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arXiv 2022年

作者： Huang, Heyu Cong, Runmin Yang, Lianhe Du, Ling Wang, Cong Kwong, Sam School of Computer Science and Technology Tiangong University Tianjin300387 China The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Distributed and Parallel Software Lab Huawei Technologies Shenzhen518129 China The Department of Computer Science City University of Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The hippocampus plays a vital role in the diagnosis and treatment of many neurological disorders. Recent years, deep learning technology has made great progress in the field of medical image segmentation, and the performance of related tasks has been constantly refreshed. In this paper, we focus on the hippocampus segmentation task and propose a novel hierarchical feedback chain network. The feedback chain structure unit learns deeper and wider feature representation of each encoder layer through the hierarchical feature aggregation feedback chains, and achieves feature selection and feedback through the feature handover attention module. Then, we embed a global pyramid attention unit between the feature encoder and the decoder to further modify the encoder features, including the pair-wise pyramid attention module for achieving adjacent attention interaction and the global context modeling module for capturing the long-range knowledge. The proposed approach achieves state-of-the-art performance on three publicly available datasets, compared with existing hippocampus segmentation approaches. The code and results can be found from the link of https://***/easymoneysniper183/sematic_seg. © 2022, CC BY-NC-SA.

关键词： Deep learning

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Pre-training for ad-hoc retrieval: Hyperlink is also you need

arXiv

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arXiv 2021年

作者： Ma, Zhengyi Dou, Zhicheng Xu, Wei Zhang, Xinyu Jiang, Hao Cao, Zhao Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Beijing China Distributed and Parallel Software Lab Huawei Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China

Designing pre-training objectives that more closely resemble the downstream tasks for pre-trained language models can lead to better performance at the fine-tuning stage, especially in the ad-hoc retrieval area. Existing pre-training approaches tailored for IR tried to incorporate weak supervised signals, such as query-likelihood based sampling, to construct pseudo query-document pairs from the raw textual corpus. However, these signals rely heavily on the sampling method. For example, the query likelihood model may lead to much noise in the constructed pre-training data. In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval. Since the anchor texts are created by webmasters and can usually summarize the target document, it can help to build more accurate and reliable pre-training samples than a specific algorithm. Considering different views of the downstream ad-hoc retrieval, we devise four pre-training tasks based on the hyperlinks. We then pre-train the Transformer model to predict the pair-wise preference, jointly with the Masked Language Model objective. Experimental results on two large-scale ad-hoc retrieval datasets show the significant improvement of our model compared with the existing methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Hypertext systems

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Improving knowledge distillation via an expressive teacher

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KNOWLEDGE-BASED SYSTEMS 2021年 218卷 106837-106837页

作者： Tan, Chao Liu, Jie Zhang, Xiang Natl Univ Def Technol Sci & Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China Natl Univ Def Technol Inst Quantum Informat Changsha 410073 Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha 410073 Peoples R China

Knowledge distillation (KD) is a widely used network compression technique for seeking a light student network with similar behaviors to its heavy teacher network. Previous studies mainly focus on training the student to mimic representation space of the teacher. However, how to be a good teacher is rarely explored. We find that if a teacher has weak ability to capture the knowledge underlying the true data in the real world, the student cannot even learn knowledge from its teacher. Inspired by that, we propose an inter-class correlation regularization to train teacher to capture a more explicit correlation among classes. Besides, we enforce student to mimic inter-class correlation of its teacher. Extensive experiments of image classification task have been conducted on four public benchmarks. For example, when the teacher and student networks are ShuffleNetV2-1.0 and ShuffleNetV2-0.5, our proposed method achieves 42.63% top-1 error rate for Tiny ImageNet. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Neural network compression Knowledge distillation Knowledge transfer

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An Efficient Scheme for Coupling OpenMC and FLUENT with Adaptive Load Balancing

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SCIENCE AND TECHNOLOGY OF NUCLEAR INSTALLATIONS 2021年第1期2021卷

作者： Zhang, Qingyang Peng, Tianji Zhang, Guangchun Liu, Jie Guo, Xiaowei Gong, Chunye Yang, Bo Fan, Xukai Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China Chinese Acad Sci Inst Modern Phys Lanzhou 730030 Peoples R China Univ Chinese Acad Sci Sch Nucl Sci & Technol Beijing 100049 Peoples R China

