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Full-Text Articles in Computer Engineering

Evaluating Load Adjusted Learning Strategies For Client Service Levels Prediction From Cloud-Hosted Video Servers, Ruairí De Fréin, Obinna Izima, Mark Davis Dec 2018

Evaluating Load Adjusted Learning Strategies For Client Service Levels Prediction From Cloud-Hosted Video Servers, Ruairí De Fréin, Obinna Izima, Mark Davis

Conference papers

Network managers that succeed in improving the accuracy of client video service level predictions, where the video is deployed in a cloud infrastructure, will have the ability to deliver responsive, SLA-compliant service to their customers. Meeting up-time guarantees, achieving rapid first-call resolution, and minimizing time-to-recovery af- ter video service outages will maintain customer loyalty.

To date, regression-based models have been applied to generate these predictions for client machines using the kernel metrics of a server clus- ter. The effect of time-varying loads on cloud-hosted video servers, which arise due to dynamic user requests have not been leveraged to improve prediction ...


Distance-Based Cluster Head Election For Mobile Sensing, Ruairí De Fréin, Liam O'Farrell Dec 2018

Distance-Based Cluster Head Election For Mobile Sensing, Ruairí De Fréin, Liam O'Farrell

Conference papers

Energy-efficient, fair, stochastic leader-selection algorithms are designed for mobile sensing scenarios which adapt the sensing strategy depending on the mobile sensing topology. Methods for electing a cluster head are crucially important when optimizing the trade-off between the number of peer-to- peer interactions between mobiles and client-server interactions with a cloud-hosted application server. The battery-life of mobile devices is a crucial constraint facing application developers who are looking to use the convergence of mobile computing and cloud computing to perform environmental sensing. We exploit the mobile network topology, specifically the location of mobiles with respect to the gateway device, to stochastically ...


A Comparative Study Of Defeasible Argumentation And Non-Monotonic Fuzzy Reasoning For Elderly Survival Prediction Using Biomarkers, Lucas Rizzo, Ljiljana Majnaric, Luca Longo Nov 2018

A Comparative Study Of Defeasible Argumentation And Non-Monotonic Fuzzy Reasoning For Elderly Survival Prediction Using Biomarkers, Lucas Rizzo, Ljiljana Majnaric, Luca Longo

Conference papers

Computational argumentation has been gaining momentum as a solid theoretical research discipline for inference under uncertainty with incomplete and contradicting knowledge. However, its practical counterpart is underdeveloped, with a lack of studies focused on the investigation of its impact in real-world settings and with real knowledge. In this study, computational argumentation is compared against non-monotonic fuzzy reasoning and evaluated in the domain of biological markers for the prediction of mortality in an elderly population. Different non-monotonic argument-based models and fuzzy reasoning models have been designed using an extensive knowledge base gathered from an expert in the field. An analysis of ...


State Acquisition In Computer Networks, Ruairí De Fréin May 2018

State Acquisition In Computer Networks, Ruairí De Fréin

Conference papers

We establish that State Acquisition should be per- formed in networks at a rate which is consistent with the rate-of-change of the element or service being observed. We demonstrate that many existing monitoring and service-level prediction tools do not acquire network state in an appropriate manner. To address this challenge: (1) we define the rate-of- change of different applications; (2) we use methods for analysis of unevenly spaced time series, specifically, time series arising from video and voice applications, to estimate the rate-of-change of these services; and finally, (3) we demonstrate how to acquire network state accurately for a number ...


Using Regular Languages To Explore The Representational Capacity Of Recurrent Neural Architectures, Abhijit Mahalunkar, John D. Kelleher Jan 2018

Using Regular Languages To Explore The Representational Capacity Of Recurrent Neural Architectures, Abhijit Mahalunkar, John D. Kelleher

Conference papers

The presence of Long Distance Dependencies (LDDs) in sequential data poses significant challenges for computational models. Various recurrent neural architectures have been designed to mitigate this issue. In order to test these state-of-the-art architectures, there is growing need for rich benchmarking datasets. However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs. This lack of control limits the analysis of model performance in relation to the specific challenge posed by LDDs. One way to address this is to use synthetic data having the properties of subregular ...


