Brazil

Identifying suspects of crimes on social networks is one of the most relevant tasks in the analysis of this type of network. Most of the computational methods focused on this task involve supervised machine learning, and, therefore, require previously labeled datasets that inform, among the registered people, which ones are suspects. However, in practice, this type of information is not available, for several reasons, among which, it is rare or even protected by secrecy guaranteed by law. This limitation makes it very difficult to effectively use these methods in real situations. Hence, the present work raises the hypothesis that the use of a controlled vocabulary on the field of application can make it possible the identification of suspects in social networks, without the need for previously labeled datasets. In order to search for experimental evidence that points to the validity of the hypothesis raised, this article proposes a method that uses a controlled vocabulary with categorized terms, according to a certain domain (e.g., pedophilia, cyberbullying, terrorism, etc.), to analyze messages exchanged on social networks, in order to identify criminal suspects. The results obtained in a preliminary experiment showed signs of adequacy of the proposed method.

WebMedia 2020

Identifying Criminal Suspects on Social Networks: A Vocabulary-Based Method

controlled vocabulary

identifying suspects

text mining

**BRACIS** originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – **SBIA** (22 editions), and the Brazilian Symposium on Neural Networks – **SBRN** (13 editions). **ENIAC** is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

To access the 10th Brazilian Conference on Intelligent Systems (**BRACIS**), you need to register for the conference.  Please visit the Conference official site to register

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BRACIS 2021

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – SBIA (22 editions), and the Brazilian Symposium on Neural Networks – SBRN (13 editions). 
ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. 
STIL is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: CE-IA, CE-IC and CE-PLN

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.

The recent growth of social networks led to an increase in the dissemination of hate speech online and, as a consequence, studies about hate speech detection have emerged. Part of them are based on supervised learning techniques, which require large labeled databases, however this is still a scarce resource. Most of the existing databases on hate speech consist of texts in English. Although there are some databases in Portuguese, they are rather small with few examples, a factor which can limit both the performance of simple deep neural networks and the usage of more complex architectures. In order to work around this setback, this paper analyses the usage of Data Augmentation in texts to improve the training performance of recursive neural networks (LSTM) and convolutional neural networks (CNN) applied to the hate speech detection task. Data Augmentation is a regularization process used on deep neural networks to avoid overfitting. These techniques are common in the field of computer vision, however, due to the complexity of natural languages, this process is not as frequently used in tasks involving texts. In this paper, we experimented on a public hate speech database in Portuguese using Data Augmentation techniques such as text generation from the original database to enhance the results. The experiments show an improvement in the results, demonstrating that the techniques are promising.

Data Augmentation for improving Hate Speech Detection on Social Networks

The constant expansion of e-commerce, recently boosted due to the coronavirus pandemic, has led to a huge increase in online shopping. More and more, customers demand online reviews of products and comments on the Web to make decisions about buying a product over another. In this context, sentiment analysis techniques constitute the traditional way to summarize user’s opinions that criticizes or highlights the positive aspects of a product. Sentiment analysis of reviews usually relies on extracting positive and negative aspects of products, neglecting comparative opinions. Such opinions do not directly express a positive or negative view but contrast aspects of products from different competitors. In this paper, we present the first effort towards detecting comparative sentences in Portuguese. Identifying comparative sentences is a key task for companies to know how users are comparing a product with their competitors and is essential for developing sentiment summarization applications for the end user. In addition, we present a supervised approach to automatically detect Portuguese comparative sentences, classifying them into five distinct groups: (1) Non-Comparative, (2) Non-Equal Gradable, (3) Equative, (4) Superlative e (5) Non-Gradable. To that end, this paper provides three main contributions: (1) a Portuguese lexicon list with words used to make comparisons; (2) two new Portuguese datasets with comparative sentences; and (3) a hierarchical approach for detecting multiple comparisons and classify the sentences in different groups by using state-of-art classification algorithms, reaching an accuracy of 87%.

Mining Portuguese Comparative Sentences in Online Reviews

The Internet of Things (IoT) is an increasingly evident reality in everyday life. IoT makes possible to interconnect physical objects through a heterogeneous computer network, creating new ways to manage infrastructures. In the IoT systems, smartphones have a fundamental role due to their computational capacity and resources. Nowadays, the major mobile devices have several types of sensors that can be used to monitor and collect data from the physical world, such as GPS, accelerometer, barometer, among others. Thus, IoT developers have used different cross-platform frameworks to improve their productivity and to make the software maintainability easily and fast. In this context, the goal of this work is to understand the performance impacts of some cross-platform frameworks for IoT application development. To do this, we built three versions of the same Android application of an IoT system using (1) Ionic, (2) React Native, and (3) Java, which uses some smartphone's sensors commonly used in IoT applications, such as GPS, WiFi, and BLE. For each resource and performance evaluation metric for each version of our application, we created a specific test-case to measure and to compare the analyzed cross-platform framework. Our results showed slight differences between the three versions of our IoT application in some analyzed metrics.

Understanding the Performance Impacts Of Cross-Platform Development On IoT Applications

Conversational user interfaces have been an increasingly popular way to obtain quick information in natural language, especially with advances in the area of natural language processing (NLP) fueled by the surge of deep neural networks. Real-world cases include IBM Watson and Azure Microsoft chatbots, among others. Such examples, however, are most present in the commercial spectrum, with few being actively used by the scientific community. In this work, we develop an Interactive Question Answering System (IQA) for the Sucupira platform, Brazil's biggest open platform of postgraduate content, deployed as conversational interfaces for two popular messaging platforms: Telegram and Discord. We also propose the use of multilingual embeddings for ordering answers by a language-independent measuer of semantic similarity. Our IQA was evaluated with an study with 16 participants. The results were mostly positive, indicating that the use of conversational user interfaces, along with NLP techniques, may be a valid alternative for retrieving scientific information from the Sucupira plataform.

SucupiraBot: An Interactive Question-Answering System from Sucupira Platform Data

Like the spoken languages, sign languages are not universal and vary in different countries. LIBRAS (Brazilian Sign Language) is the second official language of Brazil and it is the language adopted by Brazilian Deaf's community to communicate. The signs of LIBRAS are composed of hand configurations, facial expressions and are affected by space and intensity modifiers, which makes their recognition more complicated than the simple identification of hand signs. The signs are arranged, according to a grammar, respecting form phrases, clauses, and sentences like any other spoken or sign language. The automatic machine translation of a sign language typically includes an initial phase for detecting sign boundaries. In this paper, we apply a crowdsourcing method to identifying signs boundaries present in pre-recorded videos those features LIBRAS interpreters. The limits or boundaries of the signs in the videos were established from the processing of contributions from workers from different countries, who have supposedly never heard of LIBRAS nor any other sign languages. To evaluate the segmentation process, we compared the sign boundaries identified by the crowd with the ground truth provided by a team of LIBRAS experts, who also assessed the quality of the delimitation of the identified signs. Our analysis showed that our crowdsourcing method was able to get 93.75% of the sign boundaries successfully.


A Crowdsourcing Method for Sign Segmentation in Brazilian Sign Language Videos

Um paradigma emergente que tem se mostrado uma materialização do conceito de computação ubíqua é a Internet das Coisas (do inglês, Internet of Things - IoT). Porém, para que a IoT se torne uma realidade para todos os tipos de usuários, é necessário encontrar soluções para superar alguns desafios presentes neste paradigma computacional. Dentre os desafios presentes, pode-se citar: a heterogeneidade das informações geradas nestes meios; o considerável volume de dados trafegados; dispositivos potencialmente executando em redes com largura de banda reduzida, ou que possuam links de comunicação saturados. Neste trabalho é apresentado o EXEHDA-DEP, uma arquitetura de processamento de eventos complexos distribuída para a IoT, apta a lidar com a heterogeneidade proveniente destes meios e de executar em ambientes que possuam ou não restrições de conexão. Os resultados obtidos demonstram a capacidade do EXEHDA-DEP escalar o processamento horizontalmente, aplicando estratégias que favoreçam a concepção de uma arquitetura com um consumo de rede estável, onde nos testes executados é demonstrado uma redução considerável do consumo médio de rede.

EXEHDA-DEP: An Architecture to Distributed Complex Event Processing

Due to its continental size, Brazil is one of the countries with the largest territorial extension in theworld, with a total of 27 federative units, and prominent cultural diversity. Brazil is also among the five countries with the largest social inequality according to the UN, which may stimulate the interstate migration process. This process contributes to an increase in cultural diffusion across the states of origin and destination of migrants since they move with all their habits including food and drink preferences. In our work, we measured the similarity among Brazilian states, analyzing food and drink preferences as a cultural indicator. By leveraging the Facebook advertising platform, which allows us to obtain an estimate of the total number of users who satisfy a set of attributes provided, we collected the audience for food and drinks in Brazil to conduct our analysis. In order to validate our results, we compared the cultural similarity of the states with the rate of migrants they received in a given period. Our results reveal a positive correlation between cultural similarity and the rate of migration.

Inferring cultural similarity among Brazilian states based on data from social media advertising platforms

A SaaS (Software as a Service) can transparently consume a DaaS
(Data as a Service). However, heterogeneous DaaS and its evolution
can disrupt the SaaS execution. In such cases, a middleware can
provide such interoperability and monitor the DaaS trackback and
its evolution by retrieving its metadata. For instance, the middleware MIDAS manually provides such interoperability. Considering
the Web and the number of web pages and DaaS available, this task
may be time-consuming and unfeasible. To automate this task, it
is firstly important to distinguish a DaaS from a typical web page.

Classifying Web page features to detect DaaS

Worldwide, the composition of the population registers an increase in life expectancy and a reduction in the birth rate. The creative use of technology and the omnipresence of sensor-based devices allow the development of software solutions and platforms that offer services to older people towards increasing quality of life,reducing expenses, and promoting aging in place while preserving autonomy and security: a case in point is wearable sensor-based Human Activity Recognition (HAR). Given that previous work ob-served the lack of attention given to older people by the researchers investigating HAR systems, we conducted a systematic mapping aiming at identifying resources, as variables and associated values,used in the development of HAR systems for older people. The systematic mapping selected 21 papers published between 2010 and 2019, which exposed the use of 16 variables and associated values.

Recognition of Human Activities via Wearable Sensors: Variables Identified in a Systematic Mapping

Several cities around the world face security problems, such as vehicle theft. Locate and recover these vehicles are challengers for authorities. In smart cities, citizens can collaborate with authorities by sensing urban and environmental data, so-called crowdsensing. This work introduces Crowd-Auto, a crowdsensing approach that
utilizes a crowded camera network from houses and commerce to identify vehicle plates, query on official databases and inform the authorities when stolen vehicles are identified. We’ve developed a prototype and demonstrated that Crowd-Auto is viable for allowing citizens to cooperate and improve security in cities.

Identifying Criminal Suspects on Social Networks: A Vocabulary-Based Method

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Data Augmentation for improving Hate Speech Detection on Social Networks

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Identifying Criminal Suspects on Social Networks: A Vocabulary-Based Method

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Data Augmentation for improving Hate Speech Detection on Social Networks

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Text Mining for Cyberbullying Detection: a Brazilian Portuguese Evaluation

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