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Data Science and Engineering OnlineFirst articles

02.06.2020 Open Access

Semi-supervised Adversarial Domain Adaptation for Seagrass Detection Using Multispectral Images in Coastal Areas

Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida.

25.05.2020 Open Access

Transfer Metric Learning for Unseen Domains

We propose a transfer metric learning method to infer domain-specific data embeddings for unseen domains, from which no data are given in the training phase, by using knowledge transferred from related domains. When training and test distributions …

23.05.2020 Open Access

What is the Value of Experimentation and Measurement?

Quantifying the Value and Risk of Reducing Uncertainty to Make Better Decisions

Experimentation and Measurement (E&M) capabilities allow organizations to accurately assess the impact of new propositions and to experiment with many variants of existing products. However, until now, the question of measuring the measurer, or …

20.05.2020 Open Access

Contextual Sentiment Neural Network for Document Sentiment Analysis

Although deep neural networks are excellent for text sentiment analysis, their applications in real-world practice are occasionally limited owing to their black-box property. In this study, we propose a novel neural network model called contextual …

20.05.2020 | Preface Open Access

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Data Science and Engineering (DSE) is an international, peer-reviewed, and open access journal published under the brand SpringerOpen. DSE is published on behalf of the China Computer Federation (CCF). Focusing on the theoretical background and advanced engineering approaches, DSE aims to offer a prime forum for researchers, professionals, and industrial practitioners to share their knowledge in this rapidly growing area. It provides in-depth coverage of the latest advances in the closely related fields of data science and data engineering.

More specifically, DSE covers four areas: (i) the data itself, i.e., the nature and quality of the data, especially big data; (ii) the principles of information extraction from data, especially big data; (iii) the theory behind data-intensive computing; and (iv) the techniques and systems used to analyze and manage big data.

DSE publishes high-quality, original research papers, brief reports, and critical reviews in all theoretical, technological, and interdisciplinary studies that make up the fields of data science and engineering and its applications.

This is an open access journal, which is freely accessible online to anyone, anywhere. The open access fees (article-processing charges) are fully sponsored. Authors can publish in the journal without any additional charges.

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