Membership inference attacks on machine learning models have drawn significant attention. While current research primarily utilizes shadow modeling techniques, which require knowledge of the target model and training data, practical scenarios involve black-box access to the target model with no available information. Limited training data further complicate the implementation of these attacks. In this paper, we experimentally compare common data enhancement schemes and propose a data synthesis framework based on the variational autoencoder generative adversarial network (VAE-GAN) to extend the training data for shadow models. Meanwhile, this paper proposes a shadow model training algorithm based on adversarial training to improve the shadow model's ability to mimic the predicted behavior of the target model when the target model's information is unknown. By conducting attack experiments on different models under the black-box access setting, this paper verifies the effectiveness of the VAE-GAN-based data synthesis framework for improving the accuracy of membership inference attack. Furthermore, we verify that the shadow model, trained by using the adversarial training approach, effectively improves the degree of mimicking the predicted behavior of the target model. Compared with existing research methods, the method proposed in this paper achieves a 2% improvement in attack accuracy and delivers better attack performance.

An artificial rabbit optimization algorithm based on chaotic mapping and Levy flight improvement is proposed, which has the advantages of good initial population quality and fast convergence compared with the traditional artificial rabbit optimization algorithm, called CLARO. CLARO’s improvement method starts from three aspects: to optimize the quality of the initial population of the algorithm a chaotic mapping is brought in to initialize the population; to avoid the algorithm from falling into local optimum Levy flight is added in the exploration phase and the threshold of energy factor A is optimized to better balance exploration and exploitation. The efficiency of CLARO is tested on a set of 23 benchmark function sets by comparing it with ARO and different meta-heuristics algorithms. At last, the comparison experiments conclude that all three improvement strategies enhance the performance of ARO to some extent, with Levy flight providing the most significant improvement in ARO performance. The experimental results showed that CLARO has better results and faster convergence compared to other algorithms, while successfully addressing the drawbacks of ARO and being able to face more challenging problems.

To solve the privacy leakage problem of truck trajectories in intelligent logistics, this paper proposes a Quadtree-based Personalized Joint location Perturbation (QPJLP) algorithm using location generalization and local differential privacy techniques. Firstly, a flexible position encoding mechanism based on the spatial quadtree indexing is designed, and the length of the encoding can be adjusted freely according to data availability. Secondly, to meet the privacy needs of different locations of users, location categories are introduced to classify locations as sensitive and ordinary locations. Finally, the truck invokes the corresponding mechanism in the QPJLP algorithm to locally perturb the code according to the location category, allowing the protection of non-sensitive locations to be reduced without weakening the protection of sensitive locations, thereby improving data availability. Simulation experiments demonstrate that the proposed algorithm effectively meets the personalized trajectory privacy requirements while also exhibiting good performance in trajectory proportion estimation and Top-K classification.

目前，大多数构建的知识图谱无论其规模如何，大多有不完备性问题。这种不完备性会对基于知识图谱的应用产生负面影响。作为知识图谱补充的重要方法，链接预测近年来已成为热门研究课题。本文提出了一种基于半监督学习和模型汤思想的链接预测模型性能增强方案，通过对模型架构进行微小改变，有效提高了几种主流链接预测模型的性能。这一创新方案主要包括两个部分：（1）使用半监督学习策略预测图中的潜在事实三元组，（2）创造性地结合半监督学习和模型汤，进一步提高最终模型的性能，而不增加显著的计算开销。我们通过实验证实了该方案在各种链接预测模型上的有效性，特别是在具有密集关系的数据集上。对于测试的模型中整体性能最佳的模型CompGCN，在经过增强方案后，在FB15K-237数据集上的Hits@1指标提高了14.7%，在WN18RR数据集上提高了7.8%。同时，我们观察到增强方案中的半监督学习策略对于多类链接预测模型有显著改进，并且模型汤的引入带来的性能改进与具体的测试模型有关，某些模型的性能得到了改善，而其他模型的性能基本保持不变。

A new procedure of distributed self-control coverage for monitoring the dynamic targets with mobile sensor network is proposed. A special model is given for maintaining the nodes bi-connectivity and optimizing the coverage of the moving targets. The model consists of two parts, the virtual force model is proposed for motion control and the whale optimization algorithm is improved to further optimize the node positions and to reach the steady state quickly. The virtual resultant force stretches the network toward the uncovered targets by its multi-target attractive force, and maintains the network connectivity by its attractive force while network stretching, and avoids node collisions by its repulsive force while nodes moving. The operating mechanism of multi-target attractive force and other forces is also profoundly anatomized. The adjustment criteria for the model in different application scenarios are also given. Finally, the comparisons are shown to be significant advantages over other similar kinds.

