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Optical polarization imaging for underwater target detection with non-scatter background

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Journal of Measurement science and Instrumentation 2020年第4期11卷 335-342页

作者： GUAN Jin-ge ZHAO Yong ZHENG Yong-qiu MA Miao SUN Peng XUE Chen-yang School of Information and Communication Engineering North University of ChinaTaiyuan 030051China Beijing Institute of Computer Application Technology China North Industries Group Corporation LimitedBeijing 100089China Key Laboratory of Instrumentation Science and Dynamic Measurement(North University of China) Ministry of EducationTaiyuan 030051China Department of Science Taiyuan Institute of TechnologyTaiyuan 030008China

For conventional optical polarization imaging of underwater target,the polarization degree of backscatter should be pre-measured by averaging the pixel intensities in the no target region of the polarization images,and the polarization property of the target is assumed to be completely *** the scattering background is unseen in the field of view or the target is polarized,conventional method is helpless in detecting the *** improvement is to use lots of co-polarization and cross polarization detection *** propose a polarization subtraction method to estimate depolarization property of the scattering noise and target *** experiment in a quartz cuvette container is performed to demonstrate the effectiveness of the proposed *** results show that the proposed method can work without scattering background reference,and further recover the target along with smooth surface for polarization preserving *** study promotes the development of optical polarization imaging systems in underwater environments.

关键词： polarization imaging underwater optical scattering optical information processing target detection

Predicting protein functions through non-negative matrix factorization regularized by protein-protein interaction network and gene functional information

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Predicting protein functions through non-negative matrix fac...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Peng Lun Li Wei Dai Jielin Du Wei Lan Faculty of Information Engineering and Automation Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China School of Computer Electronic and Information Guangxi University Guangxi University Nanning China

Protein function prediction is necessary for understanding life and is valuable for application on drug design and health care. It is still a big challenge to predict protein function correctly by integrating multi-biological information. In this work, we propose a novel non-Negative Matrix Factorization (NMF) based method regularized by PPI network and GO similarity network, namely PONMF, for protein function prediction, which decomposes the known GO-protein association matrix into two low-rank matrixes for proteins and GO terms. In the course of factorization, PONMF incorporates the label matrix factorization term, an additional network regularization term and a GO similarity regularization term into the objective function. Finally, the potential protein functions are predicted by referring to the product of the two low-rank matrixes. PONMF not only successfully integrates diverse biological information to predict protein functions, but also naturally partitions proteins into different modules and infers functions from the proteins in the same modules. Our methods as well as the other two state-of-the-art methods (UBiRW and NMFGO) are applied to predict functions for protein of S. cerevisiae and H. sapiens. The prediction results show that PONMF outperforms the other two existing methods.

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Achieving trust-based and privacy-preserving customer selection in ubiquitous computing

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arXiv 2019年

作者： Zhang, Chuan Zhu, Liehuang Xu, Chang Sharif, Kashif Liu, Ximeng Du, Xiaojiang Guizani, Mohsen Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Application School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Information Systems Singapore Management University College of Mathematics and Computer Science Fuzhou University Department of Computer and Information Sciences Temple University Philadelphia United States Department of Electrical and Computer Engineering University of Idaho MoscowID United States

The recent proliferation of smart devices has given rise to ubiquitous computing, an emerging computing paradigm which allows anytime & anywhere computing possible. In such a ubiquitous computing environment, customers release different computing or sensing tasks, and people, also known as data processors, participate in these tasks and get paid for providing their idle computing and communication resources. Thus, how to select an appropriate and reliable customer while not disclosing processors' privacy has become an interesting problem. In this article, we present a trust-based and privacy-preserving customer selection scheme in ubiquitous computing, called TPCS, to enable potential processors select the customers with good reputation. The basic concept of TPCS is that each data processor holds a trust value, and the reputation score of the customer is calculated based on processors' trust values and feedbacks via a truth discovery process. To preserve processors' privacy, pseudonyms and Paillier cryptosystem are applied to conceal each processor's real identity. In addition, three authentication protocols are designed to ensure that only the valid data processors (i.e., the processors registering in the system, holding the truthful trust values, and joining the computing tasks) can pass the authentication. A comprehensive security analysis is conducted to prove that our proposed TPCS scheme is secure and can defend against several sophisticated attacks. Moreover, extensive simulations are conducted to demonstrate the correctness and effectiveness of the proposed scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Ubiquitous computing

