376 research outputs found
SpreadCluster: Recovering Versioned Spreadsheets through Similarity-Based Clustering
Version information plays an important role in spreadsheet understanding,
maintaining and quality improving. However, end users rarely use version
control tools to document spreadsheet version information. Thus, the
spreadsheet version information is missing, and different versions of a
spreadsheet coexist as individual and similar spreadsheets. Existing approaches
try to recover spreadsheet version information through clustering these similar
spreadsheets based on spreadsheet filenames or related email conversation.
However, the applicability and accuracy of existing clustering approaches are
limited due to the necessary information (e.g., filenames and email
conversation) is usually missing. We inspected the versioned spreadsheets in
VEnron, which is extracted from the Enron Corporation. In VEnron, the different
versions of a spreadsheet are clustered into an evolution group. We observed
that the versioned spreadsheets in each evolution group exhibit certain common
features (e.g., similar table headers and worksheet names). Based on this
observation, we proposed an automatic clustering algorithm, SpreadCluster.
SpreadCluster learns the criteria of features from the versioned spreadsheets
in VEnron, and then automatically clusters spreadsheets with the similar
features into the same evolution group. We applied SpreadCluster on all
spreadsheets in the Enron corpus. The evaluation result shows that
SpreadCluster could cluster spreadsheets with higher precision and recall rate
than the filename-based approach used by VEnron. Based on the clustering result
by SpreadCluster, we further created a new versioned spreadsheet corpus
VEnron2, which is much bigger than VEnron. We also applied SpreadCluster on the
other two spreadsheet corpora FUSE and EUSES. The results show that
SpreadCluster can cluster the versioned spreadsheets in these two corpora with
high precision.Comment: 12 pages, MSR 201
Privacy Preserving Utility Mining: A Survey
In big data era, the collected data usually contains rich information and
hidden knowledge. Utility-oriented pattern mining and analytics have shown a
powerful ability to explore these ubiquitous data, which may be collected from
various fields and applications, such as market basket analysis, retail,
click-stream analysis, medical analysis, and bioinformatics. However, analysis
of these data with sensitive private information raises privacy concerns. To
achieve better trade-off between utility maximizing and privacy preserving,
Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent
years. In this paper, we provide a comprehensive overview of PPUM. We first
present the background of utility mining, privacy-preserving data mining and
PPUM, then introduce the related preliminaries and problem formulation of PPUM,
as well as some key evaluation criteria for PPUM. In particular, we present and
discuss the current state-of-the-art PPUM algorithms, as well as their
advantages and deficiencies in detail. Finally, we highlight and discuss some
technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page
Large Language Models in Education: Vision and Opportunities
With the rapid development of artificial intelligence technology, large
language models (LLMs) have become a hot research topic. Education plays an
important role in human social development and progress. Traditional education
faces challenges such as individual student differences, insufficient
allocation of teaching resources, and assessment of teaching effectiveness.
Therefore, the applications of LLMs in the field of digital/smart education
have broad prospects. The research on educational large models (EduLLMs) is
constantly evolving, providing new methods and approaches to achieve
personalized learning, intelligent tutoring, and educational assessment goals,
thereby improving the quality of education and the learning experience. This
article aims to investigate and summarize the application of LLMs in smart
education. It first introduces the research background and motivation of LLMs
and explains the essence of LLMs. It then discusses the relationship between
digital education and EduLLMs and summarizes the current research status of
educational large models. The main contributions are the systematic summary and
vision of the research background, motivation, and application of large models
for education (LLM4Edu). By reviewing existing research, this article provides
guidance and insights for educators, researchers, and policy-makers to gain a
deep understanding of the potential and challenges of LLM4Edu. It further
provides guidance for further advancing the development and application of
LLM4Edu, while still facing technical, ethical, and practical challenges
requiring further research and exploration.Comment: IEEE BigData 2023. 10 page
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