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The availability of hyperspectral images has significantly facilitated clay minerals identification and mapping. Based on the Hyperion L1R hyperspectral dataset, this research aims to improve previous scientific work related to identifying and mapping clay minerals in the Djebel Meni region (northwestern of Algeria). Firstly, we enhance the dataset quality through pre-processing techniques like the removal of bad bands, radiometric calibration, and atmospheric correction. Secondly, the Spectral Information Divergence (SID) algorithm was employed by introducing endmembers derived with the Sequential Maximum Angle Convex Cone (SMACC) algorithm initially and then using Jet Propulsion Laboratory (JPL) spectral signatures of Illite, Kaolinite, and Montmorillonite. The classification results show a correspondence between areas occupied by endmembers and JPL spectral signatures, which helps in matching endmembers with specific minerals. Finally, we conducted a comparative analysis of the two classifications outcomes against a reference map. This last is generated using SID algorithm, which takes ground truth spectral signatures as input. Our proposed approach using the SID classifier has given an overall accuracy score of 97.13% and 84.17% using endmembers image and JPL library respectively. The Kappa coefficient is respectively, with endmembers image and JPL, 0.93 and 0.57. These results show that the SID classification with the endmembers image is better than the SID classification with the JPL library because extracting the endmembers from the image is more accurate than doing that from the pure minerals analyzed in the laboratory. These promising results suggest that our approach could be extended to the identification of clay minerals in diverse hyperspectral datasets. [ABSTRACT FROM AUTHOR]

As the complexity of modern library management tasks increases, it is difficult for traditional mobile robots to meet the task of moving and classifying books. In order to design a mobile robot that can autonomously classify and transport books, the study realizes the tasks of book classification and transportation in libraries by fusing the differential speed algorithm and the robot motion model algorithm. First, the robot operating system is utilized to scan the books, classify the books, and obtain the category information of the books. Then, the differential speed algorithm is used to control the motion of the robot to ensure that the robot can accurately transport the books to the designated location. At the same time, combined with the robot motion model algorithm, the motion trajectory of the robot is planned to ensure that the robot can avoid obstacles and stably complete the book transportation task. Finally, the deep reinforcement learning algorithm is used to train the decision-making model of the robot to improve the intelligence level of the robot. The results of simulation experiments show that the research method has the highest accuracy, with an average accuracy of 99.98%, and the robot is able to accurately categorize the books and quickly avoid obstacles with strong stability. The results of the application experiments show that the research method has the shortest moving distance, with an average moving distance of 132 m and an average completion time of 34 seconds, which are lower than the remaining three types of robots. The research robot showed high accuracy in the task of returning books in four time periods within 10 days in the library, with an average accuracy of 99.58%. The experimental results validate the superiority of the research methodology and show that the robots are capable of accurately recognizing and classifying books and can autonomously perform transportation tasks in libraries. The research results help to improve the automation level and management efficiency of libraries and have important application value. [ABSTRACT FROM AUTHOR]

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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. [ABSTRACT FROM AUTHOR]

BACKGROUND: Cardiovascular diseases remain a leading cause of global morbidity and mortality, with heart attacks and strokes representing significant health challenges. The accurate, early diagnosis and management of these conditions are paramount in improving patient outcomes. The specific disease, cardiovascular occlusions, has been chosen for the study due to the significant impact it has on public health. Cardiovascular diseases are a leading cause of mortality globally, and occlusions, which are blockages in the blood vessels, are a critical factor contributing to these conditions. OBJECTIVE: By focusing on cardiovascular occlusions, the study aims to leverage machine learning to improve the prediction and management of these events, potentially helping to reduce the incidence of heart attacks, strokes, and other related health issues. The use of machine learning in this context offers the promise of developing more accurate and timely interventions, thus improving patient outcomes. METHODS: We analyze diverse datasets to assess the efficacy of various machine learning algorithms in predicting heart attacks and strokes, comparing their performance to pinpoint the most accurate and reliable models. Additionally, we classify individuals by their predicted risk levels and examine key features that correlate with the incidence of cardiovascular events. The PyCaret machine learning library's Classification Module was key in developing predictive models which were evaluated with stratified cross-validation for reliable performance estimates. RESULTS: Our findings suggest that machine learning can significantly improve the prediction accuracy for heart attacks and strokes, facilitating earlier and more precise interventions. We also discuss the integration of machine learning models into clinical practice, addressing potential challenges and the need for healthcare professionals to interpret and apply these predictions effectively. CONCLUSIONS: The use of machine learning for risk stratification and the identification of modifiable factors may empower preemptive approaches to cardiovascular care, ultimately aiming to reduce the occurrence of life-threatening events and improve long-term patient health trajectories. [ABSTRACT FROM AUTHOR]

