Mobile learning is considered the next step of online learning by incorporating mobility as a key requirement. Indeed, the current wide spread of mobile devices and wireless technologies brings an enormous potential t...
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Mobile learning is considered the next step of online learning by incorporating mobility as a key requirement. Indeed, the current wide spread of mobile devices and wireless technologies brings an enormous potential to e-learning, in terms of ubiquity, pervasiveness, personalization, flexibility, and so on. For this reason, Mobile Learning is attracting significant research efforts covering a fairly variety of learning settings, from schools and universities to workplaces and cities. This research has evidenced that mobile technology can offer new opportunities for learners to learn inside and beyond the traditional instructor-oriented educational paradigm. However, mobile technologies are still in its infancy and many challenges arise. In this paper we analyze, from both learning and technological perspectives, the development of learning applications using mobile devices. To this end, proxy and proxy less architectures are considered as way to extend traditional virtual campuses with mobile clients. The objective is twofold: to access learning materials and to support learning activities. A prototype of a Virtual Campus is developed using MLE-Moodle -the Mobile Learning module of Moodle. The proposed Virtual Campus enables mobile clients to perform online learning activities and is a step towards achieving the “anytime, anywhere” paradigm.
With the fast development of IT technologies, virtual organizations are more and more present in the current collaborative work and learning activity. For instance, many subjects in virtual distance learning are organ...
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With the fast development of IT technologies, virtual organizations are more and more present in the current collaborative work and learning activity. For instance, many subjects in virtual distance learning are organized as online groups of students, who use groupware tools to complete their learning tasks. In this paper, we address the efficient management of peer groups in JXTA-based P2P systems as a key issue in many P2P applications that use peer group as a unit such as for remote execution of tasks in parallel and distributed applications. From this perspective, we consider peer grouping as the basis in the development of groupware tools in P2P systems. Motivated by the need to support online teams of real virtual campuses, in this work we propose the management of peer groups in JXTA-Overlay, a JXTA-based P2P middleware for the development of P2P applications. To this end, by taking advantage of the peerGroup entity in JXTA library we have designed and implemented a set of primitives as part of JXTA-Overlay aiming to support efficient peer group management. We show the usefulness of using JXTA-Overlay for the development of P2P groupware tools for supporting online teams of students in a virtual campus. Our approach distinguishes from existing studies by using groupware tools in a customized way and tailored to the specific needs of small online teams of students who consider group monitoring and autonomy, confidentiality and security as important concerns. The groupware tools developed for P2P systems include instant messaging and chat rooms, task execution in peer group's resources and file sharing system. We successfully deployed these tools based on JXTA-Overlay in a real P2P network. The experimental results showed the feasibility of our approach when applied to small groups of students who use standard desktop and laptop computers and have rather limited bandwidth of Home Net connections. On the other hand, the JXTA library showed some performance limit
Data mining algorithms have been proved to be useful for the processing of large data sets in order to extract relevant information and knowledge. Such algorithms are also important for analyzing data collected from t...
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Data mining algorithms have been proved to be useful for the processing of large data sets in order to extract relevant information and knowledge. Such algorithms are also important for analyzing data collected from the users' activity users. One family of such data analysis is that of mining of log files of online applications that register the actions of online users during long periods of time. A relevant objective in this case is to study the behavior of online users and feedback the design processes of online applications to provide better usability and adaption to users' preferences. The context of this work is that of a virtual campus in which thousands of students and tutors carry out the learning and teaching activity using online applications. The information stored in log files of virtual campuses tend to be large, complex and heterogeneous in nature. Hence, their mining requires both efficient and intelligent processing and analysis of user interaction data during long-term learning activities. In this paper, we present a bi-clustering algorithm for processing large log data sets from the online daily activity of students in a real virtual campus. Our approach is useful to extract relevant knowledge about user activity such as navigation patterns, activities performed as well as to study time parameters related to such activities. The extracted information can be useful not only to students and tutors to stimulate and improve their experience when interacting with the system but also to the designers and developers of the virtual campus in order to better support the online teaching and learning.
Memetic algorithms with continuous local search methods have arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There ex...
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We present the results of a community detection analysis of the Wikipedia graph. Distinct communities in Wikipedia contain semantically closely related articles. The central topic of a community can be identified usin...
