Community search aims to find highly cohesive connected subgraphs containing user query vertices in information networks. Cycle truss is a community search model based on cycle triangle. However, the existing index-based cycle truss community search methods suffer from the drawbacks of large index space, low search efficiency, and low community cohesion. A maximum cycle truss community search method based on hierarchical tree index was proposed to address this issue. Firstly, a k-cycle truss decomposition algorithm was proposed, and two important concepts, cycle triangle connectivity and k-level equivalence were introduced. Based on k-level equivalence, the hierarchical tree index TreeCIndex and the table index SuperTable were designed. On this basis, two efficient cycle truss community search algorithms were proposed. The proposed algorithms were compared with existing community search algorithms based on TrussIndex and EquiTruss on four real datasets. The experimental results show that the space consumptions of TreeCIndex and SuperTable are at least 41.5% lower and the index construction time is 8.2% to 98.3% lower compared to TrussIndex and EquiTruss; furthermore, the efficiencies of searching for maximum cycle truss communities is increased by one and two orders of magnitude.
To address the issue of siting for Electric EVCS (Vehicle Charging Station), an urban charging station siting method based on spatial semantics and individual activities was proposed. First, according to the urban planning, unsupervised learning was used to cluster the Point Of Interests (POIs) out of the service radius to determine the number of new charging stations. Then, Constrained Two-Archive Evolutionary Algorithm (CTAEA) was used to solve the objective function to optimize the electric vehicle siting scheme under the constraints of maximizing the distance between stations and covering the most POIs with new charging stations. The trajectory data and POIs of taxis in the second-ring road of Chengdu were used as the experimental samples, and siting scheme with 15 charging stations was planned. Experimental results show that compared with NSGA2 (Non-dominated Sorting Genetic Algorithm 2) and SPEA2 (Strength Pareto Evolutionary Algorithm 2), CTAEA improves 22.9 and 20.6 percentage points on POI coverage, and reduces 18.9% and 25.5% on driver’s average selected distance, which illustrates the convenience and rationality of the method in electric vehicle charging station siting.
Focused on the issue that the current large-scale networks are not suitable to be applied on resource-starved mobile devices like smart phones and tablet computers, and the pooling layer will lead to the sparsity of feature map, which ultimately affect the recognition accuracy of the neural network, a lightweight face recognition neural network namely ShuffaceNet was proposed, a smooth nonlinear Log-Mean-Exp function ThetaMEX was designed, and an end-to-end trainable ThetaMEX Global Pool Layer (TGPL) was proposed, so as to reduce network parameters and improve computing speed while ensuring the accuracy of the algorithm, achieving the purpose that the network can be effectively deployed on mobile devices with limited resources. ShuffaceNet has about 3 600 parameters, and the model size is only 3.5 MB. The recognition test results on LFW (Labled Faces in the Wild), AgeDB-30 (Age Database-30) and CFP (Celebrities in Frontal Profile) face datasets show that the accuracy of ShuffaceNet reaches 99.32%, 93.17%, 94.51% respectively. Compared with the traditional networks such as MobileNetV1, SqueezeNet and Xception, the proposed network has the size reduced by 73.1%, 82.1% and 78.5% respectively, and the accuracy on AgeDB-30 dataset improved by 5.0%, 6.3% and 6.7% respectively. It can be seen that the proposed network based on ThetaMEX global pooling can improve the model accuracy.
As a simplified version of Spatial Modulation (SM), Generalized Space Shift Keying (GSSK) has been widely used in massive Multiple-Input Multiple-Output (MIMO) systems. It can better solve the problems such as Inter-Channel Interference (ICI), Inter-Antenna Synchronization (IAS), and multiple Radio Frequency (RF) links in traditional MIMO technology. To solve the problem of high computational complexity of the Maximum Likelihood (ML) detection algorithm for GSSK systems, a low-complexity GSSK signal detection algorithm based on Compressed Sensing (CS) theory was proposed by combining Subspace Tracking (SP) and ML detection algorithms in CS, and presetting the threshold. First, the improved SP algorithm was used to obtain partial Transmit Antenna Combinations (TACs). Secondly, the set of search antennas was shrunk by deleting partial antenna combinations. Finally, the ML algorithm and the preset threshold were used to estimate the TACs. The results of simulation experiments show that the computational complexity of the proposed algorithm is significantly lower than that of ML detection algorithm, and the Bit Error Rate (BER) performance is almost the same as that of ML detection algorithm, which verify the effectiveness of the proposed algorithm.
For a given reference image of a person, the goal of Human Pose Transfer (HPT) is to generate an image of that person in any arbitrary pose. Many existing related methods fail to capture the details of a person’s appearance and have difficulties in predicting invisible regions, especially for complex pose transformation, and it is difficult to generate a clear and realistic person’s appearance. To address the above problems, a new HPT model that integrated convolution and multi-head attention was proposed. Firstly, the Convolution-Multi-Head Attention (Conv-MHA) block was constructed by fusing the convolution and multi-head attention, then it was used to extract rich contextual features. Secondly, to improve the learning ability of the proposed model, the HPT network was constructed by using Conv-MHA block. Finally, the self-reconstruction of the reference image was introduced as an auxiliary task to make the model more fully utilized its performance. The Conv-MHA-based human pose transfer model was validated on DeepFashion and Market-1501 datasets, and the results on DeepFashion test dataset show that it outperforms the state-of-the-art human pose transfer model, DPTN (Dual-task Pose Transformer Network), in terms of Structural SIMilarity (SSIM), Learned Perceptual Image Patch Similarity (LPIPS) and FID (Fréchet Inception Distance) indicators. Experimental results show that the Conv-MHA module, which integrates convolution and multi-head attention mechanism, can improve the representation ability of the model, capture the details of person’s appearance more effectively, and improve the accuracy of person image generation.
Concerning that the federated chain lacks visualization methods to show the resource usage, health status, mutual relationship and consensus transaction process of each node, a Fabric consensus transaction Tracking method based on custom Log (FTL) was proposed. Firstly, Hyperledger Fabric, a typical federation framework, was used as the infrastructure to build the bottom layer of FTL. Then, the custom consensus transaction logs of the Fabric were collected and parsed by using the ELK (Elasticsearch, Logstash, Kibana) tool chain, and Spring Boot was used as the business logic processing framework. Finally, Graphin which focuses on graph analysis was utilized to realize the visualization of consensus trade trajectory. Experimental results show that compared with native Fabric applications, FTL Fabric?based application framework only experienced an 8.8% average performance decline after the implementation of visual tracking basis without significant latency, which can provide a more intelligent blockchain supervision solution for regulators.