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Articles 61 - 90 of 157
Full-Text Articles in Artificial Intelligence and Robotics
Weakly-Admissible Semantics And The Propagation Of Ambiguity In Abstract Argumentation Semantics, Pierpaolo Dondio
Weakly-Admissible Semantics And The Propagation Of Ambiguity In Abstract Argumentation Semantics, Pierpaolo Dondio
Other
The concept of ambiguous literals of defeasible logics is mapped to the set of undecided arguments identified by an argumentation semantics. It follows that Dung’s complete semantics are all ambiguity propagating, since the undecided status of an attacking argument is always propagated to the attacked argument, unless the latter is defeated by another accepted argument. In this paper we investigate a novel family of abstract argumentation semantics, called weakly-admissible semantics, where we do not require an acceptable argument to be necessarily defended from the attacks of undecided arguments. Weakly-admissible semantics are conflict-free, ambiguity blocking, non-admissible (in Dung’s sense), but employing …
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 …
An Evaluation Of Learning Employing Natural Language Processing And Cognitive Load Assessment, Mrunal Tipari
An Evaluation Of Learning Employing Natural Language Processing And Cognitive Load Assessment, Mrunal Tipari
Dissertations
One of the key goals of Pedagogy is to assess learning. Various paradigms exist and one of this is Cognitivism. It essentially sees a human learner as an information processor and the mind as a black box with limited capacity that should be understood and studied. With respect to this, an approach is to employ the construct of cognitive load to assess a learner's experience and in turn design instructions better aligned to the human mind. However, cognitive load assessment is not an easy activity, especially in a traditional classroom setting. This research proposes a novel method for evaluating learning …
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 …
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 …
On The Exactitude Of Big Data: La Bêtise And Artificial Intelligence, Noel Fitzpatrick, John D. Kelleher
On The Exactitude Of Big Data: La Bêtise And Artificial Intelligence, Noel Fitzpatrick, John D. Kelleher
Articles
This article revisits the question of ‘la bêtise’ or stupidity in the era of Artificial Intelligence driven by Big Data, it extends on the questions posed by Gille Deleuze and more recently by Bernard Stiegler. However, the framework for revisiting the question of la bêtise will be through the lens of contemporary computer science, in particular the development of data science as a mode of analysis, sometimes, misinterpreted as a mode of intelligence. In particular, this article will argue that with the advent of forms of hype (sometimes referred to as the hype cycle) in relation to big data and …
Exploring Online Novelty Detection Using First Story Detection Models, Fei Wang, Robert J. Ross, John D. Kelleher
Exploring Online Novelty Detection Using First Story Detection Models, Fei Wang, Robert J. Ross, John D. Kelleher
Conference papers
Online novelty detection is an important technology in understanding and exploiting streaming data. One application of online novelty detection is First Story Detection (FSD) which attempts to find the very first story about a new topic, e.g. the first news report discussing the “Beast from the East” hitting Ireland. Although hundreds of FSD models have been developed, the vast majority of these only aim at improving the performance of the detection for some specific dataset, and very few focus on the insight of novelty itself. We believe that online novelty detection, framed as an unsupervised learning problem, always requires a …
A Multi-Task Approach To Incremental Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
A Multi-Task Approach To Incremental Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Conference papers
Incrementality is a fundamental feature of language in real world use. To this point, however, the vast majority of work in automated dialogue processing has focused on language as turn based. In this paper we explore the challenge of incremental dialogue state tracking through the development and analysis of a multi-task approach to incremental dialogue state tracking. We present the design of our incremental dialogue state tracker in detail and provide evaluation against the well known Dialogue State Tracking Challenge 2 (DSTC2) dataset. In addition to a standard evaluation of the tracker, we also provide an analysis of the Incrementality …
From Rankings To Ratings: Rank Scoring Via Active Learning, Jack O'Neill, Sarah Jane Delany, Brian Mac Namee
From Rankings To Ratings: Rank Scoring Via Active Learning, Jack O'Neill, Sarah Jane Delany, Brian Mac Namee
Conference papers
In this paper we present RaScAL, an active learning approach to predicting real-valued scores for items given access to an oracle and knowledge of the overall item-ranking. In an experiment on six different datasets, we find that RaScAL consistently outperforms the state-of-the-art. The RaScAL algorithm represents one step within a proposed overall system of preference elicitations of scores via pairwise comparisons.
Perception & Perspective: An Analysis Of Discourse And Situational Factors In Reference Frame Selection, Robert J. Ross, Kavita E. Thomas
Perception & Perspective: An Analysis Of Discourse And Situational Factors In Reference Frame Selection, Robert J. Ross, Kavita E. Thomas
Conference papers
To integrate perception into dialogue, it is necessary to bind spatial language descriptions to reference frame use. To this end, we present an analysis of discourse and situational factors that may influence reference frame choice in dialogues. We show that factors including spatial orientation, task, self and other alignment, and dyad have an influence on reference frame use. We further show that a computational model to estimate reference frame based on these features provides results greater than both random and greedy reference frame selection strategies.
