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- Dialogue state tracking (3)
- Spoken dialogue system (3)
- Background Corpus (2)
- Cognitive Load Theory (2)
- Energy-based learning (2)
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- First Story Detection (2)
- TF-IDF (2)
- Variable dependencies (2)
- Abusive context (1)
- Agent-based simulation (1)
- Anomaly detection (1)
- Artificial neural network (1)
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- Bullying (1)
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- Cloud safety (1)
- Clustering (1)
- Cognitive Load Types (1)
- Cognitive Theory of Multimedia Learning (1)
- Cognitive load types (1)
- Community of Inquiry (1)
- Computer generated imagery (1)
- Computer graphics (1)
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Articles 1 - 27 of 27
Full-Text Articles in Physical Sciences and Mathematics
How Do Spinal Surgeons Perceive The Impact Of Factors Used In Post-Surgical Complication Risk Scores?, Enea Parimbelli, Szymon Wilk, Dympna O'Sullivan, Stephen Kingwell, Wojtek Michalowski, Martin Michalowski
How Do Spinal Surgeons Perceive The Impact Of Factors Used In Post-Surgical Complication Risk Scores?, Enea Parimbelli, Szymon Wilk, Dympna O'Sullivan, Stephen Kingwell, Wojtek Michalowski, Martin Michalowski
Conference papers
When deciding about surgical treatment options, an important aspect of the decision-making process is the potential risk of complications. A risk assessment performed by a spinal surgeon is based on their knowledge of the best available evidence and on their own clinical experience. The objective of this work is to demonstrate the differences in the way spine surgeons perceive the importance of attributes used to calculate risk of post-operative and quantify the differences by building individual formal models of risk perceptions. We employ a preference-learning method - ROR-UTADIS - to build surgeon-specific additive value functions for risk of complications. Comparing …
Update Frequency And Background Corpus Selection In Dynamic Tf-Idf Models For First Story Detection, Fei Wang, Robert J. Ross, John D. Kelleher
Update Frequency And Background Corpus Selection In Dynamic Tf-Idf Models For First Story Detection, Fei Wang, Robert J. Ross, John D. Kelleher
Conference papers
First Story Detection (FSD) requires a system to detect the very first story that mentions an event from a stream of stories. Nearest neighbour-based models, using the traditional term vector document representations like TF-IDF, currently achieve the state of the art in FSD. Because of its online nature, a dynamic term vector model that is incrementally updated during the detection process is usually adopted for FSD instead of a static model. However, very little research has investigated the selection of hyper-parameters and the background corpora for a dynamic model. In this paper, we analyse how a dynamic term vector model …
Capturing Dialogue State Variable Dependencies With An Energy-Based Neural Dialogue State Tracker, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Capturing Dialogue State Variable Dependencies With An Energy-Based Neural Dialogue State Tracker, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Conference papers
Dialogue state tracking requires the population and maintenance of a multi-slot frame representation of the dialogue state. Frequently, dialogue state tracking systems assume independence between slot values within a frame. In this paper we argue that treating the prediction of each slot value as an independent prediction task may ignore important associations between the slot values, and, consequently, we argue that treating dialogue state tracking as a structured prediction problem can help to improve dialogue state tracking performance. To support this argument, the research presented in this paper is structured into three stages: (i) analyzing variable dependencies in dialogue data; …
Investigating Variable Dependencies In Dialogue States, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Investigating Variable Dependencies In Dialogue States, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Conference papers
Dialogue State Tracking is arguably one of the most challenging tasks among dialogue processing problems due to the uncertainties of language and complexity of dialogue contexts. We argue that this problem is made more challenging by variable dependencies in the dialogue states that must be accounted for in processing. In this paper we give details on our motivation for this argument through statistical tests on a number of dialogue datasets. We also propose a machine learning-based approach called energy-based learning that tackles variable dependencies while performing prediction on the dialogue state tracking tasks.
