• Title/Summary/Keyword: information representation

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Computational Processing of Korean Dialogue and the Construction of Its Representation Structure Based on Situational Information (상황정보에 기반한 한국어대화의 전산적 처리와 표상구조의 구축)

  • Lee, Dong-Young
    • The KIPS Transactions:PartB
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    • v.9B no.6
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    • pp.817-826
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    • 2002
  • In Korean dialogue honorification phenomenon may occur, an honorific pronoun may be used, and a subject or an object may be completely omitted when it can be recovered based on context. This paper proposes that in order to process Korean dialogue in which such distinct linguistic phenomena occur and to construct its representation structure we mark and use the following information explicitly, not implicitly : information about dialogue participants, information about the speech act of an utterance, information about the relative order of social status for the people involved in dialogue, and information flow among utterances of dialogue. In addition, this paper presents a method of marking and using such situational information and an appropriate representation structure of Korean dialogue. In this paper we set up Korean dialogue representation structure by modifying and extending DRT (Discourse Representation Theory) and SDRT (Segmented Discourse Representation Theory). Futhermore, this paper shows how to process Korean dialogue computationally and construct its representation structure by using Prolog programming language, and then applies such representation structure to spontaneous Korean dialogue to know its validity.

An improved kernel principal component analysis based on sparse representation for face recognition

  • Huang, Wei;Wang, Xiaohui;Zhu, Yinghui;Zheng, Gengzhong
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.6
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    • pp.2709-2729
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    • 2016
  • Representation based classification, kernel method and sparse representation have received much attention in the field of face recognition. In this paper, we proposed an improved kernel principal component analysis method based on sparse representation to improve the accuracy and robustness for face recognition. First, the distances between the test sample and all training samples in kernel space are estimated based on collaborative representation. Second, S training samples with the smallest distances are selected, and Kernel Principal Component Analysis (KPCA) is used to extract the features that are exploited for classification. The proposed method implements the sparse representation under ℓ2 regularization and performs feature extraction twice to improve the robustness. Also, we investigate the relationship between the accuracy and the sparseness coefficient, the relationship between the accuracy and the dimensionality respectively. The comparative experiments are conducted on the ORL, the GT and the UMIST face database. The experimental results show that the proposed method is more effective and robust than several state-of-the-art methods including Sparse Representation based Classification (SRC), Collaborative Representation based Classification (CRC), KCRC and Two Phase Test samples Sparse Representation (TPTSR).

Application of Fuzzy Information Representation Using Frequency Ratio and Non-parametric Density Estimation to Multi-source Spatial Data Fusion for Landslide Hazard Mapping

  • Park No-Wook;Chi Kwang-Hoon;Kwon Byung-Doo
    • Journal of the Korean earth science society
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    • v.26 no.2
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    • pp.114-128
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    • 2005
  • Fuzzy information representation of multi-source spatial data is applied to landslide hazard mapping. Information representation based on frequency ratio and non-parametric density estimation is used to construct fuzzy membership functions. Of particular interest is the representation of continuous data for preventing loss of information. The non-parametric density estimation method applied here is a Parzen window estimation that can directly use continuous data without any categorization procedure. The effect of the new continuous data representation method on the final integrated result is evaluated by a validation procedure. To illustrate the proposed scheme, a case study from Jangheung, Korea for landslide hazard mapping is presented. Analysis of the results indicates that the proposed methodology considerably improves prediction capabilities, as compared with the case in traditional continuous data representation.

Low-Rank Representation-Based Image Super-Resolution Reconstruction with Edge-Preserving

  • Gao, Rui;Cheng, Deqiang;Yao, Jie;Chen, Liangliang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.9
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    • pp.3745-3761
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    • 2020
  • Low-rank representation methods already achieve many applications in the image reconstruction. However, for high-gradient image patches with rich texture details and strong edge information, it is difficult to find sufficient similar patches. Existing low-rank representation methods usually destroy image critical details and fail to preserve edge structure. In order to promote the performance, a new representation-based image super-resolution reconstruction method is proposed, which combines gradient domain guided image filter with the structure-constrained low-rank representation so as to enhance image details as well as reveal the intrinsic structure of an input image. Firstly, we extract the gradient domain guided filter of each atom in high resolution dictionary in order to acquire high-frequency prior information. Secondly, this prior information is taken as a structure constraint and introduced into the low-rank representation framework to develop a new model so as to maintain the edges of reconstructed image. Thirdly, the approximate optimal solution of the model is solved through alternating direction method of multipliers. After that, experiments are performed and results show that the proposed algorithm has higher performances than conventional state-of-the-art algorithms in both quantitative and qualitative aspects.

Object Tracking based on Relaxed Inverse Sparse Representation

  • Zhang, Junxing;Bo, Chunjuan;Tang, Jianbo;Song, Peng
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.9
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    • pp.3655-3671
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    • 2015
  • In this paper, we develop a novel object tracking method based on sparse representation. First, we propose a relaxed sparse representation model, based on which the tracking problem is casted as an inverse sparse representation process. In this process, the target template is able to be sparsely approximated by all candidate samples. Second, we present an objective function that combines the sparse representation process of different fragments, the relaxed representation scheme and a weight reference prior. Based on some propositions, the proposed objective function can be solved by using an iteration algorithm. In addition, we design a tracking framework based on the proposed representation model and a simple online update manner. Finally, numerous experiments are conducted on some challenging sequences to compare our tracking method with some state-of-the-art ones. Both qualitative and quantitative results demonstrate that the proposed tracking method performs better than other competing algorithms.

