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IEEE International Conference on Computational Science and Engineering, CSE

作者： Honglei Zheng Hong Guo College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

In recent years, major universities have attached great importance to digital information construction, but the survey found that there are still areas for improvement in the design of the content blocks that students are most concerned about, and the processing speed of database data needs to be optimized. Campus service system platform based on the big data studies the optimization method, and five optimization ideas are proposed. The first is schema optimization; the second is index optimization; the third is query performance optimization; the fourth is based on server optimization; the fifth is based on operating system optimization. Applying these optimization schemes, it is found that the speed of data processing is obviously improved, and the response time is shortened, which proves that the implementation of the system is feasible and the optimization effect is remarkable.

关键词： Optimization Indexes Big Data Query processing Servers Memory

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Multi-view ontology alignment visualization (MOAV): A human-cognition based ontology alignment visualization technique

Journal of Network Intelligence

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Journal of Network Intelligence 2020年第4期5卷 198-210页

作者： Chen, Jie Zhang, Jie Xue, Xingsi Huang, Yikun Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.33 Xueyuan Road University Town Minhou FuzhouFujian350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Intelligent Information Processing Research Center Fujian University of Technology No.33 Xueyuan Road University Town Minhou FuzhouFujian350118 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology No.1 Jinji Road Guangxi Guilin541004 China Department of Information Technology Concord University College Fujian Normal University No.68 Xueyuan Road University Town Minhou FuzhouFujian350117 China

To further improve the quality of ontology alignment, it is necessary for an ontology matcher to introduce a user’s knowledge into its automatic matching process, which yields the development of interactive ontology matching techniques. Since validating problematic entity correspondences is a difficult cognition task, user interaction based on Ontology Alignment Visualization (OAV) has become the critical component of an interactive ontology matcher, which directly affects the quality of validating result and the efficiency of validating process. The existing OAV tools are mainly developed based on their designers’ subjective feelings and experiences, which do not take into consideration the law of human cognition. To improve the efficiency of the ontology alignment validating process, in this paper, a Multi-view OAV (MOAV) is proposed, which synthetically utilizes the human cognitive theory-information visualization and human-computer inter-action. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s benchmark to test the performance of our proposal, and the experimental results show that MOAV can effectively improve a user’s validating efficiency. © 2020, Taiwan Ubiquitous Information. All rights reserved.

关键词： Visualization

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Removing Rain from Single Image Based on Details Preservation and Background Enhancement

Removing Rain from Single Image Based on Details Preservatio...

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IEEE International Conference on Information Communication and Signal processing (ICICSP)

作者： Jiacheng Liu Shenghua Teng Zuoyong Li College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Processing and Intelligent Control Fujian Provincial Key Laboratory of Information Minjiang University Fuzhou China

Life is often accompanied by the bad weather. In the rainy days, the quality of images and videos acquired will be greatly degraded, affecting human observation and target detection and recognition in computer vision systems. In this paper, a single image rain removal algorithm is proposed based on details preservation and background enhancement. We first decompose the rainy image into low-frequency part and high-frequency part by low pass smoothing filter. Then, edge detection is performed on the low-frequency part to extract a mask, which will be used to capture rain-free image details from the high-frequency part. Next, the rain-free image details are superimposed on the low-frequency part to obtain an image without rains but well preserved details. Finally, the dark channel prior method is utilized to further alleviate the blur due to raining. Experiments on both synthetic and real rainy images demonstrate the effectiveness and efficiency of the proposed method.

关键词： Rain Image edge detection Filtering Videos Filtering algorithms Snow Computer vision

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The Structure Design and Process Analysis for a Metrorological Monitoring Device Based on GIS

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IOP Conference Series: Materials Science and Engineering 2020年第1期774卷

作者： Ying Wang Jiangxi Province Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang 330099 China

A kind of meteorological monitoring device based on geographic information system (GIS) is studied in this paper, which belongs to the field of meteorological monitoring technology. The device include base, turntable, themometer shelter, roof, bracket and processor. Specifically, batteries and turntables motors are fixed on the left side and middle on the top respectively, and motors are fixed on the middle of the bottom. The device adopts a technological design of modularity design to ensure its scientific, rigorous and flexible. Its configuration can not only meet functional requirements of meteorological monitoring and the overall performance, but also its scalability can be achieved by adjusting the hardware configuration or adding a server to achieve a smooth upgrade of the device and expanding the overall performance of the device.

