Titlebook: Nordic Artificial Intelligence Research and Development; 4th Symposium of the Evi Zouganeli,Anis Yazidi,Pedro Lind Conference proceedings‘‘

弹药

abysmal

倔强不能

征服

Andrea M. Storås,Michael A. Riegler,Trine B. Haugen,Vajira Thambawita,Steven A. Hicks,Hugo L. Hammera team working on a school level problem: Reducing grade repetition. The second case concerns a data team working on a classroom level problem: low student achievement in English language. The last part of the book explains what it means to implement the data team procedure in the school, the condit

NORM

er had always expressed his respect for her communication skills and her ability to inspire team members to take one step beyond the normal. Although his decision to leave the company might have come as a surprise to some people in the company, the choice of Joanna as the new Chief Executive Officer did not.

chronology

Meirav Segal,Anne-Marie George,Christos Dimitrakakis an orientation and action framework for the strategic and operational design of a data-driven organization and is valuable for managers who are involved in data management in companies and organizations.978-3-031-20606-1978-3-031-20604-7Series ISSN 2731-4758 Series E-ISSN 2731-4766

极端的正确性

Knowledge Infused Representations Through Combination of Expert Knowledge and Original Inputencoded expert information is transported through the new enriched input representation, leading to a beneficial impact on performance within the task domain. This opens the way for exciting future endeavors such as testing performance on different task domains and the combination of multiple expert

合乎习俗

Pattern Based Software Architecture for Predictive Maintenancelutions to general problems. The presented conceptual model enables the integration of an expert knowledge of anticipated failures and the matrix-profile technique based anomaly detection. The results so far are encouraging.

Herbivorous

客观

Fake News Detection by Weakly Supervised Learning Based on Content Featuresased approach in weak supervised learning without the use of any contextual information for fake news detection. We propose an architecture that generates weak labels. We explore the effect of using weak labels for fake news detection with five different machine learning models. We demonstrate that