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arXiv 2018年

作者： Yang, Liu Ai, Qingyao Guo, Jiafeng Bruce Croft, W. Center for Intelligent Information Retrieval University of Massachusetts AmherstMA United States CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed for semantic matching of questions and answers. To achieve good results, however, these models have been combined with additional features such as word overlap or BM25 scores. Without this combination, these models perform significantly worse than methods based on linguistic feature engineering. In this paper, we propose an attention based neural matching model for ranking short answer text. We adopt value-shared weighting scheme instead of position-shared weighting scheme for combining different matching signals and incorporate question term importance learning using question attention network. Using the popular benchmark TREC QA data, we show that the relatively simple aNMM model can significantly outperform other neural network models that have been used for the question answering task, and is competitive with models that are combined with additional features. When aNMM is combined with additional features, it outperforms all baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Deep learning based joint resource scheduling algorithms for hybrid MEC networks

arXiv

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arXiv 2019年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and Business Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Technology and Engineering Changchun Institute of Technology Changchun China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom

In this paper, we consider a hybrid mobile edge computing (H-MEC) platform, which includes ground stations (GSs), ground vehicles (GVs) and unmanned aerial vehicle (UAVs), all with mobile edge cloud installed to enable user equipments (UEs) or Internet of thing (IoT) devices with intensive computing tasks to offload. Our objective is to obtain an online offloading algorithm to minimize the energy consumption of all the UEs, by jointly optimizing the positions of GVs and UAVs, user association and resource allocation in real-time, while considering the dynamic environment. To this end, we propose a hybrid deep learning based online offloading (H2O) framework where a large-scale path-loss fuzzy c-means (LSFCM) algorithm is first proposed and used to predict the optimal positions of GVs and UAVs. Secondly, a fuzzy membership matrix U-based particle swarm optimization (U-PSO) algorithm is applied to solve the mixed integer nonlinear programming (MINLP) problems and generate the sample datasets for the deep neural network (DNN) where the fuzzy membership matrix can capture the small-scale fading effects and the information of mutual interference. Thirdly, a DNN with the scheduling layer is introduced to provide user association and computing resource allocation under the practical latency requirement of the tasks and limited available computing resource of H-MEC. In addition, different from traditional DNN predictor, we only input one UEs information to the DNN at one time, which will be suitable for the scenarios where the number of UE is varying and avoid the curse of dimensionality in DNN. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Multifractal temporally weighted detrended cross-correlation analysis of multivariate time series

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Chaos (Woodbury, N.Y.) 2020年第2期30卷 023134页

作者： Shan Jiang Bao-Gen Li Zu-Guo Yu Fang Wang Vo Anh Yu Zhou Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China. College of Information and Science Technology Hunan Agricultural University Changsha Hunan 410128 China. Faculty of Science Engineering and Technology Swinburne University of Technology PO Box 218 Hawthorn Victoria 3122 Australia. Institute of Future Cities and Department of Geography and Resource Management The Chinese University of Hong Kong Shatin Hong Kong China.

Fractal and multifractal properties of various systems have been studied extensively. In this paper, first, the multivariate multifractal detrend cross-correlation analysis (MMXDFA) is proposed to investigate the multifractal features in multivariate time series. MMXDFA may produce oscillations in the fluctuation function and spurious cross correlations. In order to overcome these problems, we then propose the multivariate multifractal temporally weighted detrended cross-correlation analysis (MMTWXDFA). In relation to the multivariate detrended cross-correlation analysis and multifractal temporally weighted detrended cross-correlation analysis, an innovation of MMTWXDFA is the application of the signed Manhattan distance to calculate the local detrended covariance function. To evaluate the performance of the MMXDFA and MMTWXDFA methods, we apply them on some artificially generated multivariate series. Several numerical tests demonstrate that both methods can identify their fractality, but MMTWXDFA can detect long-range cross correlations and simultaneously quantify the levels of cross correlation between two multivariate series more accurately.

