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With rapid development of service-oriented architecture and cloud computing, web services have been widely employed on the Internet. Quality of Service (QoS) is a very important criterion for service consumers to measure and select services. The selection of web services with respect to non-functional QoS criteria can be considered as a Multiple Criteria Decision Making (MCDM) problem when multiple consumers need to share a number of services. This paper describes a new user centric service-oriented modeling approach which is featured by integrating fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Service Component Architecture (SCA) to facilitate web service selection and composition and to effectively satisfy a group of service consumers' subjective requirements and preferences in the dynamic environment. The main contribution of this method is able to translate a group of users' fuzzy requirements to services as well as model different levels of hardware and software as services to meet the requirements. We also design a simulated environment that includes 8*8 LED matrix on a circuit board that corresponds to an office with different appliances to demonstrate the dynamic service selection and binding. The simulation is used to assess the computational efficiency of the fuzzy TOPSIS method and the effectiveness of the proposed system.

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Most existing user authentication approaches for detecting fraud in e-commerce applications have focused on Secure Sockets Layer (SSL)-based authentication to inspect a username and a password from a server, rather than the inspection of personal biometric information. Because of the lack of support for mutual authentication or twoway authentication between a consumer and a mercantile agent, one-way SSL authentication cannot prevent man-in-the-middle attacks. In practice, in user authentication systems, machine learning and the generalisation capability of support vector models (SVMs) are used to guarantee a small classification error. This study developed an online face-recognition system by training an SVM classifier based on user facial features associated with wavelet transforms and a spatially enhanced local binary pattern. A cross-validation scheme and SVMs associated with the Olivetti Research Laboratory database of user facial features were used for solving classification precision problems. Experimental results showed that the classification error decreased with an increase in the size of the training samples. By using the aggregation of both the low-resolution and the high-resolution face image samples, the global precision of face recognition was over 97% with tenfold cross-validation scheme for an image data size of 168 and 341, respectively. Overall, the proposed scheme provided a higher precision of face recognition compared with the average precision for low-resolution face image (approximately 89%) of the existing schemes.