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2nd International Conference on Artificial Intelligence and intelligent Information processing, AIIIP 2023

作者： Bian, Yongheng Ke, Xiao College of Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou350116 China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou350003 China

ISBN: (纸本)9798350371451

The purpose of domain adaptation is to transfer knowledge learned in the labeled source domain to unlabeled but related target domains without requiring a large number of target domain labels. The latest method of domain adaptation mainly adjusts the distribution of the source and target domains to adapt to the target domain. Despite significant success, when there are significant distribution differences between the source and target domains, the adaptability in the target domain still decreases, which may be due to insufficient exploration of domain specific features leading to domain offset issues. To address this issue, we propose the GSA-NET framework in this article. We designed an adversarial dropout method to improve the generalization performance of the model in the target domain by pushing the decision boundary away from the feature dense region. At the same time, in order to effectively match the distribution of advanced features across domains, we deploy feature adaptation modules before the classification layer, which can significantly reduce domain differences. Our experiments on two cross domain benchmarks, Office Home and Office-31, have shown that the proposed method is significantly superior to existing methods. © 2023 IEEE.

关键词： Computer vision

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Spring pair method of finding saddle points using the minimum energy path as a compass

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Physical Review E 2024年第6期110卷 064123-064123页

作者： Gang Cui Kai Jiang Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Xiangtan Hunan 411105 China

Finding index-1 saddle points is crucial for understanding phase transitions. In this work, we propose a simple yet efficient approach, the spring pair method (SPM), to accurately locate saddle points. Without requiring the Hessian information, the SPM evolves a single pair of spring-coupled particles on an energy surface. By designing complementary drifting and climbing dynamics based on gradient decomposition, the spring pair converges onto the minimum energy path (MEP) and spontaneously aligns its orientation with the MEP tangent, providing a reliable ascent direction for efficient convergence to saddle points. The SPM fundamentally differs from traditional surface walking methods that rely on the eigenvectors of the Hessian, which may deviate from the MEP tangent and potentially lead to convergence failure or undesired saddle points. The efficiency of the SPM for finding saddle points is verified by ample examples, including Lennard-Jones clusters, Morse clusters, water clusters, and the Landau energy functional involving quasicrystal phase transitions.

关键词： Lennard-Jones potential

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Advanced Detection and signal Reception System Utilizing Rydberg Atomic Dynamics for Applications

Advanced Detection and Signal Reception System Utilizing Ryd...

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European Conference on Antennas and Propagation, EuCAP

作者： Wenjie Ding Guoda Xie Yingsong Li Zhixiang Huang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Ministry of Education Center for Big Data and Population Health of IHM Anhui University Hefei China The Jianghuai Advance Technology Center Hefei China

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

In this paper, we introduce a novel Monte Carlo wave function method (MCWFM) for simulating the transient response of Rydberg atoms under rapidly varying electric fields, aimed at high-precision electric field measurements and wireless communication reception. This approach leverages the electromagnetically induced transparency (EIT) effect and Autler- Townes (AT) splitting in Rydberg atoms, offering enhanced efficiency compared to traditional density matrix methods by focusing solely on wave function evolution, thereby reducing computational complexity. Additionally, we account for Doppler effects in AT splitting through MCWFM, providing faster results than conventional Runge-Kutta methods. This work lays a solid foundation for advancing Rydberg atom technology in the development of high-capacity, high-fidelity wireless communication systems.

关键词： Wireless communication Atomic measurements Transient response Rydberg atoms Receivers Wave functions Optical fiber communication Doppler effect Electric fields Computational complexity

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Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models

Evolutionary Multi-Objective Neural Architecture Search for ...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Shangshang Yang Cheng Zhen Ye Tian Haiping Ma Yuanchao Liu Panpan Zhang Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures.

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A Negative Survey based Method for Preserving Topology Privacy in Social Networks 4

A Negative Survey based Method for Preserving Topology Priva...

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4th International Conference on Data Intelligence and Security, ICDIS 2022

作者： Jiang, Hao Liao, Yuerong Yu, Qianwei Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China The Second Affiliated Hospital of Anhui Medical University Department of Gastroenterology Hefei230601 China

ISBN: (数字)9781665459686

ISBN: (纸本)9781665459686

Currently, the rapid popularity of social network platforms makes the real social relations in social networks face the potential risk of disclosure. Therefore, most users may refuse to provide their social relations for the purpose of privacy preservation, which is not conducive to the development of social networks. The negative survey is an emerging sensitive information collection method, which can collect sensitive information while preserving personal privacy. In this paper, based on negative survey, we propose an algorithm called NetNS to preserve the privacy of social relations, namely the topology privacy of social networks. The proposed NetNS is simple and efficient, which only randomly divides the nodes in social networks into different groups, and disturbs the topology of each group by a dedicated negative survey model. Experiments are carried out on three real networks of different sizes. The experimental results show that compared to three algorithms tailored for topology privacy of social networks, the proposed NetNS can against graph structure attack while realizing better utility of social networks. © 2022 IEEE.

关键词： Topology

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Enhanced Breakdown Voltage of p-GaN HEMT with a Lateral Gradient in Barrier Layer Component

Enhanced Breakdown Voltage of p-GaN HEMT with a Lateral Grad...

