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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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Increasing Momentum-Like Factors:A Method for Reducing Training Errors on Multiple GPUs

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Tsinghua science and technology 2022年第1期27卷 114-126页

作者： Yu Tang Zhigang Kan Lujia Yin Zhiquan Lai Zhaoning Zhang Linbo Qiao Dongsheng Li Science and Technology on Paralled and Distributed Processing Laboratory and College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 473000China

In distributed training,increasing batch size can improve parallelism,but it can also bring many difficulties to the training process and cause training *** this work,we investigate the occurrence of training errors in theory and train ResNet-50 on CIFAR-10 by using Stochastic Gradient Descent(SGD) and Adaptive moment estimation(Adam) while keeping the total batch size in the parameter server constant and lowering the batch size on each Graphics Processing Unit(GPU).A new method that considers momentum to eliminate training errors in distributed training is *** define a Momentum-like Factor(MF) to represent the influence of former gradients on parameter updates in each ***,we modify the MF values and conduct experiments to explore how different MF values influence the training performance based on SGD,Adam,and Nesterov accelerated *** results reveal that increasing MFs is a reliable method for reducing training errors in distributed *** analysis of convergent conditions in distributed training with consideration of a large batch size and multiple GPUs is presented in this paper.

关键词： multiple Graphics Processing Units(GPUs) batch size training error distributed training momentum-like factors

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A Connectivity-Enhanced Multi-Task Learning based on Anatomical Priors for 3D Class-Balanced Pulmonary Airway Segmentation

A Connectivity-Enhanced Multi-Task Learning based on Anatomi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

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

ISBN: (纸本)9798350386226

Accurate and efficient airway segmentation is essential for evaluating pulmonary diseases, aiding diagnosis, reducing the preoperative burden of airway identification, and minimizing patient discomfort during prolonged surgeries. However, current pulmonary airway reconstruction techniques are hindered by two major challenges: difficulty in accurately reconstructing fine airway branches due to the tendency to overlook small targets, and insufficient structural connectivity leading to frequent branch discontinuities within the airway tree. These limitations directly affect the clinical applicability of reconstructed airways. To overcome these challenges, a novel 3D pulmonary airway segmentation multi-task framework is proposed, designed to enhance the performance of existing backbone models. This approach integrates Anatomical Prior-Based Multi-Task Learning (AP-MTL) through the use of Gaussian-constructed connectivity-enhanced isosurfaces, significantly improving the network's ability to maintain airway continuity. Additionally, a Class-Balanced CT Density Distribution Reconstruction mechanism (DDR-CB) is introduced, further refining the model's capability to detect and segment fine airway branches. As a result of these enhancements, the model demonstrates a 11.5% average improvement in segmentation accuracy and connectivity compared to the baseline. The source code is publicly accessible at https://***/inexhaustible419/APMTLAirwaySegment. © 2024 IEEE.

关键词： Pulmonary diseases

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Deep Time Series Anomaly Detection with Local Temporal Pattern Learning

Deep Time Series Anomaly Detection with Local Temporal Patte...

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

作者： Li, Yizhou Wang, Yijie Xu, Hongzuo Zhou, Xiaohui National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Beijing100091 China

ISBN: (纸本)9798350368741

Self-supervised time series anomaly detection (TSAD) demonstrates remarkable performance improvement by extracting high-level data semantics through proxy tasks. Nonetheless, most existing self-supervised TSAD techniques rely on manual- or neural-based transformations when designing proxy tasks, overlooking the intrinsic temporal patterns of time series. This paper proposes a local temporal pattern learning-based time series anomaly detection (LTPAD). LTPAD first generates sub-sequences. Pairwise sub-sequences naturally manifest proximity relationships along the time axis, and such correlations can be used to construct supervision and train neural networks to facilitate the learning of temporal patterns. Time intervals between two sub-sequences serve as labels for sub-sequence pairs. By classifying these labeled data pairs, our model captures the local temporal patterns of time series, thereby modeling the temporal pattern-aware "normality". Abnormal scores of testing data are acquired by evaluating their conformity to these learned patterns shared in training data. Extensive experiments show that LTPAD significantly outperforms state-of-the-art competitors. © 2025 IEEE.

