Brazil

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.

WebMedia 2021

Upper Limb Motion Tracking and Classification: A Smartphone Approach

supervised machine learning

mixed/augmented reality

motion capture

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

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NAML 2021

technical paper

### Welcome to the Brazilian Symposium on Multimedia Systems and Web<br>
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.
<br><br><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><br><br>
The Webmedia 2021 schedule will feature:
<br>
* Technical sessions for the presentation of scientific articles.
* National and international lectures
* Panel with Companies
* Workshops
* Short courses
* Project meetings open to the community
<br><br>

The topics of interest for paper submission are:
<br>
* Web and Social Networks
* Multimedia
* Digital TV, Ubiquitous and Mobile Computing
<br><br>

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.
<br><br>
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/)
<br><br>
**#PartiuWebMedia2021 #WebMedia2021**<br><br><br><br>

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.

Industry Talk - AME

Due to mobile devices' growing presence in our daily routine, mobile applications are becoming increasingly complex. They require more powerful processing capability and more extensive data storage, which characterizes a challenge when computational constraints of these devices are taken into account. Several software infrastructures proposed to help the development of mobile applications with data offloading features. However, they lack essential features for data offloading, such as configurable data synchronization policy models, privacy mechanisms for the offloaded data, and scalability and performance analyses. This work presents DOP, a solution to assist the development of mobile applications that use data migration, including contextual data, from mobile devices to a remote environment, based on a microservice architecture. The data offloading technique enables data migration into a remote environment, allowing (i) storage savings on the mobile device and (ii) sharing data among users. The experiments on DOP showed benefits in storage savings on mobile devices and new possibilities for inferring situations based on shared data from multiple users.

DOP-MS: A Microservice-based Data Offloading Service with Support for Data Anonymisation

The widespread of smart objects in our daily lives request the creation and analysis of new service discovery mechanisms and interaction techniques. In this work, we designed and evaluated a pointing-based interaction mechanism based on a Convolutional Neural network classification method. We called it ViSCo (View, Scan, and Control it), which extends the openHAB service discovery mechanism of smart objets. ViSCo aggregates the users' field of view, captured by the camera of their smartphones, to reduce the service discovery results. The final solution was evaluated by 17 users remotely in an environment with virtual devices. Participants used the ViSCo approach to find and control virtual devices by pointing to real objects in their homes (e.g., their TVs). System Usability Scale (SUS) survey about ViSCo results showed a good level of acceptance, with an average score of 83.97.

Point and Control it! Using Computer Vision for Service Discovery to Control Smart Objects

Todos os anos, cerca de 1,25 milhão de pessoas morrem em acidente
automobilísticos em todo o mundo. Destes acidentes, cerca
de 23% são motociclista, representando uma parcela considerável
destas mortes. No Brasil, exitem aproximadamente 22 milhões
de motocicletas registradas e 30 milhões de pessoas habilitadas a
guiá-las1, o que ocorre por vezes em locais remotos e de difícil
acesso, como em estradas vicinais, campos e ruas isoladas. É
comum motociclistas ficarem desacordados imediatamente após a
ocorrência do acidente, impossibilitando a solicitação de ajuda e
de serem socorridos dentro da "Hora de Ouro" . A Hora de
Ouro, termo cunhado pelo médico americano Adams Cowley,
indicando que as chances de sobrevivência de pessoas acidentadas
aumentam consideravelmente se forem socorridas em até 60 minutos.
A evolução e o barateamento das tecnologias da informação permitem a criação aplicações voltadas ao salvamento de vidas, que tornam-se cada vez mais comuns. Neste contexto, torna-se possível aplicar os conceitos relacionados à Internet das Coisas (IoT) para conectar objetos físicos, como capacetes de motociclistas
dotados de tecnologia embarcada e sensores, em rede de comunicação para transmitir dados para o monitoramento e, em caso de
necessidade, acionar socorro automaticamente em caso de acidente,
reportando a geolocalização e fornecendo informações relevantes
sobre as condições de vítimas.
Visando reduzir o tempo de socorro a motociclistas acidentados,
neste artigo descrevemos o desenvolvimento do "IoT Helmet": um
capacete inteligente para localizar e notificar sobre motociclistas
acidentados. Este capacete é dotado de tecnologia embarcada capaz
de efetuar a aquisição e o processamento de dados em tempo real
da geolocalização do motociclista e de notificar por mensagem, indivíduos previamente cadastrados pelo motociclista por telefone
celular. 


IoT Helmet: an intelligent helmet for locating accidented bikers

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.

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Using Machine Learning to Predict CASREP Occurrence and Trouble Ticket Duration

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PRESENTATIONS

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COMPANY

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