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This summary is about a special issue of the International Journal on Digital Libraries (IJDL) that features selected papers from the 23rd International Conference on Asia-Pacific Digital Libraries (ICADL 2021). ICADL is an annual interdisciplinary conference that connects digital libraries, computer science, and library and information science communities. The special issue includes five papers that have undergone a rigorous review process and cover topics such as improving scientific publication retrieval, multi-label document classification, extracting leaderboards from research papers, and AI-powered peer review systems. The articles offer diverse perspectives and high-quality research within these fields. [Extracted from the article]

This document is a summary of a special issue of the International Journal on Digital Libraries (IJDL) that features selected papers from the 24th International Conference on Asia–Pacific Digital Libraries (ICADL 2022). ICADL is an annual interdisciplinary conference that connects digital library, computer science, and information science communities. The conference was held in Ha Noi, Vietnam, with the theme "From Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries." The special issue includes four papers that cover topics such as political opinions, web archives, cultural heritage content, and literature management. The papers underwent a rigorous review process and were selected based on their high quality. The authors hope that readers will find these papers interesting and insightful. [Extracted from the article]

In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it is challenging to assign newly published papers to their respective authors. Therefore, author name ambiguity is considered a critical open problem in digital libraries. This paper proposes an author name disambiguation approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use data collected from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset. [ABSTRACT FROM AUTHOR]

Metadata enrichment through text mining techniques is becoming one of the most significant tasks in digital libraries. Due to the exponential increase of open access publications, several new challenges have emerged. Raw data are usually big, unstructured, and come from heterogeneous data sources. In this paper, we introduce a text analysis framework implemented in extended SQL that exploits the scalability characteristics of modern database management systems. The purpose of this framework is to provide the opportunity to build performant end-to-end text mining pipelines which include data harvesting, cleaning, processing, and text analysis at once. SQL is selected due to its declarative nature which offers fast experimentation and the ability to build APIs so that domain experts can edit text mining workflows via easy-to-use graphical interfaces. Our experimental analysis demonstrates that the proposed framework is very effective and achieves significant speedup, up to three times faster, in common use cases compared to other popular approaches. [ABSTRACT FROM AUTHOR]

In the past two decades, digital libraries (DL) have increasingly supported computational studies of digitized books (Jett et al. The hathitrust research center extracted features dataset (2.0), 2020; Underwood, Distant horizons: digital evidence and literary change, University of Chicago Press, Chicago, 2019; Organisciak et al. J Assoc Inf Sci Technol 73:317–332, 2022; Michel et al. Science 331:176–182, 2011). Nonetheless, there remains a dearth of DL data provisions or infrastructures for research on book reception, and user-generated book reviews have opened up unprecedented research opportunities in this area. However, insufficient attention has been paid to real-world complexities and limitations of using these datasets in scholarly research, which may cause analytical oversights (Crawford and Finn, Geo J 80:491–502, 2015), methodological pitfalls (Olteanu et al. Front Big Data 2:13, 2019), and ethical concerns (Hu et al. Research with user-generated book review data: legal and ethical pitfalls and contextualized mitigations, Springer, Berlin, 2023; Diesner and Chin, Gratis, libre, or something else? regulations and misassumptions related to working with publicly available text data, 2016). In this paper, we present three case studies that contextually and empirically investigate book reviews for their temporal, cultural, and socio-participatory complexities: (1) a longitudinal analysis of a ranked book list across ten years and over one month; (2) a text classification of 20,000 sponsored and 20,000 non-sponsored books reviews; and (3) a comparative analysis of 537 book ratings from Anglophone and non-Anglophone readerships. Our work reflects on both (1) data curation challenges that researchers may encounter (e.g., platform providers' lack of bibliographic control) when studying book reviews and (2) mitigations that researchers might adopt to address these challenges (e.g., how to align data from various platforms). Taken together, our findings illustrate some of the sociotechnical complexities of working with user-generated book reviews by revealing the transiency, power dynamics, and cultural dependency in these datasets. This paper explores some of the limitations and challenges of using user-generated book reviews for scholarship and calls for critical and contextualized usage of user-generated book reviews in future scholarly research. [ABSTRACT FROM AUTHOR]

