ABSTRACT The continuous evolution of techniques for generating manipulated audio, known as voice deepfakes, and the widespread availability of tools that produce convincing forgeries have created an urgent need for reliable detection methods. This work considers the dimensionality of Mel‐Frequency Cepstral Coefficients (MFCCs) as a core design variable for practical, deployable systems. The aim is to identify the smallest number of coefficients that preserve detection performance across heterogeneous models while reducing computational cost, a critical factor for mobile and edge deployment. This study evaluates a hybrid setting on the ASVspoof 2019 Logical Access dataset, in which the same feature family serves as input to five traditional machine learning algorithms (Random Forest, k‐Nearest Neighbours, Linear Support Vector Classification, Extreme Gradient Boosting and Support Vector Machine with radial basis function kernel) and five deep learning models (Convolutional Neural Network, Recurrent Neural Network, Convolutional Recurrent Neural Network, Xception and ResNet). Results indicate that deep models reach near‐peak performance with a small number of coefficients, whereas classical methods require a larger number to achieve stable performance (except Linear Support Vector Classification, which consistently underperforms). Accordingly, 32 coefficients are considered an effective operating point for hybrid deployments. Overall, the results provide evidence to guide the selection of the number of MFCC coefficients in voice deepfake detection, aiming for efficient, reproducible and explainable systems.

Cerebrovascular accident (CVA) or stroke is one of the main causes of mortality and morbidity today, causing permanent disabilities. Its early detection helps reduce its effects and its mortality: time is brain. Currently, non-contrast computed tomography (NCCT) continues to be the first-line diagnostic method in stroke emergencies because it is a fast, available, and cost-effective technique that makes it possible to rule out haemorrhage and focus attention on the ischemic origin, that is, due to obstruction to arterial flow. NCCT are quantified using a scoring system called ASPECTS (Alberta Stroke Program Early Computed Tomography Score) according to the affected brain structures. This paper aims to detect in an initial phase those CTs of patients with stroke symptoms that present early alterations in CT density using a binary classifier of CTs without and with stroke, to alert the doctor of their existence. For this, several well-known neural network architectures are implemented in the ImageNet challenges (VGG, NasNet, ResNet and DenseNet), with 3D images, covering the entire brain volume. The training results of these networks are exposed, in which different parameters are tested to obtain maximum performance, which is achieved with a DenseNet3D network that achieves an accuracy of 98% in the training set and 95% in the test set.

This paper describes the organization and results of several MOOCs delivered about technical topics (i.e., digital electronics, VHDL design on FPGAs, open education and OERs repositories and the use of STEAM technologies to encourage diversity and inclusion), where online laboratories have been used in some of them. An analysis of the enrollment, students participating on tasks or quizzes, drop-out rate and certifications requests have been done.

This paper proposes a new recommendation system in the context of the HOPE project with the aim of providing medical bibliographic references in a simple, up-to-date and immediate way. In addition, these references are catalogued according to the patient information and symptoms, offering a ranking mechanism which sorts them from most interesting to least relevant ones according to the feedback provided by health professionals. The proposed system has been extensively trained and validated with a set of widely used machine learning models, particularly Random Forest (RF), Multinomial Logistic Regression (MLR) and Support Vector Machines (SVMs). The results obtained over real medical data from HOPE project are quite promising, exhibiting a high precision. In particular, RF is the algorithm which the best behavior with a 89.9% of precision. It is closely followed by the SVM, which reaches great results with a 89.4% of precision, performing quite accurately with false negative cases.

The instructional design of experimental subjects with a distance methodology in Engineering disciplines is nowadays a challenge. One of the most useful applications is the use of remote and virtual laboratories technologies. The learning process must transparently integrate these technologies. It is also essential to be able to analyze the students learning and acceptance of this technology. In particular, the IoT paradigm involves the acquisition of practical skills and knowledge from the use of devices/sensors to the management of cloud environments, by including data storage and processing. A Laboratory of Things platform at UNED (called LoT@UNED) has been developed to cover all phases of a life-cycle development, both hardware infrastructures and software applications. This work focuses on studying the influence among several acceptance indicators during the learning/teaching process of students for the cyber-security topic, and with respect to their intention of employing LoT@UNED for other learning purposes. To achieve this, a new structural equation models (SEM) has been proposed, as well as an statistical analysis is included from a confirmatory point of view.

