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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Ying Shan, Chun Liu, Zhen Liao, Shuyan Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Computer School Beijing Information Science and Technology University Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9789819615476

Deep learning(DL) systems exhibit multiple behavioral characteristics such as correctness, robustness, and fairness. Ensuring that these behavioral characteristics function properly is crucial for maintaining the accuracy of DL systems’ outputs. As a specialized form of software, DL systems’ security testing techniques have increasingly become a focus of research in quality assurance. We analyze and organize the testing techniques for DL systems based on an investigation of the current state of the art both domestically and internationally. This paper categorizes existing approaches as component-oriented and attribute-oriented methods, providing a detailed review based on this classification. Additionally, we forecast the future development of testing techniques for DL systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Hyperspectral Image Cross Scene Classification Combined Domain Adaptation with XGBoost

Hyperspectral Image Cross Scene Classification Combined Doma...

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2024 International Conference on Measurement, Communication, and Virtual Reality, MCVR 2024

作者： Liu, Chengyang Wu, Haibin Li, Jiarong Liu, Xinyu Yan, Siqi Wang, Aili Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510691773

To address the issues of domain discrepancies and limited labeled samples in hyperspectral image classification of cross scene, this paper proposes an ensemble learning framework that integrates eXtreme Gradient Boosting (XGBoost) with confidence domain adaptation strategies to improve classification performance. First, the Houston dataset from different scenes is input separately into a deep embedding model. The hyperspectral images are embedded into a low-dimensional space, ensuring that similar spectra are represented more closely in this space, thereby capturing the intrinsic structure and distribution of the hyperspectral image dataset. Subsequently, these samples are mapped into a discriminator model, where domain adaptation techniques are employed to reduce differences in feature distributions across different scenes. Additionally, confidence-based learning is used to identify and remove noisy samples in the Houston dataset, thereby enhancing the alignment of feature distributions among various scenes. Finally, through ensemble learning, a weighted voting strategy is employed to combine multiple base classifiers into a strong classification model. This approach better captures the complex features within the data, thereby reducing the bias of individual classifiers and enhancing the overall generalization ability and robustness of the model. Experimental results on the Houston dataset demonstrate that the proposed method outperforms popular domain adaptation learning models and traditional methods in terms of classification accuracy and robustness, showcasing its potential and advantages in practical applications. This result confirms the effectiveness of combining XGBoost and confidence domain adaptation strategies in handling hyperspectral image classification. © 2025 SPIE.

关键词： Photointerpretation

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Multi-scale convolutional fusion network embedded with improved attention mechanism for polarimetric synthetic aperture radar image classification

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Li, Jiewen Zhu, Yanan Ai, Jiaqiu Zhao, Zhicheng Shi, Jun Hefei University of Technology School of Computer Science and Information Engineering Hefei China Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei China Hefei University of Technology School of Artificial Intelligence Hefei China Hefei University of Technology School of Software Hefei China

Convolutional neural networks (CNNs) have been widely applied in the field of polarimetric synthetic aperture radar (PolSAR) image classification and have achieved good classification results. However, due to the complexity of polarimetric information, traditional CNN structures are difficult to fully extract and integrate information from multiple polarimetric channels, resulting in insufficient feature diversity, which limits further improvement in classification accuracy. To this problem, this article proposes a multi-scale convolutional fusion network (MCF-Net). MCF-Net employs convolutional kernels of various sizes to perform parallel convolutions, extracting multi-scale features and optimally fusing them, thereby greatly enhancing the diversity and completeness of feature representation. In addition, to enhance the network's focus on important features and suppress irrelevant information, this article proposes an improved squeeze-and-excitation (SE) module, guided by mean, entropy, and Z-score (MEZ-SE), to weigh the importance of feature maps. By embedding the MEZ-SE module into MCF-Net, MEZ-MCF-Net significantly improves the completeness of feature representation and enhances the ability to distinguish different land cover categories. On the Flevoland, San Francisco, and Oberfaffenhofen PolSAR datasets, the overall accuracy of MEZ-MCF-Net exceeds that of the second-best method by 3.04%, 2.09%, and 1.99%, respectively. There is no doubt that MEZ-MCF-Net is an effective method for PolSAR image classification. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Convolutional neural networks

