Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
2.7
Journals
Symmetry
Special Issues
Symmetry in Cooperative Applications II
share announcement

Symmetry in Cooperative Applications II

A special issue of Symmetry (ISSN 2073-8994).

Deadline for manuscript submissions: closed (31 January 2017) | Viewed by 28648

Special Issue Editor

Prof. Dr. Yuhua Luo

E-Mail Website
Guest Editor
Department of Computer Science, University of Balearic Islands, Palma, Spain
Interests: computer supported cooperative work; computer vision; computer graphics; multimedia
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

This Special Issue will focus on the new findings and development in the field of cooperative design, cooperative visualization, cooperative engineering and other cooperative applications particularly related to the symmetry characteristics.

During recent years, many important technological components have emerged and we are facing a completely new computing landscape. These include the cloud computing technology, the ubiquitous broadband internet, big data and the popularity of mobile devices, etc. This new computing landscape opens the possibility of wide access to social media, e-business, e-learning, e-finance, crowd-sourcing and many other online services. The scale and popularity have reached a level that human society has never experienced. The anaysis of the nature and symmetry characteristics in cooperative applications has become an active area of research.

This Special Issue aims to gather original, state-of-the-art research and development contributions concerning symmetry of all types of cooperative working applications, particularly in cooperative design, cooperative visualization and engineering.

Prof. Dr. Yuhua Luo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • symmetry in cooperative working team modelling
  • symmetry in cooperative design
  • symmetry in cooperative visualization
  • symmetry in cooperative engineering
  • symmetry in all other cooperative team work applications

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

802 KiB  
Article
Learning Performance Enhancement Using Computer-Assisted Language Learning by Collaborative Learning Groups
by Ya-huei Wang and Hung-Chang Liao
Symmetry 2017, 9(8), 141; https://doi.org/10.3390/sym9080141 - 02 Aug 2017
Cited by 15 | Viewed by 5789
Abstract
This study attempted to test whether the use of computer-assisted language learning (CALL) and innovative collaborative learning could be more effective than the use of traditional collaborative learning in improving students’ English proficiencies. A true experimental design was used in the study. Four [...] Read more.
This study attempted to test whether the use of computer-assisted language learning (CALL) and innovative collaborative learning could be more effective than the use of traditional collaborative learning in improving students’ English proficiencies. A true experimental design was used in the study. Four randomly-assigned groups participated in the study: a traditional collaborative learning group (TCLG, 34 students), an innovative collaborative learning group (ICLG, 31 students), a CALL traditional collaborative learning group (CALLTCLG, 32 students), and a CALL innovative collaborative learning group (CALLICLG, 31 students). TOEIC (Test of English for International Communication) listening, reading, speaking, and writing pre-test and post-test assessments were given to all students at an interval of sixteen weeks. Multivariate analysis of covariance (MANCOVA), multivariate analysis of variance (MANOVA), and analysis of variance (ANOVA) were used to analyze the data. The results revealed that students who used CALL had significantly better learning performance than those who did not. Students in innovative collaborative learning had significantly better learning performances than those in traditional collaborative learning. Additionally, students using CALL innovative collaborative learning had better learning performances than those in CALL collaborative learning, those in innovative collaborative learning, and those in traditional collaborative learning. Full article
(This article belongs to the Special Issue Symmetry in Cooperative Applications II)
►▼ Show Figures

Figure 1

7163 KiB  
Article
Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine
by Qing Shen, Xiaojuan Ban and Chong Guo
Symmetry 2017, 9(5), 70; https://doi.org/10.3390/sym9050070 - 10 May 2017
Cited by 9 | Viewed by 4583
Abstract
There is always an asymmetric phenomenon between traffic data quantity and unit information content. Labeled data is more effective but scarce, while unlabeled data is large but weaker in sample information. In an urban transportation assessment system, semi-supervised extreme learning machine (SSELM) can [...] Read more.
There is always an asymmetric phenomenon between traffic data quantity and unit information content. Labeled data is more effective but scarce, while unlabeled data is large but weaker in sample information. In an urban transportation assessment system, semi-supervised extreme learning machine (SSELM) can unite manual observed data and extensively collected data cooperatively to build connection between congestion condition and road information. In our method, semi-supervised learning can integrate both small-scale labeled data and large-scale unlabeled data, so that they can play their respective advantages, while the ELM can process large scale data at high speed. Optimized by kernel function, Kernel-SSELM can achieve higher classification accuracy and robustness than original SSELM. Both the experiment and the real-time application show that the evaluation system can precisely reflect the traffic condition. Full article
(This article belongs to the Special Issue Symmetry in Cooperative Applications II)
►▼ Show Figures

