In the Developmental Intelligence Laboratory, we are interested in understanding fundamental cognitive mechanisms of human intelligence, human learning, and human interaction and communication in everyday activities. To do so, we collect and analyze micro-level multimodal behavioral data using state-of-the-art sensing and computational techniques. One of our primary research aims is to understand human learning and early development. How do young children acquire fundamental knowledge of the world? How do they select and process the information around them and learn from scratch? How do they learn to move their bodies and to communicate and interact with others? Learning this kind of knowledge and skills is the core of human intelligence. To understand how human learners achieve the learning goal, the primary approach in our research is to attach GoPro-like cameras on the head of young children to record egocentric video from their point of view. Using this innovative approach, we've been collecting video data of children’s everyday activities, such as playing with their parents and their peers, reading books with parents and caregivers, and playing outside. We've been using state-of-the-art machine learning and data mining approaches to analyze high-density behavioral data. This research line will ultimately solve the mystery on why human children are such efficient learners. Moreover, the findings from our research will be used to help improve learning of children with developmental deficits. A complimentary research line is to explore how human learning can teach us about how machines can learn. Can we model and simulate how a human child learns and develops? To this end, our research aims at bridging and connecting developmental science in psychology and machine learning and computer vision in computer science.
El Objetivo del paquete aprendeR es facilitar que nuevas personas puedan R moderno con una curva de aprendizaje baja, y evitando que el inglés sea una barrera para que se puedan centrar en el aprendizaje competencial de R. Se incluyen traducciones al castellano de tutoriales presentes en otros paquetes (learnr, tutorial.helpers, r4ds.tutorials, ...), y eventualmente nuevos tutoriales más adelante.
This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.
Today, speech technology is only available for a small fraction of the thousands of languages spoken around the world because traditional systems need to be trained on large amounts of annotated speech audio with transcriptions. Obtaining that kind of data for every human language and dialect is almost impossible.
Wav2vec works around this limitation by requiring little to no transcribed data. The model uses self-supervision to push the boundaries by learning from unlabeled training data. This enables speech recognition systems for many more languages and dialects, such as Kyrgyz and Swahili, which don’t have a lot of transcribed speech audio. Self-supervision is the key to leveraging unannotated data and building better systems.
The demand for Generative AI in Media and Entertainment Market size is expected to register USD 1,412.7 million by 2023. It is anticipated to showcase a steady CAGR of 26.3% between 2023 and 2032. Sales of generative AI in media and entertainment will likely register USD 11,570.0 million by 2032. Revenue stood at USD 1,158.5 million in 2022.
This comprehensive financial analyst course covers a wide range of financial subjects, including financial statement analysis, ratio analysis, cash flow analysis, valuation methods, bookkeeping, and VAT. Participants will also develop expertise in payroll management, functioning as an accounts assistant, and mastering basic to advanced Excel techniques.
Citation analysis was traditionally based on data from the ISI Citation indexes. Now with the appearance of Scopus, and with the free citation tool Google Scholar methods and measures are need for comparing these tools. In this paper we propose a set of measures for computing the similarity between rankings induced by ordering the retrieved publications in decreasing order of the number of citations as reported by the specific tools. The applicability of these measures is demonstrated and the results show high similarities between the rankings of the ISI Web of Science and Scopus and lower similarities between Google Scholar and the other tools.
H. Nguyen, D. Bozhkov, Z. Ahmadi, N. Nguyen, и T. Doan. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, (июля 2022)
A. Nenkova, и R. Passonneau. Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004, стр. 145--152. Boston, Massachusetts, USA, Association for Computational Linguistics, (2004)