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2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Wang, Yajie Peng, Qiyang Pei, Zhao Ma, Miao Chen, Yuli Leng, Chengcai Yang, Honghong School of Information Engineering Shaanxi Xueqian Normal University Xi'an China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China School of Computer Science Shaanxi Normal University Xi'an China School of Mathematics Northwest University Xi'an China

ISBN: (纸本)9781728152097

The social groups often represent the groups within which are dense connections and between which are sparse connections. The social groups are actually the clusters. In this paper, we propose a simple but powerful method to combine the content and link information of the social network in the class, and analyze the results of different graph clustering algorithms, our experimental result shows that social groups detection by Affinity Propagation algorithm outperforms than the other clustering algorithms. In addition, we analyze the centrality of the detected communities and find the groups detected can significantly help to improve the quality of the teaching and learning. © 2019 IEEE.

关键词： Clustering algorithms

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Architecture for Disaster Relief Networks in Underground Coal Mines: A Survey

Architecture for Disaster Relief Networks in Underground Coa...

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作者： Jie Gu Pengfei Xue Sheng Lin Guopeng Zhang Teaching and Research Support Center Airforce Logistics University School of Computer Science and Technology China University of Mining and Technology

It is increasingly being recognized that dedicated emergency networks are vital interfaces between rescue teams and trapped-miners in underground *** article proposes a network architecture for rescue applications in post-disaster mines,termed as Post-Disaster Emergency Network(PDEN).First,the special requirements for configuring a PDEN are ***,the emergency data flows transported in a PDEN are classified into uplink flows and downlink *** problem of transmission priority differentiation for uplink flows,and the energy efficiency for downlink transmissions are both ***,some primary numerical results are presented to reveal the effectiveness of the proposed PDEN architecture.

关键词： Emergency communications underground communications device-to-device communications wireless multicast communications

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Decision support system for diagnosing Rheumatic-Musculoskeletal Disease using fuzzy cognitive map technique

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Informatics in Medicine Unlocked 2020年 18卷

作者： Akinnuwesi, Boluwaji A. Adegbite, Blessing A. Adelowo, Femi Ima-Edomwonyi, U. Fashoto, Gbenga Amumeji, Olaseni T. Department of Computer Science Faculty of Science Lagos State University Ojo Lagos State Nigeria ICT Division Planning and Technology Transfer Information Management (PTTIM) Federal Institute of Industrial Research Oshodi (FIIRO) Lagos State Nigeria Consultant Rheumatologist & Head of Rheumatology Unit Department of Medicine Lagos State University Teaching Hospital (LASUTH) Ikeja Lagos State Nigeria Consultant Rheumatologist Department of Medicine Lagos University Teaching Hospital (LUTH) Idi-Araba Lagos State Nigeria Department of Computer Science University of Eswatini Kwaluseni M201 Eswatini Swaziland Department of Mathematical Sciences Faculty of Science Ondo State University of Science & Technology Okitipupa Ondo State Nigeria

Rheumatic-Musculoskeletal Disease (RMD) is a leading cause of disability worldwide. It causes inflammation of the connecting body structures, affects joints, tendons, ligaments, bones, and muscles, and is responsible for thousands of deaths per year worldwide. It is prevalent in Africa. In Nigeria, RMD constitutes 10–15% of rheumatology cases in most clinics, with a ratio of 2.4:1 (Female:Male), killing over 1652 people per year. The similarity of its symptoms with other diseases often cause misdiagnosis at an early stage among infected patients, and existing computational techniques used for diagnosis cannot address the confusability of the symptoms. Hence, there is an inability to determine the causality of symptoms. Moreover, a dearth of Rheumatologists prevents many RMD infected persons from obtaining an early and accurate diagnosis. Therefore, the need arises to develop a decision support system (DSS) for diagnosing RMD, such as by using a fuzzy cognitive map (FCM) technique. Our study focuses on the development and implementation of FCM-based DSS for RMD diagnosis (RMD-FCMDSS). RMD-FCMDSS serves as a software tool complementing physician decision-making. Our evaluation of RMD-FCMDSS suggests that it has an improved diagnostic value as compared to earlier traditional and conventional medical methods. Performance results indicate an 87% accuracy, 90% sensitivity, and 80% specificity. RMD-FCMDSS was tested on limited data due to a dearth of Rheumatologists in Nigeria and a limited population of rheumatic patients interacted with;thus in future work we shall need to increase the test sample. Hybridization of Fuzzy-Logic and Cognitive Mapping techniques helps to guarantee accuracy, specificity, sensitivity, and also speed diagnosis. © 2019

