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Subject = Artificial intelligence;
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Displaying Results 1 - 25 of 308 on page 1 of 13
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"You feel real to me, Samantha": the matter of technology in Spike Jonze's Her
(2018)
Murphy, Paula
"You feel real to me, Samantha": the matter of technology in Spike Jonze's Her
(2018)
Murphy, Paula
Abstract:
This essay will argue that Spike Jonze’s Her demonstrates a key idea in posthumanist new materialist theory: that matter is essential for posthuman interaction and communication. It also examines the requirement for embodiment on the part of the digital entity as well as the human, in this case the operating system Samantha. As the film presents an artificially intelligent operating system that ultimately moves beyond matter, it provides a case study for the importance of matter and the consequences of de-materialization. In this article, posthumanism names this era in which relationships between humans and technologies have become increasingly digitised, and the cluster of theoretical concepts which have arisen to interrogate this state of affairs. It is not seen as departure, rather as part of the continuing relationship between humans and technologies. Posthumanist new materialism is drawn on for its emphasis on and insights into embodiment and materiality. Theodore experiences S...
http://doras.dcu.ie/23086/
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“A tiny cog in a large machine”: Digital Taylorism in the Translation Industry
(2020)
Moorkens, Joss
“A tiny cog in a large machine”: Digital Taylorism in the Translation Industry
(2020)
Moorkens, Joss
Abstract:
Translators have worked with the assistance of computers for many years, usually translating whole texts, divided into segments but in sequential order. In order to maximise efficiency and inspired by similar moves in the tech industry and predictions for Industry 4.0, large translation companies have begun to break tasks down into smaller chunks and to rigidly define and monitor translation processes. This is particularly true of platform-mediated work, highly collaborative workflows, and multimedia work that requires near-live turnaround times. This article considers such workflows in the context of measures of job satisfaction and discussion of sustainable work systems, proposing that companies prioritise long-term returns and attempt to balance the needs of all stakeholders in a translation process. Translators and translator trainers also have a role to play in achieving this balance.
http://doras.dcu.ie/24968/
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#hardtoparse: POS tagging and parsing the twitterverse
(2011)
Foster, Jennifer; Cetinoglu, Ozlem; Wagner, Joachim; Le Roux, Joseph; Hogan, Stephen; N...
#hardtoparse: POS tagging and parsing the twitterverse
(2011)
Foster, Jennifer; Cetinoglu, Ozlem; Wagner, Joachim; Le Roux, Joseph; Hogan, Stephen; Nivre, Joakim; Hogan, Deirdre; van Genabith, Josef
Abstract:
We evaluate the statistical dependency parser, Malt, on a new dataset of sentences taken from tweets. We use a version of Malt which is trained on gold standard phrase structure Wall Street Journal (WSJ) trees converted to Stanford labelled dependencies. We observe a drastic drop in performance moving from our in-domain WSJ test set to the new Twitter dataset, much of which has to do with the propagation of part-of-speech tagging errors. Retraining Malt on dependency trees produced by a state-of-the-art phrase structure parser, which has itself been self-trained on Twitter material, results in a significant improvement. We analyse this improvement by examining in detail the effect of the retraining on individual dependency types.
http://doras.dcu.ie/16484/
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A Combinatorial Optimisation Approach to the Design of Dual Parented Long-Reach Passive Optical Networks
(2011)
RUFFINI, MARCO; DOYLE, LINDA; PAYNE, DAVID
A Combinatorial Optimisation Approach to the Design of Dual Parented Long-Reach Passive Optical Networks
(2011)
RUFFINI, MARCO; DOYLE, LINDA; PAYNE, DAVID
Abstract:
We present an application focused on the design of resilient long-reach passive optical networks. We specifically consider dual-parented networks whereby each customer must be connected to two metro sites via local exchange sites. An important property of such a placement is resilience to single metro node failure. The objective of the application is to determine the optimal position of a set of metro nodes such that the total optical fibre length is minimized. We prove that this problem is NP-Complete. We present two alternative combinatorial optimisation approaches to finding an optimal metro node placement using: a mixed integer linear programming (MIP) formulation of the problem; and, a hybrid approach that uses clustering as a preprocessing step. We consider a detailed case-study based on a network for Ireland. The hybrid approach scales well and finds solutions that are close to optimal, with a runtime that is two orders-of-magnitude better than the MIP model.
