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TechRxiv 2023年

作者： Wang, Junkai Quan, Lijun Jin, Zhi Wu, Hongjie Ma, Xuhao Wang, Xuejiao Xie, Jingxin Pan, Deng Chen, Taoning Wu, Tingfang Lyu, Qiang School of Computer Science and Technology of Soochow University Jiangsu215006 China Province Key Lab for Information Processing Technologies Soochow University Jiangsu215006 China Collaborative Innovation Center of Novel Software Technology and Industrialization Jiangsu210000 China School of Electronic and Information Engineering Suzhou University of Science and Technology Jiangsu215009 China

This study aims to address the complexity of predicting RNA-small molecule binding sites to explore the potential value in the field of RNA drug targets. To address this challenge, we propose the MultiModRLBP method, which integrates multi-modal features using deep learning algorithms. These features include 3D structural properties at the nucleotide base level, relational graphs based on overall RNA structure, and semantically rich RNA sequence information. Different deep modules are employed to learn high-dimensional different modility of semantic features. RNAbert is fine-tuned to learn sequence information, relational graph convolutional network focuses on capturing various relationships between different nodes in the RNA graph, and CNN directly and indirectly captures local and global 3D structural features. Consequently, MultiModRLBP can better reflect the inherent complex relationships within RNA. Furthermore, we expanded the dataset to include RNA complexes with the same RNA sequences but varying structures, enabling the model to better grasp subtle changes at the structural level. The research results demonstrate that the performance of MultiModRLBP outperforms state of the art methods across multiple classic test sets, especially in predicting binding sites located in pockets or those that are distantly distributed along the sequences. This advancement holds promise in reducing the costs of RNA-targeted drugs development. © 2023, CC BY.

关键词： Binding sites

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On automatic classification for agricultural products web pages in chinese based on LVQ network and ontology

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Journal of Computational information Systems 2008年第3期4卷 971-976页

作者： Wang, Chuanyi Fu, Zetian Zhang, Jian Hu, Liang Wang, Feng Zhang, Xiaoshuan China Agricultural University Beijing 100083 China Key Laboratory for Modern Precision Agriculture System Integration China Agricultural University Beijing 100083 China Beijing Information S and T University Beijing 100000 China Jiangsu Provincial Key Laboratory of Computer Information Processing Technology Suzhou University Suzhou 215000 China

The user often retrieves few anticipated results when searching information in Internet via generalized search engine due that the web pages also exist the synonymous and ambiguous characteristic. How to classify the webpage is the key point to construct vertical search engine, which is known as solution for the problems. This paper proposes a conceptual framework based on Learning Vector Quantization (LVQ) network and Ontology to automatic classification for the webpage from agricultural website. System test shows that the framework can automatically classify Chinese agricultural products web pages and perfectly resolve the synonymous problem.

关键词： Ontology

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Research on Remote Heterogeneous Disaster Recovery technology in Grid Computing Security

Research on Remote Heterogeneous Disaster Recovery Technolog...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Ronghui Wu Ronghui Wu Renfa Li Renfa Li Fei Yu Fei Yu Guangxue Yue Guangxue Yue Jigang Wen Jigang Wen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer & Communication Hunan University Changsha China Provincial Key Laboratory of Computer Information Processing Technology Suzhou University Suzhou Jiangsu Province China College of Informationg Engineering JiaXing University JiaXing China

The tradition standby mechanism can't carry out the dynamic Disaster Recovery of database between distributed isomeric storage systems, accordingly, research the method of allopatric isomeric backup based on grid calculation is an important research subject. In this paper, some technique and strategy of allopatric isomeric backup in grid calculation environment are researched by comparing and analyzing the existing technique of database allopatric backup, a remote-data copy method is presented, a remote-data disaster dump system and a allopatric isomeric data Disaster Recovery tool are designed and implemented based on interlayer.

