期刊：
Information Processing & Management,2023年60(2):103220 ISSN：0306-4573
通讯作者：
Quan Lu<&wdkj&>Hui Liu
作者机构：
[Zhang, Lu; Chen, Jing] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Lu, Quan] Wuhan Technol & Business Univ, Inst Digital Commerce, Wuhan 430065, Peoples R China.;[Lu, Quan] Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Peoples R China.;[Liu, Hui] Chinese Acad Med Sci & Peking Union Med Coll, Inst Med Informat, Beijing 100020, Peoples R China.;[Chen, Shuaipu] Wuhan Univ, Sch Informat Management, Wuhan 430072, Peoples R China.
通讯机构：
[Quan Lu; Hui Liu] I;Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China<&wdkj&>Institute of Digital Commerce, Wuhan Technology and Business University, Wuhan, 430065, China<&wdkj&>Center for Studies of Information Resources, Wuhan University, Wuhan, 430072, China
关键词：
Gaze;Gesture;Health information identification;Misinformation;Predicting information usefulness
期刊：
Journal of King Saud University - Computer and Information Sciences,2023年35(7):101605 ISSN：1319-1578
通讯作者：
Zhang, Q
作者机构：
[Wang, Xiaoguang; Wang, Shutong] Wuhan Univ, Sch Informat Management, Wuhan 430072, Peoples R China.;[Wang, Shutong] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Zhao, Anran] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China.;[Lai, Chenghang] Fudan Univ, Sch Comp Sci, Shanghai 200438, Peoples R China.;[Zhang, Qi; Zhang, Q] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
通讯机构：
[Zhang, Q ] C;Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
关键词：
Facial expression recognition;Graph convolutional network;Geometry cue;Uncertainty;Emotion label distribution learning
摘要：
Facial expression recognition (FER) task in the wild is challenging due to some uncertainties, such as the ambiguity of facial expressions, subjective annotations, and low-quality facial images. A novel model for FER in-the-wild datasets is proposed in this study to solve these uncertainties. The overview of the proposed method is as follows. First, the facial images are grouped into high and low uncertainties by the pre-trained network. The graph convolutional network (GCN) framework is then used for the facial images with low uncertainty to obtain geometry cues, including the relationship among action units (AUs) and the implicit connection between AUs and expressions, which help predict the probability of the underlying emotional label. The emotion label distribution is produced by combining the predicted latent label probability and the given label. For the facial images with high uncertainty, k-nearest neighbor graphs are built to determine the k facial images in the low uncertainty group with the highest similarity to the given facial image. The emotion label distribution of the given image is then replaced by fusing the emotion label distribution based on the distances between the given image and its adjacent images. Finally, the constructed emotion label distribution facilitates training in a straightforward manner using a convolutional neural network framework to identify facial expressions. Experimental results on RAF-DB, FERPlus, AffectNet, and SFEW2.0 datasets demonstrate that the proposed method achieved superior performance compared to state-of-the-art approaches. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
摘要：
Interdisciplinary topic reflects the knowledge exchange and integration between different disciplines. Analyzing its evolutionary path is beneficial for interdisciplinary research in identifying potential cooperative research direction and promoting the cross-integration of different disciplines. However, current studies on the evolution of interdisciplinary topics mainly focus on identifying interdisciplinary topics at the macro level. More analysis of the evolution process of interdisciplinary topics at the micro level is still needed. This paper proposes a framework for interdisciplinary topic identification and evolutionary analysis based on BERTopic to bridge the gap. The framework consists of four steps: (1) Extract the topics from the dataset using the BERTopic model. (2) Filter out the invalid global topics and stage topics based on lexical distribution and further filter out the invalid stage topics based on topic correlation. (3) Identify interdisciplinary topics based on disciplinary diversity and disciplinary cohesion. (4) Analyze the interdisciplinary topic evolution by inspecting the intensity and content in the evolution, and visualize the evolution using Sankey diagrams. Finally, We conduct an empirical study on a dataset collected from the Web of Science (WoS) in Library & Information Science (LIS) to evaluate the validity of the framework. From the dataset, we have identified two distinct types of interdisciplinary topics in LIS. Our findings suggest that the growth points of LIS mainly exist in the interdisciplinary research topics. Additionally, our analysis reveals that more and more interdisciplinary knowledge needs to be integrated to solve more complex problems. Mature interdisciplinary topics mainly formed from the internal core knowledge in LIS stimulated by external disciplinary knowledge, while promising interdisciplinary topics are still at the stage of internalizing and absorbing the knowledge of other disciplines. The dataset, the code for implementing the algorithms, and the complete experiment results will be released on GitHub at:
https://github.com/haihua0913/IITE-BERT
.
