• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1145-1157
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• 2020

A convolution neural networks (CNNs) has demonstrated outstanding performance compared to other algorithms in the field of face recognition. Regarding the over-fitting problem of CNN, researchers have proposed a residual network to ease the training for recognition accuracy improvement. In this study, a novel face recognition model based on game theory for call-over in the classroom was proposed. In the proposed scheme, an image with multiple faces was used as input, and the residual network identified each face with a confidence score to form a list of student identities. Face tracking of the same identity or low confidence were determined to be the optimisation objective, with the game participants set formed from the student identity list. Game theory optimises the authentication strategy according to the confidence value and identity set to improve recognition accuracy. We observed that there exists an optimal mapping relation between face and identity to avoid multiple faces associated with one identity in the proposed scheme and that the proposed game-based scheme can reduce the error rate, as compared to the existing schemes with deeper neural network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.1909-1928
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• 2020

Predicting human mobility has always been an important task in Location-based Social Network. Previous efforts fail to capture spatial dependence effectively, mainly reflected in weakening the location topology information. In this paper, we propose a neural network-based method which can capture spatial-temporal dependence to predict the next location of a person. Specifically, we involve a graph convolutional network (GCN) based on a seq2seq framework to capture the location topology information and temporal dependence, respectively. The encoder of the seq2seq framework first generates the hidden state and cell state of the historical trajectories. The GCN is then used to generate graph embeddings of the location topology graph. Finally, we predict future trajectories by aggregated temporal dependence and graph embeddings in the decoder. For evaluation, we leverage two real-world datasets, Foursquare and Gowalla. The experimental results demonstrate that our model has a better performance than the compared models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2231-2252
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• 2012

With the prevalence of diverse services-oriented applications, such as IPTV systems and on-line games, the current underlying communication networks face more and more challenges on the aspects of flexibility and adaptability. Therefore, an effective and efficient multicast routing mechanism, which can fulfill different requirements of different personalized services, is critical and significant. In this paper, we first define the neighbor gradient, which is calculated based on the weighted sum of attributes such as residual link capacity, normalized hop count, etc. Then two distributed multicast routing algorithms which are neighbor Gradient-based Multicast Routing for Static multicast membership (GMR-S) and neighbor Gradient-based Multicast Routing for Dynamic multicast membership (GMR-D), are proposed. GMR-S is suitable for static membership situation, while GMR-D can be used for the dynamic membership network environment. Experimental results demonstrate the effectiveness and efficiency of our proposed methods.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.1-6
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• 2022

Teaching and learning are one of the major issues during this pandemic (COVID-19). Since the pandemic started, there are many changes in teaching and learning styles as everything related to studies started online. Game-Based Learning has got remarkable importance in the educational system and pedagogy as an effective way of increasing student inspiration and engagement. In this field, most of the work has been carried out in digital games. This research uses an Animated Game-Based Learning design in enhancing student engagement and perception of learning. In teaching Computer Science (CS) concepts in higher education, to enhance the pedagogy activities in CS concepts, more specifically the concepts of "Data Structures (DS)" i.e., Array, Stack, and Queue concepts are focused. This study aims to observe the difference in students' learning with the use of different learning methods i.e., the traditional learning (TL) method and the Animated Game-Based Learning (AGBL) Method. The experimental results show that learning DS concepts has been improved by the AGBL method as compared to the TL method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2294-2309
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• 2020

In this article, a new Aloha-based tag identification protocol is presented to improve the reading efficiency of the EPC C1 Gen2-based UHF RFID system. Collision detection (CD) plays a vital role in tag identification process which determines the efficiency of anti-collision protocols since most Aloha-based protocols optimize the incoming frame length based on the collisions in current frame. Existing CD methods are ineffective in identifying collision, resulting in a degradation of identification performance. Our proposed algorithm adopts an enhanced CD (ECD) scheme based on the EPC C1 Gen2 standard to optimize identification performance. The ECD method can realize timely and effective CD by detecting the pulse width of the randomly sent by tags. According to the ECD, the reader detects the slot distribution and predicts tag cardinality in every collision slot. The tags involved in each collision slot are identified by independently assigned sub-frames. A large number of numerical results show that the proposed solution is superior to other existing anti-collision protocols in various performance evaluation metrics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1263-1279
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• 2020

Activating appropriate roles for a session in the role-based access control (RBAC) model has become challenging because of the so-called role explosion. In this paper, factors and issues related to user-driven role management are analysed, and a session role activation (SRA) problem based on reasonable assumptions is proposed to describe the problem of such role management. To solve the SRA problem, we propose an extended RBAC model with context-based role filtering. When a session is created, context conditions are used to filter roles that do not need to be activated for the session. This significantly reduces the candidate roles that need to be reviewed by the user, and aids the user in rapidly activating the appropriate roles. Simulations are carried out, and the results show that the extended RBAC model is effective in filtering the roles that are unnecessary for a session by using predefined context conditions. The extended RBAC model is also implemented in the Apache Shiro framework, and the modifications to Shiro are described in detail.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2180-2197
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• 2019

With the proliferation of the Android malicious applications, malware becomes more capable of hiding or confusing its malicious intent through the use of code obfuscation, which has significantly weaken the effectiveness of the conventional defense mechanisms. Therefore, in order to effectively detect unknown malicious applications on the Android platform, we propose DroidVecDeep, an Android malware detection method using deep learning technique. First, we extract various features and rank them using Mean Decrease Impurity. Second, we transform the features into compact vectors based on word2vec. Finally, we train the classifier based on deep learning model. A comprehensive experimental study on a real sample collection was performed to compare various malware detection approaches. Experimental results demonstrate that the proposed method outperforms other Android malware detection techniques.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.34-46
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• 2019

A method for detecting foreign substances in mould based on scatter grams was presented to protect moulds automatically during moulding production. This paper proposes a wavelet transform foreign detection method based on Monte Carlo analysis mechanism to identify foreign objects in the tube. We use the Monte Carlo method to evaluate the image, and obtain the width of the confidence interval by the deviation statistical gray histogram to divide the image type. In order to stabilize the performance of the algorithm, the high-frequency image and the low-frequency image are respectively drawn. By analyzing the position distribution of the pixel gray in the two images, the suspected foreign object region is obtained. The experiments demonstrate the effectiveness of our approach by evaluating the labeled data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4771-4787
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• 2019

Aimed at the disclosure risk of mobile terminal user's location privacy in location-based services, a location-privacy protection scheme based on similar trajectory substitution is proposed. On the basis of the anonymized identities of users and candidates who request LBS, this scheme adopts trajectory similarity function to select the candidate whose trajectory is the most similar to user's at certain time intervals, then the selected candidate substitutes user to send LBS request, so as to protect user's privacy like identity, query and trajectory. Security analyses prove that this scheme is able to guarantee such security features as anonymity, non-forgeability, resistance to continuous query tracing attack and wiretapping attack. And the results of simulation experiment demonstrate that this scheme remarkably improve the optimal candidate' trajectory similarity and selection efficiency.

• International Journal of Advanced Culture Technology
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• v.6 no.2
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• pp.51-57
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• 2018

The purpose of this study is to try finding the research possibilities of computer technology in art education and understand why computer technology has such a great impact on our contemporary education. The methodology of this study is based on the analysis of literature review. I have tried to find the importance articles in the journals of art education such as Studies in Art Education and Art Education published from the National Art Education Association, one of the most well-known organizations in the field of art education. To draw the purpose of this study, I found articles and categorized the information using key words such as aesthetic, feminist, gender issues, and interactivity. After analyzing, I have discussed about the research possibilities and important issues of computer technology in art education and then categorized the information found in each article into four different subheadings: 1) the visual effects of computer graphics in art education, 2) gender issues based on the computer technology, 3) interactive multimedia and social interactions among students, 4) research possibilities with computer technologies in art education. The findings are as follow. Firstly, there were many research possibilities of computer technologies in art education such as ways of criticizing the contemporary art world. Secondly, I found that computer technology has a great impact on art education because students are more eager to engage with computer technologies-based media activities and very familiar with new media either at home and school. Therefore, we, as educators, must address how the student will be systematically engaged with computer technologies in their educational environment and should determine what knowledge and skills prospective teachers bring to our teacher education programs and how and where they acquired this knowledge.