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The purpose of this paper is to provide a new and useful formulation of relevance.

This paper is formulated as a conceptual argument. It makes the case for the utility of considering relevance to be function of use in creative processes.

There are several corollaries to formulating relevance as a function of use. These include the idea that objects by themselves cannot be relevant since use assumes interaction; the affordances of objects and how they are perceived can affect what becomes relevant but are not in themselves relevant; relevance is not an essential characteristic of objects; relevance is transient; potential relevance (what might be relevant in the future) can be distinguished from what is relevant in use and from what has been relevant in the past.

The paper shows that its new formulation of relevance brings improved conceptual and terminological clarity to the discourse about relevance in information science. It demonstrates that how relevance is articulated conceptually is important as its conceptualization can affect the ways that users are able to make use of information systems and, by extension, how information systems can facilitate or disable the co-production of creative outcomes. The paper also usefully expands investigative opportunities by suggesting relevance and creativity are interrelated.

Online medical repositories provide a platform for users to share information and dynamically access abundant electronic health data. It is important to determine whether case report information can assist the general public in appropriately managing their diseases. Therefore, this paper aims to introduce a novel deep learning-based method that allows non-professionals to make inquiries using ordinary vocabulary, retrieving the most relevant case reports for accurate and effective health information.

The dataset of case reports was collected from both the patient-generated research network and the digital medical journal repository. To enhance the accuracy of obtaining relevant case reports, the authors propose a retrieval approach that combines BERT and BiLSTM methods. The authors identified representative health-related case reports and analyzed the retrieval performance, as well as user judgments.

This study aims to provide the necessary functionalities to deliver relevant health case reports based on input from ordinary terms. The proposed framework includes features for health management, user feedback acquisition and ranking by weights to obtain the most pertinent case reports.

This study contributes to health information systems by analyzing patients' experiences and treatments with the case report retrieval model. The results of this study can provide immense benefit to the general public who intend to find treatment decisions and experiences from relevant case reports.

Archives and records are important resources for individuals, organizations and the country. The academic archives are created and maintained for the effective execution of university educational and corporate functions. The archives management practices in universities are being studied in the developed countries; however, a scarcity of empirical research is observed in the context of developing countries, for instance, Pakistan. Thus, the purpose of this study is to assess the archives management practices performed in the archival units of University of the Punjab (UoP), Lahore, in association with the successful execution of university educational functions.

A structured survey questionnaire was developed to collect responses from the record-keepers and archives monitoring staff using a complete enumerative (census) approach. The collected data were analyzed in SPSS 23.0 in addition to structural equation modeling (SEM) run in AMOS 22v.

The findings of this study revealed an inconsistency regarding the policies and procedures, arrangement and filing records and access and retrieval due to the practice of self-developed procedures in the UoP archival units. Although archives management practices have significant impact on university academic as well as research-related functions, however, there is lack of centralized and standardized practices for archiving records in the UoP. Lack of professional/trained staff and policy document are key limitations in building systematic and standardized archives management system in academic intuitions, particularly in the UoP.

To the best of the authors’ knowledge, this is the first empirical study in Pakistan that has explored archives management practices used in university archives. It also contributes theoretically and methodologically through the underpinnings of archival principles in association with university functions and developing a validated scale to explore archives management practices in universities. The findings of this study may be helpful for the concerned bodies, university administrations and archives managers to establish, manage and improve the academic archives systematically.

In federated search, a query is sent simultaneously to multiple resources and each one of them returns a list of results. These lists are merged into a single list using the results merging process. In this work, the authors apply machine learning methods for results merging in federated patent search. Even though several methods for results merging have been developed, none of them were tested on patent data nor considered several machine learning models. Thus, the authors experiment with state-of-the-art methods using patent data and they propose two new methods for results merging that use machine learning models.

The methods are based on a centralized index containing samples of documents from all the remote resources, and they implement machine learning models to estimate comparable scores for the documents retrieved by different resources. The authors examine the new methods in cooperative and uncooperative settings where document scores from the remote search engines are available and not, respectively. In uncooperative environments, they propose two methods for assigning document scores.

The effectiveness of the new results merging methods was measured against state-of-the-art models and found to be superior to them in many cases with significant improvements. The random forest model achieves the best results in comparison to all other models and presents new insights for the results merging problem.

In this article the authors prove that machine learning models can substitute other standard methods and models that used for results merging for many years. Our methods outperformed state-of-the-art estimation methods for results merging, and they proved that they are more effective for federated patent search.

This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice.

This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs.

To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required.

This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

An increasing number of images are generated daily, and images are gradually becoming a search target. Content-based image retrieval (CBIR) is helpful for users to express their requirements using an image query. Nevertheless, determining whether the retrieval system can provide convenient operation and relevant retrieval results is challenging. A CBIR system based on deep learning features was proposed in this study to effectively search and navigate images in digital articles.

Convolutional neural networks (CNNs) were used as the feature extractors in the author's experiments. Using pretrained parameters, the training time and retrieval time were reduced. Different CNN features were extracted from the constructed image databases consisting of images taken from the National Palace Museum Journals Archive and were compared in the CBIR system.

DenseNet201 achieved the best performance, with a top-10 mAP of 89% and a query time of 0.14 s.

