Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

The file attached to this record is the author's final peer reviewed version.Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles

Hybrid Marker-less Camera Pose Tracking with Integrated Sensor Fusion

This thesis presents a framework for a hybrid model-free marker-less inertial-visual camera pose tracking with an integrated sensor fusion mechanism. The proposed solution addresses the fundamental problem of pose recovery in computer vision and robotics and provides an improved solution for wide-area pose tracking that can be used on mobile platforms and in real-time applications. In order to arrive at a suitable pose tracking algorithm, an in-depth investigation was conducted into current methods and sensors used for pose tracking. Preliminary experiments were then carried out on hybrid GPS-Visual as well as wireless micro-location tracking in order to evaluate their suitability for camera tracking in wide-area or GPS-denied environments. As a result of this investigation a combination of an inertial measurement unit and a camera was chosen as the primary sensory inputs for a hybrid camera tracking system. After following a thorough modelling and mathematical formulation process, a novel and improved hybrid tracking framework was designed, developed and evaluated. The resulting system incorporates an inertial system, a vision-based system and a recursive particle filtering-based stochastic data fusion and state estimation algorithm. The core of the algorithm is a state-space model for motion kinematics which, combined with the principles of multi-view camera geometry and the properties of optical flow and focus of expansion, form the main components of the proposed framework. The proposed solution incorporates a monitoring system, which decides on the best method of tracking at any given time based on the reliability of the fresh vision data provided by the vision-based system, and automatically switches between visual and inertial tracking as and when necessary. The system also includes a novel and effective self-adjusting mechanism, which detects when the newly captured sensory data can be reliably used to correct the past pose estimates. The corrected state is then propagated through to the current time in order to prevent sudden pose estimation errors manifesting as a permanent drift in the tracking output. Following the design stage, the complete system was fully developed and then evaluated using both synthetic and real data. The outcome shows an improved performance compared to existing techniques, such as PTAM and SLAM. The low computational cost of the algorithm enables its application on mobile devices, while the integrated self-monitoring, self-adjusting mechanisms allow for its potential use in wide-area tracking applications

Low-Cost Automatic Ambient Assisted Living system

The file attached to this record is the author's final peer reviewed version.The recent increase in ageing population in countries around the world has brought a lot of attention toward research and development of ambient assisted living (AAL) systems. These systems should be inexpensive to be installed in elderly homes, protecting their privacy and more importantly being non-invasive and smart. In this paper, we introduce an inexpensive system that utilises off-the-shelf sensor to grab RGB-D data. This data is then fed into different learning algorithms for classification different activity types. We achieve a very good success rate (99.9%) for human activity recognition (HAR) with the help of light-weighted and fast random forests (RF)

A Quantisation of Cognitive Learning Process by Computer Graphics-Games: Towards More Efficient Learning Models

Research group of Computer Sciences at DMU, Psychology Research Group at University of Birmingham and Reseach Group of Computer Science at University of Northumbria.With the latest developments in computer technologies and artificial intelligence (AI) techniques, more opportunities of cognitive data acquisition and stimulation via game-based systems have become available for computer scientists and psychologists. This may lead to more efficient cognitive learning model developments to be used in different fields of cognitive psychology than in the past. The increasing popularity of computer games among a broad range of age groups leads scientists and experts to seek game domain solutions to cognitive based learning abnormalities, especially for younger age groups and children. One of the major advantages of computer graphics and using game-based techniques over the traditional face-to-face therapies is that individuals, especially children immerse in the game’s virtual environment and consequently feel more open to share their cognitive behavioural characteristics naturally. The aim of this work is to investigate the effects of graphical agents on cognitive behaviours to generate more efficient cognitive models

