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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： Wangzhang, Suhui Yang, Ruizhang Gu, Rong Li, Bo Liu, Dingjin Wang, Zhaokang Nanjing University State Key Laboratory for Novel Software Technology China Sinopec Geophysical Research Institute China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China

ISBN: (纸本)9798350329223

The reverse-time migration (RTM) imaging algorithm, which is used for complex underground structure analysis, is known as one of the current mainstream method of high-precision seismic imaging. Nowadays, as the fast development of seismic data acquisition technology, there exists large amount of seismic data. Thus, the requirement for the efficient computation of the reverse-time migration (RTM) imaging algorithms over large-scale data have been increased greatly. To address this problem, in this paper, we propose a distributed reverse-time migration imaging algorithm based on the widely-used Spark platform first. Based on that, we design and implement a RTM computation solution based on Spark and GPU with several optimizations, including pre-shuffle&caching and load balancing. We also analyze the fault tolerance capability of the proposed distributed algorithm. We evaluate the proposed methods on the typical three-dimensional wavefild benchmark dataset. Experimental results show that the performance of the Spark based reverse-time migration imaging algorithm can scale up near linearly with computing nodes. In addition, the GPU+Spark based version has significant performance improvement on execution efficiency over the Spark-based algorithm. Moreover, the proposed optimization strategies also take effect. In addition, the method proposed in this paper has been applied in the real-world environment of one of the top oil exploration companies world wide, and has achieved significant improvement under the premise of the algorithm correctness guarantee. © 2023 IEEE.

关键词： Graphics processing unit

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DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices 38

DapperFL: Domain Adaptive Federated Learning with Model Fusi...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jia, Yongzhe Zhang, Xuyun Hu, Hongsheng Choo, Kim-Kwang Raymond Qi, Lianyong Xu, Xiaolong Beheshti, Amin Dou, Wanchun State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China School of Computing Macquarie University Australia School of Information and Physical Sciences University of Newcastle Australia Department of Information Systems and Cyber Security University of Texas San Antonio United States College of Computer Science and Technology China University of Petroleum [East China China School of Computer and Software Nanjing University of Information Science and Technology China

Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in edge computing, especially in the presence of domain shifts across local data. In this paper, we propose a heterogeneous FL framework DapperFL, to enhance model performance across multiple domains. In DapperFL, we introduce a dedicated Model Fusion Pruning (MFP) module to produce personalized compact local models for clients to address the system heterogeneity challenges. The MFP module prunes local models with fused knowledge obtained from both local and remaining domains, ensuring robustness to domain shifts. Additionally, we design a Domain Adaptive Regularization (DAR) module to further improve the overall performance of DapperFL. The DAR module employs regularization generated by the pruned model, aiming to learn robust representations across domains. Furthermore, we introduce a specific aggregation algorithm for aggregating heterogeneous local models with tailored architectures and weights. We implement DapperFL on a real-world FL platform with heterogeneous clients. Experimental results on benchmark datasets with multiple domains demonstrate that DapperFL outperforms several state-of-the-art FL frameworks by up to 2.28%, while significantly achieving model volume reductions ranging from 20% to 80%. Our code is available at: https://***/jyzgh/DapperFL. © 2024 Neural information processing systems foundation. All rights reserved.

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RAT: A Refactoring-Aware Tool for Tracking Code History 24

RAT: A Refactoring-Aware Tool for Tracking Code History

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46th International Conference on software Engineering: Companion, ICSE-Companion 2024

作者： Niu, Feifei Shao, Junqian Xu, Chaofan Mayr-Dorn, Christoph Assunção, Wesley K. G. Huang, Liguo Li, Chuanyi Ge, Jidong Luo, Bin Egyed, Alexander State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science North Carolina State University North Carolina United States Department of Computer Science and Engineering Southern Methodist University DallasTX United States

ISBN: (纸本)9798400705021

History of code elements is essential for software maintenance tasks. However, code refactoring is one of the main causes that makes obtaining a consistent view on code evolution difficult as renaming or moving source code elements break such history. To this end, this paper presents RAT, a refactoring-aware tool for keeping track of code elements evolution across time, not just in terms of revisions but also in terms of refactoring. This is the first tool that enables fine-grained code element traceability of the whole repository. Empirical evaluation of leveraging our tool in three bug localization techniques relying on code history shows significant improvement in localization accuracy. Based on our findings, we believe that many of the state-of-the-art approaches using past source code data would benefit from our tool. Demo Tool: https://***/feifeiniu-se/RAT-Demo. Demo Video: https://***/VI-xwUaIPp4. © 2024 IEEE computer Society. All rights reserved.