Mind The Gap: Situated Spatial Language A Case-Study In Connecting Perception And Language, John D. Kelleher
Mind The Gap: Situated Spatial Language A Case-Study In Connecting Perception And Language, John D. Kelleher
Other
This abstract reviews the literature on computational models of spatial semantics and the potential of deep learning models as an useful approach to this challenge.
An Investigation Into The Effects Of Multiple Kernel Combinations On Solutions Spaces In Support Vector Machines, Paul Kelly, Luca Longo
An Investigation Into The Effects Of Multiple Kernel Combinations On Solutions Spaces In Support Vector Machines, Paul Kelly, Luca Longo
Conference papers
The use of Multiple Kernel Learning (MKL) for Support Vector Machines (SVM) in Machine Learning tasks is a growing field of study. MKL kernels expand on traditional base kernels that are used to improve performance on non-linearly separable datasets. Multiple kernels use combinations of those base kernels to develop novel kernel shapes that allow for more diversity in the generated solution spaces. Customising these kernels to the dataset is still mostly a process of trial and error. Guidelines around what combinations to implement are lacking and usually they requires domain specific knowledge and understanding of the data. Through a brute …
Evaluating Sequence Discovery Systems In An Abstraction-Aware Manner, Eoin Rogers, Robert J. Ross, John D. Kelleher
Evaluating Sequence Discovery Systems In An Abstraction-Aware Manner, Eoin Rogers, Robert J. Ross, John D. Kelleher
Conference papers
Activity discovery is a challenging machine learning problem where we seek to uncover new or altered behavioural patterns in sensor data. In this paper we motivate and introduce a novel approach to evaluating activity discovery systems. Pre-annotated ground truths, often used to evaluate the performance of such systems on existing datasets, may exist at different levels of abstraction to the output of the output produced by the system. We propose a method for detecting and dealing with this situation, allowing for useful ground truth comparisons. This work has applications for activity discovery, and also for related fields. For example, it …
Exploring The Functional And Geometric Bias Of Spatial Relations Using Neural Language Models, Simon Dobnik, Mehdi Ghanimifard, John D. Kelleher
Exploring The Functional And Geometric Bias Of Spatial Relations Using Neural Language Models, Simon Dobnik, Mehdi Ghanimifard, John D. Kelleher
Conference papers
The challenge for computational models of spatial descriptions for situated dialogue systems is the integration of information from different modalities. The semantics of spatial descriptions are grounded in at least two sources of information: (i) a geometric representation of space and (ii) the functional interaction of related objects that. We train several neural language models on descriptions of scenes from a dataset of image captions and examine whether the functional or geometric bias of spatial descriptions reported in the literature is reflected in the estimated perplexity of these models. The results of these experiments have implications for the creation of …
Towards Dynamic Interaction-Based Reputation Models, Almas Melnikov, Manuel Mazzara, Victor Rivera, Jooyoung Lee, Luca Longo
Towards Dynamic Interaction-Based Reputation Models, Almas Melnikov, Manuel Mazzara, Victor Rivera, Jooyoung Lee, Luca Longo
Articles
In this paper, we investigate how dynamic properties of reputation can influence the quality of users’ ranking. Reputation systems should be based on rules that can guarantee high level of trust and help identify unreliable units. To understand the effectiveness of dynamic properties in the evaluation of reputation, we propose our own model (DIB-RM) that utilizes three factors: forgetting, cumulative, and activity period. In order to evaluate the model, we use data from StackOverflow which also has its own reputation model. We estimate similarity of ratings between DIB-RM and the StackOverflow reputation model to test our hypothesis. We use two …
Pseudorehearsal In Actor-Critic Agents With Neural Network Function Approximation, Vladimir Marochko, Leonard Johard, Manuel Mazzara, Luca Longo
Pseudorehearsal In Actor-Critic Agents With Neural Network Function Approximation, Vladimir Marochko, Leonard Johard, Manuel Mazzara, Luca Longo
Articles
Catastrophic forgetting has a significant negative impact in reinforcement learning. The purpose of this study is to investigate how pseudorehearsal can change performance of an actor-critic agent with neural-network function approximation. We tested agent in a pole balancing task and compared different pseudorehearsal approaches. We have found that pseudorehearsal can assist learning and decrease forgetting.
