• Journal of Information Processing Systems
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• v.9 no.2
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• pp.189-204
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• 2013

The confinement problem was first noted four decades ago. Since then, a huge amount of efforts have been spent on defining and mitigating the problem. The evolution of technologies from traditional operating systems to mobile and cloud computing brings about new security challenges. It is perhaps timely that we review the work that has been done. We discuss the foundational principles from classical works, as well as the efforts towards solving the confinement problem in three domains: operating systems, mobile computing, and cloud computing. While common issues exist across all three domains, unique challenges arise for each of them, which we discuss.

• International Journal of Contents
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• v.1 no.1
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• pp.50-58
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• 2005

Generally, multidimensional data such as image and spatial data require large amount of storage space. There is a limit to store and manage those large amounts of data in single workstation. If we manage the data on parallel computing environment which is being actively researched these days, we can get highly improved performance. In this paper, we propose a parallel multidimensional index structure that exploits the parallelism of the parallel computing environment. The proposed index structure is nP(processor)-nxmD(disk) architecture which is the hybrid type of nP-nD and 1P-nD. Its node structure in-creases fan-out and reduces the height of an index. Also, a range search algorithm that maximizes I/O parallelism is devised, and it is applied to k-nearest neighbor queries. Through various experiments, it is shown that the proposed method outperforms other parallel index structures.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4950-4966
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• 2015

Cloud providers now face the problem of estimating the amount of computing resources required to satisfy a future workload. In this paper, a virtual machine provisioning (VMP) mechanism is designed to adapt workload fluctuation. The arrival rate of forthcoming jobs is predicted for acquiring the proper service rate by adopting an exponential smoothing (ES) method. The proper service rate is estimated to guarantee the service level agreement (SLA) constraints by using a diffusion approximation statistical model. The VMP problem is formulated as a facility location problem. Furthermore, it is characterized as the maximization of submodular function subject to the matroid constraints. A greedy-based VMP algorithm is designed to obtain the optimal virtual machine provision pattern. Simulation results illustrate that the proposed mechanism could increase the average profit efficiently without incurring significant quality of service (QoS) violations.

• Journal of Computing Science and Engineering
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• v.11 no.1
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• pp.1-8
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• 2017

Naive Bayes nearest neighbor (NBNN) is a simple image classifier based on identifying nearest neighbors. NBNN uses original image descriptors (e.g., SIFTs) without vector quantization for preserving the discriminative power of descriptors and has a powerful generalization characteristic. However, it has a distinct disadvantage. Its memory requirement can be prohibitively high while processing a large amount of data. To deal with this problem, we apply a spherical hashing binary code embedding technique, to compactly encode data without significantly losing classification accuracy. We also propose using an inverted index to identify nearest neighbors among binarized image descriptors. To demonstrate the benefits of our method, we apply our method to two existing NBNN techniques with an image dataset. By using 64 bit length, we are able to reduce memory 16 times with higher runtime performance and no significant loss of classification accuracy. This result is achieved by our compact encoding scheme for image descriptors without losing much information from original image descriptors.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.23-33
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• 2023

With the widespread demand from data-intensive tasks such as machine learning and large-scale databases, the amount of data processed in modern computing systems is increasing exponentially. Such data-intensive tasks require large amounts of memory to rapidly process and analyze massive data. However, existing computing system architectures face challenges when building large-scale memory owing to various structural issues such as CPU specifications. Moreover, large-scale memory may cause problems including memory overprovisioning. The Compute Express Link (CXL) allows computing nodes to use large amounts of memory while mitigating related problems. Hence, CXL is attracting great attention in industry and academia. We describe the overarching concepts underlying CXL and explore recent research trends in this technology.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.3
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• pp.346-354
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• 2024

Deep learning models require high computing power due to a substantial amount of computation. It is difficult to use them in devices with limited computing environments, such as coastal surveillance equipments. In this study, a lightweight model is constructed by analyzing the weight changes of the convolutional layers during the training process based on MobileNet and then pruning the layers that affects the model less. The performance comparison results show that the lightweight model maintains performance while reducing computational load, parameters, model size, and data processing speed. As a result of this study, an effective pruning method for constructing lightweight deep learning models and the possibility of using equipment resources efficiently through lightweight models in limited computing environments such as coastal surveillance equipments are presented.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.50-62
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• 2024

The decision to integrate mobile cloud computing (MCC) in higher education without first defining suitable usage scenarios is a global issue as the usage of such services becomes extensive. Consequently, this study investigates the security determinants of the educational use of mobile cloud computing among universities' students. This study proposes and develops a theoretical model by adopting and modifying the Protection Motivation Theory (PMT). The study's findings show that a significant amount of variance in MCC adoption was explained by the proposed model. MCC adoption intention was shown to be highly influenced by threat appraisal and coping appraisal factors. Perceived severity alone explains 37.8% of students "Intention" to adopt MCC applications, which indicates the student's perception of the degree of harm that would happen can hinder them from using MCC. It encompasses concerns about data security, privacy breaches, and academic integrity issues. Response cost, perceived vulnerability and response efficacy also have significant influence on students "intention" by 18.8%, 17.7%, and 6.7%, respectively.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.105-118
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• 2024

The decision to integrate mobile cloud computing (MCC) in higher education without first defining suitable usage scenarios is a global issue as the usage of such services becomes extensive. Consequently, this study investigates the security determinants of the educational use of mobile cloud computing among universities students. This study proposes and develops a theoretical model by adopting and modifying the Protection Motivation Theory (PMT). The studys findings show that a significant amount of variance in MCC adoption was explained by the proposed model. MCC adoption intention was shown to be highly influenced by threat appraisal and coping appraisal factors. Perceived severity alone explains 37.8% of students "Intention" to adopt MCC applications, which indicates the student's perception of the degree of harm that would happen can hinder them from using MCC. It encompasses concerns about data security, privacy breaches, and academic integrity issues. Response cost, perceived vulnerability and response efficacy also have significant influence on students "intention" by 18.8%, 17.7%, and 6.7%, respectively.

• Journal of Service Research and Studies
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• v.2 no.2
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• pp.91-98
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• 2012

The emergence of new types of embedded computing devices and developments in wireless networking are broadening the domain of computing from the work place and home office to other facets of everyday life. This trend is expected to lead to a proliferation of Internet of Things (IoT) environments, in which inexpensive and interconnected computing devices are capable of supporting users in a range of tasks. Context-aware computing is a key source to develop such smart services. However, there are many challenges to enable services to be smart; Heterogeneous Computing Environments, Resource Limitations, Large Amount of Data Produced, and Different Requirements for Context Interpretation. Because of these challenges, there are difficulties in providing smart service by utilizing IoT devices. Currently, many researchers are conducting researches on mobile-computing based smart service development and provisioning and network infrastructure for interconnected IoT devices. Still, there are some limitations on developing core technologies for IoT computing based smart service development. In order to remedy this situation, this thesis presents a reusable framework that provides unique and noble features which are required in developing advanced context-aware IoT applications.