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International Conference on Software Engineering (ICSE)

作者： Xiang Yuan Chenggang Wu Zhenjiang Wang Jianjun Li Pen-Chung Yew Jeff Huang Xiaobing Feng Yanyan Lan Yunji Chen Yong Guan University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering University of Minnesota at Twin-Cities Minneapolis USA Department of Computer Science and Engineering Texas A&M University Texas USA CAS Key Laboratory of Network Data Science and Technology Chinese Academy of Sciences Beijing China College of Information Engineering Capital Normal University Beijing China

ISBN: (纸本)9781479919345

Multi-threaded programs play an increasingly important role in current multi-core environments. Exposing concurrency bugs and debugging such multi-threaded programs have become quite challenging due to their inherent non-determinism. In order to eliminate such non-determinism, many approaches such as record-and-replay and other similar bug reproducing systems have been proposed. However, those approaches often suffer significant performance degradation because they require a large amount of recorded information and/or long analysis and replay time. In this paper, we propose an effective approach, ReCBuLC, to take advantage of the hardware clocks available on modern processors. The key idea is to reduce the recording overhead and analyzing events' global order by using time stamps recorded in each thread. Those timestamps are used to determine the global orders of shared accesses. To avoid the large overhead incurred in accessing system-wide global clock, we opt to use local per-core clocks that incur much less access overhead. We then propose techniques to resolve differences among local clocks and obtain an accurate global event order. By using per-core clocks, state-of-the-art bug reproducing systems such as PRES and CLAP can reduce the recording overheads by 1% ~ 85%, and the analysis time by 84.66% ~ 99.99%, respectively.

关键词： Clocks Program processors Computer bugs Hardware Coherence Concurrent computing Debugging

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A generalization theory based on independent and task-identically distributed assumption

arXiv

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arXiv 2019年

作者： Zheng, Guanhua Sang, Jitao Li, Houqiang Yu, Jian Xu, Changsheng University of Science and Technology of China School of Computer and Information Technology Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Hefei230026 China National Lab of Pattern Recognition Institute of Automation CAS Beijing100190 China University of Chinese Academy of Sciences

—Existing generalization theories analyze the generalization performance mainly based on the model complexity and training process. The ignorance of the task properties, which results from the widely used IID assumption, makes these theories fail to interpret many generalization phenomena or guide practical learning tasks. In this paper, we propose a new Independent and Task-Identically Distributed (ITID) assumption, to consider the task properties into the data generating process. The derived generalization bound based on the ITID assumption identifies the significance of hypothesis invariance in guaranteeing generalization performance. Based on the new bound, we introduce a practical invariance enhancement algorithm from the perspective of modifying data distributions. Finally, we verify the algorithm and theorems in the context of image classification task on both toy and real-world datasets. The experimental results demonstrate the reasonableness of the ITID assumption and the effectiveness of new generalization theory in improving practical generalization performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

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ZSTAD: Zero-shot temporal activity detection

arXiv

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arXiv 2020年

作者： Zhang, Lingling Chang, Xiaojun Liu, Jun Luo, Minnan Wang, Sen Ge, Zongyuan Hauptmann, Alexander School of Computer Science and Technology Xian Jiaotong University Xian China Ministry of Education Key Lab For Intelligent Networks and Network Security Xian China Faculty of Information Technology Monash University Australia National Engineering Lab for Big Data Analytics Xian Jiaotong University Xian China School of Information Technology and Electrical Engineering University of Queensland Australia School of Computer Science Carnegie Mellon University United States

