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arXiv 2021年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Wu, Jia Tong, Yongxin Wang, Lihong Bai, Xu Wang, Bo Yang, Qiang Institute of Information Engineering Chinese Academy of Sciences The School of Cyber Security University of Chinese Academy of Sciences Beijing100041 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Institute of Information Engineering Chinese Academy of Sciences Beijing100041 China The Department of Computing Macquarie University Sydney Australia The State Key Laboratory of Software Development Environment The School of Computer Science and Engineering Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong AI Group WeBank Co. Ltd. China

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the more common multilingual settings and lesser-spoken languages relatively unexplored. Thus, we present a GNN that incorporates cross-lingual word embeddings for detecting events in multilingual data streams. The first exploit is to make the GNN work with multilingual data. For this, we outline a construction strategy that aligns messages in different languages at both the node and semantic levels. Relationships between messages are established by merging entities that are the same but are referred to in different languages. Non-English message representations are converted into English semantic space via the cross-lingual word embeddings. The resulting message graph is then uniformly encoded by a GNN model. In special cases where a lesser-spoken language needs to be detected, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce. © 2021, CC BY.

关键词： Embeddings

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DCQCN+: Taming Large-Scale Incast Congestion in RDMA over Ethernet Networks 26

DCQCN+: Taming Large-Scale Incast Congestion in RDMA over Et...

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26th IEEE International Conference on Network Protocols, ICNP 2018

作者： Gao, Yixiao Yang, Yuchen Chen, Tian Zheng, Jiaqi Mao, Bing Chen, Guihai State Key Laboratory for Novel Software Technology Nanjing University China Computer Science Department Brown University United States

ISBN: (纸本)9781538660430

Remote Direct Memory Access (RDMA) gains growing popularity in datacenter networks. The state-of-the-art congestion control scheme is DCQCN. However, DCQCN has performance problems when large-scale incast communication happens. DCQCN uses fixed period and steps for rate increase when probing for available bandwidth and this scheme is not scalable. Our key insight is that: senders should be aware of the scale of each incast, so that they can adjust their aggressiveness accordingly. The challenges come from different aspects. The scale of congestion is not easy to estimate while the control scheme should be cautiously designed. In this paper, we propose DCQCN+ to improve performance for large-scale incast congestion in RDMA networks. DCQCN+ adapts the rate control mechanisms to different scenarios. DCQCN+ can deal with incast congestion of at least 2,000 flows both in simulation and testbed. The scale is 10 times larger than that of DCQCN in simulation and 4 times larger in testbed. DCQCN+ also has 10 times smaller latency. © 2018 IEEE.

关键词： Testbeds

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Optimal verification of two-qubit pure states

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Physical Review A 2019年第3期100卷 032315-032315页

作者： Kun Wang Masahito Hayashi Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Graduate School of Mathematics Nagoya University Nagoya 464-8602 Japan Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 117542 Singapore

In a recent work [Pallister, Linden, and Montanaro, Phys. Rev. Lett. 120, 170502 (2018)], Pallister et al. proposed an optimal strategy to verify nonmaximally entangled two-qubit pure states under the constraint that the accessible measurements are locally projective and nonadaptive. Their good result leads naturally to the following question: What is the optimal strategy among general local operations and classical communication (LOCC) measurements? In this paper, we answer this problem completely for two-qubit pure states. To be specific, we give the optimal strategy for each of the following available classes of measurements: (i) local operations and one-way classical communication (one-way LOCC) measurements; (ii) local operations and two-way classical communication (two-way LOCC) measurements; and (iii) separable measurements. Surprisingly, our results reveal that for the two-qubit pure state verification problem two-way LOCC measurements remarkably outperform one-way LOCC measurements and have the same power as the separable measurements.

关键词： Quantum benchmarking Quantum entanglement Quantum information processing Quantum measurements Quantum protocols Quantum tomography

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Stochastic optimization for non-convex inf-projection problems

arXiv

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arXiv 2019年

作者： Yan, Yan Xu, Yi Zhang, Lijun Wang, Xiaoyu Yang, Tianbao Department of Computer Science University of Iowa Iowa CityIA52242 United States National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Intellifusion

In this paper, we study a family of non-convex and possibly non-smooth inf-projection minimization problems, where the target objective function is equal to minimization of a joint function over another variable. This problem include difference of convex (DC) functions and a family of bi-convex functions as special cases. We develop stochastic algorithms and establish their first-order convergence for finding a (nearly) stationary solution of the target non-convex function under different conditions of the component functions. To the best of our knowledge, this is the first work that comprehensively studies stochastic optimization of non-convex inf-projection minimization problems with provable convergence guarantee. Our algorithms enable efficient stochastic optimization of a family of non-decomposable DC functions and a family of bi-convex functions. To demonstrate the power of the proposed algorithms we consider an important application in variance-based regularization, and experiments verify the effectiveness of our inf-projection based formulation and the proposed stochastic algorithm in comparison with previous stochastic algorithms based on the min-max formulation for achieving the same effect. Copyright © 2019, The Authors. All rights reserved.

