Development of a web application for managing records of a law firm

La idea de este proyecto es la creación de una aplicación web que permita al público contactar vía internet con su abogado y seguir online la evolución de su caso hasta su solución final. Para ello, el cliente creará en primer lugar un expediente exponiendo su caso. Habrá varias personas definidas como telefonistas que serán las encargadas encargadas de la asignación de expedientes a los abogados. Una vez asignado un expediente a un abogado, éste será la persona encargada de resolver el expediente y podrá informar al cliente del estado en el que se encuentra. Además el cliente podrá proporcionar la información que se le solicite a través de la aplicación mediante el hilo que se crea para cada expediente o bien adjuntando documentos al expediente en cuestión. La aplicación también contará con un tipo de perfil de usuario definido como estadístico. El estadístico tiene acceso a las diferentes gráficas que permite generar la aplicación y que servirán, por ejemplo, para realizar informes de actividad o análisis de rendimiento

Are learning styles useful? A new software to analyze correlations with grades and a case study in engineering

Knowing student learning styles represents an effective way to design the most suitable methodology for our students so that performance can improve with less effort for both students and teachers. However, a methodology is usually set in teaching guides according to the previous academic year's information without any knowledge of our current audience. In this work, a new software for learning styles and grade analysis based on the Honey-Alonso Learning Styles Questionnaire has been proposed. This tool proposes the average learning style profiles of a given course by clustering student learning styles and analyzes the possible relation between grades and learning style profiles. By using that program, three different courses from Computer Sciences Engineering degrees during an academic year have been analyzed. The obtained results in our specific context exhibit that possible relation. This information could be useful to understand how students approach learning materials

Homography estimation with deep convolutional neural networks by random color transformations

Most classic approaches to homography estimation are based on the filtering of outliers by means of the RANSAC method. New proposals include deep convolutional neural networks. Here a new method for homography estimation is presented, which supplies a deep neural homography estimator with color perturbated versions of the original image pair. The obtained outputs are combined in order to obtain a more robust estimation of the underlying homography. Experimental results are shown, which demonstrate the adequate performance of our approach, both in quantitative and qualitative terms.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Foreground object detection enhancement by adaptive super resolution for video surveillance

Foreground object detection is a fundamental low level task in current video surveillance systems. It is usually accomplished by keeping a model of the background at each frame pixel. Many background learning algorithms have difficulties to attain real time operation when applied directly to the output of state of the art high resolution surveillance cameras, due to the large number of pixels. Here we propose a strategy to address this problem which consists in maintaining a low resolution model of the background which is upscaled by adaptive super resolution in order to produce a foreground detection mask of the same size as the original input frame. Extensive experimental results demonstrate the suitability of our proposal, in terms of reduction of the computational load and foreground detection accuracy.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Mitigating Carlini & Wagner attacks with EGAN

Deep Learning models are experiencing a significant surge in popularity, expanding into various domains, including critical applications like object recognition in autonomous vehicles, where any failure could have fatal consequences. Given the importance of these models, it is crucial to address potential attacks that could impact their performance and jeopardize user safety. The specialized branch of Machine Learning dedicated to this study is known as Adversarial Machine Learning. In this study, we will assess the effectiveness of Carlini & Wagner attacks. Additionally, we emphasize the importance of implementing proactive security measures to defend Deep Learning models. To enhance the model's resilience against potential threats, we employ a defense network called Encoding Generative Adversarial Networks. This comprehensive analysis will not only provide valuable insights into the vulnerability of models to different attacks but also contribute to the development of more robust and advanced strategies to protect Deep Learning models in critical applications. These findings are essential for increasing the security and reliability of artificial intelligence in environments that demand exceptional accuracy and dependability.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Anomalous trajectory detection for automated traffic video surveillance

Vehicle trajectories extracted from traffic video sequences can be helpful for many purposes. In particular, the analysis of detected anomalous trajectories may enhance drivers’ safety. This work proposes a methodology to detect anomalous vehicle trajectories by using a vehicle detection, a vehicle tracking and a processing of the tracking information steps. Once trajectories are detected, their velocity vectors are estimated and an anomaly value is computed for each trajectory by comparing its vector with those from its nearest neighbours. The management of these anomaly values allows considering which trajectories are suitable to be potentially anomalous considered. Real and synthetic videos have been included in the experiments to perform the goodness of the proposal.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech Gobierno de España bajo el proyecto RTI2018-094645-B-I00 Junta de Andalucía bajo el proyecto UMA18-FEDERJA-08

Automated detection of vehicles with anomalous trajectories in traffic surveillance videos.

