Brazil

Instant messaging apps have billions of monthly active users, which made this environment a good medium for purposes other than communication, such as education. In this context, one of the most appropriate technologies to encourage learning in these applications is chatbots. They consist of conversational applications that make use of artificial intelligence and natural language processing to interact with the user in a similar way to a human. In this scenario, this article presents the AGATE a chatbot developed for the Environmental Management Office (AGA/UEMA) in order to disseminate knowledge on environmental education, focusing on the problems of water and energy waste. The chatbot was developed for Telegram through the pyTelegramBotAPI implementation. In addition, conversation flows were created using DialogFlow. A multidisciplinary team composed of 14 people participated in the evaluation of the project, with a greater participation of professionals in the field of Biology. The initial results focused on the user experience were satisfactory.

WebMedia 2020

ÁGATA: um chatbot para difusão de práticas para Educação Ambiental

environmental education

digital education

artificial intelligence

natural language processing

chatbots

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

AIAI 2020

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.

Due to easy access and low cost, social media online news consumption has increased significantly for the last decade. Despite their benefits, some social media allow anyone to post news with intense spreading power, which amplifies an old problem: the dissemination of Fake News. In the face of this scenario, several machine learning-based methods to automatically detect Fake News (MLFN) have been proposed. All of them require Fake News datasets to train and evaluate their detection models. Although recent MLFN were designed to consider data regarding the news propagation on social media, most of the few available Fake News datasets do not contain this kind of data. Hence, comparing the performances amid those recent MLFN and the others is restricted to a very limited number of datasets. Moreover, all existing datasets with propagation data do not contain news in Portuguese, which impairs the evaluation of the MLFN in this language. Thus, this work proposes FakeNewsSetGen, a process that builds Fake News datasets that contain news propagation data and support comparison amid the state-of-the-art MLFN. FakeNewsSetGen’s software engineering process was guided to include all kind of data required by the existing MLFN. In order to illustrate FakeNewsSetGen’s viability and adequacy, a case study was carried out. It encompassed the implementation of a FakeNewsSetGen prototype and the application of this prototype to create a dataset called FakeNewsSet, with news in Portuguese. Five MLFN with different kind of data requirements (two of them demanding news propagation data) were applied to FakeNewsSet and compared, demonstrating the potential use of both the proposed process and the created dataset.

FakeNewsSetGen: a Process to Build Datasets that Support Comparison Among Fake News Detection Methods.

Many recent works have successfully applied some types of Convolutional Neural Networks (CNNs) to reduce the noticeable distortion resulting from the lossy JPEG compression technique. Most of them are built upon the processing made on the spatial domain. In this work, we propose a JPEG image decoder that is purely based on the frequency-to-frequency domain: it reads the quantized DCTcoefficients received from a low-quality image bitstream and, using a deep learning-based model, predicts the missing coefficients in order to recompose the same image with enhanced quality. In experiments with two datasets, our best model was able to improve from images with quantized DCT coefficients corresponding to quality Factor (QF) of 10 to enhanced quality images with QFslightly higher than 20.

Decoder-Side Quality Enhancement for JPEG Images Using a Deep Learning-Based Prediction Model for Quantized DCT Coefficients

Face recognition systems are present in many modern solutions and thousands of applications in our daily lives. However, current solutions are not easily scalable, especially when it comes to the addition of new targeted people. We propose a cluster-matching-based approach for face recognition in video. In our approach, we use unsupervised learning to cluster the faces present in both the dataset and targeted videos selected for face recognition. Moreover, we design a cluster matching heuristic to associate clusters in both sets that is also capable of identifying when a face belongs to a non-registered person. Our method has achieved a recall of 99.435% and a precision of 99.131% in the task of video face recognition. Besides performing face recognition, it can also be used to determine the video segments where each person is present.

A Cluster-Matching-Based Method for Video Face Recognition

A major concern when building Recommender Systems (RSs) for recruitment domains is ensuring actual matching opportunities for all candidates and job vacancies on the system as fast as possible. Indeed, long periods waiting for matchings imply losses of business opportunities, causing financial damages for both candidates and companies. We refer to these scenarios where candidates or job vacancies suffer from the absence of matching opportunities as the Matching Scarcity Problem (MaSP). First, we formalize the MaSP itself. Second, we proposed and evaluated strategies to identify automatically candidates and job vacancies suffering from MaSP. Finally, we proposed five heuristic strategies to mitigate MaSP. Our strategies consist of introducing changes in curricula and job descriptions to approximate candidates suffering from MaSP to jobs semantically related to them, and vice versa. The best strategy was able to reduce up to 50% the number of both CVs and jobs suffering from MaSP before applying it in our sample.

Mitigating Matching Scarcity in Recruitment Recommendation Domains

About 50 million people around the world have Alzheimer's disease. The main characteristic of the disease is the deterioration of cognitive function, and as a result of the fact that the disease has no cure or treatment. This article presents a systematic mapping of the literature to identify articles that use technological devices to assist patients with Alzheimer's disease, in order to show an overview of the literature, identifying gaps and research opportunities. The systematic mapping study was conducted out in six databases, including articles from January 2015 to July 2020. An initial search resulted in 12,177 articles, of which 28 were selected after applying the filter criteria. The main results obtained are: (1) most articles (25.71%, 7/28) reported solutions used in devices with sensor development; (2) most technologies (22.06%, 6/28) were focused on monitoring; (3) most papers (82.14%, 23/28) address solutions for dementia, citing Alzheimer's disease indirectly; (4) more than half of the studies aimed at monitoring patients (54%, 15/28); (5) most studies (82.14%, 23/28) performed an experiment with patients with the disease. The results corroborate the need for technological devices to help caregivers monitor patients, in addition to evidence of research opportunities in palliative care and support for daily activities.

Technologies applied in the care of patients with Alzheimer’s disease: a systematic review

The popularization of Location-based social networks (LBSNs) in last years has provided a lot of improvements in several Recommender Systems to the task of points-of-interest (PoI) recommendation. In this paper, we provide an updated view of the PoI recommendation, identifying relevant efforts, results, contributions, and limitations. Through a systematic mapping, we selected 73 relevant papers published in the last three years (2017, 2018, and 2019) in the main vehicles of the area (e.g., RecSys, VLDB, SIGIR, WWW, TKDE, etc). As major limitations, first, we identified that these works prioritize accuracy over other quality dimensions, despite the consensus in the RS community that accuracy is not enough to assess the practical effectiveness of RSs. Further, we found a low intersection of metrics and datasets used in these works, along with a large number of metrics used in a few distinct studies. These observations show restrictions for reproducibility and straightforward comparison of results in the area. Finally, we highlight as a promising future work the in-depth exploitation of textual data, since just a few of the evaluated papers marginally use this rich data source.

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