• Benefits, barriers and guideline recommendations for the implementation of serious games in education for stakeholders and policymakers

      Tsekleves, Emmanuel; Cosmas, John; Aggoun, Amar (Blackwell Publishing Ltd, 2014-10-24)
      Serious games and game-based learning have received increased attention in recent years as an adjunct to teaching and learning material. This has been well echoed in the literature with numerous articles on the use of games and game theory in education. Despite this, no policy for the incorporation of serious games in education exists to date. This review paper draws from the literature to provide guideline recommendations that would help educators and policymakers in making the first step towards this.
    • Evolving polynomial neural networks for detecting abnormal patterns

      Nyah, Ndifreke; Jakaite, Livija; Schetinin, Vitaly; Sant, Paul; Aggoun, Amar; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2016-11-10)
      Abnormal patterns, existing e.g. in raw data, affect decision making process and have to be accurately detected and removed in order to reduce the risk of making wrong decisions. Existing Machine Learning (ML) approaches known from the literature require the user to set and experimentally adjust parameters of a decision model to achieve the best result. When artificial neural networks (ANNs) are employed, a typical problem is setting of a proper network structure and learning parameters that are required to minimise possible over-fitting. We propose a new evolutionary strategy of learning an ANN structure of a near optimal connectivity from the given data and show that such structures are less prone to over-fitting. The proposed method starts to learn with one input variable and one neuron and then adds a new input and a new neuron to the network while its validation error decreases. The resultant ANN consists of a reasonably small number of neurons that are concisely described by a set of short-term polynomial functions of variables that make a distinct contribution to the output. The proposed technique has been tested on the ML benchmarks and the results showed that the performance is comparable with that obtained by the conventional ML methods that require ad hoc tuning.
    • Learning polynomial neural networks of a near-optimal connectivity for detecting abnormal patterns in biometric data

      Nyah, Ndifreke; Jakaite, Livija; Schetinin, Vitaly; Sant, Paul; Aggoun, Amar; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2016-09-01)
      Existing Machine Learning (ML) approaches known from the literature require the user to set and experimentally adjust parameters of a decision model to achieve the best result. When artificial neural networks (ANNs) are employed, a typical problem is setting of a proper network structure and learning parameters that are required to minimise possible overfitting. We propose a new evolutionary strategy of learning an ANN structure of a near-optimal connectivity from the given data and show that such structures are less prone to overfitting. The resultant ANN consists of a reasonably small number of neurons that are concisely described by a set of short-term polynomial functions of variables that make a distinct contribution to the output. The proposed technique has been tested on the ML benchmarks and the results showed that the performance is comparable with that obtained by the conventional ML methods that require ad hoc tuning.
    • Refocusing distance of a standard plenoptic camera

      Hahne, Christopher; Aggoun, Amar; Velisavljević, Vladan; Fiebig, Susanne; Pesch, Matthias; University of Bedfordshire; ARRI Cine Technik (OSA - The Optical Society, 2016-09-08)
      Recent developments in computational photography enabled variation of the optical focus of a plenoptic camera after image exposure, also known as refocusing. Existing ray models in the field simplify the camera's complexity for the purpose of image and depth map enhancement, but fail to satisfyingly predict the distance to which a photograph is refocused. By treating a pair of light rays as a system of linear functions, it will be shown in this paper that its solution yields an intersection indicating the distance to a refocused object plane. Experimental work is conducted with different lenses and focus settings while comparing distance estimates with a stack of refocused photographs for which a blur metric has been devised. Quantitative assessments over a 24 m distance range suggest that predictions deviate by less than 0.35 % in comparison to an optical design software. The proposed refocusing estimator assists in predicting object distances just as in the prototyping stage of plenoptic cameras and will be an essential feature in applications demanding high precision in synthetic focus or where depth map recovery is done by analyzing a stack of refocused photographs.
    • Speaker identification using multimodal neural networks and wavelet analysis

      Almaadeed, Noor; Aggoun, Amar; Amira, Abbes; Brunel University; Qatar University; University of Bedfordshire; University of the West of Scotland (Institution of Engineering and Technology, 2015-03-19)
      The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content. In this study, the authors designed and implemented a novel text-independent multimodal speaker identification system based on wavelet analysis and neural networks. Wavelet analysis comprises discrete wavelet transform, wavelet packet transform, wavelet sub-band coding and Mel-frequency cepstral coefficients (MFCCs). The learning module comprises general regressive, probabilistic and radial basis function neural networks, forming decisions through a majority voting scheme. The system was found to be competitive and it improved the identification rate by 15% as compared with the classical MFCC. In addition, it reduced the identification time by 40% as compared with the back-propagation neural network, Gaussian mixture model and principal component analysis. Performance tests conducted using the GRID database corpora have shown that this approach has faster identification time and greater accuracy compared with traditional approaches, and it is applicable to real-time, text-independent speaker identification systems.
    • Text-independent speaker identification using vowel formants

      Almaadeed, Noor; Aggoun, Amar; Amira, Abbes (Springer New York LLC, 2015-05-05)
      Automatic speaker identification has become a challenging research problem due to its wide variety of applications. Neural networks and audio-visual identification systems can be very powerful, but they have limitations related to the number of speakers. The performance drops gradually as more and more users are registered with the system. This paper proposes a scalable algorithm for real-time text-independent speaker identification based on vowel recognition. Vowel formants are unique across different speakers and reflect the vocal tract information of a particular speaker. The contribution of this paper is the design of a scalable system based on vowel formant filters and a scoring scheme for classification of an unseen instance. Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) have both been analysed for comparison to extract vowel formants by windowing the given signal. All formants are filtered by known formant frequencies to separate the vowel formants for further processing. The formant frequencies of each speaker are collected during the training phase. A test signal is also processed in the same way to find vowel formants and compare them with the saved vowel formants to identify the speaker for the current signal. A score-based scheme allows the speaker with the highest matching formants to own the current signal. This model requires less than 100 bytes of data to be saved for each speaker to be identified, and can identify the speaker within a second. Tests conducted on multiple databases show that this score-based scheme outperforms the back propagation neural network and Gaussian mixture models. Usually, the longer the speech files, the more significant were the improvements in accuracy.