Browsing by Author "Cardinale, Yudith"

Now showing 1 - 7 of 7
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    A multi-modal visual emotion recognition method to instantiate an ontology
    (SciTePress, 2021) A. Heredia, Juan Pablo; Cardinale, Yudith; Dongo, Irvin; Díaz-Amado, Jose
    "Human emotion recognition from visual expressions is an important research area in computer vision and machine learning owing to its significant scientific and commercial potential. Since visual expressions can be captured from different modalities (e.g., face expressions, body posture, hands pose), multi-modal methods are becoming popular for analyzing human reactions. In contexts in which human emotion detection is performed to associate emotions to certain events or objects to support decision making or for further analysis, it is useful to keep this information in semantic repositories, which offers a wide range of possibilities for implementing smart applications. We propose a multi-modal method for human emotion recognition and an ontology-based approach to store the classification results in EMONTO, an extensible ontology to model emotions. The multi-modal method analyzes facial expressions, body gestures, and features from the body and the environment to determine an emotional state; this processes each modality with a specialized deep learning model and applying a fusion method. Our fusion method, called EmbraceNet+, consists of a branched architecture that integrates the EmbraceNet fusion method with other ones. We experimentally evaluate our multi-modal method on an adaptationof the EMOTIC dataset. Results show that our method outperforms the single-modal methods."
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    A qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysis
    (Emerald Group Holdings Ltd., 2021) Dongo, Irvin; Cardinale, Yudith; Aguilera, Ana; Martinez, Fabiola; Quintero, Yuni; Robayo, German; Cabeza, David
    "purpose: This paper aims to perform an exhaustive revision of relevant and recent related studies, which reveals that both extraction methods are currently used to analyze credibility on Twitter. Thus, there is clear evidence of the need of having different options to extract different data for this purpose. Nevertheless, none of these studies perform a comparative evaluation of both extraction techniques. Moreover, the authors extend a previous comparison, which uses a recent developed framework that offers both alternates of data extraction and implements a previously proposed credibility model, by adding a qualitative evaluation and a Twitter-Application Programming Interface (API) performance analysis from different locations. Design/methodology/approach: As one of the most popular social platforms, Twitter has been the focus of recent research aimed at analyzing the credibility of the shared information. To do so, several proposals use either Twitter API or Web scraping to extract the data to perform the analysis. Qualitative and quantitative evaluations are performed to discover the advantages and disadvantages of both extraction methods. Findings: The study demonstrates the differences in terms of accuracy and efficiency of both extraction methods and gives relevance to much more problems related to this area to pursue true transparency and legitimacy of information on the Web. Originality/value: Results report that some Twitter attributes cannot be retrieved by Web scraping. Both methods produce identical credibility values when a robust normalization process is applied to the text (i.e. tweet). Moreover, concerning the time performance, Web scraping is faster than Twitter API and it is more flexible in terms of obtaining data; however, Web scraping is very sensitive to website changes. Additionally, the response time of the Twitter API is proportional to the distance from the central server at San Francisco."
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    An approach of social navigation based on proxemics for crowded environments of humans and robots
    (MDPI AG, 2021) Daza, Marcos; Barrios-Aranibar, Dennis; Diaz-Amado, José; Cardinale, Yudith; Vilasboas, João
    "Nowadays, mobile robots are playing an important role in different areas of science, industry, academia and even in everyday life. In this sense, their abilities and behaviours become increasingly complex. In particular, in indoor environments, such as hospitals, schools, banks and museums, where the robot coincides with people and other robots, its movement and navigation must be programmed and adapted to robot-robot and human-robot interactions. However, existing approaches are focused either on multi-robot navigation (robot-robot interaction) or social navigation with human presence (human-robot interaction), neglecting the integration of both approaches. Proxemic interaction is recently being used in this domain of research, to improve Human-Robot Interaction (HRI). In this context, we propose an autonomous navigation approach for mobile robots in indoor environments, based on the principles of proxemic theory, integrated with classical navigation algorithms, such as ORCA, Social Momentum, and A*. With this novel approach, the mobile robot adapts its behaviour, by analysing the proximity of people to each other, with respect to it, and with respect to other robots to decide and plan its respective navigation, while showing acceptable social behaviours in presence of humans. We describe our proposed approach and show how proxemics and the classical navigation algorithms are combined to provide an effective navigation, while respecting social human distances. To show the suitability of our approach, we simulate several situations of coexistence of robots and humans, demonstrating an effective social navigation."
