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Articles 1 - 23 of 23
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On Demonstrating The Impact Of Defeasible Reasoning Via A Multi-Layer Argument-Based Framework (Doctoral Consortium), Lucas Middeldorf Rizzo
On Demonstrating The Impact Of Defeasible Reasoning Via A Multi-Layer Argument-Based Framework (Doctoral Consortium), Lucas Middeldorf Rizzo
Conference papers
Promising results have indicated Argumentation Theory as a solid research area for implementing defeasible reasoning in practice. However, applications are usually domain dependent, not incorporating all the layers and steps required in an argumentation process, thus limit- ing their applicability in different areas. This PhD project is focused on the development of a multi-layer defeasible argument-based framework which is in turn used across different applications in the fields of decision making and knowledge representation and reasoning. The inference produced is compared against the inference of different quantitative theories of reasoning under uncertainty such as expert systems and fuzzy logic. The …
Streaming Vr For Immersion: Quality Aspects Of Compressed Spatial Audio, Miroslaw Narbutt, Sean O’Leary, Andrew Allen, Jan Skoglund, Andrew Hines
Streaming Vr For Immersion: Quality Aspects Of Compressed Spatial Audio, Miroslaw Narbutt, Sean O’Leary, Andrew Allen, Jan Skoglund, Andrew Hines
Conference papers
Delivering a 360-degree soundscape that matches full sphere visuals is an essential aspect of immersive VR. Ambisonics is a full sphere surround sound technique that takes into account the azimuth and elevation of sound sources, portraying source location above and below as well as around the horizontal plane of the listener. In contrast to channel-based methods, ambisonics representation offers the advantage of being independent of a specific loudspeaker set-up. Streaming ambisonics over networks requires efficient encoding techniques that compress the raw audio content without compromising quality of experience (QoE). This work investigates the effect of audio channel compression via the …
Rating By Ranking: An Improved Scale For Judgement-Based Labels, Jack O'Neill, Sarah Jane Delany, Brian Mac Namee
Rating By Ranking: An Improved Scale For Judgement-Based Labels, Jack O'Neill, Sarah Jane Delany, Brian Mac Namee
Conference papers
Labels representing value judgements are commonly elicited using an interval scale of absolute values. Data collected in such a manner is not always reliable. Psychologists have long recognized a number of biases to which many human raters are prone, and which result in disagreement among raters as to the true gold standard rating of any particular object. We hypothesize that the issues arising from rater bias may be mitigated by treating the data received as an ordered set of preferences rather than a collection of absolute values. We experiment on real-world and artificially generated data, finding that treating label ratings …
An Analysis Of The Application Of Simplified Silhouette To The Evaluation Of K-Means Clustering Validity, Fei Wang, Hector-Hugo Franco-Penya, John D. Kelleher, John Pugh, Robert J. Ross
An Analysis Of The Application Of Simplified Silhouette To The Evaluation Of K-Means Clustering Validity, Fei Wang, Hector-Hugo Franco-Penya, John D. Kelleher, John Pugh, Robert J. Ross
Conference papers
Silhouette is one of the most popular and effective internal measures for the evaluation of clustering validity. Simplified Silhouette is a computationally simplified version of Silhouette. However, to date Simplified Silhouette has not been systematically analysed in a specific clustering algorithm. This paper analyses the application of Simplified Silhouette to the evaluation of k-means clustering validity and compares it with the k-means Cost Function and the original Silhouette from both theoretical and empirical perspectives. The theoretical analysis shows that Simplified Silhouette has a mathematical relationship with both the k-means Cost Function and the original Silhouette, while empirically, we show that …
Reflections On An Experiment, Evaluating The Impact Of Spatialisation On Exploration, Clement Roux, John Mcauley
Reflections On An Experiment, Evaluating The Impact Of Spatialisation On Exploration, Clement Roux, John Mcauley
Conference papers
This paper reports on an experiment designed to evaluate whether visualising a digital library (using a spatialisation technique) can influence exploratory search behaviour. In the experiment we asked participants to complete a set of novel tasks using one of two interfaces - a visualisation interface, ExploViz, and its search-based equivalent, LibSearch. A set of measures were used to capture sensemaking and exploratory behaviour and to analyse cognitive load. As results were non-significant, we reflect upon the design of the experiment, consider possible issues and suggest how these could be addressed in future iterations.
