-------- Weitergeleitete Nachricht -------- Betreff: [AISWorld] Newly published papers of JCSE (Mar. 2017) Datum: Thu, 30 Mar 2017 19:55:41 +0900 Von: office@kiise.org An: 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 4000 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 11, Number 1, March 2017
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. 11, no. 1, March 2017
[Paper One]
- Title: Memory-Efficient NBNN Image Classification
- Authors: YoonSeok Lee and Sung-Eui Yoon
- Keyword: Image classification; NBNN; Hashing; Memory efficiency; Indexing
- Abstract
Naive Bayes nearest neighbor (NBNN) is a simple image classifier based on identifying nearest neighbors. NBNN uses original image descriptors (e.g., SIFTs) without vector quantization for preserving the discriminative power of descriptors and has a powerful generalization characteristic. However, it has a distinct disadvantage. Its memory requirement can be prohibitively high while processing a large amount of data. To deal with this problem, we apply a spherical hashing binary code embedding technique, to compactly encode data without significantly losing classification accuracy. We also propose using an inverted index to identify nearest neighbors among binarized image descriptors. To demonstrate the benefits of our method, we apply our method to two existing NBNN techniques with an image dataset. By using 64 bit length, we are able to reduce memory 16 times with higher runtime performance and no significant loss of classification accuracy. This result is achieved by our compact encoding scheme for image descriptors without losing much information from original image descriptors.
To obtain a copy of the entire article, click on the link below. JCSE, vol. 11, no. 1, pp.1-8 http://jcse.kiise.org/PublishedPaper/topic_abstract.asp?idx=276
[Paper Two]
- Title: NuDE 2.0: A Formal Method-based Software Development, Verification and Safety Analysis Environment for Digital I&Cs in NPPs
- Authors: Eui-Sub Kim, Dong-Ah Lee, Sejin Jung, Junbeom Yoo, Jong-Gyun Choi and Jang-Soo Lee
- Keyword: MBD; Formal methods; Safety analysis; PLC; FPGA; Digital I&C
- Abstract
NuDE 2.0 (Nuclear Development Environment 2.0) is a formal-method-based software development, verification and safety analysis environment for safety-critical digital I&Cs implemented with programmable logic controller (PLC) and field-programmable gate array (FPGA). It simultaneously develops PLC/FPGA software implementations from one requirement/design specification and also helps most of the development, verification, and safety analysis to be performed mechanically and in sequence. The NuDE 2.0 now consists of 25 CASE tools and also includes an in-depth solution for indirect commercial off-the-shelf (COTS) software dedication of new FPGA-based digital I&Cs. We expect that the NuDE 2.0 will be widely used as a means of diversifying software design/implementation and model-based software development methodology.
To obtain a copy of the entire article, click on the link below. JCSE, vol. 11, no. 1, pp.9-23 http://jcse.kiise.org/PublishedPaper/topic_abstract.asp?idx=277
[Paper Three]
- Title: A Comparative Study of Local Features in Face-based Video Retrieval
- Authors: Juan Zhou and Lan Huang
- Keyword: Video retrieval; Face matching; Harris operators; SIFT; SURF; Eigenfaces
- Abstract
Face-based video retrieval has become an active and important branch of intelligent video analysis. Face profiling and matching is a fundamental step and is crucial to the effectiveness of video retrieval. Although many algorithms have been developed for processing static face images, their effectiveness in face-based video retrieval is still unknown, simply because videos have different resolutions, faces vary in scale, and different lighting conditions and angles are used. In this paper, we combined content-based and semantic-based image analysis techniques, and systematically evaluated four mainstream local features to represent face images in the video retrieval task: Harris operators, SIFT and SURF descriptors, and eigenfaces. Results of ten independent runs of 10-fold cross-validation on datasets consisting of TED (Technology Entertainment Design) talk videos showed the effectiveness of our approach, where the SIFT descriptors achieved an average F-score of 0.725 in video retrieval and thus were the most effective, while the SURF descriptors were computed in 0.3 seconds per image on average and were the most efficient in most cases.
To obtain a copy of the entire article, click on the link below. JCSE, vol. 11, no. 1, pp.24-31 http://jcse.kiise.org/PublishedPaper/topic_abstract.asp?idx=278
[Paper Four]
- Title: Word Sense Disambiguation Using Embedded Word Space
- Authors: Myung Yun Kang, Bogyum Kim and Jae Sung Lee
- Keyword: Word sense disambiguation; Word embedding; Word space; Semantic analysis
- Abstract
Determining the correct word sense among ambiguous senses is essential for semantic analysis. One of the models for word sense disambiguation is the word space model which is very simple in the structure and effective. However, when the context word vectors in the word space model are merged into sense vectors in a sense inventory, they become typically very large but still suffer from the lexical scarcity. In this paper, we propose a word sense disambiguation method using word embedding that makes the sense inventory vectors compact and efficient due to its additive compositionality. Results of experiments with a Korean sense-tagged corpus show that our method is very effective.
To obtain a copy of the entire article, click on the link below. JCSE, vol. 11, no. 1, pp.32-38 http://jcse.kiise.org/PublishedPaper/topic_abstract.asp?idx=279
[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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