"Monsters on the Brain: An Evolutionary Epistemology of Horror"

The article discusses the evolutionary development of horror and fear in animals and humans, including in regard to cognition and physiological aspects of the brain. An overview of the social aspects of emotions, including the role that emotions play in interpersonal relations and the role that empathy plays in humans' ethics, is provided. An overview of the psychological aspects of monsters, including humans' simultaneous repulsion and interest in horror films that depict monsters, is also provided

A FUNCTIONAL ANALYSIS OF THE ACQUISITION OF LANGUAGE AS BEHAVIOR

Uma análise funcional da linguagem deve levar em consideração a diferença entre as interações entre indivíduos e os produtos ou vestígios dessa interação. Embora as características morfológicas da linguagem como comportamento convencional sejam importantes, elas não são suficientes para distinguir o comportamento lingüístico do comportamento não lingüístico. Analisamos vários aspectos envolvidos na análise funcional da linguagem como comportamento: a) a aquisição de sistemas de reação convencionais; b) o despreendimento funcional de respostas; c) o comportamento lingüístico como interações contingenciais substitutivas; d) a identificação de estágios funcionais no desenvolvimento do comportamento convencional como comportamento lingüístico; e e) a análise da linguagem como processo interativo. Palavras-chave: linguagem, comportamento convencional, despreendimento funcional, contingênciasubstitutiva, interações diádicasA functional analysis of language must take into account the difference between interactions among individuals and the products or vestiges of these interactions. Although morphological features of language as conventional behavior are important, they are not sufficient to distinguish between linguistic and non-linguistic behavior. This paper examines several aspects involved in the functional analysis of language as behaviour: a) the acquisition of conventional reactional systems, b) the functional detachment of  responses, c) linguistic behavior as substitutional contingency  interactions, d) the identification of functional stages in the development of conventional behaviour as language behavior, and, e) the analysis of language as an interactive process. Key words: language, conventional behavior, functional detachment, substitutional contingency, dyadicinteraction

A Systematic Literature Review of Hausa Natural Language Processing

The processing of natural languages is an area of computer science that has gained growing attention recently. NLP helps computers recognize, in other words, the ways in which people use their language. NLP research, however, has been performed predominantly on languages with abundant quantities of annotated data, such as English, French, German and Arabic. While the Hausa Language is Africa's second most commonly used language, only a few studies have so far focused on Hausa Natural Language Processing (HNLP). In this research paper, using a keyword index and article title search, we present a systematic analysis of the current literature applicable to HNLP in the Google Scholar database from 2015 to June 2020. A very few research papers on HNLP research, especially in areas such as part-of-speech tagging (POS), Name Entity Recognition (NER), Words Embedding, Speech Recognition and Machine Translation, have just recently been released. This is due to the fact that for training intelligent models, NLP depends on a huge amount of human-annotated data. HNLP is now attracting researchers' attention after extensive research on NLP in English and other languages has been performed. The key objectives of this paper are to promote research, to define likely areas for future studies in the HNLP, and to assist in the creation of further examinations by researchers for relevant studies

Incremental construction of LSTM recurrent neural network

Long Short--Term Memory (LSTM) is a recurrent neural network that uses structures called memory blocks to allow the net remember significant events distant in the past input sequence in order to solve long time lag tasks, where other RNN approaches fail. Throughout this work we have performed experiments using LSTM networks extended with growing abilities, which we call GLSTM. Four methods of training growing LSTM has been compared. These methods include cascade and fully connected hidden layers as well as two different levels of freezing previous weights in the cascade case. GLSTM has been applied to a forecasting problem in a biomedical domain, where the input/output behavior of five controllers of the Central Nervous System control has to be modelled. We have compared growing LSTM results against other neural networks approaches, and our work applying conventional LSTM to the task at hand.Postprint (published version

Automatic Object Detection and Categorisation in Deep Astronomical Imaging Surveys Using Unsupervised Machine Learning

I present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy the technique uses no pre-selection or pre-filtering of target galaxy type to identify galaxies that are similar. I demonstrate the technique on the Hubble Space Telescope (HST) Frontier Fields. By training the algorithm using galaxies from one field (Abell 2744) and applying the result to another (MACS0416.1-2403), I show how the algorithm can cleanly separate early and late type galaxies without any form of pre-directed training for what an ‘early’ or ‘late’ type galaxy is. I present the results of testing the technique for generalisation and to identify its optimal configuration. I then apply the technique to the HST Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) fields, creating a catalogue of 60000 labelled galaxies, grouped by their similarity. I show how the automatically identified groups contain galaxies with similar morphological (and photometric) type. I compare the catalogue to human-classifications from the Galaxy Zoo: CANDELS project. Although there is not a direct mapping, I demonstrate a good level of concordance between them. I publicly release the catalogue and a corresponding visual catalogue and galaxy similarity search facility at www.galaxyml.uk. I show how the technique can be used to identify rarer objects and present lensed galaxy candidates from the CANDELS imaging. Finally, I consider how the technique can be improved and applied to future surveys to identify transient objects

An Investigation of Spectral Subband Centroids for Speaker Authentication

Most conventional features used in speaker authentication are based on estimation of spectral envelopes in one way or another, in the form of cepstrums, e.g., Mel-scale Filterbank Cepstrum Coefficients (MFCCs), Linear-scale Filterbank Cepstrum Coefficients (LFCCs) and Relative Spectral Perceptual Linear Prediction (RASTA-PLP). In this study, Spectral Subband Centroids (SSCs) are examined. These features are the centroid frequency in each subband. They have properties similar to the formant frequency but are limited to a given subband. Preliminary empirical findings, on a subset of the XM2VTS database, using Analysis of Variance and Linear Discriminant Analysis suggest that, firstly, a certain number of centroids (up to about 16) are necessary to cover enough information about the speaker's identity; and secondly, that SSCs could provide complementary information to the conventional MFCCs. Theoretical findings suggest that mean-subtracted SSCs are more robust to additive noise. Further empirical experiments carried out on the more realistic NIST2001 database using SSCs, MFCCs (respectively LFCCs) and their combinations by concatenation suggest that SSCs are indeed robust and complementary features to conventional MFCC (respectively LFCCs) features often used in speaker authentication