網際網路技術學刊/Journal of Internet Technology
- Ahead-of-Print
台灣軟體模擬學會 & Ainosco Press,此刊物暫停授權合作
選擇卷期
- 期刊
We introduce two new interpolation strategies, SOR strategy and rotated grid strategy, to compute the fine grid high order accurate solution in multiscale multigrid computation based on the Richardson extrapolation technique for solving partial differential equations. These new interpolation strategies effectively accelerate or eliminate the iterative refinement process previously employed in multiscale multigrid computation to obtain high order accurate solution on the fine grid. Experimental results show that the proposed new interpolation strategies are much more efficient and faster than the previously used iterative refinement strategy to compute high order accurate solution on the fine grid.
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Spontaneous ad hoc cloud computing networks let us perform complex tasks in a distributed manner by sharing computing resources. This kind of infrastructure is based on mobile devices with limited processing and storage capacity. Nodes with more processing capacity and energy in a spontaneous network store data or perform computing tasks in order to increase the whole computing and storage capacity. However, these networks can also present some problems of security and data vulnerability. In this paper, we present a secure spontaneous mobile ad hoc cloud computing network to make estimations using several information sources. The application is able to create users and manage encryption methods to protect the data sent through the network. The proposal has been simulated in several scenarios. The results show that the network performance depends mainly on the network size and nodes mobility.
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The pairwise key establishment in sensor networks is a fundamental and challenge task because public key cryptography and key distribution center (KDC) are not desirable to use on strictly resource-limited sensor nodes. In order to improve the performance, some of the existing key management schemes proposed to utilize the location information of sensor nodes to do the key distribution. However, in some applications, the location information of sensor nodes may not be available. In this paper, we first take advantage of heterogeneous sensor nodes to enhance the tame-based key predistribution scheme. The analysis and simulation demonstrate that the enhanced scheme not only provides deterministic authentication service, but also has better performance in terms of the initialization time, memory overhead, communication overhead, probability to establish a pairwise key between two neighboring nodes, and resilience against node compromises. We then extend the enhanced scheme to a cluster-based key predistribution scheme. The analysis and experiment indicate that in addition to preserving the advantages of the enhanced scheme, the cluster-based scheme has more efficient key predistribution process, much less energy consumption for the pairwise key establishment, and perfect resilience against node compromises. Finally, we apply the clusterbased scheme in mobile sensor networks to support the pairwise key establishment between mobile sensor nodes.
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While measuring inequality of a social system has been a popular topic in economics and sociology, structural fairness and inequality of social networks has not been paid attention by researchers interested in web or social network analysis. In practice, measuring structural fairness and inequality has a number of applications in online social networks, for example, we can check skewness of degree distribution by simply seeing inequality index. The powerlaw exponent has often been used to measure the inequality of network structures, however, it has several drawbacks to be applied to universal networks. In this paper, we propose a novel framework to measure fairness and inequality of a given network in the context of its structure. We develop a set of centrality fairness measures by combining other well-known node centralities with Gini index. We also analyze scale-free property of our proposed centrality fairness measures in real networks. Moreover, we suggest simple and efficient methods to relax structural inequality of a network, which are based on two edge manipulations: addition and rotation. Through experiments on real networks, we show that our methods decrease inequality quite steadily and effectively, and as structural hierarchy of a network gets stronger, decreasing rate of inequality gets lower.
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The success of mobile commerce adoption hinges on their ability to cover user shopping behaviors and attract consumer interest. In addition to anytime and anywhere paradigm, mobile commerce applications required to be flexible and personalized. In this work, we develop a mobile commerce system which introduces the Personal and Entry Level Storage (PELS) technology and its integration and applications with other e-commerce modules such as POS and vendor system. The benefits of the proposed system include: (1) Against advertising harassment, (2) Expanding the ability of personal cloud storage, (3) Analyzing the shopping trend for consumers and (4) Explicit shopping recommendation and shopping discount message delivery. A system scenario process is depicted to provide a generic paradigm for consumers and vendors. Furthermore, in terms of this innovative personalized e-commerce system, we discuss three managerial implications for future research and practice.
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Managing privacy leakage is of great importance in the Android platform. The variety of new forms of user-privacy fraud reveals a new challenge in terms of predicting potential private data disclosure threats and protecting the privacy of the devices inside our pockets. In this paper, we present an analytical framework, called AppLeak, to effectively evaluate information loss and detect privacy leakage during the runtime of Android applications. Moreover, a rigorous privacy risk assessment procedure is presented in which the potential privacy risk threat associated with the running Android application can be identified. In AppLeak, we adopt new starting points such as individual perception and attack criteria as major analytical principles. With the testing scenarios, we demonstrate that the feasibility and practicability of AppLeak are guaranteed.
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In recent years, user privacy in online social networks has become an important issue. Existing researches use anonymity techniques to preserve privacy. However traditional anonymity techniques do not support profile matching, thus have limitations in providing services which are based on social network topologies. This means that anonymous users cannot establish relationship or connect with friends if such techniques are used. This paper proposes an anonymous social network framework, which implements profile matching while still allows users to remain anonymous.
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Patch prior based image regularization technique has drawn much attention recently. The Multi-Scale Expected Patch Log Likelihood (MSEPLL) algorithm as a popular method for learning multi-scale prior of image patches has shown competitive results. However, the current algorithm learns patch prior with the Gaussian Mixture Model that is sensitive to outliers commonly. In this paper, we extend the MSEPLL method and attempt to employ the student's-t mixture model (SMM) to learn multi-scale image patch prior in a more robust way. Experiment results demonstrate that our proposed method performs well both in visual effect and quantitative evaluation.
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Cloud video delivery networks (C-VDNs) have been receiving considerable attention recently. Different from traditional video delivery networks, C-VDNs can provide scalable resources without the need to invest on infrastructure deployment. However, current C-VDN strategies cannot be readily applied to mobile Internet because of the dynamic characteristics of mobile users and videos. Moreover, dealing with the surrogate site selection and delivery path building under cloud resource constraints is another challenging problem. We propose the session-based cloud video delivery network framework to solve these problems. This framework considers the mobility of mobile users and the changing of videos by employing graph extension and session construction, respectively. Furthermore, we formulate the coordination of surrogate site selection and delivery route building as a mixed integer program. We relax the integer constraints and design a heuristic algorithm to obtain the optimal solution because this is a nondeterministic-polynomial time problem. Simulation experiments show that our proposed algorithm is efficient in terms of both performance and rental cost.
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Although retrieval systems of a personally identifiable information (PII) exposed in a text have developed rapidly, but retrieval method of PII exposed in an image is a challenging task. This paper proposes an efficient PII retrieval method that performs classification of image including PII. In the proposed method, the color, texture and shape-related features from images with PII are extracted by the gray-level co-occurrence matrix (GLCM) analysis. Then, our method adopts the multiple classifiers that are histogram, features, template matching, and support vector machine (SVM)-based image classifiers to classify the images with PII. The experimental results obtained using our method show a classification rate of 82% and an execution time of approximately 0.17 seconds per image for classifying images with PII. The method can be effectively applied to systems for personal information retrieval and exposure system on the web or privacy incident response system.