Despite its great potential in a wide range of human activities, hyperspectral remote sensing imaging (HSI) exhibits several challenges that prevent full exploitation of its data. In particular, land-cover classification based on HSI data suffers significant degradation due to problematic data variability. Convolutional Neural Networks (CNNs) ability to extract spectral-spatial features has enabled the development of powerful classifiers, which achieve not yet seen accuracy results. To enhance the feature extraction procedure, this paper presents a novel HSI-CNN model (DKDCNet) which combines adaptive deforming kernels (DK) and convolutions (DC) with the aim of pinpointing the effective receptive field (ERF) on the challenging input data. Experimental results on the University of Houston benchmark show that DKDCNet is able to obtain a more accurate classification than traditional strategies with similar computational cost for HSI classification. Source code: https://github.com/mhaut/DKDCNet.

The inclusion of practical activities to train critical thinking skills is very relevant in the case of Engineering subjects. In particular, it becomes a key element in the context of cybersecurity, as our case is. Therefore, this paper analyses a gambling experience in a cybersecurity subject within a Computer Engineering degree, in order to assess whether the spaced learning methodology contributes to the improvement of learning. The gamification experience is carried out through a Flag Capture Competition (CTF) among the enrolled students in the course. The use of the platform has also been evaluated in an exploratory way.

The MUREE project addresses the needs of Jordanian Universities to modernize the learning of Engineering related to renewable energies in collaboration with European Universities. After the implementation of several courses, which combined online learning with traditional teaching in most cases, it is time to analyze and verify the benefits o f t his approach in the Jordanian society. In particular, this paper outlines the results obtained with this project, from three points of view: educational impact, research impact, and socio-economic impact. Results detail the students' participation, their profile in terms of training and required skills, and some aspects about recruitment of future professionals in renewable energies.

One of the key competences for Europe in the 21st Century is the technological competence understood as an instrument for development, aimed at encouraging society to use and like science. This competence can only be fully achieved by using practical experiments, which present use cases in a realistic, creative, and revolutionary way. This paper presents our work on the development of practical experiments in the context of several pre-university levels, from pre-school to elementary school, aimed at enhancing the appeal of Science, Technology, Engineering and Mathematics (STEM) among students.

MUREE project aims at the development of courses for training specialists about renewable energy production by combining face-to-face learning with on-line attendance. In this sense, remote laboratories are nowadays essential for distance education, even more within MUREE project, since Jordan students are not able to use face-to-face traditional laboratories due to their different physical location. These remote laboratories can be employed by instructors within their virtual classrooms, so that students can carry out their on-line experiments from anywhere and at anytime. For MUREE project, remote laboratories are seen as pedagogical elements that must be fully-integrated into a the learning/teaching process. Therefore, this work focuses its attentions toward the integration of remote laboratories into Learning Management Systems (LMSs) and, additionally, discusses the advantages and disadvantages of this approach.

The creation and publication of utilities as services (the most widely known being Infrastructure as a Service, IaaS, Platform as a Service, PaaS, and Software as a Service, SaaS) has been a hot topic of research and development for the recent years. They allow easy creation and deployment of infrastructures and applications which increase the versatility and usefulness of the Information Technology (IT) budgets of institutions that implement them. Therefore, this paper proposes the development of Laboratories as a Service (LaaS), which allow users of remote laboratories create versatile experiments adapted to their needs. These will be based on the deconstruction of remote laboratories, creation of clients, and selection of a container. An aeolian laboratory based on the Lego Mindstorms robotic kit is used as an example.

The lack of tracking and storing capabilities for the results of web-based learning activities is an issue that remains unsolved. Transitions or interactions defined by teachers through a set of conditions still require programming skills that stay far beyond the desired final results. In addition to this, authoring tools should be powerful enough to let lecturers generate contents which are high-quality, interactive, and tuned to each student's cognitive preferences and progress. Availability and processing capabilities, or motivation, relevance, etc., must also be aspects to address in this context. For these reasons, this paper aims to review the existing web application authoring toolkits focusing on distance education. In particular, we analize their main features, requirements and issues, as well as the most promising areas for future improvemenst in this field.

