67 research outputs found
Linear Spatial Pyramid Matching Using Non-convex and non-negative Sparse Coding for Image Classification
Recently sparse coding have been highly successful in image classification
mainly due to its capability of incorporating the sparsity of image
representation. In this paper, we propose an improved sparse coding model based
on linear spatial pyramid matching(SPM) and Scale Invariant Feature Transform
(SIFT ) descriptors. The novelty is the simultaneous non-convex and
non-negative characters added to the sparse coding model. Our numerical
experiments show that the improved approach using non-convex and non-negative
sparse coding is superior than the original ScSPM[1] on several typical
databases
Not Every Couple Is a Pair: A Supervised Approach for Lifetime Collaborator Identification
While scientific collaboration can be critical for a scholar, some collaborator(s) can be more significant than others, a.k.a. lifetime collaborator(s). This work-in-progress aims to investigate whether it is possible to predict/identify lifetime collaborators given a junior scholar\u27s early profile. For this purpose, we propose a supervised approach by leveraging scholars\u27 local and network properties. Extensive experiments on DBLP digital library demonstrate that lifetime collaborators can be accurately predicted. The proposed model outperforms baseline models with various predictors. Our study may shed light on the exploration of scientific collaborations from the perspective of life-long collaboration
The evolution of Turing Award Collaboration Network : bibliometric-level and network-level metrics
The year of 2017 for the 50th anniversary of the Turing Award, which represents the top-level award in the computer science field, is a milestone. We study the long-term evolution of the Turing Award Collaboration Network, and it can be considered as a microcosm of the computer science field from 1974 to 2016. First, scholars tend to publish articles by themselves at the early stages, and they began to focus on tight collaboration since the late 1980s. Second, compared with the same scale random network, although the Turing Award Collaboration Network has small-world properties, it is not a scale-free network. The reason may be that the number of collaborators per scholar is limited. It is impossible for scholars to connect to others freely (preferential attachment) as the scale-free network. Third, to measure how far a scholar is from the Turing Award, we propose a metric called the Turing Number (TN) and find that the TN decreases gradually over time. Meanwhile, we discover the phenomenon that scholars prefer to gather into groups to do research with the development of computer science. This article presents a new way to explore the evolution of academic collaboration network in the field of computer science by building and analyzing the Turing Award Collaboration Network for decades. © 2014 IEEE
Random Walks: A Review of Algorithms and Applications
A random walk is known as a random process which describes a path including a
succession of random steps in the mathematical space. It has increasingly been
popular in various disciplines such as mathematics and computer science.
Furthermore, in quantum mechanics, quantum walks can be regarded as quantum
analogues of classical random walks. Classical random walks and quantum walks
can be used to calculate the proximity between nodes and extract the topology
in the network. Various random walk related models can be applied in different
fields, which is of great significance to downstream tasks such as link
prediction, recommendation, computer vision, semi-supervised learning, and
network embedding. In this paper, we aim to provide a comprehensive review of
classical random walks and quantum walks. We first review the knowledge of
classical random walks and quantum walks, including basic concepts and some
typical algorithms. We also compare the algorithms based on quantum walks and
classical random walks from the perspective of time complexity. Then we
introduce their applications in the field of computer science. Finally we
discuss the open issues from the perspectives of efficiency, main-memory
volume, and computing time of existing algorithms. This study aims to
contribute to this growing area of research by exploring random walks and
quantum walks together.Comment: 13 pages, 4 figure
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