Many organizations and institutions around the globe are moving or planning to move their paper-and-pencil based testing to computer-based testing (CBT). However, this conversion will not be the best option for all kinds of exams and it will require significant resources. These resources may include the preparation of item banks, methods for test delivery, procedures for test administration, and last but not least test security. Security aspects may include but are not limited to the identification and authentication of examinee, the risks that are associated with cheating on the exam, and the procedures related to test delivery to the examinee. This paper will mainly investigate the security considerations associated with CBT and will provide some recommendations for the security of these kinds of tests. We will also propose a palm-based biometric authentication system incorporated with basic authentication system (username/password) in order to check the identity and authenticity of the examinee.

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Automated Visual Inspection (AVI) is the process of detecting, analyzing and classifying abnormal structures in a surface using machine vision techniques. The increasing competition in the industrial sector imposes high requirements on controlling the quality of flat surface products such as textiles, paper, steel slabs, glass, plastic films, foils, parquet slabs, ceramics etc. Automation of the visual inspection process saves companies a lot of time and raises the quality of their products by avoiding the subjectivity, boredom and slowness of the traditional human-based inspection process. When a defective product reaches the consumer, the company's reputation will suffer. AVI makes 100% quality control and documentation possible. The proposed decision fusion architecture is shown in Figure 1.The paper is organized as follows: the next section gives an overview of the existing AVI approaches. Following sections describe the role of homogeneity for faster defect detection; the adaptive window sizing; the feature sets used Abstract Defect detection of textiles is a necessary requirement for quality control and customer satisfaction. This paper presents a system for decision fusion in order to enhance the accuracy of defect detection in textiles. A multi-classifier decision fusion technique based on majority voting is presented to solve the problems of sensitivity to parameter variation and to make use of the advantages of the individual feature sets for accurate texture characterization. Features based on Gray Level Co-occurrence Matrix (GLCM), Laws Energy (LE) Filter, Local Binary Patterns (LBP), HU Moment invariants, Moment of Inertia (MOI) and Standard Deviation of Gray levels are used to train a set of Learning Vector Quantization (LVQ) classifiers. Detection accuracies of classifiers trained on single-feature sets are compared with those of the majority voting among the individual classifiers. The results obtained from majority voting indicate that the decision fusion technique improves the accuracy and reliability of the detection process. Empirical results indicate the high accuracy of the presented approach. The correct defect detection rate of the proposed approach is 98.64% with an average false acceptance rate of 0.0012.