• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.3003-3029
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• 2023

The rapid evolution of the IoT has paved the way for new opportunities in smart city domains, including e-health, smart homes, and precision agriculture. However, this proliferation of services demands effective SLAs between customers and service providers, especially for critical services. Difficulties arise in maintaining the integrity of such agreements, especially in vulnerable wireless environments. This study proposes a novel SLA management model that uses an SDN-Enabled WSN consisting of wireless nodes to interact with smart contracts in a straightforward manner. The proposed model ensures the persistence of network metrics and SLA provisions through smart contracts, eliminating the need for intermediaries to audit payment and compensation procedures. The reliability and verifiability of the data prevents doubts from the contracting parties. To meet the high-performance requirements of the blockchain in the proposed model, low-cost algorithms have been developed for implementing blockchain technology in wireless sensor networks with low-energy and low-capacity nodes. Furthermore, a cryptographic signature control code is generated by wireless nodes using the in-memory private key and the dynamic random key from the smart contract at runtime to prevent tampering with data transmitted over the network. This control code enables the verification of end-to-end data signatures. The efficient generation of dynamic keys at runtime is ensured by the flexible and high-performance infrastructure of the SDN architecture.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.438-455
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• 2024

Recently, many unsupervised learning-based models have emerged for Just Noticeable Difference (JND) estimation, demonstrating remarkable improvements in accuracy. However, these models suffer from a significant drawback is that their heavy reliance on handcrafted priors for guidance. This restricts the information for estimating JND simply extracted from regions that are highly related to handcrafted priors, while information from the rest of the regions is disregarded, thus limiting the accuracy of JND estimation. To address such issue, on the one hand, we extract the information for estimating JND in an Adversarial Complementary Learning (ACoL) way and propose an ACoL-JND network to estimate the JND by comprehensively considering the handcrafted priors-related regions and non-related regions. On the other hand, to make the handcrafted priors richer, we take two additional priors that are highly related to JND modeling into account, i.e., Patterned Masking (PM) and Contrast Masking (CM). Experimental results demonstrate that our proposed model outperforms the existing JND models and achieves state-of-the-art performance in both subjective viewing tests and objective metrics assessments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.1090-1100
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• 2024

While the incorporating ESG indicator is recognized as crucial for sustainability and increased firm value, inconsistent disclosure of ESG data and vague assessment standards have been key challenges. To address these issues, this study proposes an ambiguous text-based automated ESG rating strategy. Earnings Call Transcript data were classified as E, S, or G using the Refinitiv-Sustainable Leadership Monitor's over 450 metrics. The study employed advanced natural language processing techniques such as BERT, RoBERTa, ALBERT, FinBERT, and ELECTRA models to precisely classify ESG documents. In addition, the authors computed the average predicted probabilities for each label, providing a means to identify the relative significance of different ESG factors. The results of experiments demonstrated the capability of the proposed methodology in enhancing ESG assessment criteria established by various rating agencies and highlighted that companies primarily focus on governance factors. In other words, companies were making efforts to strengthen their governance framework. In conclusion, this framework enables sustainable and responsible business by providing insight into the ESG information contained in Earnings Call Transcript data.

• Journal of Animal Science and Technology
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• 제66권1호
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• pp.167-177
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• 2024

Iris segmentation is an initial step for identifying the biometrics of animals when establishing a traceability system for livestock. In this study, we propose a deep learning framework for pixel-wise segmentation of bovine iris with a minimized use of annotation labels utilizing the BovineAAEyes80 public dataset. The proposed image segmentation framework encompasses data collection, data preparation, data augmentation selection, training of 15 deep neural network (DNN) models with varying encoder backbones and segmentation decoder DNNs, and evaluation of the models using multiple metrics and graphical segmentation results. This framework aims to provide comprehensive and in-depth information on each model's training and testing outcomes to optimize bovine iris segmentation performance. In the experiment, U-Net with a VGG16 backbone was identified as the optimal combination of encoder and decoder models for the dataset, achieving an accuracy and dice coefficient score of 99.50% and 98.35%, respectively. Notably, the selected model accurately segmented even corrupted images without proper annotation data. This study contributes to the advancement of iris segmentation and the establishment of a reliable DNN training framework.

