Seven properties of self-organization in the human brain

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward

EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier

Emotion recognition by artificial intelligence (AI) is a challenging task. A wide variety of research has been done, which demonstrated the utility of audio, imagery, and electroencephalography (EEG) data for automatic emotion recognition. This paper presents a new automated emotion recognition framework, which utilizes electroencephalography (EEG) signals. The proposed method is lightweight, and it consists of four major phases, which include: a reprocessing phase, a feature extraction phase, a feature dimension reduction phase, and a classification phase. A discrete wavelet transforms (DWT) based noise reduction method, which is hereby named multi scale principal component analysis (MSPCA), is utilized during the pre-processing phase, where a Symlets-4 filter is utilized for noise reduction. A tunable Q wavelet transform (TQWT) is utilized as feature extractor. Six different statistical methods are used for dimension reduction. In the classification step, rotation forest ensemble (RFE) classifier is utilized with different classification algorithms such as k-Nearest Neighbor (k-NN), support vector machine (SVM), artificial neural network (ANN), random forest (RF), and four different types of the decision tree (DT) algorithms. The proposed framework achieves over 93 % classification accuracy with RFE + SVM. The results clearly show that the proposed TQWT and RFE based emotion recognition framework is an effective approach for emotion recognition using EEG signals.</p

A Review on Tomato Leaf Disease Detection using Deep Learning Approaches

Agriculture is one of the major sectors that influence the India economy due to the huge population and ever-growing food demand. Identification of diseases that affect the low yield in food crops plays a major role to improve the yield of a crop. India holds the world's second-largest share of tomato production. Unfortunately, tomato plants are vulnerable to various diseases due to factors such as climate change, heavy rainfall, soil conditions, pesticides, and animals. A significant number of studies have examined the potential of deep learning techniques to combat the leaf disease in tomatoes in the last decade. However, despite the range of applications, several gaps within tomato leaf disease detection are yet to be addressed to support the tomato leaf disease diagnosis. Thus, there is a need to create an information base of existing approaches and identify the challenges and opportunities to help advance the development of tools that address the needs of tomato farmers. The review is focussed on providing a detailed assessment and considerations for developing deep learning-based Convolutional Neural Networks (CNNs) architectures like Dense Net, ResNet, VGG Net, Google Net, Alex Net, and LeNet that are applied to detect the disease in tomato leaves to identify 10 classes of diseases affecting tomato plant leaves, with distinct trained disease datasets. The performance of architecture studies using the data from plantvillage dataset, which includes healthy and diseased classes, with the assistance of several different architectural designs. This paper helps to address the existing research gaps by guiding further development and application of tools to support tomato leaves disease diagnosis and provide disease management support to farmers in improving the crop

Artificial neural networks for problems in computational cognition

Computationally modelling human level cognitive abilities is one of the principal goals of artificial intelligence research, one that draws together work from the human neurosciences, psychology, cognitive science, computer science, and mathematics. In the past 30 years, work towards this goal has been substantially accelerated by the development of neural network approaches, at least in part due to advances in algorithms that can train these networks efficiently [Rumelhart et al., 1986b] and computer hardware that is optimised for matrix computations [Krizhevsky et al., 2012]. Parallel to this body of work, research in social robotics has developed to the extent that embodied and socially intelligent artificial agents are becoming parts of our everyday lives. Where robots were traditionally placed as tools to be used to improve the efficiency of a number of industrial tasks, now they are increasingly expected to emulate humans in complex, dynamic, and unpredictable social environments. In such cases, endowing these robotic platforms with (approaching) human–like cognitive capabilities will significantly improve the efficacy of these systems, and likely see their uptake quicken as they come to be seen as safe, effective, and flexible partners in socially oriented situations such as physical healthcare, education, mental well–being, and commerce. Taken together, it would seem that neural network approaches are well placed to allow us to bestow these agents with the kinds of cognitive abilities that they require to meet this goal. However, the nascent nature of the interaction of these two fields and the risk that comes along with integrating social robots too quickly into high risk social areas, means that there is significant work still to be done before we can convince ourselves that neural networks are the right approach to this problem. In this thesis I contribute theoretical and empirical work that lends weight to the argument that neural network approaches are well suited to modelling human cognition for use in social robots. In Chapter 1 I provide a general introduction to human cognition and neural networks and motivate the use of these approaches to problems in social robotics and human–robot interaction. This chapter is written in such a way that readers with no technical background can get a good understanding of the concepts that are at the center of the thesis’ aims. In Chapter 2, I provide a more in–depth and technical overview of the mathematical concepts that are at the heart of modern neural networks, specifically detailing the logic behind the deep learning approaches that are used in the empirical chapters of the thesis. While a full understanding of this chapter requires a stronger mathematical background than the previous chapter, the concepts are explained in such a way that a non–technical reader should come out of it with a solid high level understanding of these ideas. Chapters Chapter 3 through Chapter 5 contain the empirical work that was carried out in order to attempt to answer the above questions. Specifically, Chapter 3 explores the viability of using deep learning as an approach to modelling human social–cognitive abilities by looking at the problems of subjective psychological stress and self–disclosure. I test a number of “off-the-shelf” deep learning architectures on a novel dataset and find that in all cases these models are able to score significantly above average on the task of classifying audio segments in relation to how much the person performing the contained utterance believed themselves to be stressed and performing an act of self-disclosure. In Chapter 4, I develop the work on subjective-self disclosure modelling in human–robot social interaction by collecting a much larger multi modal dataset that contains video recorded interactions between participants and a Pepper robot. I provide a novel multi-modal deep learning attention architecture, and a custom loss function, and compare the performance of our model to a number of non-neural network approach baselines. I find that all versions of our model significantly outperform the baseline approaches, and that our novel loss improves on performance when compared to other standard loss functions for regression and classification problems for subjective self-disclosure modelling. In Chapter 5, I move away from deep learning and consider how neural network models based more concretely on contemporary computational neuroscience might be used to bestow artificial agents with human like cognitive abilities. Here, I detail a novel biological neural network algorithm that is able to solve cognitive planning problems by producing short path solutions on graphs. I show how a number of such planning problems can be framed as graph traversal problem and show how our algorithm is able to form solutions to these problems in a number of experimental settings. Finally, in Chapter 6 I provide a final overview of this empirical work and explain its impact both within and without academia before outlining a number of limitations of the approaches that were used and discuss some potentially fruitful avenues for future research in these areas

