Use of chatbots for user service in higher education institutions

Authors: J Cordero, A Toledo, F Guamán, L Barba-Guamán

Abstract:

This paper arises from the need to have new tools or communication channels that allow users to answer questions or concerns about different fields at the university level. In particular, the results of the analysis of the use of three chatbots, implemented in a higher education institution, are presented. The results obtained from the surveys, considering the usability of the chatbot and the accuracy of the responses, show that the level of satisfaction of using the chatbots is high, therefore, it is recommended to use this type of systems for the attention of users.

Keywords: chatbot, higher education, usability, ICT

Link: https://ieeexplore.ieee.org/abstract/document/9141108

Recognition of the driving style in vehicle drivers

Authors: J Cordero, J Aguilar, K Aguilar, D Chávez, E Puerto

Abstract:

This paper presents three different approaches to recognize driving style based on a hierarchical-model. Specifically, it proposes a hierarchical model for the recognition of the driving style for advanced driver-assistance systems (ADAS) for vehicles. This hierarchical model for the recognition of the style of the car driving considers three aspects: the driver emotions, the driver state, and finally, the driving style itself. In this way, the proposed hierarchical pattern is composed of three levels of descriptors/features, one to recognize the emotional states, another to recognize the driver state, and the last one to recognize the driving style. Each level has a set of descriptors, which can be sensed in a real context. Finally, the paper presents three driving style recognition algorithms based on different paradigms. One is based on fuzzy logic, another is based on chronicles (a temporal logic paradigm), and the last is based on an algorithm that uses the idea of the recognition process of the neocortex, called Ar2p (Algoritmo Recursivo de Reconocimiento de Patrones, for its acronym in Spanish). In the paper, these approaches are compared using real datasets, using different metrics of interest in the context of the Internet of the Things, in order to determine their capabilities of reasoning, adaptation, and the communication of information. In general, the initial results are encouraging, specifically in the cases of chronicles and Ar2p, which give the best results..

Keywords: pattern recognition, driving style, intelligent techniques, advanced driver-assistance systems

Link: https://www.mdpi.com/1424-8220/20/9/2597/pdf?version=1588415475

Advanced Driver Assistance System for the drowsiness detection using facial landmarks

Authors: LDS Cueva, J Cordero

Abstract:

This paper presents the development of a solution to detect a driver's drowsiness in real time and issue alerts to avoid possible traffic accidents. In particular, an analysis of the methods used for the detection of drowsiness by computer vision is performed, focusing on the use of facial reference points. Distraction, drowsiness, tiredness, speeding and fatigue are the main causes of accidents and, precisely, advanced driver assistance systems ADAS help reduce these serious human errors.

Keywords: Facial landmark, Computer vision, Drowsiness Detection

Link: https://ieeexplore.ieee.org/abstract/document/9140893 

Framework comparison of neural networks for automated counting of vehicles and pedestrians

 Authors: G Lalangui, J Cordero, O Ruiz-Vivanco, L Barba-Guamán, J Guerrero, ...

Abstract:

This paper presents a comparison of three neural network frameworks used to make volumetric counts in an automated and continuous way. In addition to cars, the application count pedestrians. Frameworks used are: SSD Mobilenet retrained, SSD Mobilenet pre-trained, and GoogLeNet pre-trained. The evaluation data set has a total duration of 60 minutes and comes from three different cameras. Images from the real deployment videos are included when training to enrich the detectable cases. Traditional detection models applied to vehicle counting systems usually provide high values for cars seen from the front. However, when the observer or camera is on the side, some models have lower detection and classification values. A new data set with fewer classes reach similar performance values as trained methods with default data sets. Results show that for the class cars, recall and precision values are 0.97 and 0.90 respectively in the best case, making use of a trained model by default, while for the class people the use of a re-trained model provides better results with precision and recall values of 1 and 0.82.

