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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Shi, Yihang Zhang, Huajun Shi, Lin Xu, Shoukun Changzhou University School of Computer Science and Artificial Intelligence Changzhou China

ISBN: (纸本)9798331515966

Ubiquitous networked hosts and Internet of Things (IoT) devices have enabled critical network applications in both enterprise and industrial environments. However, network threats have proliferated, constantly challenging normal network operations and data security of IoT devices. In facing these challenges, defenders have increasingly adopted Deep Reinforcement Learning (DRL) approaches, aiming to leverage their self-learning and adaptability to bolster network security. Despite these efforts, existing approaches often exhibit significant performance bottlenecks when navigating the complexities of network scenarios. This paper introduces a novel algorithm, the Normalized Graph-Attention Proximal Policy Optimization (NGA-PPO), which synergistically integrates Graph Attention Networks (GAT) with the existing Proximal Policy Optimization (PPO) framework. By harnessing a weighted mechanism to amalgamate intricate network connectivity data, NGA-PPO empowers intelligent defense agents to dynamically decipher dependencies and interactions within complex network architectures, thereby facilitating precise and prompt defense actions. Comprehensive experiments within the Yawning Titan network security simulation platform reveal that NGA-PPO outperforms other methods, delivering substantial improvements in performance and exhibiting distinct advantages in robustness and generalization capabilities. Specifically, as network scenarios become more complex, existing algorithms face significant performance limitations, whereas NGA-PPO consistently demonstrates high performance, thereby affirming its efficacy and viability in mitigating complex network threats. © 2024 IEEE.

关键词： Reinforcement learning

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Sparse representation scheme with enhanced medium pixel intensity for face recognition

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CAAI Transactions on intelligence technology 2024年第1期9卷 116-127页

作者： Xuexue Zhang Yongjun Zhang Zewei Wang Wei Long Weihao Gao Bob Zhang State Key Laboratory of Public Big Data Institute for Artificial IntelligenceCollege of Computer Science and TechnologyGuizhou UniversityGuiyangGuizhouChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina

Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test *** has been widely used in various image classification *** in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class-specific information of the test sample,which is very important for *** deformable images such as human faces,pixels at the same location of different images of the same subject usually have different ***,extracting features and correctly classifying such deformable objects is very ***,the lighting,attitude and occlusion cause more *** the problems and challenges listed above,a novel image representation and classification algorithm is ***,the authors’algorithm generates virtual samples by a non-linear variation *** method can effectively extract the low-frequency information of space-domain features of the original image,which is very useful for representing deformable *** combination of the original and virtual samples is more beneficial to improve the clas-sification performance and robustness of the ***,the authors’algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion *** weighting coefficients in the score fusion scheme are set entirely ***,the algorithm classifies the samples based on the final *** experimental results show that our method performs better classification than conventional sparse representation algorithms.

关键词： computer vision face recognition image classification image representation

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Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated IoT Prediction 3

Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Varun, P. Sunitha, T. Nagalingam, Mythili Nithiya, P. Selvakumaran, S. Mohanaprakash, T.A. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India St. Joseph's Institute of Technology OMR Department of Computer Science and Engineering Chennai600119 India Rajalakshmi Institue of Technology Department of Artificial Intelligence and Data Science Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities. © 2024 IEEE.

关键词： Solar power generation

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Joint DRL and ASL-Based 'Cloud-Edge-End' Collaborative Caching Optimization for Metaverse Scenarios 30

Joint DRL and ASL-Based 'Cloud-Edge-End' Collaborative Cachi...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Wan, Zheng Zhao, Shenglu Dong, Xiaogang Huang, Zhekai Tan, Yifeng Jiangxi University of Finance and Economics School of Computer and Artificial Intelligence Nanchang China Jiujiang University School of Computer and Big Data Science Jiujiang China

ISBN: (纸本)9798331515966

With the emergence of the Metaverse concept, the rendering and transmission of 3D virtual scenes demand high-bandwidth, high-quality real-time rendering technology, as well as ultra-reliable low-latency communication (URLLC). However, the current 5G network technology faces unprecedented challenges. The 3D Gaussian Splatting technique renders realistic scenes from sparse input views, providing a novel solution to these challenges. This paper proposes a cloud-edge-end collaborative caching optimization scheme based on deep reinforcement learning and asynchronous federated learning (termed Gaussian Splatting Actor-Critic, GSAC) for 3D Gaussian Splatting rendering near terminals. This scheme leverages asynchronous federated learning technology to predict user behavior trajectories in real time while ensuring user data privacy. It integrates the ActorCritic strategy of deep reinforcement learning and graph convolutional network (GCN) technology to dynamically adjust caching strategies, thereby improving cache hit rates and reducing cache switching frequency. Simulation experiments demonstrate that compared to existing technologies, the GSAC scheme significantly outperforms in terms of target sub-scenario cache hit rates and cache switching frequency. © 2024 IEEE.