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Optical RIS-aided Covert Visible Light Communications

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IEEE Transactions on Vehicular technology 2025年

作者： Qian, Lei Wu, Fangqian Wang, Di Huang, Nuo Xu, Zhengyuan Tiangong University Tianjin Key Laboratory of Optoelectronic Detection Technology and System School of Electronic and Information Engineering Tianjin300160 China University of Science and Technology of China Cas Key Laboratory of Wireless-Optical Communications School of Information Science and Technology Hefei230027 China

This paper investigates a covert visible light communication (VLC) system, where a transmitter (Alice) aims to send confidential information to a legitimate user (Bob) while avoiding being detected by an unauthorized warden (Willie). To achieve this, we propose an optimization framework that controls both reflection by mirror array-based reconfigurable intelligent surface (mRIS) and refraction by liquid crystal-based RIS (LC-RIS) to reconstruct a desired covert optical transmission environment. Considering the covertness constraint with the strongest Willie aided by LC-RIS, we jointly optimize the rotation configuration of mRIS, the parameter of Bob's LC-RIS, and the average optical intensity of Alice by a proposed stepwise decoupling method. The simulation results show that the deployment of LC-RIS on Bob's receiver has a more significant impact on enhancing covert VLC performance compared to the deployment of mRIS. © 2025 IEEE.

关键词： Optical communication

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Optimal Planning of a Standalone Microgrid by Constrained Multi-objective Population Extremal Optimization

Optimal Planning of a Standalone Microgrid by Constrained Mu...

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International Conference of Electrical, Electronic and Networked Energy systems, EENES 2024

作者： Qin, Zhen Zeng, Guo-Qiang Lu, Kang-Di Wang, Rong Wu, Jun-Yi National-Local Joint Engineering Laboratory of Digitalize Electrical Design Technology Wenzhou University Wenzhou325035 China College of Information Science and Technology Donghua University Shanghai201620 China

ISBN: (纸本)9789819618637

A standalone microgrid plays an important role in supplying power to residential and remote areas by integrating different types of distributed generation. One of the key open issues in both academic and engineering fields is how to provide an optimal planning strategy for distributed generations and storage systems in a standalone microgrid, considering multi-objective performance indices such as reliability and cost. In this paper, a novel constrained multi-objective population extremal optimization (CMOPEO) is proposed for standalone microgrid optimal planning. The fundamental concept of the suggested approach is to frame the problem as a standard constrained multi-objective problem, where power loss probability, fuel emission, and power cost of the system are considered as the objectives, and the maximum amount of extra power and the availability of renewable resources are the constraints. The superiority of the CMOPEO algorithm is demonstrated through simulation results on a standalone microgrid case over multi-objective particle swarm optimization. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Constrained optimization

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Ethiopic Cryptography: An Efficient Hybrid Encryption Algorithm for Reducing Cybersecurity Attack 11th

Ethiopic Cryptography: An Efficient Hybrid Encryption Algori...

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11th EAI International Conference on Advancement of science and technology, ICAST 2023

作者： Akele, Tadesse Birara Bahiru, Tadesse Kebede Molla, Tibebu Legesse Fentie, Shambel Dessale Belay, Tadesse Destaw Marye, Almaz Yemataw Getaneh, Mersha Derese Department of Information System Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Department of Information Technology Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Deparment of Software Engineering Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Department of Computer Science Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia

