Analysis of the need for learning media for children with a certainty by Heriani Thamrin, Sunardi Sunardi, Supriyadi Supriyadi

Subjects: “…media, learning, intellectual disability…”
Get full text

Learning to Live Through Loss: Grief and the Mourning Process by Carolyn S. Wilken

Get full text

Improving fluoroprobe sensor performance through machine learning by D. Lafer, A. Sukenik, T. Zohary, O. Tal

Subjects: “…Machine learning…”
Get full text

Machine and deep learning performance in out-of-distribution regressions by Assaf Shmuel, Oren Glickman, Teddy Lazebnik

Subjects: Get full text

Learning, Teaching and First Aid in times of COVID-19

“…The editorial cites among other factors the disparities in opportunities for schooling and online education between the wealthy and poor, stressing that shifting to online first aid education is not happening in a vacuum and is not only about changing the tools and platforms (as challenging as they might be), but also about adapting what is already available to take account of the developing situation, alterations in population needs and adapting to first aid learning in diverse settings where many of those differences are not known or obvious to us.…”
Get full text

Decentralized and Asymmetric Multi-Agent Learning in Construction Sites by Yakov Miron, Dan Navon, Yuval Goldfracht, Dotan Di Castro, Itzik Klein

Subjects: Get full text

Self-Confirming Biased Beliefs in Organizational “Learning by Doing” by Sanghyun Park, Phanish Puranam

“…Learning by doing, a change in beliefs (and consequently behaviour) due to experience, is crucial to the adaptive behaviours of organizations as well as the individuals that inhabit them. …”
Get full text

Open Educational Resources in the Development of Digital Learning Environment by N. V. Dneprovskaya, I. V. Shevtsova

Subjects: Get full text

Application of machine learning tools and immersive technologies in management by A. V. Sosnilo

Subjects: Get full text

Federated Learning Applications in Fingerprint and Finger Vein Recognition by Wang Yongchao

“…This article summarizes several approaches that combine federated learning with fingerprint and finger vein recognition to solve privacy issues. …”
Get full text

Deep Reinforcement Learning for Intraday Multireservoir Hydropower Management by Rodrigo Castro-Freibott, Álvaro García-Sánchez, Francisco Espiga-Fernández, Guillermo González-Santander de la Cruz

Subjects: Get full text

Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm by Gargi Pant Shukla, Santosh Kumar, Saroj Kumar Pandey, Rohit Agarwal, Neeraj Varshney, Ankit Kumar

Subjects: Get full text

PHILOSOPHY OF EDUCATION IN THE DIGITAL AGE: TRANSFORMATION OF KNOWLEDGE AND LEARNING by Марина ШУЛЬГА, Інна КУЗНЄЦОВА, Наталія ПОЛІЩУК

Subjects: “…simulacral knowledge, digital subject, hypertextual education, adaptive learning, informational entropy, poststructuralist approach…”
Get full text

Experimenting with twilight learnings and twilight writings for community engagement by Silvia Mugnaini, Åsa Ståhl, Leah Ireland

“… This contribution explores community engagement through the collaborative practice ‘Twilight Learnings – Seasonal experiments in the Tiny House on Wheels (THoW)’. …”
Get full text

Evaluation of the AccessIT Project and the Distance LearningProgramme on Digitization by Bülent Yılmaz, Özgür Külcü, Yurdagül Ünal, Tolga Çakmak

Get full text

Survey on intellectual property protection for deep learning model by Xinya WANG, Guang HUA, Hao JIANG, Haijian ZHANG

“…With the rapid development of deep learning technology, deep learning models have been widely used in many fields such as image classification and speech recognition.Training a deep learning model relies on a large amount of data and computing power, thus selling the trained model or providing specific services (DLaaS, e.g.) has become a new business.However, the commercial interests of model trainers and the intellectual property rights of model developers may be violated if the model is maliciously stolen.With deep neural network watermarking becoming a new research topic, multimedia copyright protection techniques were used for deep learning model protection.Numerous methods have been proposed in this field and then a comprehensive survey is needed.the existing deep neural network watermarking methods were elaborated and summarized and the future research directions of this field were discussed.The overall framework of neural network watermarking was presented, whereby the basic concepts such as classification model and model backdoor were introduced.Secondly, the existing methods were divided into two types according to the mechanism of watermark embedding, one is to embed the watermark bits into the carrier of internal information of the network, and the other one uses the established backdoor mapping as the watermark.These two existing deep neural network watermarking methods were analyzed and summarized, and attacks to the watermarks were also introduced and discussed.By analyzing the white-box and black-box conditions in watermarking scenario, it comes to the conclusion that the model is difficult to be effectively protected when it is distributed in the white-box manner, and the neural network watermark defenses in the black-box distribution and black-box verification are both worthy for further research.…”
Get full text

Development and Validation of Comic-based Learning Module in Physics by Jeah May Badeo, Bee Ching Ong Kian Koc

Subjects: Get full text

Early detection of sepsis using machine learning algorithms by Rasha M. Abd El-Aziz, Alanazi Rayan

Subjects: Get full text

Schools and New Learning Spaces in Milan. An Educational Experience by António Carvalho, Szymon Ruszczewski, Filippo Piscioli

Subjects: Get full text

Effective Dose Estimation in Computed Tomography by Machine Learning by Matteo Ferrante, Paolo De Marco, Osvaldo Rampado, Laura Gianusso, Daniela Origgi

“…E calculated by DTS was chosen as the target value for prediction. Different machine learning algorithms were selected, optimizing parameters to achieve the best performance for each algorithm. …”
Get full text