The evolution of Web 3.0 and consequently eLearning 3.0 have demanded a major change of the way learning objects are provided and implemented. It is foreseen that learning objects in eLearning 3.0 should be interoperable and easily discovered, and will represent any kind of virtual or physical object. Several approaches attempted to implement remote Laboratories as a Service (LaaS) in order to achieve such interoperability and to allow their integration into heterogeneous educational systems for pedagogical purposes and for more convenience, as well as, their coupling and mashing up with other learning objects in order to yield a scaffold and rich educational environment. This paper provides a broad study on the available middleware solutions for service-oriented remote laboratories implementation, emphasizing the pros and cons of each and the upcoming challenges in developing service-oriented remote laboratories.

Development of virtual/remote laboratories is a common task involved in the design of course's assessments (in engineering disciplines). The deployment of these laboratories must manage several aspects related to the real use of them, as user's authorization and access, tracking of usage information, loading/saving data from experiments and so on. These features must be implemented by developers in a particular way for each laboratory. This paper shows how these services can be used automatically, with no development, using the RELATED management services. The deployment task consists of making a connector to the RELATED framework in order to consume these services. A full example is shown, describing the steps followed to integrate a previously developed low cost laboratory (based on a Lego system).

Educational remote laboratories are software and hardware tools that allow students to remotely access real equipment located in universities as if they were in a hands-on-lab session. Federations of these remote laboratories have existed for years, focused on allowing two universities to share their equipment. Additionally, the integration of remote laboratories in Learning Tools-LT-(Learning Management Systems, Content Management Systems or Personal Learning Environments) has been achieved in the past in order to integrate remote laboratories as part of the learning curricula, being part of the practice exercises or even as a tool of evaluation. An cross-institutional initiative called gateway4labs has been created to perform this integration through federation protocols. In this contribution, this initiative adds support for OpenSocial as a new protocol for Learning Tools (in particular, for EPFL Graasp), as well as for the iLab Shared Architecture (in addition to WebLab-Deusto and UNR FCEIA laboratories already supported). Supporting OpenSocial opens a number of new technical and organizational challenges since public labs should be supported without registering students, teachers or schools. The focus of this contribution is to show these challenges and how they are tackled in the proposed open source implementation.

Nowadays, a large number of online laboratories are available and deployed throughout the world. Most of them are sponsored by universities and often only used by their home institution, because they tend to be dedicated to the needs of their creators. However, in many cases these labs provide successful experiences and other teachers would wish to incorporate them into their classroom activities and to embrace Learning-by-Experience methodologies. Currently, they cannot do that effectively without first tailoring the lab experience to their own teaching style and the educational background of their students. This can be a complicated affair, due the closed nature of many online labs and the steep learning curve of the few tools that allow customization. This paper describes the work on an application builder, named App Composer, which aims to make it easy for school teachers to create their own customized applications from existing ones. Different customization levels are provided, each one powered by a different type of composer. For instance, teachers will be able to easily translate an application into a new language, or to start with a template and create a customized version of an application. The App Composer makes use of Graasp and Open Social - a widely-used, interoperable and open technology.

Learning Management Systems (LMSs) are Information Technology (IT) platforms whose aim is the support of learning processes. Among the features a LMS provides we can find communication tools such as forums, evaluation tools such as questionnaires, or grade tools. A number of LMSs exist, both open-source (such as .LRN, Sakai, or Moodle) and proprietary solutions (such as Blackboard). Among the wide variety of existing LMSs, a crucial question that arises is, which LMS is more suitable for my necessities? This paper strives at answering this question based on a performance evaluation that compares three of the most widely used open-source LMS, namely .LRN, Sakai, and Moodle.

On-line laboratories (virtual or remote) are widely used in experimental engineering subjects as part of the learning process. In order to develop these laboratories, a development framework called RELATED (Remote Laboratories exTendED) is used by the Communications and Control System Department of the Spanish University for Distance Education of Spain (UNED). This framework defines a structured and methodological development procedure, allowing the students the generation of their own laboratories. Once the laboratory is developed (based in its components), students have to configure their own computing resources in order to make their labs available. However, several problems must be faced by students in the “deployment” of their labs: network configuration, hardware availability, and so on. So, in order to solve these problems, an automatic system based on cloud providers is defined to allow students having their own cloud network/resources for their developed labs. This system simplifies the lab deployment and avoids common errors/mistakes in the development of laboratories with RELATED.

