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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： ShaoQin Ma Hong Zhang Hubei Key Laboratory of intelligent information processing and real time industrial systems Wuhan University of Science and Technology Wuhan China

With the advent of the era of big data, all kinds of data are fused together which form a new modality-multimedia data, how to find information in multimedia data become a key problem, so cross-media retrieval has drawn great attention in recent year. Precisely and effectively measuring the similarity between different modalities of data is a key step of cross-media retrieval. The traditional methods usually use Cosine distance or Euclidean distance, which is difficult to reflect the real similarity between different modalities of data. For address this problem, in this paper, we use a novel approach to replace the above two methods, called efficient manifold ranking (EMR), which is able to better reflect the similarity between different modalities of data. Experiments on Wikipedia dataset and the challenging XMedia dataset which includes 5 media types show the effectiveness of the EMR algorithm, as compared with the 4 state-of the-art methods.

关键词： Media Manifolds Correlation Encyclopedias Electronic publishing Internet

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Cross — Media retrieval via CCA — BP neural network

Cross — Media retrieval via CCA — BP neural network

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： LiangMeng Xia Hong Zhang Hubei Key Laboratory of intelligent information processing and real time industrial systems Wuhan University of Science and Technology Wuhan China

With the rapid growth of multimedia content on the Internet, multimedia retrieval has been extensively studied for decades. Most of the existing methods cast low-level features of cross-media data onto a unified feature space. However, some of these feature spaces usually do not have explicit semantics. Some methods, even if they take semantic meaning into account, do not dig the semantic information of data well. By considering the above issue, a new approach to cross-media retrieval via Canonical Correlation Analysis-Back Propagation Neural Networks(CCA-BPNN) is proposed in this paper which is able to explore jointly the correlation and semantic information. The experimental results on two datasets show the effectiveness of our proposed approach, compared with state-of-the-art methods.

关键词： Semantics Correlation Media Internet Neural networks Encyclopedias

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Spatial filtering for brain computer interfaces: A comparison between the common spatial pattern and its variant

arXiv

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arXiv 2018年

作者： He, He Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China

The electroencephalogram (EEG) is the most popular form of input for brain computer interfaces (BCIs). However, it can be easily contaminated by various artifacts and noise, e.g., eye blink, muscle activities, powerline noise, etc. Therefore, the EEG signals are often filtered both spatially and temporally to increase the signal-to-noise ratio before they are fed into a machine learning algorithm for recognition. This paper considers spatial filtering, particularly, the common spatial pattern (CSP) filters for EEG classification. In binary classification, CSP seeks a set of filters to maximize the variance for one class while minimizing it for the other. We first introduce the traditional solution, and then a new solution based on a slightly different objective function. We performed comprehensive experiments on motor imagery to compare the two approaches, and found that generally the traditional CSP solution still gives better results. We also showed that adding regularization to the covariance matrices can improve the final classification performance, no matter which objective function is used. Copyright © 2018, The Authors. All rights reserved.

关键词： Brain computer interface

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Separable reversible data hiding and encryption for HEVC video

Separable reversible data hiding and encryption for HEVC vid...

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作者： Long, Min Peng, Fei Li, Han-yun College of Computer and Communication Engineering Changsha University of Science and Technology ChangshaHunan China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology ChangshaHunan410114 China College of Computer Science and Electronic Engineering Hunan University ChangshaHunan China

A separable reversible data hiding and encryption for high efficiency video coding (HEVC) video is proposed in this paper. In the encoding phase, one can encrypt the signs and amplitudes of motion vector differences and the signs of residual coefficients with an encryption key by using RC4, while another can hide data into nonzero AC residual coefficients with a hiding key. For the decoding phase, one can decrypt the HEVC video and obtain a HEVC video similar to the original one if he only has the encryption key;he can extract the hiding data, but he does not know the HEVC video content if he only has the hiding key;he can extract the hiding data and recover the original HEVC video if he has both the encryption key and hiding key. Experimental results and analysis show that the proposed scheme can obtain a good security performance and keep the HEVC video stream format compliant. © 2017, Springer-Verlag GmbH Germany.