• 한국전자거래학회지
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• 제18권3호
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• pp.211-222
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• 2013

지리적으로 인접되어 있으면서 이동관점에서 같은 역할을 수행하는 Zone의 파악은 사람들의 이동흐름을 이해하고 도시개발 및 이동편의성 개선 등을 위한 중요한 정보로 활용된다. 그러나 기존의 연구는 특정 지점간의 이동과 Zone 발견을 개별적으로 수행하여, 거시적 관점에서의 이동패턴을 이해하는 데에는 한계가 존재한다. 따라서 본 연구에서는 스마트카드 전자거래 빅데이터로부터 Zone들을 발견하고 동시에 Zone들 간의 관계를 설명하는 클러스터링 기반의 이동패턴 분석기법을 제안한다. 또한, 설명력과 종속성 관점에서 이동패턴을 정량적으로 평가하는 지표를 제안한다. 제안된 분석기법을 이용하여 서울시 지하철에서 수집된 실 데이터를 분석하여 서울시에서의 이동패턴을 밝혀내고 시각화하였다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제35권8호
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• pp.512-521
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• 2008

웹 기반 정보 서비스는, 웹 정보 서버와 상호작용하는, 다양한 인터넷 웹-브라우징 기기들과의 접근성과 사용성을 위해서 평가되어야 한다. 다양한 웹-브라우징 장치로(예: 풀부라우징 휴대폰) 접근 및 사용가능하며, 신뢰할 수 있는 유비쿼터스 웹 정보서버는 유비쿼터스 웹 정보 서비스뿐만 아니라 개인화된 광고기반의 비즈니스 모델을 위한 통합센터가 되어야 한다. 개인화된 유비쿼터스 웹 정보 서비스를 위한 웹 상호작용의 접근성과 사용성을, 실시간 평가를 위한 메트릭으로서, 연구하였다. 시험용 웹사이트 ('ktrip.net')와 1자 한글 도메인명(예: 김.net, 이.net, 박.net, 최.net, ㄱ.net, ㄴ.net ... ㅎ.net, ㅏ.net ... ㅔ.net, ㄱ.com, ㄴ.com ... ㅎ.com)을 사용하여, 구현 및 한국, 일본 및 중국에서의 실험을 바탕으로 한 실험적 결과를 소개한다.

• 한국도서관정보학회지
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• 제45권3호
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• pp.5-36
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• 2014

웹은 하이퍼링크 및 야후와 같이 수동으로 분류된 웹 디렉토리 처럼 문서의 콘텐츠를 넘어선 다양한 정보의 소스가 풍부하다. 이 연구는 웹문서 내용을 활용한 텍스트기반의 검색 방식, 하이퍼 링크를 활용한 링크 기반의 검색 방식, 그리고 야후의 카테고리를 활용한 분류 기반의 검색 방식을 융합하므로서 여러 정보소스를 결합하면 검색 성능을 향상시킬 수 있다는 기존 융합검색연구들을 확장시켰다. 텍스트, 링크, 분류 기반 검색 결과를 여러가지 선형조합식으로 생성한 융합결과를 기존의 검색 평가 지표를 사용하여 각각의 검색 결과와 비교 한 후, 검색결과 오버랩의 중요성 또한 조사 하였다. 본 연구는 텍스트, 링크, 분류 기반 검색의 솔루션 스패이스들의 다양성이 융합검색의 적합성을 제시한다는 결론과 더불어 시스템 파라미터의 영향, 그리고 오버랩, 문서순위, 관련성들의 상호 관계 같은 융합 환경의 중요한 특성들을 분석하였다.

