10,218 research outputs found
From manuscript catalogues to a handbook of Syriac literature: Modeling an infrastructure for Syriaca.org
Despite increasing interest in Syriac studies and growing digital
availability of Syriac texts, there is currently no up-to-date infrastructure
for discovering, identifying, classifying, and referencing works of Syriac
literature. The standard reference work (Baumstark's Geschichte) is over ninety
years old, and the perhaps 20,000 Syriac manuscripts extant worldwide can be
accessed only through disparate catalogues and databases. The present article
proposes a tentative data model for Syriaca.org's New Handbook of Syriac
Literature, an open-access digital publication that will serve as both an
authority file for Syriac works and a guide to accessing their manuscript
representations, editions, and translations. The authors hope that by
publishing a draft data model they can receive feedback and incorporate
suggestions into the next stage of the project.Comment: Part of special issue: Computer-Aided Processing of Intertextuality
in Ancient Languages. 15 pages, 4 figure
What country, university or research institute, performed the best on COVID-19? Bibliometric analysis of scientific literature
In this article, we conduct data mining to discover the countries,
universities and companies, produced or collaborated the most research on
Covid-19 since the pandemic started. We present some interesting findings, but
despite analysing all available records on COVID-19 from the Web of Science
Core Collection, we failed to reach any significant conclusions on how the
world responded to the COVID-19 pandemic. Therefore, we increased our analysis
to include all available data records on pandemics and epidemics from 1900 to
2020. We discover some interesting results on countries, universities and
companies, that produced collaborated most the most in research on pandemic and
epidemics. Then we compared the results with the analysing on COVID-19 data
records. This has created some interesting findings that are explained and
graphically visualised in the article
Using Machine Learning to Predict the Evolution of Physics Research
The advancement of science as outlined by Popper and Kuhn is largely
qualitative, but with bibliometric data it is possible and desirable to develop
a quantitative picture of scientific progress. Furthermore it is also important
to allocate finite resources to research topics that have growth potential, to
accelerate the process from scientific breakthroughs to technological
innovations. In this paper, we address this problem of quantitative knowledge
evolution by analysing the APS publication data set from 1981 to 2010. We build
the bibliographic coupling and co-citation networks, use the Louvain method to
detect topical clusters (TCs) in each year, measure the similarity of TCs in
consecutive years, and visualize the results as alluvial diagrams. Having the
predictive features describing a given TC and its known evolution in the next
year, we can train a machine learning model to predict future changes of TCs,
i.e., their continuing, dissolving, merging and splitting. We found the number
of papers from certain journals, the degree, closeness, and betweenness to be
the most predictive features. Additionally, betweenness increases significantly
for merging events, and decreases significantly for splitting events. Our
results represent a first step from a descriptive understanding of the Science
of Science (SciSci), towards one that is ultimately prescriptive.Comment: 24 pages, 10 figures, 4 tables, supplementary information is include
Analysis of Computer Science Communities Based on DBLP
It is popular nowadays to bring techniques from bibliometrics and
scientometrics into the world of digital libraries to analyze the collaboration
patterns and explore mechanisms which underlie community development. In this
paper we use the DBLP data to investigate the author's scientific career and
provide an in-depth exploration of some of the computer science communities. We
compare them in terms of productivity, population stability and collaboration
trends.Besides we use these features to compare the sets of topranked
conferences with their lower ranked counterparts.Comment: 9 pages, 7 figures, 6 table
An Empirical Evaluation Of Social Influence Metrics
Predicting when an individual will adopt a new behavior is an important
problem in application domains such as marketing and public health. This paper
examines the perfor- mance of a wide variety of social network based
measurements proposed in the literature - which have not been previously
compared directly. We study the probability of an individual becoming
influenced based on measurements derived from neigh- borhood (i.e. number of
influencers, personal network exposure), structural diversity, locality,
temporal measures, cascade mea- sures, and metadata. We also examine the
ability to predict influence based on choice of classifier and how the ratio of
positive to negative samples in both training and testing affect prediction
results - further enabling practical use of these concepts for social influence
applications.Comment: 8 pages, 5 figure
Crowdsourcing Linked Data on listening experiences through reuse and enhancement of library data
Research has approached the practice of musical reception in a multitude of ways, such as the analysis of professional critique, sales figures and psychological processes activated by the act of listening. Studies in the Humanities, on the other hand, have been hindered by the lack of structured evidence of actual experiences of listening as reported by the listeners themselves, a concern that was voiced since the early Web era. It was however assumed that such evidence existed, albeit in pure textual form, but could not be leveraged until it was digitised and aggregated. The Listening Experience Database (LED) responds to this research need by providing a centralised hub for evidence of listening in the literature. Not only does LED support search and reuse across nearly 10,000 records, but it also provides machine-readable structured data of the knowledge around the contexts of listening. To take advantage of the mass of formal knowledge that already exists on the Web concerning these contexts, the entire framework adopts Linked Data principles and technologies. This also allows LED to directly reuse open data from the British Library for the source documentation that is already published. Reused data are re-published as open data with enhancements obtained by expanding over the model of the original data, such as the partitioning of published books and collections into individual stand-alone documents. The database was populated through crowdsourcing and seamlessly incorporates data reuse from the very early data entry phases. As the sources of the evidence often contain vague, fragmentary of uncertain information, facilities were put in place to generate structured data out of such fuzziness. Alongside elaborating on these functionalities, this article provides insights into the most recent features of the latest instalment of the dataset and portal, such as the interlinking with the MusicBrainz database, the relaxation of geographical input constraints through text mining, and the plotting of key locations in an interactive geographical browser
Symbolic Computing with Incremental Mindmaps to Manage and Mine Data Streams - Some Applications
In our understanding, a mind-map is an adaptive engine that basically works
incrementally on the fundament of existing transactional streams. Generally,
mind-maps consist of symbolic cells that are connected with each other and that
become either stronger or weaker depending on the transactional stream. Based
on the underlying biologic principle, these symbolic cells and their
connections as well may adaptively survive or die, forming different cell
agglomerates of arbitrary size. In this work, we intend to prove mind-maps'
eligibility following diverse application scenarios, for example being an
underlying management system to represent normal and abnormal traffic behaviour
in computer networks, supporting the detection of the user behaviour within
search engines, or being a hidden communication layer for natural language
interaction.Comment: 4 pages; 4 figure
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