The Code Mini-Map Visualisation - Encoding Conceptual Structures Within Source Code, Ivan Bacher, Brian Mac Namee, John Kelleher Jan 2018

The Code Mini-Map Visualisation - Encoding Conceptual Structures Within Source Code, Ivan Bacher, Brian Mac Namee, John Kelleher

Conference papers

Modern source code editors typically include a code mini-map visualisation, which provides programmers with an overview of the currently open source code document. This paper proposes to add a layering mechanism to the code mini-map visualisation in order to provide programmers with visual answers to questions related to conceptual structures that are not manifested directly in the code.


Examining A Hate Speech Corpus For Hate Speech Detection And Popularity Prediction, Filip Klubicka, Raquel Fernandez Jan 2018

Examining A Hate Speech Corpus For Hate Speech Detection And Popularity Prediction, Filip Klubicka, Raquel Fernandez

Conference papers

As research on hate speech becomes more and more relevant every day, most of it is still focused on hate speech detection. By attempting to replicate a hate speech detection experiment performed on an existing Twitter corpus annotated for hate speech, we highlight some issues that arise from doing research in the field of hate speech, which is essentially still in its infancy. We take a critical look at the training corpus in order to understand its biases, while also using it to venture beyond hate speech detection and investigate whether it can be used to shed light on other ...


Is It Worth It? Budget-Related Evaluation Metrics For Model Selection, Filip Klubicka, Giancarlo Salton, John Kelleher Jan 2018

Is It Worth It? Budget-Related Evaluation Metrics For Model Selection, Filip Klubicka, Giancarlo Salton, John Kelleher

Conference papers

Projects that set out to create a linguistic resource often do so by using a machine learning model that pre-annotates or filters the content that goes through to a human annotator, before going into the final version of the resource. However, available budgets are often limited, and the amount of data that is available exceeds the amount of annotation that can be done. Thus, in order to optimize the benefit from the invested human work, we argue that the decision on which predictive model one should employ depends not only on generalized evaluation metrics, such as accuracy and F-score, but ...


Ambiqual – A Full Reference Objective Quality Metric For Ambisonic Spatial Audio, Miroslaw Narbutt, Andrew Allen, Jan Skoglund, Michael Chinen, Andrew Hines Jan 2018

Ambiqual – A Full Reference Objective Quality Metric For Ambisonic Spatial Audio, Miroslaw Narbutt, Andrew Allen, Jan Skoglund, Michael Chinen, Andrew Hines

Conference papers

Streaming spatial audio over networks requires efficient encoding techniques that compress the raw audio content without compromising quality of experience. Streaming service providers such as YouTube need a perceptually relevant objective audio quality metric to monitor users’ perceived quality and spatial localization accuracy. In this paper we introduce a full reference objective spatial audio quality metric, AMBIQUAL, which assesses both Listening Quality and Localization Accuracy. In our solution both metrics are derived directly from the B-format Ambisonic audio. The metric extends and adapts the algorithm used in ViSQOLAudio, a full reference objective metric designed for assessing speech and audio quality ...


On The Reliability, Validity And Sensitivity Of Three Mental Workload Assessment Techniques For The Evaluation Of Instructional Designs: A Case Study In A Third-Level Course, Luca Longo Jan 2018

On The Reliability, Validity And Sensitivity Of Three Mental Workload Assessment Techniques For The Evaluation Of Instructional Designs: A Case Study In A Third-Level Course, Luca Longo

Conference papers

Cognitive Load Theory (CLT) has been conceived for instructional designers eager to create instructional resources that are presented in a way that encourages the activities of the learners and optimise their performance, thus their learning. Although it has been researched for many years, it has been criticised because of its theoretical clarity and its methodological approach. In particular, one fundamental and open problem is the measurement of its cognitive load types and the measurement of the overall cognitive load of learners during learning tasks. This paper is aimed at investigating the reliability, validity and sensitivity of existing mental workload assessment ...