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IEEE International Workshop on Radio-Frequency Integration Technology (RFIT)

作者： Wei Zhi Xingang Ren Hui Wang Shubin Zhao Gang Wang Shuping He Zhixiang Huang Xianliang Wu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Hefei China Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei China

ISBN: (数字)9798331541095

ISBN: (纸本)9798331541101

In this paper, an optimized enhanced-mode p-GaN high electron mobility transistor (p-GaN HEMT) is proposed, in which the barrier layer is set into a composite structure with a lateral gradient of Aluminum component (LGC) from the drain edge under the gate towards both source and drain, respectively. By leveraging the polarization effects from different heterojunctions, a two-dimensional electron gas (2DEG) with gradually changing density is formed laterally in the channel layer, which more precisely modulates the electric field distribution in the gate-drain region, enhances the voltage resistance of it, and reduces leakage current, thereby enhancing the breakdown voltage of the device and improving the output performance. The breakdown voltage of the proposed optimized LGC-HEMT under simulation is 716.73V, which is 13.1 % higher than that of the conventional p-GaN HEMT (633.64V), while the specific on-resistance $(\mathrm{R}_{\mathrm{o}\mathrm{n},\mathrm{s}\mathrm{p}})$ is only 1.34 $m\Omega\cdot cm^{2}$ , and the Baliga's figure of merit (BFOM) changes from 216.42 to 382.68 $MW/cm^2$ , which is 76.82% higher, hinting at the promising potential of the proposed HEMT.

关键词： Resistance Radio frequency Performance evaluation Breakdown voltage HEMTs Logic gates Impact ionization Leakage currents MODFETs Electric fields

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DDoS Detection and Defense Based on FLAD and SDN 20

DDoS Detection and Defense Based on FLAD and SDN

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Dong, Jie Fang, Wenyu Zheng, Wanling Liu, Jinkun Liu, Yanhua College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China

ISBN: (纸本)9798350356328

In order to achieve more efficient and accurate DDoS detection while ensuring data privacy, this paper proposes a DDoS detection method based on FLAD. Firstly, this paper uses the FLAD algorithm to train a global DDoS detection model without leaving local traffic data, protecting the privacy and security of traffic data between different hosts, and improving aggregation efficiency by dynamically adjusting the aggregation weights to adapt to different sub-dataset increments. Secondly, a DDoS traffic detection method based on the integration of LSTM and CNN is proposed, which extracts and analyzes the temporal correlation of traffic data by calculating the statistical characteristics of traffic data within a time period, to achieve real-time detection of traffic feature data. Again, combined with the concept of SDN, real-time defense against DDoS based on ODL-API is implemented, and precise matching of DDoS detection results with network entity information is achieved, realizing the technology of real-time and precise issuance of multiple flow rules, effectively blocking DDoS malicious attack traffic, protecting important entities in the topology, and maintaining stable traffic in the topology. This paper focuses on solving the detection problem of DDoS traffic data increments and uneven data distribution through the FLAD algorithm. Experimental results show that the proposed method improves the accuracy of DDoS attack detection by more than 4% and the F1 Score by more than 7% compared to the FedAvg aggregation algorithm. © 2024 IEEE.

关键词： Differential privacy

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A Solver for Devices of Subwavelength Lamellar Gratings

A Solver for Devices of Subwavelength Lamellar Gratings

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Optoelectronics Global Conference (OGC)

作者： Zhuang Wang Chuan Shen Liu Wang Bin Wang Daofeng He Sui Wei Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781665486996

A solver for subwavelength lamellar gratings is presented synchronized with the development of a light modulator for holographic video display Grating Liquid Crystal on Silicon (GLCoS) and is regarded as an important part of the whole R&D work. In this way it not only gives computational support in the whole design process including physical concept descriptions, verifications, predictions, fabrications and other experimental activities but also provides a support platform for further product development. Based on the generic Fourier modal method, we focus on the electrodynamics specific to conductors (Au, Al) and charge carriers in semiconductors (Indium Tin Oxide, ITO) in SPPs (Surface Plasmon Polaritons) and permittivity characteristics of the materials to make the solver oriented towards subwavelength (metal, semiconductor) lamellar gratings. Further, we analyze and calculate the optical characteristics of subwavelength lamellar gratings composed of ITO and Au. The results of these calculations not only agree with the results of actual GLCoS device tests to the same order of accuracy, but also demonstrate the validity and accuracy of the solver.

关键词： Gold Computational modeling Indium tin oxide Optical device fabrication Modulation Holography Writing

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Metasurface Antenna Near-field Computational Imaging with Generative Adversarial Network

Metasurface Antenna Near-field Computational Imaging with Ge...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Fafa Zhao Zhenhua Wu Lixia Yang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781665482271

Considering the characteristics of electromagnetic scattering of radar observation targets, it is possible to introduce compressed sensing theory into computational imaging. For the sparse undersampling problem of scene data, it is required to achieve higher reconstruction accuracy with lower complexity. However, it is often difficult to construct and solve the sparse representation dictionary in complex scenarios, and it is difficult to fully exploit the correlation and sparsity between data. In addition, the design of observation matrix and reconstruction algorithm is also difficult. In this paper, the imaging problem is modeled as an inverse scattering problem based on the mathematical model of metasurface antenna imaging, and the feasibility of the near-field computational imaging method of generative adversarial network is analyzed from the perspective of solving the inverse problem. Using the measured radiation field data to conduct imaging experiments, the network can reconstruct the imaging target quickly and with high quality under the condition of low scene compressed ratio, which verifies the effectiveness and robustness of the method.

关键词： Microwave measurement Computational modeling Millimeter wave measurements Imaging Radar imaging Generative adversarial networks Metasurfaces

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CSCL-DTI: Predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: Predicting drug-target interaction through cross-v...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xuan Zhang, Xi Yu, Zu-Guo Long, Yahui Zeng, Xiangxiang Yu, Philip S. Xiangtan University College of Computer Science China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing Ministry of Education China Singapore Hunan University College of Information Science and Engineering China University of Illinois Chicago Department of Computer Science United States

ISBN: (纸本)9798350386226

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI1 outperforms six state-of-the-art methods. © 2024 IEEE.

关键词： Contrastive Learning

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