Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Leu-M

It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.

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Computational Thinking 計算論的思考

A review and outlook for a ‘Building Information Model’ (BIM): A multi-standpoint framework for technological development

An advocated panacea for reducing design errors and rework in construction and engineering projects is building information modeling (BIM) Yet, it would appear that advocates of BIM have overlooked why and how design errors occur In this article, the nature of error is explained and the principal underlying causes identified with reference to the normative literature and the authors phenomenological research A systemic model for reducing design errors is presented and the enabling role of BIM discussed

Design error reduction: toward the effective utilization of building information modeling

Adaptive Educational Hypermedia (AEH) e-learning models aim to personalize educational content and learning resources based on the needs of an individual learner. The Adaptive Hypermedia Architecture (AHA) is a specific implementation of the AEH model that exploits the cognitive characteristics of learner feedback to adapt resources accordingly. However, beside cognitive feedback, the learning realm generally includes both the affective and emotional feedback of the learner, which is often neglected in the design of e-learning models. This article aims to explore the potential of utilizing affect or emotion recognition research in AEH models. The framework is referred to as Multiple Kernel Learning Decision Tree Weighted Kernel Alignment (MKLDT-WFA). The MKLDT-WFA has two merits over classical MKL. First, the WFA component only preserves the relevant kernel weights to reduce redundancy and improve the discrimination for emotion classes. Second, training via the decision tree reduces the misclassification issues associated with the SimpleMKL. The proposed work has been evaluated on different emotion datasets and the results confirm the good performances. Finally, the conceptual Emotion-based E-learning Model (EEM) with the proposed emotion recognition framework is proposed for future work.

Emotion Recognition Using Multiple Kernel Learning toward E-learning Applications

Recent research has been devoted to detecting people in images and videos. In this paper, a human detection method based on Histogram of Oriented Gradients (HoG) features and human body ratio estimation is presented. We utilized the discriminative power of HoG features for human detection, and implemented motion detection and local regions sliding window classifier, to obtain a rich descriptor set. Our human detection system consists of two stages. The initial stage involves image preprocessing and image segmentation, whereas the second stage classifies the integral image as human or non-human using human body ratio estimation, local region sliding window method and HoG Human Descriptor. Subsequently, it increases the detection rate and reduces the false alarm by deducting the overlapping window. In our experiments, DaimlerChrysler pedestrian benchmark data set is used to train a standard descriptor and the results showed an overall detection rate of 80% above.

Human detection using Histogram of oriented gradients and Human body ratio estimation

Periocular recognition remains challenging for deployments in the unconstrained environments. Therefore, this paper proposes an RGB-OCLBCP dual-stream convolutional neural network, which accepts an RGB ocular image and a colour-based texture descriptor, namely Orthogonal Combination-Local Binary Coded Pattern (OCLBCP) for periocular recognition in the wild. The proposed network aggregates the RGB image and the OCLBCP descriptor by using two distinct late-fusion layers. We demonstrate that the proposed network benefits from the RGB image and thee OCLBCP descriptor can gain better recognition performance. A new database, namely an Ethnic-ocular database of periocular in the wild, is introduced and shared for benchmarking. In addition, three publicly accessible databases, namely AR, CASIA-iris distance and UBIPr, have been used to evaluate the proposed network. When compared against several competing networks on these databases, the proposed network achieved better performances in both recognition and verification tasks.

Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN

Foreign Currency Exchange Rate Prediction using Neuro-Fuzzy Systems

Previous studies in computer science education show that game playing is negatively correlated with success in introductory programming classes in (Wilson, Shrock ACM SIGCSE Bulletin, vol 33, pp 184–188, 2001). However, informally, we observed that students who have previous gaming experience take to programming tasks easier than those without gaming experience. There have also been recent studies that show that playing strategic video games can improve problem-solving skills which is an essential skill in program design. This chapter presents the findings of our study to identify if a correlation between previous gaming experience (game playing) and individual computational thinking (CT) skills exists. To achieve this, a survey was administered on undergraduate students undertaking an introductory computing course to collect data on their gaming history and an individual assignment on Scratch. Each project was subsequently analysed to determine the level of mastery of core CT skills. Cochran–Armitage test of trend was then executed on each CT skill category with respect to the coded gaming experience. The results obtained during our analysis shows a correlation between gaming experience and specific categories of the CT skills domain particularly in the area of abstraction and problem-solving and user interactivity. The outcome of our study should be beneficial as ways to leverage on students’ gaming experience in the classroom will also be discussed.

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Yunli Lee

Papers

Emotion Recognition Using Multiple Kernel Learning toward E-learning Applications

Human detection using Histogram of oriented gradients and Human body ratio estimation

Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN

Foreign Currency Exchange Rate Prediction using Neuro-Fuzzy Systems

Video Games: A Potential Vehicle for Teaching Computational Thinking