Human Performance Modelling For Adaptive Automation, Chiara Leva, M. Wilkins, F. Coster Jan 2018

Human Performance Modelling For Adaptive Automation, Chiara Leva, M. Wilkins, F. Coster

Conference papers

The relentless march of technology is increasingly opening new possibilities for the application of automation and new horizons for human machine interaction. However there is insufficient scientific evidence on human factors for modern socio-technical systems supporting the guidelines currently used to design Human Machine Interfaces (HMI) (ISA 2014). This dearth of knowledge presents a particular risk in safety critical industries. The continuing 60–90% of accidents currently that are rooted in Human Factors (HF) and the rapid developments in the Internet of Things (IoT) and its novel automation archetypes means that the requirements for new interfaces are becoming more demanding ...


Review Of The Effectiveness Of Impulse Testing For The Evaluation Of Cable Insulation Quality And Recommendations For Quality Testing, Adrian Coughlan, Joseph Kearney, Tom Looby Jan 2018

Review Of The Effectiveness Of Impulse Testing For The Evaluation Of Cable Insulation Quality And Recommendations For Quality Testing, Adrian Coughlan, Joseph Kearney, Tom Looby

Conference papers

Abstract— This project investigates impulse breakdown testing as a means of determining the as constructed standard of MV power cable. A literature survey is undertaken to elucidate the place of this test in an overall cable test regime and to determine the factors that impact on the performance of the test method. Testing was undertaken on ESB Networks cables to establish if a merit order ranking was feasible based on this test and to determine if the test could detect defects in the inner semiconducting layer. Based on this, conclusions and recommendations are made regarding the overall applicability and usefulness ...


Tiled Time Delay Estimation In Mobile Cloud Computing Environments, Ruairí De Fréin Dec 2017

Tiled Time Delay Estimation In Mobile Cloud Computing Environments, Ruairí De Fréin

Conference papers

We present a tiled delay estimation technique in the context of Mobile Cloud Computing (MCC) environments. We examine its accuracy in the presence of multiple sources for (1) sub-sample delays and also (2) in the presence of phase-wrap around. Phase wrap-around is prevalent in MCC because the separation of acoustic sources may be large. We show that tiling a histogram of instantaneous phase estimates can improve delay estimates when phase-wrap around is sig- nificantly present and also when multiple sources are present. We report that error in the delay estimator is generally less than 5% of a sample, when the ...


Bim+Blockchain: A Solution To The Trust Problem In Collaboration?, Malachy Mathews, Dan Robles, Brian Bowe Aug 2017

Bim+Blockchain: A Solution To The Trust Problem In Collaboration?, Malachy Mathews, Dan Robles, Brian Bowe

Conference papers

This paper provides an overview of historic and current organizational limitations emerging in the Architecture, Engineering, Construction, Building Owner / Operations (AECOO) Industry. It then provides an overview of new technologies that attempt to mitigate these limitations. However, these technologies, taken together, appear to be converging and creating entirely new organizational structures in the AEC industries. This may be characterized by the emergence of what is called the Network Effect and it’s related calculus. This paper culminates with an introduction to Blockchain Technology (BT) and it’s integration with the emergence of groundbreaking technologies such as Internet of Things (IoT ...


Key Inference From Irish Traditional Music Scores And Recordings, Pierre Beauguitte, Bryan Duggan, John Kelleher Jul 2017

Key Inference From Irish Traditional Music Scores And Recordings, Pierre Beauguitte, Bryan Duggan, John Kelleher

Conference papers

The aim of this paper is to present techniques and results for identifying the key of Irish traditional music melodies, or tunes. Several corpora are used, consisting of both symbolic and audio representations. Monophonic and heterophonic recordings are present in the audio datasets. Some particularities of Irish traditional music are discussed, notably its modal nature. New key-profiles are defined, that are better suited to Irish music.


