• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.242-249
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• 2022

Classical cryptography with complex computations has recently been utilized in the latest computing systems to create secret keys. However, systems can be breached by fast-measuring methods of the secret key; this approach does not offer adequate protection when depending on the computational complexity alone. The laws of physics for communication purposes are used in quantum computing, enabling new computing concepts to be introduced, particularly in cryptography and key distribution. This paper proposes a quantum computing lattice (CQL) mechanism that applies the BB84 protocol to generate a quantum key. The generated key and a one-time pad encryption method are used to encrypt the message. Then Babai's algorithm is applied to the ciphertext to find the closet vector problem within the lattice. As a result, quantum computing concepts are used with classical encryption methods to find the closet vector problem in a lattice, providing strength encryption to generate the key. The proposed approach is demonstrated a high calculation speed when using quantum computing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.117-135
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• 2016

Performance enhancement is one of the most important issues in high performance distributed computing systems. In such computing systems, online users submit their jobs anytime and anywhere to a set of dynamic resources. Jobs arrival and processes execution times are stochastic. The performance of a distributed computing system can be improved by using an effective load balancing strategy to redistribute the user tasks among computing resources for efficient utilization. This paper presents a multi-class load balancing strategy that balances different classes of user tasks on multiple heterogeneous computing nodes to minimize the per-class mean response time. For a wide range of system parameters, the performance of the proposed multi-class load balancing strategy is compared with that of the random distribution load balancing, and uniform distribution load balancing strategies using simulation. The results show that, the proposed strategy outperforms the other two studied strategies in terms of average task response time, and average computing nodes utilization.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.111-119
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• 2016

In recent years, the opinion analysis is one of the key research fronts of any domain. Opinion target extraction is an essential process of opinion analysis. Target is usually referred to noun or noun phrase in an entity which is deliberated by the opinion holder. Extraction of opinion target facilitates the opinion analysis more precisely and in addition helps to identify the opinion polarity i.e. users can perceive opinion in detail of a target including all its features. One of the most commonly employed algorithms is a sequence labeling algorithm also called Conditional Random Fields. In present article, recent opinion target extraction approaches are reviewed based on sequence labeling algorithm and it features combinations by analyzing and comparing these approaches. The good selection of features combinations will in some way give a good or better accuracy result. Features combinations are an essential process that can be used to identify and remove unneeded, irrelevant and redundant attributes from data that do not contribute to the accuracy of a predictive model or may in fact decrease the accuracy of the model. Hence, in general this review eventually leads to the contribution for the opinion analysis approach and assist researcher for the opinion target extraction in particular.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1554-1566
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• 2014

Cloud Computing allows application providers seamlessly scaling their services and enables users scaling their usage according to their needs. In this paper, using queuing game model, we present service scheduling schemes which are used in software as a service (SaaS). The object is maximizing the Cloud Computing platform's (CCP's) payoff via controlling the service requests whether to join or balk, and controlling the value of CCP's admission fee. Firstly, we treat the CCP as one virtual machine (VM) and analyze the optimal queue length with a fixed admission fee distribution. If the position number of a new service request is bigger than the optimal queue length, it balks. Otherwise, it joins in. Under this scheme, the CCP's payoff can be maximized. Secondly, we extend this achievement to the multiple VMs situation. A big difference between single VM and multiple VMs is that the latter one needs to decide which VM the service requests turn to for service. We use a corresponding algorithm solve it. Simulation results demonstrate the good performance of our schemes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.4
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• pp.245-253
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• 2013

Knowledge representation realized by information granules is one of the essential facets of granular computing and an area of intensive research. Fuzzy clustering and clustering are general vehicles to realize formation of information granules. Granulation - degranulation paradigm is one of the schemes determining and quantifying functionality and knowledge representation capabilities of information granules. In this study, we augment this paradigm by forming and optimizing a collection of associations among original and transformed information granules. We discuss several transformation schemes and analyze their properties. A series of numeric experiments is provided using which we quantify the improvement of the degranulation mechanisms offered by the optimized transformation of information granules.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.276-282
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• 2022

One of the most essential foundations upon which big institutions rely in delivering cloud computing and hosting services, as well as other kinds of multiple digital services, is the security of infrastructures for digital and information services throughout the world. Distributed denial-of-service (DDoS) assaults are one of the most common types of threats to networks and data centers. Denial of service attacks of all types operates on the premise of flooding the target with a massive volume of requests and data until it reaches a size bigger than the target's energy, at which point it collapses or goes out of service. where it takes advantage of a flaw in the Transport Control Protocol's transmitting and receiving (3-way Handshake) (TCP). The current study's major focus is on an architecture that stops DDoS attacks assaults by producing code for DDoS attacks using a cloud controller and calculating Round-Tripe Time (RTT).

• Journal of the Korean Mathematical Society
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• v.50 no.2
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• pp.445-464
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• 2013

We describe the one-dimensional and zero-dimensional cusps of the Satake compactification for the paramodular groups in degree two for arbitrary levels. We determine the crossings of the one-dimensional cusps. Applications to computing the dimensions of Siegel modular forms are given.

• Journal of Computing Science and Engineering
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• v.10 no.1
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• pp.21-31
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• 2016

With the application and development of biomedical techniques such as next-generation sequencing, mass spectrometry, and medical imaging, the amount of biomedical data have been growing explosively. In terms of processing such data, we face the problems surrounding big data, highly intensive computation, and high dimensionality data. Fortunately, cloud computing represents significant advantages of resource allocation, data storage, computation, and sharing and offers a solution to solve big data problems of biomedical research. In order to improve the efficiency of resource management in cloud computing, this paper proposes a clustering method and adopts Radial Basis Function in order to compress comprehensive data sets found in biology and medicine in high quality, and stores these data with resource management in cloud computing. Experiments have validated that with such a data-compression-based resource management in cloud computing, one can store large data sets from biology and medicine in fewer capacities. Furthermore, with reverse operation of the Radial Basis Function, these compressed data can be reconstructed with high accuracy.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1192-1200
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• 2019

Recently, artificial intelligence techniques have been widely used in the computer science field, such as the Internet of Things, big data, cloud computing, and mobile computing. In particular, resource management is of utmost importance for maintaining the quality of services, service-level agreements, and the availability of the system. In this paper, we review and analyze various ways to meet the requirements of cloud resource management based on artificial intelligence. We divide cloud resource management techniques based on artificial intelligence into three categories: fog computing systems, edge-cloud systems, and intelligent cloud computing systems. The aim of the paper is to propose an intelligent resource management scheme that manages mobile resources by monitoring devices' statuses and predicting their future stability based on one of the artificial intelligence techniques. We explore how our proposed resource management scheme can be extended to various cloud-based systems.

• The Journal of Information Systems
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• v.25 no.4
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• pp.37-61
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• 2016

Purpose This study examines firms at various industries to identify key organizational characteristics that positively drive the evaluation of cloud computing. In addition, this study tests the impact of one's evaluation has on the adoption and integration of cloud computing within their respective firm. Design/methodology/approach A total of 172 responses from various firms currently using cloud computing service were analyzed using the structural equation modeling(SEM). Findings Results show that organizational Needs(Mobility and Job Relevance), Perceived Factors(Relative Advantages and Cost Savings), and Organizational Readiness(Technical Knowledge, Financial Supports, and Managerial Supports) have a significant impact on cloud computing evaluation; and evaluation influences its adoption, and integration. However, two variables(IT Performance Gaps and Compatibility) have no significant impact on cloud computing evaluation. Finally, Evolutional Leadership has a significant moderating effect within the relationship among variables in the process of cloud computing implementation.