Figure 1

3103 KiB  
Article
Nested One-to-One Symmetric Classification Method on a Fuzzy SVM for Moving Vehicles
by Guofeng Qin, Xiaodi Huang and Yiling Chen
Symmetry 2017, 9(4), 48; https://doi.org/10.3390/sym9040048 - 26 Mar 2017
Cited by 2 | Viewed by 4364
Abstract
As a multi-classification problem, classification of moving vehicles has been studied by different statistical methods. These practical applications have various requirements, efficiencies, and performance, such as the size of training sample sets, convergence rate, and inseparable or ambiguous classification issues. With a reduction [...] Read more.
As a multi-classification problem, classification of moving vehicles has been studied by different statistical methods. These practical applications have various requirements, efficiencies, and performance, such as the size of training sample sets, convergence rate, and inseparable or ambiguous classification issues. With a reduction in its training time,the one-to-many support vector machine (SVM) method has an advantage over the standard SVM method by directly converting the binary classification problem into two multi-classification problems with short time and fast speed. When the number of training samples of a certain type is far less than the total number of samples, the accuracy of training, however, will be significantlydecreased,leading to theproblem of inseparable area. In this paper, the proposed nested one-to-one symmetric classification method on a fuzzy SVM symmetrically transforms the C multi-classification problems into the C(C-1)/2 binary classification problems with C(C-1)/2 classifiers, and solves the problem of inseparable area. According to the best combination factor of kernel function (γ, C) for the radial basis function (RBF) in the comparative experiments of training sample sets among the different algorithms, and the experimental results of many different training sample sets and test samples, the nested one-to-one symmetric classification algorithm on a fuzzy SVM for moving vehicle is able to obtain the best accuracy of recognition. Full article
(This article belongs to the Special Issue Symmetry in Cooperative Applications II)
►▼ Show Figures

Figure 1

1601 KiB  
Article
A Symmetry Particle Method towards Implicit Non‐Newtonian Fluids
by Yalan Zhang, Xiaojuan Ban, Xiaokun Wang and Xing Liu
Symmetry 2017, 9(2), 26; https://doi.org/10.3390/sym9020026 - 17 Feb 2017
Cited by 7 | Viewed by 5758
Abstract
In this paper, a symmetry particle method, the smoothed particle hydrodynamics (SPH) method, is extended to deal with non‐Newtonian fluids. First, the viscous liquid is modeled by a non‐Newtonian fluid flow and the variable viscosity under shear stress is determined by the Carreau‐Yasuda [...] Read more.
In this paper, a symmetry particle method, the smoothed particle hydrodynamics (SPH) method, is extended to deal with non‐Newtonian fluids. First, the viscous liquid is modeled by a non‐Newtonian fluid flow and the variable viscosity under shear stress is determined by the Carreau‐Yasuda model. Then a pressure correction method is proposed, by correcting density error with individual stiffness parameters for each particle, to ensure the incompressibility of fluid. Finally, an implicit method is used to improve efficiency and stability. It is found that the nonNewtonian behavior can be well displayed in all cases, and the proposed SPH algorithm is stable and efficient. Full article
(This article belongs to the Special Issue Symmetry in Cooperative Applications II)
►▼ Show Figures

Figure 1

683 KiB  
Article
Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning
by Arif Mehmood, Byung-Won On, Ingyu Lee and Gyu Sang Choi
Symmetry 2017, 9(1), 11; https://doi.org/10.3390/sym9010011 - 12 Jan 2017
Cited by 7 | Viewed by 7735
Abstract
This study develops a model for essay scoring and article relevancy. Essay scoring is a costly process when we consider the time spent by an evaluator. It may lead to inequalities of the effort by various evaluators to apply the same evaluation criteria. [...] Read more.
This study develops a model for essay scoring and article relevancy. Essay scoring is a costly process when we consider the time spent by an evaluator. It may lead to inequalities of the effort by various evaluators to apply the same evaluation criteria. Bibliometric research uses the evaluation criteria to find relevancy of articles instead. Researchers mostly face relevancy issues while searching articles. Therefore, they classify the articles manually. However, manual classification is burdensome due to time needed for evaluation. The proposed model performs automatic essay evaluation using multi-text features and ensemble machine learning. The proposed method is implemented in two data sets: a Kaggle short answer data set for essay scoring that includes four ranges of disciplines (Science, Biology, English, and English language Arts), and a bibliometric data set having IoT (Internet of Things) and non-IoT classes. The efficacy of the model is measured against the Tandalla and AutoP approach using Cohen’s kappa. The model achieves kappa values of 0.80 and 0.83 for the first and second data sets, respectively. Kappa values show that the proposed model has better performance than those of earlier approaches. Full article
(This article belongs to the Special Issue Symmetry in Cooperative Applications II)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop