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SSRN 2023年

作者： Shang, Bin Zhao, Yinliang Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D Xi’an Jiaotong University Shaanxi Xi’an710049 China National Engineering Lab for Big Data Analytics Shaanxi Xi’an710049 China

Recently, graph convolutional networks (GCNs) and graph attention networks (GATs) have been widely used in knowledge graph completion (KGC), which aims to solve the incompleteness of knowledge graphs (KGs). However, GCNs based KGC models tend to stack redundant information when one entity have several noisy neighbors. GATs based KGC models may focus on certain neighbors and lead to weakening of structural information. Furthermore, relations in KGs have their specific semantics and should be considered when aggregating neighbor information (message passing). To address the above limitations, we propose a learnable convolutional attention network for knowledge graph completion named LCA-KGC. LCA-KGC introduces a knowledge graph convolutional attention network by using the convolution operation before the attention mechanism to ensure the structural information acquisition and avoid the redundant information stacking. Furthermore, to complete the autonomous switching of GNNs types and eliminate the necessity of pre-validating the local structure of KGs, LCA-KGC designs a learnable knowledge graph convolutional attention network by comprising three types of GNNs in one learnable formulation. Moreover, a learnable message function is proposed to emphasize relational semantics when aggregating neighbor information. Extensive experiments on standard KG datasets validate the effectiveness of the proposed innovations, and LCA-KGC achieves state-of-the-art (SOTA) performance compared to existing approaches (e.g., compared to SOTA approaches, LCA-KGC improves MRR from 0.360 to 0.372 on FB15k-237 dataset, and Hits@3 from 0.561 to 0.581 on YAGO3-10 dataset). © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Camera Calibration Method Based on Sine Cosine Algorithm

Camera Calibration Method Based on Sine Cosine Algorithm

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Zhihui Feng Quan Liang Zicheng Zhang Wei Ji School of Computer Science and Mathematics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou China

ISBN: (纸本)9781665426565

Aiming at the problems of traditional camera calibration method, such as sensitivity to the initial values of camera model parameters and unstable calibration results. This paper proposes a camera calibration method based on sine cosine algorithm. After obtaining a certain initial value by Zhang's camera calibration method, use the sine cosine algorithm (SCA) to form the initial population in the field near the initial value, and perform iterative optimization. The average error between the actual projection point and the calculated projection point is the accuracy criterion. Using the volatility and periodicity of the sine function and cosine function to search and iterate, so that the solution can be oscillating towards the global optimum and achieve the purpose of optimization. Experiments have proved that the adaptive parameters and randomness parameters in the algorithm better balance the exploration and development capabilities of the algorithm. The improved algorithm has fewer parameters, simple structure, easy implementation, and fast convergence speed. The experiment proves that the camera calibration accuracy is effectively improved.

关键词： Sensitivity Sociology Parallel processing Cameras Iterative algorithms Calibration Statistics

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Origin of minimal conductivity in Dirac materials: Momentum-dependent self-energy function from long-ranged disorder scattering

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Physical Review B 2023年第17期108卷 174205-174205页

作者： Weiwei Chen Yedi Shen Bo Fu Qinwei Shi W. Zhu Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of Science China Jiliang University Hangzhou 310018 China Hefei National Laboratory for Physical Sciences at the Microscale and Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui 230026 China School of Sciences Great Bay University Dongguan 523000 Guangdong Province China Department of Physics The University of Hong Kong Pokfulam Road Hong Kong China School of Science Westlake University Hangzhou 310024 China

We present a unified understanding of the experimentally observed minimal dc conductivity in Dirac materials. First, based on the linear response theory, we reveal that the momentum-dependent self-energy function induces a tunable minimal conductivity in Dirac electrons, the magnitude of which is directly related to the coefficient of the momentum-dependent part in the self-energy function. Taking the long-ranged Gaussian and Coulomb potentials as examples, the momentum-dependent self-energy function is perturbatively derived using the Born approximation and supplemented by the self-consistent Born approximation and renormalization group analysis. Moreover, we further validate our theory via numerical simulations using the large-scale Lanczos algorithm. The explicit momentum dependence of the self-energy on the intensity, concentration, and range of potential are critically addressed. Therefore, our theory provides a reasonable interpretation of the sample-dependent minimal conductivity observed in experiments.