期刊：
International Journal of Control, Automation and Systems,2023年21(7):2398-2408 ISSN：1598-6446
通讯作者：
Feng, CY
作者机构：
[Wang, Wei] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan 430073, Peoples R China.;[Feng, Changyang; Feng, CY] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
通讯机构：
[Feng, CY ] C;Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
关键词：
Adaptive dynamic programming;consensus control;kernel-based method;multi-agent systems;optimal control
摘要：
A novel method for optimal consensus control of multi-agent systems (MASs) based on adaptive dynamic programming (ADP) is developed in this paper. Unlike neural networks (NNs) that require manually designed features in value function approximation and may effect the approximation quality. Kernel-based methods are adopted to approximate value functions without predefining the model structure. Moreover, to handle the challenge of unknown or complex system dynamics, a local action-value function is defined and kernel-based methods are used to approximate the local action-value function. Thus, an action dependent heuristic dynamic programming (ADHDP) approach that uses kernel-based local action-value function approximation to achieve the model-free optimal consensus control is developed in this paper. The developed approach learns the system dynamics from historical data and avoids the need for system identification. The effectiveness of the developed approach is demonstrated with two simulation examples.
作者机构：
[Tang, Xuli] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Li, Xin] Huazhong Univ Sci & Technol, Tongji Med Coll, Sch Med & Hlth Management, Wuhan 430030, Peoples R China.;[Ma, Feicheng] Wuhan Univ, Sch Informat Management, Wuhan 430074, Peoples R China.
通讯机构：
[Xin Li] S;School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
关键词：
International collaboration;Artificial intelligence;Geographic distance;Economic distance;Cultural distance;Academic distance;Industrial distance;AI
摘要：
International collaboration has become imperative in the field of AI. However, few studies exist concerning how distance factors have affected the international collaboration in AI research. In this study, we investigate this problem by using 1,294,644 AI related collaborative papers harvested from the Microsoft Academic Graph dataset. A framework including 13 indicators to quantify the distance factors between countries from 5 perspectives (i.e., geographic distance, economic distance, cultural distance, academic distance, and industrial distance) is proposed. The relationships were conducted by the methods of descriptive analysis and regression analysis. The results show that international collaboration in the field of AI today is not prevalent (only 15.7%). All the separations in international collaborations have increased over years, except for the cultural distance in masculinity/felinity dimension and the industrial distance. The geographic distance, economic distance and academic distances have shown significantly negative relationships with the degree of international collaborations in the field of AI. The industrial distance has a significant positive relationship with the degree of international collaboration in the field of AI. Also, the results demonstrate that the participation of the United States and China have promoted the international collaboration in the field of AI. This study provides a comprehensive understanding of internationalizing AI research in geographic, economic, cultural, academic, and industrial aspects.
摘要：
Interest in assessing research impacts is increasing due to its importance for informing actions and funding allocation decisions. The level of innovation (also called “innovation degree” in the following article), one of the most essential factors that affect scientific literature’s impact, has also received increasing attention. However, current studies mainly focus on the overall innovation degree of scientific literature at the macro level, while ignoring the innovation degree of a specific knowledge element (KE), such as the method knowledge element (MKE). A macro level view causes difficulties in identifying which part of the scientific literature contains the innovations. To bridge this gap, a more fine-grained evaluation of academic papers is urgent. The fine-grained evaluation method can ensure the quality of a paper before being published and identify useful knowledge content in a paper for academic users. Different KEs can be used to perform the fine-grained evaluation. However, MKEs are usually considered as one of the most essential knowledge elements among all KEs. Therefore, this study proposes a framework to measure the innovation degree of method knowledge elements (MIDMKE) in scientific literature. In this framework, we first extract the MKEs using a rule-based approach and generate a cloud drop for each MKE using the biterm topic model (BTM). The generated cloud drop is then used to create a method knowledge cloud (MKC) for each MKE. Finally, we calculate the innovation score of a MKE based on the similarity between it and other MKEs of its type. Our empirical study on a China National Knowledge Infrastructure (CNKI) academic literature dataset shows the proposed approach can measure the innovation of MKEs in scientific literature effectively. Our proposed method is useful for both reviewers and funding agencies to assess the quality of academic papers. The dataset, the code for implementation the algorithms, and the complete experiment results will be released at:
https://github.com/haihua0913/midmke
.
期刊：
International Journal of Environmental Research and Public Health,2022年19(18):11195- ISSN：1661-7827
通讯作者：
Sui Li<&wdkj&>Jiandong Huang
作者机构：
[Zhang, Heng] Anhui Univ Finance & Econ, Sch Management Sci & Engn, Bengbu 233030, Peoples R China.;[Chang, Qian] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Li, Sui] Anhui Univ Finance & Econ, Sch Stat & Appl Math, Bengbu 233030, Peoples R China.;[Huang, Jiandong] Guangzhou Univ, Sch Civil Engn, Guangzhou 510006, Peoples R China.;[Huang, Jiandong] China Univ Min & Technol, Sch Mines, Xuzhou 221116, Jiangsu, Peoples R China.