http://hdl.handle.net/2262/59498
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Silence and overlap in chat and chunk phases of multiparty casual conversation
(2018)
Vogel, Carl; Wade, Vincent
Silence and overlap in chat and chunk phases of multiparty casual conversation
(2018)
Vogel, Carl; Wade, Vincent
http://hdl.handle.net/2262/85118
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A comparative study of existing and new deep learning methods for detecting knee injuries using the MRNet dataset
(2020)
Azcona, David; McGuinness, Kevin; Smeaton, Alan F.
A comparative study of existing and new deep learning methods for detecting knee injuries using the MRNet dataset
(2020)
Azcona, David; McGuinness, Kevin; Smeaton, Alan F.
Abstract:
This work presents a comparative study of existing and new techniques to detect knee injuries by leveraging Stanford's MRNet Dataset. All approaches are based on deep learning and we explore the comparative performances of transfer learning and a deep residual network trained from scratch. We also exploit some characteristics of Magnetic Resonance Imaging (MRI) data by, for example, using a fixed number of slices or 2D images from each of the axial, coronal and sagittal planes as well as combining the three planes into one multi-plane network. Overall we achieved a performance of 93.4% AUC on the validation data by using the more recent deep learning architectures and data augmentation strategies. More flexible architectures are also proposed that might help with the development and training of models that process MRIs. We found that transfer learning and a carefully tuned data augmentation strategy were the crucial factors in determining best performance.
http://doras.dcu.ie/25068/
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A comparison between end-to-end approaches and feature extraction based approaches for Sign Language recognition
(2018)
Oliveira, Marlon; Chatbri, Houssem; Little, Suzanne; O'Connor, Noel E.; Sutherland...
A comparison between end-to-end approaches and feature extraction based approaches for Sign Language recognition
(2018)
Oliveira, Marlon; Chatbri, Houssem; Little, Suzanne; O'Connor, Noel E.; Sutherland, Alistair
Abstract:
In this work we use a new image dataset for Irish Sign Language (ISL) and we compare different approaches for recognition. We perform experiments and report comparative accuracy and timing. We perform tests over blurred images and compare results with non-blurred images. For classification, we use end-to-end approach, such as Convolutional Neural Networks (CNN) and feature based extraction approaches, such as Principal Component Analysis (PCA) followed by different classifiers, i.e. multilayer perceptron (MLP). We obtain a recognition accuracy over 99% for both approaches. In addition, we report different ways to split the training and testing dataset, being one iterative and the other one random selected.
http://doras.dcu.ie/22132/
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A Computational Analysis of Cognitive Effort
(2010)
Longo, Luca; Barrett, Stephen
A Computational Analysis of Cognitive Effort
(2010)
Longo, Luca; Barrett, Stephen
Abstract:
Cognitive effort is a concept of unquestionable utility in understanding human behaviour. However, cognitive effort has been defined in several ways in literature and in everyday life, suffering from a partial understanding. It is common to say “Pay more attention in studying that subject” or “How much effort did you spend in resolving that task?”, but what does it really mean? This contribution tries to clarify the concept of cognitive effort, by introducing its main influencing factors and by presenting a formalism which provides us with a tool for precise discussion. The formalism is implementable as a computational concept and can therefore be embedded in an artificial agent and tested experimentally. Its applicability in the domain of AI is raised and the formalism provides a step towards a proper understanding and definition of human cognitive effort.
