Individual and ensemble functional link neural networks for data classification

This study investigated the Functional Link Neural Network (FLNN) for solving data classification problems. FLNN based models were developed using evolutionary methods as well as ensemble methods. The outcomes of the experiments covering benchmark classification problems, positively demonstrated the efficacy of the proposed models for undertaking data classification problems

Scaffolding type-2 classifier for incremental learning under concept drifts

© 2016 Elsevier B.V. The proposal of a meta-cognitive learning machine that embodies the three pillars of human learning: what-to-learn, how-to-learn, and when-to-learn, has enriched the landscape of evolving systems. The majority of meta-cognitive learning machines in the literature have not, however, characterized a plug-and-play working principle, and thus require supplementary learning modules to be pre-or post-processed. In addition, they still rely on the type-1 neuron, which has problems of uncertainty. This paper proposes the Scaffolding Type-2 Classifier (ST2Class). ST2Class is a novel meta-cognitive scaffolding classifier that operates completely in local and incremental learning modes. It is built upon a multivariable interval type-2 Fuzzy Neural Network (FNN) which is driven by multivariate Gaussian function in the hidden layer and the non-linear wavelet polynomial in the output layer. The what-to-learn module is created by virtue of a novel active learning scenario termed the uncertainty measure; the how-to-learn module is based on the renowned Schema and Scaffolding theories; and the when-to-learn module uses a standard sample reserved strategy. The viability of ST2Class is numerically benchmarked against state-of-the-art classifiers in 12 data streams, and is statistically validated by thorough statistical tests, in which it achieves high accuracy while retaining low complexity

Multi-view Representation Learning for Unifying Languages, Knowledge and Vision

The growth of content on the web has raised various challenges, yet also provided numerous opportunities. Content exists in varied forms such as text appearing in different languages, entity-relationship graph represented as structured knowledge and as a visual embodiment like images/videos. They are often referred to as modalities. In many instances, the different amalgamation of modalities co-exists to complement each other or to provide consensus. Thus making the content either heterogeneous or homogeneous. Having an additional point of view for each instance in the content is beneficial for data-driven learning and intelligent content processing. However, despite having availability of such content. Most advancements made in data-driven learning (i.e., machine learning) is by solving tasks separately for the single modality. The similar endeavor was not shown for the challenges which required input either from all or subset of them. In this dissertation, we develop models and techniques that can leverage multiple views of heterogeneous or homogeneous content and build a shared representation for aiding several applications which require a combination of modalities mentioned above. In particular, we aim to address applications such as content-based search, categorization, and generation by providing several novel contributions. First, we develop models for heterogeneous content by jointly modeling diverse representations emerging from two views depicting text and image by learning their correlation. To be specific, modeling such correlation is helpful to retrieve cross-modal content. Second, we replace the heterogeneous content with homogeneous to learn a common space representation for content categorization across languages. Furthermore, we develop models that take input from both homogeneous and heterogeneous content to facilitate the construction of common space representation from more than two views. Specifically, representation is used to generate one view from another. Lastly, we describe a model that can handle missing views, and demonstrate that the model can generate missing views by utilizing external knowledge. We argue that techniques the models leverage internally provide many practical benefits and lot of immediate value applications. From the modeling perspective, our contributed model design in this thesis can be summarized under the phrase Multi-view Representation Learning( MVRL ). These models are variations and extensions of shallow statistical and deep neural networks approaches that can jointly optimize and exploit all views of the input content arising from different independent representations. We show that our models advance state of the art, but not limited to tasks such as cross-modal retrieval, cross-language text classification, image-caption generation in multiple languages and caption generation for images containing unseen visual object categories

