• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1953-1972
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• 2022

The COVID-19 pandemic has resulted in a profound impact on large-scale gatherings throughout the world. Social distancing has become one of the most common measures to restrict the spread of the novel Coronavirus. Islamic pilgrimage attracts millions of pilgrims to Saudi Arabia annually. One of the mandatory rituals of pilgrimage is the symbolic stoning of the devil. Every pilgrim is required to perform this ritual within a specified time on three days of pilgrimage. This ritual is prone to congestion due to strict spatiotemporal requirements. We propose a pedestrian simulation model for implementing social distancing in the stoning ritual. An agent-based simulation is designed to analyze the impact of inter-queue and intra-queue spacing between adjacent pilgrims on the throughput and congestion during the stoning ritual. After analyzing several combinations of intra-queue and inter-queue spacings, we conclude that 25 queues with 1.5 meters of intra-queue spacing result in an optimal combination of throughput and congestion. The Ministry of Hajj in Saudi Arabia may benefit from these findings to manage and plan pilgrimage more effectively.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.685-694
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• 2022

The goal of few-shot semantic segmentation is to build a network that quickly adapts to novel classes with extreme data shortage regimes. Most existing few-shot segmentation methods leverage single or multiple prototypes from extracted support features. Although there have been promising results for natural images, these methods are not directly applicable to the aerial image domain. A key factor in few-shot segmentation on aerial images is to effectively exploit information that is robust against extreme changes in background and object scales. In this paper, we propose a Mask-Guided Attention module to extract more comprehensive support features for few-shot segmentation in aerial images. Taking advantage of the support ground-truth masks, the area correlated to the foreground object is highlighted and enables the support encoder to extract comprehensive support features with contextual information. To facilitate reproducible studies of the task of few-shot semantic segmentation in aerial images, we further present the few-shot segmentation benchmark iSAID-, which is constructed from a large-scale iSAID dataset. Extensive experimental results including comparisons with the state-of-the-art methods and ablation studies demonstrate the effectiveness of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2507-2528
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• 2022

Blockchain interoperability, which refers in particular to the ability to access information across blockchain systems, plays the key role for different blockchains to communicate with each other, and further supports the superstructure built on top of the cross-chain mechanism. Nowadays, blockchain interoperability technology is still in its infancy. The existing cross-chain scheme such as BTCRelay requires that the smart contract in a blockchain to download and maintain block headers of the other blockchain, which is costly in maintenance and inefficient to use. In this paper, we propose a supervision mechanism between blockchains, called Surveillant. Specially, the new entities called dual-functional nodes are introduced to commit the real-time information from the blockchain under supervision to the supervising blockchain, which enables users to have efficient cross-chain verification. Furthermore, we introduce Merkle mountain range for blocks aggregation to deal with the large-scale committing data. We propose the design of long orphan branch counter to trace the bifurcations in the blockchain under supervision. The existing incentive mechanism is improved to encourage the behaviors of dual-functional nodes. In Surveillant, the analysis and experimental results demonstrate that users are able to have efficient cross-chain verification with low maintenance overhead.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1258-1275
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• 2023

Cloud computing is a new technology that has adapted to the traditional way of service providing. Service providers are responsible for managing the allocation of resources. Selecting suitable containers and bandwidth for job scheduling has been a challenging task for the service providers. There are several existing systems that have introduced many algorithms for resource allocation. To overcome these challenges, the proposed system introduces an Optimized Task Scheduling Algorithm with Deep Learning (OTS-DL). When a job is assigned to a Cloud Service Provider (CSP), the containers are allocated automatically. The article segregates the containers as' Long-Term Container (LTC)' and 'Short-Term Container (STC)' for resource allocation. The system leverages an 'Optimized Task Scheduling Algorithm' to maximize the resource utilisation that initially inquires for micro-task and macro-task dependencies. The bottleneck task is chosen and acted upon accordingly. Further, the system initializes a 'Deep Learning' (DL) for implementing all the progressive steps of job scheduling in the cloud. Further, to overcome container attacks and errors, the system formulates a Container Convergence (Fault Tolerance) theory with high-level security. The results demonstrate that the used optimization algorithm is more effective for implementing a complete resource allocation and solving the large-scale optimization problem of resource allocation and security issues.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.209-217
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• 2023

In this study, the evaluation items related to the effectiveness evaluation of the LVC (Live, Virtual, Constructive) training system of the Air Force were derived and the weights of each item were analyzed. The LVC training system evaluation items for AHP (Analytic Hierarchy Process) analysis were divided into three layers, and according to the level, 3 items were derived at level 1, 11 items at level 2, and 33 items at level 3. For weight analysis of evaluation items, an AHP-based pairwise comparison questionnaire was conducted for Air Force experts related to the LVC training system. As a result of the survey, related items such as (1) Achievement of education and training goals (53.8%), (1.2) Large-scale mission and operational performance (25.5%), and (1.2.1) Teamwork among training participants (19.4%) was highly rated. Also, it was confirmed that the weights of evaluation items were not different for each expert group, that is, the priority for importance was evaluated in the same order between the policy department and the working department. Through these analysis results, it will be possible to use them as evaluation criteria for new LVC-related projects of the Air Force and selection of introduction systems.

