• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.1
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• pp.9-15
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• 2021

Recently, it is increasingly necessary to run high-end neural network applications with huge computation overhead on top of resource-constrained embedded systems, such as wearable devices. While the huge computational overhead can be alleviated by distributed neural networks running on multiple separate devices, existing distributed neural network techniques suffer from a large traffic between the devices; thus are very vulnerable to communication failures. These drawbacks make the distributed neural network techniques inapplicable to wearable devices, which are connected with each other through unstable and low data rate communication medium like human body communication. Therefore, in this paper, we propose a distributed neural network partitioning technique that is resilient to communication failures. Furthermore, we show that the proposed technique also improves the inference accuracy even in case of no communication failure, thanks to the improved network partitioning. We verify through comparative experiments with a real-life neural network application that the proposed technique outperforms the existing state-of-the-art distributed neural network technique in terms of accuracy and resiliency to communication failures.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.55-66
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• 2023

Community portraits can deeply explore the characteristics of community structures and describe the personalized knowledge needs of community users, which is of great practical significance for improving community recommendation services, as well as the accuracy of resource push. The current community portraits generally have the problems of weak perception of interest characteristics and low degree of integration of topic information. To resolve this problem, the reader community portrait method based on the thematic and timeliness characteristics of interest labels (UIT) is proposed. First, community opinion leaders are identified based on multi-feature calculations, and then the topic features of their texts are identified based on the LDA topic model. On this basis, a semantic mapping including "reader community-opinion leader-text content" was established. Second, the readers' interest similarity of the labels was dynamically updated, and two kinds of tag parameters were integrated, namely, the intensity of interest labels and the stability of interest labels. Finally, the similarity distance between the opinion leader and the topic of interest was calculated to obtain the dynamic interest set of the opinion leaders. Experimental analysis was conducted on real data from the Douban reading community. The experimental results show that the UIT has the highest average F value (0.551) compared to the state-of-the-art approaches, which indicates that the UIT has better performance in the smooth time dimension.

• East Asian Journal of Business Economics (EAJBE)
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• v.10 no.1
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• pp.23-34
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• 2022

Purpose - Once individuals in an organization don't comprehend with each other's culture and their interaction context differ, there is a bigger danger of poor communication in the organizations. Therefore, people must have a sufficiently comparable interpretation of data, communicative acts and the office itself. For this reason, the purpose of this study is to investigate the relationship between organizational culture and employee communication. Research design, Data, and methodology - The present researcher used categorization matrix development to review data for content. Additionally, the present researcher coded the data to correspond with the identified categories. The role of the categorization matrix is to ensure that the categories hardly represent the concepts and thus validating the findings of the study. Result - The findings from prior resources shows that the role of organizational culture on employee communication satisfaction is a pertinent issue that must be addressed and analyzed to help organizations make the desired profits and productivity. This study provides five solutions between organizational culture and employee communication satisfaction. Conclusion - This study concludes that every employee performs well where they think they are being appreciated and their efforts are rewarded. On the other hand, different organizations have instilled different organizational cultures to ensure they promote the level of satisfaction of their employees in order to yield them an improved employee performance and overall organizational performance.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.355-369
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• 2023

Since the widespread adoption of deep-learning and related distributed representation, there have been substantial advancements in part-of-speech (POS) tagging for many languages. When training word representations, morphology and shape are typically ignored, as these representations rely primarily on collecting syntactic and semantic aspects of words. However, for tasks like POS tagging, notably in morphologically rich and resource-limited language environments, the intra-word information is essential. In this study, we introduce a deep neural network (DNN) for POS tagging that learns character-level word representations and combines them with general word representations. Using the proposed approach and omitting hand-crafted features, we achieve 90.47%, 80.16%, and 79.32% accuracy on our own dataset for three morphologically rich languages: Uyghur, Uzbek, and Kyrgyz. The experimental results reveal that the presented character-based strategy greatly improves POS tagging performance for several morphologically rich languages (MRL) where character information is significant. Furthermore, when compared to the previously reported state-of-the-art POS tagging results for Turkish on the METU Turkish Treebank dataset, the proposed approach improved on the prior work slightly. As a result, the experimental results indicate that character-based representations outperform word-level representations for MRL performance. Our technique is also robust towards the-out-of-vocabulary issues and performs better on manually edited text.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.18 no.2
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• pp.125-140
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• 2007

Cultural contents should provide all the people web services to ensure accessibility without any obstacles as a core resource of nation including a code linking people and people beyond time and space. This study achieved that automated measure including 11 checkpoints and manual analysis including 11 criteria and 26 checkpoints of three areas in selected five web sites such as Culture Portal. Art Road, Korean Patterns, MFM, and e-Museum supported by Ministry of Culture & Tourism. As a result, web accessibility level of cultural contents analyzed it staying at the beginning level. This study Provided the directions in order to improve accessibility of cultural contents through showing real cases to practice for improving web accessibility.

• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• Annual Conference of KIPS
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• 2023.11a
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• pp.30-33
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• 2023

The recent scholarly focus has been directed towards the expeditious and accurate detection of salient objects, a task that poses considerable challenges for resource-limited edge devices due to the high computational demands of existing models. To mitigate this issue, some contemporary research has favored inference speed at the expense of accuracy. In an effort to reconcile the intrinsic trade-off between accuracy and computational efficiency, we present novel model for salient object detection. Our model incorporate group-wise attentive module within the decoder of the encoder-decoder framework, with the aim of minimizing computational overhead while preserving detection accuracy. Additionally, the proposed architectural design employs attention mechanisms to generate boundary information and semantic features pertinent to the salient objects. Through various experimentation across five distinct datasets, we have empirically substantiated that our proposed models achieve performance metrics comparable to those of computationally intensive state-of-the-art models, yet with a marked reduction in computational complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.881-902
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• 2024

Fast decision support systems and accurate diagnosis have become significant in the rapidly growing healthcare sector. As the number of disparate medical IoT devices connected to the human body rises, fast and interrelated healthcare data retrieval gets harder and harder. One of the most important requirements for the Healthcare Internet of Things (HIoT) is semantic interoperability. The state-of-the-art HIoT systems have problems with bandwidth and latency. An extension of cloud computing called fog computing not only solves the latency problem but also provides other benefits including resource mobility and on-demand scalability. The recommended approach helps to lower latency and network bandwidth consumption in a system that provides semantic interoperability in healthcare organizations. To evaluate the system's language processing performance, we simulated it in three different contexts. 1. Polysemy resolution system 2. System for hyponymy-hypernymy resolution with polysemy 3. System for resolving polysemy, hypernymy, hyponymy, meronymy, and holonymy. In comparison to the other two systems, the third system has lower latency and network usage. The proposed framework can reduce the computation overhead of heterogeneous healthcare data. The simulation results show that fog computing can reduce delay, network usage, and energy consumption.

• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.331-342
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• 2023

Purpose - This study aims to develop a valid and appropriate method for measuring the economic value of intangible cultural resources. Design/methodology/approach - Building upon the concepts explored in many studies on the total value regulation of public goods or environmental goods, which are non-market value commodities, with a focus on the intangible cultural property Arirang, this study aims to formulate a new economic value concept for cultural resources that contributes to the overall economic total value, including non-use value. Based on this foundation, the study aim to identify and apply the most efficient model(CVM) among economic value measurement methods, as suggested by Tietenberg (2003). Findings - This involves estimating economic value through consumer behavior, encompassing the use or experience of cultural resources, as well as utilizing statements to estimate economic methods through consumer surveys. Only by presenting individual resource economic values of cultural resources in objective figures can a foundation be established for creating budgets and organizational structures to promote projects and policies. Research implications or Originality - Appropriate decisions can then be made by comparing these values with the expected costs in the management and planning process.

• Advances in Computational Design
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• v.9 no.3
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• pp.187-211
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• 2024

WiFi sensing integration enables non-intrusive and is utilized in applications like Human Activity Recognition (HAR) to leverage Multiple Input Multiple Output (MIMO) systems and Channel State Information (CSI) data for accurate signal monitoring in different fields, such as smart environments. The complexity of extracting relevant features from CSI data poses computational bottlenecks, hindering real-time recognition and limiting deployment on resource-constrained devices. The existing methods sacrifice accuracy for computational efficiency or vice versa, compromising the reliability of activity recognition within pervasive environments. The lightweight Compact Convolutional Transformer (CCT) algorithm proposed in this work offers a solution by streamlining the process of leveraging CSI data for activity recognition in such complex data. By leveraging the strengths of both CNNs and transformer models, the CCT algorithm achieves state-of-the-art accuracy on various benchmarks, emphasizing its excellence over traditional algorithms. The model matches convolutional networks' computational efficiency with transformers' modeling capabilities. The evaluation process of the proposed model utilizes self-collected dataset for CSI WiFi signals with few daily activities. The results demonstrate the improvement achieved by using CCT in real-time activity recognition, as well as the ability to operate on devices and networks with limited computational resources.