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 …
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 …
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 System To Monitor Cognitive Workload In Naturalistic High-Motion Environments, Bethany K. Bracken, Seth Elkin-Frankston, Noa Palmon, Michael Farry, Blaise De B Frederick
A System To Monitor Cognitive Workload In Naturalistic High-Motion Environments, Bethany K. Bracken, Seth Elkin-Frankston, Noa Palmon, Michael Farry, Blaise De B Frederick
H-Workload 2017: Models and Applications (Works in Progress)
Across many careers, individuals face alternating periods of high and low attention and cognitive workload can impair cognitive function and undermine job performance. We have designed and are developing an unobtrusive system to Monitor, Extract, and Decode Indicators of Cognitive Workload (MEDIC) in naturalistic, high-motion environments. MEDIC is designed to warn individuals, teammates, or supervisors when steps should be taken to augment cognitive readiness. We first designed and manufactured a forehead sensor device that includes a custom fNIRS sensor and a three-axis accelerometer designed to be mounted on the inside of a baseball cap or headband, or standard issue gear …
Towards A Not Obtrusive Low Cost Biosystem To Assess Risk Perception In Workplace Through Stress Detection, Emanuele Bellini, Serena Benevenuti, Chiara Batistini
Towards A Not Obtrusive Low Cost Biosystem To Assess Risk Perception In Workplace Through Stress Detection, Emanuele Bellini, Serena Benevenuti, Chiara Batistini
H-Workload 2017: Models and Applications (Works in Progress)
The main aim of the article is to build a method to assess risk perception in real time in order to early detect and prevent risk behaviors and possible human errors. To this end, the relation between mental workload and stress as critical factors affecting risk perception has been investigated. In particular the mental-physical activation generated by an increment of the workload has the effect of reducing the resources needed to perceive risk increasing the worker vulnerability. The complexity of the stress phenomenon suggested the adoption of an integrated view. The Functional Model has been adopted to for its holistic …
Reducing Peak Workload In The Cockpit: A Human In The Loop Simulation Evaluating New Runway Selection Tool, Tanja Bos, Rolf Zon, Wilfred Rouwhorst
Reducing Peak Workload In The Cockpit: A Human In The Loop Simulation Evaluating New Runway Selection Tool, Tanja Bos, Rolf Zon, Wilfred Rouwhorst
H-Workload 2017: Models and Applications (Works in Progress)
In efforts to increase safety and reduce peak workload situations in the cockpit, a tool with a different interaction style was developed for use in case of a runway change instructed by Air Traffic Control during approach. In an experiment a workload comparison was made between the new tool and the conventional cockpit. Workload was measured by means of a self-rating after each experiment run, as well as eye blink frequency during each run. Results show that the self-rated workload decreases with the new tool for one of the two crew members and the blink frequency suggests a workload decrease …
System Identification Of Motion Artifact: Noise In Eeg Headsets From Locomotion, Kaela Shea, James Tung
System Identification Of Motion Artifact: Noise In Eeg Headsets From Locomotion, Kaela Shea, James Tung
H-Workload 2017: Models and Applications (Works in Progress)
Fall prevention for geriatric populations is a growing concern among clinicians and researchers due to severe risk of morbidity and loss of independence. Emerging evidence has demonstrated that mental workload while walking influences gait stability and the risk for falling. Electroencephalography (EEG) presents a potential method to provide objective measures of mental workload, particularly during daily activities. Noise introduced to the EEG signal during motion, however, is restrictive. The study presented in the following paper isolates EEG signal noise attained from gait for a commercially accessible EEG system, the "Emotiv" Time and spectral system identification techniques were applied to model …
Online Measuring Of Available Resources, Enrique Munoz-De-Escalona, José Juan Canas
Online Measuring Of Available Resources, Enrique Munoz-De-Escalona, José Juan Canas
H-Workload 2017: Models and Applications (Works in Progress)
This paper present a proposal for measuring available mental resources during the accomplishment of a task. Our proposal consists in measuring emotions provoked by perceived self-efficacy in the execution of the task. Self-efficacy is one of the most important factors that affect the resources that a person puts at the disposal of the execution of the task. When a person perceives that he/she is not being effective he/she will activate more resources to improve his performance. This self-efficacy will be reflected in the emotions that the person experiences. A good efficacy will provoke positive emotions and a bad efficacy negative …
A Workload-Centered Perspective On Reduced Crew Operations In Commercial Aviation, Daniela Schmid
A Workload-Centered Perspective On Reduced Crew Operations In Commercial Aviation, Daniela Schmid
H-Workload 2017: Models and Applications (Works in Progress)
Mental workload of a pilot, in short workload, depends on various characteristics of different accumulated tasks on the flight deck. Exogenous task demands and endogenous supply of attentional or information processing resources determine workload [1]. Performance is expect to drop if the demand exceeds the available resources of the pilot. Expertise and experience modulate the endogenous sup- ply of resources like perceiving, updating memory, planing, making a decision, and executing and processing a response. Subsequently, workload manifests in performance variables, subjective experience, and physiological parameters [2]. This is how we can summarize workload very brie y to introduce a model …
A Systems Approach To Predicting And Measuring Workload In Rail Traffic Management Systems, Joanna Evans
A Systems Approach To Predicting And Measuring Workload In Rail Traffic Management Systems, Joanna Evans
H-Workload 2017: Models and Applications (Works in Progress)
The introduction of systems such as Traffic Management (TM) will result in a number of changes in how the railway is managed for operations and maintenance staff such as, an increase in collaborative working styles and shared responsibilities. In order to react to these changing operational demands and user needs, TM workstation designs need to have greater flexibility and be configurable to support the information requirements for each specific role as well as support each role during different scenarios. Although this flexibility in system design has the potential to enhance performance, it increases the complexity of measuring operator workload. The …
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, …