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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Yanping Zhang Shuang Li Xi Chen Fulan Qian Shu Zhao Shuwei Zhu Yulu Wang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9781728191461

ISBN: (纸本)9781728191478

With the increasing number of patents, the task of patent citation recommendation becomes more and more important and meaningful. The core of a patent is textual content, which describes technical ideas and scope of the protection. Therefore, most of the existing patent citation recommendation methods focus on textual content. Recently, some scholars introduce bibliographic (structural) data to construct heterogeneous information network (HIN) on the basis of textual content to further improve the effect of recommendation. Most of these methods build HIN directly based on candidate patents selected in advance according to text similarity, which simplifies the complexity of the problem. However, the latent semantic relationships between patents formed by the textual content are often ignored, which can be used to mine more semantic and structural relationships in HIN. In this paper, in order to capture deep semantic and structural information, we introduce Semantic based Heterogeneous Information Network Embedding (SHINE) which obtains the latent semantic relationships between patents (referred to as semantic links in this paper) by textual content similarity and topic similarity, and links the semantic relationships with network structure to build a novel HIN. First, to obtain semantic links, we not only consider the textual content similarity between patents but also the topic similarity. We use the linear fusion method to combine these two similarities. Secondly, we build a HIN with semantic links and bibliographic information to integrate semantic and structural information, and we use network embedding to map the integration of two kinds of information into a common vector space. Finally, we recommend patent citations by linear combination of multi-modal similarities. The experimental results on two U.S. patent datasets show the effectiveness of SHINE. Compared to state-of-the art of patent citation recommendation, the proposed method SHINE achieves significantl

关键词： Patents Art Computational modeling Semantics Complexity theory Task analysis Artificial intelligence

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Local Smoothing Optimization Penalized Least Squares: Application to Spectrum Baseline Correction

Local Smoothing Optimization Penalized Least Squares: Applic...

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第31届中国过程控制会议（CPCC 2020）

作者： Yuqiang Li Tianhong Pan Haoran Li Shan Chen Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University School of Electrical and Information Engineering Jiangsu University

Baseline shift, as an undesirable component of processing signal, seriously contaminates the essential information of the analysis object and deteriorates the performance of the developed model. The baseline correction methods based on penalized least squares have been widely applied in various industries and obtained better results in the single interval signal. However, the real analyzed signal is composed of multiple intervals with different smoothness, and the current baseline correction methods using constant smoothing parameters throughout the process cannot eliminate the baseline of the multi-interval signal. To address this issue, a local smoothing optimization penalized least squares(lsoPLS) method is presented in this work. The local smoothing optimization strategy using the derivative difference between the previously fitted baseline and the original signal is used to identify the interval location, while smoothing parameter is optimized by the interval characteristics. Two simulated cases and one real experiment were used to evaluate the effectiveness of the proposed lsoPLS method. Experimental results confirm that the proposed method has better performance in the multi-interval baseline correction and can be served as an effective pre-processing method for the complex spectral signal.

关键词： Baseline correction local smoothing optimization penalized least square signal pre-processing spectral analysis

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A many-objective evolutionary algorithm based on rotation and decomposition

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Swarm and Evolutionary Computation 2021年 60卷

作者： Zou, Juan Liu, Jing Yang, Shengxiang Zheng, Jinhua Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University XiangtanHunan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Information Engineering College of Xiangtan University XiangtanHunan411105 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Evolutionary algorithms have shown their promise in addressing multiobjective problems (MOPs). However, the Pareto dominance used in multiobjective optimization loses its effectiveness when addressing many-objective problems (MaOPs), which are defined as having more than three objectives. This is because the Pareto dominance loses its ability to distinguish between individuals. In this paper, a many-objective evolutionary algorithm based on rotation and decomposition is proposed (MaOEA-RD) to overcome the shortcoming of insufficient selection pressure caused by the Pareto dominance. First, the coordinates system is rotated and a hyperplane is established to distinguish between the nondominated individuals. Then, a novel individual selection mechanism incorporating decomposition is adopted to maintain the diversity of the population. In order to compensate for the deficiency of the predefined reference vectors, a reference vector adjustment mechanism is proposed. Experimental studies on several well-known benchmark problems show that the proposed algorithm is competitive compared with nine state-of-the-art many-objective algorithms. © 2020 Elsevier B.V.