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Annular pupil in optical scanning holography

Annular pupil in optical scanning holography

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2019 Conference on Digital Holography and Three-Dimensional Imaging, DH 2019

作者： Zhang, Yaping Wang, Rende Wang, Lin Tsang, Peter Poon, T.-C. Key Lab of Laser Information Processing Technology and Application Kunming University of Science and Technology Kunming650500 China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing Jiangsu210094 China Department of Electronic Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States

Optical scanning holography (OSH) is a variant of two-pupil heterodyning image processing. We investigate the use of one pupil as a delta function and the other being an annulus as an extended application of OSH. ... 详细信息

ISBN: (纸本)9781943580590

关键词： Holography

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Cross-domain attribute representation based on convolutional neural network*

arXiv

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arXiv 2018年

作者： Zhang, Guohui Liang, Gaoyuan Su, Fang Qu, Fanxin Wang, Jing-Yan Huawei Technologies Co. Ltd. Shanghai China Jiangsu University of Technology Jiangsu213001 China Shaanxi University of Science & Technology Xi’an China Northwestern Polytechnical University Xi’an China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

In the problem of domain transfer learning, we learn a model for the prediction in a target domain from the data of both some source domains and the target domain, where the target domain is in lack of lab.ls while the source domain has sufficient lab.ls. Besides the instances of the data, recently the attributes of data shared across domains are also explored and proven to be very helpful to leverage the information of different domains. In this paper, we propose a novel learning framework for domain-transfer learning based on both instances and attributes. We proposed to embed the attributes of different domains by a shared convolutional neural network (CNN), learn a domain-independent CNN model to represent the information shared by different domains by matching across domains, and a domain-specific CNN model to represent the information of each domain. The concatenation of the three CNN model outputs is used to predict the class lab.l. An iterative algorithm based on gradient descent method is developed to learn the parameters of the model. The experiments over benchmark datasets show the advantage of the proposed model. Copyright © 2018, The Authors. All rights reserved.

关键词： Gradient methods

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An improved algorithm of pattern matching for information security audit system

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Journal of Computational information Systems 2008年第5期4卷 2221-2228页

作者： Ding, Lei Yu, Fei Xu, Cheng Yue, Guangxue School of Physics Science and Information Engineering Jishou University Jishou 416000 China Provincial Key Lab. of Image Processing and Imagecom Communication Nanjing University of Post and Telecommunications Nanjing 210003 China College of Computer and Communication Hunan University Changsha 410082 China

At present, network information audit system is almost based on text information filtering. Text categorization is the basic technology of text information filtering. Pattern matching algorithms are very important for the rule based information audit system. It directly influences the accuracy and real-time performance of the system. The KMP and BM algorithms are introduced in this paper. Research is carried out to improve the BM algorithm and a better BM algorithm is proposed According to the situation that the system characters group is bigger and that characters of the pat tern are less, the thesis puts forward an improved string-matching algorithm. The results of our experiments prove that it promotes the precision of text categorization. © 2008 Binary information Press.

关键词： Pattern matching

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Research on User Authentication for Grid Computing Security

Research on User Authentication for Grid Computing Security

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Ronghui Wu Ronghui Wu Renfa Li Renfa Li Fei Yu Fei Yu Guangxue Yue Guangxue Yue Cheng Xu Cheng Xu State Key Laboratory of Information Security Graduate School of Chinese Academy of Sciences Beijing China School of Computer & Communication Hunan University Changsha China Provincial Key Laboratory of Computer Information Processing Technology Suzhou University Suzhou Jiangsu Province China College of Informationg Engineering JiaXing University JiaXing China

Recently, Authentication protocol has been recognized as an important factor for grid computing security. This paper described a new simple and efficient Grid authentication system providing user anonymity. Our system is based on hash function, and mobile users only do symmetric encryption and decryption. In our system, it takes only one round of messages exchange between the mobile user and the visited network, and one round of message exchange between the visited network and the corresponding home network. The proposed architecture possesses several desirable emerging properties that enable it to provide an improved level of security for grid computing systems..

