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6th International Conference on Advanced Aspects of Software Engineering, ICAASE 2024

作者： Ferhat, Aridj Zitouni, Farouq Limanc, Abdelhadi Lakbichi, Rihab Harous, Saad Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria College of Computing and Informatics Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798331522810

This paper introduces OBL-AHO, an enhanced Archerfish Hunting Optimizer (AHO) variant that integrates Opposition-Based Learning (OBL) to improve AHO's exploratory capabilities. By leveraging opposite solutions alongside newly generated ones, OBL-AHO broadens the search space, effectively reducing the risk of local optima entrapment-A common challenge in metaheuristic algorithms. The performance of OBL-AHO is rigorously compared with the original AHO and several prominent Metaheuristic Algorithms (MAs), including TLBO (Teaching-Learning-Based Optimization), PSO (Particle Swarm Optimization), GWO (Grey Wolf Optimizer), OOBO (OnE-To-One Based Optimizer), SSA (Salp Swarm Algorithm). Evaluations were conducted using test functions taken from CEC 2022, and the outcomes were statistically analyzed using the Friedman test. The findings demonstrate that OBL-AHO significantly improves exploration while maintaining competitive exploitation relative to its opponents. © 2024 IEEE.

关键词： Contrastive Learning

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Comparative Analysis of Four Metaheuristic Algorithms for Estimating Parameters in Solar Photovoltaic Models 6

Comparative Analysis of Four Metaheuristic Algorithms for Es...

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6th International Conference on Advanced Aspects of Software Engineering, ICAASE 2024

作者： Lakbichi, Rihab Zitouni, Farouq Limane, Abdelhadi Ferhat, Aridj Harous, Saad Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria College of Computing and Informatics University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331522810

In the last decade, Metaheuristic Algorithms (MAs) have been widely employed to estimate parameters in photovoltaic (PV) models to maximize solar energy efficiency. This paper presents a comparative study of the performance of four novel and efficient MAs applied to solve the parameter extraction problem in three ubiquitous PV models: The Single-Diode Model (SDM), the Double-Diode Model (DDM), and the Photovoltaic Module Model (PMM). The primary objective of this study is to evaluate and compare the efficiency of these MAs and identify the most effective one based on the Root Mean Square Error (RMSE) between the simulated and the actual data. This work compares the performance of the Red-Tailed Hawk Optimizer (RTH), the One-To-One Based Optimizer (OOBO), the Sand Cat Swarm Optimization (SCSO), and the Archerfish Hunting Optimizer (AHO). The numerical results demonstrate that RTH significantly outperforms its counterparts in estimating parameters for the examined models. This exceptional performance underscores RTH's efficiency and effectiveness as a powerful tool for parameter estimation in PV models. Given these promising results, RTH stands out as a preferred method and holds the potential for further refinement and enhancement in the future. © 2024 IEEE.

关键词： Mean square error

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Dense Video Captioning through Convolutional-Transformer Integration 4

Dense Video Captioning through Convolutional-Transformer Int...

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4th IEEE International Conference on Digital Twins and Parallel intelligence, DTPI 2024

作者： Cao, Jiaxi Zhang, Chunjie Zheng, Xiaolong Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology School of Computer Science and Technology Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China

ISBN: (纸本)9798350349252

Dense video captioning, a task that has recently garnered attention in the field of computer vision, focuses on generating detailed captions for multiple events along with their temporal locations within untrimmed videos. Existing transformer-based methods have achieved notable success. However, some methods utilizing deformable transformer face challenges in processing long videos, as the encoder may struggle to capture the long-term dependencies of events in the video. This issue is partly due to not integrating Convolutional Neural Networks (CNN) with the deformable transformer architecture. In this work, we introduce Dense Video Captioning through Convolutional-Transformer Integration (CTDVC), which links Layer Normalization, the Scalable-Granularity Perception (SGP), and Group Normalization into a network, replacing the encoder part of the deformable transformer. This model better accounts for the sequential relationship between events, especially those events in the video that span over a longer duration, allowing it to generate video captions that are not only more coherent but also more precise in their narrative. We evaluated our method on ActivityNet Captions and YouCook2 datasets. Experiments demonstrate that our proposed method CTDVC achieves an improvement in performance. © 2024 IEEE.

