The effective real-time face detection framework proposed by Viola and Jones gained much popularity due its computational efficiency and its simplicity. A notable variant replaces the original Haar-like features with MB-LBP (Multi-Block Local Binary Pattern) which are defined by the local binary pattern operator, both detector types are integrated into the OpenCV library. However, each descriptor and its evaluation method has its own set of strengths and setbacks. In this paper, an enhanced two-layer face detector composed of both Haar-like and MB-LBP features is presented. Haar-like features are employed as a coarse filter but with a new evaluation involving dual threshold. The already established MB-LBPs are arranged as the fine filter of the detector. The Gentle AdaBoost learning algorithm is deployed for the training of the proposed detector to reach the classification and performance potential. Experiments show that in the early stages of classification, Haar features with dual threshold are more discriminative than MB-LBP and original Haarlike features with respect to number of features required and computation. Benchmarking the proposed detector demonstrate overall 12% higher detection rate at 17% false alarm over using MB-LBP features singly while performing with ×3 speedup.

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The increase in the number of institutions offering degrees in interdis ciplinary computing raises a set of questions at the epistemological level. A set of questions is raised: is any integration of computing with some other field a representation of interdisciplinarity? What are the limits of enabling? What are the requirements of integration? Does the product of interdisciplinary computing remain computer science? These questions are urgent and answers should be provided especially that the number of such degrees is increasing accompanied by a growing demand on these jobs, as reflected on jobs and opportunities websites. A definition for interdisciplinary computing is needed. This paper attempts to answer these questions based on the huge literature available on interdisciplinarity in general, and interdisciplinary computing in specific. Special reference to career opportunities will be made. The study will be completed using document analysis examining the related documents as the data source of a qualitative research. A phenomenological study will be used to understand the meaning different schools are appropriating to interdisciplinarity. Enough documents will be consulted to extract the common themes and build a sufficient data set of emerging themes to validate the findings. While the phenomenological study aims at describing the essence interdisciplinary computing, grounded theory methods will be used to formulate a definition. As a result of this study is expected to better inform on the design and understanding of how different schools are managing their offering.Some explanatory, exploratory, or descriptive case study involving multiple types of data sources will be explored to acquire a deep understanding and provide support of the findings. The paper concludes with a proposed set of PLOs for interdisciplinary computing and reflecting its educational aspects and respecting technical norms.

Social computing systems such as Social Network Sites have become more powerful. In some universities, SNSs have been adopted as a communication method between teachers and students. In addition, educational institutions have started the initiative of using open source social networking application. This chapter discusses the benefits of adopting open source SNS in education. It is organized as follows: 1) a literature review to properly define the terms, 2) a discussion of the effect of open source social networking technologies on education systems, 3) an overview of Elgg, followed by a comparison with different social learning platforms, 4) a case study of implementing Elgg at the Computer Science Department at the University of Balamand, 5) an exhibition of the requirements for the Next Generation SCORM, 6) a case study using Tin Can API with open source SNSs (Elgg), and 7) a conclusion wrapping up the chapter.