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Comparative research on multi-source heterogeneous data fusion technologies

Procedia Computer Science 2024年 242卷 1089-1095页

作者： Wen Long Jing Gao Man Guo School of Economics and Management University of Chinese Academy of Sciences Beijing 100090 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Both structured market data and unstructured financial news data can significantly affect stock price fluctuations. Therefore, relying only on a single data source for stock price trend prediction may produce information bias. This article combines these two types of heterogeneous data and systematically compares the effectiveness of different data fusion technologies to establish the optimal data integration strategy, thereby improving the accuracy of stock price prediction. Specifically, this paper innovatively proposes a stock price trend prediction model named MVL-SVM. The model successfully combines multi-perspective learning with support vector machine, and realizes the effective fusion of stock market data and financial news data. It is worth mentioning that the prediction accuracy of MVL-SVM model using direct fusion method is more than 30% higher than that of index modeling method, which indicates that direct fusion method can fully learn effective information from multiple information sources, thus significantly improving the prediction effect.

关键词： Multi-view Learning MVL-SVM Market data Financial news data Heterogeneous data fusion

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Research and Design of Disaster Monitoring and Early Warning System Based on big data 5

Research and Design of Disaster Monitoring and Early Warning...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Wang, Pengfei Liu, Yongqiang Fang, Yudong Xu, Cheng Sun, Jiwu Lu, Lu Big Data Centre Ministry of Emergency Management Beijing100013 China Beijing Union University Beijing Key Laboratory of Information Service Engineering Beijing100101 China Institute of Urban Safety and Emergency Management Beijing Union University Beijing100101 China

ISBN: (纸本)9798350385557

Disaster prevention, mitigation and relief have always been one of the core businesses of governance. However, there are a series of problems in disaster prevention, mitigation and relief business support, especially in the prevention and control of the risk of compound chain-generated natural disasters, such as insufficient sensing capability, low accuracy of monitoring and warning, and lagging assessment models. In order to cope with these challenges, the work of using tower big data for disaster analysis in complex environments is proposed. Currently, the number of communication towers in the country is huge and widely distributed, and they are affected by various abnormalities in natural disasters. Therefore, through the construction of the tower disaster big data analysis system, big data technology and tower data are used to enhance the effectiveness of disaster monitoring and early warning, and provide brand new support for the disaster prevention, mitigation and relief operations. © 2024 IEEE.

关键词： big data

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SLMP: A Scientific Literature management Platform Based on Large Language Models 15

SLMP: A Scientific Literature Management Platform Based on L...

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15th IEEE International Conference on knowledge Graph, ICKG 2024

作者： Guo, Menghao Jiang, Jinling Wu, Fan Sun, Shanxin Zhang, Chen Li, Wenhui Sun, Zeyi Chen, Guangyong Wu, Xindong Research Center for Life Sciences Computing Zhejiang Lab Hangzhou China Research Center for Data Hub and Security Zhejiang Lab Hangzhou China Research Center for High Efficiency Computing System Zhejiang Lab Hangzhou China Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data Hefei China

ISBN: (纸本)9798331508821

This paper presents a Scientific Literature management Platform (SLMP, demo link1 ) based on large language models (LLMs). The platform consists of four modules: literature management, literature extraction, literature retrieval, and question answering. The core techniques used to support the four modules across the platform include a fine-tuned model PaperExtractGPT and a continual pre-training model ChatPaperGPT based on ChatGLM2 using the data from scientific research literature, responsible for information extraction and communication, respectively. Due to their powerful capabilities in natural language understanding and generation, LLMs can understand complex scientific concepts based on the provided contexts, and thus generate high-quality texts and conduct in-depth information retrieval and question answering. Our platform can help researchers manage and utilize literature more effectively and efficiently for finding relevant literature, obtaining required information, and generating new knowledge. © 2024 IEEE.

关键词： Question answering

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Image recognition and empirical application of desert plant species based on convolutional neural network

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Journal of Arid Land 2022年第12期14卷 1440-1455页

作者： LI Jicai SUN Shiding JIANG Haoran TIAN Yingjie XU Xiaoliang School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Xinjiang Institute of Ecology and Geography Chinese Academy of SciencesUrumqi 830011China