Deep learning in food category recognition

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8

Reliable predictions for structured and corrupted data

The burgeoning use of machine learning models has spurred the use of diverse datasets that are collated, processed, and analyzed in various manners. To facilitate storage and analysis, data is often stored in a structured format. Structured data is characterized by an organizational structure or specific constraints on certain features. Such implicit or explicit constraints impose extra considerations for building predictive models based on such data, and current methodologies grapple with capturing the complex relationships inherent in the data. Factors like measurement errors, faulty equipment, or adversarial attacks can also result in the corruption of training data, making it challenging to achieve high performance. This thesis presents several approaches that can provide reliable predictions from data in a variety of complex formats while simultaneously ensuring a model’s reliability. First, a probabilistic quantile forecasting framework is introduced to tackle the challenges associated with forecasting large-scale time series that are subject to hierarchical or grouped constraints. This framework reconciles time series across various aggregation levels, taking into account any imposed constraints. It also dynamically amalgamates heterogeneous forecasting models specifically customized for different time series. Additionally, a multilevel clustering approach is proposed to mitigate computational costs associated with a vast number of forecasts. The next set of contributions lies in novel interpretable and robust Machine Learning approaches to ensure that trustworthy inferences are drawn from corrupted data. This includes counterfactual explanations and strategies to guard against outliers and adversarial examples, offering assurances of the monotonic property of neural networks, and devising robust estimations for datasets with missing values. Finally, a conformal prediction method with conditional coverage guarantees in the asymptotic limit is introduced to furnish adaptive and informative prediction intervals for heterogeneous data free of distributional assumptions. Collectively, these contributions bolster our ability to provide reliable predictions for data with complex structures or quality issues. Moreover, they hold vast potential for applications in various sectors, including healthcare and finance.Electrical and Computer Engineerin

A multilayered approach to the automatic analysis of the multifocal electroretinogram

The multifocal electroretinogram (mfERG) provides spatial and temporal information on the retina’s function in an objective manner, making it a valuable tool for monitoring a wide range of retinal abnormalities. Analysis of this clinical test can however be both difficult and subjective, particularly if recordings are contaminated with noise, for example muscle movement or blinking. This can sometimes result in inconsistencies in the interpretation process. An automated and objective method for analysing the mfERG would be beneficial, for example in multi-centre clinical trials when large volumes of data require quick and consistent interpretation. The aim of this thesis was therefore to develop a system capable of standardising mfERG analysis. A series of methods aimed at achieving this are presented. These include a technique for grading the quality of a recording, both during and after a test, and several approaches for stating if a waveform contains a physiological response or no significant retinal function. Different techniques are also utilised to report if a response is within normal latency and amplitude values. The integrity of a recording was assessed by viewing the raw, uncorrelated data in the frequency domain; clear differences between acceptable and unacceptable recordings were revealed. A scale ranging from excellent to unreportable was defined for the recording quality, first in terms of noise resulting from blinking and loss of fixation, and secondly, for muscle noise. 50 mfERG tests of varying recording quality were graded using this method with particular emphasis on the distinction between a test which should or should not be reported. Three experts also assessed the mfERG recordings independently; the grading provided by the experts was compared with that of the system. Three approaches were investigated to classify a mfERG waveform as ‘response’ or ‘no response’ (i.e. whether or not it contained a physiological response): artificial neural networks (ANN); analysis of the frequency domain profile; and the signal to noise ratio. These techniques were then combined using an ANN to provide a final classification for ‘response’ or ‘no response’. Two methods were studied to differentiate responses which were delayed from those within normal timing limits: ANN; and spline fitting. Again the output of each was combined to provide a latency classification for the mfERG waveform. Finally spline fitting was utilised to classify responses as ‘decreased in amplitude’ or ‘not decreased’. 1000 mfERG waveforms were subsequently analysed by an expert; these represented a wide variety of retinal function and quality. Classifications stated by the system were compared with those of the expert to assess its performance. An agreement of 94% was achieved between the experts and the system when making the distinction between tests which should or should not be reported. The final system classified 95% of the 1000 mfERG waveforms correctly as ‘response’ or ‘no response’. Of those said to represent an area of functioning retina it concurred with the expert for 93% of the responses when categorising them as normal or abnormal in terms of their P1 amplitude and latency. The majority of misclassifications were made when analysing waveforms with a P1 amplitude or latency close to the boundary between normal and abnormal. It was evident that the multilayered system has the potential to provide an objective and automated assessment of the mfERG test; this would not replace the expert but can provide an initial analysis for the expert to review