This case study explores the Arabic cataloguing procedures at the Jabatan Kehakiman Syariah Malaysia Library (JKSM) and the challenges faced in organising Islamic legal resources. The study addresses key issues such as the adaptation of international cataloguing standards, inconsistencies in transliteration practices, and the lack of standardised subject headings for Arabic legal materials. Using a qualitative approach, the research gathers insights from library staff through a group interview, focusing on their experience with cataloguing procedures and the complexities of managing Arabic resources. The findings highlight significant obstacles, including transliteration inconsistencies, difficulties in Arabic input, and challenges in information retrieval. Moreover, the study underscores the need for localised classification systems like the IIUM Library Classification System for Knowledge Resources on Islam (ICSI) to better represent Islamic legal texts. The research concludes with recommendations for a phased integration of ICSI, staff training, pilot reclassification projects, and collaboration with other Islamic law libraries. These strategies aim to improve accessibility, standardisation, and the overall efficiency of cataloguing Arabic legal resources, contributing to the advancement of Islamic librarianship and supporting the broader goal of equitable access to legal information in Arabic. [ABSTRACT FROM AUTHOR]

Copyright of Religion & Tolerance / Religija & Tolerancija is the property of Center for the Empirical Researches of Religion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In today's information era, collaborative filtering algorithms are widely used and their distinct knowledge discovery techniques can effectively address numerous issues. However, conventional collaborative filtering algorithms encounter cold-start and data sparsity issues, which restrict their performance and accuracy. The study selected the multi-feature method to improve the traditional collaborative filtering algorithm, and introduced gray correlation calculation and Bayesian probability for user preference analysis. A learning resource recommendation model based on collaborative filtering was developed by comparing the target user's characteristics with those of other users, calculating their similarity, selecting users with high similarity to the target user and forming a neighbor set. Using Bayesian probability and grey correlation to analyze user preferences in library systems can be well applied in book classification and recommendation problems in university libraries. The computing layer, which includes the collaborative filtering calculation stage and the group recommendation calculation stage, is the model's main functional component. The smaller the value of mean absolute error, the higher the prediction accuracy of the model. The mean absolute error value of the multi-feature collaborative filtering algorithm was inferior to the traditional collaborative filtering algorithm, indicating that the classification accuracy of the former is higher than that of the latter. When the training set to test set ratio steadily became bigger, the mean absolute error value reached the lowest and smoothest point at 80%. In dataset A, the minimum mean absolute error values of multi-feature collaborative filtering and collaborative filtering were 0.765 and 0.809. Compared with traditional filtering algorithms, the mean absolute error value has decreased by 0.044. In dataset B, the mean absolute error values of multi-feature collaborative filtering and collaborative filtering were 0.796 and 0.836. Compared with traditional filtering algorithms, the mean absolute error value has decreased by 0.040. In dataset C, the minimum mean absolute error values of multi-feature collaborative filtering and collaborative filtering were 0.815 and 0.848. Compared with traditional filtering algorithms, the mean absolute error value has decreased by 0.033. When the accuracy was the highest; the mean absolute error value was the smallest at the grey correlation, which means that the technique improves the reliability of the recommendations compared with other methods. This means that the method has a positive impact on the accuracy of the recommendations compared to other methods. Grey correlation degree can comprehensively consider the interrelationships between multiple factors, handle uncertain and incomplete information, and explore potential user needs and behavior patterns. The implementation of the grey correlation degree has transformed the collaborative filtering algorithm into a group filtering algorithm, thereby enhancing its precision. The research on book classification and recommendation in university libraries, which enhances the group filtering algorithm, can address a range of issues such as improving classification accuracy, augmenting recommendation diversity, enhancing library management efficiency among others. This, in turn, enables more precise book recommendations to users. [ABSTRACT FROM AUTHOR]