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A Novel Multi-Agent Deep Reinforcement Learning Approach

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Journal of Physics: Conference Series 2021年第1期1757卷

作者： Dong Yin Zhe Zhao Yinglong Dai Han Long College of Intelligence Science and Technology National University of Defense Technology Changsha 410073 China College of Liberal Arts and Sciences National University of Defense Technology Changsha 410073 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha 410081 China

Borrowing the power of deep neural networks, deep reinforcement learning achieved big success in games, and it becomes a popular method to solve the sequential decision-making problems. However, the success is still restricted to single agent training environment. Multi-agent reinforcement learning still is a challenge problem. Although some multi-agent deep reinforcement learning methods have been proposed, they can only perform well when the number of agents is very limited. In this paper, by analyzing the dynamic changing observation space and action space of multi-agent environment, we propose a novel multi-agent deep RL method that compress the joint observation space and action space as the time goes on. The proposed method is potential for a large number of agents cooperative or competitive tasks

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Bilingual Word Embedding with Sentence Combination CNN for 1-to-N Sentence Alignment 20

Bilingual Word Embedding with Sentence Combination CNN for 1...

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Proceedings of the 4th International Conference on Natural Language processing and Information Retrieval

作者： Xinyuan Ren Xiangling Fu Xuesi Zhou Chunsheng Liu Songfeng Gao Lei Peng School of Software Engineering Beijing University of Posts and Telecommunications Beijing China and Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Tsinghua University Hai Dian Beijing China and Multimedia Signal and Intelligent Information Processing Lab Tsinghua University Hai Dian Beijing China BUPT and Huarong Joint lab of Smart Finance Beijing P. R. China and HuaRong RongTong (Beijing) Technology Co. Ltd Beijing P. R. China

ISBN: (纸本)9781450377607

Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly proposed, the other, which are adopted later, is based on the Neural Network method. Presently, under the limitation that the existing mainstream data corpora are mostly in the form of 1-to-1, the alignment models with relatively good performance mainly apply to the cases of 1-to-1 sentence alignment. However, under the circumstance that a sentence contains too much information, 1-to-N sentence alignment can actually have a better effect on sentence translation tasks, compared with the 1-to-1 form, since it is more flexible and can reduce the complexity of the original sentence. As a result, we attempt to exploit neural networks with relatively good performance in the cases of 1-to-1 to fit in the cases of 1-to-N. In this paper, a novel 1-N Bilingual word Embedding with Sentence Combination CNN Improved Framework (1-NBESCC) is proposed in order to align 1-to-N sentences more precisely. Experiments show that our proposed model performs as good as the traditional methods such as BLEUALIGN in 1-to-1 situation, but much better in 1-to-N situation.

关键词： Convolutional Neural Network

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Novel Color Orthogonal Moments-Based Image Representation and Recognition

Research Square

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Research Square 2020年

作者： He, Bing Liu, Jun Yang, Tengfei Xiao, Bin Peng, Yanguo School of Physics and Electrical Engineering Weinan Normal University Weinan714000 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts & Telecommunications Xi’an710121 China School of Computer Science and Technology Weinan Normal University Weinan714000 China School of Cyberspace Security Xi’an University of Posts & Telecommunications Xi’an710121 China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science and Technology Xidian University Xi’an710071 China

Inspired by quaternion algebra and the idea of fractional-order transformation, we propose a new set of quaternion fractional-order generalized Laguerre orthogonal moments (QFr-GLMs) based on fractional-order generalized Laguerre polynomials. Firstly, the proposed QFr-GLMs are directly constructed in Cartesian coordinate space, avoiding the need for conversion between Cartesian and polar coordinates;therefore, they are better image descriptors than circularly orthogonal moments constructed in polar coordinates. Moreover, unlike the latest Zernike moments based on quaternion and fractional-order transformations, which extract only the global features from color images, our proposed QFr-GLMs can extract both the global and local color features. This paper also derives a new set of invariant color-image descriptors by QFr-GLMs, enabling geometric-invariant pattern recognition in color images. Finally, the performances of our proposed QFr-GLMs and moment invariants were evaluated in simulation experiments of correlated color images. Both theoretical analysis and experimental results demonstrate the value of the proposed QFr-GLMs and their geometric invariants in the representation and recognition of color images. © 2020, CC BY.

关键词： Image reconstruction

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Weakly Supervised Object Detection With Segmentation Collaboration

Weakly Supervised Object Detection With Segmentation Collabo...

