• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.605-608
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• 2002

A text-dependent speaker recognition system using a robust matching process is studied. The feature histogram of LPC cepstral coefficients for matching is used. The matching process uses mixture network with penalty scores. Using probability and shape comparison of two feature histograms, similarity values are obtained. The experiment results will be shown to show the effectiveness of the proposed algorithm.

• 한국멀티미디어학회논문지
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• 제11권6호
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• pp.875-882
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• 2008

This research proposes an alternative approach to machine learning based ones for text categorization. For using machine learning based approaches for any task of text mining, documents should be encoded into numerical vectors; it causes two problems: huge dimensionality and sparse distribution. Although there are various tasks of text mining such as text categorization, text clustering, and text summarization, the scope of this research is restricted to text categorization. The idea of this research is to avoid the two problems by encoding a document or documents into a table, instead of numerical vectors. Therefore, the goal of this research is to improve the performance of text categorization by proposing approaches, which are free from the two problems.

• Journal of Information Processing Systems
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• 제20권4호
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• pp.432-441
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• 2024

To improve the security of gene information and the accuracy of matching, this paper designs a homomorphic encryption algorithm for gene matching based on cloud computing environment. Firstly, the gene sequences of cloud files entered by users are collected, which are converted into binary code by binary function, so that the encrypted text is obviously different from the original text. After that, the binary code of genes in the database is compared with the generated code to complete gene matching. Experimental analysis indicates that when the number of fragments in a 1 GB gene file is 65, the minimum encryption time of the algorithm is 80.13 ms. Aside from that, the gene matching time and energy consumption of this algorithm are the least, which are 85.69 ms and 237.89 J, respectively.

• Journal of Information Processing Systems
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• 제5권3호
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• pp.159-166
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• 2009

As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) - if a pattern matching algorithm is used - can be reduced to O(mlogN) - if an Information Retrieval technique is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that automatic in-text tagging with keywords filtered by Information Retrieval speeds up to about 6 $\sim$ 40 times compared with the fastest pattern matching algorithm.

• 디지털산업정보학회논문지
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• 제9권4호
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• pp.135-141
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• 2013

String matching is one of the key algorithms in network security and many areas could be benefit from a faster string matching algorithm. Based on the most efficient string matching algorithm in sual applications, the Boyer-Moore (BM) algorithm, a novel algorithm called RQS is proposed. RQS utilizes an improved bad character heuristic to achieve bigger shift value area and an enhanced good suffix heuristic to dramatically improve the worst case performance. The two heuristics combined with a novel determinant condition to switch between them enable RQS achieve a higher performance than BM both under normal and worst case situation. The experimental results reveal that RQS appears efficient than BM many times in worst case, and the longer the pattern, the bigger the performance improvement. The performance of RQS is 7.57~36.34% higher than BM in English text searching, 16.26~26.18% higher than BM in uniformly random text searching, and 9.77% higher than BM in the real world Snort pattern set searching.

• 정보과학회 논문지
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• 제43권5호
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• pp.509-515
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• 2016

순위패턴매칭문제는 텍스트 T와 패턴 P가 주어질 때, P와 각 문자들의 순위가 동일한 순서로 나타나는 T의 모든 부분문자열을 찾는 문제이다. 순위패턴매칭문제는 주가지수분석과 음악의 유사성분석과 같이 문자 자체를 비교하는 것보다 값의 변화순서가 중요한 분야에서 연구가 진행되었다. 순위다중패턴매칭문제는 텍스트 T와 여러 개의 패턴들로 이루어진 패턴집합 $\mathbb{P}$가 주어질 때, $\mathbb{P}$에 속한 패턴과 각 문자들의 순위가 동일한 순서로 나타나는 T의 모든 부분문자열을 찾는 문제이다. 본 논문에서는 순위다중패턴매칭문제를 해결하는 해싱기반 알고리즘을 제시한다.

