Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network

.In this work, a sentiment analysis method that is capable of accepting audio of any length, without being fixed a priori, is proposed. Mel spectrogram and Mel Frequency Cepstral Coefficients are used as audio description methods and a Fully Convolutional Neural Network architecture is proposed as a classifier. The results have been validated using three well known datasets: EMODB, RAVDESS and TESS. The results obtained were promising, outperforming the state-of–the-art methods. Also, thanks to the fact that the proposed method admits audios of any size, it allows a sentiment analysis to be made in near real time, which is very interesting for a wide range of fields such as call centers, medical consultations or financial brokers.S

Feature Space Augmentation: Improving Prediction Accuracy of Classical Problems in Cognitive Science and Computer Vison

The prediction accuracy in many classical problems across multiple domains has seen a rise since computational tools such as multi-layer neural nets and complex machine learning algorithms have become widely accessible to the research community. In this research, we take a step back and examine the feature space in two problems from very different domains. We show that novel augmentation to the feature space yields higher performance. Emotion Recognition in Adults from a Control Group: The objective is to quantify the emotional state of an individual at any time using data collected by wearable sensors. We define emotional state as a mixture of amusement, anger, disgust, fear, sadness, anxiety and neutral and their respective levels at any time. The generated model predicts an individual’s dominant state and generates an emotional spectrum, 1x7 vector indicating levels of each emotional state and anxiety. We present an iterative learning framework that alters the feature space uniquely to an individual’s emotion perception, and predicts the emotional state using the individual specific feature space. Hybrid Feature Space for Image Classification: The objective is to improve the accuracy of existing image recognition by leveraging text features from the images. As humans, we perceive objects using colors, dimensions, geometry and any textual information we can gather. Current image recognition algorithms rely exclusively on the first 3 and do not use the textual information. This study develops and tests an approach that trains a classifier on a hybrid text based feature space that has comparable accuracy to the state of the art CNN’s while being significantly inexpensive computationally. Moreover, when combined with CNN’S the approach yields a statistically significant boost in accuracy. Both models are validated using cross validation and holdout validation, and are evaluated against the state of the art

A survey of multiple classifier systems as hybrid systems

A current focus of intense research in pattern classification is the combination of several classifier systems, which can be built following either the same or different models and/or datasets building approaches. These systems perform information fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. This paper presents an up-to-date survey on multiple classifier system (MCS) from the point of view of Hybrid Intelligent Systems. The article discusses major issues, such as diversity and decision fusion methods, providing a vision of the spectrum of applications that are currently being developed

gLOP: A Cleaner Dirty Model for Multitask Learning

Multitask learning (MTL) was originally defined by Caruana (1997) as "an approach to inductive transfer that improves learning for one task by using the information contained in the training signals of other related tasks". In the linear model setting this is often realized as joint feature selection across tasks, where features (but not necessarily coefficient values) are shared across tasks. In later work related to MTL Jalali (2010) observed that sharing all features across all tasks is too restrictive in some cases, as commonly used composite absolute penalties (like the l(1,∞) norm) encourage not only common feature selection but also common parameter values between settings. Because of this, Jalali proposed an alternative "dirty model" that can leverage shared features even in the case where not all features are shared across settings. The dirty model decomposes the coefficient matrix Θ into a row-sparse matrix B and an elementwise sparse matrix S in order to better capture structural differences between tasks. Multitask learning problems arise in many contexts, and one of the most pertinent of these is healthcare applications in which we must use data from multiple patients to learn a common predictive model. Often it is impossible to gather enough data from any one patient to accurately train a full predictive model for that patient. Additionally, learning in this context is complicated by the presence of individual differences between patients as well as population-wide effects common to most patients, leading to the need for a dirty model. Two additional challenges for methods applied in the healthcare setting include the need for scalability so that the model can work with big data, and the need for interpretable models. While Jalali gives us a dirty model, this method does not scale as well as many other commonly used methods like the Lasso, and does not have a clean interpretation. This is particularly true in the healthcare domain, as this model does not allow us to interpret coefficients in relation to all settings. Because B coefficients in the dirty model paradigm are not required to be the same for all settings for a particular feature, departures from the global model may be captured in B or S leading to ambiguity in interpreting potential main effects. We propose a "cleaner" dirty model gLOP (global/LOcal Penalty) that is capable of representing global effects between settings as well as local setting-specific effects, much like the ANalysis Of VAriance (ANOVA) test in inferential statistics. However, the goal of the ANOVA is not to build an accurate predictive model, but to identify coefficients that are non-zero at a given level of statistical significance. By combining the dirty model's decomposed Θ matrix and the underlying concept behind the ANOVA, we get the best of both worlds: an interpretable predictive model that can accurately recover the underlying structure of a given problem. gLOP is structured as a coordinate minimization problem which decomposes Θ into a global vector of coefficients g and a matrix of local setting-specific coefficients L. At each step, g is updated using the standard Lasso paradigm applied to the composite global design matrix in which the design matrices from each setting are concatenated vertically. In contrast, L is updated at each step using the standard Lasso paradigm applied separately to each setting. Another significant advantage of our model gLOP in comparison to previous dirty models is the out-of-the-box use of standard Lasso implementations instead of less frequently implemented CAP family penalties such as the l(1,∞) norm. Additionally, gLOP has a significant advantage in lowered computational time demands as it takes larger steps towards the global optimum at each iteration. We present experimental results comparing both the runtime and structure recovered by gLOP to Jalali's dirty model