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

The use of computational intelligence for security in named data networking

Information-Centric Networking (ICN) has recently been considered as a promising paradigm for the next-generation Internet, shifting from the sender-driven end-to-end communication paradigma to a receiver-driven content retrieval paradigm. In ICN, content -rather than hosts, like in IP-based design- plays the central role in the communications. This change from host-centric to content-centric has several significant advantages such as network load reduction, low dissemination latency, scalability, etc. One of the main design requirements for the ICN architectures -since the beginning of their design- has been strong security. Named Data Networking (NDN) (also referred to as Content-Centric Networking (CCN) or Data-Centric Networking (DCN)) is one of these architectures that are the focus of an ongoing research effort that aims to become the way Internet will operate in the future. Existing research into security of NDN is at an early stage and many designs are still incomplete. To make NDN a fully working system at Internet scale, there are still many missing pieces to be filled in. In this dissertation, we study the four most important security issues in NDN in order to defense against new forms of -potentially unknown- attacks, ensure privacy, achieve high availability, and block malicious network traffics belonging to attackers or at least limit their effectiveness, i.e., anomaly detection, DoS/DDoS attacks, congestion control, and cache pollution attacks. In order to protect NDN infrastructure, we need flexible, adaptable and robust defense systems which can make intelligent -and real-time- decisions to enable network entities to behave in an adaptive and intelligent manner. In this context, the characteristics of Computational Intelligence (CI) methods such as adaption, fault tolerance, high computational speed and error resilient against noisy information, make them suitable to be applied to the problem of NDN security, which can highlight promising new research directions. Hence, we suggest new hybrid CI-based methods to make NDN a more reliable and viable architecture for the future Internet.Information-Centric Networking (ICN) ha sido recientemente considerado como un paradigma prometedor parala nueva generación de Internet, pasando del paradigma de la comunicación de extremo a extremo impulsada por el emisora un paradigma de obtención de contenidos impulsada por el receptor. En ICN, el contenido (más que los nodos, como sucede en redes IPactuales) juega el papel central en las comunicaciones. Este cambio de "host-centric" a "content-centric" tiene varias ventajas importantes como la reducción de la carga de red, la baja latencia, escalabilidad, etc. Uno de los principales requisitos de diseño para las arquitecturas ICN (ya desde el principiode su diseño) ha sido una fuerte seguridad. Named Data Networking (NDN) (también conocida como Content-Centric Networking (CCN) o Data-Centric Networking (DCN)) es una de estas arquitecturas que son objetode investigación y que tiene como objetivo convertirse en la forma en que Internet funcionará en el futuro. Laseguridad de NDN está aún en una etapa inicial. Para hacer NDN un sistema totalmente funcional a escala de Internet, todavía hay muchas piezas que faltan por diseñar. Enesta tesis, estudiamos los cuatro problemas de seguridad más importantes de NDN, para defendersecontra nuevas formas de ataques (incluyendo los potencialmente desconocidos), asegurar la privacidad, lograr una alta disponibilidad, y bloquear los tráficos de red maliciosos o al menos limitar su eficacia. Estos cuatro problemas son: detección de anomalías, ataques DoS / DDoS, control de congestión y ataques de contaminación caché. Para solventar tales problemas necesitamos sistemas de defensa flexibles, adaptables y robustos que puedantomar decisiones inteligentes en tiempo real para permitir a las entidades de red que se comporten de manera rápida e inteligente. Es por ello que utilizamos Inteligencia Computacional (IC), ya que sus características (la adaptación, la tolerancia a fallos, alta velocidad de cálculo y funcionamiento adecuado con información con altos niveles de ruido), la hace adecuada para ser aplicada al problema de la seguridad ND

2nd International Conference on Numerical and Symbolic Computation

The Organizing Committee of SYMCOMP2015 – 2nd International Conference on Numerical and Symbolic Computation: Developments and Applications welcomes all the participants and acknowledge the contribution of the authors to the success of this event. This Second International Conference on Numerical and Symbolic Computation, is promoted by APMTAC - Associação Portuguesa de Mecânica Teórica, Aplicada e Computacional and it was organized in the context of IDMEC/IST - Instituto de Engenharia Mecânica. With this ECCOMAS Thematic Conference it is intended to bring together academic and scientific communities that are involved with Numerical and Symbolic Computation in the most various scientific area