• Journal of Information Processing Systems
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• v.14 no.1
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• pp.32-55
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• 2018

The digitization of music has seen a considerable increase in audience size from a few localized listeners to a wider range of global listeners. At the same time, the digitization brings the challenge of smoothly retrieving music from large databases. To deal with this challenge, many systems which support the smooth retrieval of musical data have been developed. At the computational level, a query music piece is compared with the rest of the music pieces in the database. These systems, music information retrieval (MIR systems), work for various applications such as general music retrieval, plagiarism detection, music recommendation, and musicology. This paper mainly addresses two parts of the MIR research area. First, it presents a general overview of MIR, which will examine the history of MIR, the functionality of MIR, application areas of MIR, and the components of MIR. Second, we will investigate music similarity measurement methods, where we provide a comparative analysis of state of the art methods. The scope of this paper focuses on comparative analysis of the accuracy and efficiency of a few key MIR systems. These analyses help in understanding the current and future challenges associated with the field of MIR systems and music similarity measures.

• Journal of KIISE
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• v.43 no.10
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• pp.1067-1072
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• 2016

Software birthmarks, which are unique characteristics of the software, are used to detect software plagiarism or software similarity. Generally, software birthmarks are divided into static birthmarks or dynamic birthmarks, which have evident pros and cons depending on the extraction method. In this paper, we propose a method for extracting the API sequence birthmarks using a dynamic analysis and similarity detection between the executable codes. Dynamic birthmarks based on API sequences extract API functions during the execution of programs. The extracted API sequences often include all the API functions called from the start to the end of the program. Meanwhile, our dynamic birthmark scheme extracts the API functions only called directly from the executable code. Then, it uses a sequence alignment algorithm to calculate the similarity metric effectively. We evaluate the birthmark with several open source software programs to verify its reliability and credibility. Our dynamic birthmark scheme based on the extracted API sequence can be utilized in a similarity test of executable codes.

• Journal of KIISE
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• v.41 no.12
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• pp.1007-1012
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• 2014

Software birthmarks are used to detect software plagiarism. For binaries, however, only a few birthmarks have been developed. In this paper, we propose a static approach to generate API sequences along major paths, which are analyzed from control flow graphs of the binaries. Since our API sequences are extracted along the most plausible paths of the binary codes, they can represent actual API sequences produced from binary executions, but in a more concise form. Our similarity measures use the Smith-Waterman algorithm that is one of the popular sequence alignment algorithms for DNA sequence analysis. We evaluate our static path-based API sequence with multiple versions of five applications. Our experiment indicates that our proposed method provides a quite reliable similarity birthmark for binaries.

• Knowledge Management Research
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• v.24 no.3
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• pp.1-15
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• 2023

This paper examines research ethics in using the generative AI ChatGPT for research purposes. After reviewing traditional themes of research ethics and relevant principles, it will be argued to be inappropriate to discuss ChatGPT-related issues only from the perspective of permission, detection, and punishment. We need to consider the fundamental problem that the current rules pose concerning the way ChatGPT works. This leads to the proposal that the usage of ChatGPT should be clearly noted when it is used for research purposes and that some unresolved issues should be recognized. Although the advantages of ChatGPT cannot be denied, consensus on the appropriate scope of use is needed from perspectives of the research community and researcher's social responsibility. As generative artificial intelligence technologies are still in the early stages of development, researchers should pay attention to relevant research ethical issues, while not making hasty conclusions. In the conclusion, it will be also proposed to discuss and make a consensus regarding the definition of research that is premised on existing research ethics, but challenged with the advent of ChatGPT and AI technology.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.8
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• pp.317-326
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• 2013

In this paper, we present an effective method for comparing control flow graphs which represent static structures of binary programs. To compare control flow graphs, we measure similarities by comparing instructions and syntactic information contained in basic blocks. In addition, we also consider similarities of edges, which represent control flows between basic blocks, by edge extension. Based on the comparison results of basic blocks and edges, we match most similar basic blocks in two control flow graphs, and then calculate the similarity between control flow graphs. We evaluate the proposed edge extension method in real world Java programs with respect to structural similarities of their control flow graphs. To compare the performance of the proposed method, we also performed experiments with a previous structural comparison for control flow graphs. From the experimental results, the proposed method is evaluated to have enough distinction ability between control flow graphs which have different structural characteristics. Although the method takes more time than previous method, it is evaluated to be more resilient than previous method in comparing control flow graphs which have similar structural characteristics. Control flow graph can be effectively used in program analysis and understanding, and the proposed method is expected to be applied to various areas, such as code optimization, detection of similar code, and detection of code plagiarism.