Recommendation (recommender) systems (RS) have played a significant role in both research and industry in recent years. In the area of academia, there is a need to help researchers discover the most appropriate and relevant scientific information through recommendations. Nevertheless, we argue that there is a major gap between academic state-of-the-art RS and real-world problems. In this paper, we present a novel multi-staged RS based on clustering, graph modeling and deep learning that manages to run on a full dataset (scientific digital library) in the magnitude of millions users and items (papers). We run several tests (experiments/evaluation) as a means to find the best approach regarding the tuning of our system; so, we present and compare three versions of our RS regarding recall and NDCG metrics. The results show that a multi-staged RS that utilizes a variety of techniques and algorithms is able to face real-world problems and large academic datasets. In this way, we suggest a way to close or minimize the gap between research and industry value RS. [ABSTRACT FROM AUTHOR]

This special issue features the selected works of authors who have presented papers at the 2022 iteration of the Joint Conference on Digital Libraries (JCDL) in Cologne, Germany. The motto of the conference was "Bridging Worlds" and was run as a fully hybrid event. Ten papers covering all aspects of Digital Libraries, namely Natural Language Processing, Information Retrieval, User Behavior, Scholarly Communication, Classification, Information Extraction are included in this issue. [ABSTRACT FROM AUTHOR]

Digital Library Systems are widely used in the Higher Education sector, through the use of Institutional Repositories (IRs), to collect, store, manage and make available scholarly research output produced by Higher Education Institutions (HEIs). This wide application of IRs is a direct response to the increase in scholarly research output produced. In order to facilitate discoverability of digital content in IRs, accurate, consistent and comprehensive association of descriptive metadata to digital objects during ingestion into IRs is crucial. However, due to human errors resulting from complex IR ingestion workflows, most digital content in IRs have incorrect and inconsistent descriptive metadata. While there exists a broad spectrum of descriptive metadata elements, subject headings present a classic example of a crucial metadata element that adversely affects discoverability of digital content when incorrectly and inconsistently specified. This paper outlines a case study conducted at an HEI—The University of Zambia—in order to demonstrate the effectiveness of integrating controlled subject vocabularies during the ingestion of digital objects in to IRs. A situational analysis was conducted to understand how subject headings are associated with digital objects and to analyse subject headings associated with already ingested digital objects. In addition, an exploratory study was conducted to determine domain-specific subject headings to be integrated with the IR. Furthermore, a usability study was conducted in order to comparatively determine the usefulness of using controlled vocabularies during the ingestion of digital objects into IRs. Finally, multi-label classification experiments were carried out where digital objects were assigned with more than one class. The results of the study revealed that a noticeable number of digital content is associated with incorrect subject categories and, additionally, associated with few subjects headings: two or less subject headings (71.2 % ), with a significant number of subject headings (92.1 % ) being associated with a single publication. A comparative study conducted suggests that IRs integrated with controlled vocabularies are perceived to be more usable (SUS Score = 68.9) when compared with IRs without controlled vocabularies (SUS Score = 66.2). Furthermore, the effectiveness of the multi-label arXiv subjects classifier demonstrates the viability of integrating automated techniques for subject classification. [ABSTRACT FROM AUTHOR]

Information extraction can support novel and effective access paths for digital libraries. Nevertheless, designing reliable extraction workflows can be cost-intensive in practice. On the one hand, suitable extraction methods rely on domain-specific training data. On the other hand, unsupervised and open extraction methods usually produce not-canonicalized extraction results. This paper is an extension of our original work and tackles the question of how digital libraries can handle such extractions and whether their quality is sufficient in practice. We focus on unsupervised extraction workflows by analyzing them in case studies in the domains of encyclopedias (Wikipedia), Pharmacy, and Political Sciences. As an extension, we analyze the extractions in more detail, verify our findings on a second extraction method, discuss another canonicalizing method, and give an outlook on how non-English texts can be handled. Therefore, we report on opportunities and limitations. Finally, we discuss best practices for unsupervised extraction workflows. [ABSTRACT FROM AUTHOR]

