3,268 research outputs found
A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction
This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalized rule induction. The framework emphasizes the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization
Facial Expression Recognition from World Wild Web
Recognizing facial expression in a wild setting has remained a challenging
task in computer vision. The World Wide Web is a good source of facial images
which most of them are captured in uncontrolled conditions. In fact, the
Internet is a Word Wild Web of facial images with expressions. This paper
presents the results of a new study on collecting, annotating, and analyzing
wild facial expressions from the web. Three search engines were queried using
1250 emotion related keywords in six different languages and the retrieved
images were mapped by two annotators to six basic expressions and neutral. Deep
neural networks and noise modeling were used in three different training
scenarios to find how accurately facial expressions can be recognized when
trained on noisy images collected from the web using query terms (e.g. happy
face, laughing man, etc)? The results of our experiments show that deep neural
networks can recognize wild facial expressions with an accuracy of 82.12%
Annotation Graphs and Servers and Multi-Modal Resources: Infrastructure for Interdisciplinary Education, Research and Development
Annotation graphs and annotation servers offer infrastructure to support the
analysis of human language resources in the form of time-series data such as
text, audio and video. This paper outlines areas of common need among empirical
linguists and computational linguists. After reviewing examples of data and
tools used or under development for each of several areas, it proposes a common
framework for future tool development, data annotation and resource sharing
based upon annotation graphs and servers.Comment: 8 pages, 6 figure
A Multi-channel Application Framework for Customer Care Service Using Best-First Search Technique
It has become imperative to find a solution to the dissatisfaction in response by mobile
service providers when interacting with their customer care centres. Problems faced with
Human to Human Interaction (H2H) between customer care centres and their customers
include delayed response time, inconsistent solutions to questions or enquires and lack of
dedicated access channels for interaction with customer care centres in some cases.
This paper presents a framework and development techniques for a multi-channel
application providing Human to System (H2S) interaction for customer care centre of a
mobile telecommunication provider. The proposed solution is called Interactive Customer
Service Agent (ICSA). Based on single-authoring, it will provide three media of interaction
with the customer care centre of a mobile telecommunication operator: voice, phone and
web browsing. A mathematical search technique called Best-First Search to generate
accurate results in a search environmen
Composite Correlation Quantization for Efficient Multimodal Retrieval
Efficient similarity retrieval from large-scale multimodal database is
pervasive in modern search engines and social networks. To support queries
across content modalities, the system should enable cross-modal correlation and
computation-efficient indexing. While hashing methods have shown great
potential in achieving this goal, current attempts generally fail to learn
isomorphic hash codes in a seamless scheme, that is, they embed multiple
modalities in a continuous isomorphic space and separately threshold embeddings
into binary codes, which incurs substantial loss of retrieval accuracy. In this
paper, we approach seamless multimodal hashing by proposing a novel Composite
Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds
correlation-maximal mappings that transform different modalities into
isomorphic latent space, and learns composite quantizers that convert the
isomorphic latent features into compact binary codes. An optimization framework
is devised to preserve both intra-modal similarity and inter-modal correlation
through minimizing both reconstruction and quantization errors, which can be
trained from both paired and partially paired data in linear time. A
comprehensive set of experiments clearly show the superior effectiveness and
efficiency of CCQ against the state of the art hashing methods for both
unimodal and cross-modal retrieval
Privacy-Enhanced Query Processing in a Cloud-Based Encrypted DBaaS (Database as a Service)
In this dissertation, we researched techniques to support trustable and privacy enhanced solutions for on-line applications accessing to “always encrypted” data in
remote DBaaS (data-base-as-a-service) or Cloud SQL-enabled backend solutions.
Although solutions for SQL-querying of encrypted databases have been proposed in
recent research, they fail in providing: (i) flexible multimodal query facilities includ ing online image searching and retrieval as extended queries to conventional SQL-based
searches, (ii) searchable cryptographic constructions for image-indexing, searching and
retrieving operations, (iii) reusable client-appliances for transparent integration of multi modal applications, and (iv) lack of performance and effectiveness validations for Cloud based DBaaS integrated deployments.
At the same time, the study of partial homomorphic encryption and multimodal
searchable encryption constructions is yet an ongoing research field. In this research
direction, the need for a study and practical evaluations of such cryptographic is essential,
to evaluate those cryptographic methods and techniques towards the materialization of
effective solutions for practical applications.
