1,952 research outputs found
Topological Data Analysis for Object Data
Statistical analysis on object data presents many challenges. Basic summaries
such as means and variances are difficult to compute. We apply ideas from
topology to study object data. We present a framework for using persistence
landscapes to vectorize object data and perform statistical analysis. We apply
to this pipeline to some biological images that were previously shown to be
challenging to study using shape theory. Surprisingly, the most persistent
features are shown to be "topological noise" and the statistical analysis
depends on the less persistent features which we refer to as the "geometric
signal". We also describe the first steps to a new approach to using topology
for object data analysis, which applies topology to distributions on object
spaces.Comment: 16 pages, 12 figure
Continuous Estimation of Emotions in Speech by Dynamic Cooperative Speaker Models
Automatic emotion recognition from speech has been recently focused on the prediction of time-continuous dimensions (e.g., arousal and valence) of spontaneous and realistic expressions of emotion, as found in real-life interactions. However, the automatic prediction of such emotions poses several challenges, such as the subjectivity found in the definition of a gold standard from a pool of raters and the issue of data scarcity in training models. In this work, we introduce a novel emotion recognition system, based on ensemble of single-speaker-regression-models (SSRMs). The estimation of emotion is provided by combining a subset of the initial pool of SSRMs selecting those that are most concordance among them. The proposed approach allows the addition or removal of speakers from the ensemble without the necessity to re-build the entire machine learning system. The simplicity of this aggregation strategy, coupled with the flexibility assured by the modular architecture, and the promising results obtained on the RECOLA database highlight the potential implications of the proposed method in a real-life scenario and in particular in WEB-based applications
A Machine Learning Approach for Generating a Recursive Object Model from a Natural Language Text
This research investigates the potential of machine learning algorithms as an alternative approach to rule-based systems for generating Recursive Object Model (ROM) diagrams. The existing rule-based approach suffers from limitations and challenges, and this study aims to explore the possibility of overcoming these limitations by leveraging machine learning techniques.
To achieve the research objectives, software was developed to gather labelled data for our supervised learning problem. A model comprised of Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models was created and trained using the labelled data. The proposed model takes a pair of words and a sentence as inputs and classifies the appropriate relations among the pairs. Subsequently, a comprehensive evaluation was conducted to assess the effectiveness of the proposed model.
The evaluation process involved a comparative analysis between the proposed model and a baseline model, an evaluation of the proposed model on unseen data, and an investigation into the capability of the design model in addressing the limitations of the rule-based system. The evaluation results demonstrate the superiority of the proposed model. Firstly, the proposed model achieved an exceptional accuracy of 97 percent in the training process, surpassing the baseline model's accuracy of approximately 61 percent. Secondly, the proposed model exhibited an accuracy of 96 percent on unseen data, thus showcasing its ability to generalize effectively to new instances. Lastly, when comparing the proposed intelligent system with the rule-based system, although the proposed methodology exhibited minor errors in generating ROM diagrams for certain scenarios, the findings underscore the potential of the proposed model in mitigating the limitations of the rule-based system
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Neural Diagrammatic Reasoning
Diagrams have been shown to be effective tools for humans to represent and reason about
complex concepts. They have been widely used to represent concepts in science teaching, to
communicate workflow in industries and to measure human fluid intelligence. Mechanised
reasoning systems typically encode diagrams into symbolic representations that can be
easily processed with rule-based expert systems. This relies on human experts to define the
framework of diagram-to-symbol mapping and the set of rules to reason with the symbols.
This means the reasoning systems cannot be easily adapted to other diagrams without
a new set of human-defined representation mapping and reasoning rules. Moreover such
systems are not able to cope with diagram inputs as raw and possibly noisy images. The
need for human input and the lack of robustness to noise significantly limit the applications
of mechanised diagrammatic reasoning systems.
A key research question then arises: can we develop human-like reasoning systems that
learn to reason robustly without predefined reasoning rules? To answer this question, I
propose Neural Diagrammatic Reasoning, a new family of diagrammatic reasoning
systems which does not have the drawbacks of mechanised reasoning systems. The new
systems are based on deep neural networks, a recently popular machine learning method
that achieved human-level performance on a range of perception tasks such as object
detection, speech recognition and natural language processing. The proposed systems are
able to learn both diagram to symbol mapping and implicit reasoning rules only from data,
with no prior human input about symbols and rules in the reasoning tasks. Specifically I
developed EulerNet, a novel neural network model that solves Euler diagram syllogism
tasks with 99.5% accuracy. Experiments show that EulerNet learns useful representations
of the diagrams and tasks, and is robust to noise and deformation in the input data. I
also developed MXGNet, a novel multiplex graph neural architecture that solves Raven
Progressive Matrices (RPM) tasks. MXGNet achieves state-of-the-art accuracies on two
popular RPM datasets. In addition, I developed Discrete-AIR, an unsupervised learning
architecture that learns semi-symbolic representations of diagrams without any labels.
Lastly I designed a novel inductive bias module that can be readily used in todayās deep
neural networks to improve their generalisation capability on relational reasoning tasks.EPSRC Studentship and Cambridge Trust Scholarshi
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