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检索条件"任意字段=8th International Conference on Algorithms and Architectures for Parallel Processing"

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TP-BFT: A Faster Asynchronous BFT Consensus with parallel Structure 24th

TP-BFT: A Faster Asynchronous BFT Consensus with Parallel S...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Ye, Shunliang Chen, Qi Xiao, Fuan Ke, Zhihui Yang, Guoyu Ma, Huawei Institute of Artificial Intelligence Guangzhou University Guangzhou510006 China Guangdong Provincial Key Laboratory of Blockchain Security Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789819615445

the massive success of blockchains has significantly catalyzed interest in the extensive deployment of practical asynchronous Byzantine fault-tolerant (BFT) consensus protocols across wide area networks. However, existing asynchronous consensus protocols suffer from high communication cost and low throughput, which can’t meet the high-performance requirements of blockchain platforms. We identify two key factors that hinder the efficiency of asynchronous BFT protocols: (1) Reliable broadcast protocols incur high communication costs;(2) the broadcast and agreement phases are executed strictly sequentially, resulting in wasted bandwidth resources. To address above issues, we propose the lightweight provable broadcast and a novel asynchronous BFT protocol called TP-BFT. TP-BFT implements a lightweight broadcast followed by a recovery mechanism and supports parallel broadcast, agreement, and recovery phases. this approach offers two key advantages: (1) it mitigates the impact of low-bandwidth nodes on the efficiency of the broadcast phase;(2) it fully utilizes the bandwidth during the agreement phase to enhance the protocol’s throughput. We implement TP-BFT and conduct experiments with HoneyBadger and Dumbo. the results demonstrate that TP-BFT improves throughput by 106% and reduces latency by 64% compared to Dumbo. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bandwidth

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SwinMixer Detector: A Two-Level Traffic Flow Detection System Based on Swin Transformer and MLP-Mixer 24th

SwinMixer Detector: A Two-Level Traffic Flow Detection Syste...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Lu, Lu Fang, Menghao Li, Xia Li, Minghui Lei, Tianwei Kong, Zixiao School of Cyber Science and Engineering University of International Relations Beijing100091 China Institute of Disaster and Emergency Medicine Tianjin University Tianjin300072 China Marine Engineering of College Dalian Maritime University Dalian116026 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819615414

this paper explores the problem of boundary data classification ambiguity that arises when machine learning techniques are applied in the field of intrusion detection. the features and attributes of the boundary data place them in the boundaries or boundaries between different categories in the classification decision, and these data points may be close to the decision boundaries of different categories in the feature space, resulting in relative uncertainty or some ambiguity in their classification. To address this problem, this study innovatively proposes a secondary detection system called the SwinMixer Detector, which utilises the Swin Transformer and MLP-Mixer architecture. the system consists of a primary triage detection platform utilizing Swin Transformer classifier and a secondary detection platform utilizing MLP-Mixer, which can effectively solve the problem of fuzzy classification of boundary data. By validating on the SR-BH2020 dataset and the CIC-DDOS2019 dataset, the method in this paper achieves a precision of 96.40% and 95.81%, respectively, which provides a new research idea and solution for the field of intrusion detection. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mixers (machinery)

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Crop Classification Methods Based on Siamese CBMM-CNN architectures Using Hyperspectral Remote Sensing Data 24th

Crop Classification Methods Based on Siamese CBMM-CNN Archi...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Xie, Bin Zhang, Jiahao Li, Yuling Li, Yusong Dong, Xinyu College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Shijiazhuang China

