Mindfulness mirror

This paper explores the use of an interactive Genetic Algorithm for creating a piece of visual art intended to assist in promoting the state of mindfulness. This is determined by a Bluetooth gaming electroencephalography (EEG) headset as the fitness function. The visual display consisted of an infinity mirror with over two hundred Neopixels with fade times and colour of zones controlled by two Ardu-inos running the software. Whilst we have observed some convergence of solu-tions, the results and user observations raised some interesting questions about how this strategy might be improved

A survey of the benefits and issues arising from the deployment of physical artefacts in computer science teaching

This paper describes the introduction of the use of physical artefacts in the teaching of the curriculum in the Department of Computer Science at Middlesex University. The rationale for the change is discussed, together with a description of the various technologies and the areas in which they were deployed. We conclude with a discussion of the outcomes of the work and the conclusions reached, prime amongst which are that the policy has been successful in motivating and engaging students, with a resultant improvement in student progression. In addition to their value in the taught part of the curriculum, these technologies have enabled students to become involved in real-world projects, interacting with external organizations and producing products of value in diverse areas such as the arts and assistive technologies

Smart feedback and the challenges of virtualisation

The use of audio feedback is becoming more prevalent and it would be possible to use avatars for this purpose. When audio feedback is recorded by a human tutor, the recording contains not only the text of the feedback, but also additional information associated with the intonation and manner of delivery of the voice. Experiments were conducted to investigate student’s responses to the use of audio in comparison with other forms of feedback. Students were generally positive about audio feedback; results also indicated that the conveyed emotion or intent is significant and that it is perceived by the student as an important part of the feedback. We also explore this in the context of strategies for the deployment of virtual agents in the provision of feedback

Towards a brain controller interface for generating simple Berlin School style music with interactive genetic algorithms

A novel approach to generating music is presented using two interactive Genetic Algorithms with electroencephalogram inputs from two subjects as their fitness functions. Many interactive Genetic Algorithm approaches for generating music employ constrained solution spaces that only utilise notes from a given scale. Our work incorporates the use of mutation to extend the solution space through the inclusion of accidental notes. A thresholding approach is adopted, that allows riffs to be repeated until fitness drops, together with a ‘killswitch’ to ensure unpleasant sounding riffs are removed from the population. The development is ongoing, with more testing and calibration required to ensure that there are no timing errors in communication between the microcontroller boards and to identify the most appropriate threshold and mutation ranges, in addition to determining the most appropriate mixes for the users to hear

Embedding creativity in the university computing curriculum

We explore the need for embedding creativity in the UK Higher Education computing curriculum and some of the challenges associated with this. We identify some of the initiatives and movements in this area and discuss some of the work that has been carried out. We then describe some of the ways we have tried to meet these challenges and reflect on our degree of success with respect to the goal of producing graduates who are fit for the myriad of job opportunities they will come across in a rapidly changing technology landscape. Finally, we make a number of recommendations

The use of physical artefacts in undergraduate computer science teaching

This paper describes the introduction of the use of physical artefacts in the teaching of the undergraduate curriculum in the Department of Computer Science at Middlesex University. The rationale for the change is discussed, together with a description of the various technologies and the areas in which they were deployed. We conclude with a discussion of the outcomes of the work and the conclusions reached, prime amongst which are that the policy has been successful in motivating and engaging students, with a resultant improvement in student progression

Eugene: a generic interactive genetic algorithm controller

This paper outlines the development of an open source generic hardware-based interactive Genetic Algorithm controller (Eugene) and explores contexts in which it may be deployed. The system was first applied to the generation of synthetic sound using MIDI and a simple analogue synthesiser with 27 continuous controller values. It was then applied in the area of image evaluation using an image enhancer program with 7 continuous controller values. The system was evaluated by experimental observation of users attempting various tasks with different success criteria. This led to the identification of issues, some of which were specific to, and others divorced from the application domain. These are discussed together with areas for improvement

EEuGene: employing electroencephalograph signals in the rating strategy of a hardware-based interactive genetic algorithm

We describe a novel interface and development platform for an interactive Genetic Algorithm (iGA) that uses Electroencephalograph (EEG) signals as an indication of fitness for selection for successive generations. A gaming headset was used to generate EEG readings corresponding to attention and meditation states from a single electrode. These were communicated via Bluetooth to an embedded iGA implemented on the Arduino platform. The readings were taken to measure subjects’ responses to predetermined short sequences of synthesised sound, although the technique could be applied any appropriate problem domain. The prototype provided sufficient evidence to indicate that use of the technology in this context is viable. However, the approach taken was limited by the technical characteristics of the equipment used and only provides proof of concept at this stage. We discuss some of the limitations of using biofeedback systems and suggest possible improvements that might be made with more sophisticated EEG sensors and other biofeedback mechanisms

Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture

This research investigates the application of CT pulmonary images to the detection and characterisation of TB at five levels of severity, in order to monitor the efficacy of treatment. To contend with smaller datasets (i.e. in hundreds) and the characteristics of CT TB images in which abnormalities occupy only limited regions, a 3D block-based residual deep learning network (ResNet) coupled with injection of depth information (depth-Resnet) at each layer was implemented. Progress in evaluation has been accomplished in two ways. One is to assess the proposed depth-Resnet in prediction of severity scores and another is to analyse the probability of high severity of TB. For the former, delivered results are of 92.70 ± 5.97% and 67.15 ± 1.69% for proposed depth-Resnet and ResNet-50 respectively. For the latter, two additional measures are put forward, which are calculated using (1) the overall severity (1 to 5) probability, and (2) separate probabilities of both high severity (scores of 1 to 3) and low severity (scores of 4 and 5) respectively, when scores of 1 to 5 are mapped into initial probabilities of (0.9, 0.7, 0.5, 0.3, 0.2) respectively. As a result, these measures achieve the averaged accuracies of 75.88% and 85.29% for both methods respectively

Analysing TB severity levels with an enhanced deep residual learning– depth-resnet

This work responds to the Competition of Tuberculosis Task organised by imageCLEF 2018. While Task #3 appears to be challenging, the experience was very enjoyable. If time had been permitted, it was certain that more accurate results could have been achieved. The authors submitted 2 runs. Based on the given training datasets with severity levels of 1 to 5, an enhanced deep residual learning architecture, depthResNet, is developed and applied to train the datasets to classify 5 categories. The datasets are pre-processed with each volume being segmented into twenty- 128×128×depth blocks with ~64 pixel overlaps. While each block has been predicted with a severity level, assembling all constituent block scores together to give an overall label for the concerned volume tends to be more challenging. Since the probability of high severity is not provided from the training datasets, which bears little resemblance to the classification probability, the submission of probability for the first run was manually assigned as 0.9, 0.7, 0.5, 0.3, and 0.1 to severity levels of 1 to 5 respectively. After the deadline was extended, the model was re-trained with frame numbers increased from 1 to 8, which takes much longer to train. In addition, a new measure was introduced to calculate the overall probability of high severity based on the block scores. As a result, with regard to classification accuracy, the 2nd submitted run achieved place 14 over a total of 36 submissions, a significant improvement from position of 35 from the first run