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.

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.

This work deals with the teaching of the innovative technology, named cloud computing, using the Web of Things (WoT) platform model based on web services. These services are designed and programmed by the students to handle embedded hardware devices (things) on Internet. The course is carried out within a makerspace where our students can take advantage of valuable on-line tools which are available in a collaborative learning environment. The introduction of these innovative technological elements improves the students' interest and engagement leading to achieve better learning results.

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).

In the field of Engineering Education, performing practical experiments is essential, as an accompaniment to magisterial classes, in order to enforce theoretical and practical concepts. This task is much more difficult in distance education since students attend mainly to virtual classes. Therefore, the use of remote laboratories can help minimize this inconvenient. In addition, remote laboratories may help teachers to prepare suitable evaluation on-line experiments, in similar conditions if students were physically in the real laboratory, on the other hand, remote laboratories can be a good complement to real experimentation, as a preparatory step, preparing them to face similar tasks in the immediate future. Accordingly, the principal objective of the European TEMPUS project entitled "Modernizing Undergraduate Renewable Energy Education: EU Experience for Jordan", which supports this work is the development, integration, accreditation, and evaluation of a renewable energy course in the context of engineering degrees from several universities in Jordan. This project follows the guidelines proposed in the Bologna process, and considers the previous experimentation with low-cost renewable energy equipment in order to allow us to study the best approximation of remote laboratories. This is a previous step before addressing this task with complex and expensive equipment.

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.

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.

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.

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.

Web services secure conversation extends Web services trust to provide mechanisms for establishing security contexts. A security context is an abstract concept that refers an authenticated state where the participants have derived secure session keys for multiples request/response exchanges. In this paper we model this protocol with the HLPSL language and we analyse it with the AVISPA toolbox.

In this paper, a formal analysis of a security protocol in the field of wireless sensor networks is presented. Sensor network encryption protocol (SNEP) describes basic primitives for providing confidentiality, authentication between two nodes, data integrity and weak message freshness in a wireless sensor network. It was designed as base component of security protocols for sensor networks (SPINS). SNEP is modelled in two scenarios using the high-level formal language HLPSL, and verified using the model checking tool Avispa, where two main security properties are checked: authenticity and confidentiality of relevant messages components. The first case is the communication between the base station and networks nodes in order to retrieve node confidential information. The second case is a key distribution protocol in a sensor network using SNEP for securing messages. As a result of this analysis, one attack have been found: a false request message from an intruder. In that case, the intruder impersonates the base station and creates false requests. This way, the intruder may obtain confidential data from a node in the network. A solution to this attack is proposed in the paper.