An Outcome based approach to developing a Belarusian Qualification Framework

http://iesed.esy.es/The Higher Education landscape of Belarus is characterised by high quality institutions offering world class expertise and facilities, and a very high participation rate in higher education. However, it has also been recognised by the state that the degree of individuality and autonomy prevalent in these institutions works against the current mood of globalisation in Higher Education. An obvious example is international exchanges. It is particularly difficult in the case of students since the programmes are usually organised in an insular way and lack a precise specification of the level at which any contributory course is delivered. A stated objective of the Belarusian Ministry of Education is to seek membership of the European Higher Education Area (EHEA). To this end a road map (Eastern Partnership Civil Society Forum, 2017), designed to afford increased international compatibility of the Belarusian Higher Education Framework, has been defined and is being implemented by the Belarusian Ministry of Education. This paper considers how the EU funded project IESED could directly contribute to the realisation of this Road Map

Dealing with scarce labelled data: Semi-supervised deep learning with mix match for Covid-19 detection using chest X-ray images

The file attached to this record is the author's final peer reviewed version.Coronavirus (Covid-19) is spreading fast, infecting people through contact in various forms including droplets from sneezing and coughing. Therefore, the detection of infected subjects in an early, quick and cheap manner is urgent. Currently available tests are scarce and limited to people in danger of serious illness. The application of deep learning to chest X-ray images for Covid-19 detection is an attractive approach. However, this technology usually relies on the availability of large labelled datasets, a requirement hard to meet in the context of a virus outbreak. To overcome this challenge, a semi-supervised deep learning model using both labelled and unlabelled data is proposed. We develop and test a semi-supervised deep learning framework based on the Mix Match architecture to classify chest X-rays into Covid-19, pneumonia and healthy cases. The presented approach was calibrated using two publicly available datasets. The results show an accuracy increase of around 15% under low labelled / unlabelled data ratio. This indicates that our semi-supervised framework can help improve performance levels towards Covid-19 detection when the amount of high-quality labelled data is scarce. Also, we introduce a semi-supervised deep learning boost coefficient which is meant to ease the scalability of our approach and performance comparison

Improving Uncertainty Estimation With Semi-supervised Deep Learning for COVID-19 Detection Using Chest X-ray Images

In this work we implement a COVID-19 infection detection system based on chest Xray images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer aided diagnosis tools in medical applications. Model estimations with high uncertainty should be carefully analyzed by a trained radiologist. We aim to improve uncertainty estimations using unlabelled data through the MixMatch semi-supervised framework. We test popular uncertainty estimation approaches, comprising Softmax scores, Monte-Carlo dropout and deterministic uncertainty quantification. To compare the reliability of the uncertainty estimates, we propose the usage of the Jensen-Shannon distance between the uncertainty distributions of correct and incorrect estimations. This metric is statistically relevant, unlike most previously used metrics, which often ignore the distribution of the uncertainty estimations. Our test results show a significant improvement in uncertainty estimates when using unlabelled data. The best results are obtained with the use of the Monte Carlo dropout method

Improving uncertainty estimations for mammogram classification using semi-supervised learning

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Computer aided diagnosis for mammogram images have seen positive results through the usage of deep learning architectures. However, limited sample sizes for the target datasets might prevent the usage of a deep learning model under real world scenarios. The usage of unlabeled data to improve the accuracy of the model can be an approach to tackle the lack of target data. Moreover, important model attributes for the medical domain as model uncertainty might be improved through the usage of unlabeled data. Therefore, in this work we explore the impact of using unlabeled data through the implementation of a recent approach known as MixMatch, for mammogram images. We evaluate the improvement on accuracy and uncertainty of the model using popular and simple approaches to estimate uncertainty. For this aim, we propose the usage of the uncertainty balanced accuracy metric

Pathways to sex addiction: Relationships with adverse childhood experience, attachment, narcissism, self-compassion and motivation in a gender-balanced sample

Research about sex addiction and its relationships with other constructs remains unexplored. We recruited a gender-balanced sample (53 men, 51 women) who responded to measures of sex addiction, adverse childhood experience, adult attachment, narcissism, self-compassion and motivation. Sex addiction was found to be statistically significantly associated with these constructs. Anxious attachment statistically significantly mediated the relationship between adverse childhood experience and sex addiction and the relationship between narcissism and sex addiction. Self-compassion did not statistically significantly moderate the relationship between anxious attachment and sex addiction. Therapeutic approaches targeting attachment and narcissism such as relation-based or mindfulness-based interventions are recommended.N/