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Grain Science and Technology Policies and Food Security in China:An Empirical Study Based on a Provincial Dynamic Panel Model

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Journal of Systems Science and Information 2020年第6期11卷 504-523页

作者： Changhong NIE Mingming CUI Xiuting LI Institute of Zoology Chinese Academy of SciencesBeijing 100101China School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

China’s grain science and technology policies have played an important role in the development of China’s food *** paper aims to examine the effects of China’s grain science and technology policies on food *** quantitatively assesses China’s food security by analyzing the main contents and development trends of China’s food science technology policies through the text metrology method,and then investigates the effects of grain science and technology policies on food security by employing a provincial dynamic panel *** results show that food security in China is all-round developed,and that the release frequency and cumulative effect of grain science and technology policies play a significant role in promoting food *** grain science and technology policies can effectively guarantee China’s food security.

关键词： grain science and technology policy food security dynamic panel regression policy effect

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Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation

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ACM Transactions on Recommender Systems 2024年第4期2卷 1-27页

作者： Zhiyong Cheng Jianhua Dong Fan Liu Lei Zhu Xun Yang Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shandong Artificial Intelligence Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computing National University of Singapore Singapore Singapore School of Electronic and Information Engineering Tongji University Shanghai China School of Information Science and Technology University of Science and Technology of China Hefei China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China and Hefei Comprehensive National Science Center Hefei China

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation of benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN’s ability to effectively leverage multi-behavioral data, leading to more accurate recommendations.

关键词： Multi-Behavior Recommendation disentangled representation learning graph convolutional network multi-behavior recommendation multi-task learning

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Research on Object-oriented Exploration Algorithm 2020

Research on Object-oriented Exploration Algorithm

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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Xiajiong Shen Peng Zhang Jiyong Yang Wan Chen Lei Zhang Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng PR China Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Institute of Data and Knowledge Engineering Henan University Kaifeng PR China

ISBN: (纸本)9781450387828

In formal concept analysis, attribute exploration is a very important methodology, which can explore the logical laws between attributes in a continuous and interactive way. Object-oriented exploration is very meaningful while it is similar to attribute exploration. It can explore the implication relationship between objects. But for the existing work, almost all are attribute exploration, object-oriented exploration is rare. Research on object-oriented exploration has great practical value, such as object classification, retrieval and excavation. In order to address this problem, this article takes the object as the main research point in formal concept analysis based on related knowledge of traditional attribute exploration, and proposes theories of object-oriented exploration. According to these related theories and the framework of attribute exploration algorithm, an object-oriented exploration algorithm is proposed in this paper. The algorithm can be used to explore and understand the relationship between objects, which facilitates to understand better the relationship between objects and attributes.

关键词： Formal concept analysis Object exploration Attribute exploration Implication Concept lattice

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Search Result Diversification Framework Based on Dual Star-shaped Self-Attention Network 20

Search Result Diversification Framework Based on Dual Star-s...

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20th Chinese National Conference on Computational Linguistics, CCL 2021

作者： Qin, Xubo Dou, Zhicheng Zhu, Yutao Wen, Ji-Rong School of Information Renmin University Gaoling School of Artificial Intelligence Renmin University of China Université de Montréal Beijing Key Laboratory of Big Data Management and Analysis Methods Key Laboratory of Data Engineering and Knowledge Engineering MOE

Research shows that most queries issued by users are short queries. Those queries may be ambiguous or broad for specifying users' actual information need. The same query issued by different users may lead to different information needs. In order to satisfy different users' needs, the search engines need to rank the search result document diversely. This leads to the search result diversification technology. For those previous diversification approaches based on global document interactions, fully-connected self-attention networks are used for capturing the interactions among all the candidate documents, leading to well performance. However, those models only take the document interactions into consideration, ignoring the relevance signals of candidate documents. As the annotated datasets are not enough for model training, the effectiveness of the model will be limited. In this paper we prove a new implicit diversification framework based on self-attention network in the shape of dual-star. A star-shaped topological structure is deployed with an external query node, in order to effectively extract the global interaction signals relevant to the query. Using self-attention to learn the similarity between each document, this model can be trained easily, and perform significantly better than the state-of-the-art implicit approach indicating the potential of implicit methods. © 2021 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Search engines

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A 1D Plug-and-Play Synthetic data Deep Learning For Undersampled Magnetic Resonance Image Reconstruction

A 1D Plug-and-Play Synthetic Data Deep Learning For Undersam...

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IEEE International Conference on Image Processing

作者： Min Xiao Zi Wang Jiefeng Guo Di Guo Xiaobo Qu Institute of Artificial Intelligence Xiamen University Xiamen China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen China Department of Microelectronics and Integrated Circuit Xiamen University Xiamen China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Magnetic resonance imaging (MRI) plays a pivotal role in modern medical diagnosis yet is often hindered by the long imaging time. MRI imaging can be accelerated through undersampling, but the introduced aliasing artifacts should be removed during image reconstruction. While deep learning reconstruction methods excel at image de-aliasing, they may yield suboptimal results when training sampling settings differ from those at the time of reconstruction. To decouple from specific sampling settings, we propose using synthetic data to generate a substantial training dataset and pre-train a 1D deep denoiser. We then integrate the trained deep denoiser into the iterative reconstruction process as a replacement for the approximation operator within the deep plug-and-play framework. In vivo results indicate that the proposed method exhibits robust and visually appealing image reconstruction when there is a mismatch between the training and reconstruction undersampling settings, such as different undersampling patterns and sampling rates.

