• Journal of Information Processing Systems
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• v.10 no.2
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• pp.271-282
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• 2014

The advancement of knowledge society has enabled the social network community (SNC) to be perceived as another space for learning where individuals produce, share, and apply content in self-directed ways. The content generated within social networks provides information of value for the participants in real time. Thus, this study proposes the social network community activity-based content model (SoACo Model), which takes SNC-based activities and embodies them within learning objects. The SoACo Model consists of content objects, aggregation levels, and information models. Content objects are composed of relationship-building elements, including real-time, changeable activities such as making friends, and participation-activity elements such as "Liking" specific content. Aggregation levels apply one of three granularity levels considering the reusability of elements: activity assets, real-time, changeable learning objects, and content. The SoACo Model is meaningful because it transforms SNC-based activities into learning objects for learning and teaching activities and applies to learning management systems since they organize activities -- such as tweets from Twitter -- depending on the teacher's intention.

• Architectural research
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• v.4 no.1
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• pp.53-61
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• 2002

The typical Critical Path Method (CPM) leaves it to the construction managers to overcome two problems in developing networks. First, the construction manager needs to prepare information on the type of activities and their precedence relations in order to develop a network schedule. Second, he or she can include space information into the network schedule such as the locations where the activities take place, only with difficulty. These two problems make it difficult for an inexperienced person to create a network. The purpose of this paper is to provide construction managers with set equations of creating a network schedule for multiunit projects. A space-resource combined network creation are presented in this paper, which includes equations for generating a list of required activities, their precedence relations, and information on their location. Information on the space (location) and the resource is the required data for this method. Based on this information, this method divides a project into a number of activities so that each activity contains the information on the location where the activity takes place and the principal resource required for that activity. Precedence relations are then obtained from the sequence of space and resource. This method has the potential to reduce human efforts in scheduling activities.

• The Journal of the Korea Contents Association
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• v.12 no.5
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• pp.370-379
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• 2012

This study is identifying a social standing on adolescents' social network in offline and how the social standing influence to online social activity. For the purpose, we explore two research questions. First, How the adolescents' social standing present in their offline social network? Second, How the adolescents' social standing influence to online social activity? Using data, we first visualized 5 social network of adolescents, and deducted each ego networks and global network. Also we investigated causality between social standing and social activities. The result showed adolescents' social tie and social gregariousness influence to social activity width and depth in ego network. Based on these findings, we discussed some implications, limitations, and future direction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.229-232
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• 2008

A Neural network classification of human activity data is presented. The data acquisition system involves a tri-axial accelerometer in wireless sensor network environment. The wireless ad-hoc system has the advantage of small size, convenience for wearability and cost effectiveness. The system can further improve the range of user mobility with the inclusion of ad-hoc environment. The classification is based on the frequencies of the involved activities. The most significant Fast Fourier coefficients, of the acceleration of the body movement, are used for classification of the daily activities like, Rest walk and Run. A supervised learning approach is used. The work presents classification accuracy with the available fast batch training algorithms i.e. Levenberg-Marquardt and Resilient back propagation scheme is used for training and calculation of accuracy.

• Journal of Fisheries and Marine Sciences Education
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• v.27 no.1
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• pp.108-114
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• 2015

The purpose of this study is to find the relationships among communication competency, social network centrality(trust centrality and knowledge sharing centrality), discussion performance, and online boarding activity in the team based learning situation. For investigating this topic, 44 students are participated in the classes of educational technology. In order to find out the relationships among communication competency, social network centrality, discussion performance, and online boarding activity, compared t-test and path analysis are used. Followings are the results of the research: (a) Communication competency is improved significantly after team based learning. (b) Trust centrality effects significantly on the knowledge sharing centrality. (c) Knowledge sharing effects significantly on discussion performance. (d) Trust centrality effects on the online boarding activity in the team based learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1118-1133
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• 2017