Objective: The library, once a silent repository of knowledge, is undergoing a digital metamorphosis. At the heart of this transformation is the integration of artificial intelligence (AI). Traditionally, cataloging and classifying books was a meticulous, labor-intensive task requiring deep subject knowledge and adherence to complex classification systems. However, the advent of AI is revolutionizing this process, promising greater efficiency, accuracy, and accessibility. This research presents a pilot study on the potential use of AI for semi-automatic subject indexing, cataloging, and classification of books. Materials and Methods: To collect the data, first we searched for open access books in the Directory of Open Access Books and OAPEN (Online library of open access books). Four books in English were chosen for the current study. The book titles were searched in the Library of Congress Online Catalog to discover the subject headings, the Library of Congress Classification number and the Dewey Decimal Classification number assigned to each book, collecting the necessary information. Third, we searched for each book in ChatGPT, Copilot, and Gemini, using necessary prompts to collect the data. Results: The results indicate that the degree of cataloging and classification consistency is low. The cataloging and classification consistency are seen as the measure of the similarity of reaction of different machines and human beings processing the same book. Conclusion: By automating routine tasks, improving classification and cataloging accuracy, and enhancing metadata creation, AI is transforming the way libraries organize and share knowledge. As technology continues to advance, we can expect even more exciting developments in this field. [ABSTRACT FROM AUTHOR]

Copyright of Darulfunun Ilahiyat is the property of Istanbul University, Faculty of Theology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Within the literary domain, genres function as fundamental organizing concepts that provide readers, publishers, and academics with a unified framework. Genres are discrete categories that are distinguished by common stylistic, thematic, and structural components. They facilitate the categorization process and improve our understanding of a wide range of literary expressions. In this paper, we introduce a new dataset for genre classification of Russian books, covering 11 literary genres. We also perform dataset evaluation for the tasks of binary and multi-class genre identification. Through extensive experimentation and analysis, we explore the effectiveness of different text representations, including stylometric features, in genre classification. Our findings clarify the challenges present in classifying Russian literature by genre, revealing insights into the performance of different models across various genres. Furthermore, we address several research questions regarding the difficulty of multi-class classification compared to binary classification, and the impact of stylometric features on classification accuracy. [ABSTRACT FROM AUTHOR]

The University library is considered the heart of the educational system. Collaboration among university libraries is essential to fulfill the users' information needs. The current study is designed to examine areas of library cooperation between the central library and departmental libraries within the university domain. Benefits, obstacles, and preferences for library cooperation have also been explored. A quantitative research methodology was adopted to execute the study. A questionnaire was used as a data collection tool and data was collected only from those university libraries having central and departmental libraries. The data was collected from 110 academic librarians working in central or departmental libraries of universities across Pakistan. The findings revealed that libraries within a university are frequently cooperating in many areas. The highest cooperation is in the exchange of books followed by cooperation in the classification of books. More than half of the universities have established integrated cooperation among libraries within the university. One-fifth of the libraries have interactive relationships, and the remaining libraries have weak cooperation or no cooperation at all. Cooperation among libraries has improved access to print resources, improved quality of the library services, enhanced access to library services, and improved coordination in the acquisition of library material. Limited financial resources, lack of formal framework for library cooperation, and lack of technology infrastructure were major obstacles faced by libraries. Based on the study findings it is proposed that there should be an integrated relationship among libraries for cooperation and resource sharing within the university domain for uniformity in services. [ABSTRACT FROM AUTHOR]

Deep neural networks have shown remarkable performance on a wide range of classification tasks and applications. However, the large model size and the enormous size of the training dataset make the training process slow and often limited by the computing resources. To overcome this limitation, distributed training can be used to accelerate the process by utilizing multiple devices for a single model training. In this work, we evaluate the performance of Microsoft DeepSpeed, a distributed training library, on image classification tasks by comparing the performance of 108 trained neural networks in 27 unique settings. Our experimental results suggest that DeepSpeed may provide limited benefits for simpler learning tasks (e.g. smaller neural network models or simpler datasets). On the other hand, for more complex learning tasks, DeepSpeed can provide up to 8× faster training with possible performance improvement. Our study contributes to a better understanding of the capabilities and limitations of the DeepSpeed library, providing insights into when and where it may be most beneficial to use in image classification settings. [ABSTRACT FROM AUTHOR]

Core collections have been produced by CILIP's Health Libraries Group, then called the Library Association's Medical Section, since 1952. Maintained by a Working Group of health librarians based in the UK NHS, higher education and specialist libraries, the collections provide an up‐to‐date curated list of reliable titles essential to health libraries. The core collections currently include nursing, midwifery, medicine and dentistry. The newest core collection is being developed in collaboration with the African Hospital Libraries to provide a list of key resources relevant to sub‐Saharan Africa. Expressions of interest to help develop this latest collection are invited. [ABSTRACT FROM AUTHOR]

Data augmentation uses artificially created examples to support supervised machine learning, adding robustness to the resulting models and helping to account for limited availability of labelled data. We apply and evaluate a synthetic data approach to relationship classification in digital libraries, generating artificial books with relationships that are common in digital libraries but not easier inferred from existing metadata. Artificial books are generated by remixing existing texts into synthetically constructed formats. We find that for classification on whole–part relationships between books, synthetic data improves a deep neural network classifier by 91%. Furthermore, we consider the ability of synthetic data to learn a useful new text relationship class from fully artificial training data. [ABSTRACT FROM AUTHOR]

