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Cloud computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Yi Wei Yingxiong Nong Zhe Li Jian Pan Zhenyu Yang Xiaoqi Lu Ningjiang Chen China Tobacco Guangxi Industrial Co. Ltd Nanning China School of Computer Electronics and Information Guangxi University Nanning China School of Computer Electronics and Information Guangxi University Guangxi Intelligent Digital Services Research Center of Engineering Technology Key Laboratory of Parallel Distributed and Intelligent Computing (Guangxi University) Nanning China

To prevent the serious impact on production efficiency caused by service interruption, data loss, and other issues resulting from server faults, this paper proposes a server fault prediction model named Wavelet Packet Transform Probabilistic Neural Network(WPT-PNN). WPT-PNN loosely mixes wavelet packet transform and probabilistic neural network to achieve quick fault localization and signal denoising effects. The proposed model is validated using server operational data gathered. The experimental results suggest that the WPT-PNN model can effectively manage the challenges of complicated, non-stationary, and noisy signals in the feature extraction stage and extract signal features reliably. In the fault classification prediction stage, our method improves fault prediction accuracy to 81%, and limits the error range within [-2, 4], better matching the requirements for precise and low false-positive fault prediction in servers.

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FCloudless: A Performance-Aware Collaborative Mechanism for JointCloud Serverless

FCloudless: A Performance-Aware Collaborative Mechanism for ...

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IEEE International Conference on Joint Cloud computing (JCC)

作者： Jianfei Liu Huaimin Wang Peichang Shi Yaojie Li Penghui Ma Guodong Yi National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha 410073 China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha 410073 China Xiangjiang Lab Changsha 410073 China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha 410073 China

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

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Simulation of Nuclear Reactor Accident Scenarios using Physics-Informed Neural Networks and Transfer-Learning

Simulation of Nuclear Reactor Accident Scenarios using Physi...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Yang Yu Yufei Xie Wenlin Wang Guohua Wu Haitao Lan Enbo Lin Ping An Zibin Sun Haichuan Zhang Yixian Wu National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural Networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R 2 : 0.735).

关键词： Radioactive materials Accuracy Monte Carlo methods Transfer learning Neural networks Robustness Data models Fuels Inductors Accidents

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Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection

arXiv

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arXiv 2022年

作者： Xu, Hongzuo Wang, Yijie Jian, Songlei Liao, Qing Wang, Yongjun Pang, Guansong Beijing100091 China The National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Hunan410073 China The College of Computer National University of Defense Technology Hunan410073 China Guangdong518055 China The School of Computing and Information Systems Singapore Management University Singapore178902 Singapore

Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising advanced neural network structures and new reconstruction/prediction learning objectives. However, their one-class learning process can be misled by latent anomalies in training data (i.e., anomaly contamination) under the unsupervised paradigm. Their learning process also lacks knowledge about the anomalies. Consequently, they often learn a biased, inaccurate normality boundary. To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty modeling-based calibration and native anomaly-based calibration. Specifically, our approach adaptively penalizes uncertain predictions to restrain irregular samples in anomaly contamination during optimization, while simultaneously encouraging confident predictions on regular samples to ensure effective normality learning. This largely alleviates the negative impact of anomaly contamination. Our approach also creates native anomaly examples via perturbation to simulate time series abnormal behaviors. Through discriminating these dummy anomalies, our one-class learning is further calibrated to form a more precise normality boundary. Extensive experiments on ten real-world datasets show that our model achieves substantial improvement over sixteen state-of-the-art contenders. © 2022, CC BY.

关键词： Contamination

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Joint: Orchestrating Serverless Workflows on Jointcloud FaaS Systems

arXiv

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arXiv 2025年

作者： Liu, Jianfei Li, Rui Yang, Zhilin Shi, Peichang Yi, Guodong Wang, Huaimin National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha Hunan China Xiangjiang Lab Hunan University Of Technology and Business Changsha Hunan China

Existing serverless workflow orchestration systems are predominantly designed for a single-cloud FaaS system, leading to vendor lock-in. This restricts performance optimization, cost reduction, and availability of applications. However, orchestrating serverless workflows on Jointcloud FaaS systems faces two main challenges: 1) Additional overhead caused by centralized cross-cloud orchestration;and 2) A lack of reliable failover and fault-tolerant mechanisms for cross-cloud serverless workflows. To address these challenges, we propose Joint, a distributed runtime system designed to orchestrate serverless workflows on multiple FaaS systems without relying on a centralized orchestrator. Jointintroduces a compatibility layer, Backend-Shim, leveraging inter-cloud heterogeneity to optimize makespan and reduce costs with on-demand billing. © 2025, CC BY-NC-ND.

关键词： Cloud computing

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MDPPC: Efficient Scalable Multiparty Delegated PSI and PSI Cardinality

MDPPC: Efficient Scalable Multiparty Delegated PSI and PSI C...

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Annual Conference on Privacy, Security and Trust, PST

作者： Yihao Yang Xiaolei Dong Jiachen Shen Zhenfu Cao Yunbo Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Zongyang Hou Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan Print-Rite Technology Development Co. Ltd. Of Zhuhai China

Private Set Intersection (PSI) is one of the most important functions in secure multiparty computation (MPC). PSI protocols have been a practical cryptographic primitive and there are many privacy-preserving applications based on PSI protocols such as computing conversion of advertising and distributed computation. Private Set Intersection Cardinality (PSI-CA) is a useful variant of PSI protocol. PSI and PSI-CA allow several parties, each holding a private set, to jointly compute the intersection and cardinality, respectively without leaking any additional information. Nowadays, most PSI protocols mainly focus on two-party settings, while in multiparty settings, parties are able to share more valuable information and thus more desirable. On the other hand, with the advent of cloud computing, delegating computation to an untrusted server becomes an interesting problem. However, most existing delegated PSI protocols are unable to efficiently scale to multiple clients. In order to solve these problems, this paper proposes MDPPC, an efficient PSI protocol which supports scalable multiparty delegated PSI and PSI-CA operations. Security analysis shows that MDPPC is secure against semi-honest adversaries and it allows any number of colluding clients. For 15 parties with set size of 2 20 on server side and 2 16 on clients side, MDPPC costs only 81 seconds in PSI and 80 seconds in PSI-CA, respectively. The experimental results show that MDPPC has high scalability.