Keywords: Convolutional Neural Networks, learning transfer, automatic counter, classification, tracking, Single Shot Detector, Mobilenet

Link: https://ieeexplore.ieee.org/abstract/document/8781795

Approaches to identify student learning styles through emotions in a classroom

Sistema de Gestión de las emociones en un salón de clases inteligente basado en Modelos de confianza y reputación

Authors: J Cordero, J Aguilar, K Aguilar, M Martinez

Abstract:

In this article, we propose a model of trust and reputation, for the management of emotions in a Smart Classroom (SaCI). In general, a SaCI can model its different hardware and software components as agents. On the other hand, in intelligent environments modeled with agents, it is necessary to define the interaction mechanisms used in conversations between agents. In addition, the conversations should be enriched with models of trust and reputation of the agents involved in them, so that each agent can decide what information coming from the different agents, to consider when executing their decision-making processes. In particular, this paper defines models of trust and reputation for the SaCI agents. These models are used in a conversation that performs the management of emotions in SaCI, which allows to define the emotion that prevails at a given moment in it, which we call social emotion. The conversation is tested in several cases of study, in order to validate its ability to establish the social emotion of SaCI. The experimental results with the emotion management system based on models of trust and reputation, demonstrate that it is able to determine the social emotion in SaCI regardless of the characteristics of its context and learning from the behavior of agents, which gives it great robustness.

Keywords: trust model, reputation model, emotion management, social emotion, smart classroom, multiagent systems.

Link: https://www.proquest.com/openview/f74b61fb8680f9b19d07dd2417468366/1?pq-origsite=gscholar&cbl=1006393 

Intelligent Approaches to identify Student Learning Styles through Emotions in a Classroom

Enfoques Inteligentes para Identificar Estilos de Aprendizaje de los estudiantes mediante las Emociones en un salón de clases
Authors: J Cordero, J Aguilar, K Aguilar

Abstract:

In this article, a hierarchical pattern is proposed to identify the learning style of students in a classroom, which is composed of two levels, one to recognize the emotional state, and another to identify the learning style. Each level is defined by different types of descriptors, which are perceived from a multimodal approach. In addition, using the hierarchical pattern, two approaches are analyzed to model the learning style, which can be visual, auditory, reading/writing, kinesthetic. One of the approaches is based on fuzzy logic, and another based on chronicles, which exploit the idea of recursion and learning in the recognition process. Finally, considering the dynamic environment of a classroom, the approaches are compared, in terms of their abilities to learn and to determine the learning styles; and to communicate that information clearly in a smart classroom, based on the recognized emotions.

Keywords: Hierarchical patterns, learning style, emotion recognition, fuzzy logic, chronicles, dynamic pattern recognition.

Link: https://www.proquest.com/openview/6cd1e500aef16382c3c439403a9a06c1/1?pq-origsite=gscholar&cbl=1006393

Monitoring for the Evaluation Process On-Line Prototype Based on OpenFace Algorithm

 Authors: O Ruiz-Vivanco, A Gonzalez-Eras, J Cordero, L Barba-Guaman

Abstract:
This project was designed to present a prototype of facial authentication system that allows for the recognition of students who are using an online platform to take final evaluations in our Distance Learning Program, with the purpose of detecting fraud and identity theft. It uses the OpenFace algorithm based on neural networks, taking input from two-dimensional images of the student from time to time during the participation on the exam. We present a system for face recognition using an image database of faces in classroom setting to demonstrate the improvement using this OpenFace algorithm for the preprocessing approach. The preliminary results indicate a high accuracy in the recognition of students, in terms of brightness, size and quality of the image of the face.

Keywords: Computational intelligence, Computer vision, Face authentication, Neural networks, OpenFace algorithm

Link: https://link.springer.com/chapter/10.1007/978-3-030-05532-5_37 

Specification of the Autonomic Cycles of Learning Analytic Tasks for a Smart Classroom

Authors: Jose Aguilar, Jorge Cordero and Omar Buendía

Abstract:
In this article, we propose the concept of ‘‘Autonomic Cycle Of Learning Analysis Tasks’’ (ACOLAT), which defines a set of tasks of learning analysis, whose objective is to improve the learning process. The data analysis has become a fundamental area for the knowledge discovery from data extracted from different sources. In the autonomic cycle, each learning analysis task interacts with each other and has different roles: Some of them must observe the learning process, others must analyze and interpret what happens in it, and finally, others make decisions in order to improve the learning process. In this article, we study the application of the autonomic cycle in a smart classroom, which is composed of a set of intelligent components of hardware (e.g., smart board) and software (e.g., virtual learning environments), which must exploit the knowledge generated by the ACOLAT to improve the learning process in the smart classroom. Moreover, we present the set of ACOLATs present in a smart classroom and the implementation of some of them.

