416 research outputs found
Intelligent data leak detection through behavioural analysis
In this paper we discuss a solution to detect data leaks in an intelligent and furtive way through a real time analysis of the user’s behaviour while handling classified information. Data is based on experiences with real world use cases and a variety of data preparation and data analysis techniques have been tried. Results show the feasibility of the approach, but also the necessity to correlate with other security events to improve the precision.UID/CEC/00319/201
The transposition of the Private Enforcement Directive: a critical perspective
This article analyses the proposal of transposition of the Private Enforcement Directive into the Portuguese legal system. It examines several aspects of the preliminary draft, which went through public discussion, but it highlights, specially, the articles about definitions, liability, means of proof and the potential impacts it could have on the organisation of the Portuguese judicial system. It criticises the incoherencies and points out some problematic aspects of the proposal and the necessary amendments that should be made to make the law stronger
Durabilidade de betões reforçados com fibras de aço
A durabilidade das estruturas é uma preocupação a nÃvel mundial devido ao número crescente de estruturas degradadas. Como é sabido, de forma a garantir uma adequada durabilidade do betão, devemos especificar outros requisitos para além do comportamento mecânico. A interação entre a camada superficial do betão com o meio ambiente é essencial para o inÃcio de diversos processos de degradação e, no betão reforçado com fibras de aço, as fibras apresentam-se muitas vezes à superfÃcie. Assim, torna-se necessário avaliar as propriedades de transporte de agentes agressivos para o interior das estruturas, que a longo prazo possam, eventualmente, afetar a sua durabilidade. Para os betões convencionais, sem inclusão de fibras de aço, existem indicadores de durabilidade que são de utilização corrente. No entanto, para betões reforçados com fibras de aço a bibliografia é escassa e os aspetos relacionados com a durabilidade, nomeadamente com a resistência à corrosão, estão tratados de uma forma ainda incipiente, suscitando, por exemplo, a dúvida se a corrosão das fibras pode, ou não, provocar o destacamento do betão envolvente. Neste seguimento, desenvolveu-se um trabalho experimental com o objetivo de analisar e comparar os resultados de ensaios de durabilidade em provetes de betão reforçado com fibras de aço e em provetes de betão sem fibras. Foram realizados nove ensaios diferentes com intuito de caracterizar o comportamento mecânico (resistência à compressão, resistência à tração por compressão diametral e comportamento à tração por flexão) e avaliar parâmetros indicadores de durabilidade (absorção de água por imersão e capilaridade, profundidade de penetração de água sob pressão, resistividade elétrica e difusão por migração de cloretos em regime não estacionário). Os resultados obtidos para os diferentes betões, com perÃodos de cura até 28 dias, são apresentados e analisados
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras
In this paper, we develop deep spatio-temporal neural networks to
sequentially count vehicles from low quality videos captured by city cameras
(citycams). Citycam videos have low resolution, low frame rate, high occlusion
and large perspective, making most existing methods lose their efficacy. To
overcome limitations of existing methods and incorporate the temporal
information of traffic video, we design a novel FCN-rLSTM network to jointly
estimate vehicle density and vehicle count by connecting fully convolutional
neural networks (FCN) with long short term memory networks (LSTM) in a residual
learning fashion. Such design leverages the strengths of FCN for pixel-level
prediction and the strengths of LSTM for learning complex temporal dynamics.