In this paper, the online power control and rate adaptation for a wireless communication system with energy harvesting are investigated, in which soft decision decoding is adopted by the receiver. To efficiently utilize the harvested energy and maximize the average actual information transmission rate, transmit power, modulation order and code rate are jointly optimized. The Lyapunov framework is utilized to transform the long-term optimization problem into an optimization problem per time slot. Since there is no theoretical formula for the error rate of soft decision decoding, the optimization problem cannot be analytically solved. A table to find the optimal modulation order and code rate under the different values of signal-to-noise ratio is built first, and then a numeric algorithm to find the solution to the optimization problem is given. The feasibility and performance of the proposed algorithm are demonstrated by simulation. The simulation results show that compared with the algorithms to maximize the theoretic channel capacity, the proposed algorithm can achieve a higher actual transmission rate.

Addressing the issue of low pulse identification rates for low probability of intercept ( LPI) radar signals under low signal-to-noise ratio ( SNR) conditions, this paper aims to investigate a new method in the field of deep learning to recognize modulation types of LPI radar signals efficiently. A novel algorithm combining dual efficient network ( DEN) and non-local means ( NLM) denoising was proposed for the identification and selection of LPI radar signals. Time-domain signals for 12 radar modulation types were simulated, adding Gaussian white noise at various SNRs to replicate complex electronic countermeasure scenarios. On this basis, the noisy radar signals undergo Choi-Williams distribution ( CWD ) time-frequency transformation, converting the signals into two- dimensional (2D) time-frequency images ( TFIs). The TFIs are then denoised using the NLM algorithm. Finally, the denoised data is fed into the designed DEN for training and testing, with the selection results output through a softmax classifier. Simulation results demonstrate that at an SNR of - 8 dB, the algorithm can achieve a recognition accuracy of 97.22% for LPI radar signals, exhibiting excellent performance under low SNR conditions. Comparative demonstrations prove that the DEN has good robustness and generalization performance under conditions of small sample sizes. This research provides a novel and effective solution for further improving the accuracy of identification and selection of LPI radar signals.

Learning archives management in traditional systems faces challenges such as inadequate security, weak tamper resistance, and limited sharing capabilities. To address these issues, this paper proposes LRChain, a method based on consortium blockchain, for lifelong learning archives data protection and sharing. LRChain employs a combination of on-chain and off-chain cooperative storage using a consortium chain and InterPlanetary File System (IPFS) to enhance data security and availability. It also enables fine-grained verification of learning archives through selective disclosure principles, ensuring privacy protection of sensitive data. Furthermore, an attribute-based encryption algorithm is utilized to establish authorized access control for learning archives, facilitating safe and trusted sharing. Experimental evaluations and security analyses demonstrate that this method exhibits decentralization, strong security, tamper resistance, and performs well, effectively meeting the requirements for secure sharing of learning archive data.

In frequency division duplex ( FDD) massive multiple-input multiple-output ( MIMO) systems, a bidirectional positional attention network ( BPANet) was proposed to address the high computational complexity and low accuracy of existing deep learning-based channel state information ( CSI) feedback methods. Specifically, a bidirectional position attention module ( BPAM) was designed in the BPANet to improve the network performance. The BPAM captures the distribution characteristics of the CSI matrix by integrating channel and spatial dimension information, thereby enhancing the feature representation of the CSI matrix. Furthermore, channel attention is decomposed into two one-dimensional (1D) feature encoding processes effectively reducing computational costs. Simulation results demonstrate that, compared with the existing representative method complex input lightweight neural network ( CLNet), BPANet reduces computational complexity by an average of 19. 4% and improves accuracy by an average of 7. 1% . Additionally, it performs better in terms of running time delay and cosine similarity.

In convolutional neural networks ( CNNs), the floating-point computation in the traditional convolutional layer is enormous, and the execution speed of the network is limited by intensive computing, which makes it challenging to meet the real-time response requirements of complex applications. This work is based on the principle that the time domain convolution result equals the frequency domain point multiplication result to reduce the amount of floating- point calculations for convolution. The input feature map and the convolution kernel are converted to the frequency domain by the fast Fourier transform( FFT), and the corresponding point multiplication is performed. Then the frequency domain result is converted back to the time domain, and the output result of the convolution is obtained. In the shared CNN, the input feature map is much larger than the convolution kernel, resulting in many invalid operations. The overlap addition method is proposed to reduce invalid calculations and speed up network execution better. This work designs a hardware accelerator for frequency domain convolution and verifies its efficiency on the Xilinx Zynq UltraScale + MPSoC ZCU102 board. Comparing the calculation time of visual geometry group 16 ( VGG16 ) under the ImageNet dataset faster than the traditional time domain convolution, the hardware acceleration of frequency domain convolution is 8. 5 times.

