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2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014

作者： Lotfi, Ehsan Khosravi, Abbas Akbarzadeh, M.-R. Nahavandi, Saeid Department of Computer Engineering Torbat-e-Jam Branch Islamic Azad University Torbat-e-Jam Iran Center for Intelligence Systems Research Deakin University Geelong3217 Australia Departments of Electrical and Computer Engineering Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad Iran Centre for Intelligent Systems Research Deakin University 75 Pigdons Road Waurn PondsVIC3216 Australia

ISBN: (纸本)9781479938407

Emotional neural network (ENN) is a recently developed methodology that uses simulated emotions aiding its learning process. ENN is motivated by neurophysiological knowledge of the human's emotional brain. In this paper, ENNs are developed and examined for prediction tasks. Genetic algorithm is applied for optimal tuning of crisp numerical parameters of ENN. The performance of the proposed ENN is examined using data sets for a couple of synthetic (with constant and variable noise) and real world (wind farm power generation data) case studies. A traditional artificial neural network (ANN) is also implemented for comparison purposes. Numerical results indicate the superiority of ENN over ANN in terms of accuracy and stability. © 2014 IEEE.

关键词： Wind power

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research on patent recommendation method based on graph neural network 26

Research on patent recommendation method based on graph neur...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Enchao Liu, Weidong Liu, Shi Cao, Yan Institute of Scientific and Technical Information of China China College of Computer Science Inner Mongolia University China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350331684

Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation. © 2023 IEEE.

关键词： Patents and inventions

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Exploring Patent Transformation Event: Forecasting Patent Transfer Time 26

Exploring Patent Transformation Event: Forecasting Patent Tr...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Huo, Jinchen Liu, Weidong Li, Yang Cao, Yan Institute of Scientific and Technical Information of China China College of Computer Science Inner Mongolia University China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350331684

As the largest source of technical information around the world, patents are regarded as an essential crystallization and carrier of knowledge and technological innovation. Patent transformation is conducive not only to enhancing economic efficiency, but also to improving productivity and the rational utilization of resources. There is an imbalance between high patent ownership and low transformation rates. We try to predict the occurrence of transformation events from the patent assignment. However, there are some challenges in predicting patent transformation: (1) how to capture transformation features of patents, especially combined with the transfer time factor. (2) how to predict patent transfer time effectively. To address these challenges, a Patent Transfer Time Forecasting Model (PTTFM) is proposed. The model includes: (1) extraction of time-varying features of patents. (2) the patent transfer time is forecast using a Neural Temporal Point Process. By testing the model on patents under different classifications, the experimental results are obtained to show that the proposed model is applicable to predict the timing of patent assignment within a certain time frame, especially one month. Our work may facilitate patent transformation while interpretability is ensured for transformation events. © 2023 IEEE.

关键词： Forecasting

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Hallucinating facial images and features

Hallucinating facial images and features

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作者： Li, Bo Chang, Hong Shan, Shiguang Chen, Xilin Gao, Wen Harbin Institute of Technology Harbin China Digital Media Research Center Institute of Computing Tech. CAS Beijing China Key Laboratory of Intelligent Information Processing CAS Beijing China Digital Media Institute Peking University Beijing China

ISBN: (纸本)9781424421756

In facial image analysis, image resolution is an important factor which has great influence on the performance of face recognition systems. As for low-resolution face recognition problem, traditional methods usually carry out super-resolution firstly before passing the super-resolved image to a face recognition system. In this paper, we propose a new method which predicts high-resolution images and the corresponding features simultaneously. More specifically, we propose "feature hallucination" to project facial images with low-resolution into an expected feature space. As a result, the proposed method does not require super-resolution as an explicit preprocessing step. In addition, we explore a constrained hallucination that considers the local consistency in the image grid. In our method, we use the index of local visual primitives [5] as features and a block-based histogram distance to measure the similarity for the face recognition. Experimental results on FERET face database verify that the proposed method can improve both visual quality and recognition rate for low-resolution facial images. © 2008 IEEE.

关键词： Image resolution

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Quantitative identification of yellow rust, powdery mildew and fertilizer-water stress in winter wheat using In-Situ hyperspectral data

Quantitative identification of yellow rust, powdery mildew a...

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作者： Guan, Qingsong Huang, Wenjiang Zhao, Jinling Liu, Liangyun Liang, Dong Huang, Linsheng Wang, Li Yang, Guijun Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui China Beijing Agriculture Information Technology Research Center Beijing China

As the progressive effects of global warming, the yield loss caused by diseases and pests are increasing in winter wheat. It is necessary to distinguish different diseases for guiding variable rate spraying in wheat. Nevertheless, it is very difficult to quantitatively identify different diseases and fertilizer-water stress by specific sensitive bands selected from multi spectral data over a large area. Conversely, hyper spectral data contain more information, and provide the potential for quantitative identification of different stresses. This study focused on identification and distinction of yellow rust, powdery mildew and fertilizer-water stress by canopy spectral reflectance. Fifteen commonly used vegetation indices were selected, and independent t-test was done to get sensitivity index for each stress. Finally, a combination index was optimally selected to distinguish the three stresses. The results showed that the integrative index (NDVI-PhRI) combining normalized difference vegetation index (NDVI) and physiological reflectance index (PhRI) could be used to identify powdery mildew and yellow rust (PM-YR). A 2-dimensional spatial coordinate was established based on the NDVI and PhRI derived from hyper spectral data, then the different stress data were displayed in the spatial coordinate and the classification boundary could be used to identify the powdery mildew and yellow rust stress. Similarly, yellow rust and fertilizer-water stress (YR-n0w0) can be distinguished by the combination index (MSR-PhRI) derived from modified simple ratio (MSR) and physiological reflectance index (PhRI);and the combination index (NRI-RVSI) derived from nitrogen reflectance index (NRI) and red-edge vegetation stress index (RVSI) was accurate to identify powdery mildew and fertilizer-water stress (PM-n0w0). For the PM-YR, YR-n0w0 and PM-n0w0 models, their verification accuracies were 83.3%, 88%, 88.75%, and the kappa accuracies were 63.41%, 74.79%, 71.43%, respectively. It indicate

