Recognition and retrieval of objects in diverse applications

This work proposes and evaluates object description and retrieval techniques in different real applications. It addresses the classification of boar spermatozoa according to acrosome integrity using several methods based on invariant local features. In addition, it provides two new methods for insert localisation and an automatic solution for the recognition of broken inserts in edge profile milling heads that can be set up in-process without delaying any machining operations. Finally, it evaluates different clusterings of keypoints for object retrieval and proposes a new descriptor, named colour COSFIRE, in the scope of the European project Advisory System Against Sexual Exploitation of Children

Burr detection and classification using RUSTICO and image processing

.Machined workpieces must satisfy quality standards such as avoid the presence of burrs in edge finishing to reduce production costs and time. In this work we consider three types of burr that are determined by the distribution of the edge shape on a microscopic scale: knife-type (without imperfections), saw-type (presence of small splinters that could be accepted) and burr-breakage (substantial deformation that produces unusable workpieces). The proposed method includes RUSTICO to classify automatically the edge of each piece according to its burr type. Experimental results validate its effectiveness, yielding a 91.2% F1-Score and identifying completely the burr-breakage type.S

MeWEHV: Mel and Wave Embeddings for Human Voice Tasks

[EN] A recent trend in speech processing is the use of embeddings created through machine learning models trained on a specific task with large datasets. By leveraging the knowledge already acquired, these models can be reused in new tasks where the amount of available data is small. This paper proposes a pipeline to create a new model, called Mel and Wave Embeddings for Human Voice Tasks (MeWEHV), capable of generating robust embeddings for speech processing. MeWEHV combines the embeddings generated by a pre-trained raw audio waveform encoder model, and deep features extracted from Mel Frequency Cepstral Coefficients (MFCCs) using Convolutional Neural Networks (CNNs). We evaluate the performance of MeWEHV on three tasks: speaker, language, and accent identification. For the first one, we use the VoxCeleb1, and VBHIR datasets and present YouSpeakers204, a new and publicly available dataset for English speaker identification that contains 19607 audio clips from 204 persons speaking in six different accents, allowing other researchers to work with a very balanced dataset, and to create new models that are robust to multiple accents. For evaluating the language identification task, we use the VoxForge, Common Language, and the LRE17 datasets. Finally, for accent identification, we use the Latin American Spanish Corpora (LASC), Common Voice, and the NISP datasets. Our approach allows a significant increase in the performance of state-of-the-art embedding generation models on all the tested datasets, with a low additional computational cost.SIUnión EuropeaJunta de Castilla y León (EDU/875/2021)This work was supported in part by the European Union's Horizon 2020 Research and Innovation Framework Programme under the Global Response Against Child Exploitation (GRACE) Project under Grant 883341; and in part by the "Ayudas para financiar la contratacion predoctoral de personal investigador" grant of the Government of Castilla y Leon, Spain, under Grant EDU/875/202

Analytical Framework to Investigate Ethics, Social Responsibility and Sustainability in Engineering Project Management

[EN] Project management is part of the Engineering curricula worldwide. Frequently, project management courses are goalcentered in the pursue of a balance in the triple constrain formed by quality, cost and schedule. However, ethics, social responsibility and sustainability play a crucial role on the development of projects since their success relies on compliance with laws, regulations and local culture and values. This paper presents an in-depth analysis on the treatment of ethics, social responsibility and sustainability according to two widely-used project management standards, Project Management Book of Knowledge (PMBoK) and Individual Competence Baseline for Project, Program & Portfolio Management (ICB). We design an analytical framework to carry out a desk research to these two project management standards. Particularly, we count the number of times of appearance, present the definition, if any, determine the appearance in the different knowledge areas and process groups for PMBoK and in the different competences for ICB, and identify the proposed techniques or tools for ethics, social responsibility and sustainability management. The findings of the research demonstrate that ICB treats the three concepts more in deep than PMBoK. PMBoK refers more often to ethics and only proposes one tool for sustainability. ICB introduces the concepts throughout the standard, with repeated references to their significance. Nevertheless, the detail that standards provide can be further elaborated. As a result, we also suggest improvement proposals that could enhance the important role of these topics for students and practitioners involved in project management.S

A Video Summarization Approach to Speed-up the Analysis of Child Sexual Exploitation Material

[Abstract] Identifying key content from a video is essential for many security applications such as motion/action detection, person re-identification and recognition. Moreover, summarizing the key information from Child Sexual Exploitation Materials, especially videos, which mainly contain distinctive scenes including people’s faces is crucial to speed-up the investigation of Law Enforcement Agencies. In this paper, we present a video summarization strategy that combines perceptual hashing and face detection algorithms to keep the most relevant frames of a video containing people’s faces that may correspond to victims or offenders. Due to legal constraints to access Child Sexual Abuse datasets, we evaluated the performance of the proposed strategy during the detection of adult pornography content with the NDPI-800 dataset. Also, we assessed the capability of our strategy to create video summaries preserving frames with distinctive faces from the original video using ten additional short videos manually labeled. Results showed that our approach can detect pornography content with an accuracy of 84.15% at a speed of 8.05 ms/frame making this appropriate for realtime applications.This work was supported by the framework agreement between the Universidad de León and INCIBE (Spanish National Cybersecurity Institute) under Addendum 01. Also, this research has been funded with support from the European Commission under the 4NSEEK project with Grant Agreement 821966. This publication reflects the views only of the authors, and the European Commission cannot be held responsible for any use which may be made of the information contained therein. Finally, we acknowledge the NVIDIA Corporation for the donation of the TITAN Xp GPU