Analyzing the appearances of political figures in large-scale news archives is increasingly important with the growing availability of large-scale news archives and developments in computer vision. We present a deep learning-based method combining face detection, tracking, and classification, which is particularly unique because it does not require any re-training when targeting new individuals. Users can feed only a few images of target individuals to reliably detect, track, and classify them. Extensive validation of prominent political figures in two news archives spanning 10 to 20 years, one containing three U.S. cable news and the other including two major Japanese news programs, consistently shows high performance and flexibility of the proposed method. The codes are made readily available to the public. [ABSTRACT FROM AUTHOR]

The digital transformation of banks has led to a paradigm shift, promoting the open sharing of data and services with third-party providers through APIs, SDKs, and other technological means. While data sharing brings personalized, convenient, and enriched services to users, it also introduces security risks, including sensitive data leakage and misuse, highlighting the importance of data classification and grading as the foundational pillar of security. This paper presents a cloud-edge collaborative banking data open application scenario, focusing on the critical need for an accurate and automated sensitive data classification and categorization method. The regulatory outpost module addresses this requirement, aiming to enhance the precision and efficiency of data classification. Firstly, regulatory policies impose strict requirements concerning data protection. Secondly, the sheer volume of business and the complexity of the work situation make it impractical to rely on manual experts, as they incur high labor costs and are unable to guarantee significant accuracy. Therefore, we propose a scheme UP-SDCG for automatically classifying and grading financially sensitive structured data. We developed a financial data hierarchical classification library. Additionally, we employed library augmentation technology and implemented a synonym discrimination model. We conducted an experimental analysis using simulation datasets, where UP-SDCG achieved precision surpassing 95%, outperforming the other three comparison models. Moreover, we performed real-world testing in financial institutions, achieving good detection results in customer data, supervision, and additional in personally sensitive information, aligning with application goals. Our ongoing work will extend the model's capabilities to encompass unstructured data classification and grading, broadening the scope of application. [ABSTRACT FROM AUTHOR]

Proteomic fingerprinting using MALDI-TOF mass spectrometry is a well-established tool for identifying microorganisms and has shown promising results for identification of animal species, particularly disease vectors and marine organisms. And thus can be a vital tool for biodiversity assessments in ecological studies. However, few studies have tested species identification across different orders and classes. In this study, we collected data from 1246 specimens and 198 species to test species identification in a diverse dataset. We also evaluated different specimen preparation and data processing approaches for machine learning and developed a workflow to optimize classification using random forest. Our results showed high success rates of over 90%, but we also found that the size of the reference library affects classification error. Additionally, we demonstrated the ability of the method to differentiate marine cryptic-species complexes and to distinguish sexes within species. [ABSTRACT FROM AUTHOR]

Jesse Shera (1903–1982) was a twentieth-century American librarian and information scientist. This article discusses Shera's social epistemology (SE) and its similarities with structuralism and poststructuralism. Previous research on Shera's SE has clarified its position and influences. John Budd has pointed out that his SE was sociological rather than philosophical, resembling a sociology of knowledge, while Jonathan Furner adduced similarities between Shera's SE and related areas, such as the sociology of science, and showed that Shera's SE was influenced by Paul Otlet. Patrick Wilson frames his body of research as an extension of Shera's SE. Accordingly, I show that Shera's SE exhibits characteristics of structuralism and poststructuralism. Shera argued that the semantic relationship between two groups of written materials depends on the perceptions of the individuals (or groups) involved, rejecting traditional library classification methodology aimed at a universal classification of knowledge that was the contemporary mainstream. This article discusses the background of Shera's poststructuralism. [ABSTRACT FROM AUTHOR]

Professional discussions regarding bias in classification are occurring internationally. Libraries are investigating ways to address these issues in manageable steps as we push for broader changes. This case study details one library's process for adopting the Dewey Decimal Classification optional arrangement for religion (220-299), following the work done by Emily McDonald. This arrangement reduces the Christian bias of the standard schedule. The reclassification project is a step to improve the inclusivity and browsability of the collection while maintaining the library's current classification scheme. In addition to process, the paper highlights the advantages and potential concerns for adopting this arrangement. [ABSTRACT FROM AUTHOR]

