Outputs (114)

Texture-sensitive superpixeling and adaptive thresholding for effective segmentation of sea ice floes in high-resolution optical images. (2020)
Journal Article
CHAI, Y., REN, J., HWANG, B., WANG, J., FAN, D., YAN, Y. and ZHU, S. 2021. Texture-sensitive superpixeling and adaptive thresholding for effective segmentation of sea ice floes in high-resolution optical images. IEEE journal of selected topics in applied earth observations and remote sensing [online], 14, pages 577-586. Available from: https://doi.org/10.1109/jstars.2020.3040614

Efficient and accurate segmentation of sea ice floes from high-resolution optical (HRO) remote sensing images is crucial for understanding of sea ice evolutions and climate changes, especially in coping with the large data volume. Existing methods su... Read More about Texture-sensitive superpixeling and adaptive thresholding for effective segmentation of sea ice floes in high-resolution optical images..

Multiscale 2-D singular spectrum analysis and principal component analysis for spatial–spectral noise-robust feature extraction and classification of hyperspectral images. (2020)
Journal Article
MA, P., REN, J., ZHAO, H., SUN, G., MURRAY, P. and ZHENG, J. 2021. Multiscale 2-D singular spectrum analysis and principal component analysis for spatial–spectral noise-robust feature extraction and classification of hyperspectral images. IEEE journal of selected topics in applied earth observations and remote sensing [online], 14, pages 1233-1245. Available from: https://doi.org/10.1109/JSTARS.2020.3040699

In hyperspectral images (HSI), most feature extraction and data classification methods rely on corrected dataset, in which the noisy and water absorption bands are removed. This can result in not only extra working burden but also information loss fr... Read More about Multiscale 2-D singular spectrum analysis and principal component analysis for spatial–spectral noise-robust feature extraction and classification of hyperspectral images..

Applying acceptance requirements to requirements modeling tools via gamification: a case study on privacy and security. (2020)
Conference Proceeding
PIRAS, L., CALABRESE, F. and GIORGINI, P. 2020. Applying acceptance requirements to requirements modeling tools via gamification: a case study on privacy and security. In Grabis, J. and Bork, D. (eds.) The practice of enterprise modeling: proceedings of 13th International Federation for Information Processing (IFIP) Practice of enterprise modelling working conference 2020 (Poem 2020), 25-27 November 2020, Riga, Latvia. Lecture notes in business information processing, 400. Cham: Springer [online], pages 366-376. Available from: https://doi.org/10.1007/978-3-030-63479-7_25

Requirements elicitation, analysis and modeling are critical activities for software success. However, software systems are increasingly complex, harder to develop due to an ever-growing number of requirements from numerous and heterogeneous stakehol... Read More about Applying acceptance requirements to requirements modeling tools via gamification: a case study on privacy and security..

Toward an ensemble of object detectors. (2020)
Conference Proceeding
DANG, T., NGUYEN, T.T. and MCCALL, J. 2020. Toward an ensemble of object detectors. In Yang, H., Pasupa, K., Leung, A.C.-S., Kwok, J.T., Chan, J.H. and King, I. (eds.) Neural information processing: proceedings of 27th International conference on neural information processing 2020 (ICONIP 2020), part 5. Communications in computer and information science, 1333. Cham: Springer [online], pages, 458-467. Available from: https://doi.org/10.1007/978-3-030-63823-8_53

The field of object detection has witnessed great strides in recent years. With the wave of deep neural networks (DNN), many breakthroughs have achieved for the problems of object detection which previously were thought to be difficult. However, ther... Read More about Toward an ensemble of object detectors..

A homogeneous-heterogeneous ensemble of classifiers. (2020)
Conference Proceeding
LUONG, A.V., VU, T.H., NGUYEN, P.M., VAN PHAM, N., MCCALL, J., LIEW, A.W.-C. and NGUYEN, T.T. 2020. A homogeneous-heterogeneous ensemble of classifiers. In Yang, H., Pasupa, K., Leung, A.C.-S., Kwok, J.T., Chan, J.H. and King, I. (eds.) Neural information processing: proceedings of 27th International conference on neural information processing 2020 (ICONIP 2020), part 5. Communications in computer and information science, 1333. Cham: Springer [online], pages, 251-259. Available from: https://doi.org/10.1007/978-3-030-63823-8_30

In this study, we introduce an ensemble system by combining homogeneous ensemble and heterogeneous ensemble into a single framework. Based on the observation that the projected data is significantly different from the original data as well as each ot... Read More about A homogeneous-heterogeneous ensemble of classifiers..

Fusion of PCA and segmented-PCA domain multiscale 2-D-SSA for effective spectral-spatial feature extraction and data classification in hyperspectral imagery. (2020)
Journal Article
FU, H., SUN, G., REN, J., ZHANG, A. and JIA, X. 2020. Fusion of PCA and segmented-PCA domain multiscale 2-D-SSA for effective spectral-spatial feature extraction and data classification in hyperspectral imagery. IEEE transactions on geoscience and remote sensing [online], 60, article 5500214. Available from: https://doi.org/10.1109/TGRS.2020.3034656

As hyperspectral imagery (HSI) contains rich spectral and spatial information, a novel principal component analysis (PCA) and segmented-PCA (SPCA)-based multiscale 2-D-singular spectrum analysis (2-D-SSA) fusion method is proposed for joint spectral–... Read More about Fusion of PCA and segmented-PCA domain multiscale 2-D-SSA for effective spectral-spatial feature extraction and data classification in hyperspectral imagery..

Iterative enhanced multivariance products representation for effective compression of hyperspectral images. (2020)
Journal Article
TUNA, S., TÖREYIN, B.U., REN, J., ZHAO, H. and MARSHALL, S. 2021. Iterative enhanced multivariance products representation for effective compression of hyperspectral images. IEEE transactions on geoscience and remote sensing [online], 59(11), pages 9569-9584. Available from: https://doi.org/10.1109/TGRS.2020.3031016

Effective compression of hyperspectral (HS) images is essential due to their large data volume. Since these images are high dimensional, processing them is also another challenging issue. In this work, an efficient lossy HS image compression method b... Read More about Iterative enhanced multivariance products representation for effective compression of hyperspectral images..

Efficient task optimization algorithm for green computing in cloud. (2020)
Journal Article
G, T., CH, D.C., VARMA, G.P.S. and MEKALA, M.S. 2023. Efficient task optimization algorithm for green computing in cloud. Internet technology letters [online] 6(1): ubiquitous clouds and social Internet of Things, article e254. Available from: https://doi.org/10.1002/itl2.254

Cloud infrastructure assets are accessed by all hooked heterogeneous network servers and applications to maintain entail reliability towards global subscribers with high performance and low cost is a tedious challenging task. Most of the extant techn... Read More about Efficient task optimization algorithm for green computing in cloud..

Computing students learning outcomes in learning by developing action model. (2020)
Conference Proceeding
LINTILÄ, T. and ZARB, M. 2020. Computing students learning outcomes in learning by developing action model. In Gómez Chova, L., López Martínez, A. and Candel Torres, I. (eds.) Proceedings of 13th International conference of education, research and innovation 2020 (ICERI2020), 9-10 November 2020, [virtual conference]. Valencia: IATED [online], pages 1936-1945. Available from: https://doi.org/10.21125/iceri.2020.0477

The purpose of this paper is to present the results of research aimed at finding out the learning outcomes of computing students with a study module implementation based on the Learning by Developing (LbD) Action Model used in Laurea University of Ap... Read More about Computing students learning outcomes in learning by developing action model..

Contextualisation of data flow diagrams for security analysis. (2020)
Conference Proceeding
FAILY, S., SCANDARIATO, R., SHOSTACK, A., SION, L. and KI-ARIES, D. 2020. Contextualisation of data flow diagrams for security analysis. In Eades, H. III and Gadyatskaya, O. (eds.) Graphical models for security: revised selected papers from the proceedings of the 7th International workshop on graphical models for security (GraMSec 2020), 22 June 2020, Boston, USA. Lecture notes in computer science, 12419. Cham: Springer [online], pages 186-197. Available from: https://doi.org/10.1007/978-3-030-62230-5_10

Data flow diagrams (DFDs) are popular for sketching systems for subsequent threat modelling. Their limited semantics make reasoning about them difficult, but enriching them endangers their simplicity and subsequent ease of take up. We present an appr... Read More about Contextualisation of data flow diagrams for security analysis..