9 research outputs found

    Beyond Material Implication: An Empirical Study of Residuum in Knowledge Enhanced Neural Networks

    Get PDF
    openKnowledge Enchanced Neural Networks (KENN) is a neuro-symbolic architecture that exploits fuzzy logic for injecting prior knowledge, codified by propositional formulas, into a neural network. It works by adding a new layer at the end of a generic neural network that further elaborates the initial predictions accordingly to the knowledge. In the existing KENN, according to material implication rule, a conditional statement is represented as a conjunctive normal form formula. The following work extends this interpretation of the implication by using the fuzzy logic's Residuum semantic and shows how it has been integrated into the original KENN architecture, while keeping it reproducible. The Residuum integration allowed to evaluate KENN on MNIST Addition, a task that couldn't be approached by the original architecture, and the results obtained were comparable to others state of the art neuro-symbolic methods. The extended architecture has subsequently been evaluated also on visual relationships detection, showing that it could improve the performance of the original one.Knowledge Enchanced Neural Networks (KENN) is a neuro-symbolic architecture that exploits fuzzy logic for injecting prior knowledge, codified by propositional formulas, into a neural network. It works by adding a new layer at the end of a generic neural network that further elaborates the initial predictions accordingly to the knowledge. In the existing KENN, according to material implication rule, a conditional statement is represented as a conjunctive normal form formula. The following work extends this interpretation of the implication by using the fuzzy logic's Residuum semantic and shows how it has been integrated into the original KENN architecture, while keeping it reproducible. The Residuum integration allowed to evaluate KENN on MNIST Addition, a task that couldn't be approached by the original architecture, and the results obtained were comparable to others state of the art neuro-symbolic methods. The extended architecture has subsequently been evaluated also on visual relationships detection, showing that it could improve the performance of the original one

    SpiNNaker - A Spiking Neural Network Architecture

    Get PDF
    20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come

    SpiNNaker - A Spiking Neural Network Architecture

    Get PDF
    20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come

    Data Hiding and Its Applications

    Get PDF
    Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

    Mixed handwritten and printed digit recognition in Sudoku with convolutional deep belief network

    No full text
    In this paper, we propose a method to recognize Sudoku puzzles containing both handwritten and printed digits from images taken with a mobile camera. The grid and the digits are detected using various image processing techniques including Hough Transform and Contour Detection. A Convolutional Deep Belief Network is then used to extract high-level features from raw pixels. The features are finally classified using a Support Vector Machine. One of the scientific question addressed here is about the capability of the Deep Belief Network to learn extracting features on mixed inputs, printed and handwritten. The system is thoroughly tested on a set of 200 Sudoku images captured with smartphone cameras under varying conditions, e.g. distortion and shadows. The system shows promising results with 92% of the cells correctly classified. When cell detection errors are not taken into account, the cell recognition accuracy increases to 97.7%. Interestingly, the Deep Belief Network is able to handle the complex conditions often present on images taken with phone cameras and the complexity of mixed printed and handwritten digits

    Personality Identification from Social Media Using Deep Learning: A Review

    Get PDF
    Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed
    corecore