A Study on Indexing Efficiency and Retrieval Accuracy for Author Name Search of Academic Papers

Most academic information has its creator, that is, a subject who has created the information. The subject can be an individual, a group, or an institution, and can be a nation depending on the nature of the relevant information. Most web data are composed of a title, an author, and contents. A paper which is under the academic information category has metadata including a title, an author, keyword, abstract, data about publication, place of publication, ISSN, and the like. A patent has metadata including the title, an applicant, an inventor, an attorney, IPC, number of application, and claims of the invention. Most web-based academic information services enable users to search the information by processing the meta-information. An important element is to search information by using the author field which corresponds to a personal name. This study suggests a method of efficient indexing and using the adjacent operation result ranking algorithm to which phrase search-based boosting elements are applied, and thus improving the accuracy of the search results of author name. This method can be effectively applied to providing accurate search results in the academic information services.

Towards Effective Author Name Disambiguation by Hybrid Attention

Author name disambiguation(AND)is a central task in academic search,which has received more attention recently accompanied by the increase of authors and academic publications.To tackle the AND problem,existing studies have proposed various approaches based on different types of information,such as raw document features(e.g.,co-authors,titles,and keywords),the fusion feature(e.g.,a hybrid publication embedding based on multiple raw document features),the local structural information(e.g.,a publication's neighborhood information on a graph),and the global structural information(e.g.,interactive information between a node and others on a graph).However,there has been no work taking all the above-mentioned information into account and taking full advantage of the contributions of each raw document feature for the AND problem so far.To fill the gap,we propose a novel framework named EAND(Towards Effective Author Name Disambiguation by Hybrid Attention).Specifically,we design a novel feature extraction model,which consists of three hybrid attention mechanism layers,to extract key information from the global structural information and the local structural information that are generated from six similarity graphs constructed based on different similarity coefficients,raw document features,and the fusion feature.Each hybrid attention mechanism layer contains three key modules:a local structural perception,a global structural perception,and a feature extractor.Additionally,the mean absolute error function in the joint loss function is used to introduce the structural information loss of the vector space.Experimental results on two real-world datasets demonstrate that EAND achieves superior performance,outperforming state-of-the-art methods by at least+2.74%in terms of the micro-F1 score and+3.31%in terms of the macro-F1 score.

Beyond authorship: Analyzing contributions in PLOS ONE and the challenges of appropriate attribution

Purpose:This study aims to evaluate the accuracy of authorship attributions in scientific publications,focusing on the fairness and precision of individual contributions within academic works.Design/methodology/approach:The study analyzes 81,823 publications from the journal PLOS ONE,covering the period from January 2018 to June 2023.It examines the authorship attributions within these publications to try and determine the prevalence of inappropriate authorship.It also investigates the demographic and professional profiles of affected authors,exploring trends and potential factors contributing to inaccuracies in authorship.Findings:Surprisingly,9.14%of articles feature at least one author with inappropriate authorship,affecting over 14,000 individuals(2.56%of the sample).Inappropriate authorship is more concentrated in Asia,Africa,and specific European countries like Italy.Established researchers with significant publication records and those affiliated with companies or nonprofits show higher instances of potential monetary authorship.Research limitations:Our findings are based on contributions as declared by the authors,which implies a degree of trust in their transparency.However,this reliance on self-reporting may introduce biases or inaccuracies into the dataset.Further research could employ additional verification methods to enhance the reliability of the findings.Practical implications:These findings have significant implications for journal publishers,Beyond authorship:Analyzing contributions in PLOS ONE and Maddi,A.,&the challenges of appropriate attribution highlighting the necessity for robust control mechanisms to ensure the integrity of authorship attributions.Moreover,researchers must exercise discernment in determining when to acknowledge a contributor and when to include them in the author list.Addressing these issues is crucial for maintaining the credibility and fairness of academic publications.Originality/value:This study contributes to an understanding of critical issues within academic authorship,shedding light on the prevalence and impact of inappropriate authorship attributions.By calling for a nuanced approach to ensure accurate credit is given where it is due,the study underscores the importance of upholding ethical standards in scholarly publishing.

