Brazil

WebMedia 2021

Aprendizado de máquina para precaching de conteúdo

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

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. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

BRACIS 2020

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.

Como utilizamos design e a experiência do usuário para construir produtos tecnológicos de impacto em logística?

Recommendation Systems have concerned about the online environment of real-world scenarios where the system should continually learn and predict new recommendations. Current works have handled it as a Multi-Armed Bandit (MAB) problem by proposing parametric bandit models based on the main recommendation
concepts to handle the exploitation and exploration dilemma. However, recent works identified a new problem about the way these models handle the user cold-start. Due to the lack of information about the user, these models have intrinsically delivered naive non-personalized recommendations in their first recommendations until the system learns more about the user. The first recommendations of these bandit models are equivalent to a random selection around the items (i.e., a pure-exploration approach) or a biased selection by the most popular items in the system (i.e., a pure-exploitation approach). Thus, to mitigate this problem, we propose a new contextual approach to initialize the bandit models. This context is made by the information available about the items: their popularity and entropy. The idea is to address both exploration and exploitation goals since the first recommendations by mixing entropic and popular items. Indeed, this approach maximizes the user’s satisfaction in the long-term run. By a strong experimental evaluation, comparing our proposal with seven state-of-the-art methods in three real datasets, we demonstrate this context achieves statistically significant improvements by outperforming all baselines.

A contextual approach to improve the user's experience in interactive recommendation systems

Recipe sharing websites have become even more popular in the past few decades, and such repositories are able to keep hundreds of thousands of cooking recipes at the same time. Many recipe websites are developed with the participation of their community of users, which are allowed to upload new recipes and to provide
evaluations and comments on the available recipes. However, in such repositories, the amount of recipes that are safe for users with special needs, such as food restrictions or allergies, is much smaller than ordinary food recipes, what may restrict the access and usability provided by such websites to that public. In this work, we propose a new recipe recommendation and generation system, based on a data-driven approach for single ingredient substitution, in such a way that recipes containing forbidden ingredients, according to a category of user food restrictions, are adapted by replacing such ingredients by safe ingredients. The proposed ingredient substitute recommendation system is based on a filtering process that
takes into consideration the original recipe context, the relationship among sets of ingredients and the user preferences, towards the generation of recipes that are safe, and at the same time contemplate both user needs and tastes. The proposed system is evaluated by means of a qualitative analysis, showing promising results.

Ingredient Substitute Recommendation Based on Collaborative Filtering and Recipe Context for Automatic Allergy-Safe Recipe Generation

The number of people with access to mobile devices, as well as
applications to these devices (i.e., apps), has been increasing significantly.
Thus, users have to choose among a high number of
apps, those that better serve them. However, this is not a trivial
task, as we have seen an increasing number of apps proposing
to do the same functions. In the same way, companies are facing
difficulties to attract users through a usual marketing campaign.
A possible solution to this problem is the adoption of recommendation
systems, where it is possible to compare the similarities of
user profiles. Meanwhile, these systems often consider only users’
preferences to create a profile, or request sensitive data (e.g., call
and message logs). However, the installation of an app may involve
other factors like the capacity of the mobile device (e.g., memory
and processing power) and the demographic information of the
user’s living area. This work investigates the impact of using demographic
and handset information on app recommendation. To
do that, we use this information to enrich a user profile that has
only easy-to-obtain data (i.e., installed apps, approximate location,
and handset model). Besides, our proposal was evaluated on three
different recommending approaches: Latent Dirichlet Allocation
(LDA), Markov Transition Matrix (MTM), and Collaborative Filtering.
The general results reveal that the LDA approach achieved the
highest efficacy when added information about the user’s region
mean wage, in terms of precision (approximately 64%) and recall
(approximately 28%).

Mobile Application Recommendation based on Demographic and Device Information

The advancement of mobile technologies combined with assistive technologies can provide autonomy to people with different types of disabilities, including those with Autistic Spectrum Disorder (ASD). Technologies, such as those found in mobile devices, are very attractive to people with ASD and can be used as a valuable educational tool for these individuals. The aim of this study was to learn about how mobile apps aimed at people with ASD are developed and evaluated, carried out through a systematic mapping of applications aimed at people with ASD, in order to better understand their foundations, motivations, evaluation mode, resources and user profile. The results indicated that the main procedures used to support the applications for people with ASD are the questionnaires to identify the ASD, such as the CHAT and AQ-10, and the ABA intervention, and that these applications, mostly, seek to identify the ASD in a practical way and at a low cost. Most of the apps were evaluated by usability testing and through indirect observation through the app’s logs. The most used hardware and software resources are the mobile device camera and convolutional neural networks (CNN), respectively, with most applications aimed at children and the most common software engineering methodologies were requirements analysis and prototyping.

Systematic mapping of the literature on mobile apps for people with Autistic Spectrum Disorder

Drowsiness is a problem that affects traffic on a worldwide scale, especially among motorcyclists, who use a naturally dangerous means of transport. Nowadays, there is no product on the market that performs an anti-drowsiness action in this public. This article aims to propose the creation of a wearable device coupled to the motorcycle helmet that detects a state of drowsiness through sensors and combats it using actuators. Still at an early stage, a prototype was created, tests in a controlled environment were carried out, preliminary results were presented and a series of future works were drawn up to continue the project.

2DrowSys: an anti-drowsiness device for motorcyclists

Due to the evolution of motion capture devices, natural user interfaces have been applied in several areas, such as neuromotor rehabilitation supported by virtual environments. This paper presents a smartphone application that allows the user to interact with the virtual environment and enables the captured data to be stored, processed, and used in machine learning models. The application submits the recordings to the remote database with information about the movement and in order to apply supervised machine learning. As a proof of concept, we generated a dataset capturing movement data using our application with 232 instances divided into 8 classes of movements. Moreover, we used this dataset for training models that classifies these movements. The remarkable accuracy of the models shows the feasibility of using body articulation data for a classification task after some data transformations.

Upper Limb Motion Tracking and Classification: A Smartphone Approach

Towards a Pragmatic Interoperability on the MIDAS Middleware

The IoT's widely distributed nature has promoted demands for Over-The-Air (OTA) upgrading of embedded systems. This paper presents the conception of a new approach, called iOTA, which meets the security requirements of the SUIT specification of the Internet Engineering Task Force (IETF). The partial evaluation of the proposal points to the continuation of the research and showed that iOTA stands out in terms of security in relation to the other approaches proposed by the literature, offering protection against attacks to which a remote update of an embedded system is usually exposed.

iOTA: An Approach to Secure Over-The-Air Updates on the Internet of Things Scenario

In smart city scenarios, a plethora of data is generated from multiple, decentralized sources, e.g., systems, applications, citizen devices, sensors, etc. However, these data may be not readily available for use due to the high heterogeneity of types, formats, and protocols, thereby giving rise to vertical silos that hamper sharing, integrating, and exploiting these data. This paper introduces Aquedücte, a data integration service for smart cities. Aquedücte offers functionalities for (i) handling data from heterogeneous sources such as files, Web services, systems, etc., (ii) converting these data to a common protocol, and (iii) making them further available to platforms aimed to support the development of smart city applications.

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