• Journal of information and communication convergence engineering
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• 제7권4호
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• pp.419-430
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• 2009

Reading comprehension (RC) systems are to understand a given text and return answers in response to questions about the text. Many previous studies extract sentences that are the most similar to questions as answers. However, texts for RC tests are generally short and facts about an event or entity are often expressed in multiple sentences. The answers for some questions might be indirectly presented in the sentences having few overlapping words with the questions. This paper proposes a conceptual graph matching method towards RC tests to extract answer strings. The method first represents the text and questions as conceptual graphs, and then extracts subgraphs for every candidate answer concept from the text graph. All candidate answer concepts will be scored and ranked according to the matching similarity between their sub-graphs and question graph. The top one will be returned as answer seed to form a concise answer string. Since the sub-graphs for candidate answer concepts are not restricted to only covering a single sentence, our approach improved the performance of answer extraction on the Remedia test data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권4호
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• pp.711-725
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• 2013

A heuristic with multi-byte suffix matching plays an important role in real pattern matching algorithms. By skipping many characters at a time in the process of comparing a given pattern with the text, the pattern matching algorithm based on a heuristic with multi-byte suffix matching shows a faster average search time than algorithms based on deterministic finite automata. Based on various experimental results and simulations, the previous works show that the pattern matching algorithms with multi-byte suffix matching performs well. However, there have been limited studies on the mathematical model for analyzing the performance in a standard manner. In this paper, we propose a new probabilistic model, which evaluates the performance of a heuristic with multi-byte suffix matching in an average-case search. When the theoretical analysis results and experimental results were compared, the proposed probabilistic model was found to be sufficient for evaluating the performance of a heuristic with suffix matching in the real pattern matching algorithms.

• International Journal of Computer Science & Network Security
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• 제22권8호
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• pp.242-248
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• 2022

The article presents the analysis of modern methods of automatic comparison of original and unoriginal text to detect textual plagiarism. The study covers two types of plagiarism - literal, when plagiarists directly make exact copying of the text without changing anything, and intelligent, using more sophisticated techniques, which are harder to detect due to the text manipulation, like words and signs replacement. Standard techniques related to extrinsic detection are string-based, vector space and semantic-based. The first, most common and most successful target models for detecting literal plagiarism - N-gram and Vector Space are analyzed, and their advantages and disadvantages are evaluated. The most effective target models that allow detecting intelligent plagiarism, particularly identifying paraphrases by measuring the semantic similarity of short components of the text, are investigated. Models using neural network architecture and based on natural language sentence matching approaches such as Densely Interactive Inference Network (DIIN), Bilateral Multi-Perspective Matching (BiMPM) and Bidirectional Encoder Representations from Transformers (BERT) and its family of models are considered. The progress in improving plagiarism detection systems, techniques and related models is summarized. Relevant and urgent problems that remain unresolved in detecting intelligent plagiarism - effective recognition of unoriginal ideas and qualitatively paraphrased text - are outlined.

• 정보처리학회논문지B
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• 제12B권2호
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• pp.115-122
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• 2005

영상이 다른 영상을 포함하고 있는 경우, 이득 영상의 인치 여부를 판단하는 부분 영상 매칭 방법은 대부분 자연 영상을 대상으로 연구되고 있다. 본 논문에서는 자연 영상이 아닌 텍스트 영상을 매칭하는데 효과적인 두 가지 기법, 즉 메쉬 기반 방법과 상관성 기반 방법을 제안하고자 하다. 메쉬 기반 방법은 두 영상의 일치하는 모서리론 찾은 후 겹치는 영역에 대한 메쉬 특징을 이용하여 유사 여부를 판단하는 것으로, 일치 영역 검색 단계와 유사성 측정 단계로 구성된다. 상관성 기반 방법은 FFT를 이용하여 두 영상의 상관성을 계산함으로써 유사도를 측정한다. 우편 자동화 시스템에서 텍스트 영상을 검증하는 분야에 세안 방법을 적용한 견과, 메쉬 기반 방법은 $90.1\%$, 상관성 기반 방법은 $92.7\%$의 성능을 나타내었다.