关键词： Convolutional neural networks

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A Memristive Spiking Neural Network Circuit for Bio-Inspired Navigation Based on Spatial Cognitive Mechanisms

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IEEE Transactions on Biomedical Circuits and Systems 2025年第3期19卷 686-698页

作者： Chen, Zhanfei Wang, Xiaoping Wang, Zilu Yang, Chao Huang, Tingwen Lai, Jingang Zeng, Zhigang School of Mechanical Engineering and Automation Shenzhen518055 China Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Hubei Key Laboratory of Brain-Inspired Intelligent Systems School of Artificial Intelligence and Automation Wuhan430074 China Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Shenzhen University of Advanced Technology School of Computer Science and Control Engineering Shenzhen518055 China

Cognitive navigation, a high-level and crucial function for organisms' survival in nature, enables autonomous exploration and navigation within the environment. However, most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation, capable of online decision-making through reward-based learning. The circuit comprises three primary modules. The place cell module encodes the agent's spatial position in real-time through Poisson spiking;the action cell module determines the direction of subsequent movement;and the reward-based learning module provides a bio-inspired learning method adaptive to delayed and sparse rewards. To facilitate practical application, the entire SNN is quantized and deployed on a real memristive hardware platform, achieving about a 21 × reduction in energy consumption compared to a typical digital acceleration system in the forward computing phase. This work offers an implementation idea of neuromorphic solution for robotic navigation application in low-power scenarios. © 2007-2012 IEEE.

关键词： Intelligent robots

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Method of shapelet discovery for time series ordinal classification

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Soft Computing 2024年第20期28卷 11685-11701页

作者： Jing, Siyuan Yang, Jun Sichuan Province Key Laboratory of Philosophy and Social Science for Language Intelligence in Special Education Leshan Normal University Leshan614000 China Chengdu Institute of Computer Application Chinese Academy of Sciences Chengdu610041 China School of Electronic Information and Artificial Intelligence Leshan Normal University Leshan614000 China

Current methods for time series ordinal classification (TSOC) methods suffer from low efficiency because the measures used to evaluate the quality of the shapelet need to calculate Information Gain from the Euclidian distances between the shapelet and time series, which incurs tremendous computation for large datasets. This paper introduces a novel method of shapelet discovery for TSOC in which a new measure is adopted, which takes into account the coverage concentration and dominance of shapelet on SAX-represented time series datasets. Moreover, a trie-tree is constructed based on all candidate shapelets and aims to discover a diverse set of high-quality shapelets. The experimental results demonstrated the effectiveness and efficiency when compared to eight SOTA algorithms for time series classification/ordinal classification. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Time series

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Adversarial attack method with adaptive step size for ID card text recognition 4

Adversarial attack method with adaptive step size for ID car...

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4th IEEE International Conference on Digital Twins and Parallel intelligence, DTPI 2024

作者： Feng, Weidong Zhang, Chunjie Zheng, Xiaolong Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology School of Computer Science and Technology Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China

ISBN: (纸本)9798350349252

In the field of text recognition models, existing adversarial attack algorithms mainly target white-box scenarios, which are usually limited to single-color backgrounds and short text length. However, with the complex color textures of ID card images and long text length, these methods are not satisfactory when performing adversarial attacks on ID card text data. To overcome these challenges, this paper introduces a novel black-box attack method tailored for ID card text recognition models. The proposed approach accounts for the complexities of intricate image backgrounds, extended text data, and the limitations inherent in black-box conditions. The proposed algorithm introduces an adaptive step-size search method based on line search, aiming to overcome the limitations of traditional methods that rely on fixed step sizes and projection techniques for perturbation generation. This refinement aims to enhance the success rate of black-box attacks while maintaining efficacy in white-box scenarios. Spatial transformations and random noise are incorporated to fortify the robustness of adversarial samples. Experimental results, conducted under various norms, demonstrate that the fusion of the traditional method with the adaptive step-size search method yields superior attack performance. © 2024 IEEE.

