950 research outputs found
Relate that image: A tool for finding related cultural heritage images
Museums,galleries, art centers, etc. are increasingly seeing the benefits of digitalizing their art work collections –and acting on it. The more visible benefits usually have to do with advertising, involving the citizens, or creating interactive tools that get people interested in coming to museums or buying art. With the availability of these increasingly large collections, analysis of art images has gained attention from researchers.This master thesis proposes a tool to recommend paintingsthat are similar to a given image of an artwork. We define different similarity measures that include criteria existent in the metadata associated with the digitized pictures (e.g. style, genre, artist, etc.), but also image content similarity. The work is more closely related to existing approaches on automatic classification of paintings, but also shares techniques with other areas such as image clustering. Our goal is to offer a tool that can enable creative uses, support the work of gallery / museum curators, help create interesting and interactive educational content, or create clusters of images as training sets for further learning and analysis algorithms
Collaborative personalised dynamic faceted search
Information retrieval systems are facing challenges due to the overwhelming volume of available information online. It leads to the need of search features that have the capability to provide relevant information for searchers. Dynamic faceted search has been one of the potential tools to provide a list of multiple facets for searchers to filter their contents. However, being a dynamic system, some irrelevant or unimportant facets could be produced. To develop an effective dynamic faceted search, personalised facet selection is an important mechanism to create an appropriate personalised facet list. Most current systems have derived the searchers' interests from their own profiles. However, interests from the past may not be adequate to predict current interest due to human information-seeking behaviour. Incorporating current interests from other people's opinions to predict the interests of individual person is an alternative way to develop personalisation which is called Collaborative approach. This research aims to investigate the incorporation of a Collaborative approach to personalise facet selection. This study introduces the Artificial Neural Network (ANN)-based collaborative personalisation architecture framework and Relation-aware Collaborative AutoEncoder model (RCAE) with embedding methodology for modelling and predicting the interests in multiple facets. The study showed that incorporating collaborative approach into the proposed framework for facet selection is capable to enhance the performance of personalisation model in facet selection in comparison to the state-of-the-art techniques
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Prometheus: a generic e-commerce crawler for the study of business markets and other e-commerce problems
Dissertação de mestrado em Computer ScienceThe continuous social and economic development has led over time to an increase in consumption,
as well as greater demand from the consumer for better and cheaper products.
Hence, the selling price of a product assumes a fundamental role in the purchase decision
by the consumer. In this context, online stores must carefully analyse and define the best
price for each product, based on several factors such as production/acquisition cost, positioning
of the product (e.g. anchor product) and the competition companies strategy. The
work done by market analysts changed drastically over the last years.
As the number of Web sites increases exponentially, the number of E-commerce web
sites also prosperous. Web page classification becomes more important in fields like Web
mining and information retrieval. The traditional classifiers are usually hand-crafted and
non-adaptive, that makes them inappropriate to use in a broader context. We introduce an
ensemble of methods and the posterior study of its results to create a more generic and
modular crawler and scraper for detection and information extraction on E-commerce web
pages. The collected information may then be processed and used in the pricing decision.
This framework goes by the name Prometheus and has the goal of extracting knowledge
from E-commerce Web sites.
The process requires crawling an online store and gathering product pages. This implies
that given a web page the framework must be able to determine if it is a product page.
In order to achieve this we classify the pages in three categories: catalogue, product and
”spam”. The page classification stage was addressed based on the html text as well as on
the visual layout, featuring both traditional methods and Deep Learning approaches.
Once a set of product pages has been identified we proceed to the extraction of the pricing
information. This is not a trivial task due to the disparity of approaches to create a web
page. Furthermore, most product pages are dynamic in the sense that they are truly a page
for a family of related products. For instance, when visiting a shoe store, for a particular
model there are probably a number of sizes and colours available. Such a model may be
displayed in a single dynamic web page making it necessary for our framework to explore
all the relevant combinations. This process is called scraping and is the last stage of the
Prometheus framework.O contínuo desenvolvimento social e económico tem conduzido ao longo do tempo a um
aumento do consumo, assim como a uma maior exigência do consumidor por produtos
melhores e mais baratos. Naturalmente, o preço de venda de um produto assume um papel
fundamental na decisão de compra por parte de um consumidor. Nesse sentido, as lojas
online precisam de analisar e definir qual o melhor preço para cada produto, tendo como
base diversos fatores, tais como o custo de produção/venda, posicionamento do produto
(e.g. produto âncora) e as próprias estratégias das empresas concorrentes. O trabalho dos
analistas de mercado mudou drasticamente nos últimos anos.
O crescimento de sites na Web tem sido exponencial, o número de sites E-commerce
também tem prosperado. A classificação de páginas da Web torna-se cada vez mais importante,
especialmente em campos como mineração de dados na Web e coleta/extração
de informações. Os classificadores tradicionais são geralmente feitos manualmente e não
adaptativos, o que os torna inadequados num contexto mais amplo. Nós introduzimos
um conjunto de métodos e o estudo posterior dos seus resultados para criar um crawler
e scraper mais genéricos e modulares para extração de conhecimento em páginas de Ecommerce.
A informação recolhida pode então ser processada e utilizada na tomada de
decisão sobre o preço de venda. Esta Framework chama-se Prometheus e tem como intuito
extrair conhecimento de Web sites de E-commerce.
Este processo necessita realizar a navegação sobre lojas online e armazenar páginas de
produto. Isto implica que dado uma página web a framework seja capaz de determinar
se é uma página de produto. Para atingir este objetivo nós classificamos as páginas em
três categorias: catálogo, produto e spam. A classificação das páginas foi realizada tendo
em conta o html e o aspeto visual das páginas, utilizando tanto métodos tradicionais como
Deep Learning.
Depois de identificar um conjunto de páginas de produto procedemos à extração de
informação sobre o preço. Este processo não é trivial devido à quantidade de abordagens
possíveis para criar uma página web. A maioria dos produtos são dinâmicos no sentido
em que um produto é na realidade uma família de produtos relacionados. Por exemplo,
quando visitamos uma loja online de sapatos, para um modelo em especifico existe
a provavelmente um conjunto de tamanhos e cores disponíveis. Esse modelo pode ser
apresentado numa única página dinâmica fazendo com que seja necessário para a nossa
Framework explorar estas combinações relevantes. Este processo é chamado de scraping e
é o último passo da Framework Prometheus
Deep Learning Techniques for Music Generation -- A Survey
This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P.
Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music
Generation, Computational Synthesis and Creative Systems, Springer, 201
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