Bigger Versus Similar: Selecting A Background Corpus For First Story Detection Based On Distributional Similarity, Fei Wang, Robert J. Ross, John D. Kelleher
Bigger Versus Similar: Selecting A Background Corpus For First Story Detection Based On Distributional Similarity, Fei Wang, Robert J. Ross, John D. Kelleher
Conference papers
The current state of the art for First Story Detection (FSD) are nearest neighbour-based models with traditional term vector representations; however, one challenge faced by FSD models is that the document representation is usually defined by the vocabulary and term frequency from a background corpus. Consequently, the ideal background corpus should arguably be both large-scale to ensure adequate term coverage, and similar to the target domain in terms of the language distribution. However, given these two factors cannot always be mutually satisfied, in this paper we examine whether the distributional similarity of common terms is more important than the scale …
Estimating Distributed Representation Performance In Disaster-Related Social Media Classification, Pallavi Jain, Robert J. Ross, Bianca Schoen-Phelan
Estimating Distributed Representation Performance In Disaster-Related Social Media Classification, Pallavi Jain, Robert J. Ross, Bianca Schoen-Phelan
Conference papers
This paper examines the effectiveness of a range of pre-trained language representations in order to determine the informativeness and information type of social media in the event of natural or man-made disasters. Within the context of disaster tweet analysis, we aim to accurately analyse tweets while minimising both false positive and false negatives in the automated information analysis. The investigation is performed across a number of well known disaster-related twitter datasets. Models that are built from pre-trained word embeddings from Word2Vec, GloVe, ELMo and BERT are used for performance evaluation. Given the relative ubiquity of BERT as a standout language …
Energy-Based Modelling For Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Energy-Based Modelling For Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Conference papers
The uncertainties of language and the complexity of dialogue contexts make accurate dialogue state tracking one of the more challenging aspects of dialogue processing. To improve state tracking quality, we argue that relationships between different aspects of dialogue state must be taken into account as they can often guide a more accurate interpretation process. To this end, we present an energy-based approach to dialogue state tracking as a structured classification task. The novelty of our approach lies in the use of an energy network on top of a deep learning architecture to explore more signal correlations between network variables including …
Synthetic, Yet Natural: Properties Of Wordnet Random Walk Corpora And The Impact Of Rare Words On Embedding Performance, Filip Klubicka, Alfredo Maldonado, Abhijit Mahalunkar, John D. Kelleher
Synthetic, Yet Natural: Properties Of Wordnet Random Walk Corpora And The Impact Of Rare Words On Embedding Performance, Filip Klubicka, Alfredo Maldonado, Abhijit Mahalunkar, John D. Kelleher
Conference papers
Creating word embeddings that reflect semantic relationships encoded in lexical knowledge resources is an open challenge. One approach is to use a random walk over a knowledge graph to generate a pseudo-corpus and use this corpus to train embeddings. However, the effect of the shape of the knowledge graph on the generated pseudo-corpora, and on the resulting word embeddings, has not been studied. To explore this, we use English WordNet, constrained to the taxonomic (tree-like) portion of the graph, as a case study. We investigate the properties of the generated pseudo-corpora, and their impact on the resulting embeddings. We find …
Nurse-Led Design And Development Of An Expert System For Pressure Ulcer Management, Débora Abranches, Dympna O'Sullivan, Jon Bird
Nurse-Led Design And Development Of An Expert System For Pressure Ulcer Management, Débora Abranches, Dympna O'Sullivan, Jon Bird
Conference papers
The use of Clinical Practice Guidelines (CPGs) is known to enable better care outcomes by promoting a consistent way of treating patients. This paper describes a user-centered design approach involving nurses, to develop a prototype expert system for modelling CPGs for Pressure Ulcer management. The system was developed using Visirule, a software tool that uses a graphical approach to modeling knowledge. The system was evaluated by 5 staff nurses and compared nurses’ time and accuracy to assess a wound using CPGs accessed via the Intranet of an NHS Trust and the expert system. A post task qualitative evaluation revealed that …
Test: A Terminology Extraction System For Technology Related Terms, Murhaf Hossari, Soumyabrata Dev, John Kelleher
Test: A Terminology Extraction System For Technology Related Terms, Murhaf Hossari, Soumyabrata Dev, John Kelleher
Conference papers
Tracking developments in the highly dynamic data-technology landscape are vital to keeping up with novel technologies and tools, in the various areas of Artificial Intelligence (AI). However, It is difficult to keep track of all the relevant technology keywords. In this paper, we propose a novel system that addresses this problem. This tool is used to automatically detect the existence of new technologies and tools in text, and extract terms used to describe these new technologies. The extracted new terms can be logged as new AI technologies as they are found on-the-fly in the web. It can be subsequently classified …