Taxonomy is a classification system that helps researchers conceptualize phenomena based on their dimensions and characteristics. It is an integral process involving a high complexity in understanding a subject classification. This paper aims to represent an update for the taxonomy development of Sports Science and recreational; it contributes to the prescriptive knowledge of taxonomy design and seeks to augment both rigorous taxonomies building and evaluation, including a comprehensive analysis of the expert-assigned keywords with a comparison to Library of Congress Subject Headings. This study employs comparative subject analysis as the methodology for comparing expert-assigned keywords with LCSH. The analysis will identify the similarities and differences in the classification of the taxonomy used in the UiTM Local Content Hub. The subjects used in Sports Science & Recreational Digital Collection in PTAR Local Content Hub are very minimal compared to the structured classification of subjects in the classification web. [ABSTRACT FROM AUTHOR]

The article discusses the implementation of a donated nonclinical leisure reading collection in a medical library, inspired by the concept of Little Free Libraries. The goal is to enhance the library's appeal, provide clinicians and staff with easy access to reading material, and promote the benefits of nonmedical fiction reading, including improved clinical reasoning skills and increased empathy.

The rise of new technologies, including artificial intelligence, is changing universities' conduct of library operations and, therefore, their workflows and service to the user. Taking away from human beings formal routine dodges through artificial intelligence releases a librarian's time for more thoughtful and valuable assignments. It helps in automating the entire cataloging and classification of machine learning algorithms to reduce any form of human error related to it. This also leads to an accurate categorization to arrange stuff properly. Chatbots and virtual assistants that are AI-powered, working around the clock, help users ask questions, use library resources, and navigate research. Predictive analytics, through demand forecasting, allows the libraries to minimize the risk factor and anticipate the users' needs to effect comprehensive collection development. AI also assists in the process of data analytics and helps manage resources for better insights and concrete data-driven decision-making in the process of resource allocation and when more resources need to be procured. Technologies such as image and text recognition have made it possible to digitize and index vast amounts of archival sources, hence departing resources hidden up to universal researchers. With its intelligent recommendations and a similarly formatted interface, the entire user's experience gets personalized for a given patron; offering suggested electronic versions of materials relative to what faculty and students have used in the past. More importantly, AI-embedded systems come in handy in making the security system in a library more reliable, from detecting to shielding against plausible threats on sensitive user data or confidential intellectual properties. At the same time, AI in university libraries not only enhances library operational efficiency but also helps procure the academic environment that is made preferable and accessible. The larger the role that technology plays in the operation of libraries, the potential for innovations toward improved service shall become higher. Consequently, university libraries that incorporate AI have a crucial advantage in offering potentialities whereby they can adjust and diversify the needs of their academic fraternities into an environment of learning and research evolution. [ABSTRACT FROM AUTHOR]

Copyright of Argumenta Philosophica is the property of Herder Editorial S.L. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

During the 1930s the Japanese Government General of Korea's Society for the Compilation of Korean History commissioned facsimiles of some 21 rare historical sources to accompany the publication of the colossal History of Korea (Chōsenshi 朝鮮史), funnelling select xylographic, typographic, and chirographic products of the defunct Chosŏn dynasty's book ecology through offset lithography and collotype, and on occasion movable type. This article investigates the Society for the Compilation of Korean History's collection and classification of historical materials against the larger backdrop of colonial knowledge production, illuminates the different economic and editorial logics of the new printing technologies used to produce the facsimiles, and examines the products as one example of the significance of facsimiles in the field of history. It suggests that the interplay of traditional print media, dominated by woodblock prints, and the new photomechanical means of reproduction, allowed for the swift reproduction of the unfolded page image and the easy utilization of traditional-style binding, permitting the Society to create purposefully antiquated reproductions with a high degree of fidelity to the original. At the same time, the use of modern materials (paper, string, and covers) and certain features common to traditional Japanese book binding meant that the facsimiles were irrevocably hybrid. These facsimiles ended up in a wide range of research libraries, representing the Korean past to the scholarly community in the Japanese empire. [ABSTRACT FROM AUTHOR]

This document is a review of the book "Foundations of familiar language: formulaic expressions, lexical bundles, and collocations at work and play" by Diana Sidtis. The reviewer highly recommends the book as a valuable resource for those interested in the study of familiar language. The book covers research in the English-speaking world and provides helpful references for professionals in the field. It is written in an informal style and includes illustrations by cartoonists. The book explores the classification and acquisition of familiar language, highlighting that frequency alone does not fully capture its range. It also discusses the different approaches to language acquisition between first language learners and second language learners. The book further examines the prosodic characteristics of familiar language and how unique prosodic patterns are stored in the lexicon. It then delves into the neurological aspects of familiar language, exploring the impact of psychiatric and neurological disorders on language processing. The chapter concludes by summarizing the main points of the book, emphasizing the significance of familiarity, memory, routine behaviors, pattern recognition, and emotions in language use. [Extracted from the article]