关键词： Dirac fermions Quantum transport Disordered systems Diagrammatic methods

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

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IEEE Wireless Communications Letters 2025年第6期14卷 1618-1622页

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems China The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of HongKong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent algorithm to iteratively optimize the antenna position vector, array rotation vector, receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio. Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. © 2025 IEEE. All rights reserved.

关键词： Aircraft communication

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GTB-PPI:Predict Protein-protein Interactions Based on L1-regularized Logistic Regression and Gradient Tree Boosting

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Genomics, Proteomics & Bioinformatics 2020年第5期18卷 582-592页

作者： Bin Yu Cheng Chen Hongyan Zhou Bingqiang Liu Qin Ma School of Life Sciences University of Science and Technology of ChinaHefei 230027China College of Mathematics and Physics Qingdao University of Science and TechnologyQingdao 266061China Artificial Intelligence and Biomedical Big Data Research Center Qingdao University of Science and TechnologyQingdao 266061China School of Mathematics Shandong UniversityJinan 250100China Department of Biomedical Informatics College of MedicineThe Ohio State UniversityColumbusOH 43210USA

Protein-protein interactions(PPIs)are of great importance to understand genetic mechanisms,delineate disease pathogenesis,and guide drug *** the increase of PPI data and development of machine learning technologies,prediction and identification of PPIs have become a research hotspot in *** this study,we propose a new prediction pipeline for PPIs based on gradient tree boosting(GTB).First,the initial feature vector is extracted by fusing pseudo amino acid composition(Pse AAC),pseudo position-specific scoring matrix(Pse PSSM),reduced sequence and index-vectors(RSIV),and autocorrelation descriptor(AD).Second,to remove redundancy and noise,we employ L1-regularized logistic regression(L1-RLR)to select an optimal feature ***,GTB-PPI model is ***-fold cross-validation showed that GTB-PPI achieved the accuracies of 95.15% and 90.47% on Saccharomyces cerevisiae and Helicobacter pylori datasets,*** addition,GTB-PPI could be applied to predict the independent test datasets for Caenorhabditis elegans,Escherichia coli,Homo sapiens,and Mus musculus,the one-core PPI network for CD9,and the crossover PPI network for the Wnt-related signaling *** results show that GTB-PPI can significantly improve accuracy of PPI *** code and datasets of GTB-PPI can be downloaded from https://***/QUST-AIBBDRC/GTB-PPI/.

关键词： Protein-protein interaction Feature fusion L1-regularized logistic regression Gradient tree boosting Machine learning

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A k-core Analysis to Large-Scale Web API Collaboration Networks

A k-core Analysis to Large-Scale Web API Collaboration Netwo...

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2020 International Conference on Service science, ICSS 2020

作者： Tang, Mingdong Lei, Wenquan Lian, Sixian Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9781728185316

The Web has become a huge programmable platform in the Web 2.0 era. More and more companies are opening their data and services to the public through Web APIs. With the increasing number and variety of Web APIs, new Web applications and value-added services can be rapidly developed by combining different APIs. The ever-growing collaborations between APIs thus raise a kind of a large-scale networks, namely Web API collaboration networks. However, the structure and evolution process of Web API collaboration networks are still unclear to people so far. This paper provides a deep analysis to the internal structure of a real-world Web API collaboration network using k-core decomposition. We firstly construct the Web API collaboration network by using the data crawled from the largest Web API registry, Programmable ***, and then employ the k-core decomposition method to obtain different subgraphs of the network with different centrality or coreness (i.e., k-cores). We give an experimental analysis to the structures of the Web API collaboration network and its k-cores by using some classic statistical tools, such as degree distribution and clustering coefficient. The analysis results not only can identify the most central APIs in the Web API collaboration network, but also provides a basis for the visualization and understanding of Web API collaboration networks. © 2020 IEEE.

