• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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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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• v.18 no.2
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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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• v.18 no.4
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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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• v.66 no.1
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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.

• The Journal of Society for e-Business Studies
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• v.18 no.3
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• pp.211-222
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• 2013

Discovering zones which a1re sets of geographically adjacent regions are essential in sophisticated urban developments and people's movement improvements. While there are some studies that separately focus on movements between particular regions and zone discovery, they show limitations to understand people's movements from a wider viewpoint. Therefore, in this research, we propose a clustering based analysis method that aims at discovering movement patterns, which involves zones and their relations, based on a big data of smart card transaction systems. Moreover, the effectiveness of discovered movement patterns is quantitatively evaluated by using the proposed metrics. By using a real-world dataset obtained in Seoul metropolitan subway networks, we investigate and visualize hidden movement patterns in Seoul.

• Journal of KIISE:Software and Applications
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• v.35 no.8
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• pp.512-521
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• 2008

Web-based information services should be evaluated for accessibility and usability with various types of Internet Web-browsing devices, interacting with web information servers. A reliable ubiquitous Web information server, accessible and usable with a variety of Web-browsing devices (e.g. a full-browsing mobile phone), should be a unified center for personalized ubiquitous Web information services as well as for business models based on personalized advertisements. We studied an estimation of the accessibility and usability in Web interaction for personalized ubiquitous Web information services, as metrics for real-time estimation. We show empirical results based on implementation and experiments in Korea, Japan and China, using a test-bed Web site ('ktrip.net') and single-character Korean domain names (e.g. 김.net, 이.net, 박.net, 최.net, ㄱ.net, ㄴ.net ... ㅎ.net, ㅏ.net, ... ㅔ.net, ㄱ.com, ㄴ.com ... ㅎ.com).

• Journal of Korean Library and Information Science Society
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• v.45 no.3
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• pp.5-36
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• 2014

The Web is rich with various sources of information that go beyond the contents of documents, such as hyperlinks and manually classified directories of Web documents such as Yahoo. This research extends past fusion IR studies, which have repeatedly shown that combining multiple sources of evidence (i.e. fusion) can improve retrieval performance, by investigating the effects of combining three distinct retrieval approaches for Web IR: the text-based approach that leverages document texts, the link-based approach that leverages hyperlinks, and the classification-based approach that leverages Yahoo categories. Retrieval results of text-, link-, and classification-based methods were combined using variations of the linear combination formula to produce fusion results, which were compared to individual retrieval results using traditional retrieval evaluation metrics. Fusion results were also examined to ascertain the significance of overlap (i.e. the number of systems that retrieve a document) in fusion. The analysis of results suggests that the solution spaces of text-, link-, and classification-based retrieval methods are diverse enough for fusion to be beneficial while revealing important characteristics of the fusion environment, such as effects of system parameters and relationship between overlap, document ranking and relevance.

• The Journal of Society for e-Business Studies
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• v.14 no.2
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• pp.95-116
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• 2009

Ontologies have provided supports in integrating heterogeneous and distributed information. More and more ontologies and tools have been developed in various domains. However, building ontologies requires much time and effort. Therefore, ontologies need to be shared and reused among users. Specifically, finding the desired ontology from an ontology repository will benefit users. In the past, most of the studies on retrieving and ranking ontologies have mainly focused on lexical level supports. In those cases, it is impossible to find an ontology that includes concepts that users want to use at the semantic level. Most ontology libraries and ontology search engines have not provided semantic matching capability. Retrieving an ontology that users want to use requires a new ontology selection and ranking mechanism based on semantic similarity matching. We propose an ontology selection and ranking model consisting of selection criteria and metrics which are enhanced in semantic matching capabilities. The model we propose presents two novel features different from the previous research models. First, it enhances the ontology selection and ranking method practically and effectively by enabling semantic matching of taxonomy or relational linkage between concepts. Second, it identifies what measures should be used to rank ontologies in the given context and what weight should be assigned to each selection measure.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.2
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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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• v.28 no.8
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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.