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1st Workshop on Mobile Data, MobiData 2016, co-located with MobiSys 2016

作者： Qi, Ling Qiao, Yuanyuan Abdesslem, Fehmi Ben Ma, Zhanyu Yang, Jie Center for Data Science Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Network BUPT Beijing China Decisions Networks and Analytics Laboratory SICS Stockholm Sweden Center for Data Science Pattern Recognition and Intelligent System Lab BUPT Beijing China

ISBN: (纸本)9781450343275

Cellular towers capture logs of mobile subscribers whenever their devices connect to the network. When the logs show data traffic at a cell tower generated by a device, it reveals that this device is close to the tower. The logs can then be used to trace the locations of mobile subscribers for different applications, such as studying customer behaviour, improving location-based services, or helping urban planning. However, the logs often suffer from an oscillation phenomenon. Oscillations may happen when a device, even when not moving, does not only connect to the nearest cell tower, but is instead unpredictably switching between multiple cell towers because of random noise, load balancing, or simply dynamic changes in signal strength. Detecting and removing oscillations are a challenge when analyzing location data collected from the cellular network. In this paper, we propose an algorithm called SOL (Stable, Oscillation, Leap periods) aimed at discovering and reducing oscillations in the collected logs. We apply our algorithm on real datasets which contain about 18.9 TB of traffic logs generated by more than 3 million mobile subscribers covering about 21000 cell towers and collected during 27 days from both GSM and UMTS networks in northern China. Experimental results demonstrate the ability and effectiveness of SOL to reduce oscillations in cellular network logs. © 2016 ACM.

关键词： Location based services

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Fast KNN search for big data with set compression tree and best bin first 2

Fast KNN search for big data with set compression tree and b...

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2nd International Conference on Cloud Computing and Internet of Things, CCIOT 2016

作者： Chen, Zhenjie Yan, Jingqi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of Ministry of Education for System Control and Information Processing China Shanghai China

ISBN: (纸本)9781467398213

This paper proposes k nearest neighbors (kNN) search based on set compression tree (SCT) and best bin first (BBF) to deal with the problem for big data. The large compression rate by set compression tree is achieved by compressing the set of descriptors jointly instead of compressing on a per-descript or basis. So set compression tree has a good performance in kNN search at a low bit rate. At the same time, the best bin first (BBF) is a very efficient algorithm to find the approximately kNN from a large number of high dimensional feature descriptors. SCT-BBF is a novel exploration and it improves search performance in three aspects: First, SCT-BBF requires less memory footprint, which is important in big data age. Second, it increases accuracy compared traditional method like KD-Tree and original SCT. SCT-BBF can be used with other data processing methods like PCA and SIFT to perform better. Third, this paper adopts finer search to increase accuracy at a slight loss of speed. And it can extend to big data easily. © 2016 IEEE.

关键词： Big data

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Analysis of key Factors in Heat Demand Prediction with Neural Networks

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Energy Procedia 2017年 105卷 2965-2970页

作者： Jiyang Xie Hailong Li Zhanyu Ma Qie Sun Fredrik Wallin Zhongwei Si Jun Guo Pattern Recognition and Intelligent Systems Lab. Beijing University of Posts and Telecommunications China School of Business Society and Engineering Mälardalen University Sweden Institute of Thermal Science and Technology Shandong University China Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications China

The development of heat metering has promoted the development of statistic models for the prediction of heat demand, due to the large amount of available data, or big data. Weather data have been commonly used as input in such statistic models. In order to understand the impacts of direct solar radiance and wind speed on the model performance comprehensively, a model based on Elman neural networks (ENN) was adopted, of which the results can help heat producers to optimize their production and thus mitigate costs. Compared with the measured heat demand, the introduction of wind speed and direct solar radiation has opposite impacts on the performance of ENN and the inclusion of wind speed can improve the prediction accuracy of ENN. However, ENN cannot benefit from the introduction of both wind speed and direct solar radiation simultaneously.