关键词： Causality Confusability Decision support system Fuzzy cognitive map Rheumatic-musculoskeletal disease

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Single image super-resolution via squeeze and excitation network 29

Single image super-resolution via squeeze and excitation net...

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29th British Machine Vision Conference, BMVC 2018

作者： Jiang, Tao Zhang, Yu Wu, Xiaojun Rao, Yuan Zhou, Mingquan Key Laboratory of Modern Teaching Technology Ministry of Education Shaanxi Normal University Xi'an710062 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province SNNU Xi'an710119 China School of Computer Science Shaanxi Normal University Xi'an710119 China School of Software School Xi'an Jiaotong University Xi'an710049 China

Single Image Super-Resolution (SISR) has obtained unprecedented breakthrough with the development of Convolutional Neural Networks (CNN). A majority of these methods try to increase the depth of the network to obtain a larger receptive field. However, we found that blindly stacking feature maps and the simple cascading structure can not achieve a high rate of utilization in super-resolution reconstruction. In this paper, we propose a refined fully convolutional network for Single Image Super Resolution. Based on the assumption that features maps from different depths or the same depth but different channels have different contributions during image reconstruct, we introduce the Squeeze and Excitation (SE) network to evaluate the importance of different feature maps while building the network. Besides, densely connection operation is also conducted in the framework for a better use of the contextual information and feature maps. Extensive experiments demonstrates that the proposed method can enhance the restoration performance and achieve the state-of-the-art results in super-resolution task. © 2018. The copyright of this document resides with its authors.

关键词： Convolution

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ST-ONCODIAG: A semantic rule-base approach to diagnosing breast cancer base on Wisconsin datasets

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Informatics in Medicine Unlocked 2018年 10卷 117-125页

作者： Oyelade, O.N. Obiniyi, A.A. Junaidu, S.B. Adewuyi, S.A. Department of Computer Science Faculty of Physical Science Ahmadu Bello University Zaria Nigeria Department of Radiotheraphy and Oncology Ahmadu Bello University Teaching Hospital Zaria Nigeria

Breast cancer is a major terminal disease that occurs largely among females. This disease stems from abnormal mutations in the genes of normal cells, thereby resulting in development of cancerous cells. Though there have being several research breakthroughs in the field of medicine in taming this disease, however, computer aided diagnosis on the other hand has proven very supportive in the quest. Techniques such as Machine Learning (ML) and Medical Expert Systems (MES) algorithms have added impetus to the use of artificial intelligence in detecting and diagnosing breast cancer. While MES may seem promising in machine based diagnostic systems, their accuracy is often impaired by inefficient medical reasoning algorithms employed. This paper therefore seeks to address the limitation of one such reasoning algorithm known as Select and Test (ST). The approach in this paper is to first create an efficient input mechanism that enables the system to read, filter and clean input from datasets. Secondly, semantic web languages (ontologies and rule languages) were used to create a coordinated rule set and a knowledge representation framework was created to aid the reasoning algorithm. As a result, the reasoning structures of ST were modified to accommodate this enhancement. Thereafter, the input generating mechanism was used to transform instances of the databases of Breast Cancer Wisconsin Data set retrieved from UCI Learning Repository. The generated inputs were passed into the improved ST algorithm to diagnose breast cancer in patients captured in the datasets. Experiments were carried out, and result show that 26.60%, 56.17%, and 54.05% were diagnosed of breast cancer in Wisconsin Breast Cancer Database (WBCD), Wisconsin Diagnostic Breast Cancer (WDBC), and Wisconsin Prognostic Breast Cancer (WPBC) respectively. © 2017

