A model for providing emotion awareness and feedback using fuzzy logic in online learning

Monitoring users’ emotive states and using that information for providing feedback and scaffolding is crucial. In the learning context, emotions can be used to increase students’ attention as well as to improve memory and reasoning. In this context, tutors should be prepared to create affective learning situations and encourage collaborative knowledge construction as well as identify those students’ feelings which hinder learning process. In this paper, we propose a novel approach to label affective behavior in educational discourse based on fuzzy logic, which enables a human or virtual tutor to capture students’ emotions, make students aware of their own emotions, assess these emotions and provide appropriate affective feedback. To that end, we propose a fuzzy classifier that provides a priori qualitative assessment and fuzzy qualifiers bound to the amounts such as few, regular and many assigned by an affective dictionary to every word. The advantage of the statistical approach is to reduce the classical pollution problem of training and analyzing the scenario using the same dataset. Our approach has been tested in a real online learning environment and proved to have a very positive influence on students’ learning performance.Peer ReviewedPostprint (author's final draft

A Multivariate Fit Luminosity Function And World Model For Long Gamma-Ray Bursts

It is proposed that the luminosity function, the rest-frame spectral correlations, and distributions of cosmological long-duration (Type-II) gamma-ray bursts (LGRBs) may be very well described as a multivariate log-normal distribution. This result is based on careful selection, analysis, and modeling of LGRBs' temporal and spectral variables in the largest catalog of GRBs available to date: 2130 BATSE GRBs, while taking into account the detection threshold and possible selection effects. Constraints on the joint rest-frame distribution of the isotropic peak luminosity (L-iso), total isotropic emission (E-iso), the time-integrated spectral peak energy (E-p,E-z), and duration (T-90,T-z) of LGRBs are derived. The presented analysis provides evidence for a relatively large fraction of LGRBs that have been missed by the BATSE detector with E-iso extending down to similar to 10(49) erg and observed spectral peak energies (Ep) as low as similar to 5 keV. LGRBs with rest-frame duration T-90,T-z less than or similar to 1 s or observer-frame duration T-90 less than or similar to 2 s appear to be rare events (less than or similar to 0.1% chance of occurrence). The model predicts a fairly strong but highly significant correlation (rho = 0.58 +/- 0.04) between E-iso and E-p,E-z of LGRBs. Also predicted are strong correlations of L-iso and E-iso with T-90,T-z and moderate correlation between L-iso and E-p,E-z. The strength and significance of the correlations found encourage the search for underlying mechanisms, though undermine their capabilities as probes of dark energy's equation of Stateat high redshifts. The presented analysis favors-but does not necessitate-a cosmic rate for BATSE LGRBs tracing metallicity evolution consistent with a cutoff Z/Z(circle dot) similar to 0.2-0.5, assuming no luminosity-redshift evolution.Institute for Fusion Studie

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

MIMO PID Controller Tuning Method for Quadrotor Based on LQR/LQG Theory

In this work, a new pre-tuning multivariable PID (Proportional Integral Derivative) controllers method for quadrotors is put forward. A procedure based on LQR/LQG (Linear Quadratic Regulator/Gaussian) theory is proposed for attitude and altitude control, which suposes a considerable simplification of the design problem due to only one pretuning parameter being used. With the aim to analyze the performance and robustness of the proposed method, a non-linear mathematical model of the DJI-F450 quadrotor is employed, where rotors dynamics, together with sensors drift/bias properties and noise characteristics of low-cost commercial sensors typically used in this type of applications are considered. In order to estimate the state vector and compensate bias/drift effects in the measures, a combination of filtering and data fusion algorithms (Kalman filter and Madgwick algorithm for attitude estimation) are proposed and implemented. Performance and robustness analysis of the control system is carried out by employing numerical simulations, which take into account the presence of uncertainty in the plant model and external disturbances. The obtained results show the proposed controller design method for multivariable PID controller is robust with respect to: (a) parametric uncertainty in the plant model, (b) disturbances acting at the plant input, (c) sensors measurement and estimation errors

