Supercomputación Castilla y León

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.

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.

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.

Abstract Context awareness in ambient assisted living programmes for the elderly is a cornerstone in the current scenario of noncustomized service robots distributed around the world. This research proposes a context‐awareness system for a human–robot scene interpretation based on seven primary contexts and the American Occupational Therapy Association. The context‐awareness system defined here proposes an inference mechanism for the activity recognition supported on hierarchical Bayesian networks. However, when the information from sensors increases, the computational cost associated also increases. Thus, an evaluation of different Bayesian network models is necessary for decreasing its impact over the robot performance. Two topological models have been modelled and tested using OpenMarkov application: a two‐level approach of an input–observations layer and the activity recognition layer, and a three‐layer model setting apart a primary contexts layer, the input–observations layer, and the activity recognition layer. The qualitative and quantitative results presented here show better performance in terms of memory and memory in a three‐layer model. Besides, its effect on a hybrid architecture of a robotic platform is presented.

Version Control Systems are commonly used by Information and Communication Technology professionals. These systems allow monitoring programmers activity working in a project. Thus, Version Control Systems are also used by educational institutions. The aim of this work is to demonstrate that the academic success of students may be predicted by monitoring their interaction with a Version Control System. In order to do so, we have built a Machine Learning model to predict student results in a specific practical assignment of the Ampliatión de Sistemas Operativos subject, from the second course of the degree in Computer Science of the University of León, through their interaction with a Git repository. To build the model, several classifiers and predictors have been evaluated. In order to do so, we have developed Model Evaluator (MoEv), a tool to evaluate different Machine Learning models in order to get the most suitable for a specific problem. Prior to the model development, a feature selection of the input data is done. The resulting model has been trained using results from 2016--2017 course and later validated using results from 2017--2018 course. Results conclude that the model predict students' success with a success high percentage.

This paper presents the evolution of a robotic architecture intended for controlling autonomous social robots. The first instance of this architecture was originally designed according to behavior-based principles. The building blocks of this architecture were behaviors designed as a finite state machine and organized in an ethological inspired way. However, the need of managing explicit symbolic knowledge in human–robot interaction required the integration of planning capabilities into the architecture and a symbolic representation of the environment and the internal state of the robot. A major contribution of this paper is the description of the working memory that integrates these two approaches. This working memory has been implemented as a distributed graph. Another contribution is the use of behavior trees instead of state machine for implementing the behavior-based part of the architecture. This late version of the architecture has been tested in robotic competitions (RoboCup or European Robotics League, among others), whose performance is also discussed in this paper.

Abstract Version Control Systems are commonly used by Information and communication technology professionals. These systems allow monitoring programmers activity working in a project. Thus, Version Control Systems are also used by educational institutions. The aim of this work is to evaluate if the academic success of students may be predicted by monitoring their interaction with a Version Control System. In order to do so, we have built a Machine Learning model which predicts student results in a specific practical assignment of the Operating Systems Extension subject, from the second course of the degree in Computer Science of the University of León, through their interaction with a Git repository. To build the model, several classifiers and predictors have been evaluated. In order to do so, we have developed Model Evaluator (MoEv), a tool to evaluate Machine Learning models in order to get the most suitable for a specific problem. Prior to the model development, a feature selection from input data is done. The resulting model has been trained using results from 2016–2017 course and later validated using results from 2017–2018 course. Results conclude that the model predicts students’ success with a success high percentage.

Cyber-attacks are a major problem for users, businesses, and institutions. Classical anomaly detection techniques can detect malicious traffic generated in a cyber-attack by analyzing individual network packets. However, routers that manage large traffic loads can only examine some packets. These devices often use lightweight flow-based protocols to collect network statistics. Analyzing flow data also allows for detecting malicious network traffic. But even gathering flow data has a high computational cost, so routers usually apply a sampling rate to generate flows. This sampling reduces the computational load on routers, but much information is lost. This work aims to demonstrate that malicious traffic can be detected even on flow data collected with a sampling rate of 1 out of 1,000 packets. To do so, we evaluate anomaly-detection-based models using synthetic sampled flow data and actual sampled flow data from RedCAYLE, the Castilla y León regional subnet of the Spanish academic and research network. The results presented show that detection of malicious traffic on sampled flow data is possible using novelty-detection-based models with a high accuracy score and a low false alarm rate.

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.