Copyright of Palabra Clave (1666-2938) is the property of Universidad Nacional de La Plata and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Neutrality is one of the founding principles of library classification; however, systems reflect the biases of the societies that created them. Many articles have been written on bias in the Library of Congress Classification System (LCC) and its subject headings (LCSH). But how is bias evident in the Fine Arts (N) range? One answer to this question lays in the writings of Hope A. Olson who argues that systems like LCC are inherently prejudiced because of their use of universality, which results in hierarchical relationships and Derridean binaries. This is problematic because library classification, according to Olson, functions as a third-space, a place where meaning is created. Reading the Fine Arts range through Olson's work reveals a system that perpetuates bias by reconstructing the western canon of art history through its privileging of fine art over craft. While each of the fine arts are given their own subclasses, craft mediums are located under one subclass, Decorative Arts (NK), giving them a lesser than status. Artists and art historians have argued that the valuing of fine art over craft in the western canon, something clearly seen in LCC, is a consequence of patriarchal and colonialist power systems. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of practices of classification, organization, and presentation within U.S. academic libraries on systemic support for diversity in philosophy. It argues that present practices result in the preemptive marginalization of non-traditional perspectives and methods in the discipline of philosophy. The paper has three parts. First, an examination of the historical and philosophical roots of the predominant systems of classification in academic libraries, Library of Congress Classification (LCC) and Subject Headings (LCSH). Second, a consideration of how library classification norms the research and practice of philosophy. Third, a response to potential objections about the impact of library classification on philosophers. [ABSTRACT FROM AUTHOR]

The Pettee Collective is a support network created to assist libraries using the Pettee Classification System. The system has not been updated since 1967 and lacks modern research and ideas. The Collective, consisting of eight libraries, has created a webpage on the ANZTLA website to publish updates to the Pettee schedules and a database for new numbers. They are systematically reviewing and updating the schedules, addressing issues such as terminology and bias. The Collective welcomes new members and has already attracted international attention. [Extracted from the article]

The categories listed below are used to classify books, book reviews, journal articles, and dissertations indexed in JEL, JEL on CD, and EconLit. New changes to the classification system appear as soon as possible on www.econlit.org. The JEL classification system may be used freely for scholarly purposes. We suggest the following format: "JEL: A10, B10, etc." [ABSTRACT FROM AUTHOR]

Premise of research. Computer vision has the potential to become a transformative identification tool in biodiversity research and collections management, allowing high-throughput identification and removing the need for nonexpert end users to understand technical terminology. A major bottleneck for taxonomists is the generation of sufficient numbers of training images. Contemporary large-scale imaging projects of herbaria provide an increasing number of specimen photos, but whole-sheet images are not directly suitable for training image classification models targeted at individual taxonomically informative characters. Methodology. Here, we illustrate a time- and labor-efficient approach for generating training libraries for image classification from photos of herbarium sheets. It involves the annotation of specimen images with bounding boxes using open-source software and automated cropping of annotations with a custom script to produce the training library. We demonstrate the approach on the flower heads of a genus of Asteraceae comprising eight taxa, six species and two nontypus varieties. Pivotal results. After generating 816 training images from 33 specimen photos with a time investment of only ∼90 min, we trained an image classification model that achieved 98.2% precision and recall. Conclusions. The demonstrated approach allows taxonomists to use digitized herbarium specimens to produce training libraries for image classification models within hours. We expect that computer vision will increasingly become a part of taxonomic practice. [ABSTRACT FROM AUTHOR]

Copyright of Inönü University International Journal of Social Sciences / İnönü Üniversitesi Uluslararası Sosyal Bilimler Dergisi (INIJOSS) is the property of Inonu University International Journal of Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Automatic classification of ancient books is an important component of the digital platform of ancient books, while automatic classification of ancient books is more challenging. In view of the ancient books script identification task of different ethnic minorities in China, this paper proposes a deep convolutional neural network (CNN) ancient books script identification method with multi-branch structure and spatial pyramid pooling (SPP), called MbSPPVGG. We build a dataset of Chinese ethnic ancient handwritten books, and crop and standardize preprocessing images of ancient books. In order to improve the identification accuracy of ancient books and ability of CNN to perceive multi-scale changes in image, bottom-level and high-level features of CNN are merged by multi-branch structure to enhance the networks expression ability, and then use SPP to multi-scale de-dimensionality of convolutional features, increase the spatial scale invariance of CNN. The introduction of multi-branch structure and SPP in the CNN model constitutes a new ancient books identification model. The experimental results show that the precision, recall and F1-score of MbSPPVGG model are all 99.94%. As demonstrated by comparison experiments, the classification accuracy of MbSPPVGG model is better than that of state-of-the-art GhostNet, CSPDenseNet, MixNet and other deep learning methods, and its effectiveness is verified on multiple datasets. [ABSTRACT FROM AUTHOR]

