• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.185-186
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• 2007

ITSP(Internet Telephony Service Provider)를 이용한 VoIP 서비스의 사용자가 증가함에 따라 VoIP 스팸은 큰 문제로 대두되고 있다. 기존의 일반 전화 때부터 사용되던 스팸은 실시간적 음성 통신이라는 특성상 콘텐츠 필터링을 하기 어렵기 때문에 롤 행위 패턴 조사를 통해 스패머(Spammer)를 구분하고 있다. 그러나 잘못된 오판으로 인한 문제와 스팸으로 인식하는 임계값을 넘지 않는 한도의 스팸 전송, 그리고 여러 사용자가 하나의 번호를 공유하여 사용하는 경우에는 여전히 스팸의 위협이 남아 있다. 이에 본 논문에서는 튜링 테스트를 이용한 VoIP 스팸 방지를 제안한다. 제안된 방법은 송신자에게 튜링 테스트를 거치게 하고, 튜링 테스트를 통과한 사용자만 수신자와 연결이 되는 방식으로 동작한다. 또한 튜링 테스트를 통과한 정상적인 사용자에게는 티켓을 줌으로써 재발신시 거쳐야하는 튜링 테스트의 번거로움을 줄일 수 있다. 제안된 방법은 ASUS WL-500G 무선 공유기 및 Asterisk IP-PBX에서 구현되었고 실험을 통해 제안된 방법의 유효성을 검증하였다.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.3
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• pp.261-265
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• 2008

As increasing the user of VoIP service using ITSP(Internet Telephony Service Provider), the VoIP spam becomes a big problem. The spam used in the existing public telephone is detected by using the pattern inspection of call behavior because it is difficult to filter contents for the characteristic of real-time voice communication. However there is a false-positive problem. The threat on spam remains where spam with low threshold can't be detected or users share one number. In this paper, we propose anti-spam for VoIP based on luring test. The proposed method gives a user luring test and he/she can connect to a receiver if passing turing test. A ticket is given to a user that pass luring test and it reduces overhead of luring test in re-dial. The proposed method is implemented on ASUS WL-500G wireless router and Asterisk IP-PBX. Experimental results show the effectiveness of the proposed method.

• Korean Journal of Cognitive Science
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• v.23 no.3
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• pp.367-388
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• 2012

The centennial of Alan Mathison Turing(23 June 1912 - 7 June 1954) is an appropriate occasion on which to assess his profound influence on the development of cognitive science. His contributions to and attitudes toward that field are discussed from the metamathematical perspective. This essay addresses (i)Turing's mathematical analysis of cognition, (ii)universal Turing machines, (iii)the limitations of universal Turing machines, (iv)oracle Turing machine beyond universal Turing machine, and (v)Turing test for cognitive science. Turing was a ground-breaker, eager to move on to new fields. He actually opened wider the scientific windows to the mind. The results show that first, by means of mathematical logic Turing discovered a new bridge between the mind and the physical world. Second, Turing gave a new formal analysis of operations of the mind. Third, Turing investigated oracle Turing machines and connectionist network machines as new models of minds beyond the limitations of his own universal machines. This paper explores why the cognitive scientist would be ever expecting a new Turing Test on the shoulder of Alan Turing.

• Journal of Science and Technology Studies
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• v.17 no.1
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• pp.5-39
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• 2017

In March 2016, the computer Go program, AlphaGo, defeated Sedol Lee, a Korean professional Go player of 9-dan rank. This victory by AlphaGo shows the rise in popularity of artificial intelligence (AI). Not only was this game a testament to machine performance, it was the type of game that extended the Turing test. When the interrogator cannot differentiate between human being and machine, the machine has passed the test. This article examines the interactions between AI and human beings and studies the social nature of intelligence through the AlphaGo case. Collins insists that knowledge or intelligence is social and embodied, and the interrogators in the Turing test can identify the difference between native members and non-members through their knowledge only. Applying this concept, AlphaGo, as subject A of this test, fulfilled its role of stirring up the classical "truth of human." Meanwhile, Lee as subject B, played to speak the truth by revealing his own qualities. Here, it is also important role that interrogators judge what it is. Many spectators, as interrogators, have intervened to confirm the border between human beings and machines by using their embodied and social knowledge.

• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.11-19
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• 2022

Recently, various researches on medical image generation have been suggested, and it becomes crucial to accurately evaluate the quality and diversity of the generated medical images. For this purpose, the expert's visual turing test, feature distribution visualization, and quantitative evaluation through IS and FID are evaluated. However, there are few methods for quantitatively evaluating medical images in terms of fidelity and diversity. In this paper, images are generated by learning a chest CT dataset of non-small cell lung cancer patients through DCGAN and PGGAN generative models, and the performance of the two generative models are evaluated in terms of fidelity and diversity. The performance is quantitatively evaluated through IS and FID, which are one-dimensional score-based evaluation methods, and Precision and Recall, Improved Precision and Recall, which are two-dimensional score-based evaluation methods, and the characteristics and limitations of each evaluation method are also analyzed in medical imaging.