https://arrow.dit.ie/scschcombk/6
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A cost benefit operator for efficient multi level genetic algorithm searches
(2007)
Mitchell, George G.; McMullin, Barry; Decraene, James
A cost benefit operator for efficient multi level genetic algorithm searches
(2007)
Mitchell, George G.; McMullin, Barry; Decraene, James
Abstract:
In this paper we present a novel cost benefit operator that assists multi level genetic algorithm searches. Through the use of the cost benefit operator, it is possible to dynamically constrain the search of the base level genetic algorithm, to suit the user’s requirements. Initially we review meta-evolutionary (multi-level genetic algorithm) approaches. We note that the current literature has abundant studies on meta-evolutionary GAs. However these approaches have not identified an efficient approach to termination of base GA search or a means to balance practical consideration such as quality of solution and the expense of computation. Our Quality time tradeoff operator (QTT) is user defined, and acts as a base level termination operator and also provides a fitness value for the meta-level GA. In this manner the amount of computation time spent on less encouraging configurations can be specified by the user. Our approach has been applied to a computationally intensive test problem...
http://doras.dcu.ie/4608/
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A Dataset for Irish sign language recognition
(2017)
Oliveira, Marlon; Chatbri, Houssem; Ferstl, Ylva; Farouk, Mohamed; Little, Suzanne; O...
A Dataset for Irish sign language recognition
(2017)
Oliveira, Marlon; Chatbri, Houssem; Ferstl, Ylva; Farouk, Mohamed; Little, Suzanne; O'Connor, Noel E.; Sutherland, Alistair
Abstract:
We introduce a new image dataset for Irish Sign Language (ISL) recognition. We filmed human subjects performing ISL hand-shapes and movements, resulting in 468 videos. Then, we extracted frames from the videos. This resulted in a total of 58,114 images for the 23 common hand-shapes from the ISL language. This dataset is a part of our ongoing work on ISL recognition using pattern recognition methods. In addition to the dataset, we report experiments using Principal Component Analysis (PCA) where we reached 95% recognition accuracy.
http://doras.dcu.ie/21882/
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A deep convolutional neural network for brain tissue segmentation in Neonatal MRI
(2017)
Murphy, Keelin; Boylan, Geraldine B.; Smeaton, Alan F.; McGuinness, Kevin
A deep convolutional neural network for brain tissue segmentation in Neonatal MRI
(2017)
Murphy, Keelin; Boylan, Geraldine B.; Smeaton, Alan F.; McGuinness, Kevin
Abstract:
Brain tissue segmentation is a prerequisite for many subsequent automatic quantitative analysis techniques. As with many medical imaging tasks, a shortage of manually annotated training data is a limiting factor which is not easily overcome, particularly using recent deep-learning technology. We present a deep convolutional neural network (CNN) trained on just 2 publicly available manually annotated volumes, trained to annotate 8 tissue types in neonatal T2 MRI. The network makes use of several recent deep-learning techniques as well as artificial augmentation of the training data, to achieve state-of-the- art results on public challenge data.
http://doras.dcu.ie/22062/
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A Deep learning toolkit for high dimensional sequential data
(2017)
O'Donoghue, James
A Deep learning toolkit for high dimensional sequential data
(2017)
O'Donoghue, James
Abstract:
Deep learning is a more recent form of machine learning based on a set of algorithms that attempt to learn using a deep graph with multiple processing layers, where layers are composed of multiple linear and non-linear transformational nodes. While research in this area has shown to improve the predictive accuracy in a number of domains, deep learning systems are highly complex and experiments can be hard to manage. In this dissertation, we present a deep learning system, built from scratch, which enables fully configurable deep learning experiments. By configurable, we mean selecting the overall learning algorithm, the number of layers within the deep network, the nodes within network layers and the propagation functions deployed at each node. We use a range of deep network configurations together with different datasets to illustrate the potential of this system but also to highlight the difficulties in tuning the model and hyper-parameters to maximise accuracy. Our research als...
http://doras.dcu.ie/21943/
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A deep neural network for 12-lead electrocardiogram interpretation outperforms a conventional algorithm, and its physician overread, in the diagnosis of atrial fibrillation
(2019)
Smith, Stephen W.; Rapin, Jeremy; Li, Jia; Fleureau, Yann; Fennell, William; Walsh, Bro...