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A Train Position Based Beam Switching Mechanism in High Speed Railway Scenario 9

A Train Position Based Beam Switching Mechanism in High Spee...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Peng Ma Delin Cai Chao Wang Mingyang Zhang Qiaoling Yu Ting Zhou Key Laboratory of Intelligent Computing & Signal Processing (Anhui University) Ministry of Education Shanghai Research Center for Wireless Communications Shanghai Institute of Microsystem and Information Technology (SIMIT) Chinese Academy of Sciences (CAS)

ISBN: (纸本)9781509038237;9781509038220

According to the medium and long term development planning of railway networks, China will built "eight vertical and horizontal" high speed railway network during 13 th five-year. With the rapid development of mobile smart terminals, the rail dedicated communication system using GSM-R system cannot satisfy the passengers' communication requirement. As high frequency communication has been considered in 3 GPP new radio to achieve higher capacity, this paper studies the high-speed communication using 60 GHz millimeter wave. In this paper, we propose a beam switching mechanism based on train position information in high speed railway scenario. The proposed mechanism can realize fast intra-TRP/inter-TRP beam switching and the simulation result demonstrates Gigabit data rate can be provided for passengers.

关键词： high-speed rail communication millimeter wave beam switching train position

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ISIA at the ImageCLEF 2017 image caption task 18

ISIA at the ImageCLEF 2017 image caption task

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18th Working Notes of CLEF Conference and labs of the Evaluation Forum, CLEF 2017

作者： Liang, Sisi Li, Xiangyang Zhu, Yongqing Li, Xue Jiang, Shuqiang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Zhongguancun Haidian District Beijing100190 China

This paper describes the details of our methods for participation in the caption prediction task of ImageCLEF 2017. The dataset we use is all provided by the organizers and doesn't include any external resources. The key components of our framework include a deep model part, an SVM part and a caption retrieval part. In deep model part, we use an end to end architecture with Convolutional neural network (CNN) and a Long Short-Term Memory (LSTM) to encode and decode images and captions. According to the statistics of training dataset, we train different models with different lengths of captions. Then in SVM part, we use Support Vector Machine (SVM) to determine which model to use when generating the description for a test image. In this way, we can combine these models from the previous deep model part. In caption retrieval part, we use the image feature extracted from CNN and apply Nearest Neighbor method to retrieve the most similar image with caption in the training dataset. The final description is the aggregation of the generated sentence and the caption retrieved from the training dataset. The best performance of our 10 submitted runs ranks the 3rd in group which doesn't use external resources.

关键词： Support vector machines

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Shape DNA: Basic generating functions for geometric moment invariants

arXiv

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arXiv 2017年

作者： Erbo Li Yazhou Huang Dong Xu Li, Hua Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences

Geometric moment invariants (GMIs) have been widely used as basic tool in shape analysis and information retrieval. Their structure and characteristics determine efficiency and effectiveness. Two fundamental building blocks or generating functions (GFs) for invariants are discovered, which are dot product and vector product of point vectors in Euclidean space. The primitive invariants (PIs) can be derived by carefully selecting different products of GFs and calculating the corresponding multiple integrals, which translates polynomials of coordinates of point vectors into geometric moments. Then the invariants themselves are expressed in the form of product of moments. This procedure is just like DNA encoding proteins. All GMIs available in the literature can be decomposed into linear combinations of PIs. This paper shows that Hu's seven well known GMIs in computer vision have a more deep structure, which can be further divided into combination of simpler PIs. In practical uses, low order independent GMIs are of particular interest. In this paper, a set of PIs for similarity transformation and affine transformation in 2D are presented, which are simpler to use, and some of which are newly reported. The discovery of the two generating functions provides a new perspective of better understanding shapes in 2D and 3D Euclidean spaces, and the method proposed can be further extended to higher dimensional spaces and different manifolds, such as curves, surfaces and so on. Copyright © 2017, The Authors. All rights reserved.

关键词： DNA

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Anatomy of an online misinformation network

arXiv

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arXiv 2018年

作者： Shao, Chengcheng Hui, Pik-Mai Wang, Lei Jiang, Xinwen Flammini, Alessandro Menczer, Filippo Ciampaglia, Giovanni Luca College of Computer National University of Defense Technology China School of Informatics Computing and Engineering Indiana University Bloomington United States MOE Key Laboratory of Intelligent Computing and Information Processing Xiangtan University China Indiana University Network Science Institute Bloomington United States

Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy filters public tweets that include links to unverified claims or fact-checking articles. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network. Copyright © 2018, The Authors. All rights reserved.

