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.
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.
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.
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.
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.
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.
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.
This paper provides a summary account of Activity-Centred Analysis and Design (ACAD). ACAD offers a practical approach to analysing complex learning situations, in a way that can generate knowledge that is reusable in subsequent (re)design work. ACAD has been developed over the last two decades. It has been tested and refined through collaborative analyses of a large number of complex learning situations and through research studies involving experienced and inexperienced design teams. The paper offers a definition and high level description of ACAD and goes on to explain the underlying motivation. The paper also provides an overview of two current areas of development in ACAD: the creation of explicit design rationales and the ACAD toolkit for collaborative design meetings. As well as providing some ideas that can help teachers, design teams and others discuss and agree on their working methods, ACAD has implications for some broader issues in educational technology research and development. It questions some deep assumptions about the framing of research and design thinking, in the hope that fresh ideas may be useful to people involved in leadership and advocacy roles in the field.
Task analysis is the systematic study of how users complete tasks to achieve their goals. This knowledge ensures products and services are designed to efficiently and appropriately support those goals.
"a beautiful article. There's code, there's math. There are many plotted examples using a variety of different plotting techniques (in total, representing a 2d array as data, or in black/white, or with a terrible 'jet' colormap, or as a 3d terrain."
Altmetric measurements derived from the social web are increasingly advocated and used as early indicators of article impact and usefulness. Nevertheless, there is a lack of systematic scientific evidence that altmetrics are valid proxies of either impact or utility although a few case studies have reported medium correlations between specific altmetrics and citation rates for individual journals or fields. Finally, the coverage of all the altmetrics except for Twitter seems to be low and so it is not clear if they are prevalent enough to be useful in practice.
Explosionen in Kraftwerken, Krankenhäusern und Rüstungsanlagen erschüttern seit Tagen die Islamische Republik. Sind sie Teil einer asymmetrischen Attacke?