Computer vision-based human activity recognition (HAR) has become very famous these days due to its applications in various fields such as smart home healthcare for elderly people. A video-based activity recognition system basically has many goals such as to react based on people's behavior that allows the systems to proactively assist them with their tasks. A novel approach is proposed in this work for depth video based human activity recognition using joint-based motion features of depth body shapes and Deep Belief Network (DBN). From depth video, different body parts of human activities are segmented first by means of a trained random forest. The motion features representing the magnitude and direction of each joint in next frame are extracted. Finally, the features are applied for training a DBN to be used for recognition later. The proposed HAR approach showed superior performance over conventional approaches on private and public datasets, indicating a prominent approach for practical applications in smartly controlled environments.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.43-48
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• 2021

The article proposes a method of modeling a comprehensive indicator for evaluating the effectiveness of regional management of innovation activity. This will make it possible to assess the effectiveness of personnel, financial and credit and foreign economic activity of the regions from the standpoint of an integrated approach. The modeling technique is proposed to be carried out using the tools of taxonomic analysis and the calculation of a complex indicator of the effectiveness of the innovation activity management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4176-4197
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• 2020

Botnet is a type of dangerous malware. Botnet attack with a collection of bots attacking a similar target and activity pattern is called bot group activities. The detection of bot group activities using intrusion detection models can only detect single bot activities but cannot detect bots' behavioral relation on bot group attack. Detection of bot group activities could help network administrators isolate an activity or access a bot group attacks and determine the relations between bots that can measure the correlation. This paper proposed a new model to measure the similarity between bot activities using the intersections-probability concept to define bot group activities called as B-Corr Model. The B-Corr model consisted of several stages, such as extraction feature from bot activity flows, measurement of intersections between bots, and similarity value production. B-Corr model categorizes similar bots with a similar target to specify bot group activities. To achieve a more comprehensive view, the B-Corr model visualizes the similarity values between bots in the form of a similar bot graph. Furthermore, extensive experiments have been conducted using real botnet datasets with high detection accuracy in various scenarios.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3354-3362
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• 2009

Recently, the potential of advanced studies is being highlighted that the networking activity of entrepreneurs of venture businesses influences successful foundation. Given this background, this study intends to make an exploratory study on the influence of self-efficacy of the entrepreneur's cognitive characteristic and patience with ambiguity of the psychological characteristic upon networking activity, and the components of networking activity upon entrepreneurial performance. It was tested with sample of responses to the questionnaires for 156 entrepreneurs of venture businesses which are under 3 years since they were founded. Findings of this study are as follows: First, the entrepreneur's self-efficacy was shown to have a significant effect on networking and entrepreneurial performance. Second, patience with ambiguity was shown not to have a significant effect on networking and entrepreneurial performance. Third, networking was shown to have an effect on entrepreneurial performance, and among the components of networking activity, network frequency and reliability were shown to significantly influence entrepreneurial performance, while network range was shown not to have a significant effect on it. Therefore, for performance improvement of start-up enterprises, rather self-efficacy of the entrepreneur's cognitive characteristic than the psychological characteristic seems more important. Also, with a practical suggestion that for the networking activity of entrepreneurs of venture businesses, network frequency and reliability may have a more critical effect rather on entrepreneurial performance than network range, the limitation and direction of this study are presented.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.11-18
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• 2022

Recently, research to classify human activity using imagery has been actively conducted for the purpose of crime prevention and facility safety in public places and facilities. In order to improve the performance of human activity classification, most studies have applied deep learning based-transfer learning. However, despite the increase in the number of backbone network models that are the basis of deep learning as well as the diversification of architectures, research on finding a backbone network model suitable for the purpose of operation is insufficient due to the atmosphere of using a certain model. Thus, this study applies the transfer learning into recently developed deep learning backborn network models to build an intelligent system that classifies human activity using imagery. For this, 12 types of active and high-contact human activities based on sports, not basic human behaviors, were determined and 7,200 images were collected. After 20 epochs of transfer learning were equally applied to five backbone network models, we quantitatively analyzed them to find the best backbone network model for human activity classification in terms of learning process and resultant performance. As a result, XceptionNet model demonstrated 0.99 and 0.91 in training and validation accuracy, 0.96 and 0.91 in Top 2 accuracy and average precision, 1,566 sec in train process time and 260.4MB in model memory size. It was confirmed that the performance of XceptionNet was higher than that of other models.