关键词： Steganography

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DABC-Net for robust pneumonia segmentation and prediction of COVID-19 progression on chest CT scans

Research Square

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Research Square 2020年

作者： Zhang, Xiao-Yong Yu, Ziqi Han, Xiaoyang Zhao, Botao Zhuo, Yaoyao Ren, Yan Xue, Xiangyang Lamm, Lorenz Feng, Jianfeng Marr, Carsten Shan, Fei Peng, Tingying Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Shanghai200433 China Department of Radiology Shanghai Public Health Clinical Center Fudan University Shanghai201508 China Department of Radiology Huashan Hospital Fudan University Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Institute of Computational Biology Helmholtz Zentrum München Ingolstädter Landstraße 1 D-85764 Germany Helmholtz AI Helmholtz Zentrum München Ingolstädter Landstraße 1 NeuherbergD-85764 Germany

Currently, reliable, robust and ready-to-use CT-based tools for prediction of COVID-19 progression are still lacking. To address this problem, we present DABC-Net, a novel deep learning (DL) tool that combines a 2D U-net for intra-slice spatial information processing, and a recurrent LSTM network to leverage inter-slice context, for automatic volumetric segmentation of lung and pneumonia lesions. We evaluate DABC-Net on more than 10,000 radiologists-labeled CT slices from four different cohorts. Compared to state-of-the-art segmentation tools, DABC-Net is much faster, more robust, and able to estimate segmentation uncertainty. Based only on the first two CT scans within 3 days after admission from 656 longitudinal CT scans, the AUC of our DBAC-Net for disease progression prediction reaches 93%. We release our tool as a GUI for patient-specific prediction of pneumonia progression, to provide clinicians with additional assistance to triage patients at early days after the diagnosis and to optimize the assignment of limited medical resources, which is of particular importance in current critical COVID-19 pandemic. © 2020, CC BY.

关键词： COVID-19

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Unsupervised Ensemble Learning for Class Imbalance Problems

Unsupervised Ensemble Learning for Class Imbalance Problems

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Chinese Automation Congress

作者： Zihan Liu Dongrui Wu Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China

Ensemble learning, which aggregates multiple base (weak) learners to obtain a strong learner, is an effective approach for improving the generalization performance of a machine learning model. Several completely unsupervised ensemble learning approaches have been proposed in the literature for binary classification. However, most of them only considered the case that the two classes are balanced, and hence their performances deteriorate when there is significant class imbalance, which often happens in practice. This paper proposes a spectral meta-learner for class imbalance (SMLCI) approach to explicitly consider the class imbalance. Experiments on 12 UCI datasets from various domains verified that SMLCI significantly outperformed the individual base classifiers, and also five existing unsupervised ensemble learning approaches, when the balanced classification accuracy is used as the performance measure.

关键词： Ensemble learning Unsupervised learning Class imbalance Binary classification

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Optimal protein structure extraction with clustering algorithm

Optimal protein structure extraction with clustering algorit...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Jiafu Zhao Xiaolong Zhang Min Cao Hubei Hubei Key Laboratory of Intelligent Information Wuhan University of Science and Technology Wuhan China Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China. 430065

In this paper, we use K-means++ and AP algorithm to cluster the five protein similarity measures of RMSD, TM, MaxSub, GDT-TS and GDT-HA. As for the selection of the number of clusters, using the measures of Scikit-learn to value the cluster result to attain an optimal number of clusters. Also, we optimize the AP algorithm through the change of the preference value, whose value changes from the general mean value, max value, min value to mean value of 4 neighbour at the corresponding position. Moreover, we propose a cluster center selection algorithm based on mean distance from data points to cluster center and number of data points in a cluster, which could automatically delete exception value, thus improve the accuracy of selection cluster center. After the cluster and selection of cluster center, we get better similarity between protein structure and natural protein compare to the earlier results, which means a lot to the protein structure prediction.