关键词： Training Deep learning In vivo Magnetic resonance imaging Magnetic resonance Training data Trajectory

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Generative AI for Advanced UAV Networking

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IEEE Network 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Du, Hongyang Kang, Jiawen Wu, Jing Sun, Sumei Zhang, Ping Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong999077 Hong Kong Guangdong University of Technology School of Automation Guangzhou510006 China Agency for Science Technology and Research Institute for Infocomm Research Singapore639798 Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

With the impressive achievements of chatGPT and Sora, generative artificial intelligence (GAI) has received increasing attention. Not limited to the field of content generation, GAI is also widely used to solve the problems in wireless communication scenarios due to its powerful learning and generalization capabilities. Therefore, we discuss key applications of GAI in improving unmanned aerial vehicle (UAV) communication and networking performance in this article. Specifically, we first review the key technologies of GAI and the important roles of UAV networking. Then, we show how GAI can improve the communication, networking, and security performances of UAV systems. Subsequently, we propose a novel framework of GAI for advanced UAV networking, and then present a case study of UAV-enabled spectrum map estimation and transmission rate optimization based on the proposed framework to verify the effectiveness of GAI-enabled UAV systems. Finally, we discuss some important open directions. © 2024 IEEE.

关键词： Aircraft communication

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SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking 25

SoAy: A Solution-based LLM API-using Methodology for Academi...

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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and data mining V.1

作者： Yuanchun Wang Jifan Yu Zijun Yao Jing Zhang Yuyang Xie Shangqing Tu Yiyang Fu Youhe Feng Jinkai Zhang Jingyao Zhang Bowen Huang Yuanyao Li Huihui Yuan Lei Hou Juanzi Li Jie Tang School of Information Renmin University of China Beijing China & Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China Institute of Education Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Information Renmin University of China Beijing China & Engineering Research Center of Database and Business Intelligence MOE Beijing China School of Information Renmin University of China Beijing China Zhipu AI Beijing China

ISBN: (纸本)9798400712456

Applying large language models (LLMs) to academic API usage shows promise in reducing researchers' efforts to seek academic information. However, current LLM methods for using APIs struggle with the complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM methodology for academic information seeking. SoAy enables LLMs to generate code for invoking APIs, guided by a pre-constructed API calling sequence referred to as a solution. This solution simplifies the model's understanding of complex API relationships, while the generated code enhances reasoning efficiency. LLMs are aligned with this solution-oriented, code-based reasoning method by automatically enumerating valid API coupling sequences and transforming them into queries and executable *** evaluate SoAy, we introduce SoAyBench, an evaluation benchmark accompanied by SoAyEval, built upon a cloned environment of APIs from AMiner. Experimental results demonstrate a 34.58-75.99% performance improvement compared to state-of-the-art LLM API-based baselines. All datasets, codes, tuned models, and deployed online services are publicly accessible at https://***/RUCKBReasoning/SoAy.

关键词： academic information seeking

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Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension 2

Extremely Short Chinese Text Classification Method Based on ...

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2019 2nd International Symposium on Big data and Applied Statistics, ISBDAS 2019

作者： Yue, Yongzeng Zhang, Yuhong Hu, Xuegang Li, Peipei Key Laboratory of Knowledge Engineering with Big Data [Hefei University of Technology] Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei23061 China

Short text classification methods have achieved significant progress and wide application on text data such as Twitter and Weibo. However, the extremely short chinese texts like tax invoice data are different with traditional short texts in lackness of contextual semantic information, feature sparseness and extremely short length. The existing short text classification methods are difficult to achieve a satisfactory performance in these texts. To address these problems, this paper proposes a text classification method based on bidirectional semantic extension for extremely short texts like Chinese tax invoice data. More specifically, firstly, the Chinese knowledge graph is introduced for extending bidirectional semantic of texts and label data to expand the extremely short texts and ease the problem of feature sparseness;secondly, the hash vectorization is used to avoid the semantic problem caused by the lackness of contextual information. Experimental results conducted the real tax invoice dataset demonstrate the effectiveness of our proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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Sparse Multiple Instance Learning for Elderly People Balance Ability

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Procedia Computer Science 2022年 199卷 621-628页

作者： Yuqi Zhang Duo Su Xiaoxi Zhao Yingjie Tian School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing China

Falling occurs when a person’s body (excluding their feet) accidentally touches the ground, resulting in severe injury for the elderly. As a response, determining old people’s balance characteristics and assisting them in preventing falls is important. We proposed the sparse SyMIL with SCAD to find the important factors that may cause an elderly person to fall, which can yield realistic and accurate results. This work can also be used in future research about falling prevention and balance supervision.

关键词： Multiple Instance Learning Feature Selection Elderly People Balance Ability

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Semi-autoregressive transformer for image captioning

arXiv

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arXiv 2021年

作者： Zhou, Yuanen Zhang, Yong Hu, Zhenzhen Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Tencent AI Lab Key Laboratory of Knowledge Engineering with Big Data Ministry of Education

Current state-of-the-art image captioning models adopt autoregressive decoders, i.e. they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. To tackle this issue, non-autoregressive image captioning models have recently been proposed to significantly accelerate the speed of inference by generating all words in parallel. However, these non-autoregressive models inevitably suffer from large generation quality degradation since they remove words dependence excessively. To make a better trade-off between speed and quality, we introduce a semi-autoregressive model for image captioning (dubbed as SATIC), which keeps the autoregressive property in global but generates words parallelly in local. Based on Transformer, there are only a few modifications needed to implement SATIC. Experimental results on the MSCOCO image captioning benchmark show that SATIC can achieve a good trade-off without bells and whistles. Code is available at https://***/YuanEZhou/satic. Copyright © 2021, The Authors. All rights reserved.

关键词： Economic and social effects

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