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Construction and optimization of a file distribution model for all-to-all comparison of big dataset

Ingenierie des Systemes d'Information 2019年第3期24卷 337-342页

作者： Li, Leixiao Gao, Jing Wang, Hui Deng, Dan Lin, Hao College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China Inner Mongolia Autonomous Region Key Laboratory of big data research and application for agriculture and animal husbandry Hohhot010018 China

This paper probes into the all-to-all comparison of large dataset, and gives a formal mathematical description of the problem. Then, a multi-objective file distribution model was constructed based on the LP, aiming to localize the data, balance node storage and loads, minimize the storage occupation, and control the occupied storage within the storage limit of each node. To save storage space, the established model was further optimized, and the file distribution algorithm was designed for the distributed environment. Experimental results show that our model and algorithm successfully balanced the storage occupation and loads between computing nodes, and minimized the occupation of node storage. © 2019 International Information and engineering technology Association. All rights reserved.

关键词： Linear programming

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Exploring Patent Transformation Event: Forecasting Patent Transfer Time

Exploring Patent Transformation Event: Forecasting Patent Tr...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jinchen Huo Weidong Liu Yang Li Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering 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

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.

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Research on patent recommendation method based on graph neural network

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

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International Conference on Computer Supported Cooperative Work in Design

作者： Enchao Zhang Weidong Liu Shi Liu Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering 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

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.

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Construction of Knowledge Graph Based on Discipline Inspection and Supervision

Construction of Knowledge Graph Based on Discipline Inspecti...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yuefeng Liu Wei Guo Hanyu Zhang Haodong Bian Yingjie He Xiaoyan Zhang Yanzhang Gong Jianmin Dong Zhen Liu School of Information Engineering Inner Mongolia University of Science and Technology Baotou Inner Mongolia China Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory HohhotInner MongoliaChina Graduate School of Engineering Nagasaki Institue of Applied Science Nagasaki Japan

ISBN: (纸本)9781665416597

To solve the problems of large number of notifications, low relevance and no relevant knowledge base in the field of discipline inspection, a method of constructing a knowledge map of discipline inspection and supervision based on the BERT-BiLSTM-CRF model is proposed. Firstly, the unstructured data is collected from the content of the disciplinary inspection and supervision report. Through the bottom-up method the notification concept layer is constructed. By using deep learning models to extract entities. Then the entities and semantic relations are stored in the graph database Neo4j and displayed in the form of a knowledge graph. This method realizes the whole process from unstructured data to knowledge graph, and provides technical reference for the construction of domain-based knowledge graph. Simultaneously, the knowledge map of discipline inspection field established through the example can find the hidden association between those who break the law and discipline, prevent criminal facts in advance, and provide support and help for discipline inspection personnel to implement the spirit of the eight-point regulation of the Central Committee and continue to fight against the “four winds” and other actions.

关键词： Deep learning databases Soft sensors Knowledge based systems Semantics Inspection Transformers

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A short-term output power prediction model of wind power based on deep learning of grouped time series

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European Journal of Electrical engineering 2020年第1期22卷 29-38页

作者： Wang, Yongsheng Gao, Jing Xu, Zhiwei Li, Leixiao College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry Hohhot010018 China College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China

The output power prediction of wind power plants is an important guarantee to improve the utilization rate of wind energy and reduce wind curtailment. However, due to the strong randomness of wind energy, the ultra-short-term prediction accuracy of wind power output is poor. In view of the problem above, a prediction model based on deep learning of grouped time series (LW-CLSTM) was proposed in this paper. Based on this model, the authors attempted to explore a prediction method of wind power output. For this, first the multivariate data of wind power was fused, cleaned, dimension-reduced, and standardized, and the time period characteristics of the output power itself were extracted. Afterwards, it proposes a time sliding window (TSW) algorithm, and constructs a neural network input data set. Then a deep neural network prediction model combining the Convolutional Neutral Network (CNN) and Long-Short Term Memory (LSTM) was established, and the regression evaluation criteria for output power forecast accuracy in wind power production were designed, to compare the proposed model with four other prediction models. Finally, the experiments on the TensorFlow platform using real data show that this model has better prediction accuracy than the other four models, reaching a prediction accuracy rate of 92.5%, which verified the effectiveness of this prediction method. © 2020 International Information and engineering technology Association. All rights reserved.

关键词： Wind power

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A novel method for driving path planning with spark

TechRxiv

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TechRxiv 2021年

作者： Li, Leixiao Lin, Hao Wan, Jianxiong Wang, Yongsheng Gao, Jing College of Data Science and Application Inner Mongolia University of Technology China School of Computer Science and Engineering Tianjin University of Technology China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service China College of Computer and Information Engineering Inner Mongolia Agricultural University China Inner Mongolia Autonomous Region Key Laboratory of big data research and application for agriculture and animal husbandry China

Efficient and accurate driving path planning can help drivers drive. To solve the problem of low efficiency of traditional heuristic algorithms such as PSO and GA in solving driving path planning, we introduce Excellence Coefficient into heuristic algorithms and make a parallel design based on Spark, which called EC-SPPSOGA. Excellence Coefficient can increase the probability of good edges being left, simultaneously, preserves the possibility of longer side being selected. The parallel design is based on time-consuming analysis of heuristic algorithms. We validate the performance of EC-SPPSOGA based on the data in TSPLIB. It is verified that the EC-SPPSOGA can improve efficiency of driving path planning and has good scalability. © 2021, CC BY.