关键词： Statistical mechanics

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A novel method for driving path planning with spark

TechRxiv

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TechRxiv 2021年

作者： Li, Leixiao Lin, Hao Wan, Jianxiong Wang, Yongsheng Gao, Jing College of Data Science and Application Inner Mongolia University of Technology China School of Computer Science and Engineering Tianjin University of Technology China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service China College of Computer and Information Engineering Inner Mongolia Agricultural University China Inner Mongolia Autonomous Region Key Laboratory of big data research and application for agriculture and animal husbandry China

Efficient and accurate driving path planning can help drivers drive. To solve the problem of low efficiency of traditional heuristic algorithms such as PSO and GA in solving driving path planning, we introduce Excellence Coefficient into heuristic algorithms and make a parallel design based on Spark, which called EC-SPPSOGA. Excellence Coefficient can increase the probability of good edges being left, simultaneously, preserves the possibility of longer side being selected. The parallel design is based on time-consuming analysis of heuristic algorithms. We validate the performance of EC-SPPSOGA based on the data in TSPLIB. It is verified that the EC-SPPSOGA can improve efficiency of driving path planning and has good scalability. © 2021, CC BY.

关键词： Heuristic algorithms

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Novel method for robust bilateral filtering point cloud denoising

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Alexandria Engineering Journal 2025年 127卷 573-585页

作者： Yang, Huan Wang, Wei Wang, Yue Wang, Peng College of Computer Science and Technology Chongqing University of Posts and Telecommunications No. 2 Chongwen Road Chongqing Nan'an400065 China School of Advanced Manufacturing Engineering Chongqing University of Posts and Telecommunications No. 2 Chongwen Road Chongqing Nan'an400065 China Sevnce Robotics Co. Ltd Building 7 Phase II Internet Industrial Park Chongqing Liangjiang401123 China Big Data Institute BaoShan University No. 16 Yuanzheng Road Longyang District Yunnan Baoshan678000 China

Bilateral filtering for point cloud denoising relies on the normal of the points. However, normals are not inherent attributes of point clouds but are estimated using algorithms such as principal component analysis (PCA). Due to inaccuracies in the normals, bilateral filtering may lead to unexpected results, such as smoothing in sharp regions. This study addressed this issue by improving the accuracy of normals. Initially, PCA was employed to estimate the initial normals of the point cloud. The points were then categorized into edge and planar points by fitting a three-dimensional sphere. Subsequently, iterative weighted PCA was applied to refine the normals of the edge points. Finally, the improved normals were integrated into bilateral filtering to achieve point cloud denoising. Additionally, a robust estimator was used instead of a Gaussian function to compute the weights in the bilateral filtering. To assess and compare the performance of this algorithm with related ones, the mean square angular error (MSAE) and angular error distribution (AE) were employed to evaluate the accuracy of normal estimation, while the mean square error (MSE) and signal-to-noise ratio (SNR) were employed to assess the denoising performance. Results indicate that the proposed method yielded the most accurate normals compared to PCA, Jet, and VCM normal estimation algorithms. Moreover, when compared to algebraic point set surfaces (APSS), robust implicit moving least squares (RIMLS), anisotropic weighted locally optimal projection (AWLOP), bilateral filtering, and guided filtering point cloud denoising algorithms, the proposed method consistently achieved the smallest MSE and the highest SNR in most cases on the dataset used in this study. © 2025 The Authors

