• Asia pacific journal of information systems
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• v.13 no.3
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• pp.63-84
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• 2003

Business process redesign(BPR) has been widely adopted as an organizational change method since 1990s as the competitive pressures have forced organizations to constantly change. Although BPR has provided successful stories to gain dramatic improvement in performance and has been promoted as an enabler of organizational change, many organizations have faced serious challenges with widely mixed results due to the lack of understanding for potential organizational conflicts and the improper targeting of critical processes in the initial stage of BPR. This paper proposes a cognitive map based method to help organizational members identify potential organizational conflicts, capture core business activities, and suggests guidelines to lead to the necessary organizational change. A computerized tool has been developed to support the real world cases. Working procedure of the proposed method is illustrated with its application to the real BPR project of a dairy company.

• World Technopolis Review
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• v.7 no.1
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• pp.2-14
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• 2018

Nowadays, we are standing in front of the $4^{th}$ Industrial Revolution that is featured by a great range of new and advanced technologies that influences all the domains of economies and industries. The great question that this revolution raises is how it can lead to a future that reflects the peoples' common objectives and values on how these advanced technologies can affect the life and transform the economic, social, cultural, and human environment. It is commonly agreed that to be adapted to these changes and needs and shape a society with competitive economies with highly-skilled individuals, we need to encourage innovation, entrepreneurship, new knowledge generation and exchange and true and effective collaboration and communication. In this complex scene, education seems to have a central and critical role on finding new ways of developing expertise and innovation within the existing knowledge procedures, with more and better cooperation between the key players. This paper argues the concepts, opportunities and challenges that are related to the learning ecosystem towards the needs raised by the $4^{th}$ Industrial Revolution. The education is discussed as catalyst but also as carrier of innovation and innovation practices and the basis of a relevant framework is presented that takes into account all the aspects, domains and key players of educational world and interacting domains. Having introduced the ideas of innovation, collaboration and technology advancement in this environment, this paper also presents a real case of practice, focusing on how more than 5.000 schools around Europe succeeded the last four (4) years to implement innovation activities in a collaborative way and under a unique but also flexible pedagogical innovation framework.

• Korean Journal of Computational Design and Engineering
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• v.16 no.5
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• pp.370-379
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• 2011

The increasing complexity of complex buildings, such as high-rise buildings and underground subway stations, presents new challenges to firefighters. In a fire in complex buildings, the importance of the collaboration between firefighters is clear. The increased demand on firefighter training for such environment is now evident. Due to cost, time, and safety issues, it is impossible to experience a real fire in such environments for training. In addition, the use of real fire for training does not enable repeatable training and the evaluation of the training is difficult. We developed a team-based firefighter training simulator for complex buildings using the virtual reality technology. It provides the training and evaluation of firefighting and mission-based team training. To model real fire phenomena in virtual space, a numerical analysis method based on fire dynamics is used. To achieve an immersive virtual environment, an augmented reality technique for the compensation of real world image and a haptic technique for heat experience are adopted. The developed training simulator can help the firefighter to respond to large and complex firefighting scenarios, while maintaining the safety of the trainees.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.1010-1032
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• 2018

Recent advances in medical science have made people live longer, which has affected many aspects of life, such as caregiver burden, increasing cost of healthcare, increasing number of disabled and depressive disorder persons, and so on. Researchers are now focused on elderly living assistance services in smart home environments. In recent years, assisted living technologies have rapidly grown due to a faster growing aging society. Many smart devices are now interconnected within the home network environment and such a home setup supports collaborations between those devices based on the Internet of Things (IoT). One of the major challenges in providing elderly living assistance services is to consider each individual's requirements of different needs. In order to solve this, the virtualization of physical things, as well as the collaboration and composition of services provided by these physical things should be considered. In order to meet these challenges, Web of Objects (WoO) focuses on the implementation aspects of IoT to bring the assorted real world objects with the web applications. We proposed a semantic modelling technique for manual and semi-automated service composition. The aim of this work is to propose a framework to enable RESTful web services composition using semantic ontology for elderly living assistance services creation in WoO based smart home environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.51-73
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• 2023

Social media play a significant role in communicating information across the globe, connecting with loved ones, getting the news, communicating ideas, etc. However, a group of people uses social media to spread fake information, which has a bad impact on society. Therefore, minimizing fake news and its detection are the two primary challenges that need to be addressed. This paper presents a multi-modal deep learning technique to address the above challenges. The proposed modal can use and process visual and textual features. Therefore, it has the ability to detect fake information from visual and textual data. We used EfficientNetB0 and a sentence transformer, respectively, for detecting counterfeit images and for textural learning. Feature embedding is performed at individual channels, whilst fusion is done at the last classification layer. The late fusion is applied intentionally to mitigate the noisy data that are generated by multi-modalities. Extensive experiments are conducted, and performance is evaluated against state-of-the-art methods. Three real-world benchmark datasets, such as MediaEval (Twitter), Weibo, and Fakeddit, are used for experimentation. Result reveals that the proposed modal outperformed the state-of-the-art methods and achieved an accuracy of 86.48%, 82.50%, and 88.80%, respectively, for MediaEval (Twitter), Weibo, and Fakeddit datasets.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.160-160
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• 2019