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Handling polysemous triggers and arguments in event extraction:an adaptive semantics learning strategy with reward-penalty mechanism

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Frontiers of Information Technology & Electronic Engineering 2025年第4期26卷 534-555页

作者： Haili LI Zhiliang TIAN Xiaodong WANG Yunyan ZHOU Shilong PAN Jie ZHOU Qiubo XU Dongsheng LI National Key Laboratory of Parallel and Distributed Computing National University of Defense TechnologyChangsha 410073China College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China Unit 63891 of PLA Luoyang 471003China Unit 63893 of PLA Luoyang 471003China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang 471003China

Event extraction(EE)is a complex natural language processing(NLP)task that aims at identifying and classifying triggers and arguments in raw *** polysemy of triggers and arguments stands out as one of the key challenges affecting the precise extraction of *** approaches commonly consider the semantic distribution of triggers and arguments to be ***,the sample quantities of different semantics in the same trigger or argument vary in real-world scenarios,leading to a biased semantic *** bias introduces two challenges:(1)low-frequency semantics is difficult to identify;(2)high-frequency semantics is often mistakenly *** tackle these challenges,we propose an adaptive learning method with the reward-penalty mechanism for balancing the semantic distribution in polysemous triggers and *** reward-penalty mechanism balances the semantic distribution by enlarging the gap between the target and nontarget semantics by rewarding correct classifications and penalizing incorrect ***,we propose a sentencelevel event situation awareness(SA)mechanism to guide the encoder to accurately learn the knowledge of events mentioned in the sentence,thereby enhancing target event semantics in the distribution of polysemous triggers and ***,for various semantics in different tasks,we propose task-specific semantic decoders to precisely identify the boundaries of the predicted triggers and arguments for the *** experimental results on ACE2005 and its variants,along with the rich Entities,Relations,and Events(ERE),demonstrate the superiority of our approach over single-task and multi-task EE baselines.

关键词： Event extraction Polysemous triggers Polysemous arguments Semantic imbalance Reward-penalty mechanism

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for Highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yanlong Qiu Siqi Wang Xi Yang Xinyuan Qiu Chengkun Wu Yingbo Cui Canqun Yang Institute for Quantum Information & State Key Laboratory of High-Performance Computing College of Computer Science National University of Defense Technology Changsha P. R. China National Supercomputer Center in Tianjin Tianjin P. R. China National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha P. R. China Department of Biology and Chemistry College of Science National University of Defense Technology Changsha P. R. China

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

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A Complexity-Reduced Block-Selective Algebraic Multigrid Method for Implicitly Coupled Velocity-Pressure System on High Performance Computers

SSRN

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SSRN 2023年

作者： Liang, Yuechao Guo, Xiao-Wei Li, Chao Yuan, Fan Song, Min Zhang, Qingyang Chen, Xinhai Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Mathematics and Computational Sciences Xiangtan University Xiangtan411105 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

The implicitly coupled pressure-based algorithm is widely acknowledged for its superior convergence and robustness in solving incompressible flow problems. However, the increased expansion scale of equations and difficulty in solving the resulting equation system has limited the widespread use of the coupling algorithm. This paper presents an optimized block-SAMG method, referred to as Red-SAMG, to significantly reduce computational complexity and memory consumption. Our approach employs a parallel modified independent set algorithm, allowing each process to perform matrix coarsening individually. An aggressive coarsening strategy further reduces complexity to address larger-scale problems. We conducted numerical experiments on a serial two-dimensional backward-facing step case and a parallel three-dimensional tunnel between rod bundles in the reactor core problem. The results demonstrate that the Red-SAMG method with aggressive coarsening reduces solution time by 12\% on the backward-facing step case. For large-scale three-dimensional problems under the same parallel conditions, the Red-SAMG method shortens the solution time by 14\% compared to the existing SAMG method. The Red-SAMG method with aggressive coarsening further reduces solution time by 49\%. Our optimized Red-SAMG method effectively reduces the complexity of the linear system arising from the implicitly coupled algorithm. The aggressive coarsening strategy also represents a significant advancement in improving the computational efficiency of the SAMG method. © 2023, The Authors. All rights reserved.

关键词： Coarsening

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A clustering-based approach for mining dockerfile evolutionary trajectories

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Science China(Information Sciences) 2019年第1期62卷 211-213页

作者： Yang ZHANG Huaimin WANG Vladimir FILKOV Key Laboratory of Parallel and Distributed Computing National University of Defense Technology College of Computer National University of Defense Technology DECAL Lab University of California Computer Science Department University of California

Dear editor,Docker1), as a de-facto industry standard [1], enables the packaging of an application with all its dependencies and execution environment in a light-weight, self-contained unit, i.e., *** launching the container from Docker image, developers can easily share the same operating system, libraries, and binaries [2]. As the configuration file, the dockerfile plays an important role,

关键词： A clustering-based approach for mining dockerfile evolutionary trajectories

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