• Asia pacific journal of information systems
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• v.27 no.2
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• pp.99-125
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• 2017

In this study, we propose an operations model to automate a home energy management system (HEMS) that utilizes an energy storage system (ESS) in consideration of consumer utility. Most previous studies focused on the system for the profits obtained from trading charged energy using large-scale ESS. By contrast, the present study focuses on constructing a home-level energy management system that considers consumer's utility over energy consumption. Depending on personal preference, some residential consumers may prefer consuming additional energy to earn increased profits through price arbitrage and vice versa. However, the current system could not yet reflect on this aspect. Thus, we develop an operations model for HEMS that could automatically control energy consumption while considering the level of consumer's preference and the economic benefits of using an ESS. The results of simulations using a dataset of the Korean market show that an operations policy of charging and discharging can be changed depending on consumer's utility. The impact of this policy is not ignorable. Moreover, the technical specifications of ESS, such as self-discharge rate and round-trip efficiency, can affect the operations policy and automation of HEMS.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.571-590
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• 2019

Despite the growing amount of mobile peer-to-peer (P2P) payment applications available on mobile app stores, these applications are still in their infancy and have yet to see mass adoption. This study aims to explore the factors that influence the adoption of such mobile P2P payment applications by using a large-scale data set based on the tracking of users' actual mobile application usage behavior. Our main findings reveal that the duration of each session that users use of traditional bank application has a significant relationship with their adoption of mobile P2P payment applications. In addition, we explore the social aspect of such mobile P2P payment applications by analyzing their social network applications usage and found that the amount of social network service applications used and usage duration positively impacted one's adoption of mobile P2P payment applications. These findings have important theoretical and practical implications for stakeholders of mobile P2P payment solution providers as well as intermediaries/banks who provide their own payment applications to their customers.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.64-72
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• 2024

In recent times cyber attackers can use Artificial Intelligence (AI) to boost the sophistication and scope of attacks. On the defense side, AI is used to enhance defense plans, to boost the robustness, flexibility, and efficiency of defense systems, which means adapting to environmental changes to reduce impacts. With increased developments in the field of information and communication technologies, various exploits occur as a danger sign to cyber security and these exploitations are changing rapidly. Cyber criminals use new, sophisticated tactics to boost their attack speed and size. Consequently, there is a need for more flexible, adaptable and strong cyber defense systems that can identify a wide range of threats in real-time. In recent years, the adoption of AI approaches has increased and maintained a vital role in the detection and prevention of cyber threats. In this paper, an Ensemble Deep Restricted Boltzmann Machine (EDRBM) is developed for the classification of cybersecurity threats in case of a large-scale network environment. The EDRBM acts as a classification model that enables the classification of malicious flowsets from the largescale network. The simulation is conducted to test the efficacy of the proposed EDRBM under various malware attacks. The simulation results show that the proposed method achieves higher classification rate in classifying the malware in the flowsets i.e., malicious flowsets than other methods.

• Asia pacific journal of information systems
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• v.34 no.2
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• pp.635-660
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• 2024

A growing body of research explores how users of online communities navigate through large-scale platforms to find the information they seek. This study builds on the theories of homophily, structural embeddedness, and social exchange to investigate how interest homophily and existing communication structures serve as mechanisms driving information searches and the subsequent formation of communication networks in these communities. Specifically, we analyze comment-on-post tie formation using network data from "Today's House," the largest online user community specializing in interior design in Korea. Employing the LR-QAP method, a permutation-based hypothesis testing algorithm for social network data, our research identifies that network tie formation is driven by both homophilous information searches based on instrumental and hedonic interests, as well as by structurally induced searches such as preferential attachment, reciprocity, and transitivity. In addition, we investigate the contingent effects of communication structure on homophilous tie formation. Our findings suggest that while network-wide structural characteristics enhance homophilous tie formation based on instrumental interests, local network processes leverage homophily based on hedonic interests. We conclude by discussing the theoretical implications of the differential influence of participation motivations on information search patterns and the practical implications for the design of online communities.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.135-143
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• 2002

In this study, Multi-Values Logic processor was designed using the basic circuit of the electric current mode CMOS. First of all, binary FFT(Fast courier Transform) was extended and high-speed Multi-Valued Logic processor was constructed using a multi valued logic circuit. Compared with the existing two-valued FFT, the FFT operation can reduce the number of transistors significantly and show the simplicity of the circuit. Moreover, for the construction of amount was used inside the FFT circuit with the set of redundant numbers like {0, 1, 2, 3}. As a result, the defects in lines were reduced and it turned out to be effective in the aspect of normality an regularity when it was used designing VLSI(Very Large Scale Integration). To multiply FFT, the time and size of the operation was used toed as LUT(Lood Up Table).