9 research outputs found
Complex-valued embeddings of generic proximity data
Proximities are at the heart of almost all machine learning methods. If the
input data are given as numerical vectors of equal lengths, euclidean distance,
or a Hilbertian inner product is frequently used in modeling algorithms. In a
more generic view, objects are compared by a (symmetric) similarity or
dissimilarity measure, which may not obey particular mathematical properties.
This renders many machine learning methods invalid, leading to convergence
problems and the loss of guarantees, like generalization bounds. In many cases,
the preferred dissimilarity measure is not metric, like the earth mover
distance, or the similarity measure may not be a simple inner product in a
Hilbert space but in its generalization a Krein space. If the input data are
non-vectorial, like text sequences, proximity-based learning is used or ngram
embedding techniques can be applied. Standard embeddings lead to the desired
fixed-length vector encoding, but are costly and have substantial limitations
in preserving the original data's full information. As an information
preserving alternative, we propose a complex-valued vector embedding of
proximity data. This allows suitable machine learning algorithms to use these
fixed-length, complex-valued vectors for further processing. The complex-valued
data can serve as an input to complex-valued machine learning algorithms. In
particular, we address supervised learning and use extensions of
prototype-based learning. The proposed approach is evaluated on a variety of
standard benchmarks and shows strong performance compared to traditional
techniques in processing non-metric or non-psd proximity data.Comment: Proximity learning, embedding, complex values, complex-valued
embedding, learning vector quantizatio
Complex-valued embeddings of generic proximity data
Proximities are at the heart of almost all machine learning methods. If the
input data are given as numerical vectors of equal lengths, euclidean distance,
or a Hilbertian inner product is frequently used in modeling algorithms. In a
more generic view, objects are compared by a (symmetric) similarity or
dissimilarity measure, which may not obey particular mathematical properties.
This renders many machine learning methods invalid, leading to convergence
problems and the loss of guarantees, like generalization bounds. In many cases,
the preferred dissimilarity measure is not metric, like the earth mover
distance, or the similarity measure may not be a simple inner product in a
Hilbert space but in its generalization a Krein space. If the input data are
non-vectorial, like text sequences, proximity-based learning is used or ngram
embedding techniques can be applied. Standard embeddings lead to the desired
fixed-length vector encoding, but are costly and have substantial limitations
in preserving the original data's full information. As an information
preserving alternative, we propose a complex-valued vector embedding of
proximity data. This allows suitable machine learning algorithms to use these
fixed-length, complex-valued vectors for further processing. The complex-valued
data can serve as an input to complex-valued machine learning algorithms. In
particular, we address supervised learning and use extensions of
prototype-based learning. The proposed approach is evaluated on a variety of
standard benchmarks and shows strong performance compared to traditional
techniques in processing non-metric or non-psd proximity data.Comment: Proximity learning, embedding, complex values, complex-valued
embedding, learning vector quantizatio
El uso del chatbot como elemento de acción tutorial en la enseñanza universitaria
Abstract: It is of great importance to help and pay attention to students through different educational activities to ensure their participation in class and thus reduce the dropout rate. Traditionally, tutoring activities have been limited to face-to-face sessions in which students pose questions to the teacher. However, in a connected world with many available information systems, innovative tools are needed to facilitate and speed up both the study and the resolution of doubts in a comfortable way. Methods: This paper proposes using a chatbot based tutoring system as a novel educational experience focused on motivating universities students. Results: Besides, we provide a proof-of-concept implementation of a chatbot that answers questions as quickly and accurately possible at any time, in a comfortable way for the students, and at the same time it gathers feedback from the students regarding those topics that need to be explained in class in more detail. Conclusions: This experience is intended to increase the engagement and collaboration of both students and instructors and has helped to decrease the dropout rate in recent years.Resumen: Es de vital importancia ayudar y guiar el aprendizaje de los estudiantes a través de diferentes herramientas y actividades educativas que faciliten su participación en clase y permitan reducir la tasa de abandono. Tradicionalmente, las actividades de tutorización para estudiantes universitarios, se limita a reuniones presenciales en las que los estudiantes plantean preguntas al docente. Sin embargo, dadas las circunstancias actuales y ante un mundo conectado con muchos sistemas de información disponibles, se necesitan herramientas docentes innovadoras que faciliten el aprendizaje y una ágil resolución de dudas. Método: en este trabajo se propone la utilización de un sistema de tutorías, basado en el uso de un chatbot como experiencia educativa novedosa y orientada a motivar y facilitar el aprendizaje en estudiantes universitarios. Resultados: estudio aporta la implementación de un chatbot que responde de forma rápida y precisa, disponible en cualquier momento para solucionar dudas y facilitar el estudio de las materias a los estudiantes. Este chatbot además permite recopilar comentarios de los propios estudiantes sobre los temas que requieren ser explicados en clase con un mayor detalle. Conclusiones: El uso del chatbot tutorial, ha permitido aumentar el compromiso y la colaboración tanto de los estudiantes como de los docentes, disminuyendo la tasa del número de estudiantes que abandonan la asignatura.Ministerio Español de Economía y Competitividad - project TIN2017-85727-C4-2-P -UGR-DeepBio
An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information
As the COVID-19 pandemic rapidly spreads across the world, regrettably, misinformation
and fake news related to COVID-19 have also spread remarkably. Such misinformation has confused
people. To be able to detect such COVID-19 misinformation, an effective detection method should be
applied to obtain more accurate information. This will help people and researchers easily differentiate
between true and fake news. The objective of this research was to introduce an enhanced evolutionary
detection approach to obtain better results compared with the previous approaches. The proposed
approach aimed to reduce the number of symmetrical features and obtain a high accuracy after
implementing three wrapper feature selections for evolutionary classifications using particle swarm
optimization (PSO), the genetic algorithm (GA), and the salp swarm algorithm (SSA). The experiments
were conducted on one of the popular datasets called the Koirala dataset. Based on the obtained
prediction results, the proposed model revealed an optimistic and superior predictability performance
with a high accuracy (75.4%) and reduced the number of features to 303. In addition, by comparison
with other state-of-the-art classifiers, our results showed that the proposed detection method with
the genetic algorithm model outperformed other classifiers in the accurac
Mathematics in Software Reliability and Quality Assurance
This monograph concerns the mathematical aspects of software reliability and quality assurance and consists of 11 technical papers in this emerging area. Included are the latest research results related to formal methods and design, automatic software testing, software verification and validation, coalgebra theory, automata theory, hybrid system and software reliability modeling and assessment
Signal Processing Using Non-invasive Physiological Sensors
Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions
Incorporating spatial relationship information in signal-to-text processing
This dissertation outlines the development of a signal-to-text system that incorporates spatial relationship information to generate scene descriptions. Existing signal-to-text systems generate accurate descriptions in regards to information contained in an image. However, to date, no signalto- text system incorporates spatial relationship information. A survey of related work in the fields of object detection, signal-to-text, and spatial relationships in images is presented first. Three methodologies followed by evaluations were conducted in order to create the signal-to-text system: 1) generation of object localization results from a set of input images, 2) derivation of Level One Summaries from an input image, and 3) inference of Level Two Summaries from the derived Level One Summaries. Validation processes are described for the second and third evaluations, as the first evaluation has been previously validated in the related original works. The goal of this research is to show that a signal-to-text system that incorporates spatial information results in more informative descriptions of the content contained in an image. An additional goal of this research is to demonstrate the signal-to-text system can be easily applied to additional data sets, other than the sets used to train the system, and achieve similar results to the training sets. To achieve this goal, a validation study was conducted and is presented to the reader