A Novel Meta-Cognitive Extreme Learning Machine to Learning from Data Streams

© 2015 IEEE. Extreme Learning Machine (ELM) is an answer to an increasing demand for a low-cost learning algorithm to handle big data applications. Nevertheless, existing ELMs leave four uncharted problems: complexity, uncertainty, concept drifts, curse of dimensionality. To correct these issues, a novel incremental meta-cognitive ELM, namely Evolving Type-2 Extreme Learning Machine (eT2ELM), is proposed. Et2Elm is built upon the three pillars of meta-cognitive learning, namely what-To-learn, how-To-learn, when-To-learn, where the notion of ELM is implemented in the how-To-learn component. On the other hand, eT2ELM is driven by a generalized interval type-2 Fuzzy Neural Network (FNN) as the cognitive constituent, where the interval type-2 multivariate Gaussian function is used in the hidden layer, whereas the nonlinear Chebyshev function is embedded in the output layer. The efficacy of eT2ELM is proven with four data streams possessing various concept drifts, comparisons with prominent classifiers, and statistical tests, where eT2ELM demonstrates the most encouraging learning performances in terms of accuracy and complexity

Digital Obesity: Legacy of the COVID-19 pandemic in Brazil

The social isolation measures generated by the pandemic COVID-19 boosted the use of technologies and, consequently, the process of digital transformation in society. There is an intense and unrestrained consumption of technological tools, which can lead to the exaggerated and indiscriminate use of technology, which we call, in this reflection, digital obesity. This process of including new technologies in the routines of individuals and organizations, if not well conducted and carried out in a critical and conscious manner, can harm health, happiness, well-being. Thus, the objective of this study is to map how people are feeling in the face of the digital dependence generated during the COVID-19 pandemic. This study reveals behaviors and expands the understanding of the relationship and complementarity that technology and humanity have. The phenomenon is investigated through a survey with 323 respondents, carried out in May/2020. The results suggest a more intensive use of technology, without necessarily changing your comfort regarding use; however, it shows an increase in tiredness and anxiety with the use of technology. The combination of taste and intensity for the use of technology, together with indifference to the reduction in the use of technology, shows footprints of a digital obesity behavior. In this context, being disconnected seems to have ceased to be a reality to become an object of desire. Las medidas de aislamiento social generadas por la pandemia COVID-19 impulsaron el uso de tecnologías y, en consecuencia, el proceso de transformación digital en la sociedad. Existe un consumo intenso y desenfrenado de herramientas tecnológicas, que puede llevar al uso exagerado e indiscriminado de la tecnología, lo que llamamos, en esta reflexión, obesidad digital. Este proceso de inclusión de nuevas tecnologías en las rutinas de las personas y organizaciones, si no se realiza bien y se lleva a cabo de manera crítica y consciente, puede dañar la salud, la felicidad y el bienestar. Así, el objetivo de este estudio es mapear cómo se sienten las personas ante la dependencia digital generada durante la pandemia COVID-19. Este estudio revela comportamientos y amplía la comprensión de la relación y complementariedad que tienen la tecnología y la humanidad. El fenómeno se investiga a través de una survey a 323 encuestados, realizada en mayo / 2020. Los resultados sugieren un uso más intensivo de la tecnología, sin necesariamente cambiar su comodidad de uso; sin embargo, muestra un aumento del cansancio y la ansiedad con el uso de la tecnología. La combinación de gusto e intensidad por el uso de la tecnología, junto con la indiferencia ante la reducción en el uso de la tecnología, muestra huellas de un comportamiento de obesidad digital. En este contexto, estar desconectado parece haber dejado de ser una realidad para convertirse en un objeto de deseo. As medidas de isolamento social gerados pela pandêmica COVID-19 impulsionou o uso de tecnologias e, consequentemente, o processo de transformação digital da sociedade. Existe um consumo intenso e desenfreado de ferramentas tecnológicas, o que pode levar ao uso exagerado e indiscriminado de tecnologia, ao que chamamos, nesta reflexão, de obesidade digital. Esse processo de inclusão de novas tecnologias nas rotinas de indivíduos e organizações, se não bem conduzido e realizado de maneira crítica e consciente, pode prejudicar a saúde, a felicidade, o bem-estar. Assim, o objetivo deste estudo é identificar como as pessoas estão se sentindo perante a dependência digital gerada durante a pandemia da COVID-19. Este estudo revela comportamentos e amplia o entendimento da relação e da complementariedade que tecnologia e humanidade possuem. O fenômeno é investigado por meio de uma survey com 323 respondentes, realizada em maio/2020. Os resultados sugerem o uso mais intenso da tecnologia, sem necessariamente mudar seu conforto quanto ao uso; contudo, evidencia um aumento do cansaço e da ansiedade com o uso da tecnologia. A combinação do gosto e a intensidade pelo uso da tecnologia, juntamente com a indiferença quanto à redução do uso de tecnologia, evidencia pegadas de um comportamento de obesidade digital. Nesse contexto, estar desconectado parece ter deixado de ser uma realidade para se tornar um objeto de desejo

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

Feature Selection and Classifier Development for Radio Frequency Device Identification

The proliferation of simple and low-cost devices, such as IEEE 802.15.4 ZigBee and Z-Wave, in Critical Infrastructure (CI) increases security concerns. Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting facilitates biometric-like identification of electronic devices emissions from variances in device hardware. Developing reliable classifier models using RF-DNA fingerprints is thus important for device discrimination to enable reliable Device Classification (a one-to-many looks most like assessment) and Device ID Verification (a one-to-one looks how much like assessment). AFITs prior RF-DNA work focused on Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) and Generalized Relevance Learning Vector Quantized Improved (GRLVQI) classifiers. This work 1) introduces a new GRLVQI-Distance (GRLVQI-D) classifier that extends prior GRLVQI work by supporting alternative distance measures, 2) formalizes a framework for selecting competing distance measures for GRLVQI-D, 3) introducing response surface methods for optimizing GRLVQI and GRLVQI-D algorithm settings, 4) develops an MDA-based Loadings Fusion (MLF) Dimensional Reduction Analysis (DRA) method for improved classifier-based feature selection, 5) introduces the F-test as a DRA method for RF-DNA fingerprints, 6) provides a phenomenological understanding of test statistics and p-values, with KS-test and F-test statistic values being superior to p-values for DRA, and 7) introduces quantitative dimensionality assessment methods for DRA subset selection

A teachable semi-automatic web information extraction system based on evolved regular expression patterns

This thesis explores Web Information Extraction (WIE) and how it has been used in decision making and to support businesses in their daily operations. The research focuses on a WIE system based on Genetic Programming (GP) with an extensible model to enhance the automatic extractor. This uses a human as a teacher to identify and extract relevant information from the semi-structured HTML webpages. Regular expressions, which have been chosen as the pattern matching tool, are automatically generated based on the training data to provide an improved grammar and lexicon. This particularly benefits the GP system which may need to extend its lexicon in the presence of new tokens in the web pages. These tokens allow the GP method to produce new extraction patterns for new requirements