关键词： Attributes Breast cancer Diagnoses Instances Reasoning algorithm Select and test

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Satellite-based InSAR Geodesy and Collocation with GNSS

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Procedia computer science 2024年 239卷 2329-2340页

作者： Juraj Papco Matus Bakon Lukas Kubica Gabika Belicova Branislav Droscak Martin Ferianc Martin Rovnak Antonio M. Ruiz Joaquim J. Sousa Department of Theoretical Geodesy and Geoinformatics Faculty of Civil Engineering Slovak University of Technology in Bratislava Radlinskeho 11 813 62 Bratislava Slovakia insar.sk Ltd Konstantinova 3 080 01 Presov Slovakia Department of Finance Accounting and Mathematical Methods Faculty of Management and Business University of Presov Konstantinova 16 080 01 Presov Slovakia Department of Engineering School of Sciences and Technology Universidade de Trás-os-Montes e Alto Douro Quinta de Prados 5000-801 Vila Real Portugal Geodetic and Cartographic Institute Bratislava Chlumeckeho 4 827 45 Bratislava Slovakia Department of Cartographic Geodetic and Photogrammetry Engineering University of Jaén Campus Las Lagunillas s/n 23071 Jaén Spain Center for Advanced Studies in Earth Sciences Energy and Environment (CEACTEMA) University of Jaén Campus Las Lagunillas s/n 23071 Jaén Spain Research Group RNM-282 Microgeodesia Jaén University of Jaén Campus Las Lagunillas s/n 23071 Jaén Spain Institute for Systems and Computer Engineering Technology and Science INESC-TEC 4200-465 Porto Portugal

Satellite radar interferometry (InSAR) enables large-scale monitoring of displacement with precision akin to ground-based methods. Using measurements from Sentinel-1 satellites, persistent scatterer networks cover areas up to hundreds of kilometers in size. One of the current challenges for the InSAR community is to tie these networks into a current and country-specific global terrestrial reference system through collocation with other geodetic techniques, especially Global Navigation Satellite Systems (GNSS). In this work, an optimal procedure for assessing a suitable collocation properties for InSAR and GNSS techniques is discussed, along with the proposal for the methodology to determine the spatial relationship between the reference points of both techniques. The study analyzes three collocation stations in Slovakia using their radar backscattering characteristics. InSAR achieves an intrinsic accuracy of up to 1 mm but has uncertainties several orders of magnitude larger in 3D space. A collocation of InSAR measurements with GNSS can tie relative InSAR displacement estimates to an absolute reference frame.

关键词： InSAR geodesy collocation GNSS

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Learning to match jobs with resumes from sparse interaction data using multi-view co-teaching network

arXiv

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arXiv 2020年

作者： Bian, Shuqing Chen, Xu Zhao, Wayne Xin Zhou, Kun Hou, Yupeng Song, Yang Zhang, Tao Wen, Ji-Rong School of Information Renmin University of China China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China Department of Computer Science University College London United Kingdom BOSS Zhipin NLP Center China

With the ever-increasing growth of online recruitment data, job-resume matching has become an important task to automatically match jobs with suitable resumes. This task is typically casted as a supervised text matching problem. Supervised learning is powerful when the labeled data is sufficient. However, on online recruitment platforms, job-resume interaction data is sparse and noisy, which affects the performance of job-resume match algorithms. To alleviate these problems, in this paper, we propose a novel multi-view co-teaching network from sparse interaction data for job-resume matching. Our network consists of two major components, namely text-based matching model and relation-based matching model. The two parts capture semantic compatibility in two different views, and complement each other. In order to address the challenges from sparse and noisy data, we design two specific strategies to combine the two components. First, two components share the learned parameters or representations, so that the original representations of each component can be enhanced. More importantly, we adopt a co-teaching mechanism to reduce the influence of noise in training data. The core idea is to let the two components help each other by selecting more reliable training instances. The two strategies focus on representation enhancement and data enhancement, respectively. Compared with pure text-based matching models, the proposed approach is able to learn better data representations from limited or even sparse interaction data, which is more resistible to noise in training data. Experiment results have demonstrated that our model is able to outperform state-of-the-art methods for job-resume matching. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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Groups, platonic solids and bell inequalities

