• International Journal of Computer Science & Network Security
• /
• v.21 no.11
• /
• pp.119-130
• /
• 2021

This study aimed at Evaluate Students' Interaction and Happiness in Distance Learning Among Students with Learning-Difficulties, by identifying the level of students' interaction in distance education and differences between them, as well as its impact on their happiness to learn. To achieve the aim of the study, two scales were designed for this purpose and were applied to a sample consisting of (310) individuals. The results showed that the level of students' interaction through the e-learning platform was at a high level. The results also showed that there was no statistically significant difference between the mean scores of males and females in the scale of students' interaction through the e-learning platform. There was no statistically significant difference between them in their happiness for distance learning via the online platform. There were also no statistically significant differences related to the grade variable in the level of interaction through the electronic platform and in the happiness to learn, while there was a positive statistically significant effect of interaction through the electronic platform on students' happiness to learn.

• Journal of Digital Convergence
• /
• v.10 no.6
• /
• pp.33-39
• /
• 2012

The purpose of this study is to investigate the effect of SNS(Social Network Service) on individual learning performance. To do this, we distribute and collect data by using a survey method. Research results suggest that online social networking engagement and acculturation have an effect on interaction quality with professors. Interaction quality with professors influences individual learning performance as well as collaborative learning. The conclusion and implications are discussed.

• Journal of Korean Society of Rural Planning
• /
• v.15 no.1
• /
• pp.47-59
• /
• 2009

The aim of this study is to analyze the green-tourism centrality considering spatial interaction using Gravity Model and social network method. The degree centrality and prestige centrality were applied as green-tourism centrality index. The rural amenity resources and human resources were counted as attraction factors, and a distance among villages was used as friction factor in gravity model. The weights of rural tourism amenity resources were calculated using the analytic hierarchy process(AHP) method and applied to evaluate green-tourism potentiality. The distance was measured with the shortest path among villages using geographic information system(GIS) network analysis. The spatial interaction from gravity model were employed as link weights between nodal points; a pair villages. Using the spatial interaction, the degree-centrality and prestige-centrality indices were calculated by social network analysis and demonstrated possibility of developing integrated green-tourism region centered on high centrality villages.

• The Journal of the Korea Contents Association
• /
• v.9 no.4
• /
• pp.114-120
• /
• 2009

In this study, we employed the idea that disease-related proteins tend to be work as an important factor for architecture of the disease network. We initially obtained 43 atopy-related proteins from the Online Mendelian Inheritance in Man (OMIM) and then constructed atopy-related protein interaction network. The protein network can be derived the map of the relationship between different disease proteins, denoted disease interaction network. We demonstrate that the associations between diseases are directly correlated to their underlying protein-protein interaction networks. From constructed the disease-protein bipartite network, we derived three diseasomal proteins, CCR5, CCL11, and IL/4R. Although we use the relatively small subnetwork, an atopy-related disease network, it is sufficient that the discovery of protein interaction networks assigned by diseases will provide insight into the underlying molecular mechanisms and biological processes in complex human disease system.

• BMB Reports
• /
• v.50 no.11
• /
• pp.535-536
• /
• 2017

A novel approach has been used to identify functional interactions relevant to human disease. Using high-throughput human-yeast genetic interaction screens, a first draft of disease interactome was obtained. This was achieved by first searching for candidate human disease genes that confer toxicity in yeast, and second, identifying modulators of toxicity. This study found potentially disease-relevant interactions by analyzing the network of functional interactions and focusing on genes implicated in amyotrophic lateral sclerosis (ALS), for example. In the subsequent proof-of-concept study focused on ALS, similar functional relationships between a specific kinase and ALS-associated genes were observed in mammalian cells and zebrafish, supporting findings in human-yeast genetic interaction screens. Results of combined analyses highlighted MAP2K5 kinase as a potential therapeutic target in ALS.

