Unsupervised segmentation and clustering time series approach to Southern Africa rainfall regime changes

Lovemore  Chipindu Walter Mupangwa Isaiah Nyagumbo Mainassara Zaman-Allah (2023, [Artículo])

Autoregressive Integrated Moving Average Facebook Prophet Hidden Markov Model Regression Regression with Hidden Logistic Process CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA COASTAL AREAS SEMIARID ZONES SUBHUMID ZONES RAINFALL CLIMATE CHANGE

Detección de eventos violentos en publicaciones de redes sociales

Detection of violent events in social media publications

Esteban Ponce León (2023, [Tesis de maestría])

En los últimos años, ha habido un interés creciente en el monitoreo de redes sociales para recopilar información y, en algunos casos, para examinar la ocurrencia de delitos. Sin embargo, gran parte de las investigaciones hasta ahora solo se han centrado en ciudades de EE. UU. o extranjeras, y por ende, en publicaciones y conjuntos de datos en inglés El objetivo principal de esta tesis es diseñar un método que permita la identificación de publicaciones de eventos violentos en español y en Twitter, utilizando información multimodal y técnicas de aumento de datos que mejoren el rendimiento de los modelos. Para esto, el trabajo de investigación se dividió en dos fases experimentales. La primera orientada a identificar publicaciones a partir de solo texto, explorando diferentes técnicas de aumento de datos para texto y modelos de aprendizaje máquina y profundo. En la segunda fase, se extendió el método propuesto para abordar la identificación en un contexto multimodal, es decir, considerando tanto los textos de los tweets como las imágenes compartidas que los acompañan. En este caso el método propuesto consideró utilizar descripciones textuales de las imágenes y abordar la problemática desde el dominio textual, además se hicieron 2 tipos de aumento de datos para cada tipo de información. La evaluación de los métodos se hizo utilizando las colecciones de la tarea de evaluación DA-VINCIS 2022 y 2023. Los resultados demostraron una mejora en el rendimiento de los modelos al considerar el uso de información multimodal y el uso de aumento de datos.

In recent years, there has been a growing interest in monitoring social networks to gather information and, in some cases, to examine the occurrence of crime. However, much of the research so far has only focused on US or foreign cities, and thus on English-language publications and data sets. The main objective of this thesis is to design a method that allows the identification of publications of violent events in Spanish and on Twitter, using multimodal information and data augmentation techniques that improve the performance of the models. For this, the research work was divided into two experimental phases. The first aimed at identifying publications from only text, exploring different data augmentation techniques for text and machine and deep learning models. In the second phase, the proposed method was extended to address identification in a multimodal context, that is, considering both the texts of the tweets and the shared images that accompany them. In this case, the proposed method considered using textual descriptions of the images and addressing the problem from the textual domain, in addition, 2 types of data augmentation were made for each type of information. The evaluation of the methods was done using the collections of the DA-VINCIS 2022 and 2023 evaluation task. The results demonstrated an improvement in the performance of the models when considering the use of multimodal information and the use of data augmentation.

Detección de Violencia, Redes Sociales, Aumento de Datos, Procesamiento del Lenguaje Natural, BERT, BETO, Descripción de Imágenes Violence Detection, Social Networks, Data Augmentation, Natural Language Processing, BERT, BETO, Image Captioning INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ORDENADORES MODELOS CAUSALES MODELOS CAUSALES

Variability of Microcirculation Detected by Blood Pulsation Imaging

Alexei Kamshilin (2013, [Artículo])

The non-invasive assessment of blood flow is invaluable for the diagnostic and monitoring treatment of numerous vascular and neurological diseases. We developed a non-invasive and non-contact method of blood pulsation imaging capable of visualizing and monitoring of the two-dimensional distribution of two key parameters of peripheral blood flow: the blood pulsation amplitude and blood pulsation phase. The method is based on the photoplethysmographic imaging in the reflection mode. In contrast with previous imaging systems we use new algorithm for data processing which allows two dimensional mapping of blood pulsations in large object's areas after every cardiac cycle. In our study we carried out the occlusion test of the arm and found (i) the extensive variability of 2D-distribution of blood pulsation amplitude from one cardiac cycle to another, and (ii) existence of the adjacent spots to which the blood is asynchronously supplied. These observations show that the method can be used for studying of the multicomponent regulation of peripheral blood circulation. The proposed technique is technologically simple and cost-effective, which makes it applicable for monitoring the peripheral microcirculation in clinical settings for example, in diagnostics or testing the efficiency of new medicines. © 2013 Kamshilin et al.

algorithm, article, blood analysis, heart cycle, human, human experiment, image analysis, imaging system, male, microcirculation, peripheral circulation, photoelectric plethysmography, process development, Algorithms, Diagnostic Imaging, Diagnostic T CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA MATEMÁTICAS ANÁLISIS NUMÉRICO ANÁLISIS NUMÉRICO