关键词： Functions

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Cost-Effective Signal Map Crowdsourcing with Auto-Encoder Based Active Matrix Completion

Cost-Effective Signal Map Crowdsourcing with Auto-Encoder Ba...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Chengyong Liu Yanchao Zhao Kun Zhu Sheng Zhang Jie Wu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key laboratory for Novel Software Technology Nanjing University China Department of Computer and Information Sciences Temple University USA

ISBN: (纸本)9781728125848

Signal map is of great importance, especially in the dawn of 5G network, for site spectrum monitoring, location-based services (LBS), network construction, and cellular planning. Despite its significance, the traditional signal map construction, e.g., through full site survey, could be time-consuming and labor-intensive as the signal varies frequently over time and the accuracy requirement grows rapidly with the emergence of new applications. Even with crowdsourcing scheme, the participants tend to be unevenly distributed in space while the encouragement budgets for the participants could be far from enough to collect adequate high-quality measurements. Therefore, the signal map constructed by crowdsourcing is often sparse and incomplete. To this end, in this paper, we study how to effectively reconstruct and update the signal map in the case of partially measured signal maps with minimum cost and propose an auto-encoder-based active signal map reconstruction method (AER). Our method is mainly innovative in three parts. Firstly, AER can effectively update the signal map with only a small number of observations while also fully using the incomplete historical signals to effectively update the signal map online. Secondly, AER consists of an active query mechanism which quantitatively evaluates the most valuable measurement site for reconstruction, which further reduces the measurement cost to a large extent. Thirdly, to cope with the measurement dynamics, we give a new signal map model describing not only the signal strength but also the signal dynamics, based on which an advanced AER algorithm is proposed. The simulation results demonstrate the advantages and effectiveness of our approach in both accuracy and cost.

关键词： Signal map Active matrix completion Autoencoder Crowdsourcing Signal mapping signals maps MAPPING(MATHEMATICAL) Crowdsource ANION EXCHANGE RESIN Site surveys required precision

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Efficient and Accurate Arbitrary-Shaped Text Detection With Pixel Aggregation Network

Efficient and Accurate Arbitrary-Shaped Text Detection With ...

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International Conference on computer Vision (ICCV)

作者： Wenhai Wang Enze Xie Xiaoge Song Yuhang Zang Wenjia Wang Tong Lu Gang Yu Chunhua Shen National Key Lab for Novel Software Technology Nanjing University Tongji University Megvii (Face++) Technology Inc. University of Electronic Science and Technology of China Nanjing University The University of Adelaide

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Scene text detection, an important step of scene text reading systems, has witnessed rapid development with convolutional neural networks. Nonetheless, two main challenges still exist and hamper its deployment to real-world applications. The first problem is the trade-off between speed and accuracy. The second one is to model the arbitrary-shaped text instance. Recently, some methods have been proposed to tackle arbitrary-shaped text detection, but they rarely take the speed of the entire pipeline into consideration, which may fall short in practical applications. In this paper, we propose an efficient and accurate arbitrary-shaped text detector, termed Pixel Aggregation Network (PAN), which is equipped with a low computational-cost segmentation head and a learnable post-processing. More specifically, the segmentation head is made up of Feature Pyramid Enhancement Module (FPEM) and Feature Fusion Module (FFM). FPEM is a cascadable U-shaped module, which can introduce multi-level information to guide the better segmentation. FFM can gather the features given by the FPEMs of different depths into a final feature for segmentation. The learnable post-processing is implemented by Pixel Aggregation (PA), which can precisely aggregate text pixels by predicted similarity vectors. Experiments on several standard benchmarks validate the superiority of the proposed PAN. It is worth noting that our method can achieve a competitive F-measure of 79.9% at 84.2 FPS on CTW1500.

关键词： Kernel Detectors Pipelines Benchmark testing Fuses Shape Bones

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On the Robustness of Median Sampling in Noisy Evolutionary Optimization

arXiv

引用

arXiv 2019年

作者： Bian, Chao Qian, Chao Yu, Yang Tang, Ke State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Shenzhen Key Laboratory of Computational Intelligence Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Evolutionary algorithms (EAs) are a sort of nature-inspired metaheuristics, which have wide applications in various practical optimization problems. In these problems, objective evaluations are usually inaccurate, because noise is almost inevitable in real world, and it is a crucial issue to weaken the negative effect caused by noise. Sampling is a popular strategy, which evaluates the objective a couple of times, and employs the mean of these evaluation results as an estimate of the objective value. In this work, we introduce a novel sampling method, median sampling, into EAs, and illustrate its properties and usefulness theoretically by solving OneMax, the problem of maximizing the number of 1s in a bit string. Instead of the mean, median sampling employs the median of the evaluation results as an estimate. Through rigorous theoretical analysis on OneMax under the commonly used onebit noise, we show that median sampling reduces the expected runtime exponentially. Next, through two special noise models, we show that when the 2-quantile of the noisy fitness increases with the true fitness, median sampling can be better than mean sampling;otherwise, it may fail and mean sampling can be better. The results may guide us to employ median sampling properly in practical applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Computational complexity

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Running Time Analysis of the (1+1)-EA for Robust Linear Optimization

arXiv

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arXiv 2019年

作者： Bian, Chao Qian, Chao Tang, Ke Yu, Yang State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Shenzhen Key Laboratory of Computational Intelligence Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties. To understand the practical behaviors of EAs theoretically, there are a series of efforts devoted to analyzing the running time of EAs for optimization under uncertainties. Existing studies mainly focus on noisy and dynamic optimization, while another common type of uncertain optimization, i.e., robust optimization, has been rarely touched. In this paper, we analyze the expected running time of the (1+1)-EA solving robust linear optimization problems (i.e., linear problems under robust scenarios) with a cardinality constraint k. Two common robust scenarios, i.e., deletion-robust and worst-case, are considered. Particularly, we derive tight ranges of the robust parameter d or budget k allowing the (1+1)-EA to find an optimal solution in polynomial running time, which disclose the potential of EAs for robust optimization. Copyright © 2019, The Authors. All rights reserved.

关键词： Budget control

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CRNN Based Jersey-Bib Number/Text Recognition in Sports and Marathon Images

CRNN Based Jersey-Bib Number/Text Recognition in Sports and ...

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International Conference on Document Analysis and Recognition

作者： Sauradip Nag Raghavendra Ramachandra Palaiahnakote Shivakumara Umapada Pal Tong Lu Mohan Kankanhalli Department of Computer Science & Engineering Kalyani Government Engineering College Kalyani India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer System and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University China Department of computer science National University of Singapore Singapore

The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jersey/Bib number that may present in different regions of their outfit captured in cluttered environment conditions. In this work, we proposed a new framework based on detecting the human body parts such that both Jersey Bib number and text is localized reliably. To achieve this, the proposed method first detects and localize the human in a given image using Single Shot Multibox Detector (SSD). In the next step, different human body parts namely, Torso, Left Thigh, Right Thigh, that generally contain a Bib number or text region is automatically extracted. These detected individual parts are processed individually to detect the Jersey Bib number/text using a deep CNN network based on the 2-channel architecture based on the novel adaptive weighting loss function. Finally, the detected text is cropped out and fed to a CNN-RNN based deep model abbreviated as CRNN for recognizing jersey/Bib/text. Extensive experiments are carried out on the four different datasets including both bench-marking dataset and a new dataset. The performance of the proposed method is compared with the state-of-the-art methods on all four datasets that indicates the improved performance of the proposed method on all four datasets.

关键词： Text recognition Sports Image recognition Hip Thigh Torso Biological system modeling

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ParaAim: Testing Android Applications Parallel at Activity Granularity

ParaAim: Testing Android Applications Parallel at Activity G...

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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Chun Cao Jing Deng Ping Yu Zhiyong Duan Xiaoxing Ma State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China Shenzhen Feixiang Software Consulting Co. Ltd Shenzhen China

Widely used commercial Android applications (apps) turn to be of complex GUIs and hundreds of activities. Testing this kind of apps is challenging. Existing automated testing tools cannot complete the testing of these complex apps in a short time. However, scaling these tools for parallelism to accelerate the testing is not straightforward. In this paper, we borrow the basic concepts from parallel computing, and introduce the parallel testing platform ParaAim. ParaAim partitions an app testing job into a set of tasks at the activity granularity. Starting from a specific entrance activity, ParaAim explores the UI-states of the app and each newly discovered activity spawns a new task. ParaAim dispatches the new task to an idle device and sets it up to the entrance by replaying an event sequence. In this manner, the independent parts of the app are explored simultaneously. We focus on the efficiency of this parallel GUI exploration schema. ParaAim assigns the tasks that have more possibility to find new activities with high priority for better performance. Event sequence minimization is also studied to reduce the time cost of replay, and we design widget fuzzy match technique to handle the state inconsistency issue. We evaluated ParaAim with 20 popular commercial apps on different settings of Android device cluster. The results show that ParaAim scales well and evidently increases the average speed of exploration to nearly 2 times with two devices, and 3 times with four devices, than a single device.

关键词： Task analysis Testing Tools Graphical user interfaces Minimization Computational modeling software

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