关键词： 5G mobile communication systems

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Interpretable Multi-domain Awareness Scheme for Drug-target Interaction Prediction

Interpretable Multi-domain Awareness Scheme for Drug-target ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lian, Xiaoqing Zhu, Jie Lv, Tianxu Pan, Xiang Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China

ISBN: (纸本)9798350386226

Predicting drug-target interactions (DTI) is a fundamental step in drug discovery, where deep learning methods show promising performance. However, current approaches encounter challenges in interpretable generalizing the drug-target interactions and adapting to out-of-distribution data. To solve them, we propose a Multi-domain Awareness Drug-Target Interaction (MADTI) framework, which captures the overlooked shallow concrete features and intrinsic biological traits. Specifically, our design enhances the model's understanding of shallow features in drug graphs and target sequences while retaining the advantages of existing methods that emphasize deep features. This multi-level domain comprehension, combined with the ability of the Category Awareness Domain Adaption(CADA) module to understand biological patterns in positive and negative samples, improves predictive accuracy in cross-domain scenarios. Additionally, by employing bilinear attention and gated attention to learn the multi-level interaction patterns of drug-target pairs explicitly, we further enhance the model's biological interpretability. Through extensive experiments, MADTI demonstrates significant improvements over state-of-the-art methods, achieving higher AUC-ROC and PR-AUC in various tasks, including conventional prediction, missing data prediction, single-modality prediction, and clustering-based out-of-distribution prediction. The code are available at https://***/lian-xiao/MADTI. © 2024 IEEE.

关键词： Prediction models

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Transformers Fusing Wavelet with High-Frequency Enhancement for SAR Ship Detection 21st

Transformers Fusing Wavelet with High-Frequency Enhancement...

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21st Pacific Rim International Conference on artificial intelligence, PRICAI 2024

作者： Wang, Ziwen Yang, Jinlong Liu, Jianjun School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214000 China

ISBN: (纸本)9789819601271

In the field of computer vision, Vision Transformers (ViTs) have demonstrated superior performance across a variety of downstream tasks. However, ViTs tend to lose a significant amount of fine-grained features (e.g., texture details) in synthetic aperture radar (SAR) ship detection due to the difficulty in capturing high-frequency (HF) information. Furthermore, the minuscule size of some ship targets in SAR imagery intensifies the challenge for ViTs in detecting small targets. To address these issues, we construct a multi-scale ViT fusing wavelet with HF enhancement (WHFE-ViT), making it retain more high-frequency information and enhance its detection ability for small ships. First, we adopt discrete wavelet transform (DWT) as down-sampling operation to reduce the computational cost and generate multi-scale features to accommodate ship targets of various sizes. Then, we design a HF enhancement path to enhance the texture features of ships. Finally, a feature fusion path is proposed to fuse time-domain and frequency-domain information, compensating for the lack of small ship features. After sufficient experimental verification, we validate the effectiveness of our model on HRSID dataset, with detection accuracy exceeding classical Convolutional Neural Networks (CNNs) and ViTs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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SMFuse: Two-Stage Structural Map Aware Network for Multi-focus Image Fusion 27th

SMFuse: Two-Stage Structural Map Aware Network for Multi-foc...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Shen, Tianyu Li, Hui Cheng, Chunyang Shen, Zhongwei Song, Xiaoning International Joint Laboratory on Artificial Intelligence of Jiangsu Province School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou China

ISBN: (纸本)9783031783111

Multi-focus image fusion (MFIF) explores the positioning and reorganization of the focused parts from the input images. Focused and defocused parts have similar representations in color, contour and other appearance information, which degrades the fusion quality due to the influence of these redundant information. Currently, most MFIF methods have not identified an effective way to remove redundant information before fusion stage. Thus, in this paper, we introduce a structural map extraction strategy for multi-focus image fusion. Compared to the source image, structural map reduces redundant information, and the clearer parts of the image retain more abundant structural features. Consequently, the differences between focused part and defocused part become more pronounced based on the extracted structural map. Specifically, the proposed fusion method adopts a two-stage training strategy. Firstly, the structural map is extracted by the proposed structural map extraction network (SMENet) from the source images. Secondly, the structural map is thus applied to train the decision map generation network (DMGNet) to obtain the decision map which is utilized to generate the final fusion image. Qualitative and quantitative experiments on three public datasets demonstrate the superiority of the proposed method, compared with the advanced image fusion algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image fusion

