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An Efficient Broadcast Authentication Protocol in Wireless Sensor Networks

Chinese Journal of Electronics 2023年第2期18卷 368-372页

作者： Xin Zhao Xiaodong Wang Wanrong Yu Xingming Zhou National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China

Broadcast authentication is a critical security service in wireless sensor networks. A protocol named $\mu\text{TESLA}$ [1] has been proposed to provide efficient authentication service for such networks. However, when applied to applications such as time synchronization and fire alarm in which broadcast messages are sent infrequently, $\mu\text{TESLA}$ encounters problems of wasted key resources and slow message verification. This paper presents a new protocol named GBA (Generalized broadcast authentication), for efficient broadcast authentication in these applications. GBA utilizes the one-way key chain mechanism of $\mu\text{TESLA}$ , but modifies the keys and time intervals association, and changes the key disclosure mechanism according to the message transmission model in these applications. The proposed technique can take full use of key resources, and shorten the message verification time to an acceptable level. The analysis and experiments show that GBA is more efficient and practical than $\mu\text{TESLA}$ in applications with various message transmission models.

关键词： Wireless sensor networks Analytical models Protocols Authentication Synchronization Security Message authentication

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A New Pipeline for Knowledge Graph Reasoning Enhanced by Large Language Models Without Fine-Tuning

A New Pipeline for Knowledge Graph Reasoning Enhanced by Lar...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Chen, Zhongwu Bai, Long Li, Zixuan Huang, Zhen Jin, Xiaolong Dou, Yong National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798891761643

Conventional Knowledge Graph Reasoning (KGR) models learn the embeddings of KG components over the structure of KGs, but their performances are limited when the KGs are severely incomplete. Recent LLM-enhanced KGR models input KG structural information into LLMs. However, they require fine-tuning on open-source LLMs and are not applicable to closed-source LLMs. Therefore, in this paper, to leverage the knowledge in LLMs without fine-tuning to assist and enhance conventional KGR models, we propose a new three-stage pipeline, including knowledge alignment, KG reasoning and entity reranking. Specifically, in the alignment stage, we propose three strategies to align the knowledge in LLMs to the KG schema by explicitly associating unconnected nodes with semantic relations. Based on the enriched KGs, we train structure-aware KGR models to integrate aligned knowledge to original knowledge existing in KGs. In the reranking stage, after obtaining the results of KGR models, we rerank the top-scored entities with LLMs to recall correct answers further. Experiments show our pipeline can enhance the KGR performance in both incomplete and general situations. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Factorization Machine-based Unsupervised Model Selection Method

Factorization Machine-based Unsupervised Model Selection Met...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Zhang, Ruyi Wang, Yijie Xu, Hongzuo Zhou, Haifang National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Machine learning is broadly used in many intelligent cybernetic systems. With the burgeoning of the communities of AI, the number of machine learning-based models is rapidly increasing, but picking a suitable and optimal (or relatively good) model from overwhelming options has become a conundrum when deploying a new system. Therefore, we are motivated by an intriguing question: Can we automatically select a proper model for new data? However, unsupervised model selection poses two main challenges: (i) Evaluation and comparison of candidate models on the new data are infeasible due to the lack of labels;and (ii) It is non-trivial to build relationships between model performance and data characteristics when the interaction between these characteristics should be considered. In light of these limitations, this paper proposes a factorization machine-based unsupervised model selection method. Following mainstream model selection protocols, we also leverage model performance on prior known datasets. Differently, we learn higher-order complex relationships between model performance and dataset characteristics. Specifically, our method transfers the historical performance into a second-order function of meta-features and embedding weights by harnessing the power of factorization machine. This function can be subsequently used to select a proper model when given a new dataset. Extensive experiments show that our method obtains more superior model selection performance than five state-of-the-art approaches, and our method executes faster than its competitors by approximate three magnitudes. © 2022 IEEE.

关键词： Factorization

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A Data-Centric Approach for Efficient and Scalable CFD Implementation on Multi-GPUs Clusters 24th

A Data-Centric Approach for Efficient and Scalable CFD Imple...

