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< Page ,Total 11 >
A Novel Prediction of High-Risk Schizophrenia in Neonates Using Reconstructed Surface and Local Structure in MR Image SCIE
期刊论文 | 2018 , 8 (1) , 9-15 | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
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Abstract :

Schizophrenia is a severe mental disorder associated with delicate aberrant cortical. Currently, many methods which have been proposed for identifying possible abnormalities of surface area or cortical thickness in the high-risk neonates, have made initial progress driven by the techniques for neonates brain Magnetic Resonance (MR) image processing. However, it is in the initial study of neonates high-risk schizophrenia prediction that their methods have few achieve satisfactory accuracy because they only focus on the cortical reconstructed surface characteristic. The local structural texture can contribute the classification mode for prediction, but little attention to cortical structured MR image based feature was paid. To this end, we present a novel framework that jointed surface feature from reconstructed surface with 3D Haar-like features from structured MR image by SCAD SVM. We used this hybrid feature to train the SVM mode for neonates high-risk schizophrenia prediction. In the experiment, our method shows significant performance advance with an improvement of 8.5% in the accuracy rate on a challenging neonates high-risk schizophrenia prediction application.

Keyword :

Hybrid Features Schizophrenia Neonates Prediction SCAD SVM

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GB/T 7714 Fan, Caili , Jiang, Peilin , Chen, Lei et al. A Novel Prediction of High-Risk Schizophrenia in Neonates Using Reconstructed Surface and Local Structure in MR Image [J]. | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS , 2018 , 8 (1) : 9-15 .
MLA Fan, Caili et al. "A Novel Prediction of High-Risk Schizophrenia in Neonates Using Reconstructed Surface and Local Structure in MR Image" . | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 8 . 1 (2018) : 9-15 .
APA Fan, Caili , Jiang, Peilin , Chen, Lei , Wang, Fei , Yang, Haiwei . A Novel Prediction of High-Risk Schizophrenia in Neonates Using Reconstructed Surface and Local Structure in MR Image . | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS , 2018 , 8 (1) , 9-15 .
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SkyShield: A Sketch-Based Defense System Against Application Layer DDoS Attacks EI SCIE Scopus
期刊论文 | 2018 , 13 (3) , 559-573 | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
WoS CC Cited Count: 3
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Abstract :

Application layer distributed denial of service (DDoS) attacks have become a severe threat to the security of web servers. These attacks evade most intrusion prevention systems by sending numerous benign HTTP requests. Since most of these attacks are launched abruptly and severely, a fast intrusion prevention system is desirable to detect and mitigate these attacks as soon as possible. In this paper, we propose an effective defense system, named SkyShield, which leverages the sketch data structure to quickly detect and mitigate application layer DDoS attacks. First, we propose a novel calculation of the divergence between two sketches, which alleviates the impact of network dynamics and improves the detection accuracy. Second, we utilize the abnormal sketch to facilitate the identification of malicious hosts of an ongoing attack. This improves the efficiency of SkyShield by avoiding the reverse calculation of malicious hosts. We have developed a prototype of SkyShield and carefully evaluated its effectiveness using real attack data collected from a large-scale web cluster. The experimental results show that SkyShield can quickly reduce malicious requests, while posing a limited impact on normal users.

Keyword :

Application layer DDoS attacks intrusion prevention system sketch data structure

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GB/T 7714 Wang, Chenxu , Miu, Tony T. N. , Luo, Xiapu et al. SkyShield: A Sketch-Based Defense System Against Application Layer DDoS Attacks [J]. | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2018 , 13 (3) : 559-573 .
MLA Wang, Chenxu et al. "SkyShield: A Sketch-Based Defense System Against Application Layer DDoS Attacks" . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 13 . 3 (2018) : 559-573 .
APA Wang, Chenxu , Miu, Tony T. N. , Luo, Xiapu , Wang, Jinhe . SkyShield: A Sketch-Based Defense System Against Application Layer DDoS Attacks . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2018 , 13 (3) , 559-573 .
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Red Blood Cell Detection by the Improved Two-Layer Watershed Segmentation Method with a Full Convolutional Neural Network SCIE
期刊论文 | 2018 , 8 (1) , 50-54 | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
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Abstract :

Identifying cells in an image (cell segmentation) is essential for quantitative single-cell biology. The development of automatic medical image analysis of erythrocyte morphology has simplified the diagnosis of many diseases and combined a good reliability with high performance. However, in computational molecular biology, the elaboration of reliable and fully automated image analysis techniques for red blood cells imaging is still quite problematic. In this respect, quite lucrative is the watershed transformation concept, which can be utilized in image segmentation to generate partitions of the image corresponding to objects of interest. This approach is used in the current study, which presents a two-layer red blood cells detection framework, including a full convolutional neural network to extract the candidate cell regions with about 97% accuracy rate, and a novel labeled watershed method based on the morphology label and conditional skeleton extraction for improving the overlapped cells' segmentation. It is shown that the proposed method successfully extracts the red blood candidate regions, including single and overlapped ones. A comparative analysis of the proposed detection method and several other general methods was performed. The experimental results obtained indicate that the proposed framework is robust and accurate, with the cell extracting ratio over 87.76% accuracy.