The residual learning connection reformulates the vehicle count regression as
learning residual functions with reference to the sum of densities in each
frame, which significantly accelerates the training of networks. To preserve
feature map resolution, we propose a Hyper-Atrous combination to integrate
atrous convolution in FCN and combine feature maps of different convolution
layers. FCN-rLSTM enables refined feature representation and a novel end-to-end
trainable mapping from pixels to vehicle count. We extensively evaluated the
proposed method on different counting tasks with three datasets, with
experimental results demonstrating their effectiveness and robustness. In
particular, FCN-rLSTM reduces the mean absolute error (MAE) from 5.31 to 4.21
on TRANCOS, and reduces the MAE from 2.74 to 1.53 on WebCamT. Training process
is accelerated by 5 times on average.Comment: Accepted by International Conference on Computer Vision (ICCV), 201
Understanding Traffic Density from Large-Scale Web Camera Data
Understanding traffic density from large-scale web camera (webcam) videos is
a challenging problem because such videos have low spatial and temporal
resolution, high occlusion and large perspective. To deeply understand traffic
density, we explore both deep learning based and optimization based methods. To
avoid individual vehicle detection and tracking, both methods map the image
into vehicle density map, one based on rank constrained regression and the
other one based on fully convolution networks (FCN). The regression based
method learns different weights for different blocks in the image to increase
freedom degrees of weights and embed perspective information. The FCN based
method jointly estimates vehicle density map and vehicle count with a residual
learning framework to perform end-to-end dense prediction, allowing arbitrary
image resolution, and adapting to different vehicle scales and perspectives. We
analyze and compare both methods, and get insights from optimization based
method to improve deep model. Since existing datasets do not cover all the
challenges in our work, we collected and labelled a large-scale traffic video
dataset, containing 60 million frames from 212 webcams. Both methods are
extensively evaluated and compared on different counting tasks and datasets.
FCN based method significantly reduces the mean absolute error from 10.99 to
5.31 on the public dataset TRANCOS compared with the state-of-the-art baseline.Comment: Accepted by CVPR 2017. Preprint version was uploaded on
http://welcome.isr.tecnico.ulisboa.pt/publications/understanding-traffic-density-from-large-scale-web-camera-data
Tax efficient supply chain
This case started with the following question: How supply chains and tax policy
interact?
This question was the starting point for this study and was developed under the areas of
Logistics and International Tax Policy.
In order to support this study, the adopted methodology was the selection of a
Multinational company – LPR Portugal – which has the transportation and distribution
in the European Union as a scope of service and the customer satisfaction as the main
value. LPR is a market leader, with business in 12 countries of European Union and also
has subsidiaries in 8 countries. It is established in the market for more than 15 years,
with a good market consolidation and high reputation.
The study of LPR, as a case study, will allow a better understanding of the situation of
an international company in terms of logistics and international policy.
During this study, qualitative data were used and obtained through LPR Portugal and
EY Portugal. The results from the data shows that supply chain management and
international tax policy are not a well developed topic.
As short, this study requires a reflection about how logistic and tax policy interacts,
taking into consideration the international perspective.
This study presents as a useful and practical tool by collecting questions that will allow
a better understanding and analyzis of the problem.O presente caso de estudo teve como ponto de partida a seguinte questão: Como é que
cadeia de abastecimento e a fiscal interagem?
Esta questão serviu como ponto de partida à elaboração deste estudo e foi desenvolvida
no âmbito da Fiscalidade e da LogÃstica.
A metodologia adoptada baseou-se na selecção de uma multinacional portuguesa, a
LPR, que exerce uma actividade na área de transporte e de distribuição em toda a União
Europeia, e que tem como principal valor, a satisfação do cliente. A LPR é uma empresa
lÃder de mercado, está presente em mais de 12 paÃses e têm filiais espalhadas por 8
paÃses. Está presente no mercado há mais de 15 anos, uma boa consolidação de mercado
e uma elevada notoriedade.
O estudo da LPR, irá permitir uma maior compreensão da realidade de uma empresa
internacional e os seus problemas em termos fiscais e logÃsticos.
A análise do presente estudo integrou dados qualitativos, que foram obtidos através da
LPR Portugal e da EY Portugal. Os resultados dos dados demonstram que a
implementação de uma cadeia de abastecimento que tenha em consideração a polÃtica
fiscal internacional ainda se encontra numa fase embrionária.
Em suma, o estudo apresentado exige uma reflecção sobre a forma como as áreas de
logÃstica e fiscalidade estão relacionadas, tendo em conta, a realidade internacional. Este
estudo apresenta-se assim como um instrumento útil e prático, sistematizando questões
que permitam a consolidação dos conhecimentos e o desenvolvimento da capacidade de
análise
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