Back To The Future: Logic And Machine Learning, Simon Dobnik, John D. Kelleher
Back To The Future: Logic And Machine Learning, Simon Dobnik, John D. Kelleher
Conference papers
In this paper we argue that since the beginning of the natural language processing or computational linguistics there has been a strong connection between logic and machine learning. First of all, there is something logical about language or linguistic about logic. Secondly, we argue that rather than distinguishing between logic and machine learning, a more useful distinction is between top-down approaches and data-driven approaches. Examining some recent approaches in deep learning we argue that they incorporate both properties and this is the reason for their very successful adoption to solve several problems within language technology.
Estimation Of Train Driver Workload: Extracting Taskload Measures From On-Train-Data-Recorders, Nora Balfe, Katie Crowley, Brendan Smith, Luca Longo
Estimation Of Train Driver Workload: Extracting Taskload Measures From On-Train-Data-Recorders, Nora Balfe, Katie Crowley, Brendan Smith, Luca Longo
Conference papers
This paper presents a method to extract train driver taskload from downloads of on-train-data-recorders (OTDR). OTDR are in widespread use for the purposes of condition monitoring of trains, but they may also have applications in operations monitoring and management. Evaluation of train driver workload is one such application. The paper describes the type of data held in OTDR recordings and how they can be transformed into driver actions throughout a journey. Example data from 16 commuter journeys are presented, which highlights the increased taskload during arrival at stations. Finally, the possibilities and limitations of the data are discussed.
Tackling The Interleaving Problem In Activity Discovery, Eoin Rogers, Robert J. Ross, John D. Kelleher
Tackling The Interleaving Problem In Activity Discovery, Eoin Rogers, Robert J. Ross, John D. Kelleher
Conference papers
Activity discovery (AD) is the unsupervised process of discovering activities in data produced from streaming sensor networks that are recording the actions of human subjects. One major challenge for AD systems is interleaving, the tendency for people to carry out multiple activities at a time a parallel. Following on from our previous work, we continue to investigate AD in interleaved datasets, with a view towards progressing the state-of-the-art for AD.
A Dressing Method For Soliton Solutions Of The Camass-Holm Equation, Rossen Ivanov, Tony Lyons, Nigel Orr
A Dressing Method For Soliton Solutions Of The Camass-Holm Equation, Rossen Ivanov, Tony Lyons, Nigel Orr
Conference papers
The soliton solutions of the Camassa-Holm equation are derived by the implementation of the dressing method. The form of the one and two soliton solutions coincides with the form obtained by other methods.
Solitary Wave Solution Of Flat Surface Internal Geophysical Waves With Vorticity, Alan Compelli
Solitary Wave Solution Of Flat Surface Internal Geophysical Waves With Vorticity, Alan Compelli
Conference papers
A fluid system bounded by a flat bottom and a flat surface with an internal wave and depth-dependent current is con-sidered. The Hamiltonian of the system is presented and the dynamics of the system are discussed. A long-wave regime is then considered and extended to produce a KdV approximation. Finally, a solitary wave solution is obtained.
The Benefits Of Task And Cognitive Workload Support For Operators In Ground Handling, Maria Chiara Leva, Yilmar Builes
The Benefits Of Task And Cognitive Workload Support For Operators In Ground Handling, Maria Chiara Leva, Yilmar Builes
Conference papers
The scope of the present work is to report an action research project applied to the relationship of task and cognitive workload support on one of the most important aspects of an airport: ground handling. At the beginning of the project workload management was not in the scope of work but as the project progressed and preliminary results and feedback were gained the researcher came to realize that some form of workload management support was also achieved as a by-product. The present paper is an attempt to account for what was achieved and how. Safe and efficient ground handling during …
Abusive Text Detection Using Neural Networks, Hao Chen, Susan Mckeever, Sarah Jane Delany
Abusive Text Detection Using Neural Networks, Hao Chen, Susan Mckeever, Sarah Jane Delany
Conference papers
eural network models have become increasingly popular for text classification in recent years. In particular, the emergence of word embeddings within deep learning architectures has recently attracted a high level of attention amongst researchers. In this paper, we focus on how neural network models have been applied in text classification. Secondly, we extend our previous work [4, 3] using a neural network strategy for the task of abusive text detection. We compare word embedding features to the traditional feature representations such as n-grams and handcrafted features. In addition, we use an off-the-shelf neural network classifier, FastText[16]. Based on our results, …
How Short Is A Piece Of String?: The Impact Of Text Length And Text Augmentation On Short-Text Classification Accuracy, Austin Mccartney, Svetlana Hensman, Luca Longo
