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Global Collaboration in Artificial Intelligence:Bibliometrics and Network Analysis from 1985 to 2019

Journal of data and Information Science 2020年第4期5卷 86-115页

作者： Haotian Hu Dongbo Wang Sanhong Deng School of Information Management Nanjing UniversityNanjing 210023China College of Information and Technology Nanjing Agricultural UniversityNanjing 210095China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing 210023China

Purpose:This study aims to explore the trend and status of international collaboration in the field of artificial intelligence(AI)and to understand the hot topics,core groups,and major collaboration patterns in global AI ***/methodology/approach:We selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science(WoS)and studied international collaboration from the perspectives of authors,institutions,and countries through bibliometric analysis and social network ***:The bibliometric results show that in the field of AI,the number of published papers is increasing every year,and 84.8%of them are cooperative *** with more than three authors,collaboration between two countries and collaboration within institutions are the three main levels of collaboration *** social network analysis,this study found that the US,the UK,France,and Spain led global collaboration research in the field of AI at the country level,while Vietnam,Saudi Arabia,and United Arab Emirates had a high degree of international *** at the institution level reflects obvious regional and economic *** are the Developing Countries Institution Collaboration Group led by Iran,China,and Vietnam,as well as the Developed Countries Institution Collaboration Group led by the US,Canada,the ***,the Chinese Academy of Sciences(China)plays an important,pivotal role in connecting the these institutional collaboration *** limitations:First,participant contributions in international collaboration may have varied,but in our research they are viewed equally when building collaboration ***,although the edge weight in the collaboration network is considered,it is only used to help reduce the network and does not reflect the strength of *** implications:The findings fill the current shortage of research on international collaboration in A

关键词： Artificial intelligence International collaboration Collaboration pattern Bibliometric analysis Social network analysis

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DimDraw – A novel tool for drawing concept lattices 15

DimDraw – A novel tool for drawing concept lattices

引用

15th Supplementary International Conference on Formal Concept Analysis Workshops, ICFCA 2019

作者： Dürrschnabel, Dominik Hanika, Tom Stumme, Gerd Knowledge and Data Engineering Group University of Kassel Germany

Concept lattice drawings are an important tool to visualize complex relations in data in a simple manner to human readers. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although those methods are satisfactory for some lattices they unfortunately perform poorly in general. In this work we present a novel tool to draw concept lattices that is purely motivated by the order structure. Copyright © 2019 for this paper by its authors.

关键词： Formal concept analysis

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Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

arXiv

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arXiv 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Mei, Fang Kang, Jiawen Du, Hongyang Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn36830 United States

As a form of artificial intelligence (AI) technology based on interactive learning, deep reinforcement learning (DRL) has been widely applied across various fields and has achieved remarkable accomplishments. However, DRL faces certain limitations, including low sample efficiency and poor generalization. Therefore, we present how to leverage generative AI (GAI) to address these issues above and enhance the performance of DRL algorithms in this paper. We first introduce several classic GAI and DRL algorithms and demonstrate the applications of GAI-enhanced DRL algorithms. Then, we discuss how to use GAI to improve DRL algorithms from the data and policy perspectives. Subsequently, we introduce a framework that demonstrates an actual and novel integration of GAI with DRL, i.e., GAI-enhanced DRL. Additionally, we provide a case study of the framework on UAV-assisted integrated near-field/far-field communication to validate the performance of the proposed framework. Moreover, we present several future directions. Finally, the related code is available at: https://***/GAI-enhanced-DRL. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Automatic Textual Explanations of Concept Lattices

arXiv

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arXiv 2023年

作者： Hirth, Johannes Horn, Viktoria Stumme, Gerd Hanika, Tom Knowledge & Data Engineering Group University of Kassel Germany Partizipative IT-Gestaltung University of Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Germany School of Library and Information Science Humboldt-Universität zu Berlin Berlin Germany Institute of Computer Science University of Hildesheim Germany

Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard *** Codes 06A15 03G10 68T30 68T27 Copyright © 2023, The Authors. All rights reserved.

