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arXiv 2019年

作者： Wang, Qisheng Liu, Junyi Ying, Mingsheng The Department of Computer Science and Technology Tsinghua University China The State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China The University of Chinese Academy of Sciences China Centre for Quantum Software and Information University of Technology Sydney Australia

In this paper, we introduce the model of quantum Mealy machines and study the equivalence checking and minimisation problems of them. Two efficient algorithms are developed for checking equivalence of two states in the same machine and for checking equivalence of two machines. As an application, they are used in equivalence checking of quantum circuits. Moreover, the minimisation problem is proved to be in PSPACE. Copyright © 2019, The Authors. All rights reserved.

关键词： Timing circuits

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Root Cause Localization for Unreproducible Builds via Causality Analysis Over System Call Tracing

Root Cause Localization for Unreproducible Builds via Causal...

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IEEE International Conference on Automated software Engineering (ASE)

作者： Zhilei Ren Changlin Liu Xusheng Xiao He Jiang Tao Xie Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Department of Computer and Data Sciences Case Western Reserve University School of Software Dalian University of Technology Department of Computer Science and Technology Peking University

Localization of the root causes for unreproducible builds during software maintenance is an important yet challenging task, primarily due to limited runtime traces from build processes and high diversity of build environments. To address these challenges, in this paper, we propose RepTrace, a framework that leverages the uniform interfaces of system call tracing for monitoring executed build commands in diverse build environments and identifies the root causes for unreproducible builds by analyzing the system call traces of the executed build commands. Specifically, from the collected system call traces, RepTrace performs causality analysis to build a dependency graph starting from an inconsistent build artifact (across two builds) via two types of dependencies: read/write dependencies among processes and parent/child process dependencies, and searches the graph to find the processes that result in the inconsistencies. To address the challenges of massive noisy dependencies and uncertain parent/child dependencies, RepTrace includes two novel techniques: (1) using differential analysis on multiple builds to reduce the search space of read/write dependencies, and (2) computing similarity of the runtime values to filter out noisy parent/child process dependencies. The evaluation results of RepTrace over a set of real-world software packages show that RepTrace effectively finds not only the root cause commands responsible for the unreproducible builds, but also the files to patch for addressing the unreproducible issues. Among its Top-10 identified commands and files, RepTrace achieves high accuracy rate of 90.00% and 90.56% in identifying the root causes, respectively.

关键词： Runtime Noise measurement Process control Monitoring Task analysis software packages

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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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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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FLBench: A Benchmark suite for federated learning

arXiv

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arXiv 2020年

作者： Liang, Yuan Guo, Yange Gong, Yanxia Luo, Chunjie Zhan, Jianfeng Huang, Yunyou Guangxi Key Lab of Multi-Source Information Mining & Security Department of Computer Science Guangxi Normal University Guilin541004 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China BenchCouncil R&D Lab - Guilin Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China

Federated learning is a new machine learning paradigm. The goal is to build a machine learning model from the data sets distributed on multiple devices-so-called an isolated data island-while keeping their data secure and private. Most existing federated learning benchmarks work manually splits commonly-used public datasets into partitions to simulate real-world isolated data island scenarios. Still, this simulation fails to capture real-world isolated data island's intrinsic characteristics. This paper presents a federated learning (FL) benchmark suite named FLBench. FLBench contains three domains: medical, financial, and AIoT. By configuring various domains, FLBench is qualified to evaluate federated learning systems and algorithms' essential aspects, like communication, scenario transformation, privacy-preserving, data distribution heterogeneity, and cooperation strategy. Hence, it becomes a promising platform for developing novel federated learning algorithms. Currently, FLBench is open-sourced and in fast-evolution. We package it as an automated deployment tool. The benchmark suite is available from https://***/*** Codes 68-04 Copyright © 2020, The Authors. All rights reserved.

关键词： Learning algorithms

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Model Checking Applied to Quantum Physics

arXiv

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arXiv 2019年

作者： Guan, Ji Feng, Yuan Turrini, Andrea Ying, Mingsheng State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China Center for Quantum Software and Information University of Technology Sydney NSW2007 Australia Department of Computer Science and Technology Tsinghua University Beijing100084 China

Model checking has been successfully applied to verification of computer hardware and software, communication systems and even biological systems. In this paper, we further push the boundary of its applications and show that it can be adapted for applications in quantum physics. More explicitly, we show how quantum statistical and many-body systems can be modeled as quantum Markov chains, and some of their properties that interest physicists can be specified in linear-Time temporal logics. Then we present an efficient algorithm to check these properties. A few case studies are given to demonstrate the use of our algorithm to actual quantum physical problems. Copyright © 2019, The Authors. All rights reserved.