Retrievability measures the influence a retrieval system has on the access to information in a given collection of items. This measure can help in making an evaluation of the search system based on which insights can be drawn. In this paper, we investigate the retrievability in an integrated search system consisting of items from various categories, particularly focussing on datasets, publications and variables in a real-life digital library. The traditional metrics, that is, the Lorenz curve and Gini coefficient, are employed to visualise the diversity in retrievability scores of the three retrievable document types (specifically datasets, publications, and variables). Our results show a significant popularity bias with certain items being retrieved more often than others. Particularly, it has been shown that certain datasets are more likely to be retrieved than other datasets in the same category. In contrast, the retrievability scores of items from the variable or publication category are more evenly distributed. We have observed that the distribution of document retrievability is more diverse for datasets as compared to publications and variables. [ABSTRACT FROM AUTHOR]

This article maps considerations of inclusiveness and support for students with disabilities by reviewing articles within the field of learning analytics. The study involved a PRISMA-informed systematic review of two popular digital libraries, namely Clarivate's Web of Science, and Elsevier's Scopus for peer-reviewed journal articles and conference proceedings. A final corpus of 26 articles was analysed. Findings show that although the field of learning analytics emerged in 2011, none of the studies identified here covered topics of inclusiveness in education before the year of 2016. Screening also shows that learning analytics provides great potential to promote inclusiveness in terms of reducing discrimination, increasing retention among disadvantaged students, and validating particular learning designs for marginalised groups. Gaps in this potential are also identified. The article aims to provide valuable insight into what is known about learning analytics and inclusiveness and contribute knowledge to this particular nascent area for researchers and institutional stakeholders. [ABSTRACT FROM AUTHOR]

Finding relevant publications in the scientific domain can be quite tedious: Accessing large-scale document collections often means to formulate an initial keyword-based query followed by many refinements to retrieve a sufficiently complete, yet manageable set of documents to satisfy one's information need. Since keyword-based search limits researchers to formulating their information needs as a set of unconnected keywords, retrieval systems try to guess each user's intent. In contrast, distilling short narratives of the searchers' information needs into simple, yet precise entity-interaction graph patterns provides all information needed for a precise search. As an additional benefit, such graph patterns may also feature variable nodes to flexibly allow for different substitutions of entities taking a specified role. An evaluation over the PubMed document collection quantifies the gains in precision for our novel entity-interaction-aware search. Moreover, we perform expert interviews and a questionnaire to verify the usefulness of our system in practice. This paper extends our previous work by giving a comprehensive overview about the discovery system to realize narrative query graph retrieval. [ABSTRACT FROM AUTHOR]

The peer-review process plays a pivotal role in maintaining the quality and credibility of scientific publications. However, in recent times, there has been an increase in unhelpful and overly critical reviews, which can be detrimental to the process. This surge in unconstructive reviews can be attributed to a higher volume of paper submissions and the inclusion of inexperienced reviewers. Consequently, authors are left with limited valuable feedback, compromising the effectiveness of peer review. Peer review feedback must be not only objective but also delivered politely and constructively. Our study introduces a novel approach to assessing the constructiveness and tone of peer reviews. We propose a two-fold taxonomy that categorizes reviews into five labels for constructiveness and three labels for politeness. To facilitate this research, we have created a corpus of 2716 review sentences, which have been manually annotated with a high inter-annotation agreement of 88.27% for constructiveness and 83.49% for politeness, offering a valuable resource for the scientific community. Furthermore, we present a multi-task model named "Multi-Label Critique (MLC)"that leverages ToxicBERT representations and deep neural attention mechanisms. This model adeptly evaluates the constructiveness and politeness of review sentences, outperforming competitive baseline models with an impressive accuracy of 87.4%. Our paper includes an extensive analysis of the MLC model and its variations. Our research is a significant step towards contributing to the development of constructive peer-review reports. [ABSTRACT FROM AUTHOR]