Great challenges and demands are presented by increasing edge computing services for current power Internet of things ( Power IoT) to deal with the serious diversity and complexity of these services. To improve the matching degree between edge computing and complex services, the service identification function is necessary for Power IoT. In this paper, a naive long short-term memory ( Naive-LSTM ) based service identification scheme of edge computing devices in the Power IoT was proposed, where the Naive-LSTM model makes full use of the most simplified structure and conducts discretization of the long short-term memory ( LSTM) model. Moreover, the Naive-LSTM based service identification scheme can generate the probability output result to determine the task schedule policy of Power IoT. After well learning operation, these Naive-LSTM classification engine modules in edge computing devices of Power IoT can perform service identification, by obtaining key characteristics from various service traffics. Testing results show that the Naive-LSTM based services identification scheme is feasible and efficient in improving the edge computing ability of the Power IoT.

Due to the diversity of graph computing applications, the power-law distribution of graph data, and the high compute-to-memory ratio, traditional architectures face significant challenges regarding poor flexibility, imbalanced workload distribution, and inefficient memory access when executing graph computing tasks. Graph computing accelerator, GraphApp, based on a reconfigurable processing element ( PE) array was proposed to address the challenges above. GraphApp utilizes 16 reconfigurable PEs for parallel computation and employs tiled data. By reasonably dividing the data into tiles, load balancing is achieved and the overall efficiency of parallel computation is enhanced. Additionally, it preprocesses graph data using the compressed sparse columns independently ( CSCI) data compression format to alleviate the issue of low memory access efficiency caused by the high memory access-to-computation ratio. Lastly, GraphApp is evaluated using triangle counting ( TC) and depth-first search ( DFS) algorithms. Performance analysis is conducted by measuring the execution time of these algorithms in GraphApp against existing typical graph frameworks, Ligra, and GraphBIG, using six datasets from the Stanford Network Analysis Project ( SNAP) database. The results show that GraphApp achieves a maximum performance improvement of 30.86 % compared to Ligra and 20.43 % compared to GraphBIG when processing the same datasets.

Millimeter-wave ( mmWave) and massive multiple-input multiple-output ( MIMO) are broadly recognized as key enabling technologies for the fifth generation (5G) communication systems. In this paper, a low-complexity angle- delay parameters estimation ( ADPE) algorithm was put forward for wideband mmWave systems with uniform planar arrays ( UPAs). In particular, the ADPE algorithm effectively decouples the angle-delay parameters and converts the angle-delay estimation problem into three independent subproblems. Accordingly, the ability to devise an off- grid method based on discrete Fourier transform ( DFT) with a closed-form solution for angle-delay estimation and potential path number acquisition can be realized. In actuality, only a limited number of potential paths are close to the true paths influenced by noise. Consequently, the removal of noise paths to acquire the corresponding true path gains through a sparsity adaptive path gains estimation ( APGE) algorithm is postulated. Finally, the simulation results substantiate the effectiveness of ADPE and APGE algorithms.

In this paper, a wideband high gain millimeter wave radar array antenna based on a wavy power divider was proposed. The radar antenna comprises a wavy power divider and a 10-element array antenna. By adjusting the wavy radius of the power divider, the surface current of the power divider is altered, resulting in better impedance matching with the antenna. This ultimately leads to a significant improvement in bandwidth performance. The 4 伊 10 millimeter wave radar antenna loaded with a wavy power divider exhibits an approximate enhancement of 3 GHz compared to traditional microstrip power divider antennas, and an average gain increase of 2.42 dB within the vehicle millimeter wave radar frequency band relative to the improved gradient power divider structure. The 4 伊 10 millimeter wave radar antenna loaded with wavy power divider possesses the characteristics of high gain and broad bandwidth.

A 20 GHz - 24 GHz three-stage low noise amplifier ( LNA) was implemented using the GaAs pseudomorphic high electron mobility transistor ( PHEMT) process. The schematic design and optimization of the LNA were carried out using advanced design system ( ADS). The three-stage series structure is used to increase the gain of the amplifier. Additionally, a self-biasing network and negative feedback circuit can expand the bandwidth while increasing the stability of the circuit and obtaining better input matching and noise. The test results show that the gain in the 20 GHz - 24 GHz band is greater than 20 dB, the noise figure ( NF) is 2. 1 dB, and the input and output reflection coefficients are less than - 10 dB, which meets the design requirements. The amplifier serves a wide range of applications, including wireless communications, radar systems, satellite communications, and other areas that require high-frequency amplification to enhance system performance and sensitivity.