Keyword :

Full Convolutional Neural Network Red Blood Cells Cell Segmentation Conditional Skeleton Watershed Algorithm

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GB/T 7714 Jiang, Peilin , Zhang, Xuetao , Wang, Fei . Red Blood Cell Detection by the Improved Two-Layer Watershed Segmentation Method with a Full Convolutional Neural Network [J]. | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS , 2018 , 8 (1) : 50-54 .
MLA Jiang, Peilin et al. "Red Blood Cell Detection by the Improved Two-Layer Watershed Segmentation Method with a Full Convolutional Neural Network" . | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 8 . 1 (2018) : 50-54 .
APA Jiang, Peilin , Zhang, Xuetao , Wang, Fei . Red Blood Cell Detection by the Improved Two-Layer Watershed Segmentation Method with a Full Convolutional Neural Network . | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS , 2018 , 8 (1) , 50-54 .
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FFI4SoC: a Fine-Grained Fault Injection Framework for Assessing Reliability against Soft Error in SoC EI SCIE Scopus
期刊论文 | 2018 , 34 (1) , 15-25 | JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS
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Recently, system-on-chips (SoCs) are increasingly employed in reliable applications for their high-performance and high-densities. Moreover, the structure shrinking of SoC leads to its proneness to radiation-induced soft errors. This paper presents a fine-grained fault injection framework for SoC (FFI4SoC) to assess the reliability of SoC against soft errors. FFI4SoC facilitates fault injection for SoC by defining the primary components and rules that are required by fine-grained fault injection. Furthermore, based on FFI4SoC, we develop a fine-grained fault injection tool named SSIFFI for bare-metal MicroZed. The design of SSIFFI is presented in order to illustrate the application of FFI4SoC. Finally, SSIFFI is engaged in simulated fault injection experiments to explore the cause of single event functional interrupts (SEFIs) and to validate functional properties of FFI4SoC. The experimental results disclose detailed reasons for SEFI and prove that FFI4SoC can be employed to assess reliability of SoC well with the merit of fine-grained injection.

Keyword :

Fault injection Reliability assessment SEU Systemon Chip (SoC) Fault tolerance assessment

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GB/T 7714 Du, Xiaozhi , Luo, Dongyang , Shi, Kailun et al. FFI4SoC: a Fine-Grained Fault Injection Framework for Assessing Reliability against Soft Error in SoC [J]. | JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS , 2018 , 34 (1) : 15-25 .
MLA Du, Xiaozhi et al. "FFI4SoC: a Fine-Grained Fault Injection Framework for Assessing Reliability against Soft Error in SoC" . | JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 34 . 1 (2018) : 15-25 .
APA Du, Xiaozhi , Luo, Dongyang , Shi, Kailun , He, Chaohui , Liu, Shuhuan . FFI4SoC: a Fine-Grained Fault Injection Framework for Assessing Reliability against Soft Error in SoC . | JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS , 2018 , 34 (1) , 15-25 .
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Capturing Car-Following Behaviors by Deep Learning EI SCIE Scopus
期刊论文 | 2018 , 19 (3) , 910-920 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
WoS CC Cited Count: 3
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Abstract :

In this paper, we propose a deep neural network-based car-following model that has two distinctive properties. First, unlike most existing car-following models that take only the instantaneous velocity, velocity difference, and position difference as inputs, this new model takes the velocities, velocity differences, and position differences that were observed in the last few time intervals as inputs. That is, we assume that drivers' actions are temporally dependent in this model and try to embed prediction capability or memory effect of human drivers in a natural and efficient way. Second, this car-following model is built in a data-driven way, in which we reduce human interference to the minimum degree. Specially, we use recently developing deep neural networks rather than conventional neural networks to establish the model, since deep learning technique provides us more flexibility and accuracy to describe complicated human actions. Tests on empirical trajectory records show that this deep neural network-based car-following model yield significantly higher simulation accuracy than existing car-following models. All these findings provide a novel way to study traffic flow theory and traffic simulations.