Water is a limited source that needs to be properly managed and distributed to the ever-growing population of the world. Rapid urbanization and development have increased the overall water demand of the world drastically. However, there is loss of billions of liters of water every year due to leakages in water distribution systems. Such water loss means significant financial loss for the utilities as well. World bank estimates a loss of $14 billion annually from wasted water. To address these issues and for the development of efficient and reliable leakage management techniques, high efforts have been made by the researchers and engineers. Over the past decade, various techniques and technologies have been developed for leakage management and leak detection. These include ideas such as pressure management in water distribution networks, use of Advanced Metering Infrastructure, use of machine learning algorithms, etc. For leakage detection, techniques such as acoustic technique, and in recent yeats transient test-based techniques have become popular. Smart Water Grid uses two-way real time network monitoring by utilizing sensors and devices in the water distribution system. Hence, valuable real time data of the water distribution network can be collected. Best results and outcomes may be produced by proper utilization of the collected data in unison with advanced detection and management techniques. Long term reduction in Non Revenue Water can be achieved by detecting, localizing and repairing leakages as quickly and as efficiently as possible. However, there are still numerous challenges to be met and future research works to be conducted in this field.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.725-739
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• 2022

This study aims to analyze what elementary school pre-service teachers focused on and what challenges they faced in designing and producing a guided science inquiry program based on augmented reality (AR) and to provide some implications for teachers' professionalism and teacher education. To this end, focusing on the cases of pre-service teachers who designed and created AR-based guided inquiry programs, the researchers extracted and categorized the pre-service teachers' focus and challenges from the program design and production stages. As a result, in the program design stage, the pre-service teachers tried to construct scenarios that could promote students' active inquiry process. At the same time, drawing on the unique affordances of AR, the pre-service teachers focused on creating vivid visual data in a 3D environment and making meaningful connections between virtual and real-world activities. The pre-service teachers faced challenges in making use of the advantages of AR technology and designing an inquiry program due to a lack of background knowledge about CoSpaces, a content creation program. In the program production stage, the pre-service teachers tried to make their program easy to handle to improve students' concentration on inquiry activities. In addition, challenges of programming using CoSpaces were reported. Based on these results, educational implications were discussed in terms of the pedagogical uses of AR and teachers' professionalism in adopting AR in science inquiry.

• ETRI Journal
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• v.42 no.3
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• pp.411-419
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• 2020

In real-world intelligent transportation systems, accuracy in vehicle license plate detection and recognition is considered quite critical. Many algorithms have been proposed for still images, but their accuracy on actual videos is not satisfactory. This stems from several problematic conditions in videos, such as vehicle motion blur, variety in viewpoints, outliers, and the lack of publicly available video datasets. In this study, we focus on these challenges and propose a license plate detection and recognition scheme for videos based on a temporal matching prior network. Specifically, to improve the robustness of detection and recognition accuracy in the presence of motion blur and outliers, forward and bidirectional matching priors between consecutive frames are properly combined with layer structures specifically designed for plate detection. We also built our own video dataset for the deep training of the proposed network. During network training, we perform data augmentation based on image rotation to increase robustness regarding the various viewpoints in videos.

• ETRI Journal
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• v.43 no.6
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• pp.1004-1012
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• 2021

The collaboration productively interacting between multi-agents has become an emerging issue in real-world applications. In reinforcement learning, multi-agent environments present challenges beyond tractable issues in single-agent settings. This collaborative environment has the following highly complex attributes: sparse rewards for task completion, limited communications between each other, and only partial observations. In particular, adjustments in an agent's action policy result in a nonstationary environment from the other agent's perspective, which causes high variance in the learned policies and prevents the direct use of reinforcement learning approaches. Unexpected social loafing caused by high dispersion makes it difficult for all agents to succeed in collaborative tasks. Therefore, we address a paradox caused by the social loafing to significantly reduce total returns after a certain timestep of multi-agent reinforcement learning. We further demonstrate that the collaborative paradox in multi-agent environments can be avoided by our proposed effective early stop method leveraging a metric for social loafing.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.214-222
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• 2021

Recently, the growth of e-commerce in Saudi Arabia has been exponential, bringing new remarkable challenges. A naive approach for product matching and categorization is needed to help consumers choose the right store to purchase a product. This paper presents a machine learning approach for product matching that combines deep learning techniques with standard artificial neural networks (ANNs). Existing methods focused on product matching, whereas our model compares products based on unstructured descriptions. We evaluated our electronics dataset model from three business-to-consumer (B2C) online stores by putting the match products collectively in one dataset. The performance evaluation based on k-mean classifier prediction from three real-world online stores demonstrates that the proposed algorithm outperforms the benchmarked approach by 80% on average F1-measure.