关键词： Physiology

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BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance 26

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Feiyu Zhou, Jian-Tao Guo, Xu Inner Mongolia University College of Computer Science Inner Mongolia Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software China

ISBN: (纸本)9798350331684

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme's safety and do simulation experiments to show that the scheme has practicality. © 2023 IEEE.

关键词： Smart contract

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Blockchain-Based Multi-Cloud Data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Feiyu Zhou, Jian-Tao College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350337020

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads. © 2023 IEEE.

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Iterative methods in large field electron microscope tomography

Iterative methods in large field electron microscope tomogra...

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作者： Wan, Xiaohua Phan, Sébastien Lawrence, Albert Zhang, Fa Han, Renmin Liu, Zhiyong Ellisman, Mark Key Lab of Intelligent Information Processing and Advanced Computing Research Lab Institute of Computing Technology Chinese Academy of Sciences Beijing China National Center for Microscopy and Imaging Research University of California San Diego CA 92093 United States Chinese Academy of Sciences Graduate University Beijing China State Key Lab for Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

Electron tomography (ET) is a powerful technology allowing the three-dimensional (3D) imaging of cellular ultrastructure. These structures are reconstructed from a set of micrographs taken at different sample orientations, the final volume being the solution of a general inverse problem. Two different approaches are used in this context: iterative methods and filtered backprojection. Iterative methods are known to provide high-resolution 3D reconstructions for ET under noisy and incomplete data conditions. However, all previous implementations have been restricted to the straight-line projection models. This is not accurate since electron trajectories in electron microscopes do not follow the straight-line optics assumed for X-rays, and biological samples may warp as a result of being exposed to an electron beam. Compensation for curvilinear trajectories, nonlinear electron optics, and sample warping constitutes a major advance in large-field ET and has made possible resolution down to the molecular level in reconstructions of whole cells. At present these advances are limited to filtered backprojection and have been implemented in the software package TxBR. As the next step in this development, we have modified the ASART method in conjunction with a 3D model. By employing alignment based on general curvilinear trajectories we have been able to show that further improvements can be achieved with iterative methods. We also discuss a unified treatment of the alignment and reconstruction problems within the framework of iterative methods, and the relationship between formulas employed in the update step and cross-validation methods. Copyright © by SIAM. Unauthorized reproduction of this article is prohibited.

关键词： Iterative methods

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False news detection on social media

arXiv

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arXiv 2019年

作者： Cao, Juan Sheng, Qiang Qi, Peng Zhong, Lei Wang, Yanyan Zhang, Xueyao Key Laboratory of Intelligent Information Processing Center for Advanced Computing Research Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China

Social media has become an important information platform where people consume and share news. However, it has also enabled the wide dissemination of false news, i.e., news posts published on social media that are verifiably false, causing significant negative effects on society. To help prevent further propagation of false news on social media, we set up this competition to motivate the development of automated real-time false news detection approaches. Specifically, this competition includes three subtasks: false-news text detection, false-news image detection, and falsenews multi-modal detection, which aims to motivate participants to further explore the efficiency of multiple modalities in detecting false news and effective fusion approaches of multi-modal contents. To better support this competition, we also construct and release a multi-modal data repository about False News on Weibo Social platform(MCG-FNeWS) to help evaluate the performance of different approaches from participants. Copyright © 2019, The Authors. All rights reserved.

关键词： Social networking (online)

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Enhanced Second-Harmonic Generation Driven by Dual-Quasi Bound States in the Continuum

Enhanced Second-Harmonic Generation Driven by Dual-Quasi Bou...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Liu, Yuhang Ren, Xingang Niu, Kaikun Huang, Zhixiang School of Electronic and Information Engineer Anhui University Hefei230601 China Engineering Technology Research Center of Specific Power Devices with Novel Semiconductor Materials Anhui Province Chizhou247100 China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781733509657

Bound state in the continuum (BIC) is a dark mode with infinitely high quality factors(Q-factors), it has recently attracted considerable research attention in photonics. Here, we present a metasurface that can support two BICs, which consists of AlGaAs. By breaking the structural symmetry, the two BICs are transformed into quasi-bic modes because they are not protected by the symmetry. Furthermore, it is found that the two quasi-BICs resonate near the fundamental and the second harmonic respectively, which can be used to enhance efficiency of second harmonic generation. By exploiting the high Q characteristics of this structure at both frequencies, we can obtain a second harmonic generation efficiency of nearly 10-3 with 1GW/cm2of pump intensity. Our proposed metasurface will offer good prospects for nonlinear operation. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Harmonic generation

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