This paper presents a novel approach to clustering on the spherical condition structures to accuracy-based Learning Classifier System (sXCSc). Our approach achieves this by exploiting evolutionary computing, reinforcement learning and the generalization mechanisms inherent to XCS. The purpose of our work is to develop learning rules which accurately describe clusters without prior assumptions a...

Broad searches for continuous gravitational wave signals rely on hierarchies of follow-up stages candidates above a given significance threshold. An important step to simplify these follow-ups and reduce the computational cost is bundle together in single nearby candidates. This called clustering we investigate carrying it out with deep learning network. In our first paper [B. Beheshtipour M. A...

Personal Learning Environments have emerged as a complementary, even challenging, paradigm to Adaptive Learning Systems. One can argue that Pervasive Learning Environments aim at replacing the physical learning environment while Adaptive Learning Systems focus on replacing the instructor. We believe that amalgamation of these two approaches in a complementary manner, i.e. through setting an app...

The key challenge of co-saliency detection is to extract discriminative features distinguish the common salient foregrounds from backgrounds in a group relevant images. In this paper, we propose new framework which includes two strategies improve ability features. Specifically, on one hand, segment each image semantic superpixel clusters as well generate different scales/sizes images for input ...

In celebrating HRDR’s 20 years of publication, this study aims to shed light on research trends in the journal and future needs by examining 10 publications from a structural perspective. We used three complementary computational methods find major themes including keyword network analysis, topic modeling, bibliographic coupling. This paper presents findings themes, coherence, semantic relevanc...

This study used a sequential set-up to investigate the consecutive effects of timing of supportive information presentation (information before vs. information during the learning task clusters) in interactive digital learning materials (IDLMs) and type of collaboration (personal discussion vs. online discussion) in computer-supported collaborative learning (CSCL) on student knowledge construct...

Data clustering is one of the most important areas of research in data mining and knowledge discovery. Recent research in this area has shown that the best clustering results can be achieved using multi-objective methods. In other words, assuming more than one criterion as objective functions for clustering data can measurably increase the quality of clustering. In this study, a model with two ...

Atomically precise Au and Ag clusters protected by monolayers of organic ligands have attracted growing interests as promising building units functional materials ideal platforms to study size-dependent evolution structures physicochemical properties. The use gas-phase methods including mass spectrometry, ion mobility collision-induced dissociation photoelectron spectroscopy, photodissociation ...