Chaos-Based Cryptography For Cloud Computing, Paul Tobin, Lee Tobin, Michael Mckeever, Jonathan Blackledge Professor Jun 2017

Chaos-Based Cryptography For Cloud Computing, Paul Tobin, Lee Tobin, Michael Mckeever, Jonathan Blackledge Professor

Conference papers

Cloud computing and poor security issues have quadrupled over the last six years and with the alleged presence of backdoors in common encryption ciphers, has created a need for personalising the encryption process by the client. In 2007, two Microsoft employees gave a presentation ``On the Possibility of a backdoor in the NIST SP800-90 Dual Elliptic Curve Pseudo Random Number Generators'' and was linked in 2013 by the New York Times with notes leaked by Edward Snowden. This confirmed backdoors were placed, allegedly, in a number of encryption systems by the National Security Agency, which if true creates an urgent ...


One-To-Cloud One-Time Pad Data Encryption: Introducing Virtual Prototyping With Pspice, Paul Tobin, Lee Tobin, Roberto Gandia Blanquer Dr, Michael Mckeever, Jonathan Blackledge Professor Jun 2017

One-To-Cloud One-Time Pad Data Encryption: Introducing Virtual Prototyping With Pspice, Paul Tobin, Lee Tobin, Roberto Gandia Blanquer Dr, Michael Mckeever, Jonathan Blackledge Professor

Conference papers

In this paper, we examine the design and application of a one-time pad encryption system for protecting data stored in the Cloud. Personalising security using a one-time pad generator at the client-end protects data from break-ins, side-channel attacks and backdoors in public encryption algorithms. The one-time pad binary sequences were obtained from modified analogue chaos oscillators initiated by noise and encoded client data locally. Specific ``one-to-Cloud'' storage applications returned control back to the end user but without the key distribution problem normally associated with one-time pad encryption. Development of the prototype was aided by ``Virtual Prototyping'' in the latest version ...


Design And Implementation Of An Archetype Based Interoperable Knowledge Eco-System For Data Buoys, Paul Stacey, Damon Berry Jun 2017

Design And Implementation Of An Archetype Based Interoperable Knowledge Eco-System For Data Buoys, Paul Stacey, Damon Berry

Conference papers

This paper describes the ongoing work of the authors in translating two-level system design techniques used in Health Informatics to the Earth Systems Science domain. Health informaticians have developed a sophisticated two-level systems design approach for electronic health documentation over many years, and with the use of archetypes, have shown how knowledge interoperability among heterogeneous systems can be achieved. Translating two-level modelling techniques to a new domain is a complex task. A proof-of-concept archetype enabled data buoy eco-system is presented. The concept of operational templates-as-a service is proposed. Design recommendations and implementation experiences of re-working the proposed architecture to run ...


Propagating Degrees Of Truth On An Argumentation Framework: An Abstract Account Of Fuzzy Argumentation, Pierpaolo Dondio Apr 2017

Propagating Degrees Of Truth On An Argumentation Framework: An Abstract Account Of Fuzzy Argumentation, Pierpaolo Dondio

Conference papers

This paper proposes a computational framework to reason with conflicting and gradual evidence. The framework is a synthesis of Dung’s seminal work in argumentation semantics with multi-valued logic. Abstract grounded semantics is used to identify the conditions under which a conclusion can be accepted, while multi-valued logic operators are used to quantify the degree of truth of such conditions. We propose a truth-compositional recursive computation based on the notion of irrelevant arguments, and we discuss examples using the major multi-valued logics: Godel’s, Zadeh’s and Łukasiewicz's logic.


On The Development Of A One-Time Pad Generator For Personalising Cloud Security, Paul Tobin, Lee Tobin, Michael Mckeever, Jonathan Blackledge Profesor Feb 2017

On The Development Of A One-Time Pad Generator For Personalising Cloud Security, Paul Tobin, Lee Tobin, Michael Mckeever, Jonathan Blackledge Profesor

Conference papers

Cloud computing security issues are being reported in newspapers, television, and on the Internet, on a daily basis. Furthermore, in 2013, Edward Snowden alleged backdoors were placed in a number of encryption systems by the National Security Agency causing confidence in public encryption to drop even further. Our solution allows the end-user to add a layer of unbreakable security by encrypting the data locally with a random number generator prior to uploading data to the Cloud. The prototype one-time pad generator is impervious to cryptanalysis because it generates unbreakable random binary sequences from chaos sources initiated from a natural noise ...