Correlation of distance and damage in a ballistic setting

Forensic Investigation is a discipline which relies on various fields in order to be able to reconstruct an incident. Forensic Ballistics focuses upon the mechanics of projectile launch, flight and the effects of the projectile when impacting a target as well as firearms and ammunition. One of the most common evidence types in firearms related events is Gun Shot Residue (GSR), where typical analysis methods involves chemical confirmatory tests. Therefore, the fields traditionally associated with forensic ballistics are chemistry and physics, however there are various other scientific fields which could potentially further knowledge in this area such as radiography and computational science. Arguably one of the most important considerations within Forensic Ballistics is the ability to accurately reconstruct an incident. Currently there is limited literature aimed at understanding GSR spread at distances above 15 metres, which is a limitation for the criminal justice system (chapter 1). This work aims to further this knowledge by gaining an understanding of GSR spread at various distances, both short and long range (chapter 4), whilst combining this with Gun Shot Wound (GSW) damage using radiography (chapter 3). The data obtained will then be used for computational modelling with the aim of predicting shooter distance (chapter 5)

Mixtures of Heterogeneous Experts

Computer Scienc

Logarithmic Opinion Pools for Conditional Random Fields

Institute for Communicating and Collaborative SystemsSince their recent introduction, conditional random fields (CRFs) have been successfully applied to a multitude of structured labelling tasks in many different domains. Examples include natural language processing (NLP), bioinformatics and computer vision. Within NLP itself we have seen many different application areas, like named entity recognition, shallow parsing, information extraction from research papers and language modelling. Most of this work has demonstrated the need, directly or indirectly, to employ some form of regularisation when applying CRFs in order to overcome the tendency for these models to overfit. To date a popular method for regularising CRFs has been to fit a Gaussian prior distribution over the model parameters. In this thesis we explore other methods of CRF regularisation, investigating their properties and comparing their effectiveness. We apply our ideas to sequence labelling problems in NLP, specifically part-of-speech tagging and named entity recognition. We start with an analysis of conventional approaches to CRF regularisation, and investigate possible extensions to such approaches. In particular, we consider choices of prior distribution other than the Gaussian, including the Laplacian and Hyperbolic; we look at the effect of regularising different features separately, to differing degrees, and explore how we may define an appropriate level of regularisation for each feature; we investigate the effect of allowing the mean of a prior distribution to take on non-zero values; and we look at the impact of relaxing the feature expectation constraints satisfied by a standard CRF, leading to a modified CRF model we call the inequality CRF. Our analysis leads to the general conclusion that although there is some capacity for improvement of conventional regularisation through modification and extension, this is quite limited. Conventional regularisation with a prior is in general hampered by the need to fit a hyperparameter or set of hyperparameters, which can be an expensive process. We then approach the CRF overfitting problem from a different perspective. Specifically, we introduce a form of CRF ensemble called a logarithmic opinion pool (LOP), where CRF distributions are combined under a weighted product. We show how a LOP has theoretical properties which provide a framework for designing new overfitting reduction schemes in terms of diverse models, and demonstrate how such diverse models may be constructed in a number of different ways. Specifically, we show that by constructing CRF models from manually crafted partitions of a feature set and combining them with equal weight under a LOP, we may obtain an ensemble that significantly outperforms a standard CRF trained on the entire feature set, and is competitive in performance to a standard CRF regularised with a Gaussian prior. The great advantage of LOP approach is that, unlike the Gaussian prior method, it does not require us to search a hyperparameter space. Having demonstrated the success of LOPs in the simple case, we then move on to consider more complex uses of the framework. In particular, we investigate whether it is possible to further improve the LOP ensemble by allowing parameters in different models to interact during training in such a way that diversity between the models is encouraged. Lastly, we show how the LOP approach may be used as a remedy for a problem that standard CRFs can sometimes suffer. In certain situations, negative effects may be introduced to a CRF by the inclusion of highly discriminative features. An example of this is provided by gazetteer features, which encode a word's presence in a gazetteer. We show how LOPs may be used to reduce these negative effects, and so provide some insight into how gazetteer features may be more effectively handled in CRFs, and log-linear models in general

