• 디지털산업정보학회논문지
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• 제15권3호
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• pp.69-78
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• 2019

The amount of data is increasing significantly as information and communication technology advances, mobile, cloud computing, the Internet of Things and social network services become commonplace. As the data grows exponentially, there is a growing demand for services that recommend the information that users want from large amounts of data. Collaborative filtering method is commonly used in information recommendation methods. One of the problems with collaborative filtering-based recommendation method is the cold start problem. In this paper, we propose a method to improve the cold start problem. That is, it solves the cold start problem by mapping the item evaluation data that does not exist to the initial user to the automatically generated data from the mobile app. We describe the main contents of the proposed method and explain the proposed method through the book recommendation scenario. We show the superiority of the proposed method through comparison with existing methods.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2019년도 춘계종합학술대회
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• pp.211-212
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• 2019

The objectives of this research are to test the utility of semiparametric geographically weighted regression (SGWR, a spatial analysis method) in the small-scale urban sample, and to understand the geographic patterns of provision and pricing of sharing economy based accommodations in the tourist city. This paper focused on how network distance to heritage site, to casino, residential unit prices and other five attribute categories determine Airbnb price in Macau SAR, China. Findings show that SGWR models outperformed OLS models. Moreover, comparing with heritage sites, casinos are the stronger factors to drive up Airbnb (including hostels) rooms' provision and their prices; and residential unit prices are not related with the Airbnb price in the attraction clusters in Macau. This research showed a little example for the applications of SGWR in the small city, and for the analysis of online marketplace data as new urban study material. Practically, this study provides some scientific evidence for hosts, guests, urban planners, and policymakers' decision making in Macau.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.6123-6138
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• 2018

Named data networking (NDN) is a new network architecture designed for next generation Internet. Router-side content caching is one of the key features in NDN, which can reduce redundant transmission, accelerate content distribution and alleviate congestion. However, several security problems are introduced as well. One important security risk is cache privacy leakage. By measuring the content retrieve time, adversary can infer its neighbor users' hobby for privacy content. Focusing on this problem, we propose a cache privacy protection mechanism (named as CPPM-DAM) to identify legitimate user and adversary using Bloom filter. An optimization for storage cost is further provided to make this mechanism more practical. The simulation results of ndnSIM show that CPPM-DAM can effectively protect cache privacy.

• 센서학회지
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• 제31권1호
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• pp.16-23
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• 2022

Cardiac disease is the most common cause of death worldwide. Therefore, detection and classification of electrocardiogram (ECG) signals are crucial to extend life expectancy. In this study, we aimed to implement an artificial intelligence signal recognition system in field programmable gate array (FPGA), which can recognize patterns of bio-signals such as ECG in edge devices that require batteries. Despite the increment in classification accuracy, deep learning models require exorbitant computational resources and power, which makes the mapping of deep neural networks slow and implementation on wearable devices challenging. To overcome these limitations, spiking neural networks (SNNs) have been applied. SNNs are biologically inspired, event-driven neural networks that compute and transfer information using discrete spikes, which require fewer operations and less complex hardware resources. Thus, they are more energy-efficient compared to other artificial neural networks algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.450-470
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• 2023

Traditional cloud computing faces some challenges such as huge energy consumption, network delay and single point of failure. Edge computing is a typical distributed processing platform which includes multiple edge servers closer to the users, thus is more robust and can provide real-time computing services. Although outsourcing data to edge servers can bring great convenience, it also brings serious security threats. In order to provide image retrieval while ensuring users' data privacy, a privacy preserving image retrieval scheme in edge environment is proposed. Considering the distributed characteristics of edge computing environment and the requirement for lightweight computing, we present a privacy-preserving image retrieval scheme in edge computing environment, which two or more "honest but curious" servers retrieve the image quickly and accurately without divulging the image content. Compared with other traditional schemes, the scheme consumes less computing resources and has higher computing efficiency, which is more suitable for resource-constrained edge computing environment. Experimental results show the algorithm has high security, retrieval accuracy and efficiency.

• ETRI Journal
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• 제44권6호
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• pp.977-990
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• 2022

This study proposes a novel method for identifying the primary conspirators involved in terrorist activities. To map the information related to terrorist activities, we gathered information from different sources of real cases involving terrorist attacks. We extracted useful information from available sources and then mapped them in the form of terrorist networks, and this mapping provided us with insights in these networks. Furthermore, we came up with a novel centrality measure for identifying the primary conspirators of a terrorist attack. Because the leaders of terrorist attacks usually direct conspirators to conduct terrorist activities, we designed a novel algorithm that can identify such leaders. This algorithm can identify terrorist attack leaders even if they have less connectivity in networks. We tested the effectiveness of the proposed algorithms on four real-world datasets and conducted an experimental evaluation, and the proposed algorithms could correctly identify the primary conspirators and leaders of the attacks in the four cases. To summarize, this work may provide information support for security agencies and can be helpful during the trials of the cases related to terrorist attacks.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 춘계학술발표대회
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• pp.633-634
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• 2020