Clustering Opportunistic Ant-Based Routing Protocol For Wireless Sensor Networks, Xinlu Li, Brian Keegan, Fredrick Mtenzi Jan 2017

Clustering Opportunistic Ant-Based Routing Protocol For Wireless Sensor Networks, Xinlu Li, Brian Keegan, Fredrick Mtenzi

Conference papers

The wireless Sensor Networks (WSNs) have a wide range of applications in many ereas, including many kinds of uses such as environmental monitoring and chemical detection. Due to the restriction of energy supply, the improvement of routing performance is the major motivation in WSNs. We present a Clustering Opportunistic Ant-based Routing protocol (COAR), which comprises the following main contributions to achieve high energy efficient and well load-balance: (i) in the clustering algorithm, we caculate the theoretical value of energy dissipation, which will make the number of clusters fluctuate around the expected value, (ii) define novel heuristic function and pheromone update ...


Analysing The Behaviour Of Online Investors In Times Of Geopolitical Distress: A Case Study On War Stocks, James Usher, Pierpaolo Dondio Jan 2017

Analysing The Behaviour Of Online Investors In Times Of Geopolitical Distress: A Case Study On War Stocks, James Usher, Pierpaolo Dondio

Conference papers

In this paper we analyse how the behavior of an online financial community in time of geopolitical crises. In particular, we studied the behaviour, composition and communication patterns of online investors before and after a military geopolitical event. We selected a set of 23 key-events belonging to the 2003 US-led invasion of Iraq, the Arab Spring and the first period of the Ukraine crisis. We restricted our study to a set of eight so called military stocks, which are US-manufacturing companies active in the defence sector. We studied the resilience of the community to information shocks by comparing the community ...


Assessing The Usefulness Of Different Feature Sets For Predicting The Comprehension Difficulty Of Text, Brian Mac Namee, John Kelleher, Noel Fitzpatrick Jan 2017

Assessing The Usefulness Of Different Feature Sets For Predicting The Comprehension Difficulty Of Text, Brian Mac Namee, John Kelleher, Noel Fitzpatrick

Conference papers

Within English second language acquisition there is an enthusiasm for using authentic text as learning materials in classroom and online settings. This enthusiasm, however, is tempered by the difficulty in finding authentic texts at suitable levels of comprehension difficulty for specific groups of learners. An automated way to rate the comprehension difficulty of a text would make finding suitable texts a much more manageable task. While readability metrics have been in use for over 50 years now they only capture a small amount of what constitutes comprehension difficulty. In this paper we examine other features of texts that are related ...


Application Of Artificial Intelligence For Detecting Computing Derived Viruses, Jonathan Blackledge, Omotayo Asiru, Moses Dlamini Jan 2017

Application Of Artificial Intelligence For Detecting Computing Derived Viruses, Jonathan Blackledge, Omotayo Asiru, Moses Dlamini

Conference papers

Computer viruses have become complex and operates in a stealth mode to avoid detection. New viruses are argued to be created each and every day. However, most of these supposedly ‘new’ viruses are not completely new. Most of the supposedly ‘new’ viruses are not necessarily created from scratch with completely new (something novel that has never been seen before) mechanisms. For example, most of these viruses just change their form and signatures to avoid detection. But their operation and the way they infect files and systems is still the same. Hence, such viruses cannot be argued to be new. In ...


A Hardware One-Time Pad Prototype Generator For Localising Cloud Security, Jonathan Blackledge, Paul Tobin, Lee Tobin, Mick Mckeever Jan 2017

A Hardware One-Time Pad Prototype Generator For Localising Cloud Security, Jonathan Blackledge, Paul Tobin, Lee Tobin, Mick Mckeever

Conference papers

Abstract: In this paper, we examine a system for encrypting data before storing in the Cloud. Adopting this system gives excellent security to stored data and complete control for accessing data by the client at different locations. The motivation for developing this personal encryption came about because of poor Cloud security and doubts over the safety of public encryption algorithms which might contain backdoors. However, side-channel attacks and other unwanted third-party interventions in Cloud security, probably contribute more to the poor security record history. These factors led to the development of a prototype for personalising security locally which defeats cryptanalysis ...