关键词： Proteins Clustering algorithms Predictive models Amino acids Indexes Prediction algorithms Bioinformatics

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Towards Privacy-preserving Incentive for Mobile Crowdsensing Under An Untrusted Platform

Towards Privacy-preserving Incentive for Mobile Crowdsensing...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Zhibo Wang Jingxin Li Jiahui Hu Ju Ren Zhetao Li Yanjun Li State Key Laboratory for Novel Software Technology Nanjing University P. R. China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University P. R. China School of Information Science and Engineering Central South University P. R. China Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education College of Information Engineering Xiangtan University P. R. China School of Computer Science and Technology Zhejiang University of Technology P. R. China

Reverse auction-based incentive mechanisms have been commonly proposed to stimulate mobile users to participate in crowdsensing, where users submit bids to the platform to compete for tasks. Recent works pointed out that bid is a private information which can reveal sensitive information of users (e.g., location privacy), and proposed bid-preserving mechanisms with differential privacy against inference attack. However, all these mechanisms rely on a trusted platform, and would fail in bid protection completely when the platform is untrusted (e.g., honest-but-curious). In this paper, we focus on the bid protection problem in mobile crowdsensing with an untrusted platform, and propose a novel privacy-preserving incentive mechanism to protect users' true bids against the honest-but-curious platform while minimizing the social cost of winner selection. To this end, instead of uploading the true bid to the platform, a differentially private bid obfuscation function is designed with the exponential mechanism, which helps each user to obfuscate bids locally and submit obfuscated task-bid pairs to the platform. The winner selection problem with the obfuscated task-bid pairs is formulated as an integer linear programming problem and proved to be NP-hard. We consider the optimization problem at two different scenarios, and propose a solution based on Hungarian method for single measurement and a greedy solution for multiple measurements, respectively. The proposed incentive mechanism is proved to satisfy ε-differential privacy, individual rationality and γ-truthfulness. The extensive experiments on a real-world data set demonstrate the effectiveness of the proposed mechanism against the untrusted platform.

关键词： Task analysis Privacy Differential privacy Sensors Mobile handsets Cryptography Integer linear programming

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GLA: Global-Local Attention for Image Description

GLA: Global-Local Attention for Image Description

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作者： Li, Linghui Tang, Sheng Zhang, Yongdong Deng, Lixi Tian, Qi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of the ChineseAcademy of Sciences BeijingTX100049 China Department of Computer Science University of Texas at San Antonio San Antonio78249-1604 United States

In recent years, the task of automatically generating image description has attracted a lot of attention in the field of artificial intelligence. Benefitting from the development of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), many approaches based on the CNN-RNN framework have been proposed to solve this task and achieved remarkable process. However, two problems remain to be tackled in which the most existing methods use only the image-level representation. One problem is object missing, in which some important objects may be missing when generating the image description and the other is misprediction, when one object may be recognized in a wrong category. In this paper, to address these two problems, we propose a new method called global-local attention (GLA) for generating image description. The proposed GLA model utilizes an attention mechanism to integrate object-level features with image-level feature. Through this manner, our model can selectively pay attention to objects and context information concurrently. Therefore, our proposed GLA method can generate more relevant image description sentences and achieve the state-of-The-Art performance on the well-known Microsoft COCO caption dataset with several popular evaluation metrics-CIDEr, METEOR, ROUGE-L and BLEU-1, 2, 3, 4. © 1999-2012 IEEE.

关键词： Recurrent neural networks

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The evaluation of urban human settlements based on fuzzy matter-element model: Taking 11 prefecture-level cities in Jiangxi Province as examples 1

The evaluation of urban human settlements based on fuzzy mat...

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1st International Conference on Physics, Mathematics and Statistics, ICPMS 2018

作者： Liu, Zuhan Wang, Lili Zhang, Bin Yu, Xiang Zhou, Xideng Jiangxi Province Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang Jiangxi China School of Science Nanchang Institute of Technology Nanchang Jiangxi China Land and Resources College China West Normal University Nanchong Sichuan China Business Administration College Nanchang Institute of Technology Nanchang Jiangxi China

A case study is presented in which the fuzzy matter-element model (FMEM) is used to assess the evaluation of urban human settlements (UHS) of the basin of the 11 prefecture-level cities in Jiangxi Province. The regional samples and evaluation standards are all treated as the matter-element, and the compound fuzzy matter-element is constructed with the evaluation indicators and the corresponding fuzzy values of the indicators. Then the Euclid approach degree of the evaluated fuzzy matter-element to the standard fuzzy matter-element is calculated, thus attaining the grade classification of human settlements of the evaluated samples. The study results show that the fuzzy matter-element method is practical for comprehensive evaluation of regional human settlements. © Published under licence by IOP Publishing Ltd.

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