Audio Mixing Using Image Neural Style Transfer Networks, Susan Mckeever, Xuehao Liu, Sarah Jane Delany
Audio Mixing Using Image Neural Style Transfer Networks, Susan Mckeever, Xuehao Liu, Sarah Jane Delany
Conference papers
Image style transfer networks are used to blend images, producing images that are a mix of source images. The process is based on controlled extraction of style and content aspects of images, using pre-trained Convolutional Neural Networks (CNNs). Our interest lies in adopting these image style transfer networks for the purpose of transforming sounds. Audio signals can be presented as grey-scale images of audio spectrograms. The purpose of our work is to investigate whether audio spectrogram inputs can be used with image neural transfer networks to produce new sounds. Using musical instrument sounds as source sounds, we apply and compare …
The Use Of Deep Learning Distributed Representations In The Identification Of Abusive Text, Susan Mckeever, Hao Chen, Sarah Jane Delany
The Use Of Deep Learning Distributed Representations In The Identification Of Abusive Text, Susan Mckeever, Hao Chen, Sarah Jane Delany
Conference papers
The selection of optimal feature representations is a critical step in the use of machine learning in text classification. Traditional features (e.g. bag of words and n-grams) have dominated for decades, but in the past five years, the use of learned distributed representations has become increasingly common. In this paper, we summarise and present a categorisation of the stateof-the-art distributed representation techniques, including word and sentence embedding models. We carry out an empirical analysis of the performance of the various feature representations using the scenario of detecting abusive comments. We compare classification accuracies across a range of off-the-shelf embedding models …
Content-Based Music Retrieval Of Irish Traditional Music Via A Virtual Tin Whistle, Pierre Beauguitte, Hung-Chuan Huang
Content-Based Music Retrieval Of Irish Traditional Music Via A Virtual Tin Whistle, Pierre Beauguitte, Hung-Chuan Huang
Conference papers
We present a mobilephon eapplication associating a virtual musical instrument (emulating a tin whistle) to a content based music retrieval system for Irish Traditional Music (ITM). It performs tune recognition, following the architecture of the existing query-by-playing software Tunepal (Duggan & O’Shea, 2011). After explaining the motivation for this project in Section 2 and presenting some relatedworkinSection3,wedescribeourproposedapplicationinSection4. Section5discussescurrentshortcomings of our project and potential future directions.
Multi-Spectral Visual Crop Assessment Under Limited Data Constraints, Patricia O'Byrne, Patrick Jackman, Damon Berry, Hector-Hugo Franco-Penya, Michael French, Robert J. Ross
Multi-Spectral Visual Crop Assessment Under Limited Data Constraints, Patricia O'Byrne, Patrick Jackman, Damon Berry, Hector-Hugo Franco-Penya, Michael French, Robert J. Ross
Conference papers
In an era of climate change and global population growth, deep learning based multi-spectral imaging has the potential to significantly assist in production management across a wide range of agricultural and food production domains. A key challenge however in applying state-of-the-art methods is that they, unlike classical hand crafted methods, are usually thought of as being only useful when significant amounts of data are available. In this paper we investigate this hypothesis by examining the performance of state-of-the-art deep learning methods when applied to a restricted data set that is not easily bootstrapped through pre-trained image processing networks. We demonstrate …
On The Inability Of Markov Models To Capture Criticality In Human Mobility, Vaibhav Klukarni, Abhijit Mahalunkar, Benoit Garbinato, John Kelleher
On The Inability Of Markov Models To Capture Criticality In Human Mobility, Vaibhav Klukarni, Abhijit Mahalunkar, Benoit Garbinato, John Kelleher
Conference papers
We examine the non-Markovian nature of human mobility by exposing the inability of Markov models to capture criticality in human mobility. In particular, the assumed Markovian nature of mobility was used to establish an upper bound on the predictability of human mobility, based on the temporal entropy. Since its inception, this bound has been widely used for validating the performance of mobility prediction models. We show that the variants of recurrent neural network architectures can achieve significantly higher prediction accuracy surpassing this upper bound. The central objective of our work is to show that human-mobility dynamics exhibit criticality characteristics which …
A Self Healing Microservices Architecture: A Case Study In Docker Swarm Cluster, Basel Magableh, Muder Almiani
A Self Healing Microservices Architecture: A Case Study In Docker Swarm Cluster, Basel Magableh, Muder Almiani
Conference papers