Investigating The Impact Of Unsupervised Feature-Extraction From Multi-Wavelength Image Data For Photometric Classification Of Stars, Galaxies And Qsos, Annika Lindh Dec 2016

Investigating The Impact Of Unsupervised Feature-Extraction From Multi-Wavelength Image Data For Photometric Classification Of Stars, Galaxies And Qsos, Annika Lindh

Conference papers

Accurate classification of astronomical objects currently relies on spectroscopic data. Acquiring this data is time-consuming and expensive compared to photometric data. Hence, improving the accuracy of photometric classification could lead to far better coverage and faster classification pipelines. This paper investigates the benefit of using unsupervised feature-extraction from multi-wavelength image data for photometric classification of stars, galaxies and QSOs. An unsupervised Deep Belief Network is used, giving the model a higher level of interpretability thanks to its generative nature and layer-wise training. A Random Forest classifier is used to measure the contribution of the novel features compared to a set ...


Activist: A New Framework For Dataset Labelling, Jack O'Neill, Sarah Jane Delany, Brian Mac Namee Sep 2016

Activist: A New Framework For Dataset Labelling, Jack O'Neill, Sarah Jane Delany, Brian Mac Namee

Conference papers

Acquiring labels for large datasets can be a costly and time-consuming process. This has motivated the development of the semi-supervised learning problem domain, which makes use of unlabelled data — in conjunction with a small amount of labelled data — to infer the correct labels of a partially labelled dataset. Active Learning is one of the most successful approaches to semi-supervised learning, and has been shown to reduce the cost and time taken to produce a fully labelled dataset. In this paper we present Activist; a free, online, state-of-the-art platform which leverages active learning techniques to improve the efficiency of dataset labelling ...


Empirical Comparative Analysis Of 1-Of-K Coding And K-Prototypes In Categorical Clustering, Fei Wang, Hector Franco, John Pugh, Robert Ross Sep 2016

Empirical Comparative Analysis Of 1-Of-K Coding And K-Prototypes In Categorical Clustering, Fei Wang, Hector Franco, John Pugh, Robert Ross

Conference papers

Clustering is a fundamental machine learning application, which partitions data into homogeneous groups. K-means and its variants are the most widely used class of clustering algorithms today. However, the original k-means algorithm can only be applied to numeric data. For categorical data, the data has to be converted into numeric data through 1-of-K coding which itself causes many problems. K-prototypes, another clustering algorithm that originates from the k-means algorithm, can handle categorical data by adopting a different notion of distance. In this paper, we systematically compare these two methods through an experimental analysis. Our analysis shows that K-prototypes is more ...


Model-Based And Model-Free Active Learning For Regression, Jack O'Neill, Sarah Jane Delany, Brian Macnamee Sep 2016

Model-Based And Model-Free Active Learning For Regression, Jack O'Neill, Sarah Jane Delany, Brian Macnamee

Conference papers

Training machine learning models often requires large labelled datasets, which can be both expensive and time-consuming to obtain. Active learning aims to selectively choose which data is labelled in order to minimize the total number of labels required to train an effective model. This paper compares model-free and model-based approaches to active learning for regression, finding that model-free approaches, in addition to being less computationally intensive to implement, are more effective in improving the performance of linear regressions than model-based alternatives.


Activist: A New Framework For Dataset Labelling, Jack O'Neill, Sarah Jane Delany, Brian Macnamee Sep 2016

Activist: A New Framework For Dataset Labelling, Jack O'Neill, Sarah Jane Delany, Brian Macnamee

Conference papers

Acquiring labels for large datasets can be a costly and time-consuming process. This has motivated the development of the semi-supervised learning problem domain, which makes use of unlabelled data — in conjunction with a small amount of labelled data — to infer the correct labels of a partially labelled dataset. Active Learning is one of the most successful approaches to semi-supervised learning, and has been shown to reduce the cost and time taken to produce a fully labelled dataset. In this paper we present Activist; a free, online, state-of-the-art platform which leverages active learning techniques to improve the efficiency of dataset labelling ...