关键词： Rats

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SeeMe: An intelligent edge server selection method for location-aware business task computing over IIoT

SeeMe: An intelligent edge server selection method for locat...

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作者： Dou, Wanchun Liu, Bowen Duan, Jirun Dai, Fei Qi, Lianyong Xu, Xiaolong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China School of Computer Science Qufu Normal University Qufu China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

In the past few years, latency-sensitive task computing over the industrial internet of things (IIoT) has played a key role in an increasing number of intelligent applications, such as intelligent self-driving vehicles and unmanned aircraft systems. The edge computing paradigm provides a basic functional infrastructure for across-domain business task computing on distributed edge servers. With this observation, a trade-off between the mobile devices and the fixed edge servers is needed to run moving task computing in a low-latency way. Given this challenge, an intelligent server selection method, named SeeMe, is proposed in this paper. Technically speaking, this method aims at minimizing the communication capacity and the transferring capacity in a multiobjective optimization way to find a low-latency edge server. The experiments and comparison analysis verify the availability of our method. © 2022 John Wiley & Sons Ltd.

关键词： Economic and social effects

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Actor of Things: Resilient and Efficient Distributed AIoT Applications in an Actor System

Actor of Things: Resilient and Efficient Distributed AIoT Ap...

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Asia-Pacific Conference on software Engineering

作者： Tianqi Ren Chun Cao Jun Ma Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

Complex and distributed AIoT applications usually involve multiple interconnected heterogeneous devices, leading to requirements of modeling device states and interaction diagrams. It is found that actor model is well-suited for distributed IoT contexts due to its inherent distributive nature and concurrency safety, but existing works lack runtime scalability and support for asynchronous task execution. In this paper, we propose Actor of Things, an approach to transform AIoT applications into efficient and resilient actor systems. We first transform synchronous function calls into Actor Task Graph which decouples nodes with real actors and executes asynchronously. The graph is then executed by Actor Runtime which deploys and schedules actors on IoT devices committed to lowest latency. Preliminary evaluations of our implementation demonstrate the advantages of proposed method in building distributed AIoT applications.

关键词： Concurrent computing Schedules Runtime software architecture Scalability Buildings Transforms Safety Internet of Things Context modeling

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A survey on model-based reinforcement learning

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science China(Information sciences) 2024年第2期67卷 59-84页

作者： Fan-Ming LUO Tian XU Hang LAI Xiong-Hui CHEN Weinan ZHANG Yang YU National Key Laboratory for Novel Software Technology Nanjing University Polixir. ai Department of Computer Science and Engineering Shanghai Jiao Tong University

Reinforcement learning(RL) interacts with the environment to solve sequential decision-making problems via a trial-and-error approach. Errors are always undesirable in real-world applications, even though RL excels at playing complex video games that permit several trial-and-error attempts. To improve sample efficiency and thus reduce errors, model-based reinforcement learning(MBRL) is believed to be a promising direction, as it constructs environment models in which trial-and-errors can occur without incurring actual costs. In this survey, we investigate MBRL with a particular focus on the recent advancements in deep RL. There is a generalization error between the learned model of a non-tabular environment and the actual environment. Consequently, it is crucial to analyze the disparity between policy training in the environment model and that in the actual environment, guiding algorithm design for improved model learning, model utilization, and policy training. In addition, we discuss the recent developments of model-based techniques in other forms of RL, such as offline RL, goal-conditioned RL, multi-agent RL, and meta-RL. Furthermore,we discuss the applicability and benefits of MBRL for real-world tasks. Finally, this survey concludes with a discussion of the promising future development prospects for MBRL. We believe that MBRL has great unrealized potential and benefits in real-world applications, and we hope this survey will encourage additional research on MBRL.