A deep neural network for 12-lead electrocardiogram interpretation outperforms a conventional algorithm, and its physician overread, in the diagnosis of atrial fibrillation
(2019)
Smith, Stephen W.; Rapin, Jeremy; Li, Jia; Fleureau, Yann; Fennell, William; Walsh, Brooks M.; Rosier, Arnaud; Fiorina, Laurent; Gardella, Christophe
Abstract:
Background: Automated electrocardiogram (ECG) interpretations may be erroneous, and lead to erroneous overreads, including for atrial fibrillation (AF). We compared the accuracy of the first version of a new deep neural network 12-Lead ECG algorithm (Cardiologs®) to the conventional Veritas algorithm in interpretation of AF. Methods: 24,123 consecutive 12-lead ECGs recorded over 6 months were interpreted by 1) the Veritas® algorithm, 2) physicians who overread Veritas® (Veritas® + physician), and 3) Cardiologs® algorithm. We randomly selected 500 out of 858 ECGs with a diagnosis of AF according to either algorithm, then compared the algorithms' interpretations, and Veritas® + physician, with expert interpretation. To assess sensitivity for AF, we analyzed a separate database of 1473 randomly selected ECGs interpreted by both algorithms and by blinded experts. Results: Among the 500 ECGs selected, 399 had a final classification of AF; 101 (20.2%) had ≥1 false positive automated ...
http://hdl.handle.net/10468/8771
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A detailed analysis of phrase-based and syntax-based machine translation: the search for systematic differences
(2012)
Kaljahi, Rasoul Samed Zadeh; Rubino, Raphael; Roturier, Johann; Foster, Jennifer
A detailed analysis of phrase-based and syntax-based machine translation: the search for systematic differences
(2012)
Kaljahi, Rasoul Samed Zadeh; Rubino, Raphael; Roturier, Johann; Foster, Jennifer
Abstract:
This paper describes a range of automatic and manual comparisons of phrase-based and syntax-based statistical machine translation methods applied to English-German and English-French translation of user-generated content. The syntax-based methods underperform the phrase-based models and the relaxation of syntactic constraints to broaden translation rule coverage means that these models do not necessarily generate output which is more grammatical than the output produced by the phrase-based models. Although the systems generate different output and can potentially be fruitfully combined, the lack of systematic difference between these models makes the combination task more challenging.
http://doras.dcu.ie/17973/
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A domain ontology and software platform for collaborative personal data analytics
(2019)
Tuovinen, Lauri; Smeaton, Alan F.
A domain ontology and software platform for collaborative personal data analytics
(2019)
Tuovinen, Lauri; Smeaton, Alan F.
Abstract:
Collaborative knowledge discovery is a promising approach by which people with no data analytics expertise could benefit from an analysis of their own personal data by experts. To facilitate effective collaboration between data owners and knowledge discovery experts, we have developed a software platform that uses a domain ontology to represent knowledge relevant to the execution of the collaborative knowledge discovery process. The ontology provides classes representing the main elements of collaborations: collaborators and datasets. Furthermore, the ontology enables the specification of privacy constraints that determine the precise extent to which a given dataset of personal data is shared with a given collaborator. We have developed a client-server software platform that enables users to initiate collaborations, invite experts to join them, create datasets and share them with experts, and create visualisations of data. The collaborations are mediated through the creation, modi...
http://doras.dcu.ie/23918/
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A Framework for selecting deep learning hyper-parameters
(2015)
O'Donoghue, Jim
A Framework for selecting deep learning hyper-parameters
(2015)
O'Donoghue, Jim
http://doras.dcu.ie/21270/
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A hybrid CFGTSA based approach for scheduling problem: a case study of an automobile industry
(2006)
Chan, Felix T.S.; Kumar, Vikas; Chan, H.K.; Chung, S.H.
A hybrid CFGTSA based approach for scheduling problem: a case study of an automobile industry
(2006)
Chan, Felix T.S.; Kumar, Vikas; Chan, H.K.; Chung, S.H.
