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Ontem — 29 de Novembro de 2022J-K

Hidden scales in statistics of citation indicators

Publication date: February 2023

Source: Journal of Informetrics, Volume 17, Issue 1

Author(s): Andrey M. Tokmachev

  • 29 de Novembro de 2022, 18:39

Editorial Board

Publication date: November 2022

Source: Journal of Informetrics, Volume 16, Issue 4

Author(s):

  • 29 de Novembro de 2022, 18:39

Interpretable reparameterisations of citation models

Publication date: February 2023

Source: Journal of Informetrics, Volume 17, Issue 1

Author(s): Barbara Żogała-Siudem, Anna Cena, Grzegorz Siudem, Marek Gagolewski

  • 29 de Novembro de 2022, 18:39

Global informetric impact: A description and definition using bundles

Publication date: February 2023

Source: Journal of Informetrics, Volume 17, Issue 1

Author(s): Leo Egghe, Ronald Rousseau

  • 29 de Novembro de 2022, 18:39

The architecture of academic libraries in Israel: Knowledge and prestige

Publication date: March 2023

Source: The Journal of Academic Librarianship, Volume 49, Issue 2

Author(s): Jonathan Letzter

  • 29 de Novembro de 2022, 18:38

Microaggressions in Academic Spaces: What About the Library?

Publication date: March 2023

Source: The Journal of Academic Librarianship, Volume 49, Issue 2

Author(s): J.J. Prieto-Gutiérrez, María-Jesús Colmenero-Ruiz

  • 29 de Novembro de 2022, 18:38

LAGOS‐AND: A large gold standard dataset for scholarly author name disambiguation

Por Li Zhang, Wei Lu, Jinqing Yang

Abstract

In this article, we present a method to automatically build large labeled datasets for the author ambiguity problem in the academic world by leveraging the authoritative academic resources, ORCID and DOI. Using the method, we built LAGOS-AND, two large, gold-standard sub-datasets for author name disambiguation (AND), of which LAGOS-AND-BLOCK is created for clustering-based AND research and LAGOS-AND-PAIRWISE is created for classification-based AND research. Our LAGOS-AND datasets are substantially different from the existing ones. The initial versions of the datasets (v1.0, released in February 2021) include 7.5 M citations authored by 798 K unique authors (LAGOS-AND-BLOCK) and close to 1 M instances (LAGOS-AND-PAIRWISE). And both datasets show close similarities to the whole Microsoft Academic Graph (MAG) across validations of six facets. In building the datasets, we reveal the variation degrees of last names in three literature databases, PubMed, MAG, and Semantic Scholar, by comparing author names hosted to the authors' official last names shown on the ORCID pages. Furthermore, we evaluate several baseline disambiguation methods as well as the MAG's author IDs system on our datasets, and the evaluation helps identify several interesting findings. We hope the datasets and findings will bring new insights for future studies. The code and datasets are publicly available.

Abandoning Yesterday to Transform Tomorrow

Por Douglas Way
Volume 62, Issue 8, November-December 2022, Page 1070-1076
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  • 28 de Novembro de 2022, 03:38

Understanding Disability to Support Library Workers

Por Katelyn Quirin Manwiller
Volume 62, Issue 8, November-December 2022, Page 1077-1084
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  • 28 de Novembro de 2022, 03:38

DNETC: dynamic network embedding preserving both triadic closure evolution and community structures

Abstract

Network embedding, a central issue of deep learning preprocessing on social networks, aims to transform network elements (vertices) into low-dimensional latent vector space while preserving the topology and properties of the network. However, most of the existing methods mainly focus on static networks, neglecting the dynamic characteristics of real social networks. The explanation for the fundamental dynamic mechanism of social network evolution is still lacking. We design a novel dynamic network embedding approach preserving both triadic closure evolution and community structures (DNETC). First, three factors, the popularity of vertices, the proximity of vertices, and the community structures, are incorporated relying on the triadic closure principle in social networks. Second, the triadic closure loss function, the community loss function, and the temporal smoothness loss function are constructed and incorporated to optimize DNETC. Finally, the low-dimensional cognition presentation of a dynamic social network can be achieved, which can save both the evolution patterns of microscopic vertices and the structure information of macroscopic communities. Experiments on the classical tasks of link prediction, link reconstruction, and changed link reconstruction and prediction demonstrate the superiority of DNETC over state-of-the-art methods. The first experimental results validate the effectiveness of adopting triadic closure progress and community structures to improve the quality of the learned low-dimensional vectors. The last experimental results further verify the parameter sensitivity of DNETC to the analysis task. It provides a new idea for dynamic network embedding to reflect the real evolution characteristics of networks and enhance the effect of network analysis tasks. The code is available at https://github.com/YangMin-10/DNETC.

