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40th ACM/IEEE International Conference on Software Engineering, ICSE 2018

作者： Zhang, Jian Sun, Hailong Tian, Yanfei Liu, Xudong SKLSDE Lab School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China

ISBN: (纸本)9781450356633

Most software CQAs (e.g. Stack Overflow) mainly rely on users to assign tags for posted questions. This leads to many redundant, inconsistent and inaccurate tags that are detrimental to the communities. Therefore tag quality becomes a critical challenge to deal with. In this work, we propose STR, a deep learning based approach that automatically recommends tags through learning the semantics of both tags and questions in such software CQAs. First, word embedding is employed to convert text information to high-dimension vectors for better representing questions and tags. Second, a Multi-Tasking-like Convolutional Neural Network, the core modules of STR, is designed to capture short and long semantics. Third, the learned semantic vectors are fed into a gradient descent based algorithm for classification. Finally, we evaluate STR on three datasets collected from popular software CQAs, and experimental results show that STR outperforms state-of-The-Art approaches in terms of Precision@k, Recall@k and F1-Measure@k. © 2018 Authors.

关键词： Semantics

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GRAM: A GPU-based property graph traversal and query for HPC rich metadata management 1

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15th IFIP International Conference on Network and Parallel Computing, NPC 2018

作者： Li, Wenke Shi, Xuanhua Huang, Hong Zhao, Peng Jin, Hai Dai, Dong Chen, Yong Services Computing Technology and System Lab Big Data Technology and System Lab Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science Texas Tech University LubbockTX43104 United States

ISBN: (数字)9783030056773

ISBN: (纸本)9783030056766

In HPC systems, rich metadata are defined to describe rich information about data files, like the executions that lead to the data files, the environment variables, and the parameters of all executions, etc. Recent studies have shown the feasibility of using property graph to model rich metadata and utilizing graph traversal to query rich metadata stored in the property graph. We propose to utilize GPU to process the rich metadata graphs. There are generally two challenges to utilize GPU for metadata graph query. First, there is no proper data representation for the metadata graph on GPU yet. Second, there is no optimization techniques specifically for metadata graph traversal on GPU neither. In order to tackle these challenges, we propose GRAM, a GPU-based property graph traversal and query framework. GRAM uses GPU to express metadata graph in Compressed Sparse Row (CSR) format, and uses Structure of Arrays (SoA) layout to store properties. In addition, we propose two new optimizations, parallel filtering and basic operations merging, to accelerate the metadata graph traversal. Our evaluation results show that GRAM can be effectively applied to user scenarios in HPC systems, and the performance of metadata management is greatly improved. © IFIP International Federation for Information Processing 2018.

关键词： Metadata

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Domain Knowledge Guided Deep Learning with Electronic Health Records

Domain Knowledge Guided Deep Learning with Electronic Health...

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IEEE International Conference on data Mining (ICDM)

作者： Changchang Yin Rongjian Zhao Buyue Qian Xin Lv Ping Zhang School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China The Ohio State University Columbus Ohio USA

Due to their promising performance in clinical risk prediction with Electronic Health Records (EHRs), deep learning methods have attracted significant interest from healthcare researchers. However, there are 4 challenges: (i) data insufficiency. Many methods require large amounts of training data to achieve satisfactory results. (ii) Interpretability. Results from many methods are hard to explain to clinicians (e.g., why the models make particular predictions and which events cause clinical outcomes). (iii) Domain knowledge integration. No existing method dynamically exploits complicated medical knowledge (e.g., relations such as cause and is-caused-by between clinical events). (iv) Time interval information. Most existing methods only consider the relative order of visits from EHRs, but ignore the irregular time intervals between neighboring visits. In the study, we propose a new model, Domain Knowledge Guided Recurrent Neural Networks (DG-RNN), by directly introducing domain knowledge from the medical knowledge graph into an RNN architecture, as well as taking the irregular time intervals into account. Experimental results on heart failure risk prediction tasks show that our model not only outperforms state-of-the-art deep-learning based risk prediction models, but also associates individual medical events with heart failure onset, thus paving the way for interpretable accurate clinical risk predictions.