An efficient exploitation of the Distributed Computing Infrastructures (DCIs) is specially needed to deal with the data deluge that the scientific community, in particular the Astrophysics one, is facing. This requires a good understanding of the underlying DCIs. Science Gateways (SGs) provide the users with an environment that eases the interaction with the DCIs. As a previous step, IT skilled users should populate the SGs with friendly but advanced tools (e.g. Workflows, visualization tools) that not only support the scientists to build their own experiments but also adapt them in an optimal way to the infrastructures. In Astronomy, the Virtual Observatory provides the community with services and tools for data access and sharing. However, state of the art telescopes and the coming Square Kilometre Array (SKA), able to reach data rates in the exa-scale domain, will also require advanced tools for data analysis and visualization that should be run on DCIs as well as shared on SGs. In the here presented work, we have selected as exemplar a set of analysis tasks of interest for some SKA use cases. These analysis tasks have been implemented as web services that use the COMPSs programming model in order to achieve a more efficient use of the DCIs. At the same time, the nature of the web services turns them into blocks that the astronomers can combine with VO services to build their own workflows. The web services and the workflows built upon them form a two-level workflow system that hides the technical details of the DCIs and exploits them efficiently. This approach is used for the first time in analytical tasks of interest for the SKA that benefits from the capabilities of the DCIs.

Autonomous robots are increasingly widespread in our society. These robots need to be safe, reliable, respectful of privacy, not manipulable by external agents, and capable of offering explanations of their behavior in order to be accountable and acceptable in our societies. Companies offering robotic services will need to provide mechanisms to address these issues using High Performance Computing (HPC) facilities, where logs and off-line forensic analysis could be addressed if required, but these solutions are still not available in software development frameworks for robots. The aim of this paper is to discuss the implications and interactions among cybersecurity, safety, and explainability with the goal of making autonomous robots more trustworthy.

Facing human activity-aware navigation with a cognitive architecture raises several difficulties integrating the components and orchestrating behaviors and skills to perform social tasks. In a real-world scenario, the navigation system should not only consider individuals like obstacles. It is necessary to offer particular and dynamic people representation to enhance the HRI experience. The robot's behaviors must be modified by humans, directly or indirectly. In this paper, we integrate our human representation framework in a cognitive architecture to allow that people who interact with the robot could modify its behavior, not only with the interaction but also with their culture or the social context. The human representation framework represents and distributes the proxemic zones' information in a standard way, through a cost map. We have evaluated the influence of the decision-making system in human-aware navigation and how a local planner may be decisive in this navigation. The material developed during this research can be found in a public repository (https://github.com/IntelligentRoboticsLabs/social_navigation2_WAF) and instructions to facilitate the reproducibility of the results.

In this research, uncertainty associated with initial and boundary conditions is evaluated for short-term wind speed prediction in complex terrain. The study area is the Alaiz mountain range, a windy region in the northern Iberian Peninsula. A multiphysics and multiple initial and boundary condition ensemble prediction system (EPS) was generated using the Weather Research and Forecasting model. Uncertainty of the EPS is analyzed using an index based on the spread between ensemble members, considering its behavior under different wind speed and direction events, and also during distinct atmospheric stability conditions. The results corroborate that physical parameterization uncertainty is greater for short-term forecasts (63.5 %). However, it is also necessary to consider the uncertainty associated with initial conditions, not only for its quantitative importance (36.5 %) but also for its behavior during thermal inversion conditions in the narrow valleys surrounded by mountains.

Machine Learning and Deep Learning algorithms have become a great revolution in many research fields such as Robotics and Artificial Intelligence. They have applications in such different areas as Meteorology, Cybersecurity, Biology, etc.; though their use in these areas is not so extended. High-performance Computing (HPC) is the most powerful solution to get the best results by using these algorithms. HPC requires various skills to use, such as parallel programming and shell scripting on linux system which may require a long time to acquire and might be intimidating for research with small or no background in Information and Communications Technology (ICT) such as meteorologists or biologists. This work intends to encourage the use of HPC techniques among non-ICT researchers. In order to do so, we plan to analyze the response of such researchers when they are presented some new techniques and possibilities. A set of experiments are being carried out with a group of ethology researchers at Eötvös Loránd University. We will use a three-step methodology. First, researchers will fill out a questionnaire about their knowledge about and attitude towards HPC techniques. Then, they will attend an introductory talk about HPC in general and some specific use cases which may aid them in their research, after which they will receive a follow up questionnaire. After this a subset of the attendees will receive hands-on training in the use of specific HPC applications. Finally, a last round of questionnaires will be carried out with the participants. We expect to identify some key indicators which allow us to identify the main advantages and drawbacks that non-ICT researchers face when discovering High-performance Computing.

