arXiv cs.DL papers @arxiv_csdl@qoto.org

The world of scientific publishing is changing; the days of an old type of subscription-based earnings for publishers seem over, and we are entering a new era. It seems as if an ever-increasing number of journals from disparate publishers are going Gold, Open Access that is, yet have we rigorously ascertained the issue in its entirety, or are we touting the strengths and forgetting about constructive criticism and careful weighing of evidence? We will therefore present the current state of the art, in a compact review/bibliometrics style, of this more relevant than ever hot topic and suggest solutions that are most likely to be acceptable to all parties--while the performed analysis also shows there seems to be a link between trends in scientific publishing and tumultuous world events, which in turn has a special significance for the publishing environment in the current world stage.

Patent documents in the patent database (PatDB) are crucial for research, development, and innovation as they contain valuable technical information. However, PatDB presents a multifaceted challenge compared to publicly available preprocessed databases due to the intricate nature of the patent text and the inherent sparsity within the patent citation network. Although patent text analysis and citation analysis bring new opportunities to explore patent data mining, no existing work exploits the complementation of them. To this end, we propose a joint semantic-topological evolutionary graph learning approach (PatSTEG) to model the formation dynamics of patent citation networks. More specifically, we first create a real-world dataset of Chinese patents named CNPat and leverage its patent texts and citations to construct a patent citation network. Then, PatSTEG is modeled to study the evolutionary dynamics of patent citation formation by considering the semantic and topological information jointly. Extensive experiments are conducted on CNPat and public datasets to prove the superiority of PatSTEG over other state-of-the-art methods. All the results provide valuable references for patent literature research and technical exploration.

Medical imaging papers often focus on methodology, but the quality of the algorithms and the validity of the conclusions are highly dependent on the datasets used. As creating datasets requires a lot of effort, researchers often use publicly available datasets, there is however no adopted standard for citing the datasets used in scientific papers, leading to difficulty in tracking dataset usage. In this work, we present two open-source tools we created that could help with the detection of dataset usage, a pipeline \url{https://github.com/TheoSourget/Public_Medical_Datasets_References} using OpenAlex and full-text analysis, and a PDF annotation software \url{https://github.com/TheoSourget/pdf_annotator} used in our study to manually label the presence of datasets. We applied both tools on a study of the usage of 20 publicly available medical datasets in papers from MICCAI and MIDL. We compute the proportion and the evolution between 2013 and 2023 of 3 types of presence in a paper: cited, mentioned in the full text, cited and mentioned. Our findings demonstrate the concentration of the usage of a limited set of datasets. We also highlight different citing practices, making the automation of tracking difficult.

The research content hosted by arXiv is not fully accessible to everyone due to disabilities and other barriers. This matters because a significant proportion of people have reading and visual disabilities, it is important to our community that arXiv is as open as possible, and if science is to advance, we need wide and diverse participation. In addition, we have mandates to become accessible, and accessible content benefits everyone. In this paper, we will describe the accessibility problems with research, review current mitigations (and explain why they aren't sufficient), and share the results of our user research with scientists and accessibility experts. Finally, we will present arXiv's proposed next step towards more open science: offering HTML alongside existing PDF and TeX formats. An accessible HTML version of this paper is also available at https://info.arxiv.org/about/accessibility_research_report.html

This study presents an overview of the literature on virtual academic conferences, which have gained prominence due to the COVID-19 pandemic. We conducted a scoping review analyzing 147 documents covering the literature available up to October 5th, 2021. We examined the development of the field focusing on the evolution of virtual academic conferences, main themes in the literature, and its methodological and theoretical approaches. The results indicate that the existing literature on virtual academic conferences is mainly descriptive and lacks a theoretical framework. Future research should focus on developing a solid theoretical framework to guide empirically and methodologically robust studies on virtual academic conferences. We emphasize the importance of recognizing their advantages and disadvantages from the perspectives of different groups of scholars. This will be a crucial step in establishing a framework for identifying and addressing dilemmas in online scholarly communication according to the responsible research and innovation approach.

HERITRACE is a semantic data management system tailored for the GLAM sector. It is engineered to streamline data curation for non-technical users while also offering an efficient administrative interface for technical staff. The paper compares HERITRACE with other established platforms such as OmekaS, Semantic MediaWiki, Research Space, and CLEF, emphasizing its advantages in user friendliness, provenance management, change tracking, customization capabilities, and data integration. The system leverages SHACL for data modeling and employs the OpenCitations Data Model (OCDM) for provenance and change tracking, ensuring a harmonious blend of advanced technical features and user accessibility. Future developments include the integration of a robust authentication system and the expansion of data compatibility via the RDF Mapping Language (RML), enhancing HERITRACE's utility in digital heritage management.

