Brazil

Relationships between users in social networks are evaluated in different ways. Here, our goal is to measure the strength of the ties between GitHub users by considering the temporal aspect of the network. Specifically, we analyze the evolution of these relationships by applying a classification algorithm over a GitHub network and calculate the persistence of them over different classes. The results bring new information about the collaborative software development process on the platform.

WebMedia 2020

Classification and Persistence Analysis of Tie Strength on GitHub

tie strength

social coding

github

social networks

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### About

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
<br><br>
* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
<br><br>
* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
<br><br>

#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
<br><br>


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CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

technical paper

Promoted by the Brazilian Computer Society (SBC), the Brazilian Symposium on Multimedia and Web (WebMedia) is the main event of the theme in Brazil and an excellent opportunity for scientific and technical exchange among students, researchers and professionals in the areas of Multimedia, Hypermedia and Web. This year we will be celebrating to 26th edition of WebMedia, which will be held online from november 30th to December 4th .

Webmedia 2020’s programming includes:

– Technical sessions for the presentation of scientific papers
– National and international keynotes
– Panel with companies
– Workshops
– Short Courses
– Open community meetings

The topics of interest for submitting papers are:

– Web and Social Networks
– Multimedia
– Digital TV, Ubiquitous and Mobile Computing

Besides the main track, WebMedia 2020 will have in parallel the Tools and Applications Workshop, works Tools and Applications Workshop, the Workshop on Undergraduate Research Work and the Workshop The Future of Interactive.

The WebMedia 2020 organization is under the responsibility of the Programa de Pós-graduação Doutorado em Ciência da Computação – Associação UFMA/UFPI (DCCMAPI), do Programa de Pós-graduação em Ciência da Computação/ CCET (PPGCC), do Laboratory of Advanced Web Systems/UFMA (LAWS) e do Laboratório de Sistemas Multimídia/UFMA (TELEMÍDIA).).

#PartiuWebMedia2020 #WebMedia2020

Promoted by the Brazilian Computer Society (SBC), the Brazilian Symposium on Multimedia and Web (WebMedia) is the main event of the theme in Brazil and an excellent opportunity for scientific and technical exchange among students, researchers and professionals in the areas of Multimedia, Hypermedia and Web.

Due to easy access and low cost, social media online news consumption has increased significantly for the last decade. Despite their benefits, some social media allow anyone to post news with intense spreading power, which amplifies an old problem: the dissemination of Fake News. In the face of this scenario, several machine learning-based methods to automatically detect Fake News (MLFN) have been proposed. All of them require Fake News datasets to train and evaluate their detection models. Although recent MLFN were designed to consider data regarding the news propagation on social media, most of the few available Fake News datasets do not contain this kind of data. Hence, comparing the performances amid those recent MLFN and the others is restricted to a very limited number of datasets. Moreover, all existing datasets with propagation data do not contain news in Portuguese, which impairs the evaluation of the MLFN in this language. Thus, this work proposes FakeNewsSetGen, a process that builds Fake News datasets that contain news propagation data and support comparison amid the state-of-the-art MLFN. FakeNewsSetGen’s software engineering process was guided to include all kind of data required by the existing MLFN. In order to illustrate FakeNewsSetGen’s viability and adequacy, a case study was carried out. It encompassed the implementation of a FakeNewsSetGen prototype and the application of this prototype to create a dataset called FakeNewsSet, with news in Portuguese. Five MLFN with different kind of data requirements (two of them demanding news propagation data) were applied to FakeNewsSet and compared, demonstrating the potential use of both the proposed process and the created dataset.

FakeNewsSetGen: a Process to Build Datasets that Support Comparison Among Fake News Detection Methods.

Many recent works have successfully applied some types of Convolutional Neural Networks (CNNs) to reduce the noticeable distortion resulting from the lossy JPEG compression technique. Most of them are built upon the processing made on the spatial domain. In this work, we propose a JPEG image decoder that is purely based on the frequency-to-frequency domain: it reads the quantized DCTcoefficients received from a low-quality image bitstream and, using a deep learning-based model, predicts the missing coefficients in order to recompose the same image with enhanced quality. In experiments with two datasets, our best model was able to improve from images with quantized DCT coefficients corresponding to quality Factor (QF) of 10 to enhanced quality images with QFslightly higher than 20.

Decoder-Side Quality Enhancement for JPEG Images Using a Deep Learning-Based Prediction Model for Quantized DCT Coefficients

Instant messaging apps have billions of monthly active users, which made this environment a good medium for purposes other than communication, such as education. In this context, one of the most appropriate technologies to encourage learning in these applications is chatbots. They consist of conversational applications that make use of artificial intelligence and natural language processing to interact with the user in a similar way to a human. In this scenario, this article presents the AGATE a chatbot developed for the Environmental Management Office (AGA/UEMA) in order to disseminate knowledge on environmental education, focusing on the problems of water and energy waste. The chatbot was developed for Telegram through the pyTelegramBotAPI implementation. In addition, conversation flows were created using DialogFlow. A multidisciplinary team composed of 14 people participated in the evaluation of the project, with a greater participation of professionals in the field of Biology. The initial results focused on the user experience were satisfactory.

ÁGATA: um chatbot para difusão de práticas para Educação Ambiental

Smartphones sensing capabilities have enabled the development of Human Activity Recognition (HAR) solutions for better understanding human behavior through computational techniques. However, these solutions have been difficult to perform in dynamic scenarios because they do not observe data evolution over time and the high consumption of computational resources, such as memory, processing and energy. This occurs because the HAR problem for smartphones has been solved through classification models generated by offline machine learning algorithms that, in this case, are limited by a data history with little information about human activities. The problem with this approach is that human activities change constantly over time and are strongly influenced by the physical environment and the user’s profile. To overcome these problems this doctoral thesis proposes a new approach to recognize human activities based on the symbolic data streaming analysis. Our approach enables the development of low-cost, scalable HAR systems capable of adapting to data change over time. In this context, this thesis proposes a framework called DISTAR (DIscrete STream learning for Activity Recognition), responsible for standardizing the analysis of data stream process and generation of adaptive models that observe the data evolution over time without storing a data history. The DISTAR framework uses the symbolic representation algorithms known for reducing the dimensionality and numerosity of the data. In addition, this thesis also proposes a new adaptive online algorithm, called NOHAR (NOvelty discrete data stream for Human Activity Recognition), which uses as basis the DISTAR framework. Experimental results using three databases show that NOHAR is 13 times faster compared to the state of the art and is able to reduce memory consumption by an average of 99.97%.

Downloads

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FakeNewsSetGen: a Process to Build Datasets that Support Comparison Among Fake News Detection Methods.

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FakeNewsSetGen: a Process to Build Datasets that Support Comparison Among Fake News Detection Methods.

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