Version Control Systems are commonly used by Information and communication technology professionals. They allow monitoring the single activity of different people working in the same project through all the project's lifetime. Thus, Version Control Systems are also used by educational institutions. The aim of this work is to demonstrate that the academic success in Computer Sciences subjects may be predicted by monitoring students' interaction with a Version Control System. In order to do so, we have built a prediction model by using Model Evaluator, a tool which allows evaluating several Machine Learning models in order to select the most suitable for a specific problem. A set of experiments are being been carried out to evaluate the prediction model. At those experiments, students of different subjects of the degree in Computer Science of the University of León were involved. All the selected subjects: Operating Systems Extension, Computer Programming, and Computer Organization, require students to complete practical assignments which imply developing software solutions in different programming languages. A common requisite of those practical assignments is the use of a Git repository to store source code and documentation. Tentative results suggest that continuous interaction with the Git repositories has a big impact on academic success.

Abstract An essential requirement for increasing human confidence in computer systems is knowing an event’s origin. Therefore, it is necessary to have an efficient method to record such information. It is especially challenging in robotics, where unexpected behaviours can have unpredictable consequences, endangering the interests of people or even their safety. Furthermore, to analyse an incident’s cause or anticipate future behaviours, we must identify the events that cause a specific action. Although it is common to use logging systems for such purposes, issues such as partial recording of events, unhelpful data or the impact on robot performance suggest conceiving new accountability solutions that assist when determining the responsible entities or the provenance of specific information. This paper presents a general-purpose approach to developing an accountability system for autonomous robots. It consists of four main components: a system event logger, a message producer, a distributed event streaming platform and a database. Our proposal is completely decoupled from the monitored system and allows real-time analysis, improving flexibility, besides system protection and transparency. Finally, the need to reduce the impact of the audit process and logging tasks on robot performance has promoted the development of different assessment scenarios to determine the best strategy for providing accounting services.

Abstract Understanding why a robot's behaviour was triggered is a growing concern to get human‐acceptable social robots. Every action, expected and unexpected, should be able to be explained and audited. The formal model proposed here deals with different information levels, from low‐level data, such as sensors' data logging; to high‐level data that provide an explanation of the robot's behaviour. This study examines the impact on the robot system of a custom log engine based on a custom ROS logging node and investigates pros and cons when used together with a NoSQL database locally and in a cloud environment. Results allow to characterize these alternatives and explore the best strategy for offering a fully log‐based accountability engine that maximizes the mapping between robot behaviour and robot logs.

Many of the tasks that a service robot can perform at home involve navigation skills. In a real world scenario, the navigation system should consider individuals beyond just objects, theses days it is necessary to offer particular and dynamic representation in the scenario in order to enhance the HRI experience. In this paper, we use the proxemic theory to do this representation. The proxemic zones are not static. The culture or the context influences them and, if we have this influence into account, we can increase humans' comfort. Moreover, there are collaborative tasks in which these zones take different shapes to allow the task's best performance. This research develops a layer, the social layer, to represent and distribute the proxemics zones' information in a standard way, through a cost map and using it to perform a social navigate task. We have evaluated these components in a simulated scenario, performing different collaborative and human-robot interaction tasks and reducing the personal area invasion in a 32\%. The material developed during this research can be found in a public repository, as well as instructions to facilitate the reproducibility of the results.

The use of Supercomputers is wide spreading, constituting an essential component in many fields of science. The interest in the use of High Performance Computing (HPC) facilities is also increasing in a growing percentage of undergraduates because the use of these infrastructures allows them to improve their skills and the results of their training.

The work presents a case study related to the efficient use of energy in the Supercomputing Centre of Castile and Leon (SCAYLE) in the city of Len (Spain). In this case study, the location is important to show how weather conditions and an optimal design based in density and free cooling, among others, allows adequate performance of computing infrastructure in terms of energy efficiency, using computing resources in an environment friendly way, maintaining, at the same time, the overall computing performance. In order to analyse the information, the monitoring systems were used to calculate the indicators of performance related to energy consumption. The conclusions of this study shows that, apart from the design of the Data Processing Centre (DPC) an adequate and continuous control of all the components of the Supercomputer are the key for providing the best performance possible in order to obtain very efficient levels of electricity consumption from the computing infrastructure, compared to the total consumption of the Centre.