ISBN: (纸本)9789819615278

Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges in obtaining accurate ground truth data via remote sensing, thereby impeding effective crop classification. Traditional Convolutional Neural Network (CNN) models, although effective in image processing and feature extraction within computer vision, are notably limited in their ability to handle the high dimensionality and complex spectral information inherent in hyperspectral non-image remote sensing data. To address this problem, we designed an enhanced CNN model, CBM-CNN. the CBM-CNN incorporates additional convolutional layers (C), batch normalization (B), and MLP(M) to convert one-dimensional data into two-dimensional data for classification, thus improving the processing efficiency of highly nonlinear data features. Building on CBM-CNN, we add multiple attention mechanisms (CBMM-CNN) to enhance feature extraction capabilities. the Siamese CBMM-CNN (SCBMM-CNN) is employed for in-depth learning of data features, with supervised learning precisely adjusting the model to improve classification performance. Experimental results on hyperspectral remote sensing datasets demonstrate that SCBMM-CNN outperforms traditional machine learning models (PCA-RF, GBDT, GMO-SVM, Random Forest, Decision Tree) and deep learning models (CNN, CNN-LSTM) in terms of accuracy, recall rate, F1 score, and kappa coefficient. these results confirm that SCBMM-CNN significantly improves crop classification accuracy and provides a novel method for early crop growth classification. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Multimodal Summarization with Modality-Aware Fusion and Summarization Ranking 24th

Multimodal Summarization with Modality-Aware Fusion and Su...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Ye, Xuming Chaomurilige Liu, Zheng Luo, Haoyu Dong, Jun Luo, Yingzhe Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan572499 China Faculty of Data Science City University of Macau Macau China

ISBN: (纸本)9789819615278

Existing multimodal summarization methods primarily focus on multimodal fusion to efficiently utilize the visual information for summarization. However, they fail to exploit the deep interaction between textual and visual modality. Moreover, optimizing the model by maximum likelihood estimation (MLE) leads to exposure bias, causing the model to generate the next word that is based on the previously generated erroneous words during inference. To address these challenges, we propose a novel modality-aware fusion module (MAF) with a summarization ranking (SumR) training objective. Specifically, the MAF module exploits the interaction in the multimodal input through multiple fusion layers, and SumR aims to align the probability order predicted by the model with actual quality metrics, therefore it is able to reduce the exposure bias problem during inference. Extensive experiments on a large-scale dataset demonstrate that our method outperforms existing models, achieving superior results in both automatic and human evaluation metrics. the generated multimodal summaries provide richer context and enhance user’s comprehension by combining the key textual information with the relevant visual content. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Maximum likelihood estimation

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A Multi-holder Role and Strange Attractor-Based Data Possession Proof in Medical Clouds 24th

A Multi-holder Role and Strange Attractor-Based Data Possess...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Guo, Jinyuan Sun, Lijuan Wu, Jingchen Chu, Chiawei Gao, Yutong Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Cybersecurity Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University Beijing China Intellectual Property Information Service Center BHU Beihang University Beijing China City University of Macau Taipa China Li’an International Education Innovation Pilot Zone Hainan International College of Minzu University of China Hainan Beijing China

ISBN: (纸本)9789819615278

Proof of Data Possession is a technique for ensuring the integrity of data stored in cloud storage. However, most audit schemes assume only one role for data owners, which is not suitable for complex Smart Healthcare Network with multiple roles. In such schemes addressing the risk of third-party auditors (TPA) colluding with other entities, blockchain is typically employed. However, this necessitates extensive computation, resulting in unacceptably high energy consumption and prolonged verification times. In this paper, we propose a Proof of Data Possession scheme tailored for medical network environments, which incorporates protocols designed for both healthcare professionals and patients, considering the diverse roles that generate data within medical network applications and the varying performance of the devices they use. During the generation of metadata for verification, the protocol incorporates the varying security risk levels associated with medical data. For subsequent metadata selection, an audit block extraction strategy based on strange attractors is employed, effectively mitigating the risk of collusion by third-party TPA, thereby enhancing the security and efficiency of the scheme. Additionally, we conducted experimental analysis and comparisons, demonstrating higher security and practical applicability. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data integrity

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Enhancing Text-Image Person Re-identification via Intra-Class Relevance Learning 24th

