34,219 research outputs found
Khoo Kay Kim, professor of Malaysian history : a biobibliometric study
No full textPresents an analysis of the publication productivity, authorship pattern, channels of communication, journal preference and language preference of Professor Dato' Khoo Kay Kim, Professor of Malaysian History in the University of Malaya, Kuala Lumpur. The results of this biobibliometric study indicate that he can be a role model for future Malaysian historians to emulate his various achievements especially in the field of history education
Metadata Representations for Queryable ML Model Zoos
No full textMachine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretability purposes. The metatada is currently not standardised; its expressivity is limited; and there is no interoperable way to store and query it. Consequently, model search, reuse, comparison, and composition are hindered. In this paper, we advocate for standardized ML model metadata representation and management, proposing a toolkit supported to help practitioners manage and query that metadata.Web Information SystemsHuman-Centred Artificial Intelligenc
A Manifesto of Nodalism
No full textThis paper proposes the notion of Nodalism as a means describing contemporary culture and of understanding my own creative practice in electronic music composition. It draws on theories and ideas from Kirby, Bauman, Bourriaud, Deleuze, Guatarri, and Gochenour, to demonstrate how networks of ideas or connectionist neural models of cognitive behaviour can be used to contextualize, understand and become a creative tool for the creation of contemporary electronic music
Machine Learning (ML): A New Tool for Libraries & Archives
No full textA workshop presentation given by Bohyun Kim (Associate University Librarian for Library Information Technology at the University of Michigan Library) for the Aeolian Network - Online Workshop 4: AI/ML: Increasing Access, Visibility, and Engagement on April 22, 2022. The video recording and more information about the workshop are available at https://www.aeolian-network.net/workshop-4-bohyun-kim-machine-learning-a-new-tool-for-libraries-and-archives/http://deepblue.lib.umich.edu/bitstream/2027.42/194088/1/Aeolian2022_BKIM.pdfDescription of Aeolian2022_BKIM.pdf : Presentation SlidesSEL
Optimizing ML Inference Queries Under Constraints
No full textThe proliferation of pre-trained ML models in public Web-based model zoos facilitates the engineering of ML pipelines to address complex inference queries over datasets and streams of unstructured content. Constructing optimal plan for a query is hard, especially when constraints (e.g. accuracy or execution time) must be taken into consideration, and the complexity of the inference query increases. To address this issue, we propose a method for optimizing ML inference queries that selects the most suitable ML models to use, as well as the order in which those models are executed. We formally define the constraint-based ML inference query optimization problem, formulate it as a Mixed Integer Programming (MIP) problem, and develop an optimizer that maximizes accuracy given constraints. This optimizer is capable of navigating a large search space to identify optimal query plans on various model zoos.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information SystemsHuman-Centred Artificial Intelligenc
An invariance relation and a unified method to derive stationary queue-length distributions
No full textFor a broad class of discrete- and continuous-time queueing systems, we show that the stationary number of customers in system (queue plus servers) is the sum of two independent random variables, one of which is the stationary number of customers in queue and the other is the number of customers that arrive during the time a customer spends in service. We call this relation an invariance relation in the sense that it does not change for a variety of single-sever queues (with batch arrivals and batch services) and some multiserver queues (with batch arrivals and deterministic service times) that satisfy a certain set of assumptions. Making use of this relation, we also present a simple method of deriving the probability generating functions (PGFs) of the stationary numbers in queue and in system, as well as some of their properties. This is illustrated by several examples, which show that new simple derivations of old results as well as new results can be obtained in a unified manner. Furthermore, we show that the invariance relation and the method we are presenting are easily generalized to analyze queues with batch Markovian arrival process (BMAP) arrivals. Most of the results are presented under the discrete-time setting. The corresponding continuous-time results, however, are covered as well because deriving the results for continuous-time queues runs exactly parallel to that for their discrete-time counterparts
ANDez: An open-source tool for author name disambiguation using machine learning
No full textAuthor name disambiguation in bibliographic data is challenging due to the same names of different authors and name variations of authors. Various machine learning (ML) methods address this, but a unified framework for comparing them is lacking. This study introduces ANDez, an open-source tool that integrates top-performing ML techniques for author name disambiguation. Developed in Python using popular ML libraries, ANDez provides a transparent system, merging complex procedures from different ML approaches. This promotes the assessment, modification, and benchmarking of ML techniques in author name disambiguation. ANDez's user-friendly design also helps researchers analyze ambiguous bibliographic data without needing advanced ML coding expertise
Absence of significant association of trace elements in nails with urinary KIM-1 biomarker among residents of Addis Ababa in Upper Awash Basin, Ethiopia: a cross-sectional study
No full text\ua9 2022, The Author(s), under exclusive licence to Springer Nature B.V. The Akaki River in the Upper Awash Basin, which flows through Addis Ababa, the capital city of Ethiopia, has been highly polluted by sewage from factories and residential areas. A population-based cross-sectional study was used to assess the association between trace elements and kidney injury from residents living in polluted areas downstream (Akaki-Kality) versus upstream (Gullele) in Sub-Cities of Addis Ababa. A total of 95 individuals (53 from Akaki-Kality and 42 from Gullele) were included in the study. Kidney injury molecule 1 (KIM-1), lead, arsenic, cadmium, cobalt, lead, manganese, zinc, iron, copper, chromium and nickel were evaluated in residents’ urine and nail samples. A large proportion (74%) of the sample population contained KIM-1, including 81% residents in Akaki-Kality and 64% residents in Gullele. KIM-1 was, however, not significantly different (p = 0.05) between the two Sub-Cities, with median of 0.224 ng/mL in Akaki-Kality and 0.152 ng/mL in Gullele. Most of the analyzed elements, except Pb, As, Cd and Co, were found in all of the nail samples, with median (\ub5g/g) in the range of 442‒714 Fe, 97.0‒246 Zn, 11.6‒24.1 Mn, 4.49‒5.85 Cu, 1.46‒1.66 Cr and 1.22‒1.41 Ni. The high incidence of KIM-1 indicates a potential for long term renal tubular damage among residents of the Sub-Cities. The concentrations of the elements in nails were, however, not significantly associated (p = 0.05) with the corresponding levels of KIM-1 in urine. Hence, the observed KIM-1 might be related to exposure to toxic substances or factors other than those included in this study
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