Bilingual subtitles for second-language acquisition and application to engineering education as learning pills

This paper introduces the concept of bilingual subtitles, a kind of captioning in which a pair ofsubtitles (in the mother tongue, L1, and second language, L2) is shown at the same time on the screen. The aim ofdual subtitles is to help the final user in different learning processes, due to the fact that several capacities(listening, reading, and matching) are exercised at the same time by the learner while watching dual-captionedmedia. The contribution of this paper is threefold. First, it presents DualSub, an open source desktop tool aimed tocreate bilingual subtitles. Second, a descriptive study was designed and executed to evaluate the extent to whichbilingual subtitles are perceived by final users in the incidental vocabulary knowledge of a second language. Third,an experimental case study in which dual subtitles were used in the engineering education arena was carried out.The results of these surveys confirm that bilingual subtitles are perceived as useful in the different dimensions ofthe incidental vocabulary learning process (form, meaning, use) and are also helpful when applied to theeducational domain (deliberate learning)

Learning carbohydrate digestion and insulin absorption curves using blood glucose level prediction and deep learning models

This article belongs to the Section Biomedical Sensors.Type 1 diabetes is a chronic disease caused by the inability of the pancreas to produce insulin. Patients suffering type 1 diabetes depend on the appropriate estimation of the units of insulin they have to use in order to keep blood glucose levels in range (considering the calories taken and the physical exercise carried out). In recent years, machine learning models have been developed in order to help type 1 diabetes patients with their blood glucose control. These models tend to receive the insulin units used and the carbohydrate taken as inputs and generate optimal estimations for future blood glucose levels over a prediction horizon. The body glucose kinetics is a complex user-dependent process, and learning patient-specific blood glucose patterns from insulin units and carbohydrate content is a difficult task even for deep learning-based models. This paper proposes a novel mechanism to increase the accuracy of blood glucose predictions from deep learning models based on the estimation of carbohydrate digestion and insulin absorption curves for a particular patient. This manuscript proposes a method to estimate absorption curves by using a simplified model with two parameters which are fitted to each patient by using a genetic algorithm. Using simulated data, the results show the ability of the proposed model to estimate absorption curves with mean absolute errors below 0.1 for normalized fast insulin curves having a maximum value of 1 unit.This work was supported in part by the project "ANALISIS EN TIEMPO REAL DE SENSORES SOCIALES Y ESTIMACION DE RECURSOS PARA TRANSPORTE MULTIMODAL BASADA EN APRENDIZAJE PROFUNDO" MaGIST-RALES, funded by the Spanish Agencia Estatal de Investigación (AEI, doi 10.13039/501100011033) under grant PID2019-105221RB-C44 /AEI/10.13039/501100011033. This work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M21), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation)

Challenges of End-to-End Testing with Selenium WebDriver and How to Face Them: A Survey

Proceeding of 2023 IEEE 16th International Conference on Software Testing, Verification and Validation (ICST 2023), 16-20 April 2023, Dublin, Ireland.Modern web applications are complex and used for tasks of primary importance, so their quality must be guaranteed at the highest levels. For this reason, testing techniques (e.g., end-to-end) are required to validate the overall behavior of web applications. One of the most popular tools for testing web applications is Selenium WebDriver. Selenium WebDriver automates the browser to mimic real user actions on the web.While Selenium has made testing easier for many Teams worldwide, it still has its share of challenges. To better understand the challenges and the corresponding solutions adopted we decided to undertake a personal opinion survey from the industry (in total with 78 highly skilled participants) with a focus on the Selenium ecosystem.The results allow understanding which challenges are consid-ered more relevant by professionals in their daily practice and which are the techniques, approaches, and tools they adopt to face them. Therefore, this study is useful to (1) practitioners interested in understanding how to solve the problems they face every day and (2) researchers interested in proposing innovative solutions to problems having a solid industrial impact.This work was partially supported in part by the Ministerio de Ciencia e Innovación-Agencia Estatal de Investigación (10.13039/501100011033) through the H2O Learn project under Grant PID2020-112584RB-C31, and in part by the Madrid Regional Government through the e-Madrid-CM Project under Grant S2018/TCS-4307