关键词： District heating data processing prediction heat demand CHP plant

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Image spam classification based on convolutional neural network

Image spam classification based on convolutional neural netw...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Er-Xin Shang Hong-Gang Zhang Pattern Recognition and Intelligent System Laboratory Beijing University of Post and Telecommunication

ISBN: (纸本)9781509003914

Image classification is a fundamental problem in computer vision and pattern recognition. Feature extraction is often regarded as the key for classifying images. Traditional ways rely on handcrafted features heavily, such as SIFT and BoW. In this paper, we concentrate on recognizing some specific categories of images (e.g. adult content and political images) in Email. And most importantly we propose a novel architecture of Convolutional Neural Network (CNN) to apply on image classification. In particular, most of the previous methods often use softmax activation for prediction, while instead of utilizing a softmax layer, we present a linear support vector machine (SVM) to be a part of the learning model, minimizing a margin-based loss to obtain a lower level feature representation. Accordingly, we propose a new dataset which contains 7 categories and over 52934 images totally. Experimental results on this novel dataset validate the effectiveness of our method.

关键词： Support vector machines Feature extraction Electronic mail Databases Training Fasteners Cybernetics

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Instance-level coupled subspace learning for fine-grained sketch-based image retrieval 14

Instance-level coupled subspace learning for fine-grained sk...

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Computer Vision - ECCV 2016 Workshops, Proceedings

作者： Xu, Peng Yin, Qiyue Qi, Yonggang Song, Yi-Zhe Ma, Zhanyu Wang, Liang Guo, Jun Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China SketchX Lab School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

ISBN: (纸本)9783319466033

Fine-grained sketch-based image retrieval (FG-SBIR) is a newly emerged topic in computer vision. The problem is challenging because in addition to bridging the sketch-photo domain gap, it also asks for instance-level discrimination within object categories. Most prior approaches focused on feature engineering and fine-grained ranking, yet neglected an important and central problem: how to establish a finegrained cross-domain feature space to conduct retrieval. In this paper, for the first time we formulate a cross-domain framework specifically designed for the task of FG-SBIR that simultaneously conducts instancelevel retrieval and attribute prediction. Different to conventional phototext cross-domain frameworks that performs transfer on category-level data, our joint multi-view space uniquely learns from the instance-level pair-wise annotations of sketch and photo. More specifically, we propose a joint view selection and attribute subspace learning algorithm to learn domain projection matrices for photo and sketch, respectively. It follows that visual attributes can be extracted from such matrices through projection to build a coupled semantic space to conduct retrieval. Experimental results on two recently released fine-grained photo-sketch datasets show that the proposed method is able to perform at a level close to those of deep models, while removing the need for extensive manual annotations. © Springer International Publishing Switzerland 2016.

关键词： Learning algorithms

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Nonconvex penalties with analytical solutions for one-bit compressive sensing

arXiv

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arXiv 2017年

作者： Huang, Xiaolin Yan, Ming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing Shanghai200240 China Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East LansingMI48824 United States

One-bit measurements widely exist in the real world and can be used to recover sparse signals. This task is known as one-bit compressive sensing (1bit-CS). In this paper, we propose novel algorithms based on both convex and nonconvex sparsity-inducing penalties for robust 1bit-CS. We consider the dual problem, which has only one variable and provides a sufficient condition to verify whether a solution is globally optimal or not. For positive homogeneous penalties, a globally optimal solution can be obtained in two steps: a proximal operator and a normalization step. For other penalties, we solve the dual problem, and it needs to evaluate the proximal operators for many times. Then we provide fast algorithms for finding analytical solutions for three penalties: minimax concave penalty (MCP), 0norm, and sorted 1penalty. Specifically, our algorithm is more than 200 times faster than the existing algorithm for MCP. Its efficiency is comparable to the algorithm for the 1penalty in time, while its performance is much better than 1. Among these penalties, sorted 1is most robust to noise in different settings. Copyright © 2017, The Authors. All rights reserved.