关键词： Evolutionary algorithms

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Reconfigurable intelligent Surface-Aided Secure Integrated Radar and Communication Systems

arXiv

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arXiv 2024年

作者： Zheng, Tong-Xing Chen, Xin Lan, Lan Ju, Ying Hu, Xiaoyan Liu, Rongke Ng, Derrick Wing Kwan Cui, Tiejun School of Information and Communications Engineering Xi'an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China Purple Mountain Laboratories Nanjing211111 China National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China School of Telecommunications Engineering Xidian University Xi’an710071 China School of Electronic and Information Engineering Beihang University Beijing100191 China School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia School of Information Science and Engineering Southeast University Nanjing210096 China

Despite the enhanced spectral efficiency brought by the integrated radar and communication technique, it poses significant risks to communication security when confronted with malicious radar targets. To address this issue, a reconfigurable intelligent surface (RIS)-aided transmission scheme is proposed to improve secure communication in two systems, i.e., the radar and communication co-existing (RCCE) system, where a single transmitter is utilized for both radar sensing and communication, and the dual-functional radar and communication (DFRC) system. At the design stage, optimization problems are formulated to maximize the secrecy rate while satisfying the radar detection constraint via joint active beamforming at the base station and passive beamforming of RIS in both systems. Particularly, a zero-forcing-based block coordinate descent (BCD) algorithm is developed for the RCCE system. Besides, the Dinkelbach method combined with semidefinite relaxation is employed for the DFRC system, and to further reduce the computational complexity, a Riemannian conjugate gradient-based alternating optimization algorithm is proposed. Moreover, the RIS-aided robust secure communication in the DFRC system is investigated by considering the eavesdropper's imperfect channel state information (CSI), where a bounded uncertainty model is adopted to capture the angle error and fading channel error of the eavesdropper, and a tractable bound for their joint uncertainty is derived. Simulation results confirm the effectiveness of the developed RIS-aided transmission scheme to improve the secrecy rate even with the eavesdropper's imperfect CSI, and comparisons between both systems reveal that the RCCE system can provide a higher secrecy rate than the DFRC system. Copyright © 2024, The Authors. All rights reserved.

关键词： Conjugate gradient method

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A Wideband Millimeter Wave E-shape Antenna in Package with Embedded Wafer Level Ball Grid Array Technology

A Wideband Millimeter Wave E-shape Antenna in Package with E...

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International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： Bin Lu Haoran Zhu Zongming Duan Yuefei Dai Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhu University Hefei China Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Province Engineer Laboratory for Antennas and Microwave The 38th Research Institute of China Electronic Technology Group Corporationy Hefei China

ISBN: (数字)9781728169668

ISBN: (纸本)9781728169675

The feasibility of an antenna-in-package (AiP) concept is demonstrated for automotive radar sensors at the 77 GHz-band. Infineon's embedded wafer level ball grid array (eWLB) is employed as packaging technology. We developed a E-shape patch antenna in 6×6 mm 2 eWLB package that include 2×2 mm 2 dummy Si chips. From the simulated results of the proposed E-plane patch antenna, provides a wide bandwidth of 19.2% (|S 11 |

关键词： Patch antennas Radar antennas Sensors Broadband antennas Wideband Antenna arrays Electronics packaging

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AJANet: SAR Ship Detection Network Based on Adaptive Channel Attention and Large Separable Kernel Adaptation

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Remote Sensing 2025年第10期17卷 1745-1745页

作者： Yishuang Chen Jie Chen Long Sun Bocai Wu Hui Xu East China Research Institute of Electronic Engineering Hefei 230088 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electronics and Information Engineering Anhui University Hefei 230601 China ANHUI SUN CREATE ELECTRONICS Co. Ltd. Hefei 230031 China

Due to issues such as low resolution, scattering noise, and background clutter, ship detection in Synthetic Aperture Radar (SAR) images remains challenging, especially in inshore regions, where these factors have similar scattering characteristics. To overcome these challenges, this paper proposes a novel SAR ship detection framework that integrates adaptive channel attention with large kernel adaptation. The proposed method improves multi-scale contextual information extraction by enhancing feature map interactions at different scales. This method effectively reduces false positives, missed detections, and localization ambiguities, especially in complex inshore environments. Also, it includes an adaptive channel attention block that adjusts attention weights according to the dimensions of the input feature maps, enabling the model to prioritize local information and improve sensitivity to small object features in SAR images. In addition, a large kernel attention block with adaptive kernel size is introduced to automatically adjust the receptive field designed to extract abundant context information at different detection layers. Experimental evaluations on the SSDD and Hysid SAR ship datasets indicate that our method achieves excellent detection performance compared to current methods, as well as demonstrate its effectiveness in overcoming SAR ship detection challenges.

关键词： SAR ship detection adaptive channel attention adaptive large kernel attention

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Improving mutation pathogenicity prediction of metal-binding sites in proteins with a panoramic attention mechanism

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Pattern Recognition 2026年 169卷

作者： Yuan Zhang Jiafeng Wu Qiuye Zhao Mingyuan Dong Junsheng Deng Xieping Gao Kai Hu Dapeng Xiong Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan 411105 China Department of Computational Biology Cornell University Ithaca NY 14853 USA Weill Institute for Cell and Molecular Biology Cornell University Ithaca NY 14853 USA Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha 410081 China