ISBN: (纸本)9783031641503

Cryptography is an essential tool in ensuring the security of data during network communication. Network and service providers prioritize data security to ensure secure data exchanges. Ethiopic cryptography is a unique encryption method that utilizes the Amharic language, prevalent in Ethiopia. Unlike Latin alphabets, Amharic consists of 287 characters, making decryption challenging. The encryption process involves converting the text to Amharic and then mapping it to a pair of randomly chosen two-bit English alphabets, creating an intermediate cipher. The Ethiopic cryptography algorithm employs a hybrid approach, using the RSA algorithm for secure key exchange and the Advanced Encryption Standard algorithm for text confidentiality. The algorithm comprises three phases—translation, mapping, and encryption—contributing to its effectiveness in securing data over the network. Decryption requires familiarity with the Amharic language and mapping details. The algorithm demonstrates a stronger avalanche effect (97%) in comparison with Blowfish and DES. The algorithm is proposed and evaluated using the design science research methodology, and its performance is validated through simulation using MATLAB and BunnaScript tools, considering factors like the avalanche effect, time complexity, and memory consumption. The evaluation shows that the suggested solution operates faster, requires lesser time for data encryption, and uses less memory than other methods in use. Overall, the proposed Ethiopic cryptography algorithm proves to be an efficient and effective method for securing data during network communication, offering stronger text confidentiality and an improved avalanche effect. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： MATLAB

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A novel shilling attack on black-box recommendation systems for multiple targets

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Neural Computing and Applications 2025年第5期37卷 3399-3417页

作者： Liu, Shuangyu Yu, Siyang Li, Huan Yang, Zhibang Duan, Mingxing Liao, Xiangke School of Information Science and Engineering Hunan University Hunan Changsha410082 China College of Information Technology and Management Hunan University of Finance and Economics Hunan Changsha410000 China Hunan Province Key Laboratory of Industrial Internet Technology and Security Changsha University Hunan Changsha410022 China Shenzhen Institute Hunan University Guangdong Shenzhen518063 China School of Computer Science National University of Defense Technology Hunan Changsha410073 China

In the digital era, e-platforms ubiquitously deploy recommendation systems, utilizing machine learning paradigms to tailor content according to user preferences and needs. Yet, the integrity of these systems is often compromised by shilling attacks, where malicious entities inject fake user profiles to skew product exposure and sales. This vulnerability has catalyzed research efforts to bolster the robustness and security of recommendation systems, primarily through controlled attacks and reinforcement training. This paper introduces an innovative approach to combat the inefficiency of traditional single-target shilling attacks. We propose a novel multi-target shilling strategy for black-box recommendation systems, capable of generating convincing and aggressive fake user profiles. Given the complexity of the recommendation system algorithms, we employ surrogate models to replicate and understand the target system’s behavior. The surrogate model is then subjected to attacks using our proposed methodology. An attack is deemed successful when it achieves an 80% or higher success rate against the surrogate, preluding an attack on the actual recommendation system. Our model, RWA-GAN, stands out by integrating two components: a generative adversarial network (GAN) for crafting realistic fake user profiles, and a mechanism to enhance attack success rates. This dual approach not only expedites the attack process but also ensures the generation of more authentic and well-distributed fake profiles, making the model easier to migrate. Additionally, the paper delineates several defense strategies against such attacks, contributing significantly to the discourse on enhancing the robustness of recommender systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Generative adversarial networks

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GENERATIVE ADVERSARIAL NETWORKS FOR REMOTE SENSING IMAGE DEHAZING WITH COLOR FEATURE RESTORATION

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International Journal of Innovative Computing, information and Control 2025年第2期21卷 323-338页

作者： Zhao, Liquan Qin, Yuqing Jia, Yanfei Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University No. 169 Changchun Road Jilin132012 China College of Electric and Information Engineering Beihua University No. 3999 Beijing Road Jilin132013 China

Remote sensing images are often affected by atmospheric factors such as haze during the acquisition process, resulting in blurring and low contrast in the collected remote sensing images. This problem impacts the image quality, thereby affecting the analysis of remote sensing images. To mitigate the impact of haze on remote sensing images, a generative adversarial network is proposed. It comprises a generative network and an adversarial network. Firstly, a novel feature extraction module is designed to enhance the capability of extracting useful information from remote sensing images. It enables the network to focus more on regions with dense haze, allowing it to extract more important information while filtering out irrelevant details. Secondly, a residual attention module is designed which can allocate different weights based on varying haze density in the feature map. This module readjusts features outputted by the encoder, facilitating better image restoration. Thirdly, a multi-scale module is also incorporated to extract feature information across various image scales. Lastly, a color feature extraction module is designed to extract color features. The novel feature extraction module, residual attention module, multi-scale module, and color feature extraction module are utilized for constructing the generative network. Besides, an adversarial network is also designed to indirectly enhance the dehazing capability of the generative network. Synthetic and real datasets are used to test six different methods for dehazing remote sensing images, respectively. The proposed method achieves higher PSNR, SSIM, and lower MSE on the synthetic remote sensing dataset. On the other hand, it achieves lower PIQE, BRISQUE, and higher MetaIQA on the real remote sensing dataset. The proposed method has best performance in dehazing remote sensing images than other methods. © 2025, Int. J. Innov. Comput. Inf. Control. All rights reserved.