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Sequential online prediction in the presence of outliers and change points: An instant temporal structure learning approach

arXiv

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arXiv 2019年

作者： Liu, Bin Qi, Yu Chen, Ke-Jia School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Key Lab of Big Data Security and Intelligent Processing Nanjing Jiangsu210023 China College of Computer Science and Technology Zhejiang University Hangzhou Zhejiang310058 China

In this paper, we consider sequential online prediction (SOP) for streaming data in the presence of outliers and change points. We propose an INstant TEmporal structure Learning (INTEL) algorithm to address this problem. Our INTEL algorithm is developed based on a full consideration to the duality between online prediction and anomaly detection. We first employ a mixture of weighted Gaussian process models (WGPs) to cover the expected possible temporal structures of the data. Then, on the basis of the rich modeling capacity of this WGP mixture, we develop an efficient technique to instantly learn (capture) the temporal structure of the data that follows a regime shift. This instant learning is achieved only by adjusting one hyper-parameter value of the mixture model. A weighted generalization of the product of experts (POE) model is used for fusing predictions yielded from multiple GP models. An outlier is declared once a real observation seriously deviates from the fused prediction. If a certain number of outliers are consecutively declared, then a change point is declared. Extensive experiments are performed using a diverse of real datasets. Results show that the proposed algorithm is significantly better than benchmark methods for SOP in the presence of outliers and change points. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

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Complete phase diagrams of the coevolving spreading dynamics in complex networks

arXiv

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arXiv 2020年

作者： Pan, Liming Yang, Dan Wang, Wei Cai, Shimin Zhou, Tao Lai, Ying-Cheng School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu210023 China Complex Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Cybersecurity Research Institute Sichuan University Chengdu610065 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610054 China Big Data Research Center University of Electronic Science and Technology of China Chengdu610054 China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

Epidemic spreading processes in the real world can interact with each other in a cooperative, competitive or asymmetric way, requiring a description based on coevolution dynamics. Rich phenomena such as discontinuous outbreak transitions and hystereses can arise, but a full picture of these behaviors in the parameter space is lacking. We develop a theory for coevolving spreading dynamics on complex networks through spectral dimension reduction. In particular, we derive from the microscopic quenched mean-field equations a two-dimensional system in terms of the macroscopic variables, which enables a full Phase diagram to be determined analytically. The diagram not only generates critical phenomena that have been known, but also predicts those that were previously unknown such as the interplay between discontinuous transition and hysteresis as well as the emergence and role of tri-critical points. Copyright © 2020, The Authors. All rights reserved.

关键词： Complex networks

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A cooperative scheduling framework for shared transportation services 19

A cooperative scheduling framework for shared transportation...

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19th COTA International Conference of Transportation Professionals: Transportation in China - Connecting the World, CICTP 2019

作者： Wang, Xinli Qiao, Yanan Wang, Tianyu Guo, Jifu Zhou, Xuesong Xian, Kai Tong, Lu Carol Xie, Jindong Natl. Eng. Lab. for Compreh. Transp. Big Data Applic. Technol. Sch. of Transp. Sci. and Engineering Beihang Univ. Beijing100191 China State Key Lab of Software Development Environment School of Computer Science and Engineering Beihang Univ. Beijing100191 China Beijing Transport Institute No. 9 LiuLiQiao South Lane Fengtai District China

ISBN: (数字)9780784482292

ISBN: (纸本)9780784482292

To improve the efficiency of metropolitan shared transportation services (STS), this paper proposes a cooperative scheduling framework to avoid congestion. Two procedures are considered: shared path scheduling and congestion solving. In a known static traffic network, classic vehicle routing problem (VRP) solutions may obtain an optimal path. But with an increasing number of shared transportation services required, congestion is likely, triggered by open loop VRP scheduling, especially when shared vehicles overwhelm private ones. Dynamic traffic assignment (DTA) solutions could be employed for short-term traffic situation prediction. However, VRP and DTA cannot be combined linearly, due to uncontrolled recursive calls of routing procedures within VRP and DTA. Hence, a software structure is further studied to guarantee convergence of the cooperative framework. Using two open-source transportation packages, VRPLite and DTALite, we test our framework with the Western 3rd Ring Road of the Beijing regional network. Results verify the framework's usability and flexibility. © ASCE.

关键词： Scheduling

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Automatic Generation of Medical Imaging Diagnostic Report with Hierarchical Recurrent Neural Network

Automatic Generation of Medical Imaging Diagnostic Report wi...

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IEEE International Conference on data Mining (ICDM)

作者： Changchang Yin Buyue Qian Jishang Wei Xiaoyu Li Xianli Zhang Yang Li Qinghua Zheng School of Computer Science and Technology Xi’an Jiaotong University Xi’an Shaanxi China National Engineering Lab for Big Data Analytics Xian Jiaotong University Xi’an China HP Labs Palo Alto CA USA

Medical images are widely used in the medical domain for the diagnosis and treatment of diseases. Reading a medical image and summarizing its insights is a routine, yet nonetheless time-consuming task, which often represents a bottleneck in the clinical diagnosis process. Automatic report generation can relieve the issues. However, generating medical reports presents two major challenges: (i) it is hard to accurately detect all the abnormalities simultaneously, especially the rare diseases; (ii) a medical image report consists of many paragraphs and sentences, which are longer than natural image captions. We present a new framework to accurately detect the abnormalities and automatically generate medical reports. The report generation model is based on hierarchical recurrent neural network (HRNN). We introduce a topic matching mechanism to HRNN, so as to make generated reports more accurate and diverse. The soft attention mechanism is also introduced to HRNN model. Experimental results on two image-paragraph pair datasets show that our framework outperforms all the state-of-art methods.

