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Characterizing the Occurrence of Dockerfile Smells in Open-Source software: An Empirical Study

IEEE ACCESS 2020年 8卷 34127-34139页

作者： Wu, Yiwen Zhang, Yang Wang, Tao Wang, Huaimin Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Key Lab Software Engn Complex Syst Changsha 410073 Hunan Peoples R China

Dockerfile plays an important role in the Docker-based software development process, but many Dockerfile codes are infected with smells in practice. Understanding the occurrence of Dockerfile smells in open-source software can benefit the practice of Dockerfile and enhance project maintenance. In this paper, we perform an empirical study on a large dataset of 6,334 projects to help developers gain some insights into the occurrence of Dockerfile smells, including its coverage, distribution, co-occurrence, and correlation with project characteristics. Our results show that smells are very common in Dockerfile codes and there exists co-occurrence between different types of Dockerfile smells. Further, using linear regression analysis, when controlled for various variables, we statistically identify and quantify the relationships between Dockerfile smells occurrence and project characteristics. We also provide a rich resource of implications for software practitioners.

关键词： Containers Open source software Best practices Databases Buildings Licenses Docker Dockerfile smells Open-source software GitHub

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YES SIR!Optimizing semantic space of Negatives with Self-Involvement Ranker

arXiv

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arXiv 2021年

作者： Pu, Ruizhi Zhang, Xinyu Lai, Ruofei Guo, Zikai Zhang, Yinxia Jiang, Hao Wu, Yongkang Jia, Yantao Dou, Zhicheng Cao, Zhao Distributed and Parallel Software Lab Huawei China Gaoling School of Artificial Intelligence Renmin University of China

Pre-trained model such as BERT has been proved to be an effective tool for dealing with Information Retrieval (IR) problems. Due to its inspiring performance, it has been widely used to tackle with real-world IR problems such as document ranking. Recently, researchers have found that selecting "hard" rather than"random" negative samples would be beneficial for fine-tuning pre-trained models on ranking tasks. However, it remains elusive how to leverage hard negative samples in a principled way. To address aforementioned issues, we propose a fine-tuning strategy for document ranking, namely Self-Involvement Ranker (SIR), to dynamically select hard negative samples to construct high-quality semantic space for training a high-quality ranking model. Specifically, SIR consists of sequential compressors implemented with pre-trained models. Front compressor selects hard negative samples for rear compressor. Moreover, SIR leverages supervisory signal to adaptively adjust semantic space of negative samples. Finally, supervisory signal in rear compressor is computed based on condition probability and thus can control sample dynamic and further enhance the model performance. SIR is a lightweight and general framework for pre-trained models, which simplifies the ranking process in industry practice. We test our proposed solution on MS MARCO with document ranking setting, and the results show that SIR can significantly improve the ranking performance of various pre-trained models. Moreover, our method became the new SOTA model anonymously on MS MARCO Document ranking leaderboard in May 2021. Copyright © 2021, The Authors. All rights reserved.

关键词： Search engines

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Feedback Chain Network for Hippocampus Segmentation

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2023年第3期19卷 133-133页

作者： Hung, Heyu Cong, Runmin Yang, Lianhe Du, Ling Wang, Cong Kwong, Sam Tiangong Univ Sch Comp Sci & Technol Tianjin Peoples R China Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Huawei Technol Distributed & Parallel Software Lab Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R 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 article, 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 pairwise 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.

关键词： Deep learning hippocampus segmentation multi-level feature fusion feedback chain

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Answer Complex Questions: Path Ranker Is All You Need 21

Answer Complex Questions: Path Ranker Is All You Need

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44th International ACM SIGIR Conference on Research and Development in Information Retrieval

作者： Zhang, Xinyu Zhan, Ke Hu, Enrui Fu, Chengzhen Luo, Lan Jiang, Hao Jia, Yantao Yu, Fan Dou, Zhicheng Cao, Zhao Chen, Lei Huawei Distributed & Parallel Software Lab Shenzhen Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9781450380379

Currently, the most popular method for open-domain Question Answering (QA) adopts "Retriever and Reader" pipeline, where the retriever extracts a list of candidate documents from a large set of documents followed by a ranker to rank the most relevant documents and the reader extracts answer from the candidates. Existing studies take the greedy strategy in the sense that they only use samples for ranking at the current hop, and ignore the global information across the whole documents. In this paper, we propose a purely rank-based framework Thinking Path Re-Ranker (TPRR), which is comprised of Thinking Path Ranker (TPR) for generating document sequences called "a path" and External Path Reranker (EPR) for selecting the best path from candidate paths generated by TPR. Specifically, TPR leverages the scores of a dense model and conditional probabilities to score the full paths. Moreover, to further enhance the performance of the dense ranker in the iterative training, we propose a "thinking" negatives selection method that the top-K candidates treated as negatives in the current hop are adjusted dynamically through supervised signals. After achieving multiple supporting paths through TPR, the EPR component which integrates several fine-grained training tasks for QA is used to select the best path for answer extraction. We have tested our proposed solution on the multi-hop dataset "HotpotQA" with a full wiki setting, and the results show that TPRR significantly outperforms the existing state-of-the-art models. Moreover, our method haswon the first place(1) in the HotpotQA official leaderboard since Feb 1, 2021 under the Fullwiki setting. Code is available at https:// ***/ mindspore/ mindspore/ tree/ master/ model_zoo/ research/ nlp/ tprr.