• 한국전자거래학회지
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• 제14권2호
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• pp.95-116
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• 2009

지식 재사용 측면에서 기존의 온톨로지를 재사용할 수 있다면 많은 자원을 절약할 수 있을 것이다. 그러나 기존의 온톨로지를 활용하기 위해서는 보다 발전된 온톨로지 검색 기능이 요구된다. 현재까지 이루어진 관련 연구들에서는 주로 렉시컬 매칭기법을 사용하여 온톨로지를 검색하였다. 그러나 의미적 측면에서 문제점이 있으므로 본 연구에서는 관계의 의미적 유사성에 기반한 온톨로지 선택 랭킹 모델을 제안한다. 본 연구는 개념간 계층 구조와 관계를 온톨로지 검색에 이용함으로써 온톨로지의 선택 랭킹을 효과적이며 실질적으로 개선하였다. 또한 실험을 통해 연구 모델의 결과와 선행 연구의 결과, 온톨로지 전문가의 랭킹 결과를 비교 분석하고 연구 모델의 타당성을 검증하였다. 본 연구 결과는 온톨로지 검색 연구를 이론적으로 발전시켰을 뿐 아니라 실무적인 측면에서 실무자들이 온톨로지를 쉽게 찾아 재사용할 수 있도록 한다.

• 한국산업정보학회논문지
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• 제19권2호
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• pp.1-13
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• 2014

In IEEE 802.11p/1609-based vehicular networks, vehicles are allowed to exchange safety and control messages only within time periods, called control channel (CCH) interval, which are scheduled periodically. Currently, the length of the CCH interval is set to the fixed value (i.e. 50ms). However, the fixed-length intervals cannot be effective for dynamically changing traffic load. Hence, some protocols have been recently proposed to support variable-length CCH intervals in order to improve channel utilization. In existing protocols, the CCH interval is subdivided into safety and non-safety intervals, and the length of each interval is dynamically adjusted to accommodate the estimated traffic load. However, they do not consider the presence of hidden nodes. Consequently, messages transmitted in each interval are likely to overlap with simultaneous transmissions (i.e. interference) from hidden nodes. Particularly, life-critical safety messages which are exchanged within the safety interval can be unreliably delivered due to such interference, which deteriorates QoS of safety applications such as cooperative collision warning. In this paper, we therefore propose a new interference-aware Dynamic Safety Interval (DSI) protocol. DSI calculates the number of vehicles sharing the channel with the consideration of hidden nodes. The safety interval is derived based on the measured number of vehicles. From simulation study using the ns-2, we verified that DSI outperforms the existing protocols in terms of various metrics such as broadcast delivery ration, collision probability and safety message delay.

• Journal of Microbiology and Biotechnology
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• 제28권8호
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• pp.1247-1259
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• 2018

Raspberries are polyphenol-rich fruits with the potential to reduce the severity of the clinical signs associated with obesity, a phenomenon that may be related to changes in the gut microbiota. The aim of this study was to investigate the effect of raspberry supplementation on the fecal microbiota using an in vivo model of obesity. Obese diabetic db/db mice were used in this study and assigned to two experimental groups (with and without raspberry supplementation). Fecal samples were collected at the end of the supplementation period (8 weeks) and used for bacterial 16S rRNA gene profiling using a MiSeq instrument (Illumina). QIIME 1.8 was used to analyze the 16S data. Raspberry supplementation was associated with an increased abundance of Lachnospiraceae (p = 0.009), a very important group for gut health, and decreased abundances of Lactobacillus, Odoribacter, and the fiber degrader S24-7 family as well as unknown groups of Bacteroidales and Enterobacteriaceae (p < 0.05). These changes were enough to clearly differentiate bacterial communities accordingly to treatment, based on the analysis of UniFrac distance metrics. However, a predictive approach of functional profiles showed no difference between the treatment groups. Fecal metabolomic analysis provided critical information regarding the raspberry-supplemented group, whose relatively higher phytosterol concentrations may be relevant for the host health, considering the proven health benefits of these phytochemicals. Further studies are needed to investigate whether the observed differences in microbial communities (e.g., Lachnospiraceae) or metabolites relate to clinically significant differences that can prompt the use of raspberry extracts to help patients with obesity.