Enhancing Text-Image Person Re-identification via Intra-Cla...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： He, Wenbin Gao, Yutong Liu, Wenjian Chaomurilige Liu, Zheng Guo, Ao Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University of China Beijing100081 China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan Lingshui Li572499 China City University of Macau China

ISBN: (纸本)9789819615278

Text-Image Person Re-Identification (TIReID) is a computer vision task that involves identifying person in images or videos based on textual descriptions. Current works mainly employ Vision Language Pretrained (VLP) models for cross-modal alignment and to reestablish text-image fine-grained associations for re-identification. However, these methods neglect the variations in intra-class sample features, hindering accurate cross-modal alignment and discrimination of fine-grained features for the same person. In this paper, we propose a Cross-modal Intra-Class Learning (CICL) framework to enhance the model’s capability of learning fine-grained cross-modal features and intra-class sample variations. We propose an Image-Text Intra-class Relevance Learning (ITRL) method that considers sample relevance during text-image matching, boosting re-identification capability while maintaining fine-grained text-image alignment. Meanwhile, we propose a Bidirectional Masked Matching (BiMM) method that introduces masks to images and texts, prompting the model to attend to different content regions and establish finer-grained cross-modal associations. We test our approach using the CLIP model on three TIReID benchmarks, achieving results that surpass state-of-the-art performance on multiple metrics. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image matching

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DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Friendly Data Communication and parallelism Strategies 20th

DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Frie...

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20th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2024

作者： Guo, Mingfeng Deng, Liang Dai, Zhe Li, Ruitian Lin, Gaofeng Liu, Jie Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789819628292

Sparse triangular solve (SpTRSV) is a vital component in various scientific applications, and numerous GPU-based SpTRSV algorithms have been proposed. Synchronization-free SpTRSV is currently the mainstream algorithm on GPU due to its short preprocessing time and outstanding performance. However, we observed that this algorithm still has two performance bottlenecks. Firstly, the thread-level parallel mode can introduce to thread divergence issues within GPU warps during the writing phase. Secondly, the thread-level and warp-level fusion mode may struggles to fully exploit GPU resources due to suboptimal mapping relationships between rows and threads. To address these issues, this paper proposes DaCPSpTRSV, a new synchronization-free algorithm with GPU-friendly data communication and parallelism strategies. Specifically, we first develop a fast-forward thread-level approach, incorporating an efficient global memory access pattern and a light-weight dependency control mechanism, to optimize data communication and alleviate thread divergence. A fine-grained fusion strategy is then proposed to maximize GPU parallelism by adaptively selecting the suitable thread-level or warp-level modes. Moreover, the commonly-used compressed sparse row (CSR) format is employed in our DaCPSpTRSV, enhancing the versatility of our algorithm. We evaluate our approach using 245 matrices from the SuiteSparse Matrix Collection on two NVIDIA GPUs, demonstrating speedup ratios of up to 4.77×, 4.94×, 1.67×, and 1.62× compared to cuSPARSE, Sync-Free, CapelliniSpTRSV, and YuenyeungSpTRSV, respectively. the project is open-sourced at https://***/gmfff12334/DaCP. © IFIP international Federation for Information processing 2025.

关键词： Graphics processing unit

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Language-Based Colorization with Sparse Attention and Multi-scale Cross-Modal Semantic Alignment 24th

Language-Based Colorization with Sparse Attention and Mult...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Zhang, Ying Gao, Yutong Liu, Xuan Zhang, Lefeng Liu, Xianggan Jiang, Shan Ethnic Language Intelligent Analysis and Security Governance Minzu University of China Beijing China Faculty of Data Science City university of Macau China Natural Language Processing and Knowledge Graph Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819615476