In this paper, several points of view are adopted to present the research directions of contemporary Piagetian historiography. This article first notes the immensity of the secondary literature (20'000 texts) related to Piaget's work, between empirical and historical-epistemological works. In order to get a more precise idea, we carried out various analyses of the scope of influence of his work: on the 2000 keywords of the thesaurus of secondary literature from 1945 to 2012; on the reception of Piaget's idea in France between 1920 and 1940; and on the disciplines of insertion of a thousand works from his private library, which were dedicated to him. These analyses corroborate Piaget's multidisciplinary impact in the 20th century science and culture and indicate the existence of an international Piagetian movement, which has not yet been studied. The factors of production and the recent orientations of the secondary literature are then discussed, showing the changes in the historiography of the last 50 years. Finally, future research directions are outlined between the exploitation of unknown archives and the use of a digital edition of Piaget's work, which is currently underway. [ABSTRACT FROM AUTHOR]

Objectives: The study endeavors to undertake a bibliometric analysis on molar distalization, with the objective of illuminating its evolutionary trajectory, current status, and prognosticating future research hotspots and trends. Material and methods: A comprehensive exploration of the literature on molar distalization was carried out by conducting a search in the Web of Science (WOS) core database of the University of Hong Kong Electronic Library. The search for topic terms employed included "molar distalization," "molar distalisation," "move molar distally," "molar distal movement," and "molar backwards." The search results were subsequently subjected to meticulous analysis using CiteSpace software. This analysis encompassed various facets such as the citation count; the geographical distribution of the countries, institutions, and journals responsible for publishing the articles; the distribution of the authors; the utilization of keywords within the articles; and the analysis of references. Results: A total of 516 articles were included in the analysis. The top 5 countries in terms of the number of published papers were the United States (USA), South Korea, Turkey, Italy, and Germany, and the top 5 institutions in terms of the number of published papers were Kyung Hee University, A.T. Still University of Health Sciences, Catholic University of Korea, Seoul St. Mary's Hospital, and Universidade de Sao Paulo. The top 5 authors in terms of the number of published papers were Park, Kook, Bayome, Janson, and Lee. There was little cooperation overall. The top 3 journals in terms of the most published related articles were all orthodontic-related journals. After molar distalization and anchorage, the most frequently used keywords were distalization, movement, and pendulum appliance. Kinzinger GSM is the most frequently cited author in references, and one of his articles also has the highest centrality score in references. Conclusions: As the tides of time shift and scholars display an ever-growing dedication to unraveling the intricacies of this therapeutic modality, the realm of molar distalization has undergone notable advancements in technology. Initially, the traditional appliance suffered from aesthetic drawbacks and discomfort. However, contemporary iterations of the appliance have transcended these limitations, boasting enhanced elegance and convenience while concurrently elevating their efficacy. Nevertheless, limitations of current appliances, including their durability and propensity for recurrence post-treatment, continue to necessitate further advancement. Hence, the ongoing scientific inquiry aims to delve deeper into refining treatment modalities and fabricating cutting-edge appliances within this realm. Clinical relevance. This study holds the potential to significantly enhance the ability of orthodontists to devise treatment protocols and offer state-of-the-art clinical recommendations, thereby empowering them to deliver advanced and refined orthodontic interventions. [ABSTRACT FROM AUTHOR]

