Brazil

Adding sensory effects to applications has proven to increase users’
quality of experience. As immersive technologies advance in the fields of entertainment, education, training, simulation, and so forth,
more fine-grained functions are craved by users to have truly captivating and (nearly) realistic experiences. These new functions, in
turn, push forward both the industry and academia on developing
solutions to overcome the current limitations. This is indeed the
particular case of multimedia systems enriched with thermal wind
sensations. Whilst both wind and thermal effects have been included separately in such systems, the lack of wind at a low or high
temperature has been noticed by users. It is not trivial building,
controlling, and working with devices at different temperatures hit
by wind at the same time. This is exacerbated by the fact that different technologies are used to this end. In this paper, we introduce
a haptic system that allows the creation and delivery of hot and
cold airflow synchronised with mulsemedia content. The system
combines the vaporisation of cooled water by Peltier elements for
cold wind and hair dryers for warm wind steered by wind fans. To
assess our system, we analysed the time our devices take to deliver
these effects considering different starting temperatures in the environment. Furthermore, we annotated three videos with hot wind,
cold wind, and both effects respectively to observe the feasibility
of having those effects integrated with audiovisual content. Our
findings suggest recommendations for the appropriate triggering
of these types of sensory effects combined with multimedia content
taking into account our setup.

WebMedia 2021

A Haptic System for Switching Wind Temperatures Based on Ultrasonic Vibrations, Peltier Elements, and Electrical Resistances for Multisensory Applications

haptics

mulsemedia

simulation

hot wind

cold wind

temperature

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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technical paper

### Welcome to the Brazilian Symposium on Multimedia Systems and Web 
Promoted annually by the Brazilian Computer Society (SBC), the Brazilian Symposium on Multimedia Systems and Web (WebMedia) is the main event on the subject in Brazil and an excellent opportunity for scientific and technical exchanges between students, researchers and professionals in the areas of Multimedia, Hypermedia and Web. This year, we will be celebrating the 27th edition of WebMedia, to be held from **November 5th to 12th, 2021**, organized by the Federal University of Minas Gerais and Federal University of São João Del Rei. For the second time in its history, the event will be held online, with interaction between the participants being done through tools and web platforms.
 <iframe width="560" height="315" src="https://www.youtube.com/embed/2GvWKRUDtAY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> 
The Webmedia 2021 schedule will feature:
 
* Technical sessions for the presentation of scientific articles.
* National and international lectures
* Panel with Companies
* Workshops
* Short courses
* Project meetings open to the community
 

The topics of interest for paper submission are:
 
* Web and Social Networks
* Multimedia
* Digital TV, Ubiquitous and Mobile Computing
 

In addition to the main track, WebMedia 2021 will feature the Tools and Applications Workshop, the Scientific Initiation Papers Contest, the Theses and Dissertations Contest, the RNP CT-Video Workshop.
 
The organization of WebMedia 2021 is under the responsibility of the [Department of Computer Science at UFMG (DCC/UFMG)](http://dcc.ufmg.br/) and [Department of Computer Science at UFSJ (DCOMP/UFSJ)](https://dcomp.ufsj.edu.br/)
 
**#PartiuWebMedia2021 #WebMedia2021**

The Brazilian Symposium on Multimedia Systems and Web (WebMedia) is the main event on the subject in Brazil and an excellent opportunity for scientific and technical exchanges between students, researchers and professionals in the areas of Multimedia, Hypermedia and Web.

This work presents a systematic process for computing Bourdieusian Social Capital (BSC) within institutional Twitter profiles. Based
on the observation of sociability practices (social interactions) with
the support of Pierre Bourdieu’s Theory, we capture data related
to these practices, crystallized through performing actions such as
Tweet, Comment, Retweet and Like. Next, social interactions are
extracted from the captured data as sequences of actions and they
are used to compute the effective mobilization capacity of each
profile. Finally, the BSC volume is measured by using the effective
mobilization capacity, the number of social interactions and the
number of followers. The results obtained are aligned with the
Bourdieu theory and the systematic process allows to compute the
continuous sociability effort of each profile to maintain and expand
its BSC. Moreover, the results show that polarities positive and
neutral impacts in the composition of the BSC volume.

Link da apresentação: https://drive.google.com/file/d/1FMr2CgFZDrop4OQIB82qiBTChRpilOjr/view?usp=sharing

A Systematic Process for Computing Bourdieusian Social Capital within Institutional Profiles on Twitter.

