Digital games and mobile technology present remarkable opportunities for inclusive education when their design is constructively aligned with accessibility and learning objectives, alike. The aim of this paper is to explore the perspectives of special education teachers on the functional role, affordances, and inherent constraints of mobile games and gamification platforms when employed as a learning intervention for educating students with learning disabilities. A mixed-methods study was conducted to address the research questions. The findings highlight the importance of designing highly individualised gamified activities for learners and the need to involve special educators throughout the game design and development lifecycle.

A. Piki 1, L. Andreou2, M. Markou 2 1University of Central Lancashire - Cyprus campus (UCLan Cyprus) (CYPRUS) 2P.A. College (CYPRUS)

Games-based learning is an effective way to get learners actively involved in educational activities. This paper attempts to bridge the gap that exists between learning and engagement theory and systems analysis and design through a multidisciplinary framework for guiding the development of new educational games aimed at intellectually challenged children. Based on this framework a new web-based games platform is designed from the ground up, framed within games-based learning, learner engagement and motivation, and instructional systems development, and supported through the data gathered from a real-life case study. Mixed methods are employed for gathering insights on how intellectually challenged children at the age of 8 to 10 years old engage in learning activities, and for examining ways to help their development using educational games. Realising the role of learning and engagement theory in the development of educational games has a number of implications. Firstly, it emphasises the need to integrate theoretical constructs into the traditional systems analysis and design lifecycle when creating educational applications. Secondly, it confirms educators' central role both as informants in the game analysis and design process and as evaluators of the learning outcomes achieved by incorporating educational games within formal education. The contribution of the current work lies in the adaptation of the traditional system prototyping methodology to account for theoretical insights and on-going evaluation of the expected learning outcomes.

Scene analysis is an important area of research with the aim of identifying objects and their relationships in natural scenes. MINERVA benchmark has been recently introduced in this area for testing different image processing and classification schemes. In this paper we present results on the classification of eight natural objects in the complete set of 448 natural images using neural networks. An exhaustive set of experiments with this benchmark has been conducted using four different segmentation methods and five texture-based feature extraction methods. The results in this paper show the performance of a neural network classifier on a tenfold cross-validation task. On the basis of the results produced, we are able to rank how well different image segmentation algorithms are suited to the task of region of interest identification in these images, and we also see how well texture extraction algorithms rank on the basis of classification results.

This paper proposes a new model of "novelty detection" for image sequence analysis using neural networks. This model uses the concept of artificially generated negative data to form closed decision boundaries using a multilayer perceptron. The neural network output is novelty filtered by thresholding the output of multiple networks (one per known class) to which the sample is input and clustered for determining which clusters represent novel classes. After labeling these novel clusters, new networks are trained on this data. We perform experiments with video-based image sequence data containing a number of novel classes. The performance of the novelty filter is evaluated using two performance metrics and we compare our proposed model on the basis of these with five baseline novelty detectors. We also discuss the results of retraining each model after novelty detection. On the basis of Chi-square performance metric, we prove at 5 percent significance level that our optimized novelty detector performs at the same level as an ideal novelty detector that does not make any mistakes

One of the major issues in novelty detection for video and image analysis application is how to recognize objects that are not in full view. As the camera pans, tilts and zooms, image objects within a scene are never in full view and enter and exit image frames with time. These objects with partial view do not contain enough number of pixels that can generate robust color, texture and statistical features, and therefore result in classification samples that are often not representative of that class, e.g. outliers. From a data analysis point of view, it is impossible to known which samples are truly outliers because of partial view as opposed to outliers because of mistakes in object labeling. This can cause serious problems in novelty detection tasks using neural networks. In this paper we propose a novel methodology for automatically detecting image objects with partial view and discuss how to use this knowledge to improve novelty detection results.

A black hole is a region of space that has so much mass concentrated in it that there is no way training data for a nearby object to escape its gravitational pull. In this paper we are inspired by this phenomenon to create a new form of novelty detector. We consider the of a given class as a black hole. For multi-class data we are dealing with multiple black holes. A test point is pulled by the centroids of different black holes as well as its K nearest neighbours. The gravitational pull is modelled as an iterative process, where the forces acting on a point are constantly changing with time as the test point moves in multi-dimensional space corresponding to these forces. Once the algorithm has converged, a thresholding scheme is applied to determine whether the test point has been pulled within the boundary of the black hole or not. Any points that lie outside all known black holes are deemed to be novel. We compare this novelty detector with other well-known models of novelty detection on a video analysis application and show very promising results.