This paper develops a multi-physics interface code MC-FLUENT to couple the Monte Carlo code OpenMC with the commercial computational fluid dynamics code ANSYS FLUENT. The implementations and parallel performances of block Gauss-Seidel-type and block Jacobi-type Picard iterative algorithms have been investigated. In addition, this paper introduces two adaptive load-balancing algorithms into the neutronics and thermal-hydraulics coupled simulation to reduce the time cost of computation. Considering that the different scalability of OpenMC and FLUENT limits the performance of block Gauss-Seidel algorithm, an adaptive load-balancing algorithm that can increase the number of nodes dynamically is proposed to improve its efficiency. Moreover, with the natural parallelism of block Jacobi algorithm, another adaptive load-balancing algorithm is proposed to improve its performance. A 3 x 3 PWR fuel pin model and a 1000 MWt ABR metallic benchmark core were used to compare the performances of the two algorithms and verify the effectiveness of the two adaptive load-balancing algorithms. The results show that the adaptive load-balancing algorithms proposed in this paper can greatly improve the computing efficiency of block Jacobi algorithm and improve the performance of block Gauss-Seidel algorithm when the number of nodes is large. In addition, the adaptive load-balancing algorithms are especially effective when a case demands different computational power of OpenMC and FLUENT.

关键词： Iterative methods

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A compressed matrix sequence method for solving normal equations of bundle adjustment

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MACHINE VISION AND APPLICATIONS 2021年第4期32卷 81-81页

作者： Peng, Jiaxin Liu, Jie Wei, Hua Natl Univ Def Technol Sch Comp Sci Lab Software Engn Complex Syst Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Sch Comp Sci Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Hubei Univ Sci & Technol Sch Resources Environm Sci & Engn Water & Land Resources Res Ctr Middle Reaches Yan Xianning 437100 Hubei Peoples R China Changsha Mouri Technol Co Ltd Algorithm Lab Changsha 410073 Hunan Peoples R China

Bundle adjustment is a least squares method-based algorithm for minimizing the global reprojection error and has provided an effective solution for structure from motion (SfM). The Levenberg-Marquardt algorithm provides a feasible and convenient way for bundle adjustment and creates a system of linear equations which are normal equations. For the special sparsity structure of an augmented Hessian matrix, a Schur complement trick is introduced to reduce computation complexity. However, the general sparse matrix storage formats are not optimized for the augmented Hessian matrix and consume too much computation time. According to the Schur complement trick, this paper divides the arrow-like augmented Hessian matrix into a structure matrix, a camera matrix and an observation matrix, and then proposes a new compressed matrix sequence (CMS) method to reduce time complexity for matrix operations. Under the definition of CMS, all of the matrices are stored in a dense form to accelerate the matrix operations, of which the matrix operations are redefined as well. CMS costs little computation time to build sparse matrices or access sparse matrices. The experimental results show that CMS achieves a significant speedup over general sparse matrix storage formats. Also, CMS being insensitive to the data input is more stable.

关键词： Structure from motion Bundle adjustment Levenberg-Marquardt algorithm

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BCS-Net: Boundary, Context, and Semantic for Automatic COVID-19 Lung Infection Segmentation From CT Images

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷

作者： Cong, Runmin Yang, Haowei Jiang, Qiuping Gao, Wei Li, Haisheng Wang, Cong Zhao, Yao Kwong, Sam Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Technol & Business Univ Beijing Key Lab Big Data Technol Food Safety Beijing 100048 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing 100044 Peoples R China Ningbo Univ Sch Informat Sci & Engn Ningbo 315211 Peoples R China Peking Univ Shenzhen Grad Sch Sch Elect & Comp Engn Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Huawei Technol Distributed & Parallel Software Lab Shenzhen 518129 Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen 51800 Peoples R China

The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this article, we propose a novel network for automatic COVID-19 lung infection segmentation from computed tomography (CT) images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage that includes three progressively boundary-context-semantic reconstruction (BCSR) blocks. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder by highlighting the important spatial and boundary locations and modeling the global context dependence. Besides, a semantic guidance (SG) unit generates the SG map to refine the decoder features by aggregating multiscale high-level features at the intermediate resolution. Extensive experiments demonstrate that our proposed framework outperforms the existing competitors both qualitatively and quantitatively.

关键词： COVID-19 Image segmentation Computed tomography Lung Semantics Decoding X-rays Boundary-context-semantic reconstruction (BCSR) COVID-19 infection segmentation lung computed tomography (CT) image

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Cloud workload prediction based on workflow execution time discrepancies

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS software TOOLS AND APPLICATIONS 2019年第3期22卷 737-755页

作者： Kecskemeti, Gabor Nemeth, Zsolt Kertesz, Attila Ranjan, Rajiv Liverpool John Moores Univ Dept Comp Sci Liverpool L3 3AF Merseyside England MTA SZTAKI Lab Parallel & Distributed Syst H-1111 Budapest Hungary Univ Szeged Software Engn Dept H-6720 Szeged Hungary Newcastle Univ Sch Comp Newcastle Upon Tyne NE4 5TG Tyne & Wear England

Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase the reliability or performance of their applications, they would need solutions to detect behavioural changes in the underlying system. Existing runtime solutions for such purposes offer limited capabilities as they are mostly restricted to revealing weekly or yearly behavioural periodicity in the infrastructure. This article proposes a technique for predicting generic background workload by means of simulations that are capable of providing additional knowledge of the underlying private cloud systems in order to support activities like cloud orchestration or workflow enactment. Our technique uses long-running scientific workflows and their behaviour discrepancies and tries to replicate these in a simulated cloud with known (trace-based) workloads. We argue that the better we can mimic the current discrepancies the better we can tell expected workloads in the near future on the real life cloud. We evaluated the proposed prediction approach with a biochemical application on both real and simulated cloud infrastructures. The proposed algorithm has shown to produce significantly (similar to 20%) better workload predictions for the future of simulated clouds than random workload selection.

关键词： Workload prediction Cloud computing Simulation Scientific workflow

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Who Will Become a Long-Term Contributor? A Prediction Model based on the Early Phase Behaviors 18

Who Will Become a Long-Term Contributor? A Prediction Model ...

引用

10th Asia-Pacific Symposium on Internetware (Internetware)

作者： Wang, Tao Zhang, Yang Yin, Gang Yu, Yue Wang, Huaimin Natl Univ Def Technol Key Lab Parallel & Distributed Comp Lab Software Engn Complex Syst Coll Comp Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781450365901

The continuous contribution from peripheral participants is crucial for the success of open source projects. Thus, how to identify the potential Long-Term Contributors (LTC) early and retain them is of great importance. We propose a prediction model to measure the chance for an individual to become a LTC contributor through his capacity, willingness, and the opportunity to contribute at the time of joining. Using data of Rails hosted on GitHub, we find that the probability for a new joiner to become a LTC is associated with his willingness and environment. Specifically, future LTCs tend to be more active and show more community-oriented attitude than other joiners during their first month. This implies that the interaction between individual's attitude and project's climate are associated with the odds that an individual would become a valuable contributor or disengage from the project. We evaluated our prediction model by using the 10 crossvalidation method. Results show that our model archives the mean AUC as 0.807, which is valuable for OSS projects to identify potential long-term contributors and adopt better strategies to retain them for continuous contribution.

关键词： Long Term Contributor Developer behavior GitHub Open Source

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BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images

arXiv

引用

arXiv 2022年

作者： Cong, Runmin Yang, Haowei Jiang, Qiuping Gao, Wei Li, Haisheng Wang, Cong Zhao, Yao Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China The Department of Computer Science City University of Hong Kong Hong Kong The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The School of Information Science and Engineering Ningbo University Ningbo315211 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China The Distributed and Parallel Software Lab Huawei Technologies Shenzhen518129 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this paper, we propose a novel network for automatic COVID-19 lung infection segmentation from CT images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage that includes three progressively Boundary-Context-Semantic Reconstruction (BCSR) blocks. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder by highlighting the important spatial and boundary locations and modeling the global context dependence. Besides, a semantic guidance (SG) unit generates the semantic guidance map to refine the decoder features by aggregating multi-scale high-level features at the intermediate resolution. Extensive experiments demonstrate that our proposed framework outperforms the existing competitors both qualitatively and quantitatively. © 2022, CC BY-NC-SA.

关键词： COVID-19

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A Large-Gap Clone Detection Approach Using Sequence Alignment via Dynamic Parameter Optimization

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IEEE ACCESS 2019年 7卷 131270-131281页

作者： Liu, Jinze Wang, Tao Feng, Chenhui Wang, Huaimin Li, Dongsheng Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab Changsha 410000 Hunan Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410000 Hunan Peoples R China

Large-gap clones, a kind of clones that reuses code with many edits, are very common in software development practice and widespread in software systems. The detection of such clones is very crucial. However, due to a large number of edits, most of the current work fails to detect such clones effectively. This paper aims to find an effective approach for accurate detection of large-gap clones. We transform the code clone detection problem into a biological sequence alignment question and propose a novel approach that combines code fingerprint with sequence alignment. The sequence alignment is Smith-Waterman algorithm based, but shows significant improvements using dynamic parameter acquisition strategy. Furthermore, we design new rational criteria for clone identification. The proposed approach is automatically evaluated extensively by more than 10 million lines of code for general clones detection. We further conduct an empirical study on five large-scale Java projects to manually measure the approach for large-gap clones detection. The experimental results show that the proposed approach can effectively detect large-gap clones and exhibit good performance, and at the same time remains the competitiveness with existing advanced detection tools in detecting general clone detection.

关键词： Code clone detection dynamic parameter acquisition strategy large-gap clone sequence alignment

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