Brazil

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.

WebMedia 2020

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

libras

datasets

sign language

neural networks

deep learning

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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A ticket is required to attend this event, please register using the link below:

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AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

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 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.

Analyzing YouTube Videos Shared on Whatsapp in the Early COVID-19 Crisis

Currently, most declarative languages used to define multimedia
documents do not support the specification of an interactive ap-
plication with multiple users and multimodal interaction. To han-
dle multimodal and multi-user interaction in these languages, the
author of multimedia applications must write custom code in an
imperative language to be included in the declarative document.
To fill in this gap, this work proposes an extension of NCM (Nested
Context Language), NCL (Nested Context Language) and Ginga-
NCL to provide high-level abstraction multimodal and multi-user
interaction specifications. As a proof of concept, we developed a
multimedia application that uses multi-user voice interaction.

Extending Ginga-NCL to Specify Multimodal Interactions With
Multiple Users


Nowadays, news websites are the main sources of information for most people. But these outlets may have a bias in their publications, favoring some ideology. This can influence people’s opinion regarding several topics. Methods to automatically detect bias are usually based on aspects such as text and embedded hyperlinks. In this work, we present a strategy to detect media bias by analyzing networks that model citations between news websites, evaluating if the bias is related to structural characteristics of these networks. We show that ideological bias can be captured by citation patterns and present a method that uses these patterns to automatically identify political bias in news websites, reaching accuracy and F1 scores above 72% and 0.70, respectively.

An Evaluation of Structural Characteristics of Networks to Identify Media Bias in News Portals

Techniques in recommendation systems generally focuses on recommending the most important items for a user. The purpose of this work is to generate recommendations focusing on long tail items, leading users to less popular and at the same time highly relevant products. Two techniques from the literature were applied in this study. The first technique is through graphs to calculate node similarity between users and items. The second technique applies clustering in the set of items in a dataset. This combination was adopted in order to give more visibility to long tail items. To evaluate the proposed approach an experiment was carried out to calculate the accuracy, diversity, and popularity of the generated recommendation. We compare the proposed approach with other 3 baselines where our approach achieved better results.

Exploiting Graph Similarities with Clustering to Improve Long Tail Items Recommendations

Early fusion techniques have been proposed in video analysis tasks as a way to improve efficacy by generating compact data models capable of keeping semantic clues present on multimodal data. First attempts to fuse multimodal data employed fusion operators at low-level feature space, losing data representativeness. This drove later research efforts to evolve simple operators to complex operations, which became, in general, inseparable of the multimodal semantic clues processing. In this paper, we investigate the application of early multimodal fusion operators at the mid-level feature space. Five different operators (Concatenation, Sum, Gram, Average and Maximum) were employed to fuse mid-level multimodal video features. Fused data derived from each operator were then used as input for two different video analysis tasks: Temporal Video Scene Segmentation and Video Classification. For each task, we performed a comparative analysis among the operators and related work techniques designed for these tasks using complex fusion operations. The efficacy results reached by the operators were very close to those reached by the techniques, pointing out strong evidence that working on a more homogeneous feature space can reduce known low-level fusion drawbacks. In addition, operators make data fusion separable, allowing researchers to keep the focus on developing semantic clues representations.

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

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