How Short Is A Piece Of String?: The Impact Of Text Length And Text Augmentation On Short-Text Classification Accuracy, Austin Mccartney, Svetlana Hensman, Luca Longo
Conference papers
Recent increases in the use and availability of short messages have created opportunities to harvest vast amounts of information through machine-based classification. However, traditional classification methods have failed to yield accuracies comparable to classification accuracies on longer texts. Several approaches have previously been employed to extend traditional methods to overcome this problem, including the enhancement of the original texts through the construction of associations with external data supplementation sources. Existing literature does not precisely describe the impact of text length on classification performance. This work quantitatively examines the changes in accuracy of a small selection of classifiers using a variety …
Dynamic Behavior Analysis Of Android Applications For Malware Detection, Latika Singh, Markus Hofmann
Dynamic Behavior Analysis Of Android Applications For Malware Detection, Latika Singh, Markus Hofmann
Conference papers
Android is most popular operating system for smartphones and small devices with 86.6% market share (Chau 2016). Its open source nature makes it more prone to attacks creating a need for malware analysis. Main approaches for detecting malware intents of mobile applications are based on either static analysis or dynamic analysis. In static analysis, apps are inspected for suspicious patterns of code to identify malicious segments. However, several obfuscation techniques are available to provide a guard against such analysis. The dynamic analysis on the other hand is a behavior-based detection method that involves investigating the run-time behavior of the suspicious …
Measuring Presence: Hypothetical Quantitative Framework, Krzysztof Szczurowski, Matt Smith
Measuring Presence: Hypothetical Quantitative Framework, Krzysztof Szczurowski, Matt Smith
Conference papers
Virtual Reality Head - Mounted Display (HMD) manufacturers claim that consumer electronics can finally deliver a high degree of presence in virtual and remote environments. Certainly, current consumer-grade HMD systems offer rich and coherent mediated experiences of such environments. However, the very concept of presence is still a subject of debate, and researchers' investigation of the phenomenon of `presence' is based primarily on qualitative (i.e. questionnaire-based) assessments. Some researchers attempted to develop real-time, quantitative methods to facilitate more objective investigation of presence in mediated environments. Most such methodologies are derived from attempts to correlate presence with cardiovascular and electrodermal activity …
A Comparison Of Automatic Search Query Enhancement Algorithms That Utilise Wikipedia As A Source Of A Priori Knowledge, Kyle Goslin, Markus Hofmann
A Comparison Of Automatic Search Query Enhancement Algorithms That Utilise Wikipedia As A Source Of A Priori Knowledge, Kyle Goslin, Markus Hofmann
Conference papers
This paper describes the benchmarking and analysis of five Automatic Search Query Enhancement (ASQE) algorithms that utilise Wikipedia as the sole source for a priori knowledge. The contributions of this paper include: 1) A comprehensive review into current ASQE algorithms that utilise Wikipedia as the sole source for a priori knowledge; 2) benchmarking of five existing ASQE algorithms using the TREC-9 Web Topics on the ClueWeb12 data set and 3) analysis of the results from the benchmarking process to identify the strengths and weaknesses each algorithm. During the benchmarking process, 2,500 relevance assessments were performed. Results of these tests are …
Multiparty Computations In Varying Contexts, Paul Laird, Sarah Jane Delany, Pierpaolo Dondio
Multiparty Computations In Varying Contexts, Paul Laird, Sarah Jane Delany, Pierpaolo Dondio
Conference papers
Recent developments in the automatic transformation of protocols into Secure Multiparty Computation (SMC) interactions, and the selection of appropriate schemes for their implementation have improved usabililty of SMC. Poor performance along with data leakage or errors caused by coding mistakes and complexity had hindered SMC usability. Previous practice involved integrating the SMC code into the application being designed, and this tight integration meant the code was not reusable without modification. The progress that has been made to date towards the selection of different schemes focuses solely on the two-party paradigm in a static set-up, and does not consider changing contexts. …
Representing And Inferring Mental Workload Via Defeasible Reasoning: A Comparison With The Nasa Task Load Index And The Workload Profile, Lucas Middeldorf Rizzo, Luca Longo
Representing And Inferring Mental Workload Via Defeasible Reasoning: A Comparison With The Nasa Task Load Index And The Workload Profile, Lucas Middeldorf Rizzo, Luca Longo
Conference papers