通讯机构：
[Sui Li; Jiandong Huang] A;Authors to whom correspondence should be addressed.<&wdkj&>School of Mines, China University of Mining and Technology, Xuzhou 221116, China<&wdkj&>School of Civil Engineering, Guangzhou University, Guangzhou 510006, China<&wdkj&>Authors to whom correspondence should be addressed.<&wdkj&>School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu 233030, China
关键词：
sponge city construction;data envelopment analysis;efficiency evaluation;water ecological environment
摘要：
Sponge city construction (SCC) has improved the quality of the urban water ecological environment, and the policy implementation effect of SCC pilots is particularly remarkable. Based on the data envelopment analysis (DEA) model, this study employed the related index factors such as economy, ecology, infrastructure, and the population of the pilot city as the input, and the macro factors of SCC as the output, to scientifically evaluate the relative efficiency between the SCC pilots in China. Eleven representative SCC pilots were selected for analysis from the perspectives of static and dynamic approaches, and comparisons based on the horizontal analysis of the efficiency of SCC pilots were conducted and some targeted policy suggestions are put forward, which provide a reliable theoretical model and data support for the efficiency evaluation of SCC. This paper can be used as a reference for construction by providing a DEA model for efficiency evaluation methods and thus helps public sector decision makers choose the appropriate construction scale for SCC pilots.
期刊：
Journal of Informetrics,2022年16(4):101333 ISSN：1751-1577
通讯作者：
Xuli Tang
作者机构：
[Li, Xin] Huazhong Univ Sci & Technol, Sch Med & Hlth Management, Tongji Med Coll, Wuhan 430030, Hubei, Peoples R China.;[Tang, Xuli] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.;[Cheng, Qikai] Wuhan Univ, Sch Informat Management, Wuhan 430074, Hubei, Peoples R China.
通讯机构：
[Xuli Tang] S;School of Information Management, Central China Normal University, Wuhan 430079, Hubei, China
关键词：
Clinical citation count prediction;Multilayer perceptron neural network;Reference dimension;Biomedical paper
摘要：
The number of clinical citations received from clinical guidelines or clinical trials has been con-sidered as one of the most appropriate indicators for quantifying the clinical impact of biomedical papers. Therefore, the early prediction of clinical citation count of biomedical papers is critical to scientific activities in biomedicine, such as research evaluation, resource allocation, and clinical translation. In this study, we designed a four-layer multilayer perceptron neural network (MPNN) model to predict the clinical citation count of biomedical papers in the future by using 9,822,620 biomedical papers published from 1985 to 2005. We extracted ninety-one paper features from three dimensions as the input of the model, including twenty-one features in the paper dimen-sion, thirty-five in the reference dimension, and thirty-five in the citing paper dimension. In each dimension, the features can be classified into three categories, i.e., the citation-related features, the clinical translation-related features, and the topic-related features. Besides, in the paper di-mension, we also considered the features that have previously been demonstrated to be related to the citation counts of research papers. The results showed that the proposed MPNN model outper-formed the other five baseline models, and the features in the reference dimension were the most important. In all the three dimensions, the citation-related and topic-related features were more important than the clinical translation-related features for the prediction. It also turned out that the features helpful in predicting the citation count of papers are not important for predicting the clinical citation count of biomedical papers. Furthermore, we explored the MPNN model based on different categories of biomedical papers. The results showed that the clinical translation-related features were more important for the prediction of clinical citation count of basic papers rather than those papers closer to clinical science. This study provided a novel dimension (i.e., the ref-erence dimension) for the research community and could be applied to other related research tasks, such as the research assessment for translational programs. In addition, the findings in this study could be useful for biomedical authors (especially for those in basic science) to get more attention from clinical research.
期刊：
International Journal of Environmental Research and Public Health,2022年19(14):8675- ISSN：1661-7827
通讯作者：
Yanhui Li
作者机构：
[Yao, Qi; Li, Yanhui] Wuhan Coll, Management Sch, Wuhan 430212, Peoples R China.;[Yao, Qi; Li, Yanhui; Zhu, Shenjun] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
通讯机构：
[Yanhui Li] S;School of Information Management, Central China Normal University, Wuhan 430079, China<&wdkj&>Management School, Wuhan College, Wuhan 430212, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
Knowledge discovery in databases (KDD) has an important effect on various fields with the development of information science. Electricity energy forecasting (EEF), a primary application of KDD, aims to explore the inner potential rule of electrical data for the purpose to serve electricity-related organizations or groups. Meanwhile, the advent of the information society attracts more and more scholars to pay attention to EEF. The existing methods for EEF focus on using high-techs to improve the experimental results but fail to construct an applicable electricity energy KDD framework. To complement the research gap, our study aims to propose a gradient boosting machine-based KDD framework for electricity energy prediction and enrich knowledge discovery applications. To be specific, we draw on the traditional knowledge discovery process and techniques to make the framework reliable and extensible. Additionally, we leverage Gradient Boosting Machine (GBM) to improve the efficiency and accuracy of our approach. We also devise three metrics for the evaluation of the proposed framework including R-square (R2), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Besides, we collect the electricity energy consumption (EEC) as well as meteorological data from 2013 to 2016 in New York state and take the EEC prediction of New York State as an example. Finally, we conduct extensive experiments to verify the superior performance of our framework and the results show that our model achieves outstanding results for the three metrics (around 0.87 for R2, 60.15 for MAE, and 4.79 for MAPE). Compared with real value and the official prediction model, our approach also has a remarkable prediction ability. Therefore, we find that the proposed framework is feasible and reliable for EEF and could provide practical references for other types of energy KDD.