Keywords: Learning Analytics, Smart Classroom, Autonomic Computing, Learning Environments, Knowledge Discovery

Link: http://journals.sagepub.com/doi/abs/10.1177/0735633117727698

Competences as Services in the Autonomic Cycles of Learning Analytic Tasks for a Smart Classroom

Authors: Alexandra González-Eras, Omar Buendia, Jose Aguilar, Jorge Cordero and Taniana Rodriguez

Abstract:
Learning Analytic is a useful tool in the context of the learning process, in order to improve the educational environment. In previous works, we have proposed autonomic cycles of learning Analytic tasks, in order to improve the learning process in smart classrooms. One aspect to be considered by the autonomic cycles is their adaptability to the formation of competences, assuming that a student has competences that must be strengthened during the learning process. In this paper, we propose the utilization of competences to guide the adaptation process of a learning environment. Particularly, we propose the extensions of the autonomic cycles for smart classrooms, using the idea of competences. In this case, we define the competences as a service, to help the autonomic cycles in their processes of adaptation.

Keywords: Learning analytics, Smart classroom, Educational competences, Autonomic cycles

Link: https://link.springer.com/chapter/10.1007/978-3-319-67283-0_16

Different Intelligent Approaches for Modeling the Style of Car Driving

Authors: Jose Aguilar, Kristell Aguilar, Danilo Chávez, Jorge Cordero and Eduard Puerto

Abstract:
In this paper, we propose a hierarchical pattern of the style of driving, which is composed of three levels, one to recognize the emotional state, other to recognize the state of the driver, and finally, the last one corresponds to the style of driving. Each level is defined by different types of descriptors, which are perceived in different multi-modal ways (sound, vision, etc.). Additionally, we analyze three techniques to recognize the style of driving, using our hierarchical pattern, one based on fuzzy logic, another based on chronicles (a temporal logic paradigm), and another based on an algorithm that models the functioning of the human neocortex, exploiting the idea of recursivity and learning in the recognition process. We compare the techniques considering the dynamic context where a car driver operates.

Keywords: Hierarchical Patterns, Fuzzy Logic, Chronicles, Dynamic Pattern Recognition, Style of Driving

Link: http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=pKH2LWLdplY%3d&t=1

Towards a Fuzzy Cognitive Map for Opinion Mining

Authors: Jose Aguilar, Oswaldo Téran, Hebert Sánchez, José Gutiérrez de Meza, Jorge Cordero, and Danilo Chávez

Abstract:
In this paper, we propose a Fuzzy Cognitive Map (FCM) to opinion mining, with special attention to media influence on public opinion. Particularly, in this paper, we describe the FCM, the concepts and relationships among them. Our opinion mining model is based on a multilevel FCM, to distribute the concepts according to the aspects that describe the elements conforming public opinion, which are: social, technological and biological. We carry out preliminary tests, and the results are very encouraging.

Keywords: Fuzzy Cognitive Maps, Opinion Mining, Opinion Conformation, Media Manipulation

Link: http://www.sciencedirect.com/science/article/pii/S1877050917309432

Learning analytics tasks as services in smart classrooms

Authors: Jose Aguilar, Manuel Sánchez, Jorge Cordero, Priscila Valdiviezo-Díaz, Luis Barba-Guamán and Luis Chamba-Eras

Abstract:
A smart classroom integrates the different components in a traditional classroom, by using different technologies as artificial intelligence, ubiquitous, and cloud paradigms, among others, in order to improve the learning process. On the other hand, the learning analytics tasks are a set of tools that can be used to collect and analyze the data accumulated in a smart classroom. In this paper, we propose the definition of the learning analytics tasks as services, which can be invoked by the components of a smart classroom. We describe how to combine the cloud and multi-agent paradigms in a smart classroom, in order to provide academic services to the intelligent and non-intelligent agents in the smart classroom, to adapt and respond to the teaching and learning requirements of students. Additionally, we define a set of learning analytics tasks as services, which defines a knowledge feedback loop for the smart classroom, in order to improve the learning process in it, and we explain how they can be invoked and consumed by the agents in a smart classroom.

Keywords: Learning analytics as service, Smart classroom, Cloud computing, Ambient intelligences

Link: http://link.springer.com/article/10.1007%2Fs10209-017-0525-0
Other link: Learning analytics tasks as services in smart classrooms

Cite this article as:

Aguilar, J., Sánchez, M., Cordero, J. et al.