Language-based colorization generates realistic and aesthetically appealing colors by leveraging the guidance of intuitive and user-friendly natural language descriptions. Previous methods in language-based image colorization face several significant challenges, including limited color richness, color bleeding, color distortions, and inconsistent color styles. these issues often arise due to the reliance on dense attention mechanisms, which can lead to an overemphasis on global features at the expense of local details. Furthermore, many feature fusion modules for text and grayscale images are not designed to achieve adequate alignment of the diverse feature representations, resulting in suboptimal colorization outcomes. In this paper, we explore a novel module that employs sparse attention to mitigate the issue of color bleeding and limited colorfulness in image colorization tasks. By introducing a sparse attention mechanism, our method enables more flexible allocation of computational resources with a focus on color awareness. Additionally, we propose a module that effectively aligns grayscale images with color descriptions, thereby improving the consistency and quality of the colorization results. this module leverages the synergy between image features and language descriptions to solve long-standing issues like color bleeding. Empirical evaluations demonstrate that our approach surpasses recent state-of-the-art techniques in both automatic and language-based colorization tasks, validating the effectiveness and robustness of our proposed method in generating high-quality, visually appealing colorized images. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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A Dual-Defense Self-balancing Framework Against Bilateral Model Attacks in Federated Learning 24th

A Dual-Defense Self-balancing Framework Against Bilateral Mo...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Wu, Xiang Yao, Aiting Pal, Shantanu Jiang, Frank Li, Xuejun Xu, Jia Dong, Chengzu Chen, Xuefei Zhang, Xiuyi Liu, Xiao Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Anhui China Deakin University Melbourne Australia School of Data Science Lingnan University Hong Kong

ISBN: (纸本)9789819615247

With the rapid expansion of Artificial Intelligence (AI) services, smart devices generate a large amount of user data at the edge network, which urgently needs to be protected while effectively extracting information. Federated learning (FL) is an important technology for handling dispersed data and strict privacy requirements in this context. However, the security threats caused by model inversion attacks and poisoning attacks can affect the mutual trust between the client and server. Yet, for these two types of attacks, the existing defense mechanisms are contradictory in terms of whether the model parameters are publicly disclosed. In addition, the data distribution of the clients is imbalanced which will increase the bias of model, reducing its practicality. To address this issue, this study proposes a dual defense self-balanced federated learning (DDSFL) framework, aiming to introduce a novel lightweight defense mechanism during the model parameter aggregation stage, combating these two types of attacks simultaneously by applying differential privacy and adjusting learning rates. In addition, this method also integrates a middleware-based reordering algorithm to enhance the robustness of the framework. Experimental results show that DDSFL effectively improves the ability to resist imbalanced data, forged data, and malicious behavior, significantly enhancing the generalization performance and security of the FL system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Enhancing Delay-Sensitive Task Offloading: A Multi-agent Deep Reinforcement Learning Algorithm for MEC-Based AIoT Systems 24th

Enhancing Delay-Sensitive Task Offloading: A Multi-agent Dee...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Tang, Fengjie Xu, Jia Liu, Xiao Yao, Aiting Fang, Mingyan Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China School of Information Technology Deakin University Geelong Australia

ISBN: (纸本)9789819615414

Artificial Intelligence of things (AIoT) systems are widely utilized in various domains such as smart logistics and smart health. In AIoT systems, computational tasks earmarked for offloading by IoT devices often have different task priorities and heterogeneous resource requirements. However, these factors will affect the QoS of the solution and the task response time. therefore, motivated by a typical smart warehouse system, a Multi-access Edge Computing (MEC) based distributed task offloading framework is proposed. Afterward, a delay-sensitive mixed-integer programming model is defined to address the imbalanced utilization of computing resources between edge and cloud servers during the offloading process. Finally, based on the attention mechanism, a Priority-Driven Multi-Agent Deep Reinforcement Learning algorithm (PDMA-DRL) is proposed to fulfill real-time resource requirements while reducing the task response time. the PDMA-DRL algorithm consists of two agents, and each agent corresponds to a sub-problem. Comprehensive experimental results demonstrate that our proposed PDMA-DRL algorithm can outperform representative existing methods in generating delay-sensitive task offloading plans and effectively reducing the task response time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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