본 논문에서는 미디어영상을 기반한 무대 공연의 다양한 미디어 효과를 분석하고, 무대 공연을 위한 제스처 기반 동적 프로젝션 맵핑 프레임워크를 설계 구현한다. 이를 위하여, 동적 프로젝션 맵핑 기반 기존 공연에서 공연자의 제스처와 이에 따른 미디어 효과를 분석하고, 동적 프로젝션 맵핑기술을 효율적으로 구현하기 위하여 모션 히스토리 이미지를 이용한 CNN(Convolutional Neural Network) 기반의 제스처 인식 기술을 구현한다. 또한, 구현된 제스처인식 기술을 기반으로 공연자의 서로 다른 제스처와 미디어 효과를 매칭시킬 수 있는 프레임 워크 구현 내용을 소개한다.

• 스마트미디어저널
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• 제12권9호
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• pp.45-59
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• 2023

Weakly supervised object localization (WSOL) is a task of localizing an object in an image using only image-level labels. Previous studies have followed the conventional class activation mapping (CAM) pipeline. However, we reveal the current CAM approach suffers from problems which cause original CAM could not capture the complete defects features. This work utilizes a convolutional neural network (CNN) pretrained on image-level labels to generate class activation maps in a multi-scale manner to highlight discriminative regions. Additionally, a vision transformer (ViT) pretrained was treated to produce multi-head attention maps as an auxiliary detector. By integrating the CNN-based CAMs and attention maps, our approach localizes defective regions without requiring bounding box or pixel-level supervision during training. We evaluate our approach on a dataset of apple images with only image-level labels of defect categories. Experiments demonstrate our proposed method aligns with several Object Detection models performance, hold a promise for improving localization.

• 한국지리정보학회지
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• 제18권2호
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• pp.75-91
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• 2015

본 연구의 목적은 유시티의 진화지도를 통하여 유시티 진화 특성을 분석하는데 있다. 유시티 진화 특성은 서비스, 기술, 인프라, 관리 등으로 구성된 STIM 모형의 관점에 따라 부문별 진화지도를 구축하여 분석하였다. 분석자료는 2002년부터 2013년까지 한국정보사회진흥원에서 발간한 국가정보화백서를 활용하였다. 분석 결과는 다음과 같다. 첫째, 유시티 서비스는 행정 정보화 서비스, 기업 정보화 서비스, 행정 생활 정보화 서비스, 행정 공간 민간 정보화 서비스로 진화되었다. 둘째, 유시티 기술은 유선 네트워크, 센서 네트워크, 프로세싱 초고속 네트워크, 네트워크 및 보안, 융합기술 등으로 진화하였다. 셋째, 유시티 인프라는 유선 네트워크, 유 무선네트워크, 지능화시설(1990년대 후반), 지능형 시설 공간(2000년대 초반)으로 진화되었다. 넷째, 유시티 관리는 단위 네트워크 인프라 관리, 정보연계 및 운영 관리, 정보통합 및 참여운영 관리 등으로 진화 되었다. 따라서 유시티는 행정 중심의 정부 정보화, 기술 중심의 컴퓨터 지향 사회, 서비스 중심의 정보화 도시, 공간 중심의 유시티로 진화되었음을 알 수 있다.

• 정보처리학회논문지C
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• 제14C권6호
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• pp.537-544
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• 2007

홈 네트워크 환경은 가정내의 다양한 디지털 기기들이 네트워크로 통합한 최첨단 생활환경으로써, 이동 에이전트는 이러한 홈 네트워크 환경에서의 새로운 컴퓨팅 요소로써 활용될 것으로 기대된다. 특히, 이동 에이전트의 이동성과 비동기적 수행능력은 가정내의 디지털 기기들을 제어하고 관리하기 위해 발생하는 네트워크 트래픽을 감소 시킬 수 있다. 그러나, 이동 에이전트를 홈 네트워크 환경에서 적용하기 위해서는 이동 에이전트에 대한 접근제어가 반드시 필요하다. 기존의 홈 네트워크 시스템에서는 홈 서버를 이용하여 사용자에 대한 접근제어를 수행한다. 홈 서버는 디지털 기기와 사용자의 권한을 명시하는 접근제어 목록을 이용하여 홈 네트워크로 접근하는 사용자에 대한 접근제어를 수행한다. 이를 위해 홈 서버는 디지털 기기와 사용자의 권한 간의 최신 정보를 저장하기 위해 주기적으로 접근제어 목록을 갱신하는 추가적인 연산을 수행한다. 따라서, 본 논문에서는 홈 네트워크 환경에서 이동 에이전트의 역할에 기반한 접근제어 프레임워크(Secure-KAgent)를 보인다. 본 프레임워크는 Role-Based Access Control(RBAC)을 기초한 접근 권한의 관리가 가능하다. 또한, 본 논문에서 제안하는 롤 티켓(Role ticket)을 이용함으로써 이동 에이전트에게 안전한 역할 분배를 보장한다.