A Survey of the Selenium Ecosystem

Selenium is often considered the de-facto standard framework for end-to-end web testing nowadays. It allows practitioners to drive web browsers (such as Chrome, Firefox, Edge, or Opera) in an automated fashion using different language bindings (such as Java, Python, or JavaScript, among others). The term ecosystem, referring to the open-source software domain, includes various components, tools, and other interrelated elements sharing the same technological background. This article presents a descriptive survey aimed to understand how the community uses Selenium and its ecosystem. This survey is structured in seven categories: Selenium foundations, test development, system under test, test infrastructure, other frameworks, community, and personal experience. In light of the current state of Selenium, we analyze future challenges and opportunities around it.This work has been supported by the European Commission under the H2020 project "MICADO" (GA-822717), by the Government of Spain through the project "BugBirth" (RTI2018-101963-B-100), by the Regional Government of Madrid (CM) through the project "EDGEDATA-CM" (P2018/TCS-4499) cofunded by FSE & FEDER, and by the project "Analytics using sensor data for FlatCity" (MINECO/ERDF, EU) funded in part by the Spanish Agencia Estatal de Investigación (AEI) under Grant TIN2016-77158-C4-1-R and in part by the European Regional Development Fund (ERDF)

Enhancing Web Applications Observability through Instrumented Automated Browsers

In software engineering, observability is the ability to determine the current state of a software system based on its external outputs or signals such as metrics, logs, or traces. Web engineers rely on the web browser console as the primary tool to monitor the client-side of web applications during end-to-end tests. However, this is a manual and time-consuming task due to the different browsers available. This paper presents BrowserWatcher, an open-source browser extension providing cross-browser capabilities to observe web applications and automatically gather browser console logs in different browsers (e.g., Chrome, Firefox, or Edge). We have leveraged this extension to conduct an empirical study analyzing the browser console of the top-50 public websites manually and automatically. The results show that BrowserWatcher gathers all the well-known log categories such as console or error traces. It also reveals that each web browser additionally includes other types of logs, which differ among browsers, thus providing distinct pieces of information for the same website.This work was partially supported in part by the Ministerio de Ciencia e Innovación-Agencia Estatal de Investigación, Spain (10.13039/501100011033) through the H2O Learn project under Grant PID2020-112584RB-C31, in part by the Madrid Regional Government through the e-Madrid-CM Project, Spain under Grant S2018/TCS-4307, and in part supported by the Comunidad de Madrid and Universidad Politécnica de Madrid, Spain through the V-PRICIT Research Programme Apoyo a la realización de Proyectos de I+D para jóvenes investigadores UPM-CAM, under Grant APOYOJOVENES-QINIM8-72-PKGQ0J. Funding for Article Processing Charge (APC): Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2023)

Automated Driver Management for Selenium WebDriver

Selenium WebDriver is a framework used to control web browsers automatically. It provides a cross-browser Application Programming Interface (API) for different languages (e.g., Java, Python, or JavaScript) that allows automatic navigation, user impersonation, and verification of web applications. Internally, Selenium WebDriver makes use of the native automation support of each browser. Hence, a platform-dependent binary file (the so-called driver) must be placed between the Selenium WebDriver script and the browser to support this native communication. The management (i.e., download, setup, and maintenance) of these drivers is cumbersome for practitioners. This paper provides a complete methodology to automate this management process. Particularly, we present WebDriverManager, the reference tool implementing this methodology. WebDriverManager provides different execution methods: as a Java dependency, as a Command-Line Interface (CLI) tool, as a server, as a Docker container, and as a Java agent. To provide empirical validation of the proposed approach, we surveyed the WebDriverManager users. The aim of this study is twofold. First, we assessed the extent to which WebDriverManager is adopted and used. Second, we evaluated the WebDriverManager API following Clarke’s usability dimensions. A total of 148 participants worldwide completed this survey in 2020. The results show a remarkable assessment of the automation capabilities and API usability of WebDriverManager by Java users, but a scarce adoption for other languages.This work has been been supported in part by the "Análisis en tiempo Real de sensores sociALes y EStimación de recursos para transporte multimodal basada en aprendizaje profundo" project (MaGIST-RALES), funded by the Spanish Agencia Estatal de Investigación (AEI, doi 10.13039/501100011033) under grant PID2019-105221RB-C44. This work also received partial support from FEDER/Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación through project Smartlet (TIN2017-85179-C3-1-R), and from the eMadrid Network, which is funded by the Madrid Regional Government (Comunidad de Madrid) with grant No. S2018/TCS-4307