SIFT (Scale Invariant Feature Transform)

Cap. 8, pp. 131-157SIFT es un método que permite detectar puntos característicos en una imagen y luego describirlos mediante un histograma orientado de gradientes. Y además, lo hace de forma que la localización y la descripción presenta una gran invarianza a la orientación, la posición y la escala. Cada punto característico queda, por lo tanto, definido mediante su vector de características de 128 elementos, y se obtiene la información de su posición en coordenadas de la imagen, la escala a la que se encontró y la orientación dominante de la región alrededor de dicho punto. En este capítulo se explican los pasos necesarios para obtener descriptores SIFT en una imagen. Se presenta un ejercicio sencillo que sirve para ilustrar numéricamente cómo se obtiene el descriptor a partir de la región que rodea un punto característico. También se comentan las posibilidades de SIFT para realizar reconocimiento de objetos presentes en una imagen. Y, finalmente, se habla brevemente de algunas extensiones del método así como de otros descriptores de imagen relacionados que han surgido posteriormente

A Framework for the Optimization of Complex Cyber-Physical Systems via Directed Acyclic Graph

[EN] Mathematical modeling and data-driven methodologies are frequently required to optimize industrial processes in the context of Cyber-Physical Systems (CPS). This paper introduces the PipeGraph software library, an open-source python toolbox for easing the creation of machine learning models by using Directed Acyclic Graph (DAG)-like implementations that can be used for CPS. scikit-learn’s Pipeline is a very useful tool to bind a sequence of transformers and a final estimator in a single unit capable of working itself as an estimator. It sequentially assembles several steps that can be cross-validated together while setting different parameters. Steps encapsulation secures the experiment from data leakage during the training phase. The scientific goal of PipeGraph is to extend the concept of Pipeline by using a graph structure that can handle scikit-learn’s objects in DAG layouts. It allows performing diverse operations, instead of only transformations, following the topological ordering of the steps in the graph; it provides access to all the data generated along the intermediate steps; and it is compatible with GridSearchCV function to tune the hyperparameters of the steps. It is also not limited to (�����,�����) entries. Moreover, it has been proposed as part of the scikit-learn-contrib supported project, and is fully compatible with scikit-learn. Documentation and unitary tests are publicly available together with the source code. Two case studies are analyzed in which PipeGraph proves to be essential in improving CPS modeling and optimization: the first is about the optimization of a heat exchange management system, and the second deals with the detection of anomalies in manufacturing processes.SIEspaña : Ministerio de Economía y Competitividad : grant number DPI2016-79960-C3-2-PMCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe

Estimation of lamb weight using transfer learning and regression

Meat production needs of accurate measurement of livestock weight. In lambs, traditional scales are still used to weigh live animals, which is a tedious process for the operators and stressful for the animal. In this paper, we propose a method to estimate the weight of live lambs automatically, fast, non-invasive and affordably. The system only requires a camera like those that can be found in mobile phones. Our approach is based on the use of a known Convolutional Neural Network architecture (Xception) pre-trained on the ImageNet dataset. The acquired knowledge during training is used to estimate the weight, which is known as transfer learning. The best results are achieved with a model that receives the image, the sex of the lamb and the height from where the image is taken. A mean absolute error (MAE) of 0.58 kg and an R2 of 0.96 were obtained, improving on current techniques. Only one image and two values specified by the user (sex and height) allow to estimate with a minimum error the optimal weight of a lamb, maximising the economic profit

Repositorio Git para aprendizaje basado en resolución de problemas. Asignatura de Dirección de Proyectos

[ES] La experiencia descrita a continuación propone la implantación de un sistema de aprendizaje autónomo y guiado cuyo método didáctico está basado en el aprendizaje basado en resolución de problemas específicos de la dirección de plazos, costes y recursos, proporcionando un entorno de simulación donde el alumno puede explorar el conjunto de efectos que conllevan las decisiones organizativas que propone para el proyecto. La realización de esta experiencia se realiza mediante un servidor Git públicamente accesible responsable de almacenar la base de conocimiento de los casos de estudio, así de como de un servidor de máquinas virtuales encargadas de ejecutar el motor de simulación para cada alumno que acceda al sistema de aprendizaje. Como objetivo principal de la experiencia que se plantea está la mejora de la calidad en el proceso de enseñanzaaprendizaje en la asignatura de Dirección de Proyectos de los grados de ingenierías impartidos por el Área de Proyectos de Ingeniería. Finalmente, resaltar que la experiencia investigadora descrita ha permitido facilitar el aprendizaje basado en la resolución de problemas mediante el uso de la herramienta de cálculo y simulación para el aprendizaje desarrollada. Los resultados obtenidos en los diferentes cursos aplicados se califican como excelentes, derivados del gran interés mostrado por parte del alumno y por la mejora de los resultados académicos finales, teniéndose previsto ampliar en el futuro dicho repositorio