925 resultados para Mobile-learning
Resumo:
In this paper, we intend to present some research carried out in a state Primary school, which is very well-equipped with ICT resources, including interactive whiteboards. The interactive whiteboard was used in the context of a Unit of Work for English learning, based on a traditional oral story, ‘Jack and the Beanstalk’. It was also used for reinforcing other topics like, ‘At the beach’, ‘In the city’, ‘Jobs’, etc. An analysis of the use of the digital board, which includes observation records as well as questionnaires for teachers and pupils, was carried out.
Resumo:
Mobile applications are becoming increasingly more complex and making heavier demands on local system resources. Moreover, mobile systems are nowadays more open, allowing users to add more and more applications, including third-party developed ones. In this perspective, it is increasingly expected that users will want to execute in their devices applications which supersede currently available resources. It is therefore important to provide frameworks which allow applications to benefit from resources available on other nodes, capable of migrating some or all of its services to other nodes, depending on the user needs. These requirements are even more stringent when users want to execute Quality of Service (QoS) aware applications, such as voice or video. The required resources to guarantee the QoS levels demanded by an application can vary with time, and consequently, applications should be able to reconfigure themselves. This paper proposes a QoS-aware service-based framework able to support distributed, migration-capable, QoS-enabled applications on top of the Android Operating system.
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In this paper we propose a framework for the support of mobile application with Quality of Service (QoS) requirements, such as voice or video, capable of supporting distributed, migration-capable, QoS-enabled applications on top of the Android Operating system.
Resumo:
This paper provides a longitudinal, empirical view of the multifaceted and reciprocal processes of organizational learning in a context of self-managed teams. Organizational learning is seen as a social construction between people and actions in a work setting. The notion of learning as situated (Brown & Duguid 1989, Lave& Wenger 1991, Gherardi & al. 1998, Easterby-Smith & Araujo 1999, Abma 2003) opens up the possibility for placing the focus of research on learning in the community rather than in individual learning processes. Further, in studying processes in their social context, we cannot avoid taking power relations into consideration (Contu & Willmott 2003). The study is based on an action research with a methodology close to the ‘democratic dialogue’ presented by Gustavsen (2001). This gives a ground for research into how the learning discourse developed in the case study organization over a period of 5 years, during which time the company abandoned a middle management level of hierarchy and the teams had to figure out how to work as self-managed units. This paper discusses the (re)construction of power relations and its role in organizational learning. Power relations are discussed both in vertical and horizontal work relations. A special emphasis is placed on the dialectic between managerial aims and the space for reflection on the side of employees. I argue that learning is crucial in the search for the limits for empowerment and that these limits are negotiated both in actions and speech. This study unfolds a purpose-oriented learning process, constructing an open dialogue, and describes a favourable context for creative, knowledge building communities.
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Modelling the fundamental performance limits of wireless sensor networks (WSNs) is of paramount importance to understand the behaviour of WSN under worst case conditions and to make the appropriate design choices. In that direction, this paper contributes with a methodology for modelling cluster tree WSNs with a mobile sink. We propose closed form recurrent expressions for computing the worst case end to end delays, buffering and bandwidth requirements across any source-destination path in the cluster tree assuming error free channel. We show how to apply our theoretical results to the specific case of IEEE 802.15.4/ZigBee WSNs. Finally, we demonstrate the validity and analyze the accuracy of our methodology through a comprehensive experimental study, therefore validating the theoretical results through experimentation.
Resumo:
This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.
Resumo:
With the advent of wearable sensing and mobile technologies, biosignals have seen an increasingly growing number of application areas, leading to the collection of large volumes of data. One of the difficulties in dealing with these data sets, and in the development of automated machine learning systems which use them as input, is the lack of reliable ground truth information. In this paper we present a new web-based platform for visualization, retrieval and annotation of biosignals by non-technical users, aimed at improving the process of ground truth collection for biomedical applications. Moreover, a novel extendable and scalable data representation model and persistency framework is presented. The results of the experimental evaluation with possible users has further confirmed the potential of the presented framework.
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Dynamical systems theory is used as a theoretical language and tool to design a distributed control architecture for teams of mobile robots, that must transport a large object and simultaneously avoid collisions with (either static or dynamic) obstacles. Here we demonstrate in simulations and implementations in real robots that it is possible to simplify the architectures presented in previous work and to extend the approach to teams of n robots. The robots have no prior knowledge of the environment. The motion of each robot is controlled by a time series of asymptotical stable states. The attractor dynamics permits the integration of information from various sources in a graded manner. As a result, the robots show a strikingly smooth an stable team behaviour.