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A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

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computer Modeling in Engineering & sciences 2024年第5期139卷 1613-1633页

作者： Jingyu Zhang Pian Zhou Jin Wang Osama Alfarraj Saurabh Singh Min Zhu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410015China Science and Technology on Information Systems Engineering Laboratory School of Systems EngineeringNational University of Defense TechnologyChangsha410073China Computer Science Department King Saud UniversityRiyadh11437Saudi Arabia Department of Artificial Intelligence and Big Data Woosong UniversityDaejeon34606South Korea College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the *** technology has been widely used and has developed rapidly in big data systems across various *** increasing number of users are participating in application systems that use blockchain as their underlying *** the number of transactions and the capital involved in blockchain grow,ensuring information security becomes *** the verification of transactional information security and privacy has emerged as a critical ***-based verification methods can effectively eliminate the need for centralized third-party ***,the efficiency of nodes in storing and verifying blockchain data faces unprecedented *** address this issue,this paper introduces an efficient verification scheme for transaction ***,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all ***,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous *** analyses and simulation experiments conclusively demonstrate the superior performance of this *** verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional *** findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of *** scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

关键词： Blockchain architecture transaction verification information security heterogeneous Merkle tree distributed systems

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Decrease Longitude Diversity in Lane-conditioned Trajectory Prediction 7

Decrease Longitude Diversity in Lane-conditioned Trajectory ...

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7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Xu, Xiang Wang, Xiao Tang, Jin Wang, Futian School Of Artificial Intelligence Anhui University China Institute Of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China School Of Computer Science And Technology Anhui University Hefei China

ISBN: (纸本)9798350361445

Predicting the future trajectories of surrounding agents is crucial for achieving safe autonomous driving. In addition to the strong prior information provided by maps, our key insight is that trajectories of surrounding vehicles can also guide trajectory prediction models. This leads us to develop a road supplement framework, a solution that integrates scene information in a deterministic manner during the preprocessing stage. Subsequently, we evaluates the probability of driving on the supplemented and reachable lane. Furthermore, we focus on achieving the highest possible prediction accuracy with the fewest prediction outputs. To accomplish this, we employ a decoder based on Conditional Variational Autoencoder (CVAE) that outputs the most probable set of trajectories to the goal lane. Detailed ablation experiments demonstrate the effectiveness of decreasing longitudinal diversity using CVAE. We benchmark our model on the nuScenes dataset and achieve favorable performance on metrics with fewer outputs. © 2024 IEEE.

关键词： Trajectories

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Cloud-Based Privacy-Preserving Medical Images Storage Scheme with Low Consumption

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IEEE Transactions on Multimedia 2025年 27卷 3556-3570页

作者： Yang, Yaolin He, Hongjie Feng, Zhuo Chen, Fan Yuan, Yuan Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China

For the security risks and high transmission/storage consumption in cloud-based medical images storage systems (CMISS), reversible data hiding in encrypted images (RDHEI) provide an effective solution. Nevertheless, challenges persist concerning the security risks cause by key transmission and the large file size of encrypted medical images. Consequently, a cloud-based privacy-preserving medical images storage scheme with low consumption is proposed in this paper. First, RDHEI is applied to CMISS, where image encryption achieves privacy protection, reversible data hiding eliminates extra space consumption by index data self-hiding, and the reversibility enables lossless recovery and extraction of medical images and index data. Then, hybrid encryption is designed to achieve high security. The security of encrypted images is guaranteed by combining a one-time cryptosystem with symmetric XOR encryption, which makes our scheme can resist various attacks. Time-varying key used in XOR is encrypted by asymmetric RSA, and only public key is used in RSA, avoiding the risk of private key transmission. Finally, to reduce the file size of encrypted images and achieve low consumption, context Huffman coding is proposed to adaptively selects the block coding method by context and thresholds, and has at most 98,056 bits shorter than Huffman coding in encoded stream length. Experimental results show that the proposed scheme has better performance in terms on security, consumption, and reversibility. The minimum compression ratio in databases is 32.46%, which is 2.63% lower than the existing schemes. And the medical image and index data can be restored lossless. © 1999-2012 IEEE.

关键词： Encryption Cryptography Image coding Medical diagnostic imaging Indexes Servers Cloud computing Medical services Huffman coding Protection

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