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RH-BrainFS: regional heterogeneous multimodal brain networks fusion strategy 23

RH-BrainFS: regional heterogeneous multimodal brain networks...

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Proceedings of the 37th International Conference on Neural information processing Systems

作者： Hongting Ye Yalu Zheng Yueying Li Ke Zhang Youyong Kong Yonggui Yuan Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University and Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Ministry of Education China Department of Psychosomatics and Psychiatry Zhongda Hospital School of Medicine Southeast University

Multimodal fusion has become an important research technique in neuroscience that completes downstream tasks by extracting complementary information from multiple modalities. Existing multimodal research on brain networks mainly focuses on two modalities, structural connectivity (SC) and functional connectivity (FC). Recently, extensive literature has shown that the relationship between SC and FC is complex and not a simple one-to-one mapping. The coupling of structure and function at the regional level is heterogeneous. However, all previous studies have neglected the modal regional heterogeneity between SC and FC and fused their representations via "simple patterns", which are inefficient ways of multimodal fusion and affect the overall performance of the model. In this paper, to alleviate the issue of regional heterogeneity of multimodal brain networks, we propose a novel Regional Heterogeneous multimodal Brain networks Fusion Strategy (RH-BrainFS). Briefly, we introduce a brain subgraph networks module to extract regional characteristics of brain networks, and further use a new transformer-based fusion bottleneck module to alleviate the issue of regional heterogeneity between SC and FC. To the best of our knowledge, this is the first paper to explicitly state the issue of structural-functional modal regional heterogeneity and to propose a solution. Extensive experiments demonstrate that the proposed method outperforms several state-of-the-art methods in a variety of neuroscience tasks. The codes are availab.e at https://***/Yedaxia1/RH-BrainFS.

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Automatic categorization of bioscience literature containing QTL information

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International Journal of Simulation: Systems, Science and technology 2016年第15期17卷 5.1-5.10页

作者： Zhong, Yi Quan, Xin Yu, Xiaohan Gao, Qin Peng, Jing School of Information Engineering Wuhan University of Technology WuhanHubei China Key Lab. of Fiber Optic Sensing Technology Information Processing Ministry of Education Wuhan University of Technology WuhanHubei China School of Computer Science and Technology Wuhan University of Technology WuhanHubei China School of Computer Science and Technology Hubei Polytechnic University HuangshiHubei China

In this paper we introduce text categorization methods to address the classification problem of literature containing Quantitative Trait Locus, QTL information. Our work focused on building an automatic categorization system targeting the QTL information of various species based on Support Vector Machines, SVM. A text representation strategy is proposed combining words and phrases that effectively improve the classification accuracy. Through studying literature containing QTL information and other species-related publications, we determined representative phrases and detected abbreviations in order to form another set of features. Together with the words selected by Chi value, the two sets of features were both used to represent text samples. We employed a portion of particular species’ QTL-related literature data to conduct an experiment regarding the system’s construction, and then tested our system using the data of multiple plants and species. The experiment results indicate that our work may help further research on constructing QTL information databases. © 2016, UK Simulation Society. All rights reserved.

关键词： Support vector machines

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A peer selection prior data scheduling method for peer-to-peer media streaming system

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Beijing Gongye Daxue Xuebao/Journal of Beijing University of technology 2010年第SUPPL. 1期36卷 26-30页

作者： Ji, Qi-Jin Yang, Zhe Zhu, Yan-Qin School of Computer Science and Technology Soochow University Suzhou 215006 Jiangsu China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 Jiangsu China

Data Scheduling in P2P media streaming system considers which peer to select for interesting media data. Since data scheduling decision can only be made in terms of the local information of each peer, the commonly used data block selection prior data scheduling is prone to traffic concentration to minor peers in the system and thus degrades the system throughput greatly. A peer selection prior data scheduling method is proposed in this paper, which can direct traffic to the neighbors evenly. The simulation results show that the proposed method can mitigate the load of source server more than 25% and meanwhile improve the system throughput comparing with the data block selection prior alternatives.

关键词： Peer to peer networks

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