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Social learning: Defining learning objects from social tool

Social learning: Defining learning objects from social tool

引用

IADIS International Conference WWW/Internet 2012, ICWI 2012

作者： Menolli, André Luís Andrade Reinehr, Sheila Malucelli, Andreia Polytechnic School Postgraduate Program in Computer Science Computer Science Department State University of North Paraná PPGIa Pontifical Catholic University of Paraná PUCPR Brazil Postgraduate Program in Computer Science Polytechnic School PPGIa Pontifical Catholic University of Paraná PUCPR Curitiba Brazil

ISBN: (纸本)9789898533098

Social learning is focused for that learning occurs within a social context, in a place where people can work and learn collaboratively. New technologies, such as social network, wiki, blogs, among others social tools, enable collaborative work and are important facilitators of social learning process. These tools provide an easy mechanism for people to communicate and collaborate, which help in the creation of knowledge. However, collaboration is one of the several necessary components for learning. It is important that all acquired knowledge be organized to be reused faster, easily and efficiently. This paper aims to propose an approach to generate learning objects from social tool, in order to organize the information to be easily reused, improving social learning.

关键词： Semantic Web

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Ontology for organizational learning objects based on LOM standard

Ontology for organizational learning objects based on LOM st...

引用

Conferencia Latinoamericana En Informatica (CLEI)

作者： André Luís Andrade Menolli Sheila Reinehr Andreia Malucelli Postgraduate Program in Computer Science-PPGIa Pontifical Catholic University of Paraná Brazil Polytechnic School Postgraduate Program in Computer Science-PPGIa Pontifical Catholic University of Paraná Curitiba Brazil

Organizational learning is an area that helps companies to improve their processes significantly through the reuse of experiences. An area that may help in this way is social learning. Collaborative tools, such as social network and wiki, enable collaborative work and are important facilitators of social learning process. However, collaboration is one of the several necessary components for learning. Therefore, it is important that all acquired knowledge be organized to be reused faster, easily and efficiently. Therefore, we propose using learning objects to organize the content inserted in collaborative tools. There are some learning object metadata to describe relevant learning objects characteristics and to catalog them. As these metadata are proposed to describe educational learning objects, they do not contemplate organizational characteristics, important for knowledge-intensive organizations. Moreover, the metadata are formally modeled through the XML-Schema language, which has a lack of expressiveness. Thus, trying to solve these limitations, the paper presents an ontology for organizational learning object based on IEEE LOM standard. The paper describes the ontology building process, following all the activities proposed in Methontology. Some experiments to evaluate the ontology are also presented.

关键词： Ontologies Standards organizations Organizations Semantics Collaborative tools Interoperability

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Improving Business by Migrating Applications to the Cloud Using Cloudstep

Improving Business by Migrating Applications to the Cloud Us...

引用

International Conference on Advanced Information Networking and Applications Workshops (AINAW)

作者： Paulo R.M. Andrade Renanh G. Araújo Jose Cronemberger Filho Tiago R. Pereira Adriano B. Albuquerque Nabor C. Mendonca Postgraduate Program in Applied Computer Science (PPGIA) University of Fortaleza (UNIFOR) Fortaleza CE Brazil

The Cloud Computing means change, a task that is not always easy from the point of view of businesses, even when this has numerous benefits (high scalability, cost reduction, on demand service, and other). However, companies can benefit with the migration of their applications to the cloud. With this, companies may have reduced costs, greater reliability in its business processes, helping in his growth. In our current reality, small and medium enterprises still afraid to perform this process. They fear the unknown. The focus of this work is to show that even small and medium-sized companies may have competitive advantages to migrate their applications to the cloud.

关键词： Conferences

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Bird Species Classification Based on Color Features

Bird Species Classification Based on Color Features

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Andréia Marini Jacques Facon Alessandro L. Koerich Postgraduate Program in Computer Science (PPGIa) Pontifical Catholic University of Paraná (PUCPR) Curitiba PR Brazil 80215-901

ISBN: (纸本)9781479906505

This paper presents a novel approach for bird species classification based on color features extracted from unconstrained images. This means that the birds may appear in different scenarios as well may present different poses, sizes and angles of view. Besides, the images present strong variations in illuminations and parts of the birds may be occluded by other elements of the scenario. The proposed approach first applies a color segmentation algorithm in an attempt to eliminate background elements and to delimit candidate regions where the bird may be present within the image. Next, the image is split into component planes and from each plane, normalized color histograms are computed from these candidate regions. After aggregation processing is employed to reduce the number of the intervals of the histograms to a fixed number of bins. The histogram bins are used as feature vectors to by a learning algorithm to try to distinguish between the different numbers of bird species. Experimental results on the CUB-200 dataset show that the segmentation algorithm achieves 75% of correct segmentation rate. Furthermore, the bird species classification rate varies between 90% and 8%, depending on the number of classes taken into account.

关键词： pattern recognition color features color image segmentation machine learning bird species classification color feature bird species Pattern recognition images histograms Avifauna image segmentation illumination Imagery (Psychotherapy) Artificial Intelligence Machine Learning

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Exploring Data Augmentation to Improve Music Genre Classification with ConvNets

Exploring Data Augmentation to Improve Music Genre Classific...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Rafael L. Aguiar Yandre M.G. Costa Carlos N. Silla Postgraduate Program in Informatics (PPGIa) Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil Graduate Program in Computer Science (PCC) State University of Maringá (UEM) Maringá Brazil

In this work we address the automatic music genre classification as a pattern recognition task. The content of the music pieces were handled in the visual domain, using spectrograms created from the audio signal. This kind of image has been successfully used in this task since 2011 by extracting handcrafted features based on texture, since it is the main visual attribute found in spectrograms. In this work, the patterns were described by representation learning obtained with the use of convolutional neural network (CNN). CNN is a deep learning architecture and it has been widely used in the pattern recognition literature. Overfitting is a recurrent problem when a classification task is addressed by using CNN, it may occur due to the lack of training samples and/or due to the high dimensionality of the space. To increase the generalization capability we propose to explore data augmentation techniques. In this work, we have carefully selected strategies of data augmentation that are suitable for this kind of application, which are: adding noise, pitch shifting, loudness variation and time stretching. Experiments were conducted on the Latin Music Database (LMD), and the best obtained accuracy overcame the state of the art considering approaches based only in CNN.

关键词： Spectrogram Music Machine learning Training Task analysis Visualization Multiple signal classification

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Delay tolerant transport protocol - DTTP

Delay tolerant transport protocol - DTTP

引用

International Global Information Infrastructure Symposium (GIIS)

作者： Fabio Luiz P. Albini Anelise Munaretto Mauro Fonseca Graduate School of Electrical Engineering and Computer Science (CPGEI) Federal Technological University of Parana (UTFPR) Curitiba Brazil Postgraduate Program in Computer Science (PPGIa) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil

This paper consists in a novel transport protocol description for Delay-Tolerant Networks and Disruption Tolerant Networks (both DTN). This protocol was designed to offer a better information delivery rate in DTNs scenarios. Fountain codes techniques are used to achieve our needs. The performance results were tested taking into account the hosts buffer size, the TTL (Time To Live) of the messages and the amount of redundant information generated into the network. A better delivery rate and performance were achieved using the proposed protocol.

关键词： Transport protocols Data models Delay Error correction codes Educational institutions Process control

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Automatic music genre classification using ensemble of classifiers

Automatic music genre classification using ensemble of class...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Carlos N. Silla Jr. Celso A. A. Kaestner Alessandro L. Koerich Postgraduate Program in Computer Science (PPGIa) Pontifical Catholic University of Paraná Curitiba Parana Brazil Department of Informatics (DAINF) Technological Federal University of Paraná Curitiba Parana Brazil

This paper presents a novel approach to the task of automatic music genre classification which is based on multiple feature vectors and ensemble of classifiers. Multiple feature vectors are extracted from a single music piece. First, three 30-second music segments, one from the beginning, one from the middle and one from end part of a music piece are selected and feature vectors are extracted from each segment. Individual classifiers are trained to account for each feature vector extracted from each music segment. At the classification, the outputs provided by each individual classifier are combined through simple combination rules such as majority vote, max, sum and product rules, with the aim of improving music genre classification accuracy. Experiments carried out on a large dataset containing more than 3,000 music samples from ten different Latin music genres have shown that for the task of automatic music genre classification, the features extracted from the middle part of the music provide better results than using the segments from the beginning or end part of the music. Furthermore, the proposed ensemble approach, which combines the multiple feature vectors, provides better accuracy than using single classifiers and any individual music segment.

关键词： Feature extraction Music information retrieval Digital audio players Optical films Data mining Indexing Multiple signal classification Voting Internet Magnetic films

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