Video feeds from traffic cameras can be useful for many purposes, the most critical of which are related to monitoring road safety. Vehicle trajectory is a key element in dangerous behavior and traffic accidents. In this respect, it is crucial to detect those anomalous vehicle trajectories, that is, trajectories that depart from usual paths. In this work, a model is proposed to automatically address that by using video sequences from traffic cameras. The proposal detects vehicles frame by frame, tracks their trajectories across frames, estimates velocity vectors, and compares them to velocity vectors from other spatially adjacent trajectories. From the comparison of velocity vectors, trajectories that are very different (anomalous) from neighboring trajectories can be detected. In practical terms, this strategy can detect vehicles in wrong-way trajectories. Some components of the model are off-the-shelf, such as the detection provided by recent deep learning approaches; however, several different options are considered and analyzed for vehicle tracking. The performance of the system has been tested with a wide range of real and synthetic traffic videos

A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The implementation of deep learning-based computer-aided diagnosis systems for the classification of mammogram images can help in improving the accuracy, reliability, and cost of diagnosing patients. However, training a deep learning model requires a considerable amount of labelled images, which can be expensive to obtain as time and effort from clinical practitioners are required. To address this, a number of publicly available datasets have been built with data from different hospitals and clinics, which can be used to pre-train the model. However, using models trained on these datasets for later transfer learning and model fine-tuning with images sampled from a different hospital or clinic might result in lower performance. This is due to the distribution mismatch of the datasets, which include different patient populations and image acquisition protocols. In this work, a real-world scenario is evaluated where a novel target dataset sampled from a private Costa Rican clinic is used, with few labels and heavily imbalanced data. The use of two popular and publicly available datasets (INbreast and CBIS-DDSM) as source data, to train and test the models on the novel target dataset, is evaluated. A common approach to further improve the model’s performance under such small labelled target dataset setting is data augmentation. However, often cheaper unlabelled data is available from the target clinic. Therefore, semi-supervised deep learning, which leverages both labelled and unlabelled data, can be used in such conditions. In this work, we evaluate the semi-supervised deep learning approach known as MixMatch, to take advantage of unlabelled data from the target dataset, for whole mammogram image classification. We compare the usage of semi-supervised learning on its own, and combined with transfer learning (from a source mammogram dataset) with data augmentation, as also against regular supervised learning with transfer learning and data augmentation from source datasets. It is shown that the use of a semi-supervised deep learning combined with transfer learning and data augmentation can provide a meaningful advantage when using scarce labelled observations. Also, we found a strong influence of the source dataset, which suggests a more data-centric approach needed to tackle the challenge of scarcely labelled data. We used several different metrics to assess the performance gain of using semi-supervised learning, when dealing with very imbalanced test datasets (such as the G-mean and the F2-score), as mammogram datasets are often very imbalanced

Dealing with scarce labelled data: Semi-supervised deep learning with mix match for Covid-19 detection using chest X-ray images

The file attached to this record is the author's final peer reviewed version.Coronavirus (Covid-19) is spreading fast, infecting people through contact in various forms including droplets from sneezing and coughing. Therefore, the detection of infected subjects in an early, quick and cheap manner is urgent. Currently available tests are scarce and limited to people in danger of serious illness. The application of deep learning to chest X-ray images for Covid-19 detection is an attractive approach. However, this technology usually relies on the availability of large labelled datasets, a requirement hard to meet in the context of a virus outbreak. To overcome this challenge, a semi-supervised deep learning model using both labelled and unlabelled data is proposed. We develop and test a semi-supervised deep learning framework based on the Mix Match architecture to classify chest X-rays into Covid-19, pneumonia and healthy cases. The presented approach was calibrated using two publicly available datasets. The results show an accuracy increase of around 15% under low labelled / unlabelled data ratio. This indicates that our semi-supervised framework can help improve performance levels towards Covid-19 detection when the amount of high-quality labelled data is scarce. Also, we introduce a semi-supervised deep learning boost coefficient which is meant to ease the scalability of our approach and performance comparison