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24th International Conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2023

作者： Li, Ruitian Deng, Liang Dai, Zhe Zhang, Jian Liu, Jie Liu, Gang China Aerodynamic Research and Development Center Computational Aerodynamic Institute Mianyang China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789819982103

Scalability is a crucial factor determining the performance of massive heterogeneous parallel CFD applications on the multi-GPUs platforms, particularly after the single-GPU implementations have achieved optimal performance through numerous optimizations. A novel Data-Centric hybrid MPI-CUDA CFD model is proposed in this paper to enable efficient scalability of CFD applications on large-scale heterogeneous platforms. Based on the Data-Centric approach, Minimum-cost MPI transfer strategy and the code refactoring technique are realized for a better balance between data transfer and floating-point computation performance, which could significantly improve the scalability and reduce the time-to-solution. Subsequently, those approaches are integrated into the industrial unstructured CFD software, FlowStar, to evaluate their effectiveness. Numerical results demonstrate that Minimum-cost MPI strategy achieves more than 2.0 times performance improvement compared to the traditional Model-Centric implementation, and the code refactoring technique boosts performance by 40% to 50% over the minimum-cost MPI version. Moreover, the Data-Centric implementation on 64 A100 GPUs platform show a speedup ratio of over 120 when compared to the original MPI implementation with 64 ranks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Scalability

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AFMA-Track: Adaptive Fusion of Motion and Appearance for Robust Multi-object Tracking 27th

AFMA-Track: Adaptive Fusion of Motion and Appearance for ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Liao, Wei Luo, Lei Zhang, Chunyuan College of Computer Science and Technology National University of Defence Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9783031784439

Motion and appearance cues play a crucial role in Multi-object Tracking (MOT) algorithms for associating objects across consecutive frames. While most MOT methods prioritize accurate motion modeling and distinctive appearance representations, the use of appearance and motion cues is often confined to simplistic association techniques. For instance, fixed weights are commonly employed to combine the intersection-over-union (IoU) matrix and appearance similarity matrix, yielding an association cost matrix. To harness the full potential of motion and appearance cues across diverse scenarios, we propose an innovative approach that dynamically balances motion and appearance cues based on scene and object information during the association process. Furthermore, we introduce a new mechanism for updating appearance representations, effectively mitigating noise introduced by occlusion. Our method demonstrates state-of-the-art performance on the MOT17 and MOT20 test sets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Object detection

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DMSA: Decentralized and Multi-keyword Selective Data Sharing and Acquisition 22

DMSA: Decentralized and Multi-keyword Selective Data Sharing...

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22nd IEEE International Symposium on parallel and distributed processing with Applications, ISPA 2024

作者： Lin, Moheng Shi, Peichang Fu, Xiang Jiang, Feng Yi, Guodong National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Changsha410073 China Xiangjiang Lab Changsha410073 China

ISBN: (纸本)9798331509712

Blockchain technology has been extensively uti-lized in decentralized data-sharing applications, with the immutability of blockchain providing a witness for the circulation of data. However, current blockchain data-sharing solutions still fail to address the simultaneous screening needs of both the sender and receiver with multi-keywords. Without the capability to support bilateral simultaneous filtering, the disclosure of reasons for matching failures could inadvertently expose sensitive user data. Therefore, the challenge lies in enabling ciphertexts with multiple keywords and receivers with multiple interests to achieve mutual and simultaneous matching. Based on the technical foundations of SE (Searchable Encryption), MABE (Multi-Attribute Based Encryption), and polynomial fitting, this paper proposes a scheme called DMSA (Decentralized and Multi-keyword selective Sharing and selective Acquisition). This scheme can satisfy soundness, enabling ciphertexts carrying multiple keywords and receivers representing multiple interests to match each other simultaneously. We conducted a security analysis that confirms the security of DMSA against chosen-plaintext attacks. Our experimental results demonstrate a significant efficiency improvement, with a 67% increase over single-keyword data-sharing schemes and a 16% enhancement compared to the existing multi-keyword data-sharing solution. © 2024 IEEE.

关键词： Ciphertext

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FedEAE: Federated Learning Based Privacy-Preserving Event Argument Extraction 12th

FedEAE: Federated Learning Based Privacy-Preserving Event Ar...