This scoping review provides an overview of the conduct, origin and distribution of previous peer-reviewed research on children's and adolescents' social media use. Searches were conducted in electronic databases Scopus, Web of Science, and ACM Digital Library, identifying 244 studies that met the inclusion criteria. The review reveals a steady increase in publications over the years, with most originating from the United States, the United Kingdom, Australia, and Canada. Among these studies, 174 employ quantitative methods, 30 utilize qualitative approaches, 19 use mixed methods, and 21 are review studies. The research predominantly falls within the disciplines of medicine and health, and psychology. The findings highlight a significant imbalance between qualitative and quantitative research, with quantitative, cross-sectional studies relying on self-reported questionnaire results being the most common. However, quantitative studies do not adequately capture the details of the complex and varied nature of social media use, nor do the common cross-sectional nature of those studies enable insights to be drawn on changes and variations over time or establish causal relationships. This scoping review reveals a growing interest in this topic, but also highlights the need for greater geographical and disciplinary diversity, varied methodological approaches, and more heterogeneous participant involvement in future research. This review suggests a pressing need for more empirical, qualitative research that examines situated social media use from the perspectives of children and adolescents. Moreover, additional longitudinal studies are necessary to offer causal explanations and a deeper understanding of the evolving dynamics of social media use among younger populations. [ABSTRACT FROM AUTHOR]

Big Data is still gaining attention as a fundamental building block of the Artificial Intelligence and Machine Learning world. Therefore, a lot of effort has been pushed into Big Data research in the last 15 years. The objective of this Systematic Literature Review is to summarize the current state of the art of the previous 15 years of research about Big Data by providing answers to a set of research questions related to the main application domains for Big Data analytics; the significant challenges and limitations researchers have encountered in Big Data analysis, and emerging research trends and future directions in Big Data. The review follows a predefined procedure that automatically searches five well-known digital libraries. After applying the selection criteria to the results, 189 primary studies were identified as relevant, of which 32 were Systematic Literature Reviews. Required information was extracted from the 32 studies and summarized. Our Systematic Literature Review sketched the picture of 15 years of research in Big Data, identifying application domains, challenges, and future directions in this research field. We believe that a substantial amount of work remains to be done to align and seamlessly integrate Big Data into data-driven advanced software solutions of the future. [ABSTRACT FROM AUTHOR]

In the late twentieth century, the term "System of Systems" (SoS) became popular to describe a complex system made up of a combination of independent constituent systems. Since then, several studies have been conducted to support and assess SoS management, functionality, and performance. Due to the evolutionary nature of SoS and the non-composability of the security properties of its constituent systems, it is difficult to assess or evaluate SoS security. This paper provides an up-to-date survey on SoS security, aimed at stimulating and guiding further research efforts. This systematic mapping study (SMS) focuses on SoS security, privacy, and trust. Our SMS identified 1828 studies from 6 digital libraries, 87 of which were selected that presented approaches analyzing, evaluating, or improving security. We classified these studies using nine research questions that focused on the nature of the studies, the studied SoS, or the study validation. After examining the selected studies, we identified six gaps and as many future work directions. More precisely, we observed that few studies examine SoS problems and instead propose specific solutions, making it challenging to develop generalizable approaches. Furthermore, the lack of standardization has hindered the reuse of existing approaches, making it difficult for solutions to be generalized to other SoS. In addition, the lack of descriptions of industrial environments in the literature makes it difficult to design realistic validation environments. As a result, the validation of new SoS research remains a challenge in the field. [ABSTRACT FROM AUTHOR]

This special issue of the International Journal on Digital Libraries features extended versions of papers presented at the 2021 ACM/IEEE Joint Conference on Digital Libraries, which was held online due to the Covid-19 pandemic. The articles cover various topics related to the creation and discovery of digital content and metadata. They explore issues such as the transition from user-authored metadata to automated metadata, improving metadata generation for biological specimen images, building datasets for information extraction from electronic theses and dissertations, understanding user perceptions of relevance in different temporal eras, and addressing the accuracy and trustworthiness of archived social media posts. These articles highlight the importance of accessibility, relevance, and provenance in the digital library landscape. [Extracted from the article]