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ISBN:
(纸本)9781605584874
We present the results of a community detection analysis of the Wikipedia graph. Distinct communities in Wikipedia contain semantically closely related articles. The central topic of a community can be identified using PageRank. Extracted communities can be organized hierarchically similar to manually created Wikipedia category structure. Copyright is held by the author/owner(s).
Recently there is increasing interest in university rankings. Annual rankings of world universities are published by QS for the Times Higher Education Supplement, the Shanghai Jiao Tong University, the Higher Educatio...
Recently there is increasing interest in university rankings. Annual rankings of world universities are published by QS for the Times Higher Education Supplement, the Shanghai Jiao Tong University, the Higher Education and Accreditation Council of Taiwan and rankings based on Web visibility by the Cybermetrics Lab at CSIC. In this paper we compare the rankings using a set of similarity measures. For the rankings that are being published for a number of years we also examine longitudinal patterns. The rankings limited to European universities are compared to the ranking of the Centre for science and Technology Studies at Leiden University. The findings show that there are reasonable similarities between the rankings, even though each applies a different methodology. The biggest differences are between the rankings provided by the QS-Times Higher Education Supplement and the Ranking Web of the CSIC Cybermetrics Lab. The highest similarities were observed between the Taiwanese and the Leiden rankings from European universities. Overall the similarities are increased when the comparison is limited to the European universities.
Partial Differential Equations (PDE) are the heart of most simulations in many scientific fields, from Fluid Mechanics to Astrophysics. One the most popular mathematical schemes to solve a PDE is Finite Difference (FD...
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Partial Differential Equations (PDE) are the heart of most simulations in many scientific fields, from Fluid Mechanics to Astrophysics. One the most popular mathematical schemes to solve a PDE is Finite Difference (FD). In this work we map a PDE-FD algorithm called Reverse Time Migration to a GPU using CUDA. This seismic imaging (Geophysics) algorithm is widely used in the oil industry. GPUs are natural contenders in the aftermath of the clock race, in particular for High-performance Computing (HPC). Due to GPU characteristics, the parallelism paradigm shifts from the classical threads plus SIMD to Single Program Multiple Data (SPMD). The NVIDIA GTX 280 implementation outperforms homogeneous CPUs up to 9x (Intel Harpertown E5420) and up to 14x (IBM PPC 970). These preliminary results confirm that GPUs are a real option for HPC, from performance to programmability.
The efficient allocation of jobs to grid resources is indispensable for high performance grid-based applications. The scheduling problem is computationally hard even when there are no dependencies among jobs. Thus, we...
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ISBN:
(纸本)9781424416936
The efficient allocation of jobs to grid resources is indispensable for high performance grid-based applications. The scheduling problem is computationally hard even when there are no dependencies among jobs. Thus, we present in this paper a new tabu search (TS) algorithm for the problem of batch job scheduling on computational grids. We consider the job scheduling as a bi-objective optimization problem consisting of the minimization of the makespan and flowtime. The bi-objectivity is tackled through a hierarchic approach in which makespan is considered a primary objective and flowtime a secondary one. An extensive experimental study has been first conducted in order to fine-tune the parameters of our TS algorithm. Then, our tuned TS is compared versus two well known TS algorithms in the literature (one of them is hybridized with an ant colony optimization algorithm)for the problem. The computational results show that our TS implementation clearly outperforms the compared algorithms. Finally, we evaluated the performance of our TS algorithm on a new set of instances that better fits with the concept of computational grid. These instances are composed of a higher number of -heterogeneous- machines (up to 256) and emulate the dynamic behavior of these systems.
Visual saliency provides a filtering mechanism to focus on a set of interesting areas in the scene, but these mechanisms often overload the computational resources of many computer vision tasks. In order to reduce suc...
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ISBN:
(纸本)9789898111210
Visual saliency provides a filtering mechanism to focus on a set of interesting areas in the scene, but these mechanisms often overload the computational resources of many computer vision tasks. In order to reduce such an overload and improve the computational performance, we propose to exploit the advantages of log-polar vision to detect salient regions with economy of computational resources and quite stable results. Particularly, in this paper we study the application of the entropy-based saliency to log-polar images. Some interesting considerations are presented in reference to the concept of "scale" and the effects of space-variant sampling on scale selection. We also propose a necessary border extension to detect objects present in peripheral areas. The original entropy-based saliency algorithm can be used in log-polar images, but the results show that our adaptations allow to detect with more precision log-polar salient forms because they consider the information redundancy of space-variant sampling. Compared with cartesian, log-polar salient results allow a significant saving of computational resources.
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