This paper presents a new educational system to automatically adapt the evaluation activities to the students' needs in the context of Higher Engineering Education. As an example, a subject focused on the configuration of network services has been chosen to implement our proposal. Therefore, the system will be able to guide each student through the learning process based on his/her particular knowledge-level. In addition to this, specific techniques are needed to dynamically evolve the system depending on the students' progress. In our case, this is analyzed by using data mining techniques. Finally, we show survey results, which illustrate the ease of use and usefulness of the system.

Merging a Learning Management System (LMS) and a social network has social advantages for students. Among others, students can show off their achievements among their acquaintances, which in turn improves their social leadership and self-esteem. This is specially true in the case of distance learning, where there are no physical interactions between students, so their student life is totally isolated from their everyday life (e.g. students do not go out together, since they may live in different cities or even countries although they study the same subjects). All these points suggest that the use of social networks for distance teaching has clear advantages that can be harnessed. In order to achieve the aforementioned benefits, we are working on integrating a commonly used LMS, Moodle, into one of the most widely-used social network, Facebook. This integration allows students and faculty to use Facebook as a communication tool that improves the learning process and social life of students in the ways presented before.

Nowadays, the use of remote laboratories is a common feature in order to get students involved in practical experiences. This is particularly important in distance learning environment. The regular practice is to develop a particular laboratory, required by the student competence's curricula. Usually, this involves a lack of methodology or a standard procedure so, new developments must be done to include new laboratories, additionally so many “manual” procedures are required. RELATED (REmote LABoratory Extended) it's used to solve these issues. In this paper, a practical experience of the structured development process of a virtual laboratory, using RELATED framework, will be presented from the point of view of students. This virtual laboratory is a simple signal generator. This signal generator will be used to generate a reference position for the height of the ball of an electromagnetic levitation system in a control experiment. Finally, responses of surveys made by students will be presented, in order to get satisfaction results of using/development of virtual/remote laboratories using RELATED.

The development of cloud technologies allows the implementation of scalable, versatile, and customized systems, constructed on-demand. This allows more efficient use of computing resources, improving the revenue of the system and enhancing the Quality of Service (QoS) received by users while minimizing the power consumption of the machines. Several research works conclude that in order to efficiently manage a cloud-based infrastructure (meaning, deploy computing resources when needed without affecting negatively to the QoS perceived by users), accurate predictions on the load of machines should be made. Thanks to this, resources can be ready to use when users need them, and shutdown when they are not needed - thus reducing the power consumption and enhancing the revenue of the system. This paper presents algorithms to perform forecasts of the load of machines based on Exponential Smoothing (ES), so that the machines of the technological infrastructure of our University can be efficiently managed. Furthermore, algorithms to perform monitoring and provision of resources based on load forecasts are presented. The usefulness of these algorithms is illustrated by means of a use case based on the e-learning facilities of our University. This use case shows that thanks to the use of cloud technologies, enhanced with the developed algorithms for load forecasting and provision of resources, better use of resources and lower power consumption can be achieved, without affecting the QoS received by user.

Use of multimedia services has become very important in learning process. This is due to the traditional approach of document based assignments not provide any feature which allows to evaluate some basic competences (oral communications skill, for example). In this paper, the UNED (Spanish University for Distance Education) solution will be presented focusing on user's interaction: lecturers and students. In the first case, the lecturer has to define a task and propose a solution (available only at the end of task availability). This solution is also video/audio based, so the student can learn about it and compare it with his/her solution. The student has several attempts, in order to try better responses and decides which of these attempts will be selected as final response. The whole interaction schema will be presented and how is integrated in the e-learning platform as a usual task (like document based tasks). This integration permits the use of all features of the platform evaluation tool, simplifying the evaluation process and tasks grading (no special procedures are required).

This work presents a new system for the automatic assessment of practical activities in the context of the EHEA. A subject focused on the configuration of network services has been chosen to implement the new automatic evaluation platform. Unlike traditional platforms based on theoretical contents to evaluate the students' knowledge, the proposed system is able to immediately evaluate students' practical skills and, additionally, provide them with feedback about the correctness of their activities. Therefore, faculty can dynamically follow the students' progress in order to adjust the learning process to their needs. Since the UNED University serves a large number of students via a distance-learning methodology, the use of scalable assessment platforms within the students' learning process is crucial to keep it working efficiently. Automatic assessment systems are also of particular interest when practical activities are performed within higher engineering courses.