关键词： reinforcement learning model-based reinforcement learning planning model learning model learning with reduced error model usage

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General Category Network: Handwritten Mathematical Expression Recognition with Coarse-Grained Recognition Task 48

General Category Network: Handwritten Mathematical Expressio...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Xinyu Ying, Han Tao, Ye Xing, Youlu Feng, Guihuan Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Nanjing University Software Institute Nanjing210023 China Nanjing University School of Computer Science and Technology Nanjing210023 China

ISBN: (纸本)9781728163277

Handwritten Mathematical Expression Recognition (HMER) is an important task in pattern recognition. It is a challenging task due to symbols resembling each other in appearance("z/2", "B/β") and the complex mathematical syntax. The encoder-decoder architecture has been widely used in recent HMER methods. Several works introduce HMER-related tasks that enhance the performance of HMER. We propose the General Category Recognition Task (GCRT) and design the General Category Network(GCN) to perform HMER and GCRT in parallel. GCRT alleviates the symbol confusion problem and also helps the model generate results conforming to mathematical syntax. Compared with SOTA methods, the experimental results show that Expression Recognition Rates(ExpRate) of our method are increased by 1.12%, 2.45% and 1.84% on CROHME 2014, 2016 and 2019 test set respectively. In addition, current works explore the effect of a single auxiliary task on HMER. We investigated the effect of multiple tasks on HMER. We performed many experiments and drew multiple valuable *** will publish all codes in the future. © 2023 IEEE.

关键词： Character recognition

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent Systems and technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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MIRROR: Model Inversion for Deep Learning Network with High Fidelity 29

MIRROR: Model Inversion for Deep Learning Network with High ...

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29th Annual Network and Distributed System Security Symposium, NDSS 2022

作者： An, Shengwei Tao, Guanhong Xu, Qiuling Liu, Yingqi Shen, Guangyu Yao, Yuan Xu, Jingwei Zhang, Xiangyu Department of Computer Science Purdue University United States State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)1891562746

Model inversion reverse-engineers input samples from a given model, and hence poses serious threats to information confidentiality. We propose a novel inversion technique based on StyleGAN, whose generator has a special architecture that forces the decomposition of an input to styles of various granularities such that the model can learn them separately in training. During sample generation, the generator transforms a latent value to parameters controlling these styles to compose a sample. In our inversion, given a target label of some subject model to invert (e.g., a private face based identity recognition model), our technique leverages a StyleGAN trained on public data from the same domain (e.g., a public human face dataset), uses the gradient descent or genetic search algorithm, together with distribution based clipping, to find a proper parameterization of the styles such that the generated sample is correctly classified to the target label (by the subject model) and recognized by humans. The results show that our inverted samples have high fidelity, substantially better than those by existing state-of-the-art techniques. © 2022 29th Annual Network and Distributed System Security Symposium, NDSS 2022. All Rights Reserved.

关键词： Genetic algorithms

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Biological Classification System Knowledge Graph and Semi-automatic Construction of Its Invertebrate Fossil Branches

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Journal of Earth science 2024年第6期35卷 2119-2128页

作者： Shaochun Dong Yukun Shi Yizao Ran Haijun Wu Yiying Deng Junxuan Fan Xinyu Dai School of Earth Sciences and Engineering Frontiers Science Center for Critical Earth Material CyclingNanjing UniversityNanjing210046China Jiangsu Deep-Time Digital Earth Research Center for Excellence Suzhou215004China School of Computer Science Nanjing UniversityNanjing210046China School of Resources and Environmental Engineering Hefei University of TechnologyHefei230009China State Key Laboratory for Mineral Deposits Research Nanjing210046China National Key Laboratory for Novel Software Technology Nanjing210046China

Biological classification is the foundation of biology and paleontology,as it arranges all the organisms in a hierarchy that humans can easily follow and *** is further used to reconstruct the evolution of life.A biological classification system(BCS)that includes all the established fossil taxa would be both useful and challenging for uncovering the life *** fossil taxa were originally recorded in various published books and articles written by natural languages,the primary step is to organize all those taxa information in a manner that can be deciphered by a computer system.A Knowledge Graph(KG)is a formalized description framework of semantic knowledge,which represents and retrieves knowledge in a machine-understandable way,and therefore provides an eligible method to represent the *** this paper,a model of the BCS KG including the ontology and fact layers is *** put it into practice,the ontology layer of the invertebrate fossil branches was manually developed,while the fact layer was automatically constructed by extracting information from 46 volumes of the Treatise of Invertebrate Paleontology series with the help of natural language processing *** a result,27348 taxa nodes spanning fourteen taxonomic ranks were extracted with high accuracy and high efficiency,and the invertebrate fossil branches of the BCS KG was thus *** study demonstrates that a properly designed KG model and its automatic construction with the help of natural language processing are reliable and efficient.

关键词： biological classification system knowledge graph ontology invertebrate fossil big data

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