This work presents a mobile tool for collecting and labeling data
about Twitter’s sociability practices. Such data are related to the
execution of functionalities such as Tweet, Comment, Retweet and
Like. During the capture, the data are labeled with a sequence of
actions, which is learned via the GSP algorithm, and the polarity of
sentiment contained in the tweet. At the end, the data are processed
to compute the volume of Bourdieusian Social Capital (BSC) of the
profile and are also available for the user of the tool to export
them. In addition, the composition of the BSC from the polarity of
sentiment is computed

Link da apresentação: https://drive.google.com/file/d/1FJGUsCSDlgMaDPOJrLhK1ER2-6Gen73B/view?usp=sharing

A Mobile Tool for Collecting and Labeling Data on Twitter's Network Sociability Practices

Using word embedding models for semantic search in the online employment marketplace

The Internet’s popularization has increased the amount of content produced and consumed on the Web. To take advantage of this new market, major content producers such as Netflix and Amazon Prime have emerged focusing on video streaming services. However, despite the large number and diversity of videos made available by these content providers, few of them attract most users’ attention.
For example, in the data explored in this paper, only 6% of the most popular videos are responsible for 85% of the total views. Finding out in advance which videos will be popular is not trivial, specially because of the large amount of influencing variables. Nevertheless, a tool with this ability would be of great value to help dimensioning network infrastructure and to properly recommend new content
to users. In this work, we propose two approaches to obtaining features to classify the popularity of a video before it is published. The first one builds upon predictive attributes defined by feature engineering. The second leverages word embeddings from the descriptions and titles of the videos. We experiment with the proposed
approaches on a set of videos from GloboPlay, the largest provider of video streaming services in Latin America. A combination of both engineered features and the embeddings using Random Forest machine learning algorithm reached the best result, with an accuracy of 87%.

Extracting Textual Features from Video Streaming Services Publications to Predict their Popularity

The massive use of online social media is a reality nowadays. Suchan increasing usage also raises growth in malicious activities in social media, one of which is the use of automated users (bots)that disseminate false information and can insert bias in analyses done on gathered social media data. Based on the concept of media literacy, this research presents a method to teach the human user to identify a pattern of a text produced by a bot, providing a tool(guide) to analyze social media text. Users who learned to identify a bot user with the guide had an average of 90% accuracy in the classification of new messages, against 57% of the participants who had no contact with the guide. The produced guide received a usefulness rating between 4 and 5 by the participants (scale from 1to 5, with 5 being the highest value).

Pattern Identification of Bot Messages for Media Literacy

Online user reviews written in Portuguese language are widely used by native speakers from several countries for decision-making and a large amount user reviews are available on the Web. However, only few studies on Portuguese sentiment analysis are reported as there is a lack of resources including domain-specific sentiment lexical collections. In this paper, we present an effective method for building a set of lexicons in restaurant domain for Portuguese language. The experiments on a large data collection of user reviews illustrate that our method significantly outperforms several alternative previous approaches of building domain-specific sentiment lexicon.

Building a Restaurant-Specific Sentiment Lexicon via Probability Theory

The search for information is inherent to the human being and with the popularization of computers, search systems have become the main source of obtaining knowledge. However, they do not support greater user interaction when they want to do a complex search. It is important to evaluate the user and infer information about him, to know his intention to search to increase the performance of these systems under the principles of SaL. In this paper, we propose a model for classifying the search intention using Transformers. It was used an available dataset and we obtained 97% accuracy in the validation set and 99% f-measure in the test set, achieving better results when comparing to traditional methods.

Predicting the User Intention in Web Search

Professional fact-checking tends to be costly and scalability challenging. For this reason, a series of methods to automate this process has been emerging. Unfortunately, these methods have been created with monolithic architectures, not making use of pre-built parts, and are harder to be understood by others. This work aims to propose a framework, where the development of methods can be done in a modular manner, creating workflows based on key steps that can be linked together to generate the input classification. For this, a study of the literature was conducted to identify the main processing steps of the methods, seeking to develop a solution that would guarantee flexibility of execution and interoperability between the different processing components. With this, the automatic verification process was described in a limited set of steps that can be performed, creating independent components that implement each of them. The framework makes use of a proposed data traceability ontology to map all generated data during execution under a unified vocabulary, which facilitates communication between these independent components.

ReVera Framework: A Framework for Fact Checking Traceability

Exploring large news collections created by media outlets with traditional search engines is impractical for demanding users. We propose a temporal exploration tool that aims to facilitate the consultation of news collections. We concentrated our efforts on two fronts: (i) allowing users to make queries with the addition of information from documents represented by word embeddings, and; (ii) retrieving temporal information to generate timelines presented by an appropriate interface. We evaluated our solution in a collection of a Brazilian newspaper, demonstrating that it can draw different timelines, covering different subtopics of the same theme.

Semantically Time Tracking of Events from Web Documents

Anaglyph Stereo Matching is a particular, harder, case of the widelystudied Stereo Matching algorithms. When applied to AnaglyphReversion problem helps to improve stereo content storage andtransmission, stereoscopic visualization independence and accessto legacy anaglyph content available on the Web. However, thereversion process is not trivial since anaglyphs have high radiome-tric differences between the two views of a stereo pair of images,preventing standard stereo matching algorithms to be directly used.Different from related work, this paper presents a new pixel descrip-tor specially designed to deal with radiometric differences, allowingfor the improvement of the final reconstructed stereo pair imagequality. Experimental results show the proposed method presents,in average, better image quality when compared with related tech-niques.

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A Systematic Process for Computing Bourdieusian Social Capital within Institutional Profiles on Twitter.

Similar lecture

Scalable Optimization for Wind Farm Control using Coordination Graphs

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