Marion Godman's book, "The Epistemology and Morality of Human Kinds," explores the concept of human kinds as historical kinds. The book argues that certain categories of human classification can be understood as objectively real kinds, with shared features due to their shared causal lineages. Godman critiques alternative approaches to human kinds and develops the idea that viewing human kinds as historical kinds allows for common cause explanations and grounds multiple projectability. The book applies this historical kind account to genders, religions, and ethnicities, showing their normative significance. While the book has some flaws, it is an insightful contribution to the debates surrounding human kinds. [Extracted from the article]

This paper details an approach for accurately machine sorting Library of Congress (LC) call numbers which improves considerably upon other methods reviewed. The authors have employed this sorting method in creating an open-source software tool for library stacks maintenance, possibly the first such application capable of sorting the full range of LC call numbers. The method has potential application to any software environment that stores and retrieves LC call number information. [ABSTRACT FROM AUTHOR]

The article focuses on two resources for technical services librarianship Worcester State University's Cataloging and Classification guide and the Omniglot website. Topics include an assessment of the cataloging LibGuide's structure and broken links, suggestions for improvement, and a review of Omniglot, highlighting its extensive database of writing systems and languages beneficial for catalogers.

While quantitative methods have been used to examine changes in word usage in books, studies have focused on overall trends, such as the shapes of narratives, which are independent of book length. We instead look at how words change over the course of a book as a function of the number of words, rather than the fraction of the book, completed at any given point; we define this measure as "cumulative word-time". Using ousiometrics, a reinterpretation of the valence–arousal–dominance framework of meaning obtained from semantic differentials, we convert text into time series of power and danger scores, with time corresponding to cumulative word-time. Each time series is then decomposed using empirical mode decomposition into a sum of constituent oscillatory modes and a non-oscillatory trend. By comparing the decomposition of the original power and danger time series with those derived from shuffled text, we find that shorter books exhibit only a general trend, while longer books have fluctuations in addition to the general trend. These fluctuations typically have a period of a few thousand words regardless of the book length or library classification code but vary depending on the content and structure of the book. Our findings suggest that, in the ousiometric sense, longer books are not expanded versions of shorter books, but rather are more similar in structure to a concatenation of shorter texts. Further, they are consistent with editorial practices that require longer texts to be broken down into sections, such as chapters. Our method also provides a data-driven denoising approach that works for texts of various lengths, in contrast to the more traditional approach of using large window sizes that may inadvertently smooth out relevant information, especially for shorter texts. Altogether, these results open up avenues for future work in computational literary analysis, particularly the possibility of measuring a basic unit of narrative. [ABSTRACT FROM AUTHOR]

In addition to a more extreme network experience and larger network capacity, 5G can also be used to rapidly transmit massive global text data at higher transmission speeds and higher reliability than previous communication technologies, allowing more communication devices around the world to operate at a faster rate. The transmission speed is safe and reliable to connect with each other. The rapid development of image recognition technology will be more effective in improving the operational efficiency of various industries. At present, scholars have used 5G technology and image recognition technology in the field of book management. This paper designs a book image recognition recommendation scheme based on 5G technology, in order to improve the problems of inaccurate book sales targets and low book classification efficiency of merchants from various perspectives. The image recognition book recommendation system combined with 5G technology is mainly divided into two steps: image recognition and book recommendation. The ultra-high speed provided by 5G technology can greatly improve the speed and accuracy of image recognition. Significantly reduces program running time. This paper first analyzes the related concepts of 5G, image recognition and recommendation algorithm, then sorts out the book image recognition method, and then designs the book recommendation purchase method. Combined with 5G technology, the image recognition and recommended purchase strategy are optimized. Finally, an online questionnaire is used to investigate the merchants using this method. The survey results show that the merchants who use this identification and purchase recommendation method have better performance in book classification, book personality recommendation, customer loyalty and book sales rate have greatly improved. [ABSTRACT FROM AUTHOR]

