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So-called spatial web queries retrieve web content representing points of interest, such that the points of interest have descriptions that are relevant to query keywords and are located close to a query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, the top- $k$ k spatial textual cluster retrieval ($k$ k -STC) query that returns the top- $k$ k clusters that (i) are located close to a query location, (ii) contain objects that are relevant with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a DBSCAN-based approach and an OPTICS-based approach that rely on on-line density-based clustering and that exploit early stop conditions. Empirical studies on real data sets offer evidence that the paper's proposals can find good quality clusters and are capable of excellent performance. [ABSTRACT FROM AUTHOR]

We consider the fundamental problem of matching a template to a signal. We do so by M-estimation, which encompasses procedures that are robust to gross errors (i.e., outliers). Using standard results from empirical process theory, we derive the convergence rate and the asymptotic distribution of the M-estimator under relatively mild assumptions. We also discuss the optimality of the estimator, both in finite samples in the minimax sense and in the large-sample limit in terms of local minimaxity and relative efficiency. Although most of the paper is dedicated to the study of the basic shift model in the context of a random design, we consider many extensions towards the end of the paper, including more flexible templates, fixed designs, the agnostic setting, and more. [ABSTRACT FROM AUTHOR]

A very important function of an issue tracking system is to assign labels to issue reports, such as bug, feature, enhancement, etc., in order to categorize issues to facilitate various development activities. In practice, it is very common that an issue has multiple labels. However, current works are mainly based on single-label prediction, which are not suitable for just-in-time multi-labeling services, due to the low efficiency. Therefore, in this paper, we propose MULA, a just-in-time MUlti-LAbeling system, which learns and automatically assigns multiple labels to issue reports. We have built a dataset with 81,601 entries and 11 labels, as the first benchmark for this task, and implemented a GitHub app. To the best of our knowledge, this is the first work and tool for online multi-labeling GitHub issues based on their categories. We conduct a comprehensive empirical study, including comparisons with five commonly adopted labeling models that show the superiority of MULA, as well as an evaluation that shows high consistency between MULA’s suggestions and developers’ opinions. [ABSTRACT FROM AUTHOR]