10,301 research outputs found
The Project Scheduling Problem with Non-Deterministic Activities Duration: A Literature Review
Purpose: The goal of this article is to provide an extensive literature review of the models and solution procedures proposed by many researchers interested on the Project Scheduling Problem with nondeterministic activities duration. Design/methodology/approach: This paper presents an exhaustive literature review, identifying the existing models where the activities duration were taken as uncertain or random parameters. In order to get published articles since 1996, was employed the Scopus database. The articles were selected on the basis of reviews of abstracts, methodologies, and conclusions. The results were classified according to following characteristics: year of publication, mathematical representation of the activities duration, solution techniques applied, and type of problem solved. Findings: Genetic Algorithms (GA) was pointed out as the main solution technique employed by researchers, and the Resource-Constrained Project Scheduling Problem (RCPSP) as the most studied type of problem. On the other hand, the application of new solution techniques, and the possibility of incorporating traditional methods into new PSP variants was presented as research trends. Originality/value: This literature review contents not only a descriptive analysis of the published articles but also a statistical information section in order to examine the state of the research activity carried out in relation to the Project Scheduling Problem with non-deterministic activities duration.Peer Reviewe
Generalized Rank Pooling for Activity Recognition
Most popular deep models for action recognition split video sequences into
short sub-sequences consisting of a few frames; frame-based features are then
pooled for recognizing the activity. Usually, this pooling step discards the
temporal order of the frames, which could otherwise be used for better
recognition. Towards this end, we propose a novel pooling method, generalized
rank pooling (GRP), that takes as input, features from the intermediate layers
of a CNN that is trained on tiny sub-sequences, and produces as output the
parameters of a subspace which (i) provides a low-rank approximation to the
features and (ii) preserves their temporal order. We propose to use these
parameters as a compact representation for the video sequence, which is then
used in a classification setup. We formulate an objective for computing this
subspace as a Riemannian optimization problem on the Grassmann manifold, and
propose an efficient conjugate gradient scheme for solving it. Experiments on
several activity recognition datasets show that our scheme leads to
state-of-the-art performance.Comment: Accepted at IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR), 201
Different approaches to community detection
A precise definition of what constitutes a community in networks has remained
elusive. Consequently, network scientists have compared community detection
algorithms on benchmark networks with a particular form of community structure
and classified them based on the mathematical techniques they employ. However,
this comparison can be misleading because apparent similarities in their
mathematical machinery can disguise different reasons for why we would want to
employ community detection in the first place. Here we provide a focused review
of these different motivations that underpin community detection. This
problem-driven classification is useful in applied network science, where it is
important to select an appropriate algorithm for the given purpose. Moreover,
highlighting the different approaches to community detection also delineates
the many lines of research and points out open directions and avenues for
future research.Comment: 14 pages, 2 figures. Written as a chapter for forthcoming Advances in
network clustering and blockmodeling, and based on an extended version of The
many facets of community detection in complex networks, Appl. Netw. Sci. 2: 4
(2017) by the same author
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