Abstract:
In the global competitive world swift, reliable and cost effective production subject to uncertain situations, through an appropriate management of the available resources, has turned out to be the necessity for surviving in the market. This inspired the development of the more efficient and robust methods to counteract the existing complexities prevailing in the market. The present paper proposes a hybrid CFGTSA algorithm inheriting the salient features of GA, TS, SA, and chaotic theory to solve the complex scheduling problems commonly faced by most of the manufacturing industries. The proposed CFGTSA algorithm has been tested on a scheduling problem of an automobile industry, and its efficacy has been shown by comparing the results with GA, SA, TS, GTS, and hybrid TSA algorithms.
http://doras.dcu.ie/15768/
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A Java Framework for Computer Vision
(2015)
Sheridan, Stephen
A Java Framework for Computer Vision
(2015)
Sheridan, Stephen
Abstract:
This paper outlines a framework implemented entirely in Java that attempts to give students exposure to computer vision systems from a practical standpoint. Various tools and technologies are introduced that will allow a student to acquire an input image through a WebCam, extract useful information from that input image and finally, attempt to make sense of the input.
https://arrow.dit.ie/itbj/vol3/iss2/6
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A machine learning approach to the unsupervised segmentation of mitochondria in subcellular electron microscopy data
(2016)
Dietlmeier, Julia
A machine learning approach to the unsupervised segmentation of mitochondria in subcellular electron microscopy data
(2016)
Dietlmeier, Julia
Abstract:
Recent advances in cellular and subcellular microscopy demonstrated its potential towards unravelling the mechanisms of various diseases at the molecular level. The biggest challenge in both human- and computer-based visual analysis of micrographs is the variety of nanostructures and mitochondrial morphologies. The state-of-the-art is, however, dominated by supervised manual data annotation and early attempts to automate the segmentation process were based on supervised machine learning techniques which require large datasets for training. Given a minimal number of training sequences or none at all, unsupervised machine learning formulations, such as spectral dimensionality reduction, are known to be superior in detecting salient image structures. This thesis presents three major contributions developed around the spectral clustering framework which is proven to capture perceptual organization features. Firstly, we approach the problem of mitochondria localization. We propose a nove...
http://doras.dcu.ie/21532/
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A machine vision approach to human activity recognition using photoplethysmograph sensor data
(2018)
Brophy, Eoin; Wang, Zhengwei; Dominguez Veiga, José Juan; Ward, Tomás E.
A machine vision approach to human activity recognition using photoplethysmograph sensor data
(2018)
Brophy, Eoin; Wang, Zhengwei; Dominguez Veiga, José Juan; Ward, Tomás E.
Abstract:
Human activity recognition (HAR) is an active area of research concerned with the classification of human motion. Cameras are the gold standard used in this area, but they are proven to have scalability and privacy issues. HAR studies have also been conducted with wearable devices consisting of inertial sensors. Perhaps the most common wearable, smart watches, comprising of inertial and optical sensors, allow for scalable, non-obtrusive studies. We are seeking to simplify this wearable approach further by determining if wrist-mounted optical sensing, usually used for heart rate determination, can also provide useful data for relevant activity recognition. If successful, this could eliminate the need for the inertial sensor, and so simplify the technological requirements in wearable HAR. We adopt a machine vision approach for activity recognition based on plots of the optical signals so as to produce classifications that are easily explainable and interpretable by non-technical users...
http://doras.dcu.ie/22433/
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A molecular approach to complex adaptive systems
(2007)
Decraene, James; Mitchell, George G.; McMullin, Barry
A molecular approach to complex adaptive systems
(2007)
Decraene, James; Mitchell, George G.; McMullin, Barry
Abstract:
Complex Adaptive Systems (CAS) are dynamical networks of interacting agents which as a whole determine the behavior, adaptivity and cognitive ability of the system. CAS are ubiquitous and occur in a variety of natural and artificial systems (e.g., cells, societies, stock markets). To study CAS, Holland proposed to employ an agent-based system in which Learning Classifier Systems (LCS) were used to determine the agents behavior and adaptivity. We argue that LCS are limited for the study of CAS: the rule-discovery mechanism is pre-specified and may limit the evolvability of CAS. Secondly, LCS distinguish a demarcation between messages and rules, however operations are reflexive in CAS, e.g., in a cell, an agent (a molecule) may both act as a message (substrate) and as a catalyst (rule). To address these issues, we proposed the Molecular Classifier Systems (MCS.b), a string-based Artificial Chemistry based on Holland’s broadcast language. In the MCS.b, no explicit fitness function or r...
