• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.344-348
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• 2021

The article examines one of the most important means of visualization of mass information on the Internet - information graphics in the broadest sense of the term as a visual technology for presenting mass information. The main objectives of the article are to determine the genre-typological features of infographics and basic technological principles; identification of features of creation and use of information graphics in modern network. Certain benefits of online infographic editors include savings in resources and time. They allow the user, who has basic PC skills, to create standardized infographics based on their own data. In addition, the use of online services develops visual thinking, allows you to get an idea of quality criteria and current trends in infographics, as well as to gain initial experience in the visual presentation of data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.6
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• pp.1025-1035
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• 2013

There has been an explosive increase in the population of OSN(online social network) for 10 years. OSN provides users with many opportunities to have communication among friends, families and goes so far as to make relationships among unknown people having similar belief or interest. However, OSN also produced adverse effects such as privacy breaches, leaking uncontrolled information or disseminating false information. Access control models such as MAC, DAC, RBAC are applied to the OSN to control those problems but those models in OSN are not fit in dynamic OSN environment because user's acts in OSN are unpredictable and static access control imposes burden on users to change access control rules one by one. This paper proposes the dynamic trust-based access control to solve the problems of traditional static access control in OSN.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.59-66
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• 2023

Text mining (TM) is most widely used to find patterns from various text documents. Cyber-bullying is the term that is used to abuse a person online or offline platform. Nowadays cyber-bullying becomes more dangerous to people who are using social networking sites (SNS). Cyber-bullying is of many types such as text messaging, morphed images, morphed videos, etc. It is a very difficult task to prevent this type of abuse of the person in online SNS. Finding accurate text mining patterns gives better results in detecting cyber-bullying on any platform. Cyber-bullying is developed with the online SNS to send defamatory statements or orally bully other persons or by using the online platform to abuse in front of SNS users. Deep Learning (DL) is one of the significant domains which are used to extract and learn the quality features dynamically from the low-level text inclusions. In this scenario, Convolutional neural networks (CNN) are used for training the text data, images, and videos. CNN is a very powerful approach to training on these types of data and achieved better text classification. In this paper, an Ensemble model is introduced with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos. The proposed approach also focused on reducing the training time and memory usage which helps the classification improvement.

• Journal of Digital Convergence
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• v.18 no.1
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• pp.45-52
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• 2020

Online rumor creates psychological stress and image loss for victims. Prior studies related to online rumor did not consider the online environmental factor, despite the fact that online rumor occurs in the online space. Therefore, this study tried to investigate the influence of online characteristics on online rumor. This study considered perceived anonymity, lack of social presence, and perceived dissemination as online characteristics. We established and demonstrated a research model in which online characteristics affect online rumor behavior through attitude toward online rumor. This study obtained the sample of 201 social network users based on the survey and verified the research model using PLS tool. The results provided that perceived anonymity and perceived dissemination influenced online rumor behavior through attitude toward online rumor. On the other hand, lack of social presence was not significant. The findings of this study provide the fact that an individual's online rumor behavior can be caused by online characteristics. This study suggests that we pay attention to the role of perceived anonymity and perceived dissemination for online rumor behavior.

• ETRI Journal
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• v.35 no.5
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• pp.748-756
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• 2013

This paper addresses the problem of online hop timing detection and frequency estimation of multiple frequency-hopping (FH) signals with antenna arrays. The problem is deemed as a dynamic one, as no information about the hop timing, pattern, or rate is known in advance, and the hop rate may change during the observation time. The technique of particle filtering is introduced to solve this dynamic problem, and real-time frequency and direction of arrival estimates of the FH signals can be obtained directly, while the hop timing is detected online according to the temporal autoregressive moving average process. The problem of network sorting is also addressed in this paper. Numerical examples are carried out to show the performance of the proposed method.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.346-353
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• 2011

With the advent of ubiquitous computing, a massive amount of trajectory data has been published and shared in many websites. This type of computing also provides motivation for online mining of trajectory data, to fit user-specific preferences or context (e.g., time of the day). While many trajectory clustering algorithms have been proposed, they have typically focused on offline mining and do not consider the restrictions of the underlying road network and selection conditions representing user contexts. In clear contrast, we study an efficient clustering algorithm for Boolean + Clustering queries using a pre-materialized and summarized data structure. Our experimental results demonstrate the efficiency and effectiveness of our proposed method using real-life trajectory data.

• International Journal of Control, Automation, and Systems
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• v.4 no.1
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• pp.17-29
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• 2006

A novel inverse kinematics solution based on the back propagation neural network (NN) for redundant manipulators is developed for online obstacles avoidance. A laser transducer at the end-effctor is used for online planning the trajectory. Since the inverse kinematics in the present problem has infinite number of joint angle vectors, a fuzzy reasoning system is designed to generate an approximate value for that vector. This vector is fed into the NN as a hint input vector rather than as a training vector to guide the output of the NN. Simulations are implemented on both three- and four-link redundant planar manipulators to show the effectiveness of the proposed position control system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.330-333
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• 2005

In this study, a method of designing a neurointerface using neural network (NN) is proposed for controlling nonholonomic mobile robots. According to the concept of virtual master-slave robots, in particular, a partially stable inverse dynamic model of the master robot is acquired online through the NN by applying a feedback-error learning method, in which the feedback controller is assumed to be based on a PD compensator for such a nonholonomic robot. A tracking control problem is demonstrated by some simulations for a nonholonomic mobile robot with two-independent driving wheels.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.382-386
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• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.406-409
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• 2008

Clustering data streams has an importance over many applications like sensor networks. Existing hierarchical methods follow a semi fuzzy clustering that yields duplicate clusters. In order to solve the problems, we propose an extended online divisive agglomerative clustering on data streams. It builds a tree-like top-down hierarchy of clusters that evolves with data streams using geometric time frame for snapshots. It is an enhancement of the Online Divisive Agglomerative Clustering (ODAC) with a pruning strategy to avoid duplicate clusters. Our main features are providing update time and memory space which is independent of the number of examples on data streams. It can be utilized for clustering sensor data and network monitoring as well as web click streams.