-------- Forwarded Message -------- Subject: [AISWorld] Newly published papers of JCSE (Sept. 2019) Date: Mon, 30 Sep 2019 19:17:17 +0900 From: office@kiise.org To: aisworld@lists.aisnet.org

Dear Colleague:

We are pleased to announce the release of a new issue of Journal of Computing Science and Engineering (JCSE), published by the Korean Institute of Information Scientists and Engineers (KIISE). KIISE is the largest organization for computer scientists in Korea with over 4,000 active members.

Journal of Computing Science and Engineering (JCSE) is a peer-reviewed quarterly journal that publishes high-quality papers on all aspects of computing science and engineering. JCSE aims to foster communication between academia and industry within the rapidly evolving field of Computing Science and Engineering. The journal is intended to promote problem-oriented research that fuses academic and industrial expertise. The journal focuses on emerging computer and information technologies including, but not limited to, embedded computing, ubiquitous computing, convergence computing, green computing, smart and intelligent computing, and human computing. JCSE publishes original research contributions, surveys, and experimental studies with scientific advances.

Please take a look at our new issue posted at http://jcse.kiise.org http://jcse.kiise.org/ . All the papers can be downloaded from the Web page.

The contents of the latest issue of Journal of Computing Science and Engineering (JCSE)

Official Publication of the Korean Institute of Information Scientists and Engineers

Volume 13, Number 3, September 2019

pISSN: 1976-4677

eISSN: 2093-8020

* JCSE web page: http://jcse.kiise.org

* e-submission: http://mc.manuscriptcentral.com/jcse

Editor in Chief: Insup Lee (University of Pennsylvania)

Il-Yeol Song (Drexel University) Jong C. Park (KAIST)

Taewhan Kim (Seoul National University)

JCSE, vol. 13, no. 3, September 2019

[Paper One]

- Title: Deep-Learning Seat Selection on a Tour Bus Based on Scenery and Sunlight Information

- Authors: Ki Hong Kim and Kwanyong Lee

- Keyword: Deep learning; Transfer learning; Google Street View; Tour

- Abstract

When traveling on a tour bus, the seat one chooses for viewing scenery is one of the main factors affecting one's enjoyment of a trip. However, such scenery information is not available in advance. Therefore, it is necessary to predict the scenery for a tour bus route. In previous research, such predictions have been attempted through machine learning. However, the prediction result has only informed users about which direction is best, not about how good that direction is. Moreover, no information was given about sunlight, which can also affect the viewing of scenery. Therefore, in this paper, we propose the Beautiful Scenery & Cool Shade system that quantifies the information about scenery and sunlight in four directions using deep learning and the azimuth theory. More specifically, we used ResNet-152, DenseNet-161, and Inception v3 for the prediction, and we used Google Street View for the input data. After building the system, we tested its applications to two existing tour bus routes. The results showed that our system outperformed the previous system. The proposed system allows tourists to make satisfactory travel plans and allows tour companies to develop more valuable tour services, ultimately contributing to the development of the global tourism industry.

To obtain a copy of the entire article, click on the link below. JCSE, vol. 13, no. 3, pp.89-98 http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=336&page_url=Cur rent_Issues

[Paper Two]

- Title: Point Cloud Segmentation of Crane Parts Using Dynamic Graph CNN for Crane Collision Avoidance

- Authors: Hyeonho Jeong, Hyosung Hong, Gyuha Park, Mooncheol Won, Mingyu Kim and Hoyeong Yu

- Keyword: crane, 3D point cloud, segmentation, DBSCAN, dynamic graph, CNN

- Abstract

In this study, we have developed a point cloud segmentation algorithm for a collision avoidance system between cranes and other objects in construction yards. We used the Dynamic Graph CNN (DGCNN) algorithm to segment the point cloud of the entire yard into crane parts and backgrounds. The point cloud data were obtained from several LIDAR sensors attached to the crane. All points were grouped into specific core clusters using the DBSCAN algorithm. The core clusters were used to train the DGCNN after labeling with corresponding part names. This network classified the point cloud into crane types and their part names. Experimental results show that the crane part segmentation performance of the suggested algorithm is accurate enough to be used for collision avoidance system. It is possible to estimate the pose of a crane by comparing the segmented point clouds with those of the CAD model.

