50,587 research outputs found
Analysis and Detection of Information Types of Open Source Software Issue Discussions
Most modern Issue Tracking Systems (ITSs) for open source software (OSS)
projects allow users to add comments to issues. Over time, these comments
accumulate into discussion threads embedded with rich information about the
software project, which can potentially satisfy the diverse needs of OSS
stakeholders. However, discovering and retrieving relevant information from the
discussion threads is a challenging task, especially when the discussions are
lengthy and the number of issues in ITSs are vast. In this paper, we address
this challenge by identifying the information types presented in OSS issue
discussions. Through qualitative content analysis of 15 complex issue threads
across three projects hosted on GitHub, we uncovered 16 information types and
created a labeled corpus containing 4656 sentences. Our investigation of
supervised, automated classification techniques indicated that, when prior
knowledge about the issue is available, Random Forest can effectively detect
most sentence types using conversational features such as the sentence length
and its position. When classifying sentences from new issues, Logistic
Regression can yield satisfactory performance using textual features for
certain information types, while falling short on others. Our work represents a
nontrivial first step towards tools and techniques for identifying and
obtaining the rich information recorded in the ITSs to support various software
engineering activities and to satisfy the diverse needs of OSS stakeholders.Comment: 41st ACM/IEEE International Conference on Software Engineering
(ICSE2019
K-Space at TRECVid 2007
In this paper we describe K-Space participation in
TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance.
The first of the two systems was a āshotā based interface,
where the results from a query were presented as a ranked
list of shots. The second interface was ābroadcastā based,
where results were presented as a ranked list of broadcasts.
Both systems made use of the outputs of our high-level feature submission as well as low-level visual features
A Survey on Soft Subspace Clustering
Subspace clustering (SC) is a promising clustering technology to identify
clusters based on their associations with subspaces in high dimensional spaces.
SC can be classified into hard subspace clustering (HSC) and soft subspace
clustering (SSC). While HSC algorithms have been extensively studied and well
accepted by the scientific community, SSC algorithms are relatively new but
gaining more attention in recent years due to better adaptability. In the
paper, a comprehensive survey on existing SSC algorithms and the recent
development are presented. The SSC algorithms are classified systematically
into three main categories, namely, conventional SSC (CSSC), independent SSC
(ISSC) and extended SSC (XSSC). The characteristics of these algorithms are
highlighted and the potential future development of SSC is also discussed.Comment: This paper has been published in Information Sciences Journal in 201
Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications
This paper presents a novel pairwise constraint propagation approach by
decomposing the challenging constraint propagation problem into a set of
independent semi-supervised learning subproblems which can be solved in
quadratic time using label propagation based on k-nearest neighbor graphs.
Considering that this time cost is proportional to the number of all possible
pairwise constraints, our approach actually provides an efficient solution for
exhaustively propagating pairwise constraints throughout the entire dataset.
The resulting exhaustive set of propagated pairwise constraints are further
used to adjust the similarity matrix for constrained spectral clustering. Other
than the traditional constraint propagation on single-source data, our approach
is also extended to more challenging constraint propagation on multi-source
data where each pairwise constraint is defined over a pair of data points from
different sources. This multi-source constraint propagation has an important
application to cross-modal multimedia retrieval. Extensive results have shown
the superior performance of our approach.Comment: The short version of this paper appears as oral paper in ECCV 201
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