Volume 54, Issue 2

With a focus on bibliometric analysis, this article intends to analyze patterns in the literature regarding Cloud Computing and its uses in libraries. The authors employed the Boolean operator “AND” while searching Scopus with the terms “Cloud Computing” and “Library” to discover studies that were mainly focused on the subject of this work. The research report that was presented included a bibliometric analysis of Cloud Computing publications. A total of 1,083 articles about Cloud Computing and libraries were obtained from the Scopus Database. The retrieved data was analyzed using Biblioshiny and VosViewer software. The relationship between Cloud Computing and library development was examined using bibliometric analysis, which revealed a number of themes, significant authors, highly referenced publications, well-known organizations, and significant countries. The study also demonstrated how the relationship between Cloud Computing and libraries has evolved and how it could work with academic libraries to give them an ongoing competitive advantage. The study reveals that 2022 was the most prolific year for publications on Cloud Computing within the field of Library and Information Science. Globally, only Venezuela, the United States, and China retained their place as the most productive nations by producing the highest number of documents. Wang and Zhang are the foremost authors, while Lecture Notes in Computer Science and ACM International Conference Proceedings are the leading sources.

This study evaluates the performance of various optimizers on a ResNet-18 based Convolutional Neural Network (CNN) model for the task of recognizing Hanacaraka Javanese script characters. The image dataset is divided into three sets: training, validation, and test, with image sizes of 64 × 64 pixels and a batch size of 64. The tested optimizers include SGD, Adam, RMSprop, Adagrad, Adadelta, NAdam, and Adamax, all with a learning rate of 0.001 and trained for 10 epochs. The results show that NAdam provides the best performance with accuracy, precision, recall, and F1-Score values reaching 100 %, followed by Adamax with metrics above 97 %. Adam and Adagrad also demonstrate high performance with metric values above 97 %. Meanwhile, SGD shows fairly good performance with an accuracy of 93.72 %, and Adadelta shows adequate performance with an accuracy of 86.58 %. RMSprop yields the lowest performance with an accuracy of 81.74 %. Accuracy and loss graphs indicate that Adam and Adadelta offer the best balance between training and validation performance, while RMSprop and NAdam exhibit significant instability. This study highlights the importance of selecting the appropriate optimizer to achieve optimal performance in Hanacaraka character classification, with NAdam and Adamax being the best choices.

This article presents a detailed analysis of the Software Requirement Specification (SRS) and Use Case Diagram for the Metaverse Museum Muhammadiyah, a virtual museum platform designed to offer an immersive and interactive user experience. The study aims to assess the alignment between the functional requirements defined in the SRS and their representation in the Use Case Diagram, ensuring that all critical functionalities are accurately captured and adequately addressed in the system design. The analysis identifies key functional requirements such as user navigation, interaction, content management, and customization features, evaluating their presence and clarity within the Use Case Diagram. Additionally, the study examines the non-functional requirements, including availability, portability, response time, safety, security, and ergonomic design, highlighting their importance in delivering a reliable and user-friendly system. The findings reveal a strong correlation between the SRS and the Use Case Diagram, with most functionalities well-represented. However, the analysis also uncovers certain gaps such as the omission of features like link sharing which require further refinement in the diagram. The article concludes by emphasizing the need for continuous improvement in both the SRS and Use Case Diagram to ensure comprehensive coverage of user needs and to meet the high standards expected in a metaverse environment. This analysis serves as a critical step in guiding the successful development and implementation of the Metaverse Museum Muhammadiyah.

Indigenous cultural resurgence parallels generative AI emergence. This article synthesizes digital technology projects for Indigenous sociocultural advancement. It analyzes 69 studies in five continents through a bifocal critical apparatus. The first lens uses activity theory to explore project ecologies comprised of peoples, objectives, places, technologies, and tensions. The second lens reveals ideopolitical framing patterns as studies are strategically positioned at the interface of Euro-Western and Indigenous cultures. Eight project types, developed by and for Indigenous peoples, are identified. Although consumer technologies predominate, many complex IT assemblages are attested. However, technological complexity often requires “outsider” experts, which limits local control over processes, data, and outcomes. The sampled studies highlight three ideopolitical frames: cultural bridging, countering Euro-Western dominance, and technical problem-solving. The foregrounded political themes are digital empowerment, data sovereignty, identity expression, and online activism. This study critically organizes underexplored research and charts new pathways for exploring digital technologies, culture, and Indigeneity.

Rock art, in the form of paintings and engravings made on rock surfaces worldwide, has garnered the interest of scholars since the nineteenth century. In southern Africa, rock art is one of the most ubiquitous kinds of material cultural heritage, allowing researchers access to the lifeways and thoughts of people through millennia, whose identities were shaped and impacted by contact between African groups and, later, all but eradicated by the colonial project. In this manner, rock art stands as a statement of indigeneity for marginalized peoples across the world, in the postcolonial present, and must be preserved. The African Rock Art Digital Archive (ARADA) at the University of the Witwatersrand’s Rock Art Research Institute (RARI) contributes to such preservation by digitizing rock art collections and related materials from cultural heritage sites across the African continent. Such an initiative ensures that the materials are permanently stored in the digital archive, preventing physical damage and loss of information while providing access to researchers and the wider public. This paper describes the tools and strategies used by ARADA to achieve this important work. In turn, it aims to raise awareness of the continuing cruciality of preserving South Africa’s cultural heritage.

This paper examines the impact of the “scan and return” practice on the sustainability of community archives, presenting a case study of a local community archive in rural Colorado, United States. The “scan and return” approach refers to a practice adopted in the post-custodial digital environment where original materials are returned to owners after digital surrogates are created. The model offers benefits for diversifying representation in archival collections and expanding collaboration with communities. However, the case study demonstrates the risks that the “scan and return” approach poses to the sustainability of community archives and emphasizes the importance of practicing responsible digital stewardship. The paper also discusses social factors of sustainability and points to the value of collaboration in sustaining community archives.

This study presents a novel predictive model employing multiple linear regression to enhance the calculation of the Preservation Index (PI) for library collections. Building upon and expanding previous research on Internet of Things (IoT)-based temperature and humidity monitoring in library rooms, this investigation incorporates critical environmental variables: temperature, humidity, and light exposure. Data was collected over a three-month period using advanced IoT systems deployed in three distinct library environments, each presenting unique preservation challenges. The model’s development involved a meticulous process of interpolating existing PI data to achieve finer gradations, followed by analysis through a sophisticated multiple regression framework. Results indicate that while light exposure’s contribution to the model’s predictive capability was minimal, the overall model precision was remarkably high, demonstrated by a low Mean Absolute Percentage Error (MAPE) of 2.615. This research underscores the predominant influence of temperature and humidity in PI forecasting while also providing nuanced insights into the subtle effects of light exposure on collection preservation. A correlation heatmap is included to elucidate the interrelationships among variables, offering a visual complement to the statistical findings and enhancing the interpretation of complex data interactions. This study contributes to the advancement of preservation strategies in library science, potentially informing more effective environmental control practices in diverse library settings.