The CBIR homepage displayed image categories showing the content of the database and provided the default query images. After retrieval, the result showed the metadata of the retrieved images and links back to the original pages.

With the interface and retrieval demonstration, a novel image-based reading mode can be established via the CBIR and links to the original images and contextual descriptions.

Despite being an authentic carrier of various cultural practices, the human body is often underutilised to access the knowledge of human body. Digital inventions today have created new avenues to open up cultural data resources, yet mainly as apparatuses for well-annotated and object-based collections. Hence, there is a pressing need for empowering the representation of intangible expressions, particularly embodied knowledge within its cultural context. To address this issue, the authors propose to inspect the potential of machine learning methods to enhance archival knowledge interaction with intangible cultural heritage (ICH) materials.

This research adopts a novel approach by combining movement computing with knowledge-specific modelling to support retrieving through embodied cues, which is applied to a multimodal archive documenting the cultural heritage (CH) of Southern Chinese martial arts.

Through experimenting with a retrieval engine implemented using the Hong Kong Martial Arts Living Archive (HKMALA) datasets, this work validated the effectiveness of the developed approach in multimodal content retrieval and highlighted the potential for the multimodal's application in facilitating archival exploration and knowledge discoverability.

This work takes a knowledge-specific approach to invent an intelligent encoding approach through a deep-learning workflow. This article underlines that the convergence of algorithmic reckoning and content-centred design holds promise for transforming the paradigm of archival interaction, thereby augmenting knowledge transmission via more accessible CH materials.

The basis for this study on electronic transferable records (ETRs) in the context of digitalizing cross-border trade is established in this chapter. It illustrates how the growing trend toward paperless trade and technological improvements is changing trade operations. This chapter focuses on the need to look into ETRs because of their capacity to influence business transactions while navigating complex legal issues. The specific goals of the study are outlined, including a review of the advantages, legality, difficulties and best practices of ETRs. This study aims to shed light on the possible advantages and disadvantages of ETRs, the legal framework that controls their use and the best practices for their efficient implementation. This study also seeks to provide informative recommendations for businesses and people that are considering using ETRs.

This study explores the evolving world of ETRs and their crucial function in international trade. Multidimensional technique is used to examine the transformative potential of ETRs from a variety of research angles. The research design is based on a comprehensive evaluation of the literature that includes a wide range of reliable sources, including academic papers, business reports and legal documents. The comprehensive retrieval of essential material is ensured through keyword searches in renowned academic databases and industry resources. The qualitative synthesis of secondary sources further enhances this methodology and allows for a complex examination of the implications of ETRs. The case study analysis provides practical information on the benefits, hazards and practical applications of ETRs. Multifaceted aspects are uncovered via a thematic approach and qualitative investigation, including potential advantages, hazards, implementation plans and regulatory frameworks.

ETRs offer a range of potential advantages for cross-border trade, encompassing augmented efficiency, reduced costs and heightened security. Nonetheless, their implementation also presents legal challenges and risks, spanning security and privacy concerns, legal ambiguities and technical complexities. Consequently, it is crucial for individuals and businesses to meticulously assess and mitigate these risks through the integration of robust security protocols, staying informed about legal developments and adhering to pertinent regulatory stipulations. In spite of these hurdles, the trajectory of ETR adoption is anticipated to remain on an upward trajectory, driven by increasing recognition of their potential benefits and the concurrent evolution of legal frameworks and technical standards.

Research limitations included the following: lack of adoption of ETRs internationally; and legal diversity and different legal systems results in different consideration of the ETRs. It makes reaching a unified ETR system more difficult.

It is necessary to develop clear policies and procedures and establish well-defined policies and procedures governing ETR use. These should encompass security guidelines, data protection measures and adherence to legal mandates. Regular review and updates are imperative. Stay current on legal developments: In light of the continuously evolving legal and regulatory landscape pertaining to ETRs, businesses and individuals must stay abreast of pertinent changes and seek professional counsel when necessary. Collaborate with partners and stakeholders: To ensure harmonization and standardization in ETR deployment, active collaboration with partners, regulators and industry associations is vital.

Enhance awareness and education: Investment in awareness and educational initiatives is crucial. Decision-makers should organize training programs, workshops and seminars to enhance understanding of ETRs’ potential benefits in cross-border trade among stakeholders. Socially, the use of ETR can achieve several political advantages for the society. It minimizes risks of corruption through enhancing tracing and auditing abilities for relevant authorities making it more difficult to engage in corrupt practices. That can promote integrity within government and public procurement system.

The development of standardized technical frameworks and interoperable platforms for ETRs could enhance their seamless integration into existing trade systems. Additionally, investigating the integration of emerging technologies like blockchain, IoT and AI into ETR ecosystems could unlock innovative solutions to security, authenticity and data management concerns. This study examines how ETRs can radically alter how trade is conducted on a global scale. This paper examines ETRs’ role in improving cross-border trade digitization by examining their advantages, legal difficulties and implementation techniques. The conclusions will aid firms, decision-makers and attorneys in navigating the constantly changing world of trade agreements. The study’s ultimate goal is to offer takeaways that support effective, secure and legally compliant integration of ETRs, ensuring that they operate as a catalyst for improved global trade efficacy and efficiency.