关键词： Adversarial machine learning

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Structure-Aware Adaptive Hybrid Interaction Modeling for Image-Text Matching 30th

Structure-Aware Adaptive Hybrid Interaction Modeling for Im...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Liu, Wei Wang, Jiahuan Wang, Chao Peng, Yan Xie, Shaorong School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Artificial Intelligence Laboratory Shanghai201114 China School of Future Technology Shanghai University Shanghai200444 China Institute of Artificial Intelligence Shanghai University Shanghai200444 China

ISBN: (纸本)9783031533044

Image-text matching is a rapidly evolving field in multimodal learning, aiming to measure the similarity between image and text. Despite significant progress in image-text matching in recent years, most existing methods for image-text interaction rely on static ways, often overlooking the substantial variations in scene complexity among different samples. Actually, multimodal interaction strategies should be flexibly adjusted according to the scene complexity of different inputs. For instance, excessive multimodal interactions may introduce noise when dealing with simple samples. In this paper, we propose a novel Structure-aware Adaptive Hybrid Interaction Modeling (SAHIM) network, which can adaptively adjust the image-text interaction strategies based on varying inputs. Moreover, we design the Multimodal Graph Inference (MGI) module to explore potential structural connections between global and local features, as well as the Entity Attention Enhancement (EAE) module to filter out irrelevant local segments. Finally, we align the image and text features with the bidirectional triplet loss function. To validate the proposed SAHIM model, we design and conduct comprehensive experiments on Flickr30K and MSCOCO. Experimental results show that SAHIM outperforms state-of-the-art methods on both datasets, demonstrating the superiority of our model. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Complex networks

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Centroid-based clustering validity: method and application to quantification of optimal cluster-data space

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Soft Computing 2024年第19期28卷 10853-10872页

作者： Nguyen, Sy Dzung Laboratory for Computational Mechatronics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh Viet Nam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh Viet Nam

Evaluation of clustering validity to set up an optimal cluster-data space (CDS) is a vital task in many fields related to data mining. Almost existing clustering validity indexes (CVIs) lack stability due to being too sensitive to noise, especially impulse noise. Here, we (1) propose a new CVI named DzI (Dzung Index) or fRisk2 using analysis of fuzzy-set-based accumulated risk degree (FARD), and (2) present a new algorithm named fRisk2-bA for determining the optimal number of data clusters. It is a method of evaluation of the centroid-based fuzzy clustering validity. In essence, the fRisk2 still focuses on enhancing the data compression in each cluster and expanding the separation between cluster centroids. However, these features are exploited indirectly through FARD. As a result, the proposed method not only can avoid the difficulties of the traditional ones relying on the compression and separation properties directly but also can distill better local and global attributes in the data distribution to estimate the CDS more fully. Along with the proved theory basis, surveys, including the ones based on noisy datasets from measurements, showed the compared advantages of fRisk2 as follows. (1) The accuracy, stability, and convergence of the fRisk2 are outstanding. (2) Its total calculating cost is lower than the other surveyed CVIs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Fuzzy clustering

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TRNet: Two-Tier Recursion Network for Co-Salient Object Detection

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5844-5857页

作者： Cong, Runmin Yang, Ning Liu, Hongyu Zhang, Dingwen Huang, Qingming Kwong, Sam Zhang, Wei Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi'an710129 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Lingnan University Hong Kong

Co-salient object detection (CoSOD) is to find the salient and recurring objects from a series of relevant images, where modeling inter-image relationships plays a crucial role. Different from the commonly used direct learning structure that inputs all the intra-image features into some well-designed modules to represent the inter-image relationship, we resort to adopting a recursive structure for inter-image modeling, and propose a two-tier recursion network (TRNet) to achieve CoSOD in this paper. The two-tier recursive structure of the proposed TRNet is embodied in two stages of inter-image extraction and distribution. On the one hand, considering the task adaptability and inter-image correlation, we design an inter-image exploration with recursive reinforcement module to learn the local and global inter-image correspondences, guaranteeing the validity and discriminativeness of the information in the step-by-step propagation. On the other hand, we design a dynamic recursion distribution module to fully exploit the role of inter-image correspondences in a recursive structure, adaptively assigning common attributes to each individual image through an improved semi-dynamic convolution. Experimental results on five prevailing CoSOD benchmarks demonstrate that our TRNet outperforms other competitors in terms of various evaluation metrics. The code and results of our method are available at https://***/rmcong/TRNetTCSVT2025. © 1991-2012 IEEE.

关键词： HTTP

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