关键词： Generative adversarial networks

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Utilizing the modified Fourier neural operator to predict the dynamics of vector optical solitons

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Optics Express 2025年第4期33卷 7345-7361页

作者： Wan, Zelin Qi, Mei Yan, Yi Lv, Chenyue Lu, Baole School of Information Science and Technology Northwest University Xi’an710127 China State Key Laboratory of Energy Photon-Technology in Western China International Collaborative Center on Photoelectric Technology and Nano Functional Materials Institute of Photonics Photon-technology Northwest University Xi’an710127 China Shaanxi Engineering Technology Research Center for Solid State Lasers and Application Provincial Key Laboratory of Photo-Electronic Technology Northwest University Xi’an710127 China

This paper introduces the study of operator learning into the research of the generalized coupled nonlinear Schrödinger equations, utilizing the modified Fourier neural operator (MFNO) to learn the nonlinear mapping from any functional parameter dependency to the solution, thereby obtaining a complete soliton solution space. The number of variable parameters in the soliton solutions used for training has been expanded from one to multiple. In terms of details, this paper studies the bright-bright one-soliton and two-soliton solutions in the Manakov model, the bright-dark one-soliton and two-soliton solutions in the mixed model, as well as rogue wave solutions and Akhmediev breather solutions in the generalized model. It also explores the MFNO’s extreme learning capabilities. These theoretical findings provide valuable insights into optical fiber telecommunications and lay a foundation for enhancing modern communication systems by using temporal optical solitons as information carriers. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Schrodinger equation

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Bioconvection Cross nanofluid flow with Cattaneo-Christov heat and mass fluxes past plate, wedge and cone

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Multiscale and Multidisciplinary Modeling, Experiments and Design 2025年第6期8卷 1-20页

作者： Shah, Muhammad Zameer Khan, Noor Saeed Khan, Waris Boukhris, Imed Kouki, Marouan Agha, Afnan Al Garalleh, Hakim Al Al-Buriahi, M.S. Division of Science and Technology Department of Mathematics University of Education Lahore54000 Pakistan Department of Mathematics and Statistics Hazara University Mansehra Pakistan Central Labs King Khalid University Abha AlQura’aP.O. Box 960 Saudi Arabia Department of Information System Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Mathematical Science College of Engineering University of Business and Technology Jeddah21361 Saudi Arabia Department of Physics Sakarya University Sakarya Turkey

This study examines the bioconvection flow of a Cross nanofluid containing gyrotactic microorganisms over three geometrical configurations: a flat plate, a wedge, and a cone. The significance of this research lies in its incorporation of the Cattaneo-Christov heat and mass flux theory, which accounts for thermal relaxation effects-critical in optimizing heat and mass transfer processes in bioengineering and industrial applications. The study aims to analyze the combined effects of radiative heat transfer, thermophoresis, heat generation/absorption, magnetic field strength and other embedded parameters on fluid behavior. The research employs the Homotopy Analysis Method (HAM) to obtain analytical solution for velocity, temperature, nanoparticle concentration, and gyrotactic microorganism density distribution. The key findings indicate that heat transfer is most efficient over the cone, whereas the wedge exhibits the highest mass transfer rate and microbial concentration. The study further highlights the influence of governing parameters such as buoyancy ratio, relaxation time, and external forces on fluid behavior. These results contribute to a deeper understanding of nanofluid dynamics in biomedical and industrial applications, including microbial-enhanced oil recovery and nanofluid-based drug delivery systems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： SARS

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