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain

A framework for context-aware sensor fusion

Mención Internacional en el título de doctorSensor fusion is a mature but very active research field, included in the more general discipline of information fusion. It studies how to combine data coming from different sensors, in such way that the resulting information is better in some sense –more complete, accurate or stable– than any of the original sources used individually. Context is defined as everything that constraints or affects the process of solving a problem, without being part of the problem or the solution itself. Over the last years, the scientific community has shown a remarkable interest in the potential of exploiting this context information for building smarter systems that can make a better use of the available information. Traditional sensor fusion systems are based in fixed processing schemes over a predefined set of sensors, where both the employed algorithms and domain are assumed to remain unchanged over time. Nowadays, affordable mobile and embedded systems have a high sensory, computational and communication capabilities, making them a perfect base for building sensor fusion applications. This fact represents an opportunity to explore fusion system that are bigger and more complex, but pose the challenge of offering optimal performance under changing and unexpected circumstances. This thesis proposes a framework supporting the creation of sensor fusion systems with self-adaptive capabilities, where context information plays a crucial role. These two aspects have never been integrated in a common approach for solving the sensor fusion problem before. The proposal includes a preliminary theoretical analysis of both problem aspects, the design of a generic architecture capable for hosting any type of centralized sensor fusion application, and a description of the process to be followed for applying the architecture in order to solve a sensor fusion problem. The experimental section shows how to apply this thesis’ proposal, step by step, for creating a context-aware sensor fusion system with self-adaptive capabilities. This process is illustrated for two different domains: a maritime/coastal surveillance application, and ground vehicle navigation in urban environment. Obtained results demonstrate the viability and validity of the implemented prototypes, as well as the benefit of including context information to enhance sensor fusion processes.La fusión de sensores es un campo de investigación maduro pero no por ello menos activo, que se engloba dentro de la disciplina más amplia de la fusión de información. Su papel consiste en mezclar información de dispositivos sensores para proporcionar un resultado que mejora en algún aspecto –completitud, precisión, estabilidad– al que se puede obtener de las diversas fuentes por separado. Definimos contexto como todo aquello que restringe o afecta el proceso de resolución de un problema, sin ser parte del problema o de su solución. En los últimos años, la comunidad científica ha demostrado un gran interés en el potencial que ofrece el contexto para construir sistemas más inteligentes, capaces de hacer un mejor uso de la información disponible. Por otro lado, el desarrollo de sistemas de fusión de sensores ha respondido tradicionalmente a esquemas de procesado poco flexibles sobre un conjunto prefijado de sensores, donde los algoritmos y el dominio de problema permanecen inalterados con el paso del tiempo. En la actualidad, el abaratamiento de dispositivos móviles y embebidos con gran capacidad sensorial, de comunicación y de procesado plantea nuevas oportunidades. La comunidad científica comienza a explorar la creación de sistemas con mayor grado de complejidad y autonomía, que sean capaces de adaptarse a circunstancias inesperadas y ofrecer un rendimiento óptimo en cada caso. En esta tesis se propone un framework que permite crear sistemas de fusión de sensores con capacidad de auto-adaptación, donde la información contextual juega un papel fundamental. Hasta la fecha, ambos aspectos no han sido integrados en un enfoque conjunto. La propuesta incluye un análisis teórico de ambos aspectos del problema, el diseño de una arquitectura genérica capaz de dar cabida a cualquier aplicación de fusión de sensores centralizada, y la descripción del proceso a seguir para aplicar dicha arquitectura a cualquier problema de fusión de sensores. En la sección experimental se demuestra cómo aplicar nuestra propuesta, paso por paso, para crear un sistema de fusión de sensores adaptable y sensible al contexto. Este proceso de diseño se ilustra sobre dos problemas pertenecientes a dominios tan distintos como la vigilancia costera y la navegación de vehículos en entornos urbanos. El análisis de resultados incluye experimentos concretos que demuestran la validez de los prototipos implementados, así como el beneficio de usar información contextual para mejorar los procesos de fusión de sensores.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Javier Bajo Pérez.- Secretario: Antonio Berlanga de Jesús.- Vocal: Lauro Snidar

Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection

A significant challenge in object detection is accurate identification of an object's position in image space, whereas one algorithm with one set of parameters is usually not enough, and the fusion of multiple algorithms and/or parameters can lead to more robust results. Herein, a new computational intelligence fusion approach based on the dynamic analysis of agreement among object detection outputs is proposed. Furthermore, we propose an online versus just in training image augmentation strategy. Experiments comparing the results both with and without fusion are presented. We demonstrate that the augmented and fused combination results are the best, with respect to higher accuracy rates and reduction of outlier influences. The approach is demonstrated in the context of cone, pedestrian and box detection for Advanced Driver Assistance Systems (ADAS) applications.Comment: 21 pages, 12 figures, journal paper, MDPI Sensors, 201

Influence of Portfolio Management in Decision-Making

Purpose: Today’s manufacturing facilities are challenged by highly customized products and just in time manufacturing and delivery of these products. In this study, a batch scheduling problem has been addressed to enable on-time completion of customer orders in a lean manufacturing environment. The problem is optimizing the partitioning of product components into batches and scheduling of the resulting batches where each customer order is received as a set of products made of various components. Design/methodology/approach: Three different mathematical models for minimization of total earliness and tardiness of customer orders are developed to provide on-time completion of customer orders and also, to avoid excess final product inventory. The first model is a non-linear integer programming model whereas the second is a linearized version of the first. Finally, to solve larger sized instances of the problem, an alternative linear integer model is presented. Findings: Computational study using a suit set of test instances showed that the alternative linear integer model is able to solve all test instances in varying sizes within quite shorter computer times compared to the other two models. It has also been showed that the alternative model is able to solve moderate sized real-world problems. Originality/value: The problem under study differentiates from existing batch scheduling problems in the literature owing to the inclusion of new circumstances that are present in real-world applications. Those are: customer orders consisting of multi-products made of multi-parts, processing of all parts of the same product from different orders in the same batch, and delivering the orders only when all related products are completed. This research also contributes to the literature of batch scheduling problem by presenting new optimization models.Peer Reviewe