Univ Access Inf Soc (2017).

doi:10.1007/s10209-017-0525-0

A Dynamic Recognition Approach of Emotional States for Car Drivers

Authors: Jose Aguilar, Danilo Chavez, and Jorge Cordero

Abstract:
In this paper, we propose a recognition model of emotional state using multi-modal perception, a temporal logic paradigm (in particular, we use chronicles), and dynamical patterns. In this way, our recognition approach is based on chronicles to model the patterns, a definition of the emotions as dynamic patterns, and the idea that they are perceived in a multi-modal way (sound, vision, etc.). In this paper, we present these elements of our approach, and give one example of an application for the recognition of the emotions of the driver of a vehicle.

Keywords: Recognition of emotions, Chronicles, Dynamic patterns recognition

Link: http://link.springer.com/chapter/10.1007/978-3-319-48024-4_13

DOI: 10.1007/978-3-319-48024-4_13

A general framework for learning analytic in a smart classroom

Authors: Jose Aguilar, Priscila Valdiviezo, Jorge Cordero, Guido Riofrio, and Eduardo Encalada

Abstract. In this paper, we propose the utilization of the “Learning Analytics” paradigm in a Smart Classroom, a classroom that integrates artificial intelligence technology on the educational process. Learning Analytics can extract knowledge from the Smart Classroom platform, to better understand students and his/her learning processes. In this way, a Smart Classroom can understand and optimize
the learning process and the teaching environments proposed. The smart classroom can adapt its components to improve students’ performance, among other aspects. Particularly, this paper proposes a framework about how the Learning Analytics paradigm can be used in a Smart Classroom, in order to provide knowledge about the activities taking place within it. The framework is defined like a closed cycle of Learning Analytics tasks, which generate metrics used like feedback to optimize the pedagogical model proposed by the smart Classroom. The metrics evaluate the learning process and pedagogical practice provided by the smart Classroom. So, our main contribution is about how the Learning Analytics paradigm can be used in a Smart Classroom in order to improve the students’
performance.

Keywords: Learning analytics, Smart classroom,  Ambient intelligence, Data mining

Link: http://link.springer.com/chapter/10.1007/978-3-319-48024-4_17

DOI: 10.1007/978-3-319-48024-4_17

Reconocimiento multimodal de emociones en un entorno inteligente basado en crónicas

Autores—Jorge Cordero, Jose Aguilar

Resumen—En este trabajo se presenta un modelo de reconocimiento multimodal de emociones en tiempo real, para un salón de clases inteligente, basado en crónicas. En nuestro modelo se analizan las emociones a reconocer que están relacionadas con el proceso de aprendizaje, como son: felicidad, tristeza, ira, miedo, y sorpresa. El reconocimiento es multimodal por considerarse diferentes tipos de eventos y formas sensoriales en el proceso de reconocimiento: facial, acústico, lenguaje corporal, y otras variables propias del salón de clases inteligente, como la temperatura, el ruido la luminosidad, entre otros. Este enfoque multimodal permite modelar más precisamente las emociones del usuario, respecto a sistemas de reconocimiento de emociones unimodales.

Palabras claves— computación afectiva, reconocimiento de emociones, crónicas, ambientes inteligentes.

Conferencia
Congreso Internacional de Sistemas Inteligentes y Nuevas Tecnologías -COISINT 2016

Link: https://www.researchgate.net/publication/307888062_Reconocimiento_multimodal_de_emociones_en_un_entorno_inteligente_basado_en_cronicas

Link2: http://www.academia.edu/28352784/Reconocimiento_multimodal_de_emociones_en_un_entorno_inteligente_basado_en_cr%C3%B3nicas

Specification of a Smart Classroom Based on Agent Communities

Authors: Jose Aguilar, Luis Chamba-Eras,  Jorge Cordero

Abstract
For the development of distributed applications, it is required to define a formalization of the process of implementation. Particularly, we are interested in one type of Ambient Intelligence (AmI), the Smart Classroom. In this paper we propose the implementation of a Smart Classroom, called SaCI, using the concept of communities of agents. With this concept, we carry out the definition and implementation of sets of agents according to their roles, functionalities, characteristics, among others, in SaCI. Each community can be designed and implemented independently and later be integrated in SaCI. In this paper we present this approach and its implementation in SaCI.