Accurately identifying the pathogenicity of mutations at protein-metal binding sites is crucial for uncovering the structural and functional complexity of metalloproteins, and the molecular mechanisms behind numerous diseases. In this study, we present a novel deep learning framework, CASTLE, for the accurate pathogenicity prediction of mutations at metal-binding sites through an effective depiction of the message-passing in local environment surrounding both the metals and metal-binding sites. Specifically, CASTLE weaves multiple attention-driven units to construct comprehensive panoramic message-passing paths, enabling the capture of intricate structural patterns associated with the metal-binding conformations. In addition, CASTLE seamlessly integrates structural information at both residue and atomic levels, and further deeply fuses structural and sequence representations to ensure the incorporation of more comprehensive information. We demonstrate that CASTLE significantly outperforms other state-of-the-art methods across all datasets we evaluated, showcasing its robustness and generalization abilities. Our interpretability analysis illustrates CASTLE’s capability in capturing meaningful representations from the metal coordination-dependent environments. Moreover, the model optimization reaffirms the advantages of our model building strategy, which can effectively capture distinct binding patterns for different metal types. Overall, CASTLE provides a powerful deep learning tool that may offer valuable insights into the study of metalloprotein-related disease mechanisms and drug design.

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On the error coefficients of asymptotic frame error rate optimal binary linear codes

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IEEE Transactions on Information Theory 2025年

作者： Li, Shitao Luo, Gaojun Shi, Minjia Ling, San Anhui University Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui Hefei230601 China Nanjing University of Aeronautics and Astronautics School of Mathematics Jiangsu Province Nanjing211106 China Nanyang Technological University School of Physical and Mathematical Sciences 637371 Singapore

A binary linear code is called asymptotic frame error rate (AFER)-optimal if it achieves the maximum possible value of the minimum distance while having the smallest value of the corresponding error coefficient. Over the additive white Gaussian noise channel and under maximum-likelihood decoding, AFER-optimal codes attain the best possible asymptotic frame error rate at high signal-to-noise ratio. In this paper, we present several bounds on the smallest error coefficients of binary linear codes and give several constructions of AFER-optimal binary linear codes. Many examples confirm that our bounds are sharp on numerous occasions. In addition, we give two families of AFER-optimal codes that respectively attain the proposed bounds with equality. © 1963-2012 IEEE.

关键词： Asymptotic analysis

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Decentralized Nonconvex Robust Optimization over Unsafe Multiagent Systems: System Modeling, Utility, Resilience, and Privacy Analysis

arXiv

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arXiv 2024年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Cheng, Huqiang Guo, Xiaoyu Huang, Tingwen School of Automation Central South University Changsha410083 China Department of Biomedical Engineering City University of Hong Kong Kowloon Hong Kong School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Key Laboratory of Dependable Services Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing400044 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Privacy leakage and Byzantine failures are two adverse factors to the intelligent decision-making process of multiagent systems (MASs). Considering an unsafe MAS with these two issues, this paper targets the resolution of a class of nonconvex optimization problems under the Polyak-Lojasiewicz (P-L) condition. To address this problem, we first identify and construct the unsafe MAS model. Under this kind of unfavorable MASs, we mask the local gradients with Gaussian noises and adopt a resilient aggregation method self-centered clipping (SCC) to design a differentially private (DP) decentralized Byzantine-resilient algorithm, namely DP-SCC-PL, which simultaneously achieves differential privacy and Byzantine resilience. The convergence analysis of DP-SCC-PL is challenging since the convergence error can be contributed jointly by privacy-preserving and Byzantine-resilient mechanisms, as well as the nonconvex relaxation, which is addressed via seeking the contraction relationships among the disagreement measure of reliable agents before and after SCC aggregation, together with the optimal gap. Theoretical results reveal that DP-SCC-PL achieves consensus among all reliable agents and sublinear (inexact) convergence with well-designed step-sizes. It has also been proved that if there are no privacy issues and Byzantine agents, then the asymptotic exact convergence can be recovered. Numerical experiments verify the utility, resilience, and differential privacy of DP-SCC-PL by tackling a nonconvex optimization problem satisfying the P-L condition under various Byzantine attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Fine-grained Pseudo-code Generation Method via Code Feature Extraction and Transformer

Fine-grained Pseudo-code Generation Method via Code Feature ...

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Asia-Pacific Conference on Software Engineering

作者： Guang Yang Yanlin Zhou Xiang Chen Chi Yu School of Information Science and Technology Nantong University China Key Laboratory of Safety-Critical Software Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University China

ISBN: (纸本)9781665437851

Pseudo-code written by natural language is helpful for novice developers' program comprehension. However, writing such pseudo-code is time-consuming and laborious. Motivated by the research advancements of sequence-to-sequence learning and code semantic learning, we propose a novel deep pseudo-code generation method DeepPseudo via code feature extraction and Transformer. In particular, DeepPseudo utilizes a Transformer encoder to perform encoding for source code and then use a code feature extractor to learn the knowledge of local features. Finally, it uses a pseudo-code generator to perform decoding, which can generate the corresponding pseudo-code. We choose two corpora (i.e., Django and SPoC) from real-world large-scale projects as our empirical subjects. We first compare DeepPseudo with seven state-of-the-art baselines from pseudo-code generation and neural machine translation domains in terms of four performance measures. Results show the competitiveness of DeepPseudo. Moreover, we also analyze the rationality of the component settings in DeepPseudo.

关键词： Codes Semantics Natural languages Writing Feature extraction Transformers Generators

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