Brazil

The COVID-19 pandemic had substantial impacts on society, both socially and economically, with devastating impacts on our country. Aiming at assisting on coping with such pandemic and providing relevant information for society on matters such as the portion of the population under quarantine, details on philanthropic institutions and the location of health Units, we developed FiscalCovid. Using geoprocessing and crowdsourcing techniques, this system enables the social control as a support tool for the battle against COVID-19.

WebMedia 2020

FiscalCovid: exercendo o controle social na temática da COVID-19

geoprocessing

ecitizenship

crowdsourcing

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

technical paper

Currently, so-called Recommendation Systems (SRs) have been used to assist users in discovering relevant Points of Interest (POIs) on Location-Based Social Networks (LBSN), such as FourSquare and Yelp. Given the main challenges of data-sparse and geographic influence in this scenario, most of the work on POI recommendations has focused only on improving the effectiveness (i.e. accuracy) of the systems. However, there is a growing consensus that just effectiveness is not sufficient to assess the practical utility of these systems. In real scenarios, categorical and geographic diversities were identified as the main complementary dimensions for assessing user satisfaction and the usefulness of recommendations. The works in the literature are concentrated on only one of these concepts. In this work, we propose a new post-processing strategy, which combines these concepts in order to improve the user’s interest in POIs. Our experimental results in the Yelp data sets show that our strategy can improve user satisfaction, considering different SRs and multiple diversification metrics. Our method is capable of improving diversity by up to 120% without significant losses in terms of effectiveness.

Estratégias para Aprimorar a Diversidade Categórica e Geográfica de Sistemas de Recomendação de POIs

Learning verification is the target of numerous studies that aim to
transform it from its solely classificatory character, in a process of
diagnosing the student's evolution in the learning goals of a
subject. This research proposes a software for monitoring and
authoring evaluative activities, modelled in order to support
teachers and students in the learning verification process. For this,
using the DSR methodology, a bibliographic study was carried out
in the context of school evaluation to support the designing of the
tool. The presented solution was validated through tests and
questionnaires on user experience. The results demonstrated the
potential of the tool to support the school evaluation process.

Avaliar: Sistema para Autoria e acompanhamento de recursos avaliativos

Lack of physical distancing, isolation, and social interactions are
some of the key factors that have contributed to the spread of
COVID-19 and transformed it into this global pandemic. Combining
and correlating human mobility with the COVID-19 cases being
reported may help to determine possible hotspots and provide
guidance on how to possibly make lifestyle changes and choices
so as to avert/limit future waves of this pandemic or a similar one.
This project aims to cope with the research problems involved in
the development of a graphical and interactive tool that performs
intelligent data analysis of visually selected geo-temporal subsets
of collected information. As a result of the project, it was launched
the COVID-19 Borescope, which is shown in Figure 1.

COVID-19 Borescope

In different countries, govern transparency is widely accepted as an effective democratic process. In Brazil, the information-access law contributes to public administration transparency since various public data are available on the Web. However, such data are in different formats and not standardized which makes it difficult to obtain information, mainly in an automated way. Indeed, this work presents an enrichment in an existing open Brazilian judiciary database, a characterization of the available data, and an analysis of the data about judiciary professionals and their earns. The results reveal a lack of linear correlations between the high salaries and their personal benefits. Also, there is a discrepancy between Judiciary salaries and advantages through Brazilian regions, which may be explained by the higher living costs in some regions.

Characterization and Analysis of Open Brazilian Judiciary Data

Context-aware recommendation systems use contextual data to recommend a fully personalized suggestion to their users, for instance, using the massive workloads produced by the usage of mobile apps. In this paper, we collected and analyzed a dataset from social media related to the Belo Horizonte's 2020 Carnival to understand how this event attracts tourists (or Belo Horizonte's non-resident), analyzing their interactions with a large recommendation system. We built a point-of-view of an event mixing its features in two social networks: Twitter and Google Review. Our results show that combining traits lead to better information about the given context using social networks. It helps both the tourists choosing where to travel and the local establishments to provide better services.