This work introduces a new model of feature selection based on a pattern recognition model using the concept of black holes. We show that this method of feature selection is robust and provides an efficient subset of features for classification.

We present a new framework for novelty detection. The framework evaluates neural networks as adaptive classifiers that are capable of novelty detection and retraining on the basis of newly discovered information. We apply our newly developed model to the application area of object recognition in video. We detail the tools and methods needed for novelty detection such that data from unknown classes can be reliably rejected without any a priori knowledge of its characteristics. The rejected data is postprocessed to determine which samples can be manually labeled of a new type and used for retraining. We compare the proposed framework with other novelty detection methods and discuss the results of adaptive retraining of neural network to recognize further unseen data containing the newly added objects.

Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time of training the model. In this paper we provide state-of-the-art review in the area of novelty detection based on statistical approaches. The second part paper details novelty detection using neural networks. As discussed, there are a multitude of applications where novelty detection is extremely important including signal processing, computer vision, pattern recognition, data mining, and robotics.

Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. In this paper we focus on neural network-based approaches for novelty detection. Statistical approaches are covered in Part 1 paper.

Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. In this paper we focus on neural network-based approaches for novelty detection. Statistical approaches are covered in Part 1 paper.

Abstract Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time of training the model In this paper we provide state - of - the - art review in the area of novelty detection based on statistical approaches The second part paper details novelty detection using neural networks As discussed, there are a multitude of applications where novelty detection is extremely important including signal processing, computer vision, pattern recognition

In this paper we investigate the role of colour spaces on texture analysis. We extract a range of correlogram and colour moment features for the VisTex colour texture benchmark in different colour spaces and find the average probabilistic distance of separation across different objects for different features and suggest the colour spaces that are best suited for the classification process. We also show the results of kNN classification for different features and their combined set.

Feature selection is an important consideration in several applications where one needs to choose a smaller subset of features from a complete set of raw measurements such that the improved subset generates as good or better classification performance compared to original data. In this paper, we describe a novel feature selection approach that is based on the estimation of classification complexity through data partitioning. This approach allows us to select the N best features from a given set in an order of their ability to separate data from different classes. In this paper, we perform our experiments on the ORL face database that consists of 400 images. The results show that the proposed approach outperforms the probability distance approach and is a viable method for implementing more advanced search methods of feature selection.

The recognition and analysis of natural objects is an important area of research. In this paper, we demonstrate the results of natural object recognition using colour texture features. The results on colour features are generated using a large scene analysis benchmark and neural network classifier. These results are compared to the use of grey-scale features on the same data and found to be much superior.

It is now well-established that k nearest-neighbour classifiers offer a quick and reliable method of data classification. In this paper we extend the basic definition of the standard k nearest-neighbour algorithm to include the ability to resolve conflicts when the highest number of nearest neighbours are found for more than one training class (model-1). We also propose model-2 of nearest-neighbour algorithm that is based on finding the nearest average distance rather than nearest maximum number of neighbours. These new models are explored using image understanding data. The models are evaluated on pattern recognition accuracy for correctly recognising image texture data of five natural classes: grass, trees, sky, river reflecting sky and river reflecting trees. On noise contaminated test data, the new nearest neighbour models show very promising results for further studies. We evaluate their performance with increasing values of neighbours (k) and discuss their future in scene analysis research.

In this paper we compare four classification techniques for classifying texture data of various natural objects found in forward-looking infrared (FLIR) images. The techniques compared include linear discriminant analysis, mean classifier and two different models of k-nearest neighbour methods. Hermite functions are used for texture feature extraction from segmented regions of interest in natural scenes taken as a video sequence. A total of 2680 samples for a total of twelve different classes are used for object recognition. The results on correctly identifying twelve natural objects in scenes are compared across the four classifiers on both unnormalised and normalised data. On unnormalised data, the average best recognition rate obtained using a ten fold cross-validation is 96.5%, and on unnormalised data it is 86.1% with a single nearest neighbour technique