Keyword :

recurrent neural network (RNN) deep learning Microscopic car-following model gated recurrent unit (GRU) neural networks

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GB/T 7714 Wang, Xiao , Jiang, Rui , Li, Li et al. Capturing Car-Following Behaviors by Deep Learning [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2018 , 19 (3) : 910-920 .
MLA Wang, Xiao et al. "Capturing Car-Following Behaviors by Deep Learning" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 19 . 3 (2018) : 910-920 .
APA Wang, Xiao , Jiang, Rui , Li, Li , Lin, Yilun , Zheng, Xinhu , Wang, Fei-Yue . Capturing Car-Following Behaviors by Deep Learning . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2018 , 19 (3) , 910-920 .
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Large-scale vocabularies with local graph diffusion and mode seeking EI SCIE Scopus
期刊论文 | 2018 , 63 , 1-8 | SIGNAL PROCESSING-IMAGE COMMUNICATION
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In this work, we propose a large-scale clustering method that captures the intrinsic manifold structure of local features by graph diffusion for image retrieval. The proposed method is a mode seeking like algorithm, and it finds the mode of each data point with the defined stochastic matrix resulted by a same local graph diffusion process. While mode seeking algorithms are normally costly, our method is efficient to generate large-scale vocabularies as it is not iterative, and the major computational steps ere done in parallel. Furthermore, unlike other clustering methods, such as k-means and spectral clustering, the proposed clustering algorithm does not need to empirically appoint the number of clusters beforehand, and its time complexity is independent on the number of clusters. Experimental results on standard image retrieval datasets demonstrate that the proposed method compaies favorably to previous large-scale clustering methods. (C) 2018 Elsevier B.V. All rights reserved.

Keyword :

Image retrieval Large-scale clustering Local graph diffusion Mode-seeking

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GB/T 7714 Pang, Shanmin , Xue, Jianru , Gao, Zhanning et al. Large-scale vocabularies with local graph diffusion and mode seeking [J]. | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2018 , 63 : 1-8 .
MLA Pang, Shanmin et al. "Large-scale vocabularies with local graph diffusion and mode seeking" . | SIGNAL PROCESSING-IMAGE COMMUNICATION 63 (2018) : 1-8 .
APA Pang, Shanmin , Xue, Jianru , Gao, Zhanning , Zheng, Lihong , Zhu, Li . Large-scale vocabularies with local graph diffusion and mode seeking . | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2018 , 63 , 1-8 .
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Towards traffic matrix prediction with LSTM recurrent neural networks EI SCIE Scopus
期刊论文 | 2018 , 54 (9) , 566-567 | ELECTRONICS LETTERS
WoS CC Cited Count: 1
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This Letter investigates traffic matrix (TM) prediction that is widely used in various network management tasks. To fastly and accurately attain timely TM estimation in large-scale networks, the authors propose a deep architecture based on LSTM recurrent neural networks (RNNs) to model the spatio-temporal features of network traffic and then propose a novel TM prediction approach based on deep LSTM RNNs and a linear regression model. By training and validating it on real-world data from Abilene network, the authors show that the proposed TM prediction approach can achieve state-of-the-art TM prediction performance.

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GB/T 7714 Zhao, Jianlong , Qu, Hua , Zhao, Jihong et al. Towards traffic matrix prediction with LSTM recurrent neural networks [J]. | ELECTRONICS LETTERS , 2018 , 54 (9) : 566-567 .
MLA Zhao, Jianlong et al. "Towards traffic matrix prediction with LSTM recurrent neural networks" . | ELECTRONICS LETTERS 54 . 9 (2018) : 566-567 .
APA Zhao, Jianlong , Qu, Hua , Zhao, Jihong , Jiang, Dingchao . Towards traffic matrix prediction with LSTM recurrent neural networks . | ELECTRONICS LETTERS , 2018 , 54 (9) , 566-567 .
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Listwise approach based on the cross-correntropy for learning to rank EI SCIE Scopus
期刊论文 | 2018 , 54 (14) , 878-879 | ELECTRONICS LETTERS
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The problem of learning to rank is addressed and a novel listwise approach by taking document retrieval as an example is proposed. It first introduces the concept of cross-correntropy into learning to rank and then proposes the listwise loss function based on the cross-correntropy between the ranking list given by the label and the one predicted by training model. The use of the cross-correntropy loss leads to the development of the listwise approach called ListCCE, which employs the gradient descent algorithm to train a neural network model. Experimental results tested on publicly available data sets show that the proposed approach performs better than some existing approaches.