关键词： Medical diagnostic imaging Feature extraction Task analysis Diseases Recurrent neural networks Deep learning

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A novel Differential Evolution approach to scheduling the freight trains in intervals of passenger trains

A novel Differential Evolution approach to scheduling the fr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jeng-Shyang Pan Zhenyu Meng Shaoquan Ni Shu-Chuan Chu Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China School of Transportation and Logistics Southwest Jiaotong University Chengdu China School of Computer Science and Technology Shandong University of Science and Technology Qingdao China

Differential Evolution (DE) is a very simple but powerful Evolutionary Algorithm (EA) for real-world applications. There are two aspects affecting the overall optimization performance significantly, one is parameter control scheme and the other is trial vector generation strategy. As all the control parameters are employed in generating trial vectors, from this perspective of view, the trial vector generation strategy dominates the overall optimization performance. Therefore, trial vector generation strategies are often designed first, and then the corresponding parameter control schemes are well-tuned thereafter. Both of them constitute the main body of a new DE variant. Here in the paper, a novel DE variant with a new designed trial vector generation strategy as well as novel parameter control scheme is proposed to tackle freight trains scheduling problem. By incorporating both a time-stamp mechanism of the external archive and a novel parameter control scheme, the new algorithm can secure better optimization performance not only on man-made benchmarks but also on real-world applications. Both the man-made benchmarks from Congress on Evolutionary Computation (CEC) test suite and the real-world freight train scheduling problem are employed in the validation of the new DE variant. The experiment results show that it is competitive with other state-of-the-art DE techniques for these optimization problems.

关键词： Benchmark testing Scheduling Evolutionary computation Rail transportation Processor scheduling Simulated annealing

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Classify EEG and Reveal Latent Graph Structure with Spatio-Temporal Graph Convolutional Neural Network

Classify EEG and Reveal Latent Graph Structure with Spatio-T...

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IEEE International Conference on data Mining (ICDM)

作者： Xiaoyu Li Buyue Qian Jishang Wei An Li Xuan Liu Qinghua Zheng School of Computer Science and Technology Xi’an Jiaotong University Xi’an Shaanxi China HP Labs Palo Alto CA USA National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an Shaanxi China

Electroencephalogram(EEG) is a test that detect brain activities using multiple electrodes placed on the scalp. Multiple channels of EEG signals are recorded through the electrodes and are widely used in applications such as neurological disease diagnosis, emotion recognition, and behavior modeling. Recently, deep learning methods have been applied to classify EEG signals, where the different EEG channels are almost treated as a 2D grid input to the machine learning model. This data formation doesn't consider The complex connection among the EEG channels is not considered in such data formation. In our work, we treat EEG signals as frames of graph, and propose an end-to-end edge-aware spatio-temporal graph convolutional neural network for EEG classification. Specifically, we iteratively apply graph convolutional layer spatially and standard convolutional layer temporally. Since there is no prior knowledge about the exact connection among EEG channels, in our model, we initialize the connection as complete graph and apply learnable mask to capture graph structure at different levels. Furthermore, we also propose an iterative method based on information aggregation in graph convolution mechanism to reveal the latent graph structure. Empirical evaluation shows that our model achieves superior performance over state-of-the-art methods for EEG classification, and the learnt and revealed latent EEG graph structure is verified to be meaningful by neuroscientists.

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Erratum to “Entropy-based fuzzy support vector machine for imbalanced datasets” [Knowl.-Based Syst. 115 (2017) 87–99]

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Knowledge-Based Systems 2020年 192卷

作者： Salim Rezvani Xizhao Wang Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen 518060 Guangdong China

In this note, we show that the calculation of statistics X F 2 and F F in sections 4.5 and 4.6 of the paper (Fan et al., 2017) is not correct. Also, based on the calculation of Critical Difference (CD) of Bonferroni–Dunn test, we show that the conclusion on ”significantly outperforming the compared algorithms”, drawn by the authors using the P -value test, is not convincing.

关键词： Information entropy fuzzy support vector machine Bonferroni–Dunn test Friedman test

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