Authentication, Authorization, and Accounting in WebRTC PaaS Infrastructures: The Case of Kurento

Server infrastructures for Web Real-Time Communications (WebRTC) are useful for creating rich applications. Developers commonly use them to access capabilities such as group communications, archiving, and transcoding. Kurento is an open source project that provides a WebRTC media server and a platform as a service (PaaS) cloud built on top. The authors present its API and analyze different security models for it, investigating the suitability of using simple access control lists and capability-based security schemes to provide authorization.This work is supported by the European Commission under projects FI-WARE FP7-2011-ICT-FI, GA-285248, and NUBOMEDIA FP7-ICT-2013-1.6, GA-610576

The Effects of the COVID-19 Pandemic on the Digital Competence of Educators

This article belongs to the Special Issue: The Effects of the COVID-19 Pandemic on the Digital Competence of EducatorsThe COVID-19 pandemic is having an undeniable impact on all aspects of society. Regarding teaching and learning activities, most educational institutions suspended in-person instruction and moved to remote emergency teaching during the lockdown of March and April 2020. Although many countries progressively re-opened their educational systems, online and hybrid education became a common practice aimed at reducing the spread of the COVID-19 disease. This disruption has caused an unprecedented acceleration in the digitalization of teaching and learning. Teaching professionals have been forced to develop their digital competence quickly, achieving mastery in the management of information, creation of audiovisual content, and use of technology to keep their students engaged. This Special Issue (SI) presents contributions regarding adopting distance learning strategies, experiences, or lessons learned in this domain.Acknowledgments: The authors acknowledge PROF-XXI, which is an Erasmus+ Capacity Building in the Field of Higher Education project funded by the European Commission (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP).Publicad

System Virtualization Tools for Software Development

The configuration complexity of preproduction sites coupled with access-control mechanisms often impede the software development life cycle. Virtualization is a cost-effective way to remove such barriers and provide a test environment similar to the production site, reducing the burden in IT administrators. An Eclipse-based virtualization tool framework can offer developers a personal runtime environment for launching and testing their applications. The authors have followed a model-driven architecture (MDA) approach that integrates best-of-breed virtualization technologies, such as Xen and VDE.ITECBAN is an IT innovation project partially funded by CENIT (a Spanish public R&D program). We're grateful to MITYC (Ministerio de Industria, Turismo y Comercio) and CDTI (Centro para el Desarrollo Tecnológico e Industrial) for supporting ITECBAN through CENIT

A systematic review on cloud testing

A systematic literature review is presented that surveyed the topic of cloud testing over the period (2012-2017). Cloud testing can refer either to testing cloud-based systems (testing of the cloud), or to leveraging the cloud for testing purposes (testing in the cloud): both approaches (and their combination into testing of the cloud in the cloud) have drawn research interest. An extensive paper search was conducted by both automated query of popular digital libraries and snowballing, which resulted into the final selection of 147 primary studies. Along the survey a framework has been incrementally derived that classifies cloud testing research along six main areas and their topics. The paper includes a detailed analysis of the selected primary studies to identify trends and gaps, as well as an extensive report of the state of art as it emerges by answering the identified Research Questions. We find that cloud testing is an active research field, although not all topics have received so far enough attention, and conclude by presenting the most relevant open research challenges for each area of the classification framework.This paper describes research work mostly undertaken in the context of the European Project H2020 731535: ElasTest. This work has also been partially supported by: the Italian MIUR PRIN 2015 Project: GAUSS; the Regional Government of Madrid (CM) under project Cloud4BigData (S2013/ICE-2894) cofunded by FSE & FEDER; and the Spanish Government under project LERNIM (RTC-2016-4674-7) cofunded by the Ministry of Economy and Competitiveness, FEDER & AEI