The NASA Task Load Index (NASA − TLX) and the Workload Profile (WP) are likely the most employed instruments for subjective mental workload (MWL) measurement. Numerous areas have made use of these methods for assessing human performance and thusly improving the design of systems and tasks. Unfortunately, MWL is still a vague concept, with different definitions and no universal measure. This research investigates the use of defeasible reasoning to represent and assess MWL. Reasoning is defeasible when a conclusion, supported by a set of premises, can be retracted in the light of new information. In this empirical study, this type …
On The Relationship Between Sampling Rate And Hidden Markov Models Accuracy In Non-Intrusive Load Monitoring, Steven Lynch, Luca Longo
On The Relationship Between Sampling Rate And Hidden Markov Models Accuracy In Non-Intrusive Load Monitoring, Steven Lynch, Luca Longo
Conference papers
Providing domestic energy consumers with a detailed breakdown of their electricity consumption, at the appliance level, empowers the consumer to better manage that consumption and reduce their over- all electricity demand. Non-Intrusive Load Monitoring (NILM) is one method of achieving this breakdown and makes use of one sensor which measures overall combined electricity usage. As all appliances are measured in combination in NILM this consumption must be disaggregated to extract appliance level consumption. Machine learning techniques can be adopted to perform this disaggregation with various levels of accuracy, with Hidden Markov Model (HMM) derivatives ordering among the most accurate results. …
The Influence Of Values On The Intention And Usage Of Mobile Phone Technology:A Case Of Tanzanian Smes, Renatus Mushi, Almar Ennis, Deirdre Lillis, Said Jafari
The Influence Of Values On The Intention And Usage Of Mobile Phone Technology:A Case Of Tanzanian Smes, Renatus Mushi, Almar Ennis, Deirdre Lillis, Said Jafari
Conference papers
Mobile phone technology has been relied upon in performing a number of activities in the SMEs. In less developed regions, computing infrastructures are very poor thereby depending highly on mobile phones. The improvement of technology in the mobile phones has seen more applications and services being accessed through them. This gives SMEs, especially in developing countries, a preferable alternative to desktop computing technology. However, to maximise the usability of mobile phone technology in SMEs context, key factors which influence users’ perception on its acceptance need to be explained clearly. This study explains the factors influencing employees’ intentions and use of …
A Comparison On The Classification Of Short-Text Documents Using Latent Dirichlet Allocation And Formal Concept Analysis, Noel Rogers, Luca Longo
A Comparison On The Classification Of Short-Text Documents Using Latent Dirichlet Allocation And Formal Concept Analysis, Noel Rogers, Luca Longo
Conference papers
With the increasing amounts of textual data being collected online, automated text classification techniques are becoming increasingly important. However, a lot of this data is in the form of short-text with just a handful of terms per document (e.g. Text messages, tweets or Facebook posts). This data is generally too sparse and noisy to obtain satisfactory classification. Two techniques which aim to alleviate this problem are Latent Dirichlet Allocation (LDA) and Formal Concept Analysis (FCA). Both techniques have been shown to improve the performance of short-text classification by reducing the sparsity of the input data. The relative performance of classifiers …
Uav Data For Coastal Dune Mapping, Chen Suo, Eugene Mcgovern, Alan Gilmer
Uav Data For Coastal Dune Mapping, Chen Suo, Eugene Mcgovern, Alan Gilmer
Conference papers
High resolution topographic maps are critical for the development of rigorous and quantitative numerical simulation landscape models. These models can inform targeted land management actions that maintain biodiversity and ecological functions. Mapping functional vegetation communities to obtain accurate distribution and population estimates is an important element of landscape models and is a challenging task which requires a considerable investment in time and resources. A recent development in surveying technologies, Unmanned Aerial Vehicles (UAV’s), also known as drones, has enabled high resolution and high accuracy ground-based data to be gathered quickly and easily on-site. The application of UAV’s represents a new …
Presenting A Labelled Dataset For Real-Time Detection Of Abusive User Posts, Hao Chen, Susan Mckeever, Sarah Jane Delany
Presenting A Labelled Dataset For Real-Time Detection Of Abusive User Posts, Hao Chen, Susan Mckeever, Sarah Jane Delany
Conference papers
Social media sites facilitate users in posting their own personal comments online. Most support free format user posting, with close to real-time publishing speeds. However, online posts generated by a public user audience carry the risk of containing inappropriate, potentially abusive content. To detect such content, the straightforward approach is to filter against blacklists of profane terms. However, this lexicon filtering approach is prone to problems around word variations and lack of context. Although recent methods inspired by machine learning have boosted detection accuracies, the lack of gold standard labelled datasets limits the development of this approach. In this work, …