Lambs’ live weight estimation using 3D images

[EN] The sheep sector has not suffered the technification that other livestock sectors have. The lack of technological knowledge of the farmers and the economic limitations of the sector have made this technification difficult. One of the most widely used technologies is Precision Livestock Farming (PLF). PLF has already been used in other livestock sectors to improve farming efficiency. In the light of the problem that sheep farmers have in weighing lambs and their low precision, this paper proposes a system for estimating weight by means of 3D image capture. Thus, zenithal images of 272 lambs have been recorded. They have been processed using the capture of the upper area and the average depth of the pixels of the lamb. This estimates the weight of the animal with an error of less than 6%. This technology has a low economic cost and is easy to operate, helping farmers to be more willing to use it. This method manages to reduce the duration of the process, the stress of the animal and to improve the overall accuracy in weight estimation. Thus, it will help to have a greater control of the weight of the animal and to improve the economic profitability that the farmer obtains for the lambsSIGobierno de AragónAsociación Nacional de Criadores de Ganado Ovino de Raza Rasa Aragones

MERLIN a Cognitive Architecture for Service Robots

[EN] Many social robots deployed in public spaces hide hybrid cognitive architectures for dealing with daily tasks. Mostly, two main blocks sustain these hybrid architectures for robot behavior generation: deliberative and behavioral-based mechanisms. Robot Operating System offers different solutions for implementing these blocks, however, some issues arise when both are released in the robot. This paper presents a software engineering approach for normalizing the process of integrating them and presenting them as a fully cognitive architecture named MERLIN. Providing implementation details and diagrams for established the architecture, this research tests empirically the proposed solution using a variation from the challenge defined in the SciRoc @home competition. The results validate the usability of our approach and show MERLIN as a hybrid architecture ready for short and long-term tasks, showing better results than using a by default approach, particularly when it is deployed in highly interactive scenarios.SIAgencia Estatal de Investigació

Analyzing the influence of the sampling rate in the detection of malicious traffic on flow data

[EN] Cyberattacks are a growing concern for companies and public administrations. The literature shows that analyzing network-layer traffic can detect intrusion attempts. However, such detection usually implies studying every datagram in a computer network. Therefore, routers routing a significant volume of network traffic do not perform an in-depth analysis of every packet. Instead, they analyze traffic patterns based on network flows. However, even gathering and analyzing flow data has a high-computational cost, and therefore routers usually apply a sampling rate to generate flow data. Adjusting the sampling rate is a tricky problem. If the sampling rate is low, much information is lost and some cyberattacks may be neglected, but if the sampling rate is high, routers cannot deal with it. This paper tries to characterize the influence of this parameter in different detection methods based on machine learning. To do so, we trained and tested malicious-traffic detection models using synthetic flow data gathered with several sampling rates. Then, we double-check the above models with flow data from the public BoT-IoT dataset and with actual flow data collected on RedCAYLE, the Castilla y León regional academic network.S

Biometric recognition through gait analysis

[EN] The use of people recognition techniques has become critical in some areas. For instance, social or assistive robots carry out collaborative tasks in the robotics field. A robot must know who to work with to deal with such tasks. Using biometric patterns may replace identification cards or codes on access control to critical infrastructures. The usage of Red Green Blue Depth (RGBD) cameras is ubiquitous to solve people recognition. However, this sensor has some constraints, such as they demand high computational capabilities, require the users to face the sensor, or do not regard users' privacy. Furthermore, in the COVID-19 pandemic, masks hide a significant portion of the face. In this work, we present BRITTANY, a biometric recognition tool through gait analysis using Laser Imaging Detection and Ranging (LIDAR) data and a Convolutional Neural Network (CNN). A Proof of Concept (PoC) has been carried out in an indoor environment with five users to evaluate BRITTANY. A new CNN architecture is presented, allowing the classification of aggregated occupancy maps that represent the people's gait. This new architecture has been compared with LeNet-5 and AlexNet through the same datasets. The final system reports an accuracy of 88%.SIInstituto Nacional de Ciberseguridad de Espana (INCIBE)The research described in this article has been funded by the Instituto Nacional de Ciberseguridad de España (INCIBE), under the grant ”ADENDA 4: Detección de nuevas amenazas y patrones desconocidos (Red Regional de Ciencia y Tecnología)”, addendum to the framework agreement INCIBE-Universidad de León, 2019-2021. Miguel Ángel González-Santamarta would like to thank Universidad de León for its funding support for his doctoral studies

Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot

[EN] Convolutional Neural Networks are usually fitted with manually labelled data. The labelling process is very time-consuming since large datasets are required. The use of external hardware may help in some cases, but it also introduces noise to the labelled data. In this paper, we pose a new data labelling approach by using bootstrapping to increase the accuracy of the PeTra tool. PeTra allows a mobile robot to estimate people's location in its environment by using a LIDAR sensor and a Convolutional Neural Network. PeTra has some limitations in specific situations, such as scenarios where there are not any people. We propose to use the actual PeTra release to label the LIDAR data used to fit the Convolutional Neural Network. We have evaluated the resulting system by comparing it with the previous one-where LIDAR data were labelled with a Real Time Location System. The new release increases the MCC-score by 65.97%.SIAgencia Estatal de InvestigaciónUniversidad de LeónInstituto Nacional de CiberseguridadThe research described in this article has been partially funded by the grant RTI2018-100683-B-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”; Instituto Nacional de Ciberseguridad de España (INCIBE), under the grant “ADENDA 4: Detección de nuevas amenazas y patrones desconocidos (Red Regional de Ciencia y Tecnología)”, addendum to the framework agreement INCIBE–Universidad de León, 2019–2021; and the regional Government of Castilla y León under under the grant BDNS (487971)

