Automatic crash bucketing is a crucial phase in the software development process for efficiently triaging bug reports. It generally consists in grouping similar reports through clustering techniques. However, with real-time streaming bug collection, ...

Nowadays, e-commerce has become one of the most popular shopping ways for worldwide customers especially after the outbreak of COVID-19 worldwide. To aid the scientific shopping decision-makings of customers, collaborative filtering is often used to ...

In computational biology, k-mers and edit distance are fundamental concepts. However, little is known about the metric space of all k-mers equipped with the edit distance. In this work, we explore the structure of the k-mer space by studying its ...

With the continuous increment of city volume and size, a number of traffic-related urban units (e.g., vehicles, roads, buildings, etc.) are emerging rapidly, which plays a heavy burden on the scientific traffic control of smart cities. In this situation, ...

Large, sparse categorical data is a natural way to represent complex data like sequences, trees, and graphs. Such data is prevalent in many applications, e.g., Criteo released a terabyte size click log data of 4 billion records with millions of ...

An important question that arises in the study of high-dimensional vector representations learned from data are, given a set D of vectors and a query q, estimate the number of points within a specified distance threshold of q. We develop two estimators, ...

A massive amount of research on graph representation learning has been carried out to learn dense features as graph embedding for information networks, thereby capturing the semantics in complex networks and benefiting a variety of downstream tasks. ...

We show that approximate near neighbor search in high dimensions can be solved in a Las Vegas fashion (i.e., without false negatives) for ℓ_p (1≤ p≤ 2) while matching the performance of optimal locality-sensitive hashing. Specifically, we construct a data-...

Differential privacy is a compelling privacy definition that explains the privacy-utility tradeoff via formal, provable guarantees. In machine learning, we often wish to release a function over a dataset while preserving differential privacy. Although ...

Feature selection algorithms, such as ReliefF, are very important for processing high‐dimensionality data sets. However, widespread use of popular and effective such algorithms is limited by their computational cost. We describe an adaptation of ...

The advancement in Internet of Things (IoT) technology has transformed our daily lifestyle. Particularly, voice assistants such as Amazon's Alexa and Google Assistant are commonly deployed in households. These voice assistants enable users to interact ...

SCAN (Structural Clustering Algorithm for Networks) is a well-studied, widely used graph clustering algorithm. For large graphs, however, sequential SCAN variants are prohibitively slow, and parallel SCAN variants do not effectively share work among ...

Point-to-Hyperplane Nearest Neighbor Search (P2HNNS) is a fundamental yet challenging problem, and it has plenty of applications in various fields. Existing hyperplane hashing schemes enjoy sub-linear query time and achieve excellent performance on ...

Smart home speakers are deployed in millions of homes around the world. These speakers enable users to interact with other IoT devices in the household and provide voice assistance such as telling the weather and reminding appointments. Although smart ...

The k-nearest-neighbors (kNN) graph is a popular and powerful data structure that is used in various areas of Data Science, but the high computational cost of obtaining it hinders its use on large datasets. Approximate solutions have been described in ...

With the advent of next-generation sequencing technologies, computational transcriptome assembly of RNA-Seq data has become a critical step in many biological and biomedical studies. The accuracy of these transcriptome assembly methods is hindered by ...

Locality-Sensitive Hashing (LSH) is one of the most popular methods for c-Approximate Nearest Neighbor Search (c-ANNS) in high-dimensional spaces. In this paper, we propose a novel LSH scheme based on the Longest Circular Co-Substring (LCCS) search ...

In this column, we review the most recent results on processing persistence diagrams as purely geometric objects. (Yes, this is not a typo! While persistence diagrams originally come from computational topology, this column will mainly focus on the ...

EDA tools are necessary to assist complicated flow of advanced IC design and verification in nowadays industry. After synthesis or simulation, the same signal could be viewed as different hierarchical names, especially for mixed-language designs. This ...

Background: Automatic program repair aims to reduce costs associated with defect repair. The detection and characterization of common bug‐fix patterns in software repositories play an important role in advancing this field. Aim: In this paper, we ...

1. We propose a novel automatic technique for unveiling the most prevalent and pervasive repair actions in Java. Our approach includes a preprocessing step based on locality-sensitive hashing (LSH) to remove outliers from the search space before clustering ...