Micro-supervised disturbance learning: A perspective of representation probability distribution

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arXiv 2020年

作者： Chu, Jielei Liu, Jing Wang, Hongjun Meng, Hua Gong, Zhiguo Li, Tianrui The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China The School of Business Sichuan University Sichuan Chengdu610065 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

The instability is shown in the existing methods of representation learning based on Euclidean distance under a broad set of conditions. Furthermore, the scarcity and high cost of labels prompt us to explore more expressive representation learning methods which depends on the labels as few as possible. To address these issues, the small-perturbation ideology is firstly introduced on the representation learning model based on the representation probability distribution. The positive small-perturbation information (SPI) which only depend on two labels of each cluster is used to stimulate the representation probability distribution and then two variant models are proposed to fine-tune the expected representation distribution of RBM, namely, Micro-supervised Disturbance GRBM (Micro-DGRBM) and Micro-supervised Disturbance RBM (Micro-DRBM) models. The Kullback-Leibler (KL) divergence of SPI is minimized in the same cluster to promote the representation probability distributions to become more similar in Contrastive Divergence (CD) learning. In contrast, the KL divergence of SPI is maximized in the different clusters to enforce the representation probability distributions to become more dissimilar in CD learning. To explore the representation learning capability under the continuous stimulation of the SPI, we present a deep Micro-supervised Disturbance Learning (Micro-DL) framework based on the Micro-DGRBM and Micro-DRBM models and compare it with a similar deep structure which has not any external stimulation. Experimental results demonstrate that the proposed deep Micro-DL architecture shows better performance in comparison to the baseline method, the most related shallow models and deep frameworks for clustering. © 2020, CC0.

关键词： Probability distributions

Gowl: A fast ontology-mediated query answering

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Gowl: A fast ontology-mediated query answering

2018 ISWC Posters and Demonstrations, Industry and Blue Sky Ideas Tracks, ISWC-P and D-Industry-BlueSky 2018

作者： Meng, Chenhong Zhang, Xiaowang Xiao, Guohui Feng, Zhiyong Qi, Guilin School of Computer Science and Technology Tianjin University Tianjin300350 China School of Computer Software Tianjin University Tianjin300350 China Faculty of Computer Science Free University of Bozen-Bolzano BolzanoI-39100 Italy School of Computer Science and Engineering Southeast University Nanjing211189 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China

This poster shows an ontology-mediated query answering system (gOWL) based on pure materialization to avoid query rewriting online. gOWL shows that answering queries over a partial materialization of the chase is complete for a large fragment of practical queries with bounded depth. From a system engineering perspective, the materialization approach allows us to design a modular architecture to integrate off-the-shelf efficient SPARQL query engines. The poster will be based on DBpedia and UOBM datasets. We will compare the performance of gOWL with PAGOdA, Ontop, and Pellet (with speedup up to three orders of magnitude). © 2018 CEUR-WS. All rights reserved.

关键词： Ontology

A Holistic Representation Guided Attention Network for Scene Text Recognition

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arXiv 2019年

作者： Yang, Lu Dang, Fan Wang, Peng Li, Hui Li, Zhen Zhang, Yanning School of Computer Science Northwestern Polytechnical University Xi'an China Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China School of Computer Science University of Adelaide Australia MinSheng FinTech Corp. Ltd. China

Reading irregular scene text of arbitrary shape in natural images is still a challenging problem, despite the progress made recently. Many existing approaches incorporate sophisticated network structures to handle various shapes, use extra annotations for stronger supervision, or employ hard-to-train recurrent neural networks for sequence modeling. In this work, we propose a simple yet strong approach for scene text recognition. With no need to convert input images to sequence representations, we directly connect two-dimensional CNN features to an attention-based sequence decoder which guided by holistic representation. The holistic representation can guide the attentionbased decoder focus on more accurate area. As no recurrent module is adopted, our model can be trained in parallel. It achieves 1:5_to 9:4_acceleration to backward pass and 1:3_to 7:9_acceleration to forward pass, compared with the RNN counterparts. The proposed model is trained with only word-level annotations. With this simple design, our method achieves state-of-the-art or competitive recognition performance on the evaluated regular and irregular scene text benchmark datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

M-geoEduNet: Mobile SDI model for education information infrastructure network

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M-geoEduNet: Mobile SDI model for education information infr...

