Results 81 to 90 of about 274,696 (183)

TMRGNN: Temporal Multi-Relation Graph Neural Network for Malicious Client Detection in Federated Learning

IEEE Open Journal of the Communications Society
Federated learning remains inherently vulnerable in practical deployments, as malicious clients can behave unpredictably, launch coordinated delayed attacks, and exploit non-IID data heterogeneity to obscure their adversarial behavior. Existing defenses,
Anuja Kunjumon, E. V. Sunitha, Jyothisha J. Nair   +2 more
doaj   +1 more source

Evaluation of teacher candidates' attitudes towards the use of smart learning technologies

Journal of Technology and Science Education
Smart learning technologies employed in education encompass a range of tools and platforms that facilitate learning and instructional activities. Here are several significant smart learning technologies: e-learning platforms, smart learning content tools,
Galiya Abayeva, Saule T. Issaliyeva, Saltanat Zh. Omirbek, Klara B. Smatova, Saule K. Alimbayeva, Damir R. Rashidinov   +5 more
doaj   +1 more source

Explainable judgment prediction and article-violation analysis using deep LexFaith hierarchical BERT model

Scientific Reports
The use of Artificial Intelligence has already changed every sphere of our lives and legal field is not an exception as cases are being prepared by analyzing content of legal documents.
Xiaoyue Zhang, Shuang Liu
doaj   +1 more source

Made in the EU

European Journal for Research on the Education and Learning of Adults
This paper addresses the processes involved in the Europeanisation of adult education, with a particular focus on the limitations of EU policies aimed at increasing adult participation in education and learning.
Fátima Antunes
doaj   +1 more source

Game-based application for normalization learning

Journal of Computing Research and Innovation, 2018
Normalization is a relational database design for the organizing data process to minimize redundancy. The main aim for this study is to develop a game application for normalization learning.
Nik Nuru Izzasalwani Nik Pa, Norfiza Ibrahim, Azmi Abu Seman   +2 more
doaj  

Network-based Anomaly Detection in Waste Transportation Data with Limited Supervision

Applied Network Science
Detecting anomalies in Waste transportation networks is vital for uncovering illegal or unsafe activities, that can have serious environmental and regulatory consequences. Identifying anomalies in such networks presents a significant challenge due to the
Nirbhaya Shaji, Shazia Tabassum, Rita P. Ribeiro, João Gama, Joana Gorgulho, Ana Garcia, Pedro Santana   +6 more
doaj   +1 more source

Change of representation for statistical relational learning

, 2007
Statistical relational learning (SRL) algorithms learn statistical models from relational data, such as that stored in a relational database. We previously introduced view learning for SRL, in which the view of a relational database can be automatically modified, yielding more accurate statistical models.
Davis, Jesse, Ong, Irene, Struyf, Jan, Burnside, Elizabeth, Page, David, Santos Costa, Vitor   +5 more
openaire   +1 more source

Scalable Statistical Relational Learning for NLP [PDF]

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts, 2016
William Yang Wang, William Cohen
openaire   +1 more source

An exercise with statistical relational learning systems

, 2009
sponsorship: This work was supported by the GOA/08/008 "Probabilistic Logic Learning", by the Research Foundation-Flanders (FWO-Vlaanderen) and by IWT Vlaanderen.
Bruynooghe, Maurice, De Cat, Broes, Drijkoningen, Jochen, Fierens, Daan, Goos, Jan, Gutmann, Bernd, Kimmig, Angelika, Labeeuw, Wouter, Langenaken, Steven, Landwehr, Niels, Meert, Wannes, Nuyts, Ewoud, Pellegrims, Robin, Rymenants, Roel, Segers, Stefan, Thon, Ingo, Van Eyck, Jelle, Van den Broeck, Guy, Vangansewinkel, Tine, Van Hove, Lucie, Vennekens, Joost, Weytjens, Timmy, De Raedt, Luc   +22 more
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Directed Models For Statistical Relational Learning

, 2012
Statistical Relational Learning is a new branch of machine learning that aims to model a joint distribution over relational data. Relational data consists of different types of objects where each object is characterized with a different set of attributes.
openaire   +1 more source