Technology and Universal Design for Learning (UDL): experiences in the university context and implications for teacher training

La utilización de tecnologías como medio para garantizar la accesibilidad al aprendizaje y atender la diversidad del alumnado en el aula ha sido objeto de numerosos estudios y experiencias educativas. Entre las propuestas teóricoprácticas más prometedoras se encuentran los enfoques basados en el Diseño Universal (DU), que han trasladado la aplicación de los principios de este movimiento arquitectónico al ámbito educativo. Se presentan brevemente los cuatro enfoques más relevantes a los que ha dado lugar, se describe con más detalle el Diseño Universal para el Aprendizaje (DUA) y el potencial que reconoce a las tecnologías para lograr un curriculum accesible para todos los estudiantes. Se analizan los resultados de su utilización en la formación de maestros y de profesorado universitario, sobre la integración práctica de sus principios en relación al uso de las TIC y en los cursos que se apoyan o imparten en entornos virtuales de aprendizaje. Los diferentes autores valoran positivamente el potencial de este modelo en la mejora de la atención a la diversidad y de la educación de calidad para todos los estudiantes universitarios y su papel estratégico en la formación de docentes.The use of technology as a means of ensuring accessibility to learning and to meet the diversity of students in the classroom has been the subject of numerous studies and educational experiences. Some of the most promising theoretical and practical approaches proposed are based on the Universal Design (UD), which apply the principles of this architectural movement in education. The four most relevant approaches that have led are briefly introduced. The Universal Design for Learning (DUA) framework and its recognized potential to technologies to provide an accessible curriculum for all students are described more in detail. It analyses practical experiences in the training of teachers and faculty about UDL and the use of ICT in the curriculum, and in relation to the integration of its principles in courses that are supported or taught in virtual learning environments. Different authors are positive about the potential of this model in meeting the needs of diversity and improving quality education for all college students, and in considering the strategic role of UDL in teacher and faculty training.Beca Complutense del Amo concedida a la Prof. Carmen Alba Pastor por el Vicerrectorado de Relaciones Internaciones de la UCM para la realización de una estancia en la Escuela de Educación de la Universidad Loyola Marymount de Los Ángeles, California.peerReviewe

A MapReduce Approach to Address Big Data Classification Problems Based on the Fusion of Linguistic Fuzzy Rules

The big data term is used to describe the exponential data growth that has recently occurred and represents an immense challenge for traditional learning techniques. To deal with big data classification problems we propose the Chi-FRBCS-BigData algorithm, a linguistic fuzzy rule-based classification system that uses the MapReduce framework to learn and fuse rule bases. It has been developed in two versions with different fusion processes. An experimental study is carried out and the results obtained show that the proposal is able to handle these problems providing competitive resultsSpanish Government TIN2011-28488Andalusian Research Plans P12-TIC-2958 P11-TIC-7765 P10-TIC-685

A Generic Parallel Pattern Interface for Stream and Data Processing

Current parallel programming frameworks aid developers to a great extent in implementing applications that exploit parallel hardware resources. Nevertheless, developers require additional expertise to properly use and tune them to operate efficiently on specific parallel platforms. On the other hand, porting applications between different parallel programming models and platforms is not straightforward and demands considerable efforts and specific knowledge. Apart from that, the lack of high-level parallel pattern abstractions, in those frameworks, further increases the complexity in developing parallel applications. To pave the way in this direction, this paper proposes GRPPI, a generic and reusable parallel pattern interface for both stream processing and data-intensive C++ applications. GRPPI accommodates a layer between developers and existing parallel programming frameworks targeting multi-core processors, such as C++ threads, OpenMP and Intel TBB, and accelerators, as CUDA Thrust. Furthermore, thanks to its high-level C++ application programming interface and pattern composability features, GRPPI allows users to easily expose parallelism via standalone patterns or patterns compositions matching in sequential applications. We evaluate this interface using an image processing use case and demonstrate its benefits from the usability, flexibility, and performance points of view. Furthermore, we analyze the impact of using stream and data pattern compositions on CPUs, GPUs and heterogeneous configurations.This work has been partially supported by the EU project ICT 644235 “REPHRASE: REfactoring Parallel Heterogeneous Resource-aware Applications” and the Spanish “Ministerio de Economía y Competitividad” under the grant TIN2016-79673-P “Towards Unification of HPC and Big Data Paradigms.