An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-to-end deep network based on R-C3D as the architecture for this solution. The proposed network is optimized with an innovative loss function that considers the embeddings of activity labels and their super-classes while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS14 and the Charades datasets show promising performance in terms of detecting unseen activities. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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A Margin-based MLE for Crowdsourced Partial Ranking

arXiv

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arXiv 2018年

作者： Xu, Qianqian Xiong, Jiechao Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab of Intell. Info. Process. Inst. of Comput. Tech. Cas Beijing100190 Tencent Ai Lab Shenzhen518057 School of Mathematical Sciences Peking University Beijing100871 China DeepWise Ai Lab Beijing100085 Inst. of Info. Engin. Cas Beijing100093 China University of Chinese Academy of Sciences Beijing100049 China Key Lab of Big Data Mining and Knowledge Management Cas Beijing100190 Department of Mathematics Hong Kong University of Science and Technology Hong Kong Hong Kong

A preference order or ranking aggregated from pairwise comparison data is commonly understood as a strict total order. However, in real-world scenarios, some items are intrinsically ambiguous in comparisons, which may very well be an inherent uncertainty of the data. In this case, the conventional total order ranking can not capture such uncertainty with mere global ranking or utility scores. In this paper, we are specifically interested in the recent surge in crowdsourcing applications to predict partial but more accurate (i.e., making less incorrect statements) orders rather than complete ones. To do so, we propose a novel framework to learn some probabilistic models of partial orders as a margin-based Maximum Likelihood Estimate (MLE) *** prove that the induced MLE is a joint convex optimization problem with respect to all the parameters, including the global ranking scores and margin parameter. Moreover, three kinds of generalized linear models are studied, including the basic uniform model, Bradley-Terry model, and Thurstone-Mosteller model, equipped with some theoretical analysis on FDR and Power control for the proposed methods. The validity of these models are supported by experiments with both simulated and real-world datasets, which shows that the proposed models exhibit improvements compared with traditional state-ofthe-art algorithms. Copyright © 2018, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Weakly supervised learning for textbook question answering

TechRxiv

引用

TechRxiv 2021年

作者： Ma, Jie Chai, Qi Huang, Jingyue Liu, Jun You, Yang Zheng, Qinghua National Engineering Lab for Big Data Analytics School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The High Performance Computing for Artificial intelligence Lab Department of Computer Science National University of Singapore 117417 Singapore

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multimodal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, i.e., multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of 5.02% and 4.12% on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on https://***/dr-majie/WSTQ. © 2021, CC BY.

关键词： Semantics

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Determination of the cross section from 10 to 200 keV at the Back-n facility at CSNS

引用

The European Physical Journal A 2023年第10期59卷 1-11页

作者： Wang, Jincheng Ren, Jie Jiang, Wei Ruan, Xichao Sun, Qi Hu, Jifeng Jiang, Bing Bao, Jie Zhang, Qiwei Luan, Guangyuan Huang, Hanxiong Nie, Yangbo Ge, Zhigang An, Qi Bai, Haofan Bai, Jiangbo Cao, Ping Chen, Qiping Chen, Yonghao Chen, Zhen Cui, Zengqi Fan, Anchuan Fan, Ruirui Feng, Changqing Feng, Fanzhen Gao, Keqing Gu, Minhao Han, Changcai Han, Zijie He, Guozhu He, Yongcheng Hong, Yang Hu, Yiwei Jia, Weihua Jiang, Haoyu Jiang, Zhijie Jin, Zhengyao Kang, Ling Li, Bo Li, Chao Li, Gong Li, Jiawen Li, Qiang Li, Yang Liu, Jie Liu, Rong Liu, Shubin Ning, Changjun Qi, Binbin Ren, Zhizhou Song, Zhaohui Sun, Kang Tan, Zhixin Tang, Jingyu Tang, Shengda Wang, Lijiao Wang, Pengcheng Wang, Zhaohui Wen, Zhongwei Wu, Xiaoguang Wu, Xuan Xie, Likun Yang, Yiwei Yi, Han Yu, Yongji Zhang, Guohui Zhang, Linhao Zhang, Mohan Zhang, Xianpeng Zhang, Yuliang Zhang, Yue Zhang, Zhiyong Zhao, Maoyuan Zhou, Luping Zhu, Kejun Zhang, Jie Key Laboratory of Nuclear Data China Institute of Atomic Energy Beijing China Institute of High Energy Physics Chinese Academy of Sciences (CAS) Beijing China Spallation Neutron Source Science Center Dongguan China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei China Department of Modern Physics University of Science and Technology of China Hefei China State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing China Institute of Nuclear Physics and Chemistry China Academy of Engineering Physics Mianyang China School of Nuclear Science and Technology University of Science and Technology of China Hefei China USTC Archaeometry Lab University of Science and Technology of China Hefei China State Key Laboratory of Particle Detection and Electronics Institute of High Energy Physics Chinese Academy of Sciences Beijing China Northwest Institute of Nuclear Technology Xi’an China University of Chinese Academy of Sciences Beijing China