关键词： Formal concept analysis

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Extracting commuter-specific destination hotspots from trip destination data-comparing the boro taxi service with Citi Bike in NYC

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Geo-Spatial Information Science 2020年第2期23卷 141-152页

作者： Andreas Keler Jukka M.Krisp Linfang Ding Department of Civil Geo and Environmental EngineeringTechnical University of MunichMunichGermany Applied Geoinformatics Institute of GeographyUniversity of Augsburg(UniA)AugsburgGermany The KRDB Research Centre for Knowledge and Data Free University of Bozen-BolzanoBozen-BolzanoItaly Department of Cartography Technical University of MunichMunichGermany

Taxi trajectories from urban environments allow inferring various information about the transport service qualities and commuter *** is possible to associate starting and end points of taxi trips with requirements of individual groups of people and even social *** research shows that due to service restrictions,boro taxis have typical customer destination locations on selected Saturdays:many drop-off clusters appear near the restricted zone,where it is not allowed to pick up customers and only few drop-off clusters appear at complicated *** crossings imply recent infrastructural *** want to follow up on these results and add one additional group of commuters:Citi Bike *** selected Saturdays in June 2015,we want to compare the destinations of boro taxi and Citi Bike *** is challenging due to manifold differences between active mobility and motorized road users,and,due to the fact that station-based bike sharing services are restricted to *** and end points of trips,as well as the volumes in between rely on specific numbers of bike sharing ***,we introduce a novel spatiotemporal assigning procedure for areas of influence around static bike sharing stations for extending available computational methods.

关键词： Urban transportation spatial analysis mobility patterns boro taxis vehicle fleets bicycle-sharing service

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Recovering accurate labeling information from partially valid data for effective multi-label learning 29

Recovering accurate labeling information from partially vali...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Li, Ximing Wang, Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University China Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology China School of Computer Sci and Information Engineering Hefei University of Technology China

ISBN: (纸本)9780999241165

Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, i.e., the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to recover the ground-truth labels, i.e., estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels. Upon this observation, we further develop a novel two-stage PML method, namely Partial MultiLabel Learning with Label Enrichment-Recovery (PML3ER), where in the first stage, it estimates the label enrichment with unconstrained label propagation, then jointly learns the ground-truth confidence and multi-label predictor given the label enrichment. Experimental results validate that PML3ER outperforms the state-of-the-art PML methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Learning systems

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Mandari: Multi-Modal Temporal knowledge Graph-aware Sub-graph Embedding for Next-POI Recommendation

Mandari: Multi-Modal Temporal Knowledge Graph-aware Sub-grap...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoqian Liu Xiuyun Li Yuan Cao Fan Zhang Xiongnan Jin Jinpeng Chen School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China The Technology Innovation Center of Cultural Tourism Big Data of Hebei Province Chengde China Hebei Normal University for Nationalities Chengde China Knowledge Discovery and Data Mining Research Center Zhejiang Lab Hangzhou China

Next-POI recommendation aims to explore from user check-in sequence to predict the next possible location to be visited. Existing methods are often difficult to model the implicit association of multi-modal data with user choices. Moreover, traditional methods struggle to fully explore the variation of user preferences at variable time intervals. To tackle these limitations, we propose a Multi-Modal Temporal knowledge Graph-aware Sub-graph Embedding approach (Mandari). We first construct a novel Multi-Modal Temporal knowledge Graph. Based on the proposed knowledge graph, we integrate multi-modal information and leverage the graph attention network to calculate sub-graph prediction probability. Next, we implement a temporal knowledge mining method to model the segmentation and periodicity of user check-in and obtain temporal prediction probability. Finally, we fuse temporal prediction probability with the previous sub-graph prediction probability to obtain the final result. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art methods.

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Implementing Predictive Model for Low Birth Weight in Afghanistan

Implementing Predictive Model for Low Birth Weight in Afghan...