Thanks to the development of cloud technologies, the way how computing is understood has evolved from "computer guided" to "user guided" systems. That is, initially, computers had static software features in which users sharing them had to "fit", but now there is a shift to dynamic systems in which it is the computer which has to fit into the users' needs. This allows more efficient use of computing resources, improving the revenue and enhancing the Quality of Service (QoS) received by users. In order to deploy computing resources when needed without affecting negatively to the QoS perceived by users, accurate predictions on the load of machines should be made. Thanks to this, resources can be ready to use when users need them, and shutdown when they are not needed. This reduces the power consumption and enhances the revenue of the system. This paper presents an algorithm to perform resource provisioning on the machines of the technological infrastructure of our University, so that they can be efficiently managed. This algorithm is based on load forecasts created using Exponential Smoothing.

This work presents a new e-learning architecture for the automatic assessment of practical activities in the context of Higher Engineering Education. As an example, a subject focused on the configuration of network services has been chosen. Unlike traditional platforms based on theoretical contents, the proposed architecture will be able to immediately evaluate students' practical skills and provide them with feedback about the correctness of their activities. Therefore, lecturers will dynamically follow the students' progress.

Personal Learning Environments (PLEs) have gained importance over recent years thanks to the wide use of the Web 2.0. An interesting functionality is the creation of customized PLEs, to improve the learning process. This work proposes a new paradigm to create customized PLEs by deconstructing a Virtual Learning Environment (VLE).

Computer performance has significantly increased in recent years and, consequently, communication subsystems have become bottlenecks within systems. To counter this problem, current high-performance distributed systems employ switch-based interconnection networks. In this scenario, after the occurrence of a topological change, a management mechanism must reestablish connectivity between network devices. This requires performing a network reconfiguration, which consists in updating the routing function. The main challenge involved in network reconfiguration is the reduction of performance degradation during the change assimilation process. As shown in the performance evaluation section, former reconfiguration techniques significantly reduce network service since the application traffic is temporally stopped in order to avoid deadlocks. In addition, current solutions are only designed for networks that use distributed routing. In this paper, we propose and evaluate a first reconfiguration method for source routing networks that does not restrict the injection of packets during the change assimilation process. Without requiring additional network resources, our scheme is able to recover topology connectivity maintaining network throughput.

Overlapping Reconfiguration is currently the most efficient method to reconfigure an interconnection network, but is only valid for systems that apply distributed routing. This paper proposes a solution which enables utilization of Overlapping Reconfiguration in a source routed environment. We demonstrate how a synchronized injection of tokens has a significant impact on the performance of the method. Furthermore, we propose and evaluate an optimization of the original algorithm that reduces (and in some cases even eliminates) performance issues caused by the token forwarding regime, such as increased latency and decreased throughput.

Recent years, computer performance has been significantly increased. As a consequence, data I/O systems have become bottlenecks within systems. In order to alleviate this problem, Advanced Switching Interconnect was proposed as a new standard for future high-performance interconnects. The Advanced Switching specification establishes a fabric management infrastructure, which is in charge of maintaining the connectivity between fabric endpoints after fabric initialization and each time a topological change takes place. Once the change has been detected, a fabric manager must discover the new topology, obtain a new set of valid fabric paths, and distribute them to the fabric endpoints. This paper evaluates a first implementation for this management mechanism, by analyzing the influence of application traffic on its performance, and the way in which network service can be affected by the change assimilation process.

Advanced switching is a new high-speed industrial standard serial interconnect. It is defined as a switching fabric architecture based on the PCI express technology. The advanced switching specification establishes a management infrastructure which maintains the fabric operation. The topology discovery process is triggered after fabric initialization and every time a topological change is detected. The information gathered by this process is used to build a set of paths between fabric endpoints. This work analyzes the performance of several possible implementations for this management task.

The advanced switching technology is a new high-performance standard serial inter-connect. Its specification establishes a management infrastructure in charge of maintaining the fabric operation after the occurrence of a topological change. When the change is detected, the management mechanism discovers the new topology, obtains a set of fabric paths, and finally distributes them to the endpoints. The main contribution of this paper is a completely functional mechanism that implements all these management tasks involved in handling topological changes in source routing switched networks, as advanced switching. We also analyze the behavior of this first proposal, in order to identify its bottlenecks and define future improvements.