One of the core functions of an academic institution is to generate knowledge, disseminate it to the intended audiences, and preserve it for future use. Academic institutions are now establishing Institutional Repositories (IRs) to collect produced resources to facilitate accessibility, dissemination, utilization, and management of intellectual materials produced within an institution. This study aimed to assess postgraduate students motives for utilizing IR resources and the challenges they encounter when utilizing IR resources at the University of Dar es Salaam. This study was conducted using a descriptive study design whereby it used both qualitative and quantitative research approaches. The population of this study comprised postgraduate students, librarians, and ICT personnel from the University of Dar es Salaam. A sample of 102 respondents was drawn conveniently and purposively for this study. Data were collected through questionnaires, interviews, as well as a review of documentary sources. Quantitative data were analyzed through a Version 16 Statistics Package for Social Science and qualitative data were analyzed using content analysis. The findings indicate that access to fulltext documents, the relevance of IR resources, and easy searching of the materials in the repository system motivate the utilization of IR resources. However, several challenges impede the utilization of these resources including unreliable internet access, inaccessibility of full-text and lack of guiding policy have been revealed as the major challenges toward utilization of IR resources. The study recommends training postgraduate students on the general use of IRs. Also, the University management should develop an IR policy that will guide the utilization of IR resources

MediaWiki and Wikipedia authors usually use LaTeX to define mathematical formulas in the wiki text markup. In the Wikimedia ecosystem, these formulas were processed by a long cascade of web services and finally delivered to users' browsers in rendered form for visually readable representation as SVG. With the latest developments of supporting MathML Core in Chromium-based browsers, MathML continues its path to be a de facto standard markup language for mathematical notation in the web. Conveying formulas in MathML enables semantic annotation and machine readability for extended interpretation of mathematical content, in example for accessibility technologies. With this work, we present WikiTexVC, a novel method for validating LaTeX formulas from wiki texts and converting them to MathML, which is directly integrated into MediaWiki. This mitigates the shortcomings of previously used rendering methods in MediaWiki in terms of robustness, maintainability and performance. In addition, there is no need for a multitude of web services running in the background, but processing takes place directly within MediaWiki instances. We validated this method with an extended dataset of over 300k formulas which have been incorporated as automated tests to the MediaWiki continuous integration instances. Furthermore, we conducted an evaluation with 423 formulas, comparing the tree edit distance for produced parse trees to other MathML renderers. Our method has been made available Open Source and can be used on German Wikipedia and is delivered with recent MediaWiki versions. As a practical example of enabling semantic annotations within our method, we present a new macro that adds content to formula disambiguation to facilitate accessibility for visually impaired people.

Newspapers are important sources for historians interested in past societies' cultural values, social structures, and their changes. Since the 19th century, newspapers have been widely available and spread regionally. Today, historical newspapers are digitized but unavailable in a separate metadata-enhanced form. Machine-readable metadata, however, is a prerequisite for a mass statistical analysis of this source. This paper focuses on parsing the complex layout of historic newspaper pages, which today's machines do not understand well. We argue for using neural networks, which require detailed annotated data in large numbers. Our Bonn newspaper dataset consists of 486 pages of the \textit{Kölnische Zeitung} from the years 1866 and 1924. We propose solving the newspaper-understanding problem by training a U-Net on our new dataset, which delivers satisfactory performance.

As legal case law databases such as HUDOC continue to grow rapidly, it has become essential for legal researchers to find efficient methods to handle such large-scale data sets. Such case law databases usually consist of the textual content of cases together with the citations between them. This paper focuses on case law from the European Court of Human Rights on Article 8 of the European Convention of Human Rights, the right to respect private and family life, home and correspondence. In this study, we demonstrate and compare the potential of topic modelling and citation network to find and organize case law on Article 8 based on their general themes and citation patterns, respectively. Additionally, we explore whether combining these two techniques leads to better results compared to the application of only one of the methods. We evaluate the effectiveness of the combined method on a unique manually collected and annotated dataset of Aricle 8 case law on evictions. The results of our experiments show that our combined (text and citation-based) approach provides the best results in finding and grouping case law, providing scholars with an effective way to extract and analyse relevant cases on a specific issue.

This study introduces an approach to estimate the uncertainty in bibliometric indicator values that is caused by data errors. This approach utilizes Bayesian regression models, estimated from empirical data samples, which are used to predict error-free data. Through direct Monte Carlo simulation -- drawing predicted data from the estimated regression models a large number of times for the same input data -- probability distributions for indicator values can be obtained, which provide the information on their uncertainty due to data errors. It is demonstrated how uncertainty in base quantities, such as the number of publications of a unit of certain document types and the number of citations of a publication, can be propagated along a measurement model into final indicator values. This method can be used to estimate the uncertainty of indicator values due to sources of errors with known error distributions. The approach is demonstrated with simple synthetic examples for instructive purposes and real bibliometric research evaluation data to show its possible application in practice.

The rise of Generative Artificial Intelligence systems ("AI systems") has created unprecedented social engagement. AI code generation systems provide responses (output) to questions or requests by accessing the vast library of open-source code created by developers over the past few decades. However, they do so by allegedly stealing the open-source code stored in virtual libraries, known as repositories. This Article focuses on how this happens and whether there is a solution that protects innovation and avoids years of litigation. We also touch upon the array of issues raised by the relationship between AI and copyright. Looking ahead, we propose the following: (a) immediate changes to the licenses for open-source code created by developers that will limit access and/or use of any open-source code to humans only; (b) we suggest revisions to the Massachusetts Institute of Technology ("MIT") license so that AI systems are required to procure appropriate licenses from open-source code developers, which we believe will harmonize standards and build social consensus for the benefit of all of humanity, rather than promote profit-driven centers of innovation; (c) we call for urgent legislative action to protect the future of AI systems while also promoting innovation; and (d) we propose a shift in the burden of proof to AI systems in obfuscation cases.