To include the title of the paper, name of the author, institution (affiliation), city.

To be placed on the lower part of the title page, or on a separate pageIt should be no longer than 5% of the article.

To include the title of the paper, name of the author, institution (affiliation), city.

To be placed on the lower Part of title title Page,or on a separate Page,It should be no loflger than 5％ of the article.

Peculiarities of gender disambiguation and ordering of non-English authors’names for Economic papers beyond core databases

Purpose:To supplement the quantitative portrait of Ukrainian Economics discipline with the results of gender and author ordering analysis at the level of individual authors,special methods of working with bibliographic data with a predominant share of non-English authors are used.The properties of gender mixing,the likelihood of male and female authors occupying the first position in the authorship list,as well as the arrangements of names are studied.Design/methodology/approach:A data set containing bibliographic records related to Ukrainian journal publications in the field of Economics is constructed using Crossref metadata.Partial semi-automatic disambiguation of authors’names is performed.First names,along with gender-specific ethnic surnames,are used for gender disambiguation required for further comparative gender analysis.Random reshuffling of data is used to determine the impact of gender correlations.To assess the level of alphabetization for our data set,both Latin and Cyrillic versions of names are taken into account.Findings:The lack of well-structured metadata and the poor use of digital identifiers lead to numerous problems with automatization of bibliographic data pre-processing,especially in the case of publications by non-Western authors.The described stages for working with such specific data help to work at the level of authors and analyse,in particular,gender issues.Despite the larger number of female authors,gender equality is more likely to be reported at the individual level for the discipline of Ukrainian Economics.The tendencies towards collaborative or solo-publications and gender mixing patterns are found to be dependent on the journal:the differences for publications indexed in Scopus and/or Web of Science databases are found.It has also been found that Ukrainian Economics research is characterized by rather a non-alphabetical order of authors.Research limitations:Only partial authors’name disambiguation is performed in a semi-automatic way.Gender labels can be derived only for authors declared by full First names or gender-specific Last names.Practical implications:The typical features of Ukrainian Economic discipline can be used to perform a comparison with other countries and disciplines,to develop an informed-based assessment procedure at the national level.The proposed way of processing publication data can be borrowed to enrich metadata about other research disciplines,especially for non-English speaking countries.Originality/value:To our knowledge,this is the first large-scale quantitative study of Ukrainian Economic discipline.The results obtained are valuable not only at the national level,but also contribute to general knowledge about Economic research,gender issues,and authors’names ordering.An example of the use of Crossref data is provided,while this data source is still less used due to a number of drawbacks.Here,for the first time,attention is drawn to the explicit use of the features of the Slavic authors’names.

To include the title of the paper, name of the author,institution(affiliation),city.

To be placed on the lower part of the title page, or on a separate page,It should be no longer than 5% of the article.

Information for Authors

Chinese Phaysics Letters(CPL)is a peer-reviewed,inter-national and multidisciplinary journal sponsored by the Chi-nese Phaysical Society(CPS)and Institute of Physics,CAS,and hosted online by IOP Publishing Ltd.Launched in 1984 asthe flagship journal of CPS,CPL has become one of the mostprestigious periodicals published in China,and been among thegood choices for worldwide physicists to disseminate their mostimportant breakthroughs.

Information for Authors

Chinese Physics Letters(CPL)is a peer-reviewed,international and multidisciplinary journal sponsored by the Chinese Physical Society(CPS)and Institute of Physics,CAS,and hosted online by IOP Publishing Ltd.Launched in 1984 as the flagship journal of CPS,CPL has become one of the most prestigious periodicals published in China.

Guide for Authors

Introduction Types ofpaper Contributions falling into the following categories will be considered for publication:Reviews,Technical papers,Theoretical papers,and Editorial.Please ensure that you select the appropriate article type from the list of options when making your submission.Authors contributing to special issues should ensure that they select the special issue article type from this list.