关键词： Heuristic algorithms

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GK: A Graphormer Architecture Based Knowledge Graph Reasoning Model for Link Prediction

GK: A Graphormer Architecture Based Knowledge Graph Reasonin...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Yueiia Wu Jian-tao Zhou Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.

关键词： Training Knowledge engineering Knowledge graphs Predictive models Prediction algorithms Feature extraction Cognition

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Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer features

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Genomics and Informatics 2024年第1期22卷 1-10页

作者： Meng, Chaolu Hou, Yongqi Zou, Quan Shi, Lei Su, Xi Ju, Ying College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Hohhot China School of Computer Science Inner Mongolia University Hohhot China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China Department of Spine Surgery Changzheng Hospital Naval Medical University Huangpu District No. 415 Fengyang Road Shanghai China Foshan Women and Children Hospital Foshan China School of Informatics Xiamen University Xiamen China

In protein identification, researchers increasingly aim to achieve efficient classification using fewer features. While many feature selection methods effectively reduce the number of model features, they often cause information loss caused by merely selecting or discarding features, which limits classifier performance. To address this issue, we present Rore, an algorithm based on a feature-dimensionality reduction strategy. By mapping the original features to a latent space, Rore retains all relevant feature information while using fewer representations of the latent features. This approach significantly preserves the original information and overcomes the information loss problem associated with previous feature selection. Through extensive experimental validation and analysis, Rore demonstrated excellent performance on an antioxidant protein dataset, achieving an accuracy of 95.88% and MCC of 91.78%, using vectors including only 15 features. The Rore algorithm is available online at http://112.124.26.17:8021/Rore. © The Author(s) 2024.

关键词： Dimensionality reduction Protein classifier Protein sequence Robust feature

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A secure rational quantum state sharing protocol

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Science China(Information Sciences) 2018年第2期61卷 204-215页

作者： Zhao DOU Gang XU Xiu-Bo CHEN Xin LIU Yi-Xian YANG Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and Telecommunications School of Software Engineering Beijing University of Posts and Telecommunications Gui Zhou University Guizhou Provincial Key Laboratory of Public Big Data School of Computer Science Shaanxi Normal University School of Information Engineering Inner Mongolia University of Science and Technology

A novel rational protocol to share two arbitrary qubits among multiple parties is investigated in this paper. First, the protocol is presented, which is learned from Li et al.'s protocol. Second, the utility,security, correctness, fairness, Nash equilibrium, and Pareto optimality of our scheme are discussed in detail,where the utility, correctness, and fairness of rational quantum state sharing protocols are creatively given because the agent who recovers the state plays a different and more important role. Another important point is that assumptions about our protocol are more practical and suitable than existing protocols.

关键词： rational quantum state sharing Nash equilibrium secure correct

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Enhanced Adsorption of Copper Citrate Complex by Ferromanganese Oxides-Biochar from Water: Performance and Mechanism

SSRN

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SSRN 2024年

作者： Zhu, Ying Lei, Xiang Liu, Jianguo Li, Yiting Fan, Wenhong Key Laboratory of Environmental Pollution Control and Remediation Universities of Inner Mongolia Autonomous Region College of Resources and Environmental Engineering Inner Mongolia University of Technology Inner Mongolia Hohhot010051 China School of Materials Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing100191 China

Due to the stable structure of complex metals, their removal is more challenging compared to metal ions. Additionally, the removal mechanism of metal complexes is closely related to their structure. This study developed a low-cost and economically efficient ferromanganese oxide-modified biochar and explored the preparation conditions in detail. The surface of ferromanganese oxide-modified biochar was uniformly loaded with 100 nm nanoparticles, mainly comprising Mn3O4 and (FeO)0.099(MnO)0.901, and exhibited excellent copper citrate (CuCA) adsorption capacity. The removal rate of CuCA at an initial concentration of 10 mg/L by 1 g/L modified biochar reached 99.5% for Cu and 87.7% for TOC, with a copper adsorption capacity of 3.79 mg/L. The mechanism of using modified biochar to remove CuCA includes chemical adsorption at the bimetallic oxide active sites and oxygen-containing functional groups on the surface of the biochar, as well as physical adsorption due to biochar’s porous structure and other effects. The modified biochar shows good performance and stability in wide pH range and certain coexisting ions. Ferromanganese oxide-modified biochar own the advantage of efficient, economical and easy to preparation, which has significant potential for development in the removal of metal complexes in water. © 2024, The Authors. All rights reserved.

关键词： Adsorption

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