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Dynamical systems theory is used here as a theoretical language and tool to design a distributed control architecture for a team of two mobile robots that must transport a long object and simultaneously avoid obstacles. In this approach the level of modeling is at the level of behaviors. A “dynamics” of behavior is defined over a state space of behavioral variables (heading direction and path velocity). The environment is also modeled in these terms by representing task constraints as attractors (i.e. asymptotically stable states) or reppelers (i.e. unstable states) of behavioral dynamics. For each robot attractors and repellers are combined into a vector field that governs the behavior. The resulting dynamical systems that generate the behavior of the robots may be nonlinear. By design the systems are tuned so that the behavioral variables are always very close to one attractor. Thus the behavior of each robot is controled by a time series of asymptotically stable states. Computer simulations support the validity of our dynamic model architectures.
Resumo:
The aim of this essay is to discuss the thesis of the German Sociologist Günter Burkhart that in modern societies a phenomenon appeared which he calls “handymania”, an excessive and nearly addictive use of the mobile phones especially from adolescents. After a short overview about the history of the cell phone, I will relate this development to Jürgen Habermas “theory of communicative action”, more precisely to his diagnosis of a pathological society (“lifeworld”) to find out if the “handymania” could be one expression of it. Adjacent I will present social-psychological theories from E.H.Erikson and Tilmann Habermas to ascertain whether juveniles could really be a high-risk group for this kind of addiction. I will focus on the ability to communicate in an Habermasian way that could be seriously harmed by the unregulated usage of cell phones.
Resumo:
Although the computational power of mobile devices has been increasing, it is still not enough for some classes of applications. In the present, these applications delegate the computing power burden on servers located on the Internet. This model assumes an always-on Internet connectivity and implies a non-negligible latency. The thesis addresses the challenges and contributions posed to the application of a mobile collaborative computing environment concept to wireless networks. The goal is to define a reference architecture for high performance mobile applications. Current work is focused on efficient data dissemination on a highly transitive environment, suitable to many mobile applications and also to the reputation and incentive system available on this mobile collaborative computing environment. For this we are improving our already published reputation/incentive algorithm with knowledge from the usage pattern from the eduroam wireless network in the Lisbon area.
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Worldwide competitiveness poses enormous challenges on managers, demanding a continuous quest to increase rationality in the use of resources. As a management philosophy, Lean Manufacturing focuses on the elimination of activities that do not create any type of value and therefore are considered waste. For companies to successfully implement the Lean Manufacturing philosophy it is crucial that the human resources of the organization have the necessary training, for which proper tools are required. At the same time, higher education institutions need innovative tools to increase the attractiveness of engineering curricula and develop a higher level of knowledge among students, improving their employability. This paper describes how Lean Learning Academy, an international collaboration project between five EU universities and five companies, from SME to Multinational/Global companies, developed and applied an innovative training programme for Engineers on Lean Manufacturing, a successful alternative to the traditional teaching methods in engineering courses.
Resumo:
Remote experimentation laboratories are systems based on real equipment, allowing students to perform practical work through a computer connected to the internet. In engineering fields lab activities play a fundamental role. Distance learning has not demonstrated good results in engineering fields because traditional lab activities cannot be covered by this paradigm. These activities can be set for one or for a group of students who work from different locations. All these configurations lead to considering a flexible model that covers all possibilities (for an individual or a group). An inter-continental network of remote laboratories supported by both European and Latin American institutions of higher education has been formed. In this network context, a learning collaborative model for students working from different locations has been defined. The first considerations are presented.
Resumo:
In this paper the authors intend to demonstrate the utilization of remote experimentation (RE) using mobile computational devices in the Science areas of the elementary school, with the purpose to develop practices that will help in the assimilation process of the subjects taught in classroom seeking to interlink them with the daily students? activities. Allying mobility with RE we intend to minimize the space-temporal barrier giving more availability and speed in the information access. The implemented architecture utilizes technologies and freely distributed softwares with open code resources besides remote experiments developed in the Laboratory of Remote Experimentation (RExLab) of Federal University of Santa Catarina (UFSC), in Brazil, through the physical computation platform of the ?open hardware of construction of our own. The utilization of open code computational tools and the integration of hardware to the 3D virtual worlds, accessible through mobile devices, give to the project an innovative face with a high potential for reproducibility and reusability.
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This paper presents a collaborative virtual learning environment, which includes technologies such as 3D virtual representations, learning and content management systems, remote experiments, and collaborative learning spaces, among others. It intends to facilitate the construction, management and sharing of knowledge among teachers and students, in a global perspective. The environment proposes the use of 3D social representations for accessing learning materials in a dynamic and interactive form, which is regarded to be closer to the physical reality experienced by teachers and students in a learning context. A first implementation of the proposed extended immersive learning environment, in the area of solid mechanics, is also described, including the access to theoretical contents and a remote experiment to determine the elastic modulus of a given object.These instructions give you basic guidelines for preparing camera-ready papers for conference proceedings. Use this document as a template if you are using Microsoft Word 6.0 or later. Otherwise, use this document as an instruction set. The electronic file of your paper will be formatted further. Define all symbols used in the abstract. Do not cite references in the abstract.