http://doras.dcu.ie/4594/
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A Multi-Domain Analysis of Explanation-Based Recommendation using User-Generated Reviews
(2019)
Muhammad, Khalil; Lawlor, Aonghus; Smyth, Barry
A Multi-Domain Analysis of Explanation-Based Recommendation using User-Generated Reviews
(2019)
Muhammad, Khalil; Lawlor, Aonghus; Smyth, Barry
Abstract:
The Thirty-First International FLAIRS Conference (FLAIRS-31), Florida, United States of America, 21-23 May 2018
This paper extends recent work on the use of explanations in recommender systems. In particular, we show how explanations can be used to rank as well as justify recommendations, then we compare the results to more conventional recommendation approaches, in three large-scale application domains.
Science Foundation Ireland
http://hdl.handle.net/10197/10126
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A neural basis for the implementation of deep learning and artificial intelligence
(2017)
Smeaton, Alan F.
A neural basis for the implementation of deep learning and artificial intelligence
(2017)
Smeaton, Alan F.
Abstract:
One of the mathematical cornerstones of modern data analytics is machine learning whereby we automatically learn subtle patterns which may be hidden in training data, we associate those patterns with outcomes and we apply these patterns to new and unseen data and make predictions about as yet unseen outcomes. This form of data analytics allows us to bring value to the huge volumes of data that is collected from people, from the environment, from commerce, from online activities, from scientific experiments, from many other sources. The mathematical basis for this form of machine learning has led to tools like Support Vector Machines which have shown moderate effectiveness and good efficiency in their implementation. Recently, however, these have been usurped by the emergence of deep learning based on convolutional neural networks. In this presentation we will examine the basis for why such deep networks are remarkably successful and accurate, their similarity to ways in which the hu...
http://doras.dcu.ie/22109/
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A neural basis for the implementation of deep learning and artificial intelligence
(2018)
Smeaton, Alan F.
A neural basis for the implementation of deep learning and artificial intelligence
(2018)
Smeaton, Alan F.
Abstract:
One of the mathematical cornerstones of modern data ana- lytics is machine learning whereby we automatically learn subtle patterns which may be hidden in training data, we associate those patterns with outcomes and we apply these patterns to new and unseen data and make predictions about as yet unseen outcomes. This form of data analytics al- lows us to bring value to the huge volumes of data that is collected from people, from the environment, from commerce, from online activities, from scientific experiments, from many other sources. The mathematical basis for this form of machine learning has led to tools like Support Vector Machines which have shown moderate effectiveness and good efficiency in their implementation. Recently, however, these have been usurped by the emergence of deep learning based on convolutional neural networks. In this presentation we will examine the basis for why such deep net- works are remarkably successful and accurate, their similarity to ways in which ...
http://doras.dcu.ie/22929/
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A new multi-modal dataset for human affect analysis
(2014)
Wei, Haolin; Monaghan, David; O'Connor, Noel E.; Scanlon, Patricia
A new multi-modal dataset for human affect analysis
(2014)
Wei, Haolin; Monaghan, David; O'Connor, Noel E.; Scanlon, Patricia
Abstract:
In this paper we present a new multi-modal dataset of spontaneous three way human interactions. Participants were recorded in an unconstrained environment at various locations during a sequence of debates in a video conference, Skype style arrangement. An additional depth modality was introduced, which permitted the capture of 3D information in addition to the video and audio signals. The dataset consists of 16 participants and is subdivided into 6 unique sections. The dataset was manually annotated on a continuously scale across 5 different affective dimensions including arousal, valence, agreement, content and interest. The annotation was performed by three human annotators with the ensemble average calculated for use in the dataset. The corpus enables the analysis of human affect during conversations in a real life scenario. We first briefly reviewed the existing affect dataset and the methodologies related to affect dataset construction, then we detailed how our unique dataset w...
http://doras.dcu.ie/20141/
Displaying Results 1 - 25 of 308 on page 1 of 13
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