As legal case law databases such as HUDOC continue to grow rapidly, it has become essential for legal researchers to find efficient methods to handle such large-scale data sets. Such case law databases usually consist of the textual content of cases together with the citations between them. This paper focuses on case law from the European Court of Human Rights on Article 8 of the European Convention of Human Rights, the right to respect private and family life, home and correspondence. In this study, we demonstrate and compare the potential of topic modelling and citation network to find and organize case law on Article 8 based on their general themes and citation patterns, respectively. Additionally, we explore whether combining these two techniques leads to better results compared to the application of only one of the methods. We evaluate the effectiveness of the combined method on a unique manually collected and annotated dataset of Aricle 8 case law on evictions. The results of our experiments show that our combined (text and citation-based) approach provides the best results in finding and grouping case law, providing scholars with an effective way to extract and analyse relevant cases on a specific issue.

MediaWiki and Wikipedia authors usually use LaTeX to define mathematical formulas in the wiki text markup. In the Wikimedia ecosystem, these formulas were processed by a long cascade of web services and finally delivered to users' browsers in rendered form for visually readable representation as SVG. With the latest developments of supporting MathML Core in Chromium-based browsers, MathML continues its path to be a de facto standard markup language for mathematical notation in the web. Conveying formulas in MathML enables semantic annotation and machine readability for extended interpretation of mathematical content, in example for accessibility technologies. With this work, we present WikiTexVC, a novel method for validating LaTeX formulas from wiki texts and converting them to MathML, which is directly integrated into MediaWiki. This mitigates the shortcomings of previously used rendering methods in MediaWiki in terms of robustness, maintainability and performance. In addition, there is no need for a multitude of web services running in the background, but processing takes place directly within MediaWiki instances. We validated this method with an extended dataset of over 300k formulas which have been incorporated as automated tests to the MediaWiki continuous integration instances. Furthermore, we conducted an evaluation with 423 formulas, comparing the tree edit distance for produced parse trees to other MathML renderers. Our method has been made available Open Source and can be used on German Wikipedia and is delivered with recent MediaWiki versions. As a practical example of enabling semantic annotations within our method, we present a new macro that adds content to formula disambiguation to facilitate accessibility for visually impaired people.

Newspapers are important sources for historians interested in past societies' cultural values, social structures, and their changes. Since the 19th century, newspapers have been widely available and spread regionally. Today, historical newspapers are digitized but unavailable in a separate metadata-enhanced form. Machine-readable metadata, however, is a prerequisite for a mass statistical analysis of this source. This paper focuses on parsing the complex layout of historic newspaper pages, which today's machines do not understand well. We argue for using neural networks, which require detailed annotated data in large numbers. Our Bonn newspaper dataset consists of 486 pages of the \textit{Kölnische Zeitung} from the years 1866 and 1924. We propose solving the newspaper-understanding problem by training a U-Net on our new dataset, which delivers satisfactory performance.

This study introduces an approach to estimate the uncertainty in bibliometric indicator values that is caused by data errors. This approach utilizes Bayesian regression models, estimated from empirical data samples, which are used to predict error-free data. Through direct Monte Carlo simulation -- drawing predicted data from the estimated regression models a large number of times for the same input data -- probability distributions for indicator values can be obtained, which provide the information on their uncertainty due to data errors. It is demonstrated how uncertainty in base quantities, such as the number of publications of a unit of certain document types and the number of citations of a publication, can be propagated along a measurement model into final indicator values. This method can be used to estimate the uncertainty of indicator values due to sources of errors with known error distributions. The approach is demonstrated with simple synthetic examples for instructive purposes and real bibliometric research evaluation data to show its possible application in practice.

The rise of Generative Artificial Intelligence systems ("AI systems") has created unprecedented social engagement. AI code generation systems provide responses (output) to questions or requests by accessing the vast library of open-source code created by developers over the past few decades. However, they do so by allegedly stealing the open-source code stored in virtual libraries, known as repositories. This Article focuses on how this happens and whether there is a solution that protects innovation and avoids years of litigation. We also touch upon the array of issues raised by the relationship between AI and copyright. Looking ahead, we propose the following: (a) immediate changes to the licenses for open-source code created by developers that will limit access and/or use of any open-source code to humans only; (b) we suggest revisions to the Massachusetts Institute of Technology ("MIT") license so that AI systems are required to procure appropriate licenses from open-source code developers, which we believe will harmonize standards and build social consensus for the benefit of all of humanity, rather than promote profit-driven centers of innovation; (c) we call for urgent legislative action to protect the future of AI systems while also promoting innovation; and (d) we propose a shift in the burden of proof to AI systems in obfuscation cases.