Paving the way towards high-level parallel pattern interfaces for data stream processing

The emergence of the Internet of Things (IoT) data stream applications has posed a number of new challenges to existing infrastructures, processing engines, and programming models. In this sense, high-level interfaces, encapsulating algorithmic aspects in pattern-based constructions, have considerably reduced the development and parallelization efforts of this type of applications. An example of parallel pattern interface is GrPPI, a C++ generic high-level library that acts as a layer between developers and existing parallel programming frameworks, such as C++ threads, OpenMP and Intel TBB. In this paper, we complement the basic patterns supported by GrPPI with the new stream operators Split-Join and Window, and the advanced parallel patterns Stream-Pool, Windowed-Farm and Stream-Iterator for the aforementioned back ends. Thanks to these new stream operators, complex compositions among streaming patterns can be expressed. On the other hand, the collection of advanced patterns allows users to tackle some domain-specific applications, ranging from the evolutionary to the real-time computing areas, where compositions of basic patterns are not capable of fully mimicking the algorithmic behavior of their original sequential codes. The experimental evaluation of the new advanced patterns and the stream operators on a set of domain-specific use-cases, using different back ends and pattern-specific parameters, reports considerable performance gains with respect to the sequential versions. Additionally, we demonstrate the benefits of the GrPPI pattern interface from the usability, flexibility and readability points of view.This work was partially supported by the EU project ICT 644235 “RePhrase: REfactoring Parallel Heterogeneous Resource-Aware Applications” and the project TIN2013-41350-P “Scalable Data Management Techniques for High-End Computing Systems” from the Ministerio de Economía y Competitividad, Spai

An adaptive offline implementation selector for heterogeneous parallel platforms

Heterogeneous Parallel Platforms, Comprising Multiple Processing Units And Architectures, Have Become A Cornerstone In Improving The Overall Performance And Energy Efficiency Of Scientific And Engineering Applications. Nevertheless, Taking Full Advantage Of Their Resources Comes Along With A Variety Of Difficulties: Developers Require Technical Expertise In Using Different Parallel Programming Frameworks And Previous Knowledge About The Algorithms Used Underneath By The Application. To Alleviate This Burden, We Present An Adaptive Offline Implementation Selector That Allows Users To Better Exploit Resources Provided By Heterogeneous Platforms. Specifically, This Framework Selects, At Compile Time, The Tuple Device-Implementation That Delivers The Best Performance On A Given Platform. The User Interface Of The Framework Leverages Two C&#43 &#43 Language Features: Attributes And Concepts. To Evaluate The Benefits Of This Framework, We Analyse The Global Performance And Convergence Of The Selector Using Two Different Use Cases. The Experimental Results Demonstrate That The Proposed Framework Allows Users Enhancing Performance While Minimizing Efforts To Tune Applications Targeted To Heterogeneous Platforms. Furthermore, We Also Demonstrate That Our Framework Delivers Comparable Performance Figures With Respect To Other Approaches.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been partially supported by the Spanish ‘Ministerio de Economía y Competitividad’ under the project grant TIN2016-79637-P ‘Towards Unification of High Performance Computing (HPC) and Big Data Paradigms’ and the EU Projects ICT 644235 ‘RePhrase: REfactoring Parallel Heterogeneous Resource-Aware Applications’ and the FP7 609666 ‘Repara: Reengineering and Enabling Performance And poweR of Applications’

The Influence of Experiential Avoidance and Cognitive Fusion in People Diagnosed With Panic Disorder and Agoraphobia and OCD

La terapia de aceptación y compromiso (ACT) pone de manifiesto la importancia de la evitación experiencial y la fusión cognitiva en un amplio espectro de problemas psicológicos. El propósito de este estudio es conocer cómo influyen la gravedad del trastorno de pánico y agorafobia (TPA) y la gravedad del trastorno obsesivo-compulsivo (TOC) en los niveles de evitación experiencial y fusión cognitiva. Se pretende examinar, además, si existen diferencias entre ambos grupos diagnósticos. La muestra estaba constituida por 67 personas diagnosticadas de TPA y TOC. Se utilizaron los siguientes instrumentos de evaluación: Acceptance and Action Questionnaire (AAQ), Cognitive Fusion Questionnaire (CFQ), Yale-Brown Obsessive Compulsive Scale (Y-BOCS) y Beck Anxiety Inventory (BAI). Los resultados mostraron que los pacientes con TPA presentan mayores niveles de fusión cognitiva frente a los pacientes con TOC, que elevados niveles de ansiedad se asocian a mayor grado de evitación experiencial y que una mayor gravedad del TOC correlacionaba con elevados niveles de evitación experiencial y fusión cognitiva. Se propone una intervención temprana en pacientes con TOC para prevenir la cronicidad del trastorno y se recalca la importancia de los aspectos de aceptación en el trascurso de la terapia de estos pacientesAcceptance and Commitment Therapy (ACT) highlights the importance of experiential avoidance and cognitive fusion in a wide spectrum of psychological problems. The purpose of this study is to know how the severity of panic disorder and agoraphobia (APT) and the severity of obsessive-compulsive disorder (OCD) influence levels of experiential avoidance and cognitive fusion. It is also intended to examine whether there are differences between the two diagnostic groups. The sample consisted of 67 people diagnosed with APT and OCD. The following assessment instruments were used: Acceptance and Action Questionnaire (AAQ), Cognitive Fusion Questionnaire (CFQ), Yale-Brown Obsessive Compulsive Scale (Y-BOCS) and Beck Anxiety Inventory (BAI). The results showed that patients with APT present higher levels of cognitive fusion compared to patients with OCD, that high levels of anxiety are associated with a higher degree of experiential avoidance, and that greater severity of OCD correlated with high levels of experiential avoidance and fusion cognitive. Early intervention in OCD patients is proposed to prevent the chronicity of the disorder and the importance of the acceptance aspects is emphasized in the course of therapy of these patients