Robust Online Object Tracking with a Structured Sparse Representation Model

  • Bo, Chunjuan;Wang, Dong
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.5
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    • pp.2346-2362
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    • 2016
  • As one of the most important issues in computer vision and image processing, online object tracking plays a key role in numerous areas of research and in many real applications. In this study, we present a novel tracking method based on the proposed structured sparse representation model, in which the tracked object is assumed to be sparsely represented by a set of object and background templates. The contributions of this work are threefold. First, the structure information of all the candidate samples is utilized by a joint sparse representation model, where the representation coefficients of these candidates are promoted to share the same sparse patterns. This representation model can be effectively solved by the simultaneous orthogonal matching pursuit method. In addition, we develop a tracking algorithm based on the proposed representation model, a discriminative candidate selection scheme, and a simple model updating method. Finally, we conduct numerous experiments on several challenging video clips to evaluate the proposed tracker in comparison with various state-of-the-art tracking algorithms. Both qualitative and quantitative evaluations on a number of challenging video clips show that our tracker achieves better performance than the other state-of-the-art methods.

Development of Information Visualization Tool using Knowledge Representation (지식 표상 방법을 이용한 정보 검색 시각화 도구 개발)

  • Ji, Hye-Sung;Park, Ki-Nam;Lim, Heui-Seok
    • Journal of Digital Convergence
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    • v.10 no.9
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    • pp.383-390
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    • 2012
  • In this paper, we suggest an information retrieval visualization tool using the knowledge representation method. Information retrieval visualization tool suggested in this paper is designed to automatically extract retrieval intention using user's search history data and visualize extracted retrieval intention in the knowledge representation method structure. A psychological knowledge representation methodology was adopted for schema for retrieval intention representation and its effectiveness has been proved through the behavioral experiments. Result of experiment revealed that information retrieval visualization tool has been improved approximately 39% in user satisfaction compared to existing retrieval method, suggesting a measure to solve re-retrieval problem in the process of information retrieval.

Multi-view Clustering by Spectral Structure Fusion and Novel Low-rank Approximation

  • Long, Yin;Liu, Xiaobo;Murphy, Simon
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.3
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    • pp.813-829
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    • 2022
  • In multi-view subspace clustering, how to integrate the complementary information between perspectives to construct a unified representation is a critical problem. In the existing works, the unified representation is usually constructed in the original data space. However, when the data representation in each view is very diverse, the unified representation derived directly in the original data domain may lead to a huge information loss. To address this issue, different to the existing works, inspired by the latest revelation that the data across all perspectives have a very similar or close spectral block structure, we try to construct the unified representation in the spectral embedding domain. In this way, the complementary information across all perspectives can be fused into a unified representation with little information loss, since the spectral block structure from all views shares high consistency. In addition, to capture the global structure of data on each view with high accuracy and robustness both, we propose a novel low-rank approximation via the tight lower bound on the rank function. Finally, experimental results prove that, the proposed method has the effectiveness and robustness at the same time, compared with the state-of-art approaches.

A Flexible and Expandable Representation Framework for Computational Science Data

  • Kim, Jaesung;Ahn, Sunil;Lee, Jeongchoel;Lee, Jongsuk Ruth
    • Journal of Internet Computing and Services
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    • v.21 no.3
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    • pp.41-51
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    • 2020
  • EDISON is a web-based platform that provides easy and convenient use of simulation software on high-performance computers. One of the most important roles of a computational science platform, such as EDISON, is to post-process and represent the simulation results data so that the user can easily understand the data. We interviewed EDISON users and collected requirements for post-processing and represent of simulation results, which included i) flexible data representation, ii) supporting various data representation components, and iii) flexible and easy development of view template. In previous studies, it was difficult to develop or contribute data representation components, and the view templates were not able to be shared or recycled. This causes a problem that makes it difficult to create ecosystems for the representation tool development of numerous simulation software. EDISON-VIEW is a framework for post-processing and representing simulation results produced from the EDISON platform. This paper proposes various methods used in the design and development of the EDISON-VIEW framework to solve the above requirements and problems. We have verified its usefulness by applying it to simulation software in various fields such as material, computational fluid dynamics, computational structural dynamics, and computational chemistry.

Development of a Rhythm Editing and Playing System Based on XML (XML 기반 리듬 편집 및 재생 시스템 개발)

  • Son, Won-Seong;Lee, Yong-Gyu
    • The Transactions of the Korea Information Processing Society
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    • v.7 no.5
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    • pp.1341-1350
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    • 2000
  • Much research has been performed to represent music information on computers. However, one problem of the previous approaches is that music information cannot be hared with other music systems since they have used their own representation schemes. In order to resolve this problem, we have developed a new scheme for representing, editing, and playing rhythms among music information based on XML which is a web standard language. We present a RDML (Rhythm Description Markup Language) to exactly describe rhythm information such as beats, musical instruments, and performance information, which is much simpler thant previous representation schemes. Moreover, the RDML representation can be played by converting it into SMIL representation. Because our approach is based on the web standard, the music information can be shared on the web.

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