关键词： Model checking

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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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Speech recognition algorithm based on neural network and hidden Markov model

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The Journal of China Universities of Posts and Telecommunications 2018年第2期25卷 28-37,102页

作者： Zhao Jianhui Gao Hongbo Liu Yuchao Cheng Bo Department of Computer Science and Technology Tsinghua University State Key Laboratory of Automotive Safety and Energy Tsinghua University Center for Intelligent Connected Vehicles and Transportation Tsinghua University

This study proposes a hybrid model of speech recognition parallel algorithm based on hidden Markov model( HMM) and artificial neural network( ANN). First,the algorithm uses HMM for time-series modeling of speech signals and calculates the voice to the HMM of the output probability score. Second,with the probability score as input to the neural network,the algorithm gets information for classification and recognition and makes a decision based on the hybrid model. Finally,Matlab software is used to train and test sample data. Simulation results show that using the strong time-series modeling ability of HMM and the classification features of the neural network,the proposed algorithm possesses stronger noise immunity than the traditional HMM. Moreover,the hybrid model corrects the individual flaws of the HMM and the neural network,and greatly improves the speed and performance of speech recognition.

关键词： speech recognition HMM neural network

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3d-cnn based tree species classification using mobile lidar data 4

3d-cnn based tree species classification using mobile lidar ...

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4th ISPRS Geospatial Week 2019

作者： Guan, H. Yu, Y. Yan, W. Li, D. Li, J. School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian223003 China School of Geographical Science Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Department of Geography and Environmental Management University of Waterloo WaterlooONN2L 3G1 Canada

Our work addresses the problem of classifying tree species from mobile LiDAR data. The work is a two step-wise strategy, including tree segmentation and tree species classification. In the tree segmentation step, a voxel-based upward growing filtering is proposed to remove terrain points from the mobile laser scanning data. Then, individual trees are segmented via a Euclidean distance clustering approach and Voxel-based Normalized Cut (VNCut) segmentation approach. In the tree species classification, a voxel-based 3D convolutional neural network (3D-CNN) model is developed based on intensity information. A road section data acquired by a RIEGL VMX-450 system are selected for evaluating the proposed tree classification method. Qualitative analysis shows that our algorithm achieves a good performance. © Authors 2019.

关键词： Optical radar

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The role of positive and negative citations in scientific evaluation

arXiv

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arXiv 2020年

作者： Bai, Xiaomei Lee, Ivan Ning, Zhaolong Tolba, Amr Xia, Feng Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China Computing Center Anshan Normal University Anshan114007 China School of Information Technology and Mathematical Sciences University of South Australia Australia Riyadh Community College King Saud University Riyadh11437 Saudi Arabia Mathematics and Computer Science Department Faculty of Science Menoufia University Egypt

Quantifying the impact of scientific papers objectively is crucial for research output assessment, which subsequently affects institution and country rankings, research funding allocations, academic recruitment and national/international scientific priorities. While most of the assessment schemes based on publication citations may potentially be manipulated through negative citations, in this study, we explore Conflict of Interest (COI) relationships and discover negative citations and subsequently weaken the associated citation strength. PANDORA (Positive And Negative COI- Distinguished Objective Rank Algorithm) has been developed, which captures the positive and negative COI, together with the positive and negative suspected COI relationships. In order to alleviate the influence caused by negative COI relationship, collaboration times, collaboration time span, citation times and citation time span are employed to determine the citing strength;while for positive COI relationship, we regard it as normal citation relationship. Furthermore, we calculate the impact of scholarly papers by PageRank and HITS algorithms, based on a credit allocation algorithm which is utilized to assess the impact of institutions fairly and objectively. Experiments are conducted on the publication dataset from American Physical Society (APS) dataset, and the results demonstrate that our method significantly outperforms the current solutions in Recommendation Intensity of list R at top-K and Spearman’s rank correlation coefficient at top-K. Copyright © 2020, The Authors. All rights reserved.

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