The neutron capture cross section of $$^{232}Th$$ has been measured with the time-of-flight technique in the energy range from 10 to 200 keV at the back-streaming white neutron beam-line (Back-n) of China Spallation Neutron Source (CSNS). The pulse height weighting technique (PHWT) was applied with four C $$_{6}$$ D $$_{6}$$ liquid scintillators to measure the prompt gamma-ray energy release following neutron capture. The measurement data, corrected with the PHWT, have been normalized to the saturated resonances at 21.8 eV. The background was determined by a lead sample measurement and detailed Monte Carlo simulations. The $$^{232}Th(n,\gamma )$$ average cross sections have been determined relative to the $$^{197}Au(n,\gamma )$$ reaction cross sections. The results are consistent with the evaluation values of CENDL-3.2 and JENDL-5. The total uncertainties, including the PHWT, normalization, background subtraction, corrections, and relative measurement, are in the range of 4.5–4.8%.

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MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization

arXiv

引用

arXiv 2022年

作者： Duan, Yue Zhao, Zhen Qi, Lei Wang, Lei Zhou, Luping Shi, Yinghuan Gao, Yang The National Key Laboratory for Novel Software Technology The National Institute of Healthcare Data Science Nanjing University Nanjing210023 China The School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia The School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China The School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia

The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples. In this paper, we aim to utilize low-confidence samples in a novel way with our proposed mutex-based consistency regularization, namely MutexMatch. Specifically, the high-confidence samples are required to exactly predict "what it is" by conventional True-Positive Classifier, while low-confidence samples are employed to achieve a simpler goal — to predict with ease "what it is not" by True-Negative Classifier. In this sense, we not only mitigate the pseudo-labeling errors but also make full use of the low-confidence unlabeled data by consistency of dissimilarity degree. MutexMatch achieves superior performance on multiple benchmark datasets, i.e., CIFAR-10, CIFAR-100, SVHN, STL-10, mini-ImageNet and Tiny-ImageNet. More importantly, our method further shows superiority when the amount of labeled data is scarce, e.g., 92.23% accuracy with only 20 labeled data on CIFAR-10. Our code and model weights have been released at https://***/NJUyued/MutexMatch4SSL. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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A Blockchain-based Fast Authentication and Collaborative Video data Forwarding Scheme for Vehicular networks

A Blockchain-based Fast Authentication and Collaborative Vid...

引用

IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC

作者： Weihui Qiu Xin Yang Ming Wei Wei Ren Tianqing Zhu School of Computer Science China University of Geosciences Wuhan P.R. China Wuhan Institute of Marine Electric Propulsion CSSC P.R. China Key Laboratory of Network Assessment Technology CAS Chinese Academy of Sciences Institute of Information Engineering Beijing P.R. China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang P.R. China

ISBN: (纸本)9781665400374

Internet of Things (IoT) current present two trends with respect to ubiquity and mobility. The number of devices increases remarkably and most forthcoming devices are mobile, e.g., Internet of Vehicles (IoV). In large scale mobile IoT, the management of device identification, fast authentication, and certification of public keys experiences upcoming difficulties. Centralized security architecture may not be suitable and in contrast cross-domain security architecture will tackle the ubiquity and mobility better. We note that blockchain is a new decentralized architecture equipped with basic cryptographic settings, which provides new tools such as token and guarantees many security properties such as block integrity. In this paper we design a blockchain-based fast authentication video data forwarding scheme for IoV. Compared with existing protocols, it has the advantages of decentralization, high authentication efficiency, strong trust, and resistance to common attacks. We evaluate our scheme with real experiments over Hyperledger Fabric and the authentication delay is manageable (e.g, 4 seconds in IoV).