In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching *** plant taxonomic identification requires high expertise,which is *** nature reserves have problems such as incomplete species surveys,inaccurate taxonomic identification,and untimely updating of status *** and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network *** 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects,this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang,such as species investigation and monitoring,by using deep *** desert plant species were not included in the public dataset,the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China(PPBC).After the sorting process and statistical analysis,a total of 2331 plant images were finally collected(2071 images from field collection and 260 images from the PPBC),including 24 plant species belonging to 14 families and 22 genera.A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance,from different perspectives,to find the optimal network model that is most suitable for the image recognition of desert plant species in *** results revealed 24 models with a recognition Accuracy,of greater than 70.000%.Among which,Residual Network X_8GF(RegNetX_8GF)performs the best,with Accuracy,Precision,Recall,and F1(which refers to the harmonic mean of the Precision and Recall values)values of 78.33%,77.65%,69.55%,and 71.26%,*** t

关键词： desert plants image recognition deep learning convolutional neural network Residual Network X_8GF(RegNetX_8GF) Mobile Network V2(MobileNetV2) nature reserves

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Sparrow Search Algorithm with Adaptive Collaborative Updating

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1706-1724页

作者： Hong, Chuanbo Liang, Quan Liang, Qiaoxin Yu, Wenjie Yu, Wenze Xiong, Neng School of Mechanical and Electrical Engineering Quanzhou University of Information Engineering Quanzhou362000 China College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Quanzhou Management Branch Fujian Expressway Group Cp. Ltd Quanzhou362000 China

The research of swarm intelligence shows a rapid development trend. People have studied a lot of swarm intelligence algorithms inspired by biology. Sparrow search algorithm is concerned by many scholars because of its powerful search performance. However, in the case of high-dimension optimization problems, if only a single sparrow search algorithm is used to solve optimization problems, the optimal solution often cannot be found, and the phenomenon of dimension disaster will occur or it is easy to fall into the local optimal solution. In order to make sparrow search algorithm have better performance in solving high-dimensional complex problems, In this paper, we propose a new Sparrow Search Algorithm with Adaptive Collaborative Updating (SSA-ACU). The SSA-ACU algorithm initializes the population through the chaotic search strategy to make the population distribution more diverse. Then, adaptive parameters are introduced to update the position of the leader to provide a more reliable direction for particle search. Finally, in order to enhance the development ability of the population, the position of the adjoint is updated through the pseudo-affine transformation mechanism to change the mode of its position update. Then CEC2013 was used to test and compare SSA-ACU algorithm with other sparrow search algorithm variants and classical algorithms, and it was verified that SSA-ACU algorithm has good performance in convergence speed and optimization ability. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Swarm intelligence

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Impact of Consumer Perceived Characteristics on Fruit Sales: Evidence from Community Group-buying 11

Impact of Consumer Perceived Characteristics on Fruit Sales:...

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Jiang, Hailiang Tian, Xin Tao, Ye Wang, Lulu Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Beijing HAOLINJU CVS Co. Ltd Beijing100088 China Shanghai HEADING Information Engineering Co. Ltd. Shanghai201112 China

There is a growing trend among retailers to sell fruits through their community group-buying (CGB) channels. Based on real operational data, we employ econometric models to empirically analyze how consumer perception characteristics in CGB affect fruit sales. Our findings suggest that incorporating subjective experiences into product descriptions can positively impact fruit sales for retailers;fresh produce stores outperform regular stores in fruit sales of CGB;Dark-colored fruits sell more than light-colored fruits in the CGB channel. Our study offers valuable insights for retailers selling fruits through CGB channels. © 2024 The Authors.

关键词： community group-buying dark-colored fruits fresh produce stores subjectivity

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Movie Recommendation Algorithm Based on Sentiment Analysis and LDA 8

Movie Recommendation Algorithm Based on Sentiment Analysis a...

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8th International Conference on Information Technology and Quantitative management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Zhang, Yilin Zhang, Lingling Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Traditional recommendation algorithms have problems such as data sparseness and not paying attention to the diversity of recommendation results. In this paper, we use LDA to extract topics of comments about movies, and identify the emotional tendencies related to topics. As a result, we enrich user interest model and product feature model based on emotional tendencies to improve content-based recommendation algorithms. Most of prior work on applying sentiment classification to recommendation systems only consider the use of sentiment dictionaries to judge polarity, and adopt pattern matching methods to identify features. This paper uses BERT to train sentiment classification models and uses LDA to extract topics. The algorithm is run on the movie review database crawled from Douban, and the experimental result showed that the diversity of recommendation lists had been significantly improved. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Sentiment Analysis Topic Extraction Content-based Recommendation Movie Reviews

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UNIKGQA: UNIFIED RETRIEVAL AND REASONING FOR SOLVING MULTI-HOP QUESTION ANSWERING OVER knowledge GRAPH 11

UNIKGQA: UNIFIED RETRIEVAL AND REASONING FOR SOLVING MULTI-H...