Isabel Hofmeyr's latest book begins with stories around and about the colonial port, though the initial spotlight is on decidedly nonnarrative texts such as classification lists of cargo items, customs handbooks, and what she intriguingly calls the "book-as-form," namely diaries and registers. These, she says, "offered one unwitting model of colonial writing in which a template from the metropolis was filled with local scribblings" (12). The port is, by definition, a liminal, watery, zone, with uncertain borders between land and sea, but which often acts as the site of border policing that regulates entry into and out of the colony and nation-state. It is a powerfully evocative place around which to set Hofmeyr's ambitious and wide-ranging book, and the port's polysemous implications allow her to intervene across a series of disparate fields: climate humanities, postcolonial studies, object-oriented ontology, South African literary histories, and studies of custom and copyright. It is a masterly and original revisioning of what it means to do book history, offering a radically new method of reading. Even more importantly, it proposes a new definition of the book as object: as customs cargo, as charismatic "thing" that creates literary canonicity far from the metropole, and as an epidemiological vector of "contamination" in the mind of the colonial customs official on the alert for seditious or obscene texts, among other suggestive meanings. [ABSTRACT FROM AUTHOR]

The categories listed below are used to classify books, book reviews, journal articles, and dissertations indexed in JEL, JEL on CD, and EconLit. New changes to the classification system appear as soon as possible on www.econlit.org. The JEL classification system may be used freely for scholarly purposes. We suggest the following format: "JEL: A10, B10, etc." [ABSTRACT FROM AUTHOR]

From the dual perspective of the DC pension plan members and the man- ager, we investigate the asset allocation of a DC pension plan under a VaR-PI constraint by maximizing a weighted utility of the two parties. Assuming that the DC pension plan members and the manager are loss averse and we use two S-shaped utility functions to demonstrate the loss aversion behavior. The VaR constraint and a weighed utility lead to a complex, nonconcave utility maximization problem. We apply the Lagrange duality theory and the concavification technique to derive the optimal wealth and the optimal portfolio processes. Numerical results show that the manager shall take a much riskier portfolio strategy when the benefits of the DC pension plan members are paid much more attention. The VaR constraint can improve the risk management of the DC pension plan. Keywords VaR constraint, DC pension plan, loss aversion, weighted utility Chinese Library Classification F832.48, F840.62 [ABSTRACT FROM AUTHOR]

Acknowledging the importance of classification not only for library and information science but also for the study and mapping of the world phenomena, in this paper we revisit and systematize the main types of classifications and focus on the species of classification mainly drawing on the work of S. R. Ranganathan. We trace the evolution of library classification systems by their structures and modes of design of various shades of classification systems and make a comparative study of enumerative and faceted species of library classifications. The value of this paper is to have a picture of the whole spectrum of existing classifications, which may serve for the study of future developments and constructions of new systems. This paper updates previous works by Comaromi and Ranganathan and is also theoretically inspired by them. [ABSTRACT FROM AUTHOR]

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The article presents a pair of Stanomin-type anklets discovered in eastern Mazovia, at Siwianka, Otwock County, in an area that yielded several other late Hallstatt hoards. The study includes comments on the manufacturing technique of the anklets, a detailed archaeological analysis and a new classification of all the ornaments of the Mazovian version known to date, which, unlike the Kuyavian patterns, cover the entire outer surface of the artefacts. An analysis of the distribution of anklets decorated with the same ornamental motifs made it possible to identify patterns typical of western and eastern Mazovia and Podlachia. [ABSTRACT FROM AUTHOR]

The science of discrepancies of hadith task sciences needed by scientists and researchers in all sciences Sharia. This science needs hadith, jurist, fundamentalist and interpreter, each according to their specialization in the field of research in reaching the appropriate Sharia judgment in the matter presented to them. The disinterested and skeptics of the Sunnah of the Prophet and the enemies of the religion of the Nile and the deceitful for the hadith and challenge it, claiming the difference and contradiction between the hadiths of the Prophet Muhammad (PBUH) and the narratives contained about the imams of the people of the house(RA). Because of the importance of this science and to respond to the skeptics and appellants of the Sunnah, this study came to identify the approach of the two honorable sheikhs Ibn Qutaybah and al-Tusi in how to reconcile the difference and contradictions between the hadiths divided into a preamble and three demands. One of the most prominent findings of the study is that they agree and differ in some issues, and among the participants between them is their classification of two books in the Discrepancies of hadith, which are my book (the interpretation of the different hadith for Ibn Qutaybah) and the insight into what differed from the news for Tusi. They differ in several issues, the most prominent of which are that the ways to reconcile the difference between the hadiths in Ibn Qutaybah in his book are: 1-the plural 2-the copies 3-the preferences, while the ways to reconcile the difference between the hadiths in Tusi in his book were: 1-the preferences 2-the plural 3-the selection. [ABSTRACT FROM AUTHOR]