Finding parallel patterns through static analysis in C++ applications

Since The 'Free Lunch' Of Processor Performance Is Over, Parallelism Has Become The New Trend In Hardware And Architecture Design. However, Parallel Resources Deployed In Data Centers Are Underused In Many Cases, Given That Sequential Programming Is Still Deeply Rooted In Current Software Development. To Address This Problem, New Methodologies And Techniques For Parallel Programming Have Been Progressively Developed. For Instance, Parallel Frameworks, Offering Programming Patterns, Allow Expressing Concurrency In Applications To Better Exploit Parallel Hardware. Nevertheless, A Large Portion Of Production Software, From A Broad Range Of Scientific And Industrial Areas, Is Still Developed Sequentially. Considering That These Software Modules Contain Thousands, Or Even Millions, Of Lines Of Code, An Extremely Large Amount Of Effort Is Needed To Identify Parallel Regions. To Pave The Way In This Area, This Paper Presents Parallel Pattern Analyzer Tool, A Software Component That Aids The Discovery And Annotation Of Parallel Patterns In Source Codes. This Tool Simplifies The Transformation Of Sequential Source Code To Parallel. Specifically, We Provide Support For Identifying Map, Farm, And Pipeline Parallel Patterns And Evaluate The Quality Of The Detection For A Set Of Different C++ Applications.This work was partially supported by the EU Projects ICT 644235 “RePhrase: Refactoring Parallel Heterogeneous Resource-Aware Applications” and the FP7 609666 “Repara: Reengineering and Enabling Performance and Power of Application

Evolutionary Feature Selection for Big Data Classification: A MapReduce Approach

Nowadays, many disciplines have to deal with big datasets that additionally involve a high number of features. Feature selection methods aim at eliminating noisy, redundant, or irrelevant features that may deteriorate the classification performance. However, traditional methods lack enough scalability to cope with datasets of millions of instances and extract successful results in a delimited time. This paper presents a feature selection algorithm based on evolutionary computation that uses the MapReduce paradigm to obtain subsets of features from big datasets. The algorithm decomposes the original dataset in blocks of instances to learn from them in the map phase; then, the reduce phase merges the obtained partial results into a final vector of feature weights, which allows a flexible application of the feature selection procedure using a threshold to determine the selected subset of features. The feature selection method is evaluated by using three well-known classifiers (SVM, Logistic Regression, and Naive Bayes) implemented within the Spark framework to address big data problems. In the experiments, datasets up to 67 millions of instances and up to 2000 attributes have been managed, showing that this is a suitable framework to perform evolutionary feature selection, improving both the classification accuracy and its runtime when dealing with big data problems.This work is supported by the Research Projects TIN2014-57251-P, P10-TIC-6858, P11-TIC-7765, P12-TIC-2958, and TIN2013-47210-P. D. Peralta and S. Ramírez-Gallego hold two FPU scholarships from the Spanish Ministry of Education and Science (FPU12/04902, FPU13/00047). I. Triguero holds a BOF postdoctoral fellowship from the Ghent University

Towards automatic parallelization of stream processing applications

Parallelizing and optimizing codes for recent multi-/many-core processors have been recognized to be a complex task. For this reason, strategies to automatically transform sequential codes into parallel and discover optimization opportunities are crucial to relieve the burden to developers. In this paper, we present a compile-time framework to (semi) automatically find parallel patterns (Pipeline and Farm) and transform sequential streaming applications into parallel using GrPPI, a generic parallel pattern interface. This framework uses a novel pipeline stage-balancing technique which provides the code generator module with the necessary information to produce balanced pipelines. The evaluation, using a synthetic video benchmark and a real-world computer vision application, demonstrates that the presented framework is capable of producing parallel and optimized versions of the application. A comparison study under several thread-core oversubscribed conditions reveals that the framework can bring comparable performance results with respect to the Intel TBB programming framework.This work was supported in part by the Spanish Ministerio de Economía y Competitividad through the Project Toward Uni cation of HPC and Big Data Paradigms under Grant TIN2016-79637-P and in part by the EU Project RePhrase: REfactoring Parallel Heterogeneous Resource-Aware Applications under Grant ICT 644235