Information for Authors

General Journal of Beijing Institute of Technology(JBIT)is a periodical publication on science and technology published by Beijing Institute of Technology under the sponsorship of the Ministry of Industry and Information Technology of the People's Republic of China.JBIT was inaugurated in 1992.

Information for Authors

General Journal of Bejing Institute of Technology(JBIT)is a periodical publication on science andtechnology published by Beijing Institute of Technology under the sponsorship of the Ministry ofIndustry and Information Technology of the People's Republic of China.JBIT was inaugurated in 1992.

Predicting Age and Gender in Author Profiling: A Multi-Feature Exploration

Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author’s personalinformation, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic,semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, andmarketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French,etc.However, the research on RomanUrdu is not up to the mark.Hence, this study focuses on detecting the author’sage and gender based on Roman Urdu text messages. The dataset used in this study is Fire’18-MaponSMS. Thisstudy proposed an ensemble model based on AdaBoostM1 and Random Forest (AMBRF) for AP using multiplelinguistic features that are stylistic, character-based, word-based, and sentence-based. The proposed model iscontrasted with several of the well-known models fromthe literature, including J48-Decision Tree (J48),Na飗e Bays(NB), K Nearest Neighbor (KNN), and Composite Hypercube on Random Projection (CHIRP), NB-Updatable,RF, and AdaboostM1. The overall outcome shows the better performance of the proposed AdaboostM1 withRandom Forest (ABMRF) with an accuracy of 54.2857% for age prediction and 71.1429% for gender predictioncalculated on stylistic features. Regarding word-based features, age and gender were considered in 50.5714% and60%, respectively. On the other hand, KNN and CHIRP show the weakest performance using all the linguisticfeatures for age and gender prediction.

Information for Authors

General Journal of Bejing Institute of Technology(JBIT)is a periodical publication on science andtechnology published by Beijing Institute of Technology under the sponsorship of the Ministry ofIndustry and Information Technology of the People's Republic of China.JBIT was inaugurated in 1992.

Instructions for Authors

The Chinese Journal of Multiple Organ Diseases in the Elderly(Zhonghua Laonian Duoqiguan Jibing Zazhi)(ISSN 1671-5403)is launched in 2002 and published monthly by Chinese PLA General Hospital in Beijing,China.

High-speed Railway Guide for Authors

Types of paper Contributions falling into the following categories will be considered for publication:Original research papers,reviews,etc.Please ensure that you select the appropriate article type from the list of options when making your submission.Authors contributing to special issues should ensure that they select the special issue article type from this list.

ABMRF:An Ensemble Model for Author Profiling Based on Stylistic Features Using Roman Urdu

This study explores the area of Author Profiling(AP)and its importance in several industries,including forensics,security,marketing,and education.A key component of AP is the extraction of useful information from text,with an emphasis on the writers’ages and genders.To improve the accuracy of AP tasks,the study develops an ensemble model dubbed ABMRF that combines AdaBoostM1(ABM1)and Random Forest(RF).The work uses an extensive technique that involves textmessage dataset pretreatment,model training,and assessment.To evaluate the effectiveness of several machine learning(ML)algorithms in classifying age and gender,including Composite Hypercube on Random Projection(CHIRP),Decision Trees(J48),Na飗e Bayes(NB),K Nearest Neighbor,AdaboostM1,NB-Updatable,RF,andABMRF,they are compared.The findings demonstrate thatABMRFregularly beats the competition,with a gender classification accuracy of 71.14%and an age classification accuracy of 54.29%,respectively.Additional metrics like precision,recall,F-measure,Matthews Correlation Coefficient(MCC),and accuracy support ABMRF’s outstanding performance in age and gender profiling tasks.This study demonstrates the usefulness of ABMRF as an ensemble model for author profiling and highlights its possible uses in marketing,law enforcement,and education.The results emphasize the effectiveness of ensemble approaches in enhancing author profiling task accuracy,particularly when it comes to age and gender identification.