The objective of the dissertation is to design, implement and perform experimental
evaluation of a security middleware solution, implementing a client/client-proxy/server appliance software architecture, to support the execution of applications requiring on line multimodal queries on “always encrypted” data maintained in outsourced cloud
DBaaS backends. In this objective we include the support for SQL-based text-queries
enhanced with searchable encrypted image-retrieval capabilities. We implemented a
prototype of the proposed solution and we conducted an experimental benchmarking
evaluation, to observe the effectiveness, latency and performance conditions in support ing those queries. The dissertation addressed the envisaged security middleware solution,
as an experimental and usable solution that can be extended for future experimental
testbench evaluations using different real cloud DBaaS deployments, as offered by well known cloud-providers.Nesta dissertação foram investigadas técnicas para suportar soluções com garantias de
privacidade para aplicações que acedem on-line a dados que são mantidos sempre cifrados em nuvens que disponibilizam serviços de armazenamento de dados, nomeadamente
soluções do tipo bases de dados interrogáveis por SQL. Embora soluções para suportar interrogações SQL em bases de dados cifradas tenham sido propostas anteriormente, estas
falham em providenciar: (i) capacidade de efectuar pesquisas multimodais que possam
incluir pesquisa combinada de texto e imagem com obtenção de imagens online, (ii) suporte de privacidade com base em construções criptograficas que permitam operações
de indexacao, pesquisa e obtenção de imagens como dados cifrados pesquisáveis, (iii)
suporte de integração para aplicações de gestão de dados em contexto multimodal, e (iv)
ausência de validações experimentais com benchmarking dobre desempenho e eficiência
em soluções DBaaS em que os dados sejam armazenados e manipulados na sua forma
cifrada.
A pesquisa de soluções de privacidade baseada em primitivas de cifras homomórficas
parciais, tem sido vista como uma possível solução prática para interrogação de dados e
bases de dados cifradas. No entanto, este é ainda um campo de investigação em desenvolvimento. Nesta direção de investigação, a necessidade de estudar e efectuar avaliações
experimentais destas primitivas em bibliotecas de cifras homomórficas, reutilizáveis em
diferentes contextos de aplicação e como solução efetiva para uso prático mais generalizado, é um aspeto essencial.
O objectivo da dissertação e desenhar, implementar e efectuar avalições experimentais
de uma proposta de solução middleware para suportar pesquisas multimodais em bases
de dados mantidas cifradas em soluções de nuvens de armazenamento. Esta proposta visa
a concepção e implementação de uma arquitectura de software client/client-proxy/server appliance para suportar execução eficiente de interrogações online sobre dados cifrados,
suportando operações multimodais sobre dados mantidos protegidos em serviços de
nuvens de armazenamento. Neste objectivo incluímos o suporte para interrogações estendidas de SQL, com capacidade para pesquisa e obtenção de dados cifrados que podem
incluir texto e pesquisa de imagens por similaridade. Foi implementado um prototipo da
solução proposta e foi efectuada uma avaliação experimental do mesmo, para observar as condições de eficiencia, latencia e desempenho do suporte dessas interrogações. Nesta
avaliação incluímos a análise experimental da eficiência e impacto de diferentes construções criptográficas para pesquisas cifradas (searchable encryption) e cifras parcialmente
homomórficas e que são usadas como componentes da solução proposta.
A dissertaçao aborda a soluçao de seguranca projectada, como uma solução experimental que pode ser estendida e utilizavel para futuras aplcações e respetivas avaliações
experimentais. Estas podem vir a adoptar soluções do tipo DBaaS, oferecidos como serviços na nuvem, por parte de diversos provedores ou fornecedores
Visual Information Retrieval in Endoscopic Video Archives
In endoscopic procedures, surgeons work with live video streams from the
inside of their subjects. A main source for documentation of procedures are
still frames from the video, identified and taken during the surgery. However,
with growing demands and technical means, the streams are saved to storage
servers and the surgeons need to retrieve parts of the videos on demand. In
this submission we present a demo application allowing for video retrieval
based on visual features and late fusion, which allows surgeons to re-find
shots taken during the procedure.Comment: Paper accepted at the IEEE/ACM 13th International Workshop on
Content-Based Multimedia Indexing (CBMI) in Prague (Czech Republic) between
10 and 12 June 201
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