In democratic contexts, the discussion of digital technology in the field of archival heritage highlights its potential benefits for expanding access to archives to the wider public. It also focuses on the legal, moral, and ethical issues raised by copyright or privacy concerns. Using the digital archives of the Islamic Republic of Iran (IRI) as a case study, this article thoroughly analyses another facet of digital technology, namely its role in building and perpetuating ignorance about the past through the mass digitization of archives in authoritarian contexts. The analysis scrutinizes digitally processed archives that are accessible as carefully curated data—some digitized and some born digital—through a network of open access web-resources developed by several institutions in the IRI. The article briefly considers the broader context of access restrictions to archives and information, and of the intentional and institutionalized opacity of this field in the IRI. These digitally processed archives are evaluated through the lens of archival science theory. Several macro- and micro-aspects of the kind of knowledge that scholars can produce from these digitally processed historical sources are considered. [ABSTRACT FROM AUTHOR]

When searching within an academic digital library, a variety of information seeking strategies may be employed. The purpose of this study is to determine whether graduate students choose appropriate information seeking strategies for the complexity of a given search scenario and to explore among other factors that could influence their decisions. We used a survey method in which participants ( n = 176 ) were asked to recall their most recent instance of an academic digital library search session that matched two given scenarios (randomly chosen from four alternatives) and, for each scenario, identify whether they employed search strategies associated with four different information seeking models. Among the search strategies, only lookup search was used in a manner that was consistent with the complexity of the search scenario. Other factors that influenced the choice of strategy were the discipline of study and the type of academic search training received. Patterns of search tool use with respect to the complexity of the search scenarios were also identified. These findings highlight that not only is it important to train graduate students on how to conduct academic digital library searches, more work is needed to train them on matching the information seeking strategies to the complexity of their search tasks and developing interfaces that guide their search process. [ABSTRACT FROM AUTHOR]

This study explores the role of financial support in the digital transformation of Chinese A-share-listed companies from 2001 to 2020. By utilizing the moderating effect model and threshold regression model, this study finds the following results: (1) Digital transformation positively impacts innovation, and the support of banking and capital markets further strengthens this impact. (2) With the development of banking and capital markets, the impact of digital transformation on innovation changes from negative to positive, which is also reflected in the subsamples of Eastern companies, small and medium-sized companies (SMEs), and non-SMEs. (3) The study reveals that only the capital market in the non-Eastern region has no threshold, and capital market support is effective only for non-SMEs when it reaches a higher level. These findings have important implications for policymakers in promoting digital transformation through financial support and help companies understand how to use financial support to improve competitiveness. [ABSTRACT FROM AUTHOR]

Diabetes mellitus remains a major global health issue, and great attention is directed at natural therapeutics. This systematic review aimed to assess the potential of flavonoids as antidiabetic agents by investigating their inhibitory effects on α-glucosidase and α-amylase, two key enzymes involved in starch digestion. Six scientific databases (PubMed, Virtual Health Library, EMBASE, SCOPUS, Web of Science, and WHO Global Index Medicus) were searched until August 21, 2022, for in vitro studies reporting IC50 values of purified flavonoids on α-amylase and α-glucosidase, along with corresponding data for acarbose as a positive control. A total of 339 eligible articles were analyzed, resulting in the retrieval of 1643 flavonoid structures. These structures were rigorously standardized and curated, yielding 974 unique compounds, among which 177 flavonoids exhibited inhibition of both α-glucosidase and α-amylase are presented. Quality assessment utilizing a modified CONSORT checklist and structure–activity relationship (SAR) analysis were performed, revealing crucial features for the simultaneous inhibition of flavonoids against both enzymes. Moreover, the review also addressed several limitations in the current research landscape and proposed potential solutions. The curated datasets are available online at https://github.com/MedChemUMP/FDIGA. [ABSTRACT FROM AUTHOR]