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International Conference on knowledge and Smart Technology (KST)

作者： Abdullah Zahirzada Kittichai Lavangnananda Data and Knowledge Engineering Lab. School of Information Technology (SIT) King Mongkut's University of Technology Thonburi (KMUTT) Bangkok Thailand

ISBN: (纸本)9781728176024;9781728176031

Birth weight is a significant determinant of the likelihood of survival of an infant. Low Birth Weight (LBW) has become an increasingly common problem, especially in developing and underdeveloped countries. Therefore, an ability to predict the LBW is beneficial and a good preventive measure. It is also a good indicator of future health risks of that infant. This study is concerned about the implementation of predictive LBW models for rural and urban areas of Afghanistan based on the data obtained from the Afghanistan Demographic and Health Survey 2015. The main objective of the study is to identify the most suitable machine learning techniques (i.e. classifiers) among the five popular ones. These are K-Nearest Neighbor (K-NN), Naïve Bayes, Neural Network, Random Forest, and Support Vector Machine (SVM). Prior to implementing the predictive models, data preprocessing is carried out. This is done by means of data cleansing and feature selection. The well-known Correlation based Feature Selection algorithm (CFS) is employed to select the top ten attributes. The classifier in this study comprises two categories, Normal and LBW. The preparation of the dataset is carefully done to ensure well-balanced samples in each category. The study reveals that Random Forest is the best classifier with an accuracy of 84.70% and 85.2% and with the Area Under the Curve (AUC) of 91.0% and 92.1% for both rural and urban areas, respectively. The study has a direct benefit to the health and prevention policy making in Afghanistan.

关键词： Support vector machines Pediatrics Urban areas Neural networks data preprocessing Predictive models Feature extraction

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Convergence of sequences: A survey

arXiv

引用

arXiv 2021年

作者： Franci, Barbara Grammatico, Sergio Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Convergent sequences of real numbers play a fundamental role in many different problems in system theory, e.g., in Lyapunov stability analysis, as well as in optimization theory and computational game theory. In this survey, we provide an overview of the literature on convergence theorems and their connection with Féjer monotonicity in the deterministic and stochastic settings, and we show how to exploit these results. © 2021, CC BY.

关键词： Surveys

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Quaternion factorization machines: A lightweight solution to intricate feature interaction modelling

arXiv

引用

arXiv 2021年

作者： Chen, Tong Yin, Hongzhi Zhang, Xiangliang Huang, Zi Wang, Yang Wang, Meng The School of Information Technology and Electrical Engineering The University of Queensland Australia The Machine Intelligence and Knowledge Engineering Laboratory King Abdullah University of Science and Technology Saudi Arabia The Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China

Due to the sparsity of available features in web-scale predictive analytics, combinatorial features become a crucial means for deriving accurate predictions. As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering. With the prominent development of deep neural networks (DNNs), there is a recent and ongoing trend of enhancing the expressiveness of FM-based models with DNNs. However, though better results are obtained with DNN-based FM variants, such performance gain is paid off by an enormous amount (usually millions) of excessive model parameters on top of the plain FM. Consequently, the heavy parameterization impedes the real-life practicality of those deep models, especially efficient deployment on resource-constrained IoT and edge devices. In this paper, we move beyond the traditional real space where most deep FM-based models are defined, and seek solutions from quaternion representations within the hypercomplex space. Specifically, we propose the quaternion factorization machine (QFM) and quaternion neural factorization machine (QNFM), which are two novel lightweight and memory-efficient quaternion-valued models for sparse predictive analytics. By introducing a brand new take on FM-based models with the notion of quaternion algebra, our models not only enable expressive inter-component feature interactions, but also significantly reduce the parameter size due to lower degrees of freedom in the hypercomplex Hamilton product compared with real-valued matrix multiplication. Extensive experimental results on three large-scale datasets demonstrate that QFM achieves 4.36% performance improvement over the plain FM without introducing any extra parameters, while QNFM outperforms all baselines with up to two magnitudes' parameter size reduction in comparison to state-of-the-art peer methods. Copyright © 2021, The Authors. All

关键词： Large dataset

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