Bryan Scotney

Ulster University

The strength of cryptographic keys rely on the random number generators (RNGs) to produce random seed values. Unfortunately there are not many RNGs options suitable for Internet of Things (IoTs) scenario, due to limited processing resources and bulk quantity of IoT data that needs to be secured. In this article, we studied sawtooth map which is a c...

Image and video data make up a significant portion of the content shared over the Internet and social media. The use of image and video communication allows more information to be shared while simultaneously presenting higher risks in terms of data security. The traditional encryption schemes are general purpose; however, to encrypt image and video...

In this paper, we describe a Proof-of-Concept implementation of an automated system to drive one aspect of a personalised, object-based TV experience, on sports content, such as football and rugby. Our Proof-of-Concept uses a sequence of analytics processes, which can be performed offline or in real time, to allow a new media object to be automatic...

Chaos-based cryptosystems have been an active area of research in recent years. Although these algorithms are not standardized like AES, DES, RSA, etc., chaos-based cryptosystems like Chebyshev polynomials can provide additional security when used with standard public key cryptosystems like RSA and El-gamal. Standard encryption algorithms such as A...

Pseudo-random number generators (PRNGs) are one of the building blocks of cryptographic methods and therefore, new and improved PRNGs are continuously developed. In this study, a novel method to generate pseudo-random sequences using coupled map lattices is presented. Chaotic maps only show their chaotic behaviour for a specified range of control p...

Market making (MM) is an important means of providing liquidity to the stock markets. Recent research suggests that reinforcement learning (RL) can improve MM significantly in terms of returns. In the latest work on RL-based MM, the reward is a function of equity returns, calculated based on its current price, and the inventory of MM agent. As a re...

Market making (MM) is a trading activity by an individual market participant or a member firm of an exchange that buys and sells same securities with the primary goal of profiting on the bid-ask spread, which contributes to the market liquidity. Reinforcement learning (RL) is emerging as a quite popular method for automated market making, in additi...

Reinforcement learning (RL) problems with continuous states and discrete actions (CSDA) can be found in classic examples such as Cart Pole and Puck World, as well as real world applications such as Market Making. Solutions to CSDA problems typically involve a function approximation (FA) of the mapping from states to actions and can be linear or non...

Background: The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized...

Internet of Things (IoT) technology is increasingly pervasive in all aspects of our life and its usage is anticipated to significantly increase in future Smart Cities to support their myriad of revolutionary applications. This paper introduces a new architecture that can support several IoT-enabled smart home use cases, with a specified level of se...

This paper studies the effect of the amalgamation of two market prices, namely the current price and the future price, on the net investment returns of a market maker and the overall market liquidity. We introduce the “consolidated price equation” for the market makers and developed a new approach, known as “Predictive market making (PMM)”.

This paper presents a novel non-linear gaussian distribution based function approximator for continuous states and discrete action spaces RL.

In many real-world classification problems there exist multiple subclasses (or clusters) within a class; in other words, the underlying data distribution is within-class multimodal. One example is face recognition where a face (i.e. a class) may be presented in frontal view or side view, corresponding to different modalities. This issue has been la...

BACKGROUND The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized b...

Over the last 2 decades, face identification has been an active field of research in computer vision. As an important class of image representation methods for face identification, fused descriptor-based methods are known to lack sufficient discriminant information, especially when compared with deep learning-based methods. This paper presents a ne...

In this paper, the issue of scale is addressed in the context of salient object detection. To date, many single scale models have been proposed for detecting salient objects within a scene. Scale is a fundamental problem within image processing, and therefore, multiple scale techniques are investigated and evaluated, as well the presentation of a n...

Salient object detection is a prominent research topic, based on a human's ability to selectively process conspicuous objects/regions within a scene. With many low-level features being adopted into saliency models, gradient is often overlooked. We investigate the effectiveness of gradient as a feature, applying and evaluating multiple image gradien...

Face recognition has achieved great success owing to the fast development of deep neural networks in the past few years. Different loss functions can be used in a deep neural network resulting in different performance. Most recently some loss functions have been proposed, which have advanced the state of the art. However, they cannot solve the prob...

