Browsing by Author "Dongo, Irvin"

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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    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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    Structural and semantic similarity for XML comparison
    (Scopus, 2013) Guzman, Renato; Dongo, Irvin; Ticona Herrera, Regina
    XML has experimented a rapid growth mostly because of its application on the Web. Application varies from version control management, data storage to clustering and information retrieval. In this context, it is necessary to develop efficient techniques for comparing XML documents. Many method proposed are based only on structural commonalities, ignoring semantics. In this paper, we propose a new method for comparing XML documents based on LevelEdge combining tag structural and semantic similarities. © 2013 Authors.
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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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    Toward RDF Normalization
    (Springer Verlag, 2015) Ticona Herrera, Regina; Tekli, Joe; Chbeir, Richard; Laborie, Sébastien; Dongo, Irvin; Guzman, Renato
    Billions of RDF triples are currently available on the Web through the Linked Open Data cloud (e.g., DBpedia, LinkedGeoData and New York Times). Governments, universities as well as companies (e.g., BBC, CNN) are also producing huge collections of RDF triples and exchanging them through different serialization formats (e.g., RDF/XML, Turtle, N-Triple, etc.). However, RDF descriptions (i.e., graphs and serializations) are verbose in syntax, often contain redundancies, and could be generated differently even when describing the same resources, which would have a negative impact on their processing. Hence, we propose here an approach to clean and eliminate redundancies from such RDF descriptions as a means of transforming different descriptions of the same information into one representation, which can then be tuned, depending on the target application (information retrieval, compression, etc.). Experimental tests show significant improvements, namely in reducing RDF description loading time and file size. © Springer International Publishing Switzerland 2015.
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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"