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Latest Papers and Publications

Latest Papers and Publications

- 31st May 2017

 

AEGLE: Harnessing Big Data to find tomorrow’s cures

Published in:

Newsletter HiPEACinfo50

Date of publication:

April 2017

The article "AEGLE: HARNESSING BIG DATA TO FIND TOMORROW’S CURES" has been published in the most recent HiPEAC Newsletter (HiPEACinfo50). The HiPEAC Newsletter is a quarterly publication providing the latest news on the activities within the European HiPEAC network, as well as activities on high-performance embedded architectures and compilers at large.

AEGLE was invited to write an article for the section Healthcare Special of HiPEAC Newsletter info50, aiming to present AEGLE's contribution to unlock the value of data in the healthcare.

Find the article in page 14 HERE

Andreas Raptopoulos (EXUS S.A.)

 

AEGLE’s Cloud Infrastructure for Resource Monitoring and Containerized Accelerated Analytics

Published in:

Conference proceedings: ISVLSI 2017: IEEE Computer Society Annual Symposium on VLSI

Date of publication:

July 3-5, 2017 – Bochum, Germany

This paper presents the cloud infrastructure of the AEGLE project, that targets to integrate cloud technologies together with heterogeneous reconfigurable computing in large scale healthcare systems for Big Bio-Data analytics. AEGLEs engineering concept brings together the hot big-data engines with emerging acceleration technologies, putting the basis for personalised and integrated health-care services, while also promoting related research activities.

We introduce the design of AEGLE’s accelerated infrastructure along with the corresponding software and hardware acceleration stacks to support various big data analytics workloads showing that through effective resource containerization AEGLE’s cloud infrastructure is able to support high heterogeneity regarding to storage types, execution engines, utilized tools and execution platforms. Special care is given to the integration of high performance accelerators within the overall software stack of AEGLE’s infrastructure, which enable efficient execution of analytics, up to 140× according to our preliminary evaluations, over pure software executions.

Sotiris Xydis (ICCS - Institute of Communications and Computer Systems)

 

Dataflow Acceleration of Scikit-Learn Gaussian Process Regression

Published in:

PARMA-DITAM 2017 proceedings

Date of publication:

January 25, 2017 – Stockholm, Sweden

Big data revolution has sparked the widespread use of predictive data analytics based on sophisticated machine learning tasks. Fast data analysis has become very important, and this fact stresses software developers and computer architects to deliver more efficient design solutions able to address the increased performance requirements. Dataflow computing engines from Maxeler has been recently emerged as a promising way of performing high performance computation, utilising FPGA devices.

In this paper, we focus on exploiting Maxeler's dataflow computing for accelerating Gaussian Process Regression from scikit-learn Python library, one of the most computationally intensive and with poor scaling characteristics machine learning algorithm. Through extensive analysis over diverse datasets, we point out which NumPy and SciPy functions pose the major performance bottlenecks that should be implemented in a dataflow acceleration engine and then discuss the mapping decisions that enable the generation of parameterized dataflow engines. Finally, we show that the proposed acceleration solution delivers significant speedups for the examined datasets, while it also exhibits good scalability in respect to increased dataset sizes.

Sotiris Xydis, Dimitrios Soudris (ICCS - Institute of Communications and Computer Systems)

 

 

 

 

Does your electronic butler owe you a duty of confidentiality? An Ethico-Legal Analysis of Legal Personality and Artificial Intelligence as Applied to Robotic Carers

Published in:

Computer Law Review International 2017; 2: 48-54

Date of publication:

April 2017

This paper describes the ethico-legal considerations for artificially intelligent agents for assistive living e.g. an electronic butler. The relevance of this to the AEGLE is in the retention of data from various devices associated with assistive living, whether robots or simply Internet of Things devices.

As artificial intelligence (AI) advances the legal issues have not progressed in step and principles that exist have become outdated in a relatively short time. Privacy is a major concern and the myriad of devices that store data for wide ranging purposes risk breaches of privacy. Treating such a breach as a design defect or technical fault, does not reflect the complexities of legal liability that apply to robotics. Where advanced levels of AI are involved, such as with electronic butlers and carers used increasingly to assist vulnerable and ageing populations, the question of whether a robot owes a duty of confidentiality to the person for whom they are caring is becoming ever more pertinent. This question is considered in detail and it is concluded that a duty may be owed in some cases.

John Rumbold, Barbara Pierscionek (Kingston University)

 

A critique of the regulation of data science in healthcare research in the European Union

Published in:

BMC Medical Ethics 2017; 18:27

Date of publication:

April 2017

This article offers a critique of the current regulatory framework for healthcare data and the effect this has on the governance of multinational Big Data projects. In particular, it criticizes the variability in protection due to the vague and weak definition of personal data which fails to fully take into account possible future advances. This puts great onus on research ethics committees to provide adequate public protection, and so introduces more variability. It is suggested that data projects will benefit from imposing a standard of anonymisation above the legal requirement.

Paper available HERE

John Rumbold, Barbara Pierscionek (Kingston University)

 

The Effect of the General Data Protection Regulation on Medical Research

Published in:

Journal of Medical Internet Research 2017; 19(2):e47

Date of publication:

February 2017

The enactment of the General Data Protection Regulation (GDPR) will impact on European data science. Particular concerns relating to consent requirements that would severely restrict medical data research have been raised.

Our objective is to explain the changes in data protection laws that apply to medical research and to discuss their potential impact.

From the analysis of ethico-legal requirements imposed by the GDPR, it is observed that GDPR makes the classification of pseudonymised data as personal data clearer, although it has not been entirely resolved. Biomedical research on personal data where consent has not been obtained must be of substantial public interest.

In conclusion, the GDPR introduces protections for data subjects that aim for consistency across the EU. The proposed changes will make little impact on biomedical data research.

John Rumbold, Barbara Pierscionek (Kingston University)

More details at: