Brazil

Research indicates that people can receive more useful product recommendations if the filtering process considers their personality. In this paper, we propose a hybrid strategy for Recommender Systems (using matrix factorization and personality-based neighborhood) to recommend the best products calculated for a particular customer (user). The proposed user profile used in the definition of the neighborhood involves these three personality models: Big Five (or OCEAN, or Five-Factor Model), Needs, and Values. We experimented with data from more than 10,000 Amazon customers. We inferred their personality characteristics from the analysis of reviews via IBM Watson Personality Insights. The results indicated that the proposed strategy&#39;s performance was better than that of the state-of-the-art algorithms analyzed. Besides, there was no statistical difference between using only the Big Five model or using it together with the Needs and Values models.

WebMedia 2020

Collaborative Filtering Strategy for Product Recommendation Using Personality Characteristics of Customers

personality-based recommendation

product recommendation

collaborative filtering

recommender systems

**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

[Register now](http://c4ai.inova.usp.br/bracis2021/)

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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

Research indicates that people can receive more useful product recommendations if the filtering process considers their personality. In this paper, we propose a hybrid strategy for Recommender Systems (using matrix factorization and personality-based neighborhood) to recommend the best products calculated for a particular customer (user). The proposed user profile used in the definition of the neighborhood involves these three personality models: Big Five (or OCEAN, or Five-Factor Model), Needs, and Values. We experimented with data from more than 10,000 Amazon customers. We inferred their personality characteristics from the analysis of reviews via IBM Watson Personality Insights. The results indicated that the proposed strategy's performance was better than that of the state-of-the-art algorithms analyzed. Besides, there was no statistical difference between using only the Big Five model or using it together with the Needs and Values models.

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.

Sistemas de recomendação são ferramentas que auxiliam usuários em seu processo de decisão e quando aplicados ao turismo, esses sistemas visam facilitar o planejamento de viagens e escolha de destinos. Apesar de todos os esforços para se construir tais sistemas e otimizar o tempo dos usuários, questões como o atendimento das expectativas dos turistas e a motivação para viajar ainda são aspectos negligenciados. Neste trabalho, será apresentado uma abordagem que considera a perspectiva de visitas, considerando práticas de turismo alternativas que possibilitam experiências diversificadas, proporcionando aos turistas uma experiência diferenciada, permitindo que os mesmos vivenciem as cidades sob a perspectiva dos moradores locais.

Uma abordagem para sistemas de recomendação orientado a perspectivas de experiências turísticas


The widespread of fake news is increasingly worrying society anddemanding approaches for mitigation. Although many approacheshave been proposed to fake news detection, there is still a lack ofworks that deeply investigate their structure. Our study has beenmotivated by two findings discussed in existing works: the firstis the fact that fake news usually mix real and fake informationto mislead readers; the second is that subjective language is a re-source commonly exploited by fake news producers. Therefore,to better understand how fake news is structured, we perform ananalysis on the way the subjective language is exploited in differ-ent situations inside the fake news documents. For this, we builta dataset by manually identifying fake parts of news articles, andwe also propose tags to categorize the fragments in documents.The proposed tags categorize the fake news fragments according totheir kind of falsehood in document. To reveal subjectivity nuanceswithin the fragments, we use the Word Movers Distance and a setof subjectivity lexicons in the Portuguese language. Our resultsindicate that the fragmentation of news allows the identificationof subjectivity markers that cannot be identified when consideringthe entire documents.

Analysis of the Subjectivity Level in Fake News Fragments

We present a multimodal dataset of songs with structured information to be applied in various machine learning tasks. The data comes from a platform focused on lyrics information, but contain several other annotations provided by their users. Our dataset, called 4MuLA (Multitask, Multimodal, and Multilingual Music Lyrics and Audio features dataset), contains features extracted from 96,458 songs distributed by 15,310 artists in 76 genres. Most of the songs in 4MuLA are protected by copyrights, so we can not provide the original audio. For each track, we make available various acoustic features, extracted tags, and their lyrics in English, Portuguese, or Spanish. With these features, researchers can use our dataset for, at least, lyrics-, audio- or multimodal-based genre classification, music and artist similarity, and popularity regression. Moreover, we can perform cross- or multi-lingual text analysis on lyrics, such as discourse analysis or measuring the differences between emotion transmitted by audio and lyrics.

4MuLA: A Multitask, Multimodal, and Multilingual Dataset of Music Lyrics and Audio Features

Deaf people are a considerable part of the world population and communicate naturally using sign languages. However, although many countries adopt their sign language as an official language, there are linguistics barriers to accessing fundamental rights, especially access to health services, even more critical situation in the midst of the COVID-19 crisis. This situation has been the focus of some government policies that oblige essential service providers to provide sign language interpreters to assist Deaf people. However, this type of solution has high operating costs, mainly to serve the entire Deaf community in all environments. These setbacks motivate the investigation of methodologies and automated tools to
support this type of problem. In this paper, we address this problem by proposing two-stream model for the recognition of the Brazilian Sign Language (Libras) in the health context. The proposed solution does not use any additional capture sensor or hardware, being entirely base on images or sequences of images (videos). The
results show that the solution is able to recognize the Libras signs in the test dataset reasonably well, achieved an average accuracy of approximately 96,12% considering a scenario where the interpreter used in the test set was not used in the training set, which shows that there are good evidence that it can assist in the communication
process with Deaf people. Besides, an additional contribution of this paper is the introduction of a new dataset in the Brazilian sign language (Libras) containing 5000 videos of 50 signs in the health context, which may assist the development and research of other solutions.

A Two-Stream Model Based on 3D Convolutional Neural Networks for the Recognition of Brazilian Sign Language in the Health Context

The Internet of Things (IoT) is a Ubiquitous Computing materialization that is gaining prominence. Security has become potentialized as a necessary feature for a viable IoT. Promoting security over this dynamic and heterogeneous environment with pre-defined and static security mechanisms is an unfeasible task. Therefore, solutions for adaptive security are required. This paper has as main objective to present the conception of a proposal for adaptive security through progressive context-based enrichment considering the challenges of IoT scenarios, offering as a differential the contextual information acquisition in the opportune moment and contemplating heterogeneous sources. To evaluate this proposal was developed one case study to validate the progressive contextualization of security events. Finally, the strengths and limitations of our approach are discussed, followed by future works.

AS-ProCBE: An Architectural Model for Adaptive Security through Progressive Context-Based Enrichment

The growth and diversity of data sources have been a challenge for data interoperability. Although there are several initiatives to face this challenge, there are still difficulties especially when data are at different levels of abstraction. In this sense, this article proposes INTEGRA, an RDF-based approach, which takes advantage of the graph flexibility. It allows the integration of heterogeneous data sources through interconnections between schemas, between instances, and between instance-schema. Additionally, we present a case study in the Software Development Management scenario, which shows INTEGRA feasibility and its benefits.

An RDF based approach for integrating data at different levels of abstraction

Lexical Substitution has a noticeable evolution in the literature,
mainly in the data sources used for the generation of substitutes
that feed the process. Of course, dictionaries and thesauri are widely
used for grouping synonyms in their structure, but the polysemy of
words prevents a direct exchange of terms without analyzing the
context. Vector space models, such as embeddings, are used to rep-
resent substitutes and also contexts. However, the representation of
words considering only contextual factors, in many cases, may in-
cur the approximation of terms that are not exactly synonyms. The
characteristics mentioned above suggest that the simultaneous use
of dictionaries and embeddings is a promising alternative for the
process. Thus, we present a method using information contained in
merged dictionaries, in addition to their linguistic relations struc-
tured in taxonomies. The method measures the preservation of the
meaning of the sentence with the potential synonym by observing
its frequency of application in small contexts. In addition, we also
consider a complete context to generate input from vector opera-
tions highlighting the best synonyms in a previously selected set.
The results show the efficiency of the method, surpassing many
methods consolidated in the literature in the prediction of the best
substitute for words contained in instances of a known benchmark.

MSL-DE: Um Método de Substituição Lexical Apoiado em Dicionários e Embeddings

Eye-tracking sensors allow users to interact by eye gaze with multimedia applications, providing a new interaction modality for those who do not want or cannot use the traditional ones (mouse, keyboard, and remote control). Considering digital TV applications, middleware Ginga-NCL does not support this type of interaction yet. In this paper, we propose a Ginga-NCL extension to provide eye-gaze interaction using eye-tracking technology and enable the use of a new event type for NCL multimedia authoring, called EyeGaze.

Providing Eye Gaze Interaction for Ginga-NCL Applications

Extracted from Wikipedia content, DBpedia is considered one of the most important knowledge bases of the Semantic Web, which has editions in several languages, among which those in English (DBpedia EN) and Portuguese (DBpedia PT). All DBpedia editions are subject to quality issues, more especially DBpedia PT suffers from inconsistencies and lack of data in several domains. In this talk, we describe a semi-automatic and incremental process for publishing data on DBpedia, coming from reliable external sources, while seeking to improve aspects of its quality. In an open science context, the proposal aims at consolidating DBpedia as a reference hub for research data, so that research from any area supported by the Semantic Web data can use its data reliably.

An Approach for Improving DBpedia as a Research Data Hub

Whatsapp is the most popular messaging app in the world. It is not only used as a one-to-one messaging app but also as a platform for group discussion. Recently, Whatsapp has gained the spotlight for its role in disseminating (often low-quality) information. Our study focuses on YouTube videos shared by political-oriented public groups on Whatsapp for a month during the COVID-19 pandemic. Through a careful analysis of the topical distribution and the lexicon present in the videos shared, we shed light on the COVID-19 debate happening in these groups. Moreover, we compare COVID-19 related videos with other political videos being shared in the groups. We observe that videos that discuss political themes have more emotional attributes as well as topics related to typical right-wing concerns such as family, work, and religion than videos discussing both pandemic and politics.

Collaborative Filtering Strategy for Product Recommendation Using Personality Characteristics of Customers

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Uma abordagem para sistemas de recomendação orientado a perspectivas de experiências turísticas

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Collaborative Filtering Strategy for Product Recommendation Using Personality Characteristics of Customers

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