• Journal of Information Processing Systems
• /
• v.13 no.4
• /
• pp.987-999
• /
• 2017

Extraction of influential people from their respective domains has attained the attention of scholastic community during current epoch. This study introduces an innovative interaction strength metric for retrieval of the most influential users in the online social network. The interactive strength is measured by three factors, namely re-tweet strength, commencing intensity and mentioning density. In this article, we design a novel algorithm called IPRank that considers the communications from perspectives of followers and followees in order to mine and rank the most influential people based on proposed interaction strength metric. We conducted extensive experiments to evaluate the strength and rank of each user in the micro-blog network. The comparative analysis validates that IPRank discovered high ranked people in terms of interaction strength. While the prior algorithm placed some low influenced people at high rank. The proposed model uncovers influential people due to inclusion of a novel interaction strength metric that improves results significantly in contrast with prior algorithm.

• Proceedings of the KIEE Conference
• /
• 2005.07d
• /
• pp.2771-2773
• /
• 2005

In network human-robot interaction, human can access services of a robot system through the network The communication is done by interacting with the distributed sensors via voice, gestures or by using user network access device such as computer, PDA. The service organization and exploration is very important for this distributed system. In this paper we propose a new agent-based framework to integrate partners of this distributed system together and help users to explore the service effectively without complicated configuration. Our system consists of several robots. users and distributed sensors. These partners are connected in a decentralized but centralized control system using agent-based technology. Several experiments are conducted successfully using our framework The experiments show that this framework is good in term of increasing the availability of the system, reducing the time users and robots needs to connect to the network at the same time. The framework also provides some coordination methods for the human robot interaction system.

• Genomics & Informatics
• /
• v.21 no.1
• /
• pp.6.1-6.8
• /
• 2023

Glaucoma is the second leading cause of irreversible blindness, and primary open-angle glaucoma (POAG) is the most common type. Due to inadequate diagnosis, treatment is often not administered until symptoms occur. Hence, approaches enabling earlier prediction or diagnosis of POAG are necessary. We aimed to identify novel drugs for glaucoma through bioinformatics and network analysis. Data from 36 samples, obtained from the trabecular meshwork of healthy individuals and patients with POAG, were acquired from a dataset. Next, differentially expressed genes (DEGs) were identified to construct a protein-protein interaction (PPI) network. In both stages, the genes were enriched by studying the critical biological processes and pathways related to POAG. Finally, a drug-gene network was constructed, and novel drugs for POAG treatment were proposed. Genes with p < 0.01 and |log fold change| > 0.3 (1,350 genes) were considered DEGs and utilized to construct a PPI network. Enrichment analysis yielded several key pathways that were upregulated or downregulated. For example, extracellular matrix organization, the immune system, neutrophil degranulation, and cytokine signaling were upregulated among immune pathways, while signal transduction, the immune system, extracellular matrix organization, and receptor tyrosine kinase signaling were downregulated. Finally, novel drugs including metformin hydrochloride, ixazomib citrate, and cisplatin warrant further analysis of their potential roles in POAG treatment. The candidate drugs identified in this computational analysis require in vitro and in vivo validation to confirm their effectiveness in POAG treatment. This may pave the way for understanding life-threatening disorders such as cancer.

• 한국HCI학회:학술대회논문집
• /
• 2009.02a
• /
• pp.45-51
• /
• 2009

In this paper, a transparency analysis and network-adaptive transport scheme are proposed in order to improve transparency of EBA-based force-reflecting teleoperation. EBA guarantees stability of force-reflecting teleoperation over network delay and loss but has limitation that it cannot overcome transparency deterioration of haptic interactions. The proposed transparency analysis quantifies the force feedback distortion caused by network delay and loss. Based on the analysis, the proposed haptic data synchronization and transmission rate control schemes adapt synchronization delay and transmission rate to current network state for more transparent haptic interaction. Through Matlab/Simulink simulations, it is confirmed that the proposed analysis provides an acceptable quantification method about haptic interaction quality and that the proposed haptic data transport scheme effectively improves haptic interaction quality with respect to network delays and losses.

• Genomics & Informatics
• /
• v.10 no.4
• /
• pp.256-262
• /
• 2012

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene ($G{\times}G$) interactions. However, the biological interpretation of $G{\times}G$ interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified $G{\times}G$ interactions. The proposed network graph analysis consists of three steps. The first step is for performing $G{\times}G$ interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified $G{\times}G$ interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform $G{\times}G$ interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified $G{\times}G$ interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of $G{\times}G$ interactions.