Keyword :

cross-correntropy loss training model learning to rank document retrieval neural network model listwise approach learning (artificial intelligence) information retrieval gradient methods entropy listwise loss function ListCCE gradient descent algorithm

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GB/T 7714 Wu, Mintao , Zhu, Jihua , Wang, Jun et al. Listwise approach based on the cross-correntropy for learning to rank [J]. | ELECTRONICS LETTERS , 2018 , 54 (14) : 878-879 .
MLA Wu, Mintao et al. "Listwise approach based on the cross-correntropy for learning to rank" . | ELECTRONICS LETTERS 54 . 14 (2018) : 878-879 .
APA Wu, Mintao , Zhu, Jihua , Wang, Jun , Pang, Shanmin , Li, Yaochen . Listwise approach based on the cross-correntropy for learning to rank . | ELECTRONICS LETTERS , 2018 , 54 (14) , 878-879 .
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A coarse-to-fine scene text detection method based on Skeleton-cut detector and Binary-Tree-Search based rectification EI SCIE Scopus
期刊论文 | 2018 , 112 , 27-33 | PATTERN RECOGNITION LETTERS
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Scene text detection has been a long standing hot and challenging research topic in pattern recognition. In this paper a novel coarse-to-fine text detection method is proposed to solve edge-adhesion problem. In coarse detection stage, Skeleton-cut detector is proposed. At first, 8-Neighborhoods-Search is applied on skeletons map to find the adhesion junctions between text and background skeletons. Then junctions in disordered skeletons are picked out by hysteresis selection and cut to separate text skeletons from background. And the text skeletons are verified through a two-stage classifier to obtain the coarse detection result. In fine detection stage, bounding boxes of all these filtered skeletons are weighted accumulated to obtain the Static Skeleton Response(SSR). Then many finer text lines candidates can be calculated through the gradient operation to the SSR's horizontal projection. And the text rectification based on Binary-Tree-Search is proposed to find a path from text lines' search space to the fine detection result. Experimental results on ICDAR dataset, SVT dataset and MSRA-TD500 dataset demonstrate that our algorithm achieves state of art performance in scene text detection. (C) 2018 Elsevier B.V. All rights reserved.

Keyword :

SSR and BTS Skeleton-cut detector Coarse-to-fine Scene text

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GB/T 7714 He, Xiang , Song, Yonghong , Zhang, Yuanlin . A coarse-to-fine scene text detection method based on Skeleton-cut detector and Binary-Tree-Search based rectification [J]. | PATTERN RECOGNITION LETTERS , 2018 , 112 : 27-33 .
MLA He, Xiang et al. "A coarse-to-fine scene text detection method based on Skeleton-cut detector and Binary-Tree-Search based rectification" . | PATTERN RECOGNITION LETTERS 112 (2018) : 27-33 .
APA He, Xiang , Song, Yonghong , Zhang, Yuanlin . A coarse-to-fine scene text detection method based on Skeleton-cut detector and Binary-Tree-Search based rectification . | PATTERN RECOGNITION LETTERS , 2018 , 112 , 27-33 .
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Multi-view registration of unordered range scans by fast correspondence propagation of multi-scale descriptors SCIE PubMed Scopus
期刊论文 | 2018 , 13 (9) | PLOS ONE
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This paper proposes a global approach for the multi-view registration of unordered range scans. Our method starts with the pair-wise registration, where multi-scale descriptor is selected for feature point and the propagation of feature correspondence is accordingly accelerated. Subsequently, we design an effective rule to judge the reliability of these pair-wise registration results. According to the judgment of reliability, we propose a model fusion method, which can utilize reliable results of pair-wise registration to augment the model shape. Finally, multi-view registration can be achieved by operating the pair-wise registration, reliability judgment, and model fusion alternately. The proposed approach can be applied to scene reconstruction and robot mapping. Experimental results conducted on public datasets show that the proposed approach can automatically achieve multi-view registration of unordered range scans. Compared with other related approaches, the proposed approach has superior performances in accuracy and effectiveness.

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GB/T 7714 Xu, Siyu , Zhu, Jihua , Jiang, Zutao et al. Multi-view registration of unordered range scans by fast correspondence propagation of multi-scale descriptors [J]. | PLOS ONE , 2018 , 13 (9) .
MLA Xu, Siyu et al. "Multi-view registration of unordered range scans by fast correspondence propagation of multi-scale descriptors" . | PLOS ONE 13 . 9 (2018) .
APA Xu, Siyu , Zhu, Jihua , Jiang, Zutao , Lin, Zhiyang , Lu, Jian , Li, Zhongyu . Multi-view registration of unordered range scans by fast correspondence propagation of multi-scale descriptors . | PLOS ONE , 2018 , 13 (9) .
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