One desired aspect of a self-adapting microservices architecture is the ability to continuously monitor the operational environment, detect and observe anomalous behaviour as well as implement a reasonable policy for self-scaling, self-healing, and self-tuning the computational resources in order to dynamically respond to a sudden change in its operational environment. Often the behaviour of a microservices architecture continuously changes over time and the identification of both normal and abnormal behaviours of running services becomes a challenging task. This paper proposes a self-healing Microservice architecture that continuously monitors the operational environment, detects and observes anomalous behaviours, and provides a reasonable adaptation …
An Evaluation Of The Reliability, Validity And Sensitivity Of Three Human Mental Workload Measures Under Different Instructional Conditions In Third-Level Education, Luca Longo, Giuliano Orru
An Evaluation Of The Reliability, Validity And Sensitivity Of Three Human Mental Workload Measures Under Different Instructional Conditions In Third-Level Education, Luca Longo, Giuliano Orru
Conference papers
Although Cognitive Load Theory (CLT) has been researched for many years, it has been criticised for its theoretical clarity and its methodological approach. A crucial issue is the measurement of three types of cognitive load conceived in the theory, and the assessment of overall human cognitive load during learning tasks. This research study is motivated by these issues and it aims to investigate the reliability, validity and sensitivity of three existing self-reporting mental workload instruments, mainly used in Ergonomics, when applied to Education and in particular to the field of Teaching and Learning. A primary research study has been designed …
We Have Always Been Virtual: Gilles Deleuze And The Computer-Generated Image, Hugh Mccabe
We Have Always Been Virtual: Gilles Deleuze And The Computer-Generated Image, Hugh Mccabe
Conference papers
The use of computer-generated imagery is becoming increasingly ubiquitous across many fields including media, advertising, architecture and art. This represents a fundamental shift within visual culture, as imagery can now be produced routinely by means of rendering algorithms based on spatial representations. We propose that the account of the image provided by Gilles Deleuze in his books on cinema provides a rich philosophical framework for understanding such contemporary imaging practices. By providing a Deleuzian reading of James Kajiya's 1986 rendering equation we argue that there is a tacit ontology of the image underwriting both Deleuze’s work on cinema and current …
Towards Linked Data For Wikidata Revisions And Twitter Trending Hashtags, Paula Dooley, Bojan Bozic
Towards Linked Data For Wikidata Revisions And Twitter Trending Hashtags, Paula Dooley, Bojan Bozic
Conference papers
This paper uses Twitter as a microblogging platform to link hashtags, which relate the message to a topic that is shared among users, to Wikidata, a central knowledge base of information relying on its members and machine bots to keeping its content up to date. The data is stored in a highly structured format, with the added SPARQL Protocol And RDF Query Language (SPARQL) endpoint to allow users to query its knowledge base. Our research, designs and implements a process to stream live Twitter tweets and to parse existing Wikidata revision XML files provided by Wikidata. Furthermore, we identify if …
The Evolution Of Cognitive Load Theory And The Measurement Of Its Intrinsic, Extraneous And Germane Loads: A Review, Giuliana Orru, Luca Longo
The Evolution Of Cognitive Load Theory And The Measurement Of Its Intrinsic, Extraneous And Germane Loads: A Review, Giuliana Orru, Luca Longo
Conference papers
Cognitive Load Theory has been conceived for supporting instructional design through the use of the construct of cognitive load. This is believed to be built upon three types of load: intrinsic, extraneous and germane. Although Cognitive Load Theory and its assumptions are clear and well-known, its three types of load have been going through a continuous investigation and re-definition. Additionally, it is still not clear whether these are independent and can be added to each other towards an overall measure of load. The purpose of this research is to inform the reader about the theoretical evolution of Cognitive Load Theory …
The Political Power Of Twitter, James Usher, Pierpaolo Dondio, Lucia Morales
The Political Power Of Twitter, James Usher, Pierpaolo Dondio, Lucia Morales
Conference papers
In June 2016, the British voted by 52 per cent to leave the EU, a club the UK joined in 1973. This paper examines Twitter public and political party discourse surrounding the BREXIT withdrawal agreement. In particular, we focus on tweets from four different BREXIT exit strategies known as “Norway”, “Article 50”, the “Backstop” and “No Deal” and their effect on the pound and FTSE 100 index from the period of December 10th 2018 to February 24th 2019. Our approach focuses on using a Naive Bayes classification algorithm to assess political party and public Twitter sentiment. A Granger causality analysis …
Brexit: A Granger Causality Of Twitter Political Polarisation On The Ftse 100 Index And The Pound, James Usher, Lucia Morales, Pierpaolo Dondio
Brexit: A Granger Causality Of Twitter Political Polarisation On The Ftse 100 Index And The Pound, James Usher, Lucia Morales, Pierpaolo Dondio
Conference papers