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International Conference on Computer Vision (ICCV)

作者： Xiaoyan Li Meina Kan Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Weakly supervised object detection aims at learning precise object detectors, given image category labels. In recent prevailing works, this problem is generally formulated as a multiple instance learning module guided by an image classification loss. The object bounding box is assumed to be the one contributing most to the classification among all proposals. However, the region contributing most is also likely to be a crucial part or the supporting context of an object. To obtain a more accurate detector, in this work we propose a novel end-to-end weakly supervised detection approach, where a newly introduced generative adversarial segmentation module interacts with the conventional detection module in a collaborative loop. The collaboration mechanism takes full advantages of the complementary interpretations of the weakly supervised localization task, namely detection and segmentation tasks, forming a more comprehensive solution. Consequently, our method obtains more precise object bounding boxes, rather than parts or irrelevant surroundings. Expectedly, the proposed method achieves an accuracy of 53.7% on the PASCAL VOC 2007 dataset, outperforming the state-of-the-arts and demonstrating its superiority for weakly supervised object detection.

关键词： Proposals Object detection Feature extraction Collaboration Task analysis Detectors Image segmentation

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Relationship between pulmonary nodule malignancy and surrounding pleurae, airways and vessels: a quantitative study using the public LIDC-IDRI dataset

arXiv

引用

arXiv 2021年

作者： Qin, Yulei Gu, Yun Zhang, Hanxiao Yang, Jie Wang, Lihui Wang, Zhexin Yao, Feng Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China CREATIS INSA Lyon CNRS UMR 5220 INSERM U1206 Université de Lyon Villeurbanne69621 France Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang550025 China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai200025 China

Objectives: To investigate whether the pleurae, airways and vessels surrounding a nodule on non-contrast computed tomography (CT) can discriminate benign and malignant pulmonary nodules. Materials and Methods: The LIDC-IDRI dataset, one of the largest publicly available CT database, was exploited for study. A total of 1556 nodules from 694 patients were involved in statistical analysis, where nodules with average scorings 3 were respectively denoted as benign and malignant. Besides, 339 nodules from 113 patients with diagnosis ground-truth were independently evaluated. Computer algorithms were developed to segment pulmonary structures and quantify the distances to pleural surface, airways and vessels, as well as the counting number and normalized volume of airways and vessels near a nodule. Odds ratio (OR) and Chi-square (χ2) testing were performed to assess the correlation between features of surrounding structures and nodule malignancy. A non-parametric receiver operating characteristic (ROC) analysis was conducted in logistic regression to evaluate discrimination ability of each structure. Results: For the benign and malignant groups, the average distances from nodules to pleural surface, airways and vessels are respectively (6.56, 5.19), (37.08, 26.43) and (1.42, 1.07) mm. The correlation between nodules and the counting number of airways and vessels that contact or project towards nodules are respectively (OR=22.96, χ2=105.04) and (OR=7.06, χ2=290.11). The correlation between nodules and the volume of airways and vessels are (OR=9.19, χ2=159.02) and (OR=2.29, χ2=55.89). The areas-under-curves (AUCs) for pleurae, airways and vessels are respectively 0.5202, 0.6943 and 0.6529. Conclusion: Our results show that malignant nodules are often surrounded by more pulmonary structures compared with benign ones, suggesting that features of these structures could be viewed as lung cancer biomarkers. © 2021, CC BY.

关键词： Computerized tomography

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TSW-FD: A Novel Temporal and Spatial Domain Weight Analysis of Feature Difference for Micro-Expression Spotting

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Zhihao Zhang Fan Mo Ke Zhao Tong Chen Xiaolan Fu State Key Laboratory of Brain and Cognitive Science Institute of Psychology Chinese Academy of Sciences Beijing 100101 China Department of Psychology University of Chinese Academy of Sciences Beijing 100049 China Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Southwest University Chongqing 400715 China Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology Chongqing 400715 China

The micro-expression spotting has recently attracted increasing attention from psychology and computer vision community, since embraced in the second facial Micro-Expression Grand Challenge (MEGC 2019). Different from the original feature difference (FD) analysis, in this paper, we proposed a novel temporal and spatial domain weight analysis of feature difference (TSW-FD) to achieve micro-expression spotting. The experimental results showed that TSW-FD improved 17.86% and 24.21% in F1-Score comparing to the FD in CASME II and SMIC-E-HS.

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