• Journal of Internet Computing and Services
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• v.19 no.3
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• pp.7-14
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• 2018

As SNS(Social Network Service) becomes a part of our life, new information can be derived through various information provided by SNS. Through the public timeline analysis of SNS, we can extract the latest tour trends for the public and the intimacy through the social relationship analysis in the SNS. The extracted intimacy can also be used to make the personalized recommendation by adding the weights to friends with high intimacy. We apply SNS elements such as analyzed latest trends and intimacy to item-based collaborative filtering techniques to achieve better accuracy and satisfaction than existing travel recommendation services in a new way. In this paper, we propose a social travel recommendation system using item - based collaborative filtering.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.7-14
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• 2022

Sensor Registry System (SRS) has been devised for maintaining semantic interoperability of data on heterogeneous sensor networks. SRS measures the connectability of the mobile device to ambient sensors based on positions and only provides metadata of sensors that may be successfully connected. The step of identifying the ambient sensors which can be successfully connected is called sensor filtering. Improving the performance of sensor filtering is one of the core issues of SRS research. In reality, GPS sometimes shows the wrong position and thus leads to failed sensor filtering. Therefore, this paper proposes a new sensor filtering strategy using geographical embedding and neural network-based path prediction. This paper also evaluates the service provision rate with the Monte Carlo approach. The empirical study shows that the proposed method can compensate for position abnormalities and is an effective model for sensor filtering in SRS.

• Journal of KIISE
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• v.43 no.12
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• pp.1325-1333
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• 2016

Software classification has several applications such as copyright infringement detection, malware classification, and software automatic categorization in software repositories. It can be also employed by software filtering systems to prevent the transmission of illegal software. If illegal software is identified by measuring software similarity in software filtering systems, the average number of comparisons can be reduced by shrinking the search space. In this study, we focused on the classification of Windows executables using API call information and machine learning. We evaluated the classification performance of machine learning-based classifier according to the refinement method for API information and machine learning algorithm. The results showed that the classification success rate of SVM (Support Vector Machine) with PolyKernel was higher than other algorithms. Since the API call information can be extracted from binary executables and machine learning-based classifier can identify tampered executables, API call information and machine learning-based software classifiers are suitable for software filtering systems.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.4
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• pp.500-507
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• 2016

In order to increase the survivability of combatant ship, measuring and analyzing the infrared radiation is important. Consequently, providing analysis report is also important for the progress of the new combatant ship design. This paper proposes a design and software implementation of filtering for the noise reduction of mid-wave IR camera image. We reduced the total test cost by using the suggested software filtering technique instead of hardware replacement or re-calibration. In addition, we enhanced the accuracy of analysis results by adjusting the parameters of software filtering according to the results of filtered image.

• Journal of the Korean Society for information Management
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• v.24 no.3
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• pp.271-297
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• 2007

Since the use of the Internet has proliferated, the availability of illegal and harmful content has been a great concern to both governments and Internet users. Among various solutions for issues related to such content, Internet content filtering technologies have been developed for enabling users to deal with harmful content. In recent years, commercial filtering has become massively popular. Many parents, teachers and even governments have chosen commercial filtering software as a feasible technical solution for protecting minors from harmful information on the Internet. The Internet content filtering software market has grown significantly. However, Internet content filtering software has led to intense debate among civil liberties groups, They deem this to be censorship and argue that Internet filtering technologies are simply unworkable because they have inherent weaknesses. They are critical of the fact that most filtering has violated free speech rights and will eventually wipe out honor and controversial, yet innocent incidences of free speech on the Internet. In this article Internet content filtering, in particular PICS/RDF-based label filtering, so-called Internet content rating system, will be explored and its advantages and drawbacks relating to end-users' autonomy and freedom of expression will be discussed.

• Journal of Korea Multimedia Society
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• v.19 no.10
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• pp.1767-1774
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• 2016

In this paper, we propose the adaptation filtering for correcting the movement path of the recognized object by the depth information. When we recognize the object by the depth information, the path error should be occurred because of the noises in the depth information. The path error is corrected by appling the lowpass filtering, but the lowpass filtering is not efficient when the changes of the object's movement are rapid. In this paper, we apply the adaptation filtering that it gives weights adaptively as the difference between the predicted location and the measured location. To apply the adaptation filtering, we can see that the proposed method can correct accurately the path error of the radical change from simulation results.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.279-280
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• 2020

최근 소셜 미디어로 이름을 알린 이색 카페와 맛집을 찾아다니는 문화가 확산되는 추세이다. 블로그 포털 검색을 통해 찾아본 맛집은 광고성 게시물이 많아서 신뢰도가 떨어지고, 맛집 관련 게시물 수가 많아서 모든 게시물들을 수동으로 읽기는 불가능하다. 본 논문에서는 사용자들이 선호해서 자발적으로 공유하는 신뢰도 높은 인스타그램의 맛집 포스트 데이터를 이용하여 아이템 기반의 협업 필터링(Item-based Collaborative Filtering) 기법을 통해 사용자의 취향에 맞고 선호할 만한 맛집을 자동으로 추천해주는 알고리즘 및 시스템을 소개한다.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.1-7
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• 2017

In this paper, we introduce three monitoring filtering techniques which reduce the overheads of dynamic data race detection. It is well known that detecting data races dynamically in multi-threaded programs is quite hard and troublesome task, because the dynamic detection techniques need to monitor all execution of a multi-threaded program and to analyse every conflicting memory and thread operations in the program. Thus, the main drawback of the dynamic analysis for detecting data races is the heavy additional time and space overheads for running the program. For the practicality, we also empirically compare the efficiency of three monitoring filtering techniques. The results using OpenMP benchmarks show that the filtering techniques are practical for dynamic data race detection, since they reduce the average runtime overhead to under 10% of that of the pure detection.

• The KIPS Transactions:PartC
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• v.16C no.5
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• pp.543-554
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• 2009

The openness of the Web allows any user to access any type of information easily at any time and anywhere. However, with function of easy access for useful information, internet has dysfunctions of providing users with harmful contents indiscriminately. Some information, such as adult content, is not appropriate for children. To protect children from these harmful contents, many filtering softwares are developed. However, these softwares can not prevent harmful contents, perfectly, because of some limitations. In this paper, we analyze existing eleven filtering softwares and state the limitation of these softwares. Furthermore, we propose requirements for new filtering software which overcomes the limitations, and describe framework of the new software.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.385-387
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• 2022

With the tremendous rise in popularity of the Internet and technological advancements, many news keeps generating every day from multiple sources. As a result, the information (News) on the network has been highly increasing. The critical problem is that the volume of articles or news content can be overloaded for the readers. Therefore, the people interested in reading news might find it difficult to decide which content they should choose. Recommendation systems have been known as filtering systems that assist people and give a list of suggestions based on their preferences. This paper studies a personalized news recommendation system to help users find the right, relevant content and suggest news that readers might be interested in. The proposed system aims to build a hybrid system that combines collaborative filtering with content-based filtering to make a system more effective and solve a cold-start problem. Twitter social media data will analyze and build a user's profile. Based on users' tweets, we can know users' interests and recommend personalized news articles that users would share on Twitter.