BREXIT is the single biggest geopolitical event in British history since WWII. Whilst the political fallout has become a tragicomedy, the political ramifications has had a profound impact on the Pound and the FTSE 100 index. This paper examines Twitter political discourse surrounding the BREXIT withdrawal agreement. In particular we focus on the discussions around four different exit strategies known as “Norway”, “Article 50”, the“Backstop” and “No Deal” and their effect on the pound and FTSE 100 index from the period of rumblings of the cancellation of the Meaning Vote on December 10th 2018 inclusive of second defeat on the …
On The Mental Workload Assessment Of Uplift Mapping Representations In Linked Data, Ademar Crotti Junior, Christophe Debruyne, Luca Longo, Declan O'Sullivan
On The Mental Workload Assessment Of Uplift Mapping Representations In Linked Data, Ademar Crotti Junior, Christophe Debruyne, Luca Longo, Declan O'Sullivan
Conference papers
Self-reporting procedures have been largely employed in literature to measure the mental workload experienced by users when executing a specific task. This research proposes the adoption of these mental workload assessment techniques to the task of creating uplift mappings in Linked Data. A user study has been performed to compare the mental workload of “manually” creating such mappings, using a formal mapping language and a text editor, to the use of a visual representation, based on the block metaphor, that generate these mappings. Two subjective mental workload instruments, namely the NASA Task Load Index and the Workload Profile, were applied …
Analysing The Impact Of Machine Learning To Model Subjective Mental Workload: A Case Study In Third-Level Education, Karim Moustafa, Luca Longo
Analysing The Impact Of Machine Learning To Model Subjective Mental Workload: A Case Study In Third-Level Education, Karim Moustafa, Luca Longo
Conference papers
Mental workload measurement is a complex multidisciplinary research area that includes both the theoretical and practical development of models. These models are aimed at aggregating those factors, believed to shape mental workload, and their interaction, for the purpose of human performance prediction. In the literature, models are mainly theory-driven: their distinct development has been influenced by the beliefs and intuitions of individual scholars in the disciplines of Psychology and Human Factors. This work presents a novel research that aims at reversing this tendency. Specifically, it employs a selection of learning techniques, borrowed from machine learning, to induce models of mental …
Fuzzy-Gra Trust Model For Cloud Risk Management, Abdul Razaque, Muder Almiani, Meer Jaro Khan, Basel Magableh, Ayman Al-Dmour, Amer Al-Rahayfeh
Fuzzy-Gra Trust Model For Cloud Risk Management, Abdul Razaque, Muder Almiani, Meer Jaro Khan, Basel Magableh, Ayman Al-Dmour, Amer Al-Rahayfeh
Conference papers
Cloud computing is not adequately secure due to the currently used traditional trust methods such as global trust model and local trust model. These are prone to security vulnerabilities. This paper introduces a trust model based on the fuzzy mathematics and gray relational theory. Fuzzy mathematics and gray relational analysis (Fuzzy-GRA) aims to improve the poor dynamic adaptability of cloud computing. Fuzzy-GRA platform is used to test and validate the behavior of the model. Furthermore, our proposed model is compared to other known models. Based on the experimental results, we prove that our model has the edge over other existing …
Intelligent Intrusion Detection Using Radial Basis Function Neural Network, Alia Abughazleh, Muder Almiani, Basel Magableh, Abdul Razaque
Intelligent Intrusion Detection Using Radial Basis Function Neural Network, Alia Abughazleh, Muder Almiani, Basel Magableh, Abdul Razaque
Conference papers
Recently we witness a booming and ubiquity evolving of internet connectivity all over the world leading to dramatic amount of network activities and large amount of data and information transfer. Massive data transfer composes a fertile ground to hackers and intruders to launch cyber-attacks and various types of penetrations. As a consequence, researchers around the globe have devoted a large room for researches that can handle different types of attacks efficiently through building various types of intrusion detection systems capable to handle different types of attacks, known and unknown (novel) ones as well as have the capability to deal with …
Agent-Based Iot Coordination For Smart Cities Considering Security And Privacy, Iván García-Magariño, Geraldine Gray, Rajarajan Muttukrishnan, Waqar Asif
Agent-Based Iot Coordination For Smart Cities Considering Security And Privacy, Iván García-Magariño, Geraldine Gray, Rajarajan Muttukrishnan, Waqar Asif
Conference papers
The interest in Internet of Things (IoT) is increasing steeply, and the use of their smart objects and their composite services may become widespread in the next few years increasing the number of smart cities. This technology can benefit from scalable solutions that integrate composite services of multiple-purpose smart objects for the upcoming large-scale use of integrated services in IoT. This work proposes an agent-based approach for supporting large-scale use of IoT for providing complex integrated services. Its novelty relies in the use of distributed blackboards for implicit communications, decentralizing the storage and management of the blackboard information in the …