Analyzing A Touristic Event Popularity Using Social Networks

Wikipedia, with more than 50 billion pages, is one of the most popular free online encyclopedias in the world. It is based on user-generated content, in other words, anyone with internet access can write and make changes to its articles, hence the information quality of the encyclopedias is often criticized. Therefore, assigning the correct class of quality to Wikipedia articles is crucial for the experience of both authors and readers when using this large repository of information. In this paper, we present an approach relying on deep neural networks for quality classification of Wikipedia articles. The experiments consisted of testing three different models to achieve the best possible result: the first one using a model without multiview, the second using multiview with three views and the third one using multiview with six views. Finally, we compared our results with a model that used Support Vector Regression as the learning algorithm, achieving similar values with our multiview models.

Automatic Content Quality Estimation Using Deep Neural Networks in Collaborative Encyclopedias on the Web

In Brazil, 2010, more than 6.5 million people claimed to have some visual impairments. Software and Web accessibility are becoming first-class requirements to promote a better relationship between those people with visual impairments and the internet as well as with new digital technologies. However, designers and software developers need more empathy and knowledge about these groups’ issues to implement accessibility features into their digital products correctly (e.g., apps, games, Web sites). This paper has the goal of developing an eye disease simulator (SDO - Simulador de Doenças Oculares) for mobile devices, which could help better teach and illustrate the effects of these diseases. First, we investigated how to simulate the symptoms of each disease better and have developed the SDO mobile application. SDO is capable of both simulating different types of Colorblindness as well as other more dangerous eye illnesses (i.e. Glaucoma, cataracts). The current version also supports the use of Head-Mounted Displays to increase user immersion.

SDO: Simulador de Doenças Oculares

Regarding ontology editors, it is observed that there are several gaps left by the main tools available on the web for editing and viewing ontologies. The proposal for this work is to provide more efficient and effective editing interfaces, providing a practical tool that improves the visualizing OWL graphs.

KGWE - A customizable editor for OWL ontologies

The spread of misinformation, hate speech, or sexist discourse had become a significant problem, especially on chat platforms. This article describes a data collection and processing tool to support research on users’ behavior exposed to these contents. Our motivation is the recent need to study profiles that propagate this type of information and the need to understand these new dissemination mechanisms. This tool provides a web configuration panel and a mobile application for data collection. Using a chatbot mechanism, the panel allows researchers to perform personalized experiments to analyze specific issues. The application collects the user’s basic profile, allows interaction with the chatbots sent by the researcher, and runs on the volunteers’ devices, similar to a real messaging application such as WhatsApp or Telegram. The data generated regarding the user’s interactions with the application can be exported for analysis through the panel. Also, we describe the insertion of optional codes that can be executed in parallel to the conversations, helping in sophisticated personalizations. We believe that the proposed open-source tool will help researchers from different areas, even without computer programming skills, to understand fundamental mechanisms of user behavior in mobile chat systems.

Rechat: Ferramenta para Estudo do Comportamento de Usuáriosem Sistemas de Bate-papo do Estilo WhatsApp

Technological resources have become indispensable in professional sports activities due to the critical level of competitiveness of athletes required in tournaments. One of the main aspects that characterize a volleyball athlete's success is his performance in the jumps performed during a match. This physical action is performed several times by the athlete to attack or prevent the success of an opposing team's attack. Wearable computing has become common in sports training, just as there are approaches that use sensors attached to the athletes' body to study and quantify the jump's movement. However, these marketable portable devices tend to be used as activity trackers that provide simple recommendations, such as the number of steps, heart rate, distance traveled, and amount of calories lost. Besides, real-time monitoring and evaluation solutions to provide personalized feedback and decisions are expensive and not viable for athletes without sponsorship.
For this reason, this work presents VolleyJump 2.0, a computational approach to classify volleyball athletes jumps using techniques of extraction and selection of features from digital signals provided by inertial sensors. The performance results of the data set are also presented, in a promising way, in some machine learning models developed to predict volleyball jumps. Recent studies dealing with this topic show that classification models have good results for distinguishing jumps using a device that contains an accelerometer and gyroscope sensor. In this study, we proposed using prototypes of electronic devices of inertial sensors attached to the wrist and waist of the user. These sensors emit digital signals via WiFi connection, which will be processed to extract features and, from there, create a data-set of jump activity. With this, it will be possible to analyze the selection of characteristics and measure the performance of these variables using some classification models.

FiscalCovid: exercendo o controle social na temática da COVID-19

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FiscalCovid: exercendo o controle social na temática da COVID-19

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Next from WebMedia 2020

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PRESENTATIONS

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