关键词： Resistance Protocols Authentication Public key Computer architecture Near field communication Ubiquitous computing

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Exploring outliers in crowdsourced ranking for QoE

arXiv

引用

arXiv 2017年

作者： Xu, Qianqian Yan, Ming Huang, Chendi Xiong, Jiechao Huang, Qingming Yao, Yuan Institute of Information Engineering Cas Beijing100093 Department of Computational Mathematics Michigan State University East LansingMI48824 United States BICMR-LMAM-LMEQF-LMP School of Mathematical Sciences Peking University Beijing100871 Tencent Ai Lab Shenzhen518057 University of Chinese Academy of Sciences Beijing100049 China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. Cas Beijing100190 Key Lab of Big Data Mining and Knowledge Management Cas Beijing100190 Department of Mathematics Hong Kong University of Science and Technology 100871 Hong Kong

Outlier detection is a crucial part of robust evaluation for crowd-sourceable assessment of Quality of Experience (QoE) and has attracted much attention in recent years. In this paper, we propose some simple and fast algorithms for outlier detection and robust QoE evaluation based on the nonconvex optimization principle. Several iterative procedures are designed with or without knowing the number of outliers in samples. Theoretical analysis is given to show that such procedures can reach statistically good estimates under mild conditions. Finally, experimental results with simulated and real-world crowdsourcing datasets show that the proposed algorithms could produce similar performance to Huber-LASSO approach in robust ranking, yet with nearly 8 or 90 times speed-up, without or with a prior knowledge on the sparsity size of outliers, respectively. Therefore the proposed methodology provides us a set of helpful tools for robust QoE evaluation with crowdsourcing data. Copyright © 2017, The Authors. All rights reserved.

关键词： Crowdsourcing

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DDistill-SR: Reparameterized Dynamic Distillation network for Lightweight Image Super-Resolution

arXiv

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arXiv 2023年

作者： Wang, Yan Su, Tongtong Li, Yusen Cao, Jiuwen Wang, Gang Liu, Xiaoguang Nankai-Baidu Joint Lab School of Computer Science Nankai University Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Tianjin300071 China Machine Learning and I-health International Cooperation Base of Zhejiang Province and Artificial Intelligence Institute Hangzhou Dianzi University 300018 China

Recent research on deep convolutional neural networks (CNNs) has provided a significant performance boost on efficient super-resolution (SR) tasks by trading off the performance and applicability. However, most existing methods focus on subtracting feature processing consumption to reduce the parameters and calculations without refining the immediate features, which leads to inadequate information in the restoration. In this paper, we propose a lightweight network termed DDistill- SR, which significantly improves the SR quality by capturing and reusing more helpful information in a static-dynamic feature distillation manner. Specifically, we propose a plug-in reparameterized dynamic unit (RDU) to promote the performance and inference cost trade-off. During the training phase, the RDU learns to linearly combine multiple reparameterizable blocks by analyzing varied input statistics to enhance layerlevel representation. In the inference phase, the RDU is equally converted to simple dynamic convolutions that explicitly capture robust dynamic and static feature maps. Then, the information distillation block is constructed by several RDUs to enforce hierarchical refinement and selective fusion of spatial context information. Furthermore, we propose a dynamic distillation fusion (DDF) module to enable dynamic signals aggregation and communication between hierarchical modules to further improve performance. Empirical results show that our DDistill- SR outperforms the baselines and achieves state-of-the-art results on most super-resolution domains with much fewer parameters and less computational overhead. We have released the code of DDistill-SR at https://***/icandle/DDistill-SR. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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