arXiv

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arXiv 2020年

作者： Bolonek-Lasoń, Katarzyna Kosiński, Piotr Department of Statistical Methods Faculty of Economics and Sociology University of Lodz 41/43 Rewolucji 1905 St. Lodz90-214 Poland Department of Computer Science Faculty of Physics and Applied Informatics University of Lodz 149/153 Pomorska St. Lodz90-236 Poland

The construction of Bell inequalities based on Platonic and Archimedean solids (Quantum 4 (2020), 293) is generalized to the case of orbits generated by the action of some finite groups. A number of examples with cons...

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Efficient Small Object Detection with an Improved Region Proposal Networks

Efficient Small Object Detection with an Improved Region Pro...

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International Conference on Electrical Engineering, Control and Robotics

作者： DongWen Ma XiaoJun Wu Honghong Yang Key Laboratory of Modern Teaching Technology Ministry of Education(Shaanxi Normal University) School of Computer Science(Shaanxi Normal University)

ISBN: (纸本)9781510887374

Although the state-of-the-art object detection methods, which depend on region proposal algorithms to hypothesize object locations, have achieved high detection accuracy, it still struggles in small-size object detection. In this paper, we present a novel method with a multi-scale and multi-tasking region proposal method to effectively detect small object. In the proposed method, multi-scale features and high-level features are employed to locate object position and identify object category, respectively. The main contributions of the proposed approach are two-fold:(1) A simpler way is used to improve the accuracy performance of small object detection, instead of the complex image pyramids and the complex combination framework, and make the object detection task more flexible.(2) Based on multi-scale and multi-tasking approaches, object location information in low layers and object semantic information in deep layers are made fully advantage respectively. The experimental results on the PASCAL VOC dataset show that the proposed method achieves the state-of-the art object detection accuracy.

关键词： Efficient Small Object Detection with an Improved Region Proposal Networks Object

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An Improved Noise Elimination Model of EEG Based on Second Order Volterra Filter

An Improved Noise Elimination Model of EEG Based on Second O...

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作者： Xia Wu Yumei Zhang Xiaojun Wu Key Laboratory of Modern Teaching technology Ministry of Education (Shaanxi Normal University) School of Computer Science Shaanxi Normal University

Recently, electroencephalogram(EEG) is widely applied for physiological research and clinical diagnosis of brain diseases. Therefore, how to eliminate noise to gain a pure EEG signal becomes a common difficulty in this field. As a typical method for chaotic time series, Volterra is widely used to study EEG signal. However, the calculation of Volterra coefficients is likely to cause dimensionality disaster. In addition, EEG signals collected in real environment are not easy to extract the prior information, which is related to the quality of the reconstructed phase space. In order to overcome these two problems, we introduce a uniform searching particle swarm optimization(UPSO) algorithm to optimize the coefficients of Volterra then a noise elimination method based on UPSO second order Volterra filter(UPSO-SOVF) can be constructed. The proposed model can improve the quality of phase-space reconstruction by implicating the phase space reconstruction process in the model solving process and then get the embedding dimension and delay time dynamically. In this paper, some experiments are made on different EEG signals and compared with the particle swarm optimization second order Volterra filter(PSO-SOVF). The result shows that the proposed model has a better performance in avoiding the dimensional disaster and can better reflect regularities of the EEG signal series than PSO-SOVF. It can fully meet the requirements for noise elimination of EEG signal.

关键词： Nonlinear model EEG signal Volterra filter UPSO algorithm

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