Uma abordagem de Ecossistemas de Software para o domínio de e-Learning
Resumo
O domínio de e-learning é caracterizado pela fragmentação de soluções e múltiplas implementações similares. Nesse artigo é apresentada uma abordagem para permitir o desenvolvimento, compartilhamento e reuso de serviços educacionais por meio da perspectiva de Ecossistemas de Software. Através da extensão dos atuais sistemas de informação dos ambientes de e-learning, conhecidos como Ambientes Virtuais de Aprendizagem, busca-se criar uma plataforma de um ecossistema que permita colaboração inter-organizacional. A proposta apresentada foi avaliada através um estudo de caso, verificando os conceitos, a arquitetura e as tecnologias utilizadas.
Palavras-chave:
Ecossistemas de e-Learning, Ecossistemas de Software
Referências
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K. Killinger, C. R. Calkins, W. J. Umberger, D. M. Feuz, and K. M. Eskridge. Consumer sensory acceptance and value for beef steaks of similar tenderness, but differing in marbling level. Journal of Animal Science, 82(11):3294–3301, 2004.
N. Lambe, D. Ross, E. Navajas, J. Hyslop, N. Prieto, C. Craigie, L. B¨unger, G. Simm, and R. Roehe. The prediction of carcass composition and tissue distribution in beef cattle using ultrasound scanning at the start and/or end of the finishing period. Livestock Science, 131(2):193–202, 2010.
D. Liu, H. Pu, D.-W. Sun, L. Wang, and X.-A. Zeng. Combination of spectra and texture data of hyperspectral imaging for prediction of pH in salted meat. Food chemistry, 160:330–337, 2014.
L. Liu, M. Ngadi, S. Prasher, and C. Gariépy. Objective determination of pork marbling scores using the wide line detector. Journal of Food Engineering, 110(3):497–504, 2012.
N. Otsu. A threshold selection method from gray-level histograms. Systems, Man and Cybernetics, IEEE Transactions on, 9(1):62–66, 1979.
S. Sharifzadeh, L. H. Clemmensen, C. Borggaard, S. Støier, and B. K. Ersbøll. Supervised feature selection for linear and non-linear regression of L*a*b* color from multispectral images of meat. Engineering Applications of Artificial Intelligence, 27(0):211 – 227, 2014.
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A. v. WANGENHEIM et al. Seminário introdução `a visão computacional. Visão Computacional–Aldon von Wangenheim’s HomePage, 2001.
Z. Xiong, D.-W. Sun, X.-A. Zeng, and A. Xie. Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats: A review. Journal of food engineering, 132:1–13, 2014.
H. Yuen, J. Princen, J. Illingworth, and J. Kittler. Comparative study of hough transform methods for circle finding. Image and vision computing, 8(1):71–77, 1990.
A. M. S. Association et al. Meat evaluation handbook. American Meat Science Association National Cattlemen’s Beef Association (US) National Pork Producers Council (US), 2001.
S. Beucher et al. The watershed transformation applied to image segmentation. SCANNING MICROSCOPY-SUPPLEMENT-, pages 299–299, 1992.
K. Chen and C. Qin. Segmentation of beef marbling based on vision threshold. computers and electronics in agriculture, 62(2):223–230, 2008.
Y. Cho and S. Kang. Emerging Technologies for Food Quality and Food Safety Evaluation. Contemporary Food Engineering. CRC Press, 2011.
R. C. Gonzalez and R. E. Woods. Digital Image Processing (3rd Edition). Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 2006.
H. Huang, L. Liu, M. Ngadi, and C. Gariepy. Prediction of pork marbling scores using pattern analysis techniques. Food Control, 31(1):224–229, 2013.
P. Jackman, D.-W. Sun, and P. Allen. Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm. Meat Science, 83(2):187–194, 2009.
P. Jackman, D.-W. Sun, P. Allen, K. Brandon, and A.-M. White. Correlation of consumer assessment of longissimus dorsi beef palatability with image colour, marbling and surface texture features. Meat Science, 84(3):564 – 568, 2010.
P. Jackman, D.-W. Sun, C.-J. Du, and P. Allen. Prediction of beef eating qualities from colour, marbling and wavelet surface texture features using homogenous carcass treatment. Pattern Recognition, 42(5):751 – 763, 2009.
K. Killinger, C. R. Calkins, W. J. Umberger, D. M. Feuz, and K. M. Eskridge. Consumer sensory acceptance and value for beef steaks of similar tenderness, but differing in marbling level. Journal of Animal Science, 82(11):3294–3301, 2004.
N. Lambe, D. Ross, E. Navajas, J. Hyslop, N. Prieto, C. Craigie, L. B¨unger, G. Simm, and R. Roehe. The prediction of carcass composition and tissue distribution in beef cattle using ultrasound scanning at the start and/or end of the finishing period. Livestock Science, 131(2):193–202, 2010.
D. Liu, H. Pu, D.-W. Sun, L. Wang, and X.-A. Zeng. Combination of spectra and texture data of hyperspectral imaging for prediction of pH in salted meat. Food chemistry, 160:330–337, 2014.
L. Liu, M. Ngadi, S. Prasher, and C. Gariépy. Objective determination of pork marbling scores using the wide line detector. Journal of Food Engineering, 110(3):497–504, 2012.
N. Otsu. A threshold selection method from gray-level histograms. Systems, Man and Cybernetics, IEEE Transactions on, 9(1):62–66, 1979.
S. Sharifzadeh, L. H. Clemmensen, C. Borggaard, S. Støier, and B. K. Ersbøll. Supervised feature selection for linear and non-linear regression of L*a*b* color from multispectral images of meat. Engineering Applications of Artificial Intelligence, 27(0):211 – 227, 2014.
D. Sun. Computer Vision Technology in the Food and Beverage Industries. Woodhead Publishing Series in Food Science, Technology and Nutrition. Elsevier Science, 2012.
A. v. WANGENHEIM et al. Seminário introdução `a visão computacional. Visão Computacional–Aldon von Wangenheim’s HomePage, 2001.
Z. Xiong, D.-W. Sun, X.-A. Zeng, and A. Xie. Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats: A review. Journal of food engineering, 132:1–13, 2014.
H. Yuen, J. Princen, J. Illingworth, and J. Kittler. Comparative study of hough transform methods for circle finding. Image and vision computing, 8(1):71–77, 1990.
Publicado
17/05/2016
Como Citar
VEIGA, Welington; CAMPOS, Fernanda; DAVID, José Maria; BRAGA, Regina.
Uma abordagem de Ecossistemas de Software para o domínio de e-Learning. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 12. , 2016, Florianópolis.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 574-581.
DOI: https://doi.org/10.5753/sbsi.2016.6009.