International Conference on Emerging Trends and Advances in Electrical engineering and Renewable Energy, ETAEERE 2016

作者： Barik, R.K. Lenka, R.K. Samaddar, A.B. Pattnaik, J. Prakash, B. Agarwal, V. School of Computer Application KIIT University Bhubaneswar India Department of Computer Science & Engineering IIIT Bhubaneswar Bhubaneswar India NIT Sikkim Ravangla India CSM Technologies Pvt Ltd Bhubaneswar India

ISBN: (纸本)9789811047640

This paper represents scope for development and implementation of m-GeoEduNet prototype to share educational institute information infrastructure framework in India. It uses modern innovation on cloud computing, mobile technology, API technology, and spatial technology. The established prototype facilitates interface to collect the information related to institutes particularly IITs, NITs, and IIITs in terms of geo-spatial data. It can be attained by interacting between the users and mobile platform to achieve mobile-based Spatial Data Infrastructure (SDI) model. The collected data being documented and uploaded in Cloud database server. The present paper uses Firebase for storing of cloud database services and NOSQL database used for storing geo-spatial data. It has also been underlying third-party API, i.e., GeoFire;for efficiently handling queries on geo-spatial data. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Database systems

SDN controllers: Benchmarking & performance evaluation

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arXiv 2019年

作者： Zhu, Liehuang Karim, Md Monjurul Sharif, Kashif Li, Fan Du, Xiaojiang Guizani, Mohsen Beijing Engineering Research Center for Massive Language Information Processing & Cloud Computing Application School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Computer and Information Sciences Temple University Philadelphia United States Department of Electrical and Computer Engineering University of Idaho MoscowID United States

Software Defined Networks offer flexible and intelligent network operations by splitting a traditional network into a centralized control plane and a programmable data plane. The intelligent control plane is responsible for providing flow paths to switches and optimizes network performance. The controller in the control plane is the fundamental element used for all operations of data plane management. Hence, the performance and capabilities of the controller itself are extremely important. Furthermore, the tools used to benchmark their performance must be accurate and effective in measuring different evaluation parameters. There are dozens of controller proposals available in existing literature. However, there is no quantitative comparative analysis for them. In this article, we present a comprehensive qualitative comparison of different SDN controllers, along with a quantitative analysis of their performance in different network scenarios. More specifically, we categorize and classify 34 controllers based on their capabilities, and present a qualitative comparison of their properties. We also discuss in-depth capabilities of benchmarking tools used for SDN controllers, along with best practices for quantitative controller evaluation. This work uses three benchmarking tools to compare nine controllers against multiple criteria. Finally, we discuss detailed research findings on the performance, benchmarking criteria, and evaluation testbeds for SDN controllers. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images

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arXiv 2022年

作者： Mo, Yuhao Han, Chu Liu, Yu Liu, Min Shi, Zhenwei Lin, Jiatai Zhao, Bingchao Huang, Chunwang Qiu, Bingjiang Cui, Yanfen Wu, Lei Pan, Xipeng Xu, Zeyan Huang, Xiaomei Liu, Zaiyi Wang, Ying Liang, Changhong The School of Medicine South China University of Technology Guangdong Guangzhou510006 China The Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Department of Ultrasound Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The Department of Medical Ultrasonics The First Hospital of Guangzhou Medical University Guangdong Guangzhou China The Department of Ultrasound Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou510060 China

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiationfree and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learningbased computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter-and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and two vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance with the best model interpretability. In the mean-while, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given. © 2022, CC BY.

关键词： Diseases