A number of new architectures for data centre networks employing reconfigurable, SDN controlled, all-optical networks have been reported in recent years. In most cases, additional capacity was added to the system which unsurprisingly improved performance. In this study, a generalised network model that emulates the behaviour of these types of netwo...

Market Making (also known as liquidity providing service) is a well-known trading problem studied in multiple disciplines including Finance, Economics and Artificial Intelligence. This paper examines the impact of Market Spread over the market maker’s (or liquidity provider’s) convergence ability through testing the hypothesis that “Knowledge of ma...

This paper presents the local septenary patterns (LSP) operator, which is a variant of the local binary patterns (LBP) texture descriptor inspired from local ternary patterns (LTP) and local quinary patterns (LQP). We introduce a seven-encoding system approach to capture more texture details and resulting in more discriminant features. Unlike the L...

Efficient processing of digital images is a key consideration in many machine vision tasks. Traditional image processing approaches often struggle to meet this demand, particularly at the initial low-level of processing image pixels. To overcome this, we propose a spiral based processing approach which takes inspiration from the asymmetric lattice...

This paper presents an efficient approach to corner detection for images using a spiral addressing scheme in conjunction with simulated, biological involuntary eye movements. As part of this approach, a combined gradient detection and smoothing operation is used to quickly obtain a feature representation that can be used with a standard 'cornerness...

Face recognition has achieved great progress owing to the fast development of the deep neural network in the past a few years. As the baton in a deep neural network, a number of the loss functions have been proposed which significantly improve the state-of-the-art methods. In this paper, we proposed a new loss function called Minimum Margin Loss (M...

This paper presents an extension of work from our previous study by investigating the use of Local Quinary Patterns (LQP) for breast density classification in mammograms on various neighbourhood topologies. The LQP operators are used to capture the texture characteristics of the fibro-glandular disk region ( F G D r o i ) instead of the whole breas...

The Network Management System (NMS) consists of Management Information Base (MIB) objects that enable policy based monitoring and management of Simple Network Management Protocol (SNMP) infrastructure. The emerging technological architecture of Software Defined Networks (SDN) is related to the NMS on the north-bound interface and the concept of pol...

Humans have a distinct ability to process only the information that is of interest within a scene, however, this is not an easy task for computers. Trying to replicate this behaviour, many methods have been proposed to generate saliency maps that segment the object of interest within an image. In this paper, we investigate the problem of object cla...

Fast image processing is a key element in achieving real-time image and video analysis. We propose a novel framework based on a square spiral (denoted as “squiral”) architecture to facilitate fast image processing. Unlike conventional image pixel addressing schemes, where the pixel indices are based on two-dimensional Cartesian coordinates, the spi...

This paper presents a preliminary quantitative study for breast cancer risk assessment in mammography using mathematical operators called Local Ternary Patterns. The study covers three different mapping patterns namely uniform (‘u2’), nonuniform (‘ri’) and a combination of uniform and nonuniform (‘riu2’). These patterns are used as texture features...

This paper presents a method for breast density classification using local quinary patterns (LQP) in mammograms. LQP operators are used to capture the texture characteristics of the fibroglandular disk region (\(FGD_{roi}\)) instead of the whole breast region as the majority of current studies have done. To maximise the local information, a multire...

This paper presents a method for breast density classification. Local ternary pattern operators are employed to model the appearance of the fibroglandular disk region instead of the whole breast region as the majority of current studies have done. The Support Vector Machine classifier is used to perform the classification and a stratified ten-fold...

Breast and pectoral muscle segmentation is an essential pre-processing step for the subsequent processes in computer aided diagnosis (CAD) systems. Estimating the breast and pectoral boundaries is a difficult task especially in mammograms due to artifacts, homogeneity between the pectoral and breast regions, and low contrast along the skin-air boun...

Technology trends such as Software-Defined Networking (SDN) are transforming networking services in terms of flexibility and faster deployment times. SDN separates the control plane from the data plane with its centralised architecture compared with the distributed approach used in other management systems. However, management systems are still req...

