Review of Neural Network Algorithms

The artificial neural network is the core tool of machine learning to realize intelligence. It has shown its advantages in the fields of sound, image, sound, picture, and so on. Since entering the 21st century, the progress of science and technology and people\u27s pursuit of artificial intelligence have introduced the research of artificial neural networks into an upsurge. Firstly, this paper introduces the application background and development process of the artificial neural network in order to clarify the research context of neural networks. Five branches and related applications of single-layer perceptron, linear neural network, BP neural network, Hopfield neural network, and depth neural network are analyzed in detail. The analysis shows that the development trend of the artificial neural network is developing towards a more general, flexible, and intelligent direction. Finally, the future development of the artificial neural network in training mode, learning mode, function expansion, and technology combination has prospected

A review of sentiment analysis research in Arabic language

Sentiment analysis is a task of natural language processing which has recently attracted increasing attention. However, sentiment analysis research has mainly been carried out for the English language. Although Arabic is ramping up as one of the most used languages on the Internet, only a few studies have focused on Arabic sentiment analysis so far. In this paper, we carry out an in-depth qualitative study of the most important research works in this context by presenting limits and strengths of existing approaches. In particular, we survey both approaches that leverage machine translation or transfer learning to adapt English resources to Arabic and approaches that stem directly from the Arabic language

Deep Learning para BigData

We live in a world where data is becoming increasingly valuable and increasingly abundant in volume. Every company produces data, be it from sales, sensors, and various other sources. Since the dawn of the smartphone, virtually every person in the world is connected to the internet and contributes to data generation. Social networks are big contributors to this Big Data boom. How do we extract insight from such a rich data environment? Is Deep Learning capable of circumventing Big Data’s challenges? This is what we intend to understand. To reach a conclusion, Social Network data is used as a case study for predicting sentiment changes in the Stock Market. The objective of this dissertation is to develop a computational study and analyse its performance. The outputs will contribute to understand Deep Learning’s usage with Big Data and how it acts in Sentiment analysis.Vivemos num mundo onde dados são cada vez mais valiosos e abundantes. Todas as empresas produzem dados, sejam eles provenientes de valores de vendas, parâmetros de sensores bem como de outras diversas fontes. Desde que os smartphones se tornaram pessoais, o mundo tornou-se mais conectado, já que virtualmente todas as pessoas passaram a ter a internet na ponta dos dedos. Esta explosão tecnológica foi acompanhada por uma explosão de dados. As redes sociais têm um grande contributo para a quantidade de dados produzida. Mas como se analisam estes dados? Será que Deep Learning poderá dar a volta aos desafios que Big Data traz inerentemente? É isso se pretende perceber. Para chegar a uma conclusão, foi utilizado um caso de estudo de redes sociais para previsão de alterações nas ações de mercados financeiros relacionadas com as opiniões dos utilizadores destas. O objetivo desta dissertação é o desenvolvimento de um estudo computacional e a análise da sua performance. Os resultados contribuirão para entender o uso de Deep Learning com Big Data, com especial foco em análise de sentimento. The objective of this dissertation is to develop a computational study and analyse its performance. The outputs will contribute to understand Deep Learning’s usage with Big Data and how it acts in Sentiment analysis

Distributed Representations for Compositional Semantics

The mathematical representation of semantics is a key issue for Natural Language Processing (NLP). A lot of research has been devoted to finding ways of representing the semantics of individual words in vector spaces. Distributional approaches --- meaning distributed representations that exploit co-occurrence statistics of large corpora --- have proved popular and successful across a number of tasks. However, natural language usually comes in structures beyond the word level, with meaning arising not only from the individual words but also the structure they are contained in at the phrasal or sentential level. Modelling the compositional process by which the meaning of an utterance arises from the meaning of its parts is an equally fundamental task of NLP. This dissertation explores methods for learning distributed semantic representations and models for composing these into representations for larger linguistic units. Our underlying hypothesis is that neural models are a suitable vehicle for learning semantically rich representations and that such representations in turn are suitable vehicles for solving important tasks in natural language processing. The contribution of this thesis is a thorough evaluation of our hypothesis, as part of which we introduce several new approaches to representation learning and compositional semantics, as well as multiple state-of-the-art models which apply distributed semantic representations to various tasks in NLP.Comment: DPhil Thesis, University of Oxford, Submitted and accepted in 201

Felt_space infrastructure: Hyper vigilant spatiality to valence the visceral dimension

Felt_space infrastructure: Hypervigilant spatiality to valence the visceral dimension. This thesis evolves perception as a hypothesis to reframe architectural praxis negotiated through agent-situation interaction. The research questions the geometric principles of architectural ordination to originate the ‘felt_space infrastructure’, a relational system of measurement concerned with the role of perception in mediating sensory space and the cognised environment. The methodological model for this research fuses perception and environmental stimuli, into a consistent generative process that penetrates the inner essence of space, to reveal the visceral parameter. These concepts are applied to develop a ‘coefficient of affordance’ typology, ‘hypervigilant’ tool set, and ‘cognitive_tope’ design methodology. Thus, by extending the architectural platform to consider perception as a design parameter, the thesis interprets the ‘inference schema’ as an instructional model to coordinate the acquisition of spatial reality through tensional and counter-tensional feedback dynamics. Three site-responsive case studies are used to advance the thesis. The first case study is descriptive and develops a typology of situated cognition to extend the ‘granularity’ of perceptual sensitisation (i.e. a fine-grained means of perceiving space). The second project is relational and questions how mapping can coordinate perceptual, cognitive and associative attention, as a ‘multi-webbed vector field’ comprised of attractors and deformations within a viewer-centred gravitational space. The third case study is causal, and demonstrates how a transactional-biased schema can generate, amplify and attenuate perceptual misalignment, thus triggering a visceral niche. The significance of the research is that it progresses generative perception as an additional variable for spatial practice, and promotes transactional methodologies to gain enhanced modes of spatial acuity to extend the repertoire of architectural practice

Classification algorithms for Big Data with applications in the urban security domain

A classification algorithm is a versatile tool, that can serve as a predictor for the future or as an analytical tool to understand the past. Several obstacles prevent classification from scaling to a large Volume, Velocity, Variety or Value. The aim of this thesis is to scale distributed classification algorithms beyond current limits, assess the state-of-practice of Big Data machine learning frameworks and validate the effectiveness of a data science process in improving urban safety. We found in massive datasets with a number of large-domain categorical features a difficult challenge for existing classification algorithms. We propose associative classification as a possible answer, and develop several novel techniques to distribute the training of an associative classifier among parallel workers and improve the final quality of the model. The experiments, run on a real large-scale dataset with more than 4 billion records, confirmed the quality of the approach. To assess the state-of-practice of Big Data machine learning frameworks and streamline the process of integration and fine-tuning of the building blocks, we developed a generic, self-tuning tool to extract knowledge from network traffic measurements. The result is a system that offers human-readable models of the data with minimal user intervention, validated by experiments on large collections of real-world passive network measurements. A good portion of this dissertation is dedicated to the study of a data science process to improve urban safety. First, we shed some light on the feasibility of a system to monitor social messages from a city for emergency relief. We then propose a methodology to mine temporal patterns in social issues, like crimes. Finally, we propose a system to integrate the findings of Data Science on the citizenry’s perception of safety and communicate its results to decision makers in a timely manner. We applied and tested the system in a real Smart City scenario, set in Turin, Italy

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform