9,476 research outputs found
ServeNet: A Deep Neural Network for Web Services Classification
Automated service classification plays a crucial role in service discovery,
selection, and composition. Machine learning has been widely used for service
classification in recent years. However, the performance of conventional
machine learning methods highly depends on the quality of manual feature
engineering. In this paper, we present a novel deep neural network to
automatically abstract low-level representation of both service name and
service description to high-level merged features without feature engineering
and the length limitation, and then predict service classification on 50
service categories. To demonstrate the effectiveness of our approach, we
conduct a comprehensive experimental study by comparing 10 machine learning
methods on 10,000 real-world web services. The result shows that the proposed
deep neural network can achieve higher accuracy in classification and more
robust than other machine learning methods.Comment: Accepted by ICWS'2
Evolution of statistical analysis in empirical software engineering research: Current state and steps forward
Software engineering research is evolving and papers are increasingly based
on empirical data from a multitude of sources, using statistical tests to
determine if and to what degree empirical evidence supports their hypotheses.
To investigate the practices and trends of statistical analysis in empirical
software engineering (ESE), this paper presents a review of a large pool of
papers from top-ranked software engineering journals. First, we manually
reviewed 161 papers and in the second phase of our method, we conducted a more
extensive semi-automatic classification of papers spanning the years 2001--2015
and 5,196 papers. Results from both review steps was used to: i) identify and
analyze the predominant practices in ESE (e.g., using t-test or ANOVA), as well
as relevant trends in usage of specific statistical methods (e.g.,
nonparametric tests and effect size measures) and, ii) develop a conceptual
model for a statistical analysis workflow with suggestions on how to apply
different statistical methods as well as guidelines to avoid pitfalls. Lastly,
we confirm existing claims that current ESE practices lack a standard to report
practical significance of results. We illustrate how practical significance can
be discussed in terms of both the statistical analysis and in the
practitioner's context.Comment: journal submission, 34 pages, 8 figure
Nowcasting with Google Trends : a keyword selection method
Search engines, such as Google, keep a log of searches entered into their websites. Google makes this data publicly available with Google Trends in the form of aggregate weekly search term volume. Aggregate search volume has been shown to be able to nowcast (i.e. compute real-time assessment of current activity) a variety of variables such as influenza outbreaks, financial market fluctuations, unemployment and retail sales. Although identifying appropriate keywords in Google Trends is an essential element of using search data, the recurring difficulty identified in the literature is the lack of a technique to do so. Given this, the main goal of this paper is to put forward a method (the "backward induction method") of identifying and extracting keywords from Google Trends relevant to economic variables
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