关键词： Passage ranking Path ranker Multi-hop QA Multi-turn ranking

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Discriminative object tracking by domain contrast

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COMPUTER VISION AND IMAGE UNDERSTANDING 2024年 239卷

作者： Cai, Huayue Zhang, Xiang Lan, Long Xiao, Changcheng Xu, Chuanfu Liu, Jie Luo, Zhigang Natl Univ Def Technol Inst Quantum Changsha Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Lab Changsha Peoples R China Natl Univ Def Technol Coll Comp Lab Software Engn Complex Syst Changsha Peoples R China

Multi-domain tracking method as a novel learning paradigm improves object tracking by sharing domain information with a common backbone whilst learning private information with domain-specific layers. In that context, each individual video sequence as a specific domain serves for a domain-specific layer. In this paper, we observe an intriguing finding that target features from different domains are highly confused with each other, thus having weak discriminative ability, for lack of domain interaction. To this end, we propose a simple yet effective domain interaction training paradigm called domain contrast to boost discriminative object features by effectively using amounts of instances from all the domains in two novel aspects: (1) a light-weight memory-saving training algorithm is proposed to solve the "out-of-the-memory"problem, which paves the way to couple with previous multi-domain trackers, and (2) a composite class-balanced loss is explored to tackle a more practical imbalanced problem, which not only involves the usual class imbalance problem but also accounts for the case of the totally mere negative instances. Experiments on multiple popular tracking benchmark datasets show that our mechanism consistently achieves the tracking performance gain of both base multi-domain tracker and its real-time variant thereof, without any other changes made on the original network.

关键词： Multi-domain tracking Domain interaction Discriminative features Class-balanced loss

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Transfer mechanism of data decryption authority in JointCloud computing 12

Transfer mechanism of data decryption authority in JointClou...

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12th IEEE International Conference on Joint Cloud Computing (IEEE JCC) / 9th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (IEEE MobileCloud)

作者： Xiang, Liangliang Qin, Ying Shi, Peichang Li, Rui Yang, Shangzhi Parallel & Distributed Proc Lab PDL Sci & Technol Changsha Peoples R China Natl Univ Def Technol Engn Res Ctr Natl Fundamental Software Coll Comp Changsha 410073 Peoples R China Tianjin Univ Tianjin 300350 Peoples R China

ISBN: (纸本)9781665434799

Data security sharing in the Jointcloud has always been a difficult problem for researchers. Some cloud data sharing solutions have a series of problems, such as high communication overhead, difficult key management, data tampering, single cloud failure, etc. We propose a data-sharing mechanism based on blockchain and threshold proxy re-encryption to solve the problems listed above. The proxy re-encryption algorithm we adopted is improved based on the Conditional Proxy Broadcast Re-Encryption. By combining the secret sharing scheme to realize trust decentralization. The re-encryption computing is delivered to multiple cloud service providers, which reduces the centralization problem caused by single cloud processing. At the same time, blockchain is used to manage metadata information and data access control rights to solve data loss and tampering problems. Experimental results show that our mechanism achieves low re-encryption computing overhead.

关键词： data sharing JointCloud proxy reencryption blockchain

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Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need 21

Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Nee...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Ma, Zhengyi Dou, Zhicheng Xu, Wei Zhang, Xinyu Jiang, Hao Cao, Zhao Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Huawei Distributed & Parallel Software Lab Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450384469

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.

关键词： Ad-hoc Retrieval Pre-training Hyperlinks Anchor Texts

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Quantization improvements for LU Decomposition to Solve Linear Equations 19

Quantization improvements for LU Decomposition to Solve Line...

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19th IEEE International Symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Chen, Lin He, Kang Jiang, Hao Barrio, Roberto Liu, Jie Li, Tiejun Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Lab Software Engn Complex Syst Changsha Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Changsha Peoples R China Univ Zaragoza Dept Appl Math E-50009 Zaragoza Spain

