Brazil

In order for subtitles to fulfill their objective of enriching users’ experience, it is necessary that, during the subtitling process (creation of subtitles), “good practices”, usually specified in subtitling guides, are taken into account. In this context, this work uses an approach based on the processing of SRT files to assess the degree of compliance of subtitles in relation to the recommendations of a reference guide considered in the subtitling process. Our preliminary results show how meticulous is the process performed by those who aim at providing a satisfactory quality of experience (QoE) for viewers of the audiovisual content.

WebMedia 2020

Toward Assessing the Quality of Subtitles based on SRT Files Processing

subtitling guidelines

subtitles

captioning

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

Registration is now open

ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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.

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.

Classification and Persistence Analysis of Tie Strength on GitHub

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%.

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Classification and Persistence Analysis of Tie Strength on GitHub

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SMURF: SeMantic and linguistic UndeRstanding Fusion for Caption Evaluation via Typicality Analysis

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