Adaptation and Evaluation of the Multisplitting-Newton and Waveform Relaxation Methods Over Distributed Volatile Environments

International audienceThis paper presents new adaptations of two methods that solve large differential equations systems, to the grid context. The first method isbased on the Multisplitting concept and the second on the Waveform Relaxation concept. Their adaptations are implemented according to the asynchronous iteration model which is well suited to volatile architectures that suffer from high latency networks. Many experiments were conducted to evaluate and compare the accuracy and performance of both methods while solving the advection-diffusion problem over heterogeneous, distributed and volatile architectures. The JACEP2P-V2 middleware provided the fault tolerant asynchronous environment, required for these experiments

An Easy-to-use and Robust Approach for the Differentially Private De-Identification of Clinical Textual Documents

Unstructured textual data is at the heart of healthcare systems. For obvious privacy reasons, these documents are not accessible to researchers as long as they contain personally identifiable information. One way to share this data while respecting the legislative framework (notably GDPR or HIPAA) is, within the medical structures, to de-identify it, i.e. to detect the personal information of a person through a Named Entity Recognition (NER) system and then replacing it to make it very difficult to associate the document with the person. The challenge is having reliable NER and substitution tools without compromising confidentiality and consistency in the document. Most of the conducted research focuses on English medical documents with coarse substitutions by not benefiting from advances in privacy. This paper shows how an efficient and differentially private de-identification approach can be achieved by strengthening the less robust de-identification method and by adapting state-of-the-art differentially private mechanisms for substitution purposes. The result is an approach for de-identifying clinical documents in French language, but also generalizable to other languages and whose robustness is mathematically proven

{MAHEVE}: An Efficient Reliable Mapping of Asynchronous Iterative Applications on volatile and Heterogeneous Environments

International audienceThe asynchronous iteration model, called AIAC, has been proven to be an efficient solution for heterogeneous and distributed architectures. An efficient mapping of application tasks is essential to reduce their execution time. In this paper we present a new mapping algorithm, called MAHEVE (Mapping Algorithm for HEterogeneous and Volatile Environments) which is efficient on such architectures and integrates a fault tolerance mechanism to resist computing node failures. Our experiments show gains on a typical AIAC application execution time up to 65%, executed on distributed clusters architectures containing more than 400 computing cores with the JaceP2P-V2 environment

DTM: a service for managing data persistency and data replication in network-enabled server environments

International audienceNetwork-Enabled Servers (NES) environments are valuable candidates to provide simple computing grid access. These environments allow transparent access to a set of computational servers via Remote Procedure Call mechanisms. In this context, a challenge is to increase performances by decreasing data tra?c. This paper presents DTM (Data Tree Manager) a data management service for NES environments. Based on the notions of data persistency and data replication, DTM proposes a set of e?cient policies which minimise computation times by decreasing data transfers between the clients and the platform. From the end-user point of view, DTM is accessible through a simple and transparent API. In the remainder, we describe DTM and its implementation in the DIET platform. We also present a set of experimental results which exhibit the feasibility and the e?ciency of our approach

De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks

Unstructured textual data are at the heart of health systems: liaison letters between doctors, operating reports, coding of procedures according to the ICD-10 standard, etc. The details included in these documents make it possible to get to know the patient better, to better manage him or her, to better study the pathologies, to accurately remunerate the associated medical acts\ldots All this seems to be (at least partially) within reach of today by artificial intelligence techniques. However, for obvious reasons of privacy protection, the designers of these AIs do not have the legal right to access these documents as long as they contain identifying data. De-identifying these documents, i.e. detecting and deleting all identifying information present in them, is a legally necessary step for sharing this data between two complementary worlds. Over the last decade, several proposals have been made to de-identify documents, mainly in English. While the detection scores are often high, the substitution methods are often not very robust to attack. In French, very few methods are based on arbitrary detection and/or substitution rules. In this paper, we propose a new comprehensive de-identification method dedicated to French-language medical documents. Both the approach for the detection of identifying elements (based on deep learning) and their substitution (based on differential privacy) are based on the most proven existing approaches. The result is an approach that effectively protects the privacy of the patients at the heart of these medical documents. The whole approach has been evaluated on a French language medical dataset of a French public hospital and the results are very encouraging

Adaptive data collection approach for periodic sensor networks

International audienceData collection from unreachable terrain and then transmit the information to the sink is a fundamental task in periodic sensor networks. Energy is a major constraint for this network as the only source of energy is a battery with limited lifetime. Therefore, in order to keep the networks operating for long time, adaptive sampling approach to periodic data collection constitutes a fundamental mechanism for energy optimization. The key idea behind this approach is to allow each sensor node to adapt its sampling rates to the physical changing dynamics. In this way, over-sampling can be minimised and power efficiency of the overall network system can be further improved. In this paper, we present an efficient adaptive sampling approach based on the dependence of conditional variance on measurements varies over time. Then, we propose a multiple levels activity model that uses behavior functions modeled by modified Bezier curves to define application classes and allow for sampling adaptive rate. The proposed method was successfully tested in a real sensor data set

A Decentralized and Fault Tolerant Convergence Detection Algorithm for Asynchronous Iterative Algorithms

International audienceThis article presents an algorithm that performs a decentralized detection of the global convergence of parallel asynchronous iterative applications. This algorithm is fault tolerant. It runs a decentralized saving procedure which enables this algorithm, after a node's crash, to replace the dead node by a new one which will continue the computing task from the last check point. Combined with the advantages of the asynchronous iteration model, this method allows us to compute very large scale problems using highly volatile parallel architectures like Peer-to-Peer and distributed clusters architectures. We also present the implementation of this algorithm in the JaceP2P platform which is dedicated to designing and executing parallel asynchronous iterative applications in volatile environments. Numerous experiments show the robustness and the efficiency of our algorithm

Mapping Asynchronous Iterative Applications on Heterogeneous Distributed Architectures

International audienceTo design parallel numerical algorithms on large scale distributed and heterogeneous platforms, the asynchronous iteration model (AIAC) may be an efficient solution. This class of algorithm is very suitable since it enables communication/computation overlapping and it suppresses all synchronizations between computation nodes. Since target architectures are composed of more than one thousand heterogeneous nodes connected through heterogeneous networks, the need for mapping algorithms is crucial. In this paper, we propose a new mapping algorithm dedicated to the AIAC model. To evaluate our mapping algorithm we implemented it in the JaceP2P programming and executing environment dedicated to AIAC applications and we conducted a set of experiments on the Grid'5000 testbed. Results are very encouraging and show that the use of our algorithm brings an important gain in term of execution time (about 40%)

JACEP2P-V2: a Fully Decentralized and Fault Tolerant Environment for Executing Parallel Iterative Asynchronous Applications on Volatile Distributed Architectures

International audienceThis article presents JACEP2P-V2, a Java environment dedicated to designing parallel iterative asynchronous algorithms(with direct communications between nodes) and executing them on global computing architectures or distributed clusters composed by alarge number of volatile heterogeneous distant computing nodes. This platform is fault tolerant, multi-threaded and completely decentralized.In this paper, we describe the different components of JACEP2P-V2 and the various mechanisms used for scalability and fault tolerance purposes. We also evaluate the performance of this platform and we compare it to JACEP2P by implementing a parallel iterative asynchronous application and by executing it on a volatile distributed architecture using both platforms