To obtain a copy of the entire article, click on the link below. JCSE, vol. 13, no. 3, pp.99-106 http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=337&page_url=Cur rent_Issues

[Paper Three]

- Title: An Experimental Investigation into Data Flow Annotated-Activity Diagram-Based Testing

- Authors: Aman Jaffari and Cheol-Jung Yoo

- Keyword: model-based testing, activity diagram-based testing, data flow-annotated activity diagram, data flow information

- Abstract

With the acceptance of Unified Modeling Language (UML) as the de-facto standard for modeling software systems, many research studies have addressed the necessity for utilizing models of systems under testing as inputs for test automation. Recently, activity diagrams have been used as a basis to derive test cases. Current studies have focused on analyzing the control flow of activities. However, examining the control flow among activities is quite simple and such testing on its own is insufficient. This study proposes technique for test case generation that complements an activity diagram with data flow information. To investigate the potential benefits of this technique, we performed an experimental investigation of well-known systems in testing literature. The experimental results were analyzed and compared with a state-of-the-art test suite generation tool as an alternative approach to fault detection effectiveness and efficiency. Overall, the results indicate that the proposed technique outperforms the alternative approach by detecting 27.3% more faults on average. In particular, the proposed technique yielded the best results in detecting faults related to arithmetic operations or parts used for calculation in our context.

To obtain a copy of the entire article, click on the link below. JCSE, vol. 13, no. 3, pp.107-123 http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=338&page_url=Cur rent_Issues

[Paper Four]

- Title: Fish Species Recognition Using VGG16 Deep Convolutional Neural Network

- Authors: Praba Hridayami, I Ketut Gede Darma Putra and Kadek Suar Wibawa

- Keyword: Fish Recognition; Deep Convolutional Neural Network; Transfer Learning; Canny Filter; VGG16

- Abstract

Conservation and protection of fish species is very important in aquaculture and marine biology. A few studies have introduced the concept of fish recognition; however, it resulted in poor rates of error recognition and conservation of a small number of species. This study presents a fish recognition method based on deep convolutional neural networks such as VGG16, which was pre-trained on ImageNet via transfer learning method. The fish dataset in this study consists of 50 species, each covered by 15 images including 10 images for training purpose and 5 images for testing. In this study, we trained our model on four different types of dataset: RGB color space image, canny filter image, blending image, and blending image mixed with RGB image. The results showed that blending image mixed with RGB image trained model exhibited the best genuine acceptance rate (GAR) value of 96.4%, following by the RGB color space image trained model with a GAR value of 92.4%, the canny filter image trained model with a GAR value of 80.4%, and the blending image trained model showed the least GAR value of 75.6%.

To obtain a copy of the entire article, click on the link below. JCSE, vol. 13, no. 3, pp.124-130 http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=339&page_url=Cur rent_Issues

[Call For Papers]

Journal of Computing Science and Engineering (JCSE), published by the Korean Institute of Information Scientists and Engineers (KIISE) is devoted to the timely dissemination of novel results and discussions on all aspects of computing science and engineering, divided into Foundations, Software & Applications, and Systems & Architecture. Papers are solicited in all areas of computing science and engineering. See JCSE home page at http://jcse.kiise.org http://jcse.kiise.org/ for the subareas.

The journal publishes regularly submitted papers, invited papers, selected best papers from reputable conferences and workshops, and thematic issues that address hot research topics. Potential authors are invited to submit their manuscripts electronically, prepared in PDF files, through http://mc.manuscriptcentral.com/jcse http://mc.manuscriptcentral.com/jcse, where ScholarOne is used for on-line submission and review. Authors are especially encouraged to submit papers of around 10 but not more than 30 double-spaced pages in twelve point type. The corresponding author's full postal and e-mail addresses, telephone and FAX numbers as well as current affiliation information must be given on the manuscript. Further inquiries are welcome at JCSE Editorial Office, mailto:office@kiise.org office@kiise.org (phone: +82-2-588-9240; FAX: +82-2-521-1352).

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