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In the current internet of things era, various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause cloud core overload and significant network delays. To overcome these overload and delay problems, the mobile edge computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal. Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, while using the service, it may also encounter a large amount of popular data being accessed in a short period of time. Without proper handling of replica generation and deployment, even in low-latency environments, it can still kill the benefits of MEC due to increased access time. Although many scholars have proposed related issues for copy replication, there are still.parts that can be improved. To avoid the situation of insufficient availability of replicas, replica replication is performed, but infinite replicas may lead to a significant increase in traffic and waste of resources. And when deploying replicas, it is necessary to avoid placing them on congested nodes and consider how to achieve better load balancing. To improve the above problems, we synthesize the advantages of previous algorithms, make up for the shortcomings, and propose an adaptive replica configuration mechanism to predict the popularity of files and replicate replicas to low-blocking nodes. This method spreads the subsequent access workload by copying the popular file in advance to improve the system's overall performance. [ABSTRACT FROM AUTHOR]

Detecting clandestine, intermittent release of heavy metal pollution into natural and man-made water ways is challenging. Conventional chemical methods are both labor intensive and expensive. A recent approach combining ion-exchange resins with the capabilities of X-ray fluorescence core scanners (XRF-CS) therefore is of great interest. In short, ion-exchange resin is deployed in the water using small sachets, the resin is then collected, dried, filled into sample holders and scanned using XRF-CS. Ion-exchange resins take up heavy metals in proportion to the concentration in the ambient water, with a correlation coefficient (R2) between concentration and XRF-CS counts better than 0.96 for most elements. However, a number of parameters influence the measurements. Different drying methods introduce differences in the XRF counts because of lattice bound water, resin shrinkage, and disaggregation of the resin particles. Furthermore, the newly developed sample carrier, which was constructed using 3D printed polymers, contains trace amounts of elements that may influence the sample measurements through edge effects and secondary fluorescence. In the tested sample carrier materials, substantial levels of Cr, Fe, Co, and Zn were detected, while Ca, Ti, Ni, Cu, Ga showed variable levels. Ba, Tl and Bi show very low levels, and Pb is only of importance in the PLA carrier. It is therefore necessary to streamline the analysis-process to ensure that the variations in sample treatment and drying and filling methods are minimized. It is also recommended that only spectra from the center of the compartments are used for the evaluation to avoid edge effects caused by secondary fluorescence of metals in the compartment walls. Although the technique of using ion-exchange resin sachets and XRF-CS analysis is only semi-quantitative, it is a cost effective and fast way to monitor large areas for environmental pollution, and the new sample carrier greatly contributes to make the process faster and less error prone. [ABSTRACT FROM AUTHOR]

In recent years, some researches on free text authentication by keystroke dynamics have been proposed. The main problem of these proposed researches is the requirement of a very long training time. To increase users' willingness to use the proposed authentication system, one simple and feasible way is shortening the training time. In this case, the training data is limited and is known as the limited resource problem. In this paper, we propose new soft biometrics and a new classifier for limited resources in free text authentication in English. Our new soft biometrics combines the idea of data mining and statistical prediction. Because our soft biometric is a mining result of limited resources, it is sensitive to outliers and a traditional statistical classifier cannot be applied. To solve this problem, the proposed classifiers considered the problem of an outlier and calculated the difference of cluster distribution. There are 114 participants in our experiments. Experimental results show that our approach can improve the accuracy of free text authentication in the case of limited resources. [ABSTRACT FROM AUTHOR]

Identification of changes in customer behavior is a major challenge that must be tackled in order to survive in a rapidly changing business environment. For example, because information technology has advanced and data-storage costs have declined, for the purpose of serving customers, numerous enterprises have employed information systems and have directly logged customer behavior in databases. This trend has motivated the development of data mining applications. Fuzzy quantitative sequential pattern mining is a functional data mining technique that is used for discovering customer behavioral patterns over time and determining the quantities of goods or services they purchase. The example term used in shopping 〈[(Beer, Low)(Milk, High)] (Cola, Middle)〉 means that customers will first buy Beer and Milk in Low and High quantities, respectively, and then purchase Cola in Middle quantities on their next shopping trip, where Low, Middle, and High are predefined linguistic terms assigned by managers. A term such as this one provides managers with general and concise knowledge related to customer behavior and allows them to rapidly make decisions in response, especially in a competitive setting. However, literature searches indicate that no previous study has addressed the issue of changes in fuzzy quantitative sequential patterns. The aforementioned example pattern might have been available last year but might not be used this year, and it could have been substituted by 〈(Beer, Middle) {(Cola, Low)(Milk, Low)}〉. If this knowledge is not renewed, managers might develop inappropriate marketing plans for their products or services and use inventory strategies that are outdated with respect to time and quantities. To solve this problem, we propose a novel change-mining model that can be used for detecting changes in fuzzy quantitative sequential patterns. We conducted experiments in which we used real-world and synthetic datasets in order to evaluate the proposed model. When the pattern change was detected using the real-world dataset, the results showed that the model reveals 3 considerations that can help managers with their handling of products' marketing and production. When we studied the model's scalability by using the synthetic dataset, the results showed that even though all run times increased when parameter values were decreased, the model remained scalable. [ABSTRACT FROM AUTHOR]

The simultaneously construct IRT-based ( Item Response Theory) parallel tests problem requires large numbers of variables and constraints, which leads to high computational complexities and now there is no polynomial time algorithm that exists for finding the optimal solution. This article proposes an adapted CLONALG algorithm to simultaneously construct IRT-based parallel tests. Based on the CLONALG features, the proposed scheme can use a single test construction model to simultaneously construct multiple parallel tests. At the same time, it avoids the inequality problem in the sequential construction and solves the drawback of larger numbers of variables and constraints in the simultaneous construction. The serial experiments show that the proposed scheme has a lower deviation in simultaneously constructing parallel tests than the Linear Programming (LP) and the Genetic Algorithm (GA). It is also able to construct parallel tests with identical test specifications from a large item bank. [ABSTRACT FROM AUTHOR]