3,185 research outputs found
Design Challenges for GDPR RegTech
The Accountability Principle of the GDPR requires that an organisation can
demonstrate compliance with the regulations. A survey of GDPR compliance
software solutions shows significant gaps in their ability to demonstrate
compliance. In contrast, RegTech has recently brought great success to
financial compliance, resulting in reduced risk, cost saving and enhanced
financial regulatory compliance. It is shown that many GDPR solutions lack
interoperability features such as standard APIs, meta-data or reports and they
are not supported by published methodologies or evidence to support their
validity or even utility. A proof of concept prototype was explored using a
regulator based self-assessment checklist to establish if RegTech best practice
could improve the demonstration of GDPR compliance. The application of a
RegTech approach provides opportunities for demonstrable and validated GDPR
compliance, notwithstanding the risk reductions and cost savings that RegTech
can deliver. This paper demonstrates a RegTech approach to GDPR compliance can
facilitate an organisation meeting its accountability obligations
Interrelationships among international stock market indices: Europe, Asia and the Americas
In this paper, we investigate the price interdependence between seven international stock markets, namely Irish, UK, Portuguese, US, Brazilian, Japanese and Hong Kong, using a new testing method, based on the wavelet transform to reconstruct the data series, as suggested by Lee (2002). We find evidence of intra-European (Irish, UK and Portuguese) market co-movements with the US market also
weakly influencing the Irish market. We also find co-movement between the US and Brazilian markets and similar intra-Asian co-movements (Japanese and Hong Kong). Finally, we conclude that the circle of impact
is that of the European markets (Irish, UK and Portuguese) on both American markets (US and Brazilian), with these in turn impacting on the Asian markets (Japanese and Hong Kong) which in turn influence the European markets. In summary, we find evidence for intra-continental relationships and an increase in importance of
international spillover effects since the mid 1990’s, while the importance of historical transmissions has decreased since the beginning of this century
Interdependence between emerging and major markets
In this paper, we investigate the price spillover effects among two developed
markets, (the US and the UK ), and two developing markets, (Irish
and Portuguese), using a new testing method suggested by Lee (2002).
We find that there are interrelationships between any two of the Irish,
the UK and Portuguese markets and that the co-movements between the
emerging markets and the US are statistically significant but weak. We
also found that the US market is slightly influenced by the UK but not
vice versa
Modelling drug coatings: A parallel cellular automata model of ethylcellulose-coated microspheres
Pharmaceutical companies today face a growing demand for more complex drug designs. In the past few decades, a number of probabilistic models have been developed, with the aim of improving insight on microscopic features of these complex designs. Of particular interest are models of controlled release systems, which can provide tools to study targeted dose delivery. Controlled release is achieved by using polymers with different dissolution characteristics. We present here an approach for parallelising a large-scale model of a drug delivery system based on Monte Carlo methods, as a framework for Cellular Automata mobility. The model simulates drug release in the gastro-intestinal tract, from coated ethylcellulose microspheres. The objective is high performance simulation of coated drugs for targeted delivery. The overall aim is to understand the importance of various molecular effects with respect to system evolution over time. Important underlying mechanisms of the process, such as erosion and diffusion, are described
Apples and oranges: the difference between the reaction of the emerging and mature markets to crashes
We study here the behavior of the eigenvalues of the covariance matrices of returns for emerging and mature markets at times of crises. Our results appear to
indicate that mature markets respond to crashes differently to emerging ones and that emerging markets take longer to recover than mature markets. In addition, the results appear to indicate that the second largest eigenvalue gives additional information on market movement and that a study of the behavior of the other eigenvalues may provide insight on crash dynamics
Techniques for clustering gene expression data
Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognises these limitations and implements procedures to overcome them. It provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for the clustering methods considered
Day ahead forecasting of FAANG stocks using ARIMA, LSTM networks and wavelets.
Abstract. Facebook Inc., Apple Inc., Amazon.com Inc., Net
ix Inc. and Alphabet Inc., known collectively as FAANG, are a group of the best performing tech stocks in recent years. In this study, we present linear
and non-linear methods for predicting the closing price of each stock
on the following day. We decompose each time series into component
series using wavelet methods and develop an novel ensemble approach to
improve forecast accuracy
Students’ Behaviours in using Learning Resources in Higher Education: How do behaviours reflect success in Programming Education?
[EN] Programming education traditionally has been an important part of Information Technology-related degrees but, more recently, it is also becoming essential in many STEM domains as well. Despite this, drop-out rates in programming courses in higher education institutions are considerable and cannot be ignored. At the same time, analysing learning behaviours has been reported to be an effective way to support the improvement of teaching and learning quality. This article aims to deliver an in-depth analysis of students’ learning behaviours when using course material items. We analyse an introductory programming course at a University in Dublin. The dataset is extracted from automatically logged learning data from a bespoke online learning system. The analysis makes use of the power of Principal Component Analysis and Random Matrix Theory to reduce dimensionality in, and to extract information from, the data, verifying the results with rigorous statistical tests. Overall, we found that all the students follow a common learning pattern in accessing all given learning items. However, there is a noticeable difference between higher and lower-performing cohorts of students when using practical and theoretical learning items. The high performing students have been consistently active in practice during the study progress. On the other hand, the students who failed the exam have more recorded activities in reading lecture notes and appear to become discouraged and unmotivated from the practical activities, especially in the later stage of the semester.This research is financially supported by Irish Research Council.Mai, T.; Crane, M.; Bezbradica, M. (2021). Students’ Behaviours in using Learning Resources in Higher Education: How do behaviours reflect success in Programming Education?. En 7th International Conference on Higher Education Advances (HEAd'21). Editorial Universitat Politècnica de València. 47-55. https://doi.org/10.4995/HEAd21.2021.12939OCS475
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