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11th International Conference on Learning Representations, ICLR 2023

作者： Jiang, Jinhao Zhou, Kun Zhao, Wayne Xin Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China China School of Information Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China

Multi-hop Question Answering over knowledge Graph (KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question on a large-scale knowledge Graph (KG).To cope with the vast search space, existing work usually adopts a two-stage approach: it first retrieves a relatively small subgraph related to the question and then performs the reasoning on the subgraph to find the answer entities *** these two stages are highly related, previous work employs very different technical solutions for developing the retrieval and reasoning models, neglecting their relatedness in task *** this paper, we propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter *** model architecture, UniKGQA consists of a semantic matching module based on a pre-trained language model (PLM) for question-relation semantic matching, and a matching information propagation module to propagate the matching information along the directed edges on *** parameter learning, we design a shared pre-training task based on question-relation matching for both retrieval and reasoning models, and then propose retrieval-and reasoning-oriented fine-tuning *** with previous studies, our approach is more unified, tightly relating the retrieval and reasoning *** experiments on three benchmark datasets have demonstrated the effectiveness of our method on the multi-hop KGQA *** codes and data are publicly available at https://***/RUCAIBox/UniKGQA. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： knowledge graph

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NetNDP: Nonoverlapping (delta, gamma)-approximate pattern matching

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INTELLIGENT data ANALYSIS 2022年第6期26卷 1661-1682页

作者： Wu, Youxi Jian, Bojing Li, Yan Jiang, He Wu, Xindong Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China Hebei Key Lab Big Data Comp Tianjin Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin Peoples R China Dalian Univ Technol Sch Software Dalian Liaoning Peoples R China Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Hefei Anhui Peoples R China

Pattern matching can be used to calculate the support of patterns, and is a key issue in sequential pattern mining (or sequence pattern mining). Nonoverlapping pattern matching means that two occurrences cannot use the same character in the sequence at the same position. Approximate pattern matching allows for some data noise, and is more general than exact pattern matching. At present, nonoverlapping approximate pattern matching is based on Hamming distance, which cannot be used to measure the local approximation between the subsequence and pattern, resulting in large deviations in matching results. To tackle this issue, we present a Nonoverlapping Delta and gamma approximate Pattern matching (NDP) scheme that employs the (delta, gamma)-distance to give an approximate pattern matching, where the local and the global distances do not exceed delta and gamma, respectively. We first transform the NDP problem into a local approximate Nettree and then construct an efficient algorithm, called the local approximate Nettree for NDP (NetNDP). We propose a new approach called the Minimal Root Distance which allows us to determine whether or not a node has root paths that satisfy the global constraint and to prune invalid nodes and parent-child relationships. NetNDP finds the rightmost absolute leaf of the max root, searches for the rightmost occurrence from the rightmost absolute leaf, and deletes this occurrence. We iterate the above steps until there are no new occurrences. Numerous experiments are used to verify the performance of the proposed algorithm.

关键词： Patterns matching approximate pattern matching nonoverlapping condition (delta, gamma)-distance Nettree structure

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Partial Label Feature Selection: An Adaptive Approach

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IEEE Transactions on knowledge and data Engineering 2024年第8期36卷 4178-4191页

作者： Zan Zhang Jialu Yao Lin Liu Jiuyong Li Lei Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei Anhui China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei Anhui China UniSA STEM University of South Australia Adelaide SA Australia Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei Anhui China

As an emerging weakly supervised learning framework, partial label learning aims to induce a multi-class classifier from ambiguous supervision information where each training example is associated with a set of candidate labels, among which only one is the true label. Traditional feature selection methods, either for single label and multiple label problems, are not applicable to partial label learning as the ambiguous information contained in the label space obfuscates the importance of features and misleads the selection process. This makes the selection of a proper feature subset from partial label examples particularly challenging, and therefore has rarely been investigated. In this paper, we propose a novel feature selection algorithm for partial label learning, named PLFS, which considers not only the relationships between features and labels, but also exploits the relationships between instances to select the most informative and important features to enhance the performance of partial label learning. PLFS constructs an adaptive weighted graph to exploit the similarity information among instances, differentiate the label space and weight the feature space, which leads to the selection of a proper feature subset. Extensive experiments over a broad range of benchmark data sets clearly validate the effectiveness of our proposed feature selection approach.

关键词： Feature extraction Supervised learning Training knowledge engineering Labeling Classification algorithms big data

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