关键词： Local Temporal Pattern Self-supervised Learning Time Series Anomaly Detection

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A Float-Point Arithmetic Logic Unit for High Performance RISC-V Processor 6

A Float-Point Arithmetic Logic Unit for High Performance RIS...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Wei, Chunyuan Yuan, Chuan Qi, Yu Xu, Bangjian Niu, Xin College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electrical and Information Engineering Hunan University Changsha China College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Laboratory Changsha China

ISBN: (纸本)9798331541750

With the rapid advancement of artificial intelligence, chips have become increasingly important. The emerging RISC-V instruction set gradually provides powerful computing support for this field. In this context, along with the computing requirements of deep learning, this paper presents the design of a high-performance floating-point arithmetic logic unit (FALU) that facilitates calculations with double-precision, single-precision, half-precision, and Bfloat16 precision data. This design is based on a single-channel algorithm with merged rounding. It improves and implements a composite adder that combines high and low bits. It also proposes a tree-like floating-point comparator based on the Kogge-Stone parallel prefix network. To ensure that the FALU components meet performance requirements, we undergo functional verification in the Vivado simulation environment. Operating at 1.47GHz under the 28nm CMOS process, the components achieve the predetermined performance indicators. © 2024 IEEE.

关键词： computer circuits

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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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Communication Analysis for Multidimensional parallel Training of Large-scale DNN Models 25

Communication Analysis for Multidimensional Parallel Trainin...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Lai, Zhiquan Hao, Yanqi Li, Shengwei Li, Dongsheng College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798350330014

Multidimensional parallel training has been widely applied to train large-scale deep learning models like GPT-3. The efficiency of parameter communication among training devices/processes is often the performance bottleneck of large model training. Analysis of parameter communication mode and traffic has important reference significance for the research of interconnection network design and computing task scheduling to improve the training performance. In this paper, we analyze the parametric communication modes in typical 3D parallel training (data parallelism, pipeline parallelism, and tensor parallelism), and model the traffic in different communication modes. Finally, taking GPT-3 as an example, we present the communication in its 3D parallel training. © 2023 IEEE.

关键词： Deep neural networks

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MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework 24th

MARO: Enabling Full MPI Automatic Refactoring in DSL-Based ...

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

作者： Lei, Tong Chen, Zongjing Che, Yonggang Xu, Chuanfu Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819615445

Currently, the landscape of computer hardware architecture presents the characteristics of heterogeneity and diversity, prompting widespread attention to cross-platform portable parallel programming techniques. Most existing portable programming approaches adopt the "MPI+X" strategy, focusing mainly on node-level "X" parallelism, while inter-node parallelization still relies on manual MPI programming. OP2 is a domain-specific language-based portable parallel programming framework for unstructured mesh applications that supports the automatic generation of MPI parallel code. However, programmers must manually write pre-processing code during code refactoring, such as handling the mapping relationship between global and process-local variables and the initial distribution of process-local data. In this paper, we propose MARO (MPI Automatic Refactoring for OP2), an automatic refactoring method for generating pre-processing code targeting MPI code generation within the OP2 programming framework for unstructured mesh applications. This method automatically generates the pre-processing code required for MPI parallelism through the source-to-source translator, enabling OP2 to generate MPI parallel code without requiring users to write MPI-related code manually. We compare the automatically refactored OP2 applications using the proposed method with those manually refactored to validate its effectiveness. The proposed method enhances OP2’s automatic parallel code generation capability, enabling it to achieve fully automatic MPI parallelization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automatic programming

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CD-Sched: An Automated Scheduling Framework for Accelerating Neural Network Training on Shared Memory CPU-DSP Platforms

CD-Sched: An Automated Scheduling Framework for Accelerating...

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2023 International Conference on Power, Communication, Computing and Networking Technologies, PCCNT 2023

作者： Xiao, Yuanyuan Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Processing Computer College National University of Defense Technology Changsha China

ISBN: (纸本)9781450399951

DSP holds significant potential for important applications in Deep Neural Networks. However, there is currently a lack of research focused on shared-memory CPU-DSP heterogeneous chips. This paper proposes CD-Sched, an automated scheduling framework that aims to address this gap. By predicting the latency of operators on both CPU and DSP, CD-Sched automatically schedules the computation of operators to the appropriate computing device. This scheduling optimization accelerates the computation of individual operators and ultimately improves the overall training time of neural networks. In end-to-end training tasks, CD-Sched can significantly reduce the overall training time, with an average reduction of approximately 10.77%. © 2023 ACM.

关键词： Deep neural networks

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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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