Keywords
Smart educational environment, Multi-agent system, Ambient intelligence

Link: http://link.springer.com/chapter/10.1007%2F978-3-319-31232-3_95

DOI: 10.1007/978-3-319-31232-3_95

Cloud Computing in Smart Educational Environments: Application in Learning Analytics as Service

Authors: Manuel Sánchez, Jose Aguilar, Jorge Cordero, Priscila Valdiviezo-Díaz, Luis
Barba-Guamán, Luis Chamba-Eras

Abstract
In this paper, we present an extension of a Middleware for Smart Educational Environments based in agents, using the paradigm of Cloud Computing. In that sense, we detail the Middleware components, which enable the process of management of the Cloud Computing. We also present the utilization of this Middleware to provide services on the cloud about task of Learning Analytics that allow processing of data of students and learning environments, to understand and optimize the learning processes.

Keywords
Cloud computing, Smart educational environment, Learning analytics

Link: http://link.springer.com/chapter/10.1007/978-3-319-31232-3_94

DOI: 10.1007/978-3-319-31232-3_94

A business intelligence model for online tutoring process

Authors: Priscila Valdiviezo-Díaz, Jorge Cordero, Ruth Reátegui, Jose Aguilar

Abstract:
This work aims to implement business intelligence strategies in an educational institution based on the distance education, particularly in the online tutoring process. In this paper we propose to use the business intelligence paradigm to analyze the online tutoring process, based on the data collected on the interactions of students and teachers in a virtual learning environment, and the results recorded in the institutional academic system of evaluations. This analysis should answer the following questions: 1) Can we define a model of online tutoring that can adapt to each student profile? 2) Can we predict the success of an online tutoring process for a course and a student given? To this purpose, this paper presents three aspects: characterize and determine the key elements in an online tutoring process, build a descriptive model of the online tutoring process, and build a predictive model of the success of the online tutoring process. The models to be defined will be based on data mining techniques, and will be obtained from the current data stored in transactional databases of the University. This data is preprocessed with ETL techniques to build a multidimensional model, and the key elements are obtained through operations OLAP.

Keywords—Business Intelligence Systems, Data Warehouses, Learning Analytics, Online Tutoring

Published in: Frontiers in Education Conference (FIE), 2015. 32614 2015. IEEE
DOI: 10.1109/FIE.2015.7344385

Link: http://ieeexplore.ieee.org/document/7344385/

Conceptual Design of a Smart Classroom Based on Multiagent Systems

Authors: Jose Aguilar, Priscila Valdiviezo, Jorge Cordero and Manuel Sánchez

Abstract - The smart environments have been used in different domains: home, educational and health centers, etc. Particularly, a smart environment in education must integrate different aspects linked to virtual and presencial education, the profile of the students, to the pedagogical paradigm used, etc., in real time. In this paper we characterize a smart classroom considering these aspects, using the multiagent systems paradigm. Particularly, we define the different components of a smart classroom with their properties. Based on that, we describe these components like agents using MASINA, a methodology to specify multiagent systems. We define two frameworks of agents which describe the different types of components in a smart classroom (of software and of hardware), and give examples of applications of these two frameworks in a device and a software of a smart classroom. Finally, we show an example of conversation in a smart classroom based on our multiagents approach, specifically in a work session.

Keywords: Smart Classroom, Multiagent System, AmI, Middleware

Proceedings on the International Conference on Artificial Intelligence (ICAI): 471-477. Athens: The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). (2015)

Link: http://search.proquest.com/openview/930c8d0a31e0faf9bbd8a2a44137e85a/1?pq-origsite=gscholar

A Smart Learning Environment based on Cloud Learning

Authors: Sánchez, M., J. Aguilar, J. Cordero, and P. Valdiviezo.

Abstract—In this paper is presentedan architecture of a Reflective Middlewarebased in Cloud Learning, for Intelligent Learning Environments.The middleware is defined using theMultiagent Systemsparadigm, and propose academic services on the cloud based on the current context.That is, the middlewaremanages educational services in the cloud to enhance the learning experience of students, either collaboratively or individually.In that sense, in this paper is detailed the middleware components, which enable the process of management of the cloud computing. The paper also presents examples of the utilization of the middleware to provide services on the cloud about tasks of learning analytics, which allow processing of data of students and learning environment in order to understand and optimize their learning processes.

Index terms -Intelligent Learning Environment; Cloud Learning; Reflective middleware; Ambient Intelligence

Link: http://www.academia.edu/download/41072580/A_Smart_Learning_Environment_based_on_Cloud_Learning.pdf