An Investigation into the Performance of Ethnicity Verification Between Humans and Machine Learning Algorithms

There has been a significant increase in the interest for the task of classifying demographic profiles i.e. race and ethnicity. Ethnicity is a significant human characteristic and applying facial image data for the discrimination of ethnicity is integral to face-related biometric systems. Given the diversity in the application of ethnicity-specific information such as face recognition and iris recognition, and the availability of image datasets for more commonly available human populations, i.e. Caucasian, African-American, Asians, and South-Asian Indians. A gap has been identified for the development of a system which analyses the full-face and its individual feature-components (eyes, nose and mouth), for the Pakistani ethnic group. An efficient system is proposed for the verification of the Pakistani ethnicity, which incorporates a two-tier (computer vs human) approach. Firstly, hand-crafted features were used to ascertain the descriptive nature of a frontal-image and facial profile, for the Pakistani ethnicity. A total of 26 facial landmarks were selected (16 frontal and 10 for the profile) and by incorporating 2 models for redundant information removal, and a linear classifier for the binary task. The experimental results concluded that the facial profile image of a Pakistani face is distinct amongst other ethnicities. However, the methodology consisted of limitations for example, low performance accuracy, the laborious nature of manual data i.e. facial landmark, annotation, and the small facial image dataset. To make the system more accurate and robust, Deep Learning models are employed for ethnicity classification. Various state-of-the-art Deep models are trained on a range of facial image conditions, i.e. full face and partial-face images, plus standalone feature components such as the nose and mouth. Since ethnicity is pertinent to the research, a novel facial image database entitled Pakistani Face Database (PFDB), was created using a criterion-specific selection process, to ensure assurance in each of the assigned class-memberships, i.e. Pakistani and Non-Pakistani. Comparative analysis between 6 Deep Learning models was carried out on augmented image datasets, and the analysis demonstrates that Deep Learning yields better performance accuracy compared to low-level features. The human phase of the ethnicity classification framework tested the discrimination ability of novice Pakistani and Non-Pakistani participants, using a computerised ethnicity task. The results suggest that humans are better at discriminating between Pakistani and Non-Pakistani full face images, relative to individual face-feature components (eyes, nose, mouth), struggling the most with the nose, when making judgements of ethnicity. To understand the effects of display conditions on ethnicity discrimination accuracy, two conditions were tested; (i) Two-Alternative Forced Choice (2-AFC) and (ii) Single image procedure. The results concluded that participants perform significantly better in trials where the target (Pakistani) image is shown alongside a distractor (Non-Pakistani) image. To conclude the proposed framework, directions for future study are suggested to advance the current understanding of image based ethnicity verification.Acumé Forensi