关键词： Compressed sensing

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Correction to: Internet gaming disorder: deficits in functional and structural connectivity in the ventral tegmental area-Accumbens pathway

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Brain imaging and behavior 2019年第4期13卷 1182页

作者： Ruonan Wang Min Li Meng Zhao Dahua Yu Yu Hu Corinde E Wiers Gene-Jack Wang Nora D Volkow Kai Yuan School of Life Science and Technology Xidian University Xi'an Shaanxi 710071 People's Republic of China. Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Xi'an People's Republic of China. Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Baotou Inner Mongolia 014010 People's Republic of China. National Institute on Alcoholism and Alcohol Abuse National Institutes of Health Bethesda MD 20892 USA. National Institute on Drug Abuse National Institutes of Health Bethesda MD 20892 USA. School of Life Science and Technology Xidian University Xi'an Shaanxi 710071 People's Republic of China. kyuan@***. Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Xi'an People's Republic of China. kyuan@***. Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Baotou Inner Mongolia 014010 People's Republic of China. kyuan@***. National Institute on Alcoholism and Alcohol Abuse National Institutes of Health Bethesda MD 20892 USA. kyuan@***. Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University Guilin People's Republic of China. kyuan@***.

The original version of this article contained mistakes, and the authors would like to correct them. The correct details are given below.

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An Image Segmentation Algorithm Based on Gray Bit Plane for Flame

An Image Segmentation Algorithm Based on Gray Bit Plane for ...

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2016 4th International Conference on Electrical&Electronics Engineering and Computer Science(ICEEECS 2016)

作者： Xing Deng Jinlan Li Fujian Feng Lin Wang Zhongming Yu School of physics and electronic science Guizhou Normal University Guizhou provincial key laboratory of pattern recognition and intelligent system Liupanshui Normal University

ISBN: (纸本)9781510835429

Flame region judgment is an important part of fire detection system;meanwhile the flame of intelligent monitoring system also has important practical significance to national fire control safety. Therefore, this article puts forward the flame image segmentation method based on gray level bit plane. Firstly, by studying the distribution of flame image in OHTA color space, we found that it can accurately segment the flame area by using the method of the gray level bit plane for component image in OHTA color space. The experiments results show that the algorithm in different scenarios for segmenting flame has a good effectiveness and robustness.

关键词： Fire detect OHTA color space gray bit plane image segmentation

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A MIL-based interactive approach for hotspot segmentation from bone scintigraphy

A MIL-based interactive approach for hotspot segmentation fr...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shijie Geng Jingyang Ma Xiaoguang Niu Shaoyong Jia Yu Qiao Jie Yang Institute of Image Processing and Pattern Recognition The Key Laboratory of System Control and Information Processing Ministry of Education China

ISBN: (纸本)9781479999897

Bone scintigraphy is widely used to diagnose bone diseases. Accurate hotspot segmentation is a critical task for tumor metastasis diagnosis. In this paper, we propose an interactive approach to detect and extract hotspots in thoracic region based on a new multiple instance learning (MIL) method called EM-MILBoost. We convert the segmentation problem to a multiple instance learning task by constructing positive and negative bags according to the input bounding box. In order to be robust against noisy input, we train a region-level hotspot classifier with EM-MILBoost and develop several segmentation strategies based on it. The experimental results demonstrate that our method outperforms other methods and is robust against various noisy input.

关键词： Image segmentation Noise measurement Bones Training Indexes Belts Standards

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MRI denoising using randomized version of nonlocal means method

MRI denoising using randomized version of nonlocal means met...

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2016 International Conference on Image Processing, Computer Vision, and pattern recognition, IPCV 2016

作者： Hu, Jinrong He, Jia Fu, Ying Wu, Xi Zhou, Jiliu School of Computer and Soft Engineering Xihua University Chengdu610039 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Chengdu University Chengdu610106 China School of Computer Science Chengdu University of Information Technology Chengdu610225 China

ISBN: (纸本)1601324421

- Non-local mean (NLM) algorithm has been implemented effectively in MRI denoising and is always limited by its computational complexity. To reduce the computational burden of NLM in 3D MRI dataset, in this paper, we used a randomized version of NLM algorithm to remove the noise in MRI data. The random NLM algorithm seeds up the classical NLM by computing a small subset of image patch distances, which are randomly select according to the uniform sampling pattern. Numerical experiments demonstrate that the random NLM can achieve a competitive denoising result at a low sampling rate (0.05) in 3D MRI dataset while reducing the runtime dramatically. © CSREA Press.

关键词： Magnetic resonance imaging

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