关键词： Principal component analysis

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A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition

arXiv

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arXiv 2023年

作者： Shou, Yuntao Liu, Huan Cao, Xiangyong Meng, Deyu Dong, Bo School of Computer Science and Technology The Ministry of Education Key Laboratory for Intelligent Networks and Network Security The Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong Shaanxi Xi’an710049 China School of Computer Science and Technology Xi’an Jiaotong Shaanxi Xi’an710049 China The BigKE Joint Innovation Center SHMEEA Shanghai China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China

Conversational emotion recognition (CER) is an important research topic in human-computer interactions. Although recent advancements in transformer-based cross-modal fusion methods have shown promise in CER tasks, they tend to overlook the crucial intra-modal and inter-modal emotional interaction or suffer from high computational complexity. To address this, we introduce a novel and lightweight cross-modal feature fusion method called Low-Rank Matching Attention Method (LMAM). LMAM effectively captures contextual emotional semantic information in conversations while mitigating the quadratic complexity issue caused by the self-attention mechanism. Specifically, by setting a matching weight and calculating inter-modal features attention scores row by row, LMAM requires only one-third of the parameters of self-attention methods. We also employ the low-rank decomposition method on the weights to further reduce the number of parameters in LMAM. As a result, LMAM offers a lightweight model while avoiding overfitting problems caused by a large number of parameters. Moreover, LMAM is able to fully exploit the intra-modal emotional contextual information within each modality and integrates complementary emotional semantic information across modalities by computing and fusing similarities of intra-modal and inter-modal features simultaneously. Experimental results verify the superiority of LMAM compared with other popular cross-modal fusion methods on the premise of being more lightweight. Also, LMAM can be embedded into any existing state-of-the-art CER methods in a plug-and-play manner, and can be applied to other multi-modal recognition tasks, e.g., session recommendation and humour detection, demonstrating its remarkable generalization ability. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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ATHENA: An In-vehicle CAN Intrusion Detection Framework Based on Physical Characteristics of Vehicle Systems

arXiv

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arXiv 2025年

作者： Wang, Kai Sun, Zhen Wang, Bailing Fan, Qilin Li, Ming Zhang, Hongke School of Computer Science and Technology Harbin Institute of Technology Weihai China Shandong Key Laboratory of Industrial Network Security China School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing China Jinan Key Laboratory of Distributed Databases Shandong Inspur Database Technology Co. Ltd Jinan China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

With the growing interconnection between In-Vehicle Networks (IVNs) and external environments, intelligent vehicles are increasingly vulnerable to sophisticated external network attacks. This paper proposes ATHENA, the first IVN intrusion detection framework that adopts a vehicle-cloud integrated architecture to achieve better security performance for the resource-constrained vehicular environment. Specifically, in the cloud with sufficient resources, ATHENA uses the clustering method of multi-distribution mixture model combined with deep data mining technology to generate the raw Payload Rule Bank of IVN CAN messages, and then improves the rule quality with the help of exploitation on the first-principled physical knowledge of the vehicle system, after which the payload rules are periodically sent to the vehicle terminal. At the vehicle terminal, a simple LSTM component is used to generate the Time Rule Bank representing the long-term time series dependencies and the periodic characteristics of CAN messages, but not for any detection tasks as in traditional usage scenarios, where only the generated time rules are the candidates for further IVN intrusion detection tasks. Based on both the payload and time rules generated from cloud and vehicle terminal, ATHENA can achieve efficient intrusion detection capability by simple rule-base matching operations, rather than using complex black-box reasoning of resource-intensive neural network models, which is in fact only used for rule logic generation phase instead of the actual intrusion detection phase in our framework. Comparative experimental results on the ROAD dataset, which is current the most outstanding real-world in-vehicle CAN dataset covering new instances of sophisticated and stealthy masquerade attacks, demonstrate ATHENA significantly outperforms the state-of-the-art IVN intrusion detection methods in detecting complex attacks. We make the code available at https://***/wangkai-tech23/ATHENA. Copyright

关键词： Network intrusion

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