Development of Cognitive Capabilities in Humanoid Robots

Merged with duplicate record 10026.1/645 on 03.04.2017 by CS (TIS)Building intelligent systems with human level of competence is the ultimate grand challenge for science and technology in general, and especially for the computational intelligence community. Recent theories in autonomous cognitive systems have focused on the close integration (grounding) of communication with perception, categorisation and action. Cognitive systems are essential for integrated multi-platform systems that are capable of sensing and communicating. This thesis presents a cognitive system for a humanoid robot that integrates abilities such as object detection and recognition, which are merged with natural language understanding and refined motor controls. The work includes three studies; (1) the use of generic manipulation of objects using the NMFT algorithm, by successfully testing the extension of the NMFT to control robot behaviour; (2) a study of the development of a robotic simulator; (3) robotic simulation experiments showing that a humanoid robot is able to acquire complex behavioural, cognitive, and linguistic skills through individual and social learning. The robot is able to learn to handle and manipulate objects autonomously, to cooperate with human users, and to adapt its abilities to changes in internal and environmental conditions. The model and the experimental results reported in this thesis, emphasise the importance of embodied cognition, i.e. the humanoid robot's physical interaction between its body and the environment

Recognize basic emotional statesin speech by machine learning techniques using mel-frequency cepstral coefficient features

Speech Emotion Recognition (SER) has been widely used in many fields, such as smart home assistants commonly found in the market. Smart home assistants that could detect the user’s emotion would improve the communication between a user and the assistant enabling the assistant to offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable algorithm considering performance verses complexity for deployment in smart home devices. The four emotional speech sets were selected from the Berlin Emotional Database (EMO-DB) as experimental data, 26 MFCC features were extracted from each type of emotional speech to identify the emotions of happiness, anger, sadness and neutrality. Then, speaker-independent experiments for our Speech emotion Recognition (SER) were conducted by using the Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). Synthesizing the recognition accuracy and processing time, this work shows that the performance of SVM was the best among the four methods as a good candidate to be deployed for SER in smart home devices. SVM achieved an overall accuracy of 92.4% while offering low computational requirements when training and testing. We conclude that the MFCC features and the SVM classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion

Simulating sensorimotor systems with cortical topology

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references.Not availabl

Análisis comparativo sobre modelos de redes neuronales profundas para la detección de ciberbullying en redes sociales

Social media usage has been increased and it consists of both positive and negative effects. By considering the misusage of social media platforms by various cyberbullying methods like stalking, harassment there should be preventive methods to control these and to avoid mental stress. These extra words will expand the size of the vocabulary and influence the performance of the algorithm. Therefore, we come up with variant deep learning models like LSTM, BI-LSTM, RNN, BI-RNN, GRU, BI-GRU to detect cyberbullying in social media. These models are applied on Twitter, public comments data and performance were observed for these models and obtained improved accuracy of 90.4%.Introducción: el uso de las redes sociales se ha incrementado y tiene efectos tanto positivos como negativos. Al considerar el uso indebido de las plataformas de redes sociales a través de varios métodos de acoso cibernético, como el acecho y el acoso, debe haber métodos preventivos para controlarlos y evitar el estrés mental.Problema: estas palabras adicionales ampliarán el tamaño del vocabulario e influirán en el rendimiento del algoritmo.Objetivo: Detectar el ciberacoso en las redes sociales.Metodología: en este documento, presentamos variantes de modelos de aprendizaje profundo como la memoria a largo plazo (LSTM), memoria bidireccional a largo plazo (BI-LSTM), redes neuronales recurrentes (RNN), redes neuronales recurrentes bidireccionales (BI-RNN), unidad recurrente cerrada (GRU) y unidad recurrente cerrada bidireccional (BI-GRU) para detectar el ciberacoso en las redes sociales.Resultados: El mecanismo propuesto ha sido realizado, analizado e implementado sobre datos de Twitter con Accuracy, Precision, Recall y F-Score como medidas. Los modelos de aprendizaje profundo como LSTM, BI-LSTM, RNN, BI-RNN, GRU y BI-GRU se aplican en Twitter a los datos de comentarios públicos y se observó el rendimiento de estos modelos, obteniendo una precisión mejorada del 90,4 %.Conclusiones: Los resultados indican que el mecanismo propuesto es eficiente en comparación con los es-quemas del estado del arte.Originalidad: la aplicación de modelos de aprendizaje profundo para realizar un análisis comparativo de los datos de las redes sociales es el primer enfoque para detectar el ciberacoso.Restricciones: estos modelos se aplican solo en comentarios de datos textuales. El trabajo propio no se ha concentrado en datos multimedia como audio, video e imágenes