  • 27 de Novembro de 2022, 00:00

Dynamic ensemble selection classification algorithm based on window over imbalanced drift data stream

Abstract

Data stream classification is an important research direction in the field of data mining, but in many practical applications, it is impossible to collect the complete training set at one time, and the data may be in an imbalanced state and interspersed with concept drift, which will greatly affect the classification performance. To this end, an online dynamic ensemble selection classification algorithm based on window over imbalanced drift data stream (DESW-ID) is proposed. The algorithm employs various balancing measures, first resampling the data stream using Poisson distribution, and if it is in a highly imbalanced state then secondary sampling is performed using a window storing a minority class instances to achieve the current balanced state of the data. To improve the processing efficiency of the algorithm, a classifier selection ensemble is proposed to dynamically adjust the number of classifiers, and the algorithm runs with an ADWIN detector to detect the presence of concept drift. The experimental results show that the proposed algorithm ranks first on average in all five classification performance metrics compared to the state-of-the-art methods. Therefore, the proposed algorithm has better classification performance for imbalanced data streams with concept drift and also improves the operation efficiency of the algorithm.

  • 27 de Novembro de 2022, 00:00

Sage research methods core review

Por Georgette Nicolosi
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  • 24 de Novembro de 2022, 01:35
Antes de ontemJ-K

KLECA: knowledge-level-evolution and category-aware personalized knowledge recommendation

Abstract

Knowledge recommendation plays a crucial role in online learning platforms. It aims to optimize the service quality so as to improve users’ learning efficiency and outcomes. Existing approaches generally leverage RNN-based methods in combination with attention mechanisms to learn user preference. There is a lack of in-depth understanding of users’ knowledge-level changes over time and the impact of knowledge item categories on recommendation performance. To this end, we propose the knowledge-level-evolution and category-aware personalized knowledge recommendation (KLECA) model. The model firstly leverages bidirectional GRU and the time adjustment function to understand users’ learning evolution by analyzing their learning trajectory data. Secondly, it considers the effect of item categories and descriptive information and enhances the accuracy of knowledge recommendation by introducing a cross-head decorrelation module to capture the information of knowledge items based on a multi-head attention mechanism. In addition, a personalized attention mechanism and gated function are introduced to grab the relationship between items, item categories and user learning trajectory to strengthen the representation of information. Through extensive experiments on real-world data collected from an online learning platform, the proposed approach has been shown to significantly outperform other approaches.

  • 24 de Novembro de 2022, 00:00

Concept drift detection and accelerated convergence of online learning

Abstract

Streaming data has become an important form in the era of big data, and the concept drift, as one of the most important problem of it, is often studied deeply. However, similar to true concept drift, noise and too small training samples will also lead to the classification performance fluctuation, which is easy to confuse with true concept drift. To solve this problem, an improved concept drift detection method is proposed, and the accelerated convergence of the model after concept drift is also studied. Firstly, the effective fluctuation sites can be obtained by group detection method. Secondly, the authenticity of concept drift can be determined by tracking the testing accuracy of reference sites near the effective fluctuation site. Lastly, in the convergence acceleration stage, the time sequential distance is designed to measure the similarity of these sequential data blocks during different time periods, and the noncritical disturbance data with the largest time sequential distance are removed sequentially to improve the convergence speed of the model after concept drift occurs. The experimental results demonstrate that the proposed method not only produces better identification results in distinguishing true and false concept drift but also improves the convergence speed of the model.

  • 23 de Novembro de 2022, 00:00

Conscientiousness predicts doctoral students’ research productivity

Publication date: February 2023

Source: Journal of Informetrics, Volume 17, Issue 1

Author(s): Jonas Lindahl

  • 24 de Novembro de 2022, 10:33

Patent landscape and key technology interaction roadmap using graph convolutional network – Case of mobile communication technologies beyond 5G

Publication date: February 2023

Source: Journal of Informetrics, Volume 17, Issue 1

Author(s): Amy J.C. Trappey, Ann Y.E. Wei, Neil K.T. Chen, Kuo-An Li, L.P. Hung, Charles V. Trappey

  • 24 de Novembro de 2022, 10:33

Big Five Personality Traits and Knowledge Sharing Intentions of Academic Librarians

Publication date: March 2023

Source: The Journal of Academic Librarianship, Volume 49, Issue 2

Author(s): Azeem Akbar, Amara Malik, Nosheen Fatima Warraich

  • 24 de Novembro de 2022, 10:32

Implementing Project READY at an academic library: Survey analysis of a DEI training experience

Publication date: March 2023

Source: The Journal of Academic Librarianship, Volume 49, Issue 2

Author(s): Emy Nelson Decker, Lance Simpson

  • 24 de Novembro de 2022, 10:32

The user experience: Student perspectives on library course reserve

Publication date: March 2023

Source: The Journal of Academic Librarianship, Volume 49, Issue 2

Author(s): Sara Foster, Duane Wilson, Shannon Sanders, Justin Johnson

  • 24 de Novembro de 2022, 10:32

Faculty perceptions, use, and needs of library resource and services in a public research university

Publication date: January 2023

Source: The Journal of Academic Librarianship, Volume 49, Issue 1

Author(s): Jung Mi Scoulas, Sandra L. De Groote

  • 24 de Novembro de 2022, 10:32
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