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    Application of a methodological approach to compare ontologies
    (Emerald Group Holdings Ltd., 2021) Cardinale, Yudith; Cornejo-Lupa, Maria Alejandra; Pinto-De la Gala, Alexander; Ticona-Herrera, Regina
    "Purpose: This study aims to the OQuaRE quality model to the developed methodology. Design/methodology/approach: Ontologies are formal, well-defined and flexible representations of knowledge related to a specific domain. They provide the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. Then, it is necessary to define how to compare such ontologies to decide which one is the most suitable for the specific needs of users/developers. As the emerging development of ontologies, several studies have proposed criteria to evaluate them. Findings: In a previous study, the authors propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural and Domain Knowledge levels, considering correctness and quality perspectives. As the evaluation methods of the proposal are based on a golden-standard, it can be customized to compare ontologies in any domain. Practical implications: To show the suitability of the proposal, the authors apply the methodological approach to conduct comparative studies of ontologies in two different domains, one in the robotic area, in particular for the simultaneous localization and mapping (SLAM) problem; and the other one, in the cultural heritage domain. With these cases of study, the authors demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domains. Originality/value: Using these metrics and the quality model from OQuaRE, the authors are incorporating a standard of software engineering at the quality validation into the Semantic Web. "
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    Emotion detection for social robots based on nlp transformers and an emotion ontology
    (MDPI AG, 2021) Graterol, Wilfredo; Diaz-Amado, Jose; Cardinale, Yudith; Dongo, Irvin; Lopes-Silva, Edmundo; Santos-Libarino, Cleia
    For social robots, knowledge regarding human emotional states is an essential part of adapting their behavior or associating emotions to other entities. Robots gather the information from which emotion detection is processed via different media, such as text, speech, images, or videos. The multimedia content is then properly processed to recognize emotions/sentiments, for example, by analyzing faces and postures in images/videos based on machine learning techniques or by converting speech into text to perform emotion detection with natural language processing (NLP) techniques. Keeping this information in semantic repositories offers a wide range of possibilities for implementing smart applications. We propose a framework to allow social robots to detect emotions and to store this information in a semantic repository, based on EMONTO (an EMotion ONTOlogy), and in the first figure or table caption. Please define if appropriate. an ontology to represent emotions. As a proof-of-concept, we develop a first version of this framework focused on emotion detection in text, which can be obtained directly as text or by converting speech to text. We tested the implementation with a case study of tour-guide robots for museums that rely on a speech-to-text converter based on the Google Application Programming Interface (API) and a Python library, a neural network to label the emotions in texts based on NLP transformers, and EMONTO integrated with an ontology for museums; thus, it is possible to register the emotions that artworks produce in visitors. We evaluate the classification model, obtaining equivalent results compared with a state-of-the-art transformer-based model and with a clear roadmap for improvement.
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    T-CrEO: A twitter credibility analysis frameworkT-CrEO: A twitter credibility analysis framework
    (Institute of Electrical and Electronics Engineers Inc., 2021) Cardinale, Yudith; Dongo, Irvin; Robayo, Germán; Cabeza, David; Medina, Sergio; Aguilera, Ana
    "Social media and other platforms on Internet are commonly used to communicate and generate information. In many cases, this information is not validated, which makes it difficult to use and analyze. Although there exist studies focused on information validation, most of them are limited to specific scenarios. Thus, a more general and flexible architecture is needed, that can be adapted to user/developer requirements and be independent of the social media platform. We propose a framework to automatically and in real-time perform credibility analysis of posts on social media, based on three levels of credibility: Text, User, and Social. The general architecture of our framework is composed of a front-end, a light client proposed as a web plug-in for any browser; a back-end that implements the logic of the credibility model; and a third-party services module. We develop a first version of the proposed system, called T-CREo (Twitter CREdibility analysis framework) and evaluate its performance and scalability. In summary, the main contributions of this work are: the general framework design; a credibility model adaptable to various social networks, integrated into the framework; and T-CREo as a proof of concept that demonstrates the framework applicability and allows evaluating its performance for unstructured information sources; results show that T-CREo qualifies as a highly scalable real-time service. The future work includes the improvement of T-CREo implementation, to provide a robust architecture for the development of third-party applications, as well as the extension of the credibility model for considering bots detection, semantic analysis and multimedia analysis"
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    Towards an ontology for urban tourism
    (Association for Computing Machinery, 2021) Pinto De La Gala, Alexander; Cardinale, Yudith; Dongo, Irvin; Ticona-Herrera, Regina
    "Nowadays, diffusion and preservation of cultural heritage are being supported by technology on the Web. Thus, the online availability of urban tourism information, as part of cultural heritage, has been of enormous relevance to activate the tourism in many countries. The necessity of a well-defined and standard model for representing this knowledge is being managed by semantic web technologies, such as ontologies. However, current proposals represent partial knowledge of cultural heritage. In this context, this work proposes an ontology for indoor and outdoor environments of a city to represent the cultural heritage knowledge based on the UNESCO definitions. This ontology has a three-level architecture (Upper, Middle, and Lower ontologies) in accordance with a purpose of modularity and levels of specificity. To demonstrate the utility and suitability of our proposal, we have developed a parser to map and convert a museum repository (in CSV format) to RDF triples. With this case of study, we demonstrated that, by using our ontology, it is possible to represent the knowledge of urban tourism domains of a city. © 2021 Owner/Author"