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12th national CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2023

作者： Hu, Fei Dong, Shenpo Chang, Tao Zhou, Jie Li, Haili Wang, Jingnan Chen, Rui Liu, Haijiao Wang, Xiaodong National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783031446955

Benefiting from Pre-trained Language Model (PLM), Event Argument Extraction (EAE) methods have achieved SOTA performance in general scenarios of Event Extraction (EE). However, with increasing concerns and regulations on data privacy, aggregating distributed data among different institutions in some privacy-sensitive territories (e.g., medical record analysis, financial statement analysis, etc.) becomes very difficult, and it’s hard to train an accurate EAE model with limited local data. Federated Learning (FL) provides promising methods for a large number of clients to collaboratively learn a shared global model without the need to exchange privacy-sensitive data. Therefore, we propose a privacy-preserving EAE method named FedEAE based on FL to solve the current difficulties. To better adapt to federated scenarios, we design a dataset named FedACE generated from the ACE2005 dataset under IID and Non-IID for our experiments. Extensive experiments show that FedEAE achieves promising performance compared to existing baselines, thus validates the effectiveness of our method. To the best of our knowledge, FedEAE is the first to apply FL in the EAE task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Extraction

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Jia, Yan Cheng, Yuqing Xu, Kele Dou, Yong Qiao, Peng He, Zhouyu National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue's surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network's ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI. © 2025 IEEE.

关键词： contour extraction counterfactual inference self-supervised learning speech production ultrasound image

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parallel Implementation of SHA256 on Multizone Heterogeneous Systems 21

Parallel Implementation of SHA256 on Multizone Heterogeneous...

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21st IEEE International Symposium on parallel and distributed processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Luo, Yongtao Liu, Jie Xiao, Tiaojie Gong, Chunye National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing Program for Frontier Equipment Changsha China National Supercomputer Center in Tianjin Tianjin China

ISBN: (纸本)9798350329223

SHA-256 plays an important role in widely used applications, such as data security, data integrity, digital signatures, and cryptocurrencies. However, most of the current optimized implementations of SHA-256 are based on CPUs or dedicated hardware, such as ASICs and FPGAs. Consequently, there is a need to explore whether new heterogeneous parallel framework can improve the computational performance of the hash function. To address this issue, we conducted a study on the MT-3000 platform, which is a special architecture processor for the next-generation exascale prototype supercomputer. We proposed MT-SHA256, a heterogeneous multistage parallel implementation for hashing multiple messages on the MT-3000. Combining the architectural features of this processor, we developed an effective solution that significantly improved the computational performance of SHA-256. As a result, MT-SHA256 achieved a maximum throughput of 1045.68 MB/s on a single acceleration core of MT-3000. This is 9.84x higher than the C code implementation on one CPU core of MT-3000. We also performed a scalability test and found that MT-SHA256 achieved a throughput of 98.04 GB/s on a computing node, and extended to 512 nodes (2048 acceleration clusters) on this system with good scalability. © 2023 IEEE.

关键词： Hash functions

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ROGC: Role-Oriented Graph Convolution Based Multi-Agent Reinforcement Learning

ROGC: Role-Oriented Graph Convolution Based Multi-Agent Rein...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Liu, Yuntao Li, Yuan Xu, Xinhai Liu, Donghong Dou, Yong National University of Defense Technology National Laboratory for Parallel and Distributed Processing China Academy of Military Sciences China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

The role-oriented learning approach could improve the performance of multi-agent reinforcement learning by decomposing complex multi-agent tasks into different roles. However, due to the dynamic environment and interactions among agents, the role undertaken by an agent changes rapidly with time going on. Therefore, the roles of agents should be adapted to the varying situation during the learning process. In this paper, we propose a role-oriented graph convolution based multi-agent reinforcement learning framework (ROGC). Firstly, we design a role assigner based on samples generated from the environment to learn roles for classifying agents into different groups. To further enhance cooperation among agents in the same group for higher performance, we design a graph convolutional module to achieve intra-role communications based on discovered roles. With roles and extracted role features, we design a role-oriented policy learning module that embeds the role information into the algorithm and generates effective policies for individuals. Further, we introduce an auto-encoder to learn the intra-role cooperation knowledge in the graph convolutional module, which ensures our framework executes in a decentralized way. Extensive experiments show that our framework can learn dynamic roles and make full use of learned roles, which makes it outperform popular MARL methods. © 2022 IEEE.

关键词： Reinforcement learning

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