关键词： Social networking (online)

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Deep Multi-Metric Learning for Person Re-Identification

Deep Multi-Metric Learning for Person Re-Identification

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yongxin Ge Xinqian Gu Min Chen Hongxing Wang Dan Yang Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education (Chongqing University) Chongqing China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Big Data & Software Engineering Chongqing University Chongqing China

In this paper, to exploit more discriminative information of the global-body and body-parts features, we present a novel deep multi-metric learning (DMML) network for person re-identification under the triplet framework. The main novelty of our learning framework lies in two aspects: 1) Unlike most existing metric learning-based approaches, which learn only one distance metric for comparison, our DMM-L method aims to learn different metrics for the global-body and body-parts features respectively by using convolutional neural network (CNN); 2) A new multi-metric loss function is proposed to train the DMML network, under which the distance of each negative pair is greater than that of each positive pair by a predefined margin, and the correlations of different metrics are maximized. Compared with the previous person re-identification methods that have shown state-of-the-art performances, our DMML approach can achieve competitive results on the challenging CUHK03, CUHKOl, VIPeR and iLIDS datasets.

关键词： Measurement Image color analysis Feature extraction Training Machine learning Kernel Learning systems

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A survey on context-aware mobile visual recognition

A survey on context-aware mobile visual recognition

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作者： Min, Weiqing Jiang, Shuqiang Wang, Shuhui Xu, Ruihan Cao, Yushan Herranz, Luis He, Zhiqiang Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing100190 China Higher Education Institution Teacher Online Training Center Beijing China Lenovo Corporate Research Beijing100085 China

The phenomenal growth of the usage of mobile devices (e.g., mobile phones and tablet PCs) opens up a new service, namely mobile visual recognition, which has been widely used in many areas, such as mobile shopping and augmented reality. The rich contextual information (e.g., location, time and direction information), easily acquired by the mobile devices, provides useful clues to facilitate mobile visual recognition, including speeding up the recognition time and improving the recognition performance. This survey focuses on recent advances in Context-Aware Mobile Visual Recognition (CAMVR) and reviews related work regarding to different contextual information, recognition methods, recognition types, and various application scenarios. Finally, we discuss future research directions in this field. © 2016, Springer-Verlag Berlin Heidelberg.

关键词： Surveys

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Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma

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Cancer Communications 2018年第1期38卷 50-62页

作者： Jin-Cheng Guo Yang Wu Yang Chen Feng Pan Zhi-Yong Wu Jia-Sheng Zhang Jian-Yi Wu Xiu-E Xu Jian-Mei Zhao En-Min Li Yi Zhao Li-Yan Xu Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Chaoshan Area of Guangdong Higher Education Institutes Shantou University Medical CollegeShantouGuangdong 515041P.R.China Department of Biochemistry and Molecular Biology Shantou University Medical CollegeShantouGuangdong 515041P.R.China Departments of Oncology Surgery Shantou Central HospitalAffiliated Shantou Hospital of Sun Yat-Sen UniversityShantouGuangdong 515041P.R.China Institute of Oncologic Pathology Shantou University Medical CollegeShantouGuangdong 515041P.R.China Key Laboratory of Intelligent Information Processing Advanced Computer Research CenterState Key Laboratory of Computer ArchitectureInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190P.R.China

Background:Esophageal squamous cell carcinoma(ESCC)is the predominant subtype of esophageal carcinoma in *** study was to develop a staging model to predict outcomes of patients with ***:Using Cox regression analysis,principal component analysis(PCA),partitioning clustering,Kaplan-Meier analysis,receiver operating characteristic(ROC)curve analysis,and classification and regression tree(CART)analysis,we mined the Gene Expression Omnibus database to determine the expression profiles of genes in 179 patients with ESCC from GSE63624 and GSE63622 ***:Univariate cox regression analysis of the GSE63624 dataset revealed that 2404 protein-coding genes(PCGs)and 635 long non-coding RNAs(lncRNAs)were associated with the survival of patients with *** categorized these PCGs and lncRNAs into three principal components(PCs),which were used to cluster the patients into three *** analysis demonstrated that the predictive ability of PCG-lncRNA PCs when applied to new patients was better than that of the tumor-node-metastasis staging(area under ROC curve[AUC]:0.69 vs.0.65,P<0.05).Accord-ingly,we constructed a molecular disaggregated model comprising one lncRNA and two PCGs,which we desig-nated as the LSB staging model using CART analysis in the GSE63624 *** LSB staging model classified the GSE63622 dataset of patients into three different groups,and its effectiveness was validated by analysis of another cohort of 105 ***:The LSB staging model has clinical significance for the prognosis prediction of patients with ESCC and may serve as a three-gene staging microarray.

关键词： Long non-coding RNA Protein-coding gene Esophageal squamous cell carcinoma Overall survival Staging model Transcriptome

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