall rate,and lower computational cost.
In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect anal...
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In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching ***,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design refe
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