ISBN: (纸本)9781665435741

Quantization is an effective method for compressing Deep Neural Networks. Now, it is considered to accelerate the traditional HPC applications. In this article, we present a quantization method (q8gemm) using integer 8-bits(INT8) for matrix multiplication, which is implemented by block techniques to overcome the overflow problem. We choose different scales for one matrix's each row and those for the other matrix's each column to keep the numbers in range. With the intel advanced matrix extension cblas_gemm_s8u8s32, our q8gemm can accelerate panel-matrix multiplication in LU factorization. Furthermore, we propose a new mixed-precision solver (q8gesv(1)) for the linear equations. Using q8gemm in INT8, q8gesv modifies LU factorization in FP32 and then utilizes GMRES iterative refinement in FP 64 to bring the solution back to 64 bits accuracy. Numerical tests show that on two Intel CPU platforms with avx2 and avx512, q8gemm achieves a speedup of 1.313-1.621 over cblas_sgemm of MKL. The execution time of cblas_gemm_s8u8s32 makes up 86.78%-0.85% of the q8gemm. By means of q8gemm, our q8gesv can achieve a speedup of 1.176-1.493 over the optimized Higham's mixed-precision algorithm utilized in the HPL-AI benchmark and a speedup of 1.1-1.459 over the LAPACKE_dsgesv function from MKL LAPACK. Meanwhile, q8gesv runs up to 2.859X faster on Intel(R) Core(TM) i5-10600KF and to 2.224X faster on Intel(R) Xeon(R) Gold 6130 than the LAPACKE_dgesv function from MKL LAPACK.

关键词： GMRES-IR LU Factorization Matrix Multiplication Mixed-precision Quantization

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Feasibility analysis of the Sanchez-Pomraning method to treat the particle size distribution in particle-dispersed fuel

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NUCLEAR ENGINEERING AND DESIGN 2024年 428卷

作者： Qin, Shuai Zhang, Qian Feng, Xing Liang, Yuechao Zhejiang Univ Zhejiang Inst Modern Phys Sch Phys Lab Adv Nucl Energy Theory & Applicat Hangzhou 310027 Zhejiang Peoples R China Huaneng Shandong Shidao Bay Nucl Power Co Ltd Rongcheng 264300 Shandong Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Hunan Peoples R China

The non-uniform size of particles appears in the particle-dispersed fuel generally, but this phenomenon is usually neglected. This study aims to evaluate the feasibility of the Sanchez-Pomraning method to solve the particle size distribution problem in particle-dispersed fuel. The Pu-rich agglomerate phenomenon in the MOX fuel and manufacturing tolerance in the Fully Ceramic Microencapsulated (FCM) fuel are calculated by ALPHA-DH code, which implements the Sanchez-Pomraning method based on the MOC transport calculation and improved subgroup resonance calculation. Numerical results show that the Sanchez-Pomraning method has the capability to treat the particle size distribution effect accurately, where the differences between the Sanchez-Pomraning method and the Monte Carlo method are generally lower than 160 pcm. For the Pu-rich agglomerate phenomenon, the results show that ignoring the Pu-rich agglomerate effect introduces the reactivity difference of around 600 pcm, and ignoring the size distribution of the Pu agglomerate will cause the reactivity of up to 200 pcm in the extra-high or extra-low Pu enrichment. The more discrete size distribution and the smaller Pu-spots, the more significant influence will occur in the MOX fuel. For the FCM fuel, the influence brought by the TRISO particle size distribution can be ignored when only TRISO particles are filled. However, ignoring the size distribution when TRISO particles and poison particles are both added into the FCM fuel introduces the differences reaching 600-1000 pcm. These results demonstrate the importance of considering the particle size distribution for analyzing the MOX fuel and FCM fuel in practice. And the Sanchez-Pomraning method is reliable to analyze the effect of particle size distribution.

关键词： Double heterogeneous Particle size distribution Pu spot Fully ceramic microencapsulated fuel Sanchez-Pomraning method

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Online intervention siamese tracking

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INFORMATION SCIENCES 2023年第1期637卷

作者： Cai, Huayue Lan, Long Zhang, Jing Zhang, Xiang Xiao, Changcheng Luo, Zhigang Natl Univ Def Technol Inst Quantum Changsha Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha Peoples R China Univ Sydney Sch Comp Sci Sydney Australia Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Lab Changsha Peoples R China

Online target update excels in helping a visual tracking algorithm adapt to variations in target appearance during inference, and thus is preferred by recent advances. However, this technique typically operates in a heuristic manner by updating numerous network parameters with large amounts of online-collected data. This poses a distinct challenge: can the online target update be effectively executed without using the aforementioned approach? To this end, we propose a novel target feature update scheme to reduce the need for tedious data collection and computation-intensive parameter updates. This scheme operates on the principle of causal intervention and is just as effective as default parameter updates in visual tracking. Besides, we explore a novel video-specific target label to capture the context of a specific target in video frames for feature discrimination. This makes target features better fit for appearance changes. Such schemes together with the off-shelf pre-trained classification backbone form a novel online intervention siamese tracker (OIS). When equipped with an unsupervised pre-trained backbone, OIS outperforms current state-of-the-art unsupervised trackers on the OTB and VOT. When exploiting a supervised trained backbone, it competes with typical supervised trackers trained on massive offline training and online tracking data.

关键词： Online siamese tracking Causal intervention Training-data free

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