Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

Topic modeling, or identifying the set of topics that occur in a collection of articles, is one of
the primary objectives of text mining. One of the big challenges in topic modeling is …

We propose SiCaGCN, a learning system to predict the similarity of a given software code to
a set of codes that are permitted to run on a computational resource, such as a …

Non-negative matrix factorization (NMF) has proven to be a powerful unsupervised learning
method for uncovering hidden features in complex and noisy data sets with applications in …

Distinguishing malicious anomalous activities from unusual but benign activities is a
fundamental challenge for cyber defenders. Prior studies have shown that statistical user …

Non-negative Matrix Factorization (NMF) models the topics of a text corpus by decomposing
the matrix of term frequency-inverse document frequency (TF-IDF) representation, X, into two …

Triangle centrality is introduced for finding important vertices in a graph based on the
concentration of triangles surrounding each vertex. It has the distinct feature of allowing a …

The era of exascale computing opens new venues for innovations and discoveries in many
scientific, engineering, and commercial fields. However, with the exaflops also come the …

With the boom in the development of computer hardware and software, social media, IoT
platforms, and communications, there has been exponential growth in the volume of data …

We propose an efficient distributed out-of-memory implementation of the non-negative
matrix factorization (NMF) algorithm for heterogeneous high-performance-computing …