In this paper we study the use of medical history information extracted from the Utah Population Database (UPDB) to predict adoption of a reminder solution for people with dementia. The adoption model was built using 24 categorised features. The kNN classification algorithm gave the best performance with 85.8% accuracy. Whilst data from the UPDB is...

Purpose: Assistive technologies have been identified as a potential solution for the provision of elderly care. Such technologies have in general the capacity to enhance the quality of life and increase the level of independence among their users. Nevertheless, the acceptance of these technologies is crucial to their success. Generally speaking, t...

A wide range of assistive technologies have been developed to support the elderly population with the goal of promoting independent living. The adoption of these technology based solutions is, however, critical to their overarching success. In our previous research we addressed the significance of modelling user adoption to reminding technologies b...

This paper presents a low-complexity mobile application for automatically diagnosing crop diseases in the field. In an initial pre-processing stage, the system leverages the capability of a smartphone device and basic image processing algorithms to obtain consistent leaf orientation and to remove the background. A number of different features are t...

Virtualisation technology has become a very common trend in modern datacentres as Virtual Machine (VM) migration brings several benefits like improved performance, high manageability, resource consolidation and fault tolerance. Live Migration (LM) of VMs is used for transferring a working VM from one host to another host of a different physical mac...

Flood event monitoring plays an important role for emergency management. With the fast growth of social media, a large number of images and videos are uploaded and searched on the internet during disasters, which can be used as “sensors” for improving efficiency of emergency management. This work proposes a novel framework in which the rich informa...

We provide a framework for simulating the entire patient journey across different phases (such as diagnosis, treatment, rehabilitation and long-term care) and different sectors (such as GP, hospital, social and community services), with the aim of providing better understanding of such processes and facilitating evaluation of alternative clinical a...

Image processing has traditionally involved the use of square operators on regular rectangular image lattices. For many years the concept of using hexagonal pixels for image capture has been investigated, and several advantages of such an approach have been highlighted. We present a design procedure for hexagonal gradient operators, developed withi...

In recent years the processing of hexagonal pixelbased images has been investigated, and as a result, a number of edge detection algorithms for direct application to such image structures have been developed. We build on this research by presenting a novel and efficient approach to the design of hexagonal image processing operators using linear bas...

Wireless sensor networks have increasingly become contributors of very large amounts of data. The recent deployment of wireless sensor networks in Smart City infrastructures has led to very large amounts of data being generated each day across a variety of domains, with applications including environmental monitoring, healthcare monitoringand trans...

Wireless sensor networks have increasingly become contributors of very large amounts of data. The recent deployment of wireless sensor networks in Smart City infrastructures have led to very large amounts of data being generated each day across a variety of domains, with applications including environmental monitoring, healthcare monitoring and tra...

This paper presents a practical classification system for recognising diseased wheat leaves and consists of a number of components. Pre-processing is performed to adjust the orientation of the primary leaf in the image using a Fourier Transform. A Wavelet Transform is then applied to partially remove low frequency information or background in the i...

Spiral architectures have been employed as an efficient addressing scheme in hexagonal image processing (HIP), whereby the image pixel indices can be stored in a one-dimensional vector that enables fast image processing. However, this computational advance of HIP is hindered by the additional time and effort required for conversion of image data to...

With recent advances in technology, resource control is a significant challenge for geographically distributed clouds. Users geographically close to the server get better services due to low latency. A few existing scheduling algorithms can provide better strategies through efficient job scheduling and resource allocation techniques. However, these...

The Bag-of-Words (BoW) model has been increasingly applied in the field of computer vision, in which the local features are first mapped to a codebook produced by clustering method and then represented by histogram of the words. One of drawbacks in BoW model is that the orderless histogram ignores the valuable spatial relationships among the featur...

The acceptance of technology is a crucial factor in successfully deploying technology solutions in healthcare. Our previous research has highlighted the potential of modelling user adoption from a range of environmental, social and physical parameters. This current work aims to build on the notion of predicting technology adoption through a study i...

Healthcare stakeholders require accurate and reliable models to aid performance management, but there is an issue as to whether deterministic or stochastic modelling is more appropriate. Deterministic models are usually simpler, more easily understood, and less data intensive. However, stochastic models tend to be more realistic, but require signif...