By leveraging the high maneuverability of the unmanned aerial vehicle ( UAV) and the expansive coverage of the intelligent reflecting surface ( IRS), a multi-IRS-assisted UAV communication system aimed at maximizing the sum data rate of all users was investigated in this paper. This is achieved through the joint optimization of the trajectory and transmit beamforming of the UAV, as well as the passive phase shift of the IRS. Nevertheless, the initial problem exhibits a high degree of non-convexity, posing challenges for conventional mathematical optimization techniques in delivering solutions that are both quick and efficient while maintaining low complexity. To address this issue, a novel scheme called the deep reinforcement learning ( DRL) -based enhanced cooperative reflection network ( DCRN) was proposed. This scheme effectively identifies optimal strategies, with the long short-term memory ( LSTM) network improving algorithm convergence by extracting hidden state information. Simulation results demonstrate that the proposed scheme outperforms the baseline scheme, manifesting substantial enhancements in sum rate and superior performance.

Since the release of the first version of the 5 th generation ( 5 G) mobile networks standard: Release-15 ( Rel- 15 ) , the 3 rd Generation Partnership Project ( 3 GPP) made significant efforts in the field of indoor and outdoor wireless positioning. Notably, Release-16 ( Rel-16 ) augmented support for enhanced mobile broadband ( eMBB) and ultra-reliable low-latency communication ( uRLLC) , particularly within complex indoor settings. To further meet the diverse application needs of positioning scenarios, the 3 GPP standards for Release-17 ( Rel-17 ) and Release-18 ( Rel-18 ) propose new enhancement measures to continuously provide more accurate positioning services. In this paper, the scholarly discourse on 5 G positioning was critically examined, providing a systematic review of the 5 G positioning standards as delineated in 3 GPP’s Rel-16 and Rel-17 , and extended the discussion to the anticipated enhancements in 5 G Rel-18 , along with their underlying motivations. Through these discussions, not only a comprehensive perspective on the current development of 5 G positioning technology was provided but also forward-looking analysis and predictions for the evolution of positioning technology in the upcoming 3 GPP Release-19 ( Rel-19 ) was offered. Additionally, it serves as a reference for researchers interested in understanding the development of positioning within the standard framework in the field of 5 G indoor positioning, which holds significant meaning for promoting research and application of 5 G positioning technology.

Aiming at the problems of poor initial population quality, slow convergence, and long-running time of optical microscope algorithm ( OMA), a multiple-strategy improved OMA based on periodical variation and encircling mechanism, called MOMA, was proposed in this paper. Firstly, the good point set population initialization is introduced to obtain a uniform initial population. Secondly, the periodic mutation and encircling mechanism are successively used to improve the convergence speed. Finally, the MOMA’s running time is optimized by introducing the conversion factor and the corresponding threshold, while balancing the exploration and exploitation. Experimental and analytical comparisons are made with OMA and 7 other excellent optimizers on 21 benchmark functions. The results show that MOMA largely outperforms the original algorithm. Furthermore, by applying MOMA to the optimization experiments of the no-wait flow-shop scheduling problem ( NWFSP), MOMA can obtain the optimal completion time and the fastest convergence speed compared to modified particle swarm optimization ( PSO) using adaptive strategy, grey wolf optimizer ( GWO), golden jackal optimization ( GJO), and OMA.

As a fundamental component of intelligent transportation systems, existing urban traffic flow forecasting models tend to overlook the spatio-temporal and long-term time-dependent patterns that characterize transportation networks. Among these, the long sequence time-series forecasting ( LSTF) model is susceptible to the issue of gradient disappearance, which can be attributed to the influence of a multitude of intricate factors. Accordingly, in this paper, the standpoint of multi-feature fusion was studied, and a traffic flow forecasting network model based on feature fusion and spatio-temporal transformer ( S-T transformer) ( STFFN) was proposed. The model combined predictive recurrent neural network ( Pred RNN) and S-T transformer to dynamically capture the spatio-temporal dependence and long-term time-dependence of traffic flow, thereby achieving a certain degree of model interpretability. A novel gated residual network-2 ( GRN-2) was proposed to investigate the potential relationship between multivariate features and target values. Furthermore, a hybrid quantile loss function was devised to alleviate the gradient disappearance in LSTF problems effectively. In extensive real experiments, the rationality and effectiveness of each network of the model were demonstrated, and the superior forecasting performance was verified in comparison to existing benchmark models.

The Nakagami-Gamma ( NG) shadow fading model based on the moment-based method ( MoM) generates lower tail approximation, which is inaccuracy when the gamma random variables are replaced by the lognormal random variables. The channel parameters of composite NG shadow fading based on the method of minimizing the Kullback- Leibler ( KL) divergence were estimated and a closed-form expression for the system bit error rate ( BER) was derived in this paper. The simulation results show that the KL estimated parameters solve the lower tail approximation problem, and the replacement effect of the lognormal function by the gamma function is better than the MoM when the shading parameters are around the typical value of 4 dB - 9 dB. Moreover, the KL method has a lower mean square error ( MSE) value for the channel analysis.