People Detection and Tracking Using LIDAR Sensors

Special Issue Robotics in Spain 2019[EN] The tracking of people is an indispensable capacity in almost any robotic application. A relevant case is the @home robotic competitions, where the service robots have to demonstrate that they possess certain skills that allow them to interact with the environment and the people who occupy it; for example, receiving the people who knock at the door and attending them as appropriate. Many of these skills are based on the ability to detect and track a person. It is a challenging problem, particularly when implemented using low-definition sensors, such as Laser Imaging Detection and Ranging (LIDAR) sensors, in environments where there are several people interacting. This work describes a solution based on a single LIDAR sensor to maintain a continuous identification of a person in time and space. The system described is based on the People Tracker package, aka PeTra, which uses a convolutional neural network to identify person legs in complex environments. A new feature has been included within the system to correlate over time the people location estimates by using a Kalman filter. To validate the solution, a set of experiments have been carried out in a test environment certified by the European Robotic League.SIJunta de Castilla y León (LE028P17)Comunidad de Madrid (RoboCity2030-Fase 3

Benchmark Dataset for Evaluation of Range-Based People Tracker Classifiers in Mobile Robots

[EN] Tracking people has many applications, such as security or safe use of robots. Many onboard systems are based on Laser Imaging Detection and Ranging (LIDAR) sensors. Tracking peoples' legs using only information from a 2D LIDAR scanner in a mobile robot is a challenging problem because many legs can be present in an indoor environment, there are frequent occlusions and self-occlusions, many items in the environment such as table legs or columns could resemble legs as a result of the limited information provided by two-dimensional LIDAR usually mounted at knee height in mobile robots, etc. On the other hand, LIDAR sensors are affordable in terms of the acquisition price and processing requirements. In this article, we present a new dataset.Data actually contained in the dataset allow evaluating two people trackers, both neural network-based: leg detector (LD), a widely used solution by the Robot Operating System (ROS) community; and a people-tracker tool developed by the Robotics Group at the University of Leon, known as PeTra.S

SQL injection attack detection in network flow data

[EN] SQL injections rank in the OWASP Top 3. The literature shows that analyzing network datagrams allows for detecting or preventing such attacks. Unfortunately, such detection usually implies studying all packets flowing in a computer network. Therefore, routers in charge of routing significant traffic loads usually cannot apply the solutions proposed in the literature. This work demonstrates that detecting SQL injection attacks on flow data from lightweight protocols is possible. For this purpose, we gathered two datasets collecting flow data from several SQL injection attacks on the most popular database engines. After evaluating several machine learning-based algorithms, we get a detection rate of over 97% with a false alarm rate of less than 0.07% with a Logistic Regression-based model.SIInstituto Nacional de Ciberseguridad de España (INCIBE)Universidad de Leó

Tracking People in a Mobile Robot From 2D LIDAR Scans Using Full Convolutional Neural Networks for Security in Cluttered Environments

[EN] Tracking people has many applications, such as security or safe use of robots. Many onboard systems are based on Laser Imaging Detection and Ranging (LIDAR) sensors. Tracking peoples' legs using only information from a 2D LIDAR scanner in a mobile robot is a challenging problem because many legs can be present in an indoor environment, there are frequent occlusions and self-occlusions, many items in the environment such as table legs or columns could resemble legs as a result of the limited information provided by two-dimensional LIDAR usually mounted at knee height in mobile robots, etc. On the other hand, LIDAR sensors are affordable in terms of the acquisition price and processing requirements. In this article, we describe a tool named PeTra based on an off-line trained full Convolutional Neural Network capable of tracking pairs of legs in a cluttered environment. We describe the characteristics of the system proposed and evaluate its accuracy using a dataset from a public repository. Results show that PeTra provides better accuracy than Leg Detector (LD), the standard solution for Robot Operating System (ROS)-based robots.SIJunta de Castilla y León (LE028P17)Instituto Nacional de Cibersegurida

SEPAR Recommendations for COVID-19 Vaccination in Patients With Respiratory Diseases

[ES] La Sociedad Española de Neumología y Cirugía Torácica (SEPAR) ha elaborado este documento de recomendaciones sobre la vacuna para la COVID-19 en las enfermedades respiratorias, con el objetivo de ayudar al personal sanitario en la toma de decisiones sobre cómo actuar en la vacunación de estos pacientes. Las recomendaciones han sido elaboradas por un grupo de expertos en la materia, tras la revisión de la literatura recopilada hasta el 7 de marzo del 2021, y de la información aportada por distintas sociedades científicas, agencias del medicamento y estrategias de organismos gubernamentales hasta esa fecha. Podemos concluir que las vacunas para la COVID-19 no solo son seguras y eficaces, sino que, en aquellos pacientes vulnerables con enfermedades respiratorias crónicas, son prioritarias. Además, la implicación activa de los profesionales sanitarios que manejan estas patologías en la estrategia de vacunación es clave para lograr una buena adherencia y coberturas vacunales elevadas.[EN] The Spanish Society of Pneumonology and Thoracic Surgery (SEPAR) has elaborated this document of recommendations for COVID-19 vaccination in patients with respiratory diseases aimed to help healthcare personnel make decisions about how to act in case of COVID-19 vaccination in these patients. The recommendations have been developed by a group of experts in this field after reviewing the materials published up to March 7, 2021, the information provided by different scientific societies, drug agencies and the strategies of the governmental bodies up to this date. We can conclude that COVID-19 vaccines are not only safe and effective, but also prior in vulnerable patients with chronic respiratory diseases. In addition, an active involvement of healthcare professionals, who manage these diseases, in the vaccination strategy is the key to achieve good adherence and high vaccination coverage