The Experts below are selected from a list of 269640 Experts worldwide ranked by ideXlab platform
Michael F P Oboyle - One of the best experts on this subject based on the ideXlab platform.
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partitioning streaming parallelism for multi cores a Machine learning based Approach
International Conference on Parallel Architectures and Compilation Techniques, 2010Co-Authors: Zheng Wang, Michael F P OboyleAbstract:Stream based languages are a popular Approach to expressing parallelism in modern applications. The efficient mapping of streaming parallelism to multi-core processors is, however, highly dependent on the program and underlying architecture. We address this by developing a portable and automatic compiler-based Approach to partitioning streaming programs using Machine learning. Our technique predicts the ideal partition structure for a given streaming application using prior knowledge learned off-line. Using the predictor we rapidly search the program space (without executing any code) to generate and select a good partition. We applied this technique to standard StreamIt applications and compared against existing Approaches. On a 4-core platform, our Approach achieves 60% of the best performance found by iteratively compiling and executing over 3000 different partitions per program. We obtain, on average, a 1.90x speedup over the already tuned partitioning scheme of the StreamIt compiler. When compared against a state-of-the-art analytical, model-based Approach, we achieve, on average, a 1.77x performance improvement. By porting our Approach to a 8-core platform, we are able to obtain 1.8x improvement over the StreamIt default scheme, demonstrating the portability of our Approach.
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mapping parallelism to multi cores a Machine learning based Approach
ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2009Co-Authors: Zheng Wang, Michael F P OboyleAbstract:The efficient mapping of program parallelism to multi-core processors is highly dependent on the underlying architecture. This paper proposes a portable and automatic compiler-based Approach to mapping such parallelism using Machine learning. It develops two predictors: a data sensitive and a data insensitive predictor to select the best mapping for parallel programs. They predict the number of threads and the scheduling policy for any given program using a model learnt off-line. By using low-cost profiling runs, they predict the mapping for a new unseen program across multiple input data sets. We evaluate our Approach by selecting parallelism mapping configurations for OpenMP programs on two representative but different multi-core platforms (the Intel Xeon and the Cell processors). Performance of our technique is stable across programs and architectures. On average, it delivers above 96% performance of the maximum available on both platforms. It achieve, on average, a 37% (up to 17.5 times) performance improvement over the OpenMP runtime default scheme on the Cell platform. Compared to two recent prediction models, our predictors achieve better performance with a significant lower profiling cost.
Zheng Wang - One of the best experts on this subject based on the ideXlab platform.
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partitioning streaming parallelism for multi cores a Machine learning based Approach
International Conference on Parallel Architectures and Compilation Techniques, 2010Co-Authors: Zheng Wang, Michael F P OboyleAbstract:Stream based languages are a popular Approach to expressing parallelism in modern applications. The efficient mapping of streaming parallelism to multi-core processors is, however, highly dependent on the program and underlying architecture. We address this by developing a portable and automatic compiler-based Approach to partitioning streaming programs using Machine learning. Our technique predicts the ideal partition structure for a given streaming application using prior knowledge learned off-line. Using the predictor we rapidly search the program space (without executing any code) to generate and select a good partition. We applied this technique to standard StreamIt applications and compared against existing Approaches. On a 4-core platform, our Approach achieves 60% of the best performance found by iteratively compiling and executing over 3000 different partitions per program. We obtain, on average, a 1.90x speedup over the already tuned partitioning scheme of the StreamIt compiler. When compared against a state-of-the-art analytical, model-based Approach, we achieve, on average, a 1.77x performance improvement. By porting our Approach to a 8-core platform, we are able to obtain 1.8x improvement over the StreamIt default scheme, demonstrating the portability of our Approach.
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mapping parallelism to multi cores a Machine learning based Approach
ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2009Co-Authors: Zheng Wang, Michael F P OboyleAbstract:The efficient mapping of program parallelism to multi-core processors is highly dependent on the underlying architecture. This paper proposes a portable and automatic compiler-based Approach to mapping such parallelism using Machine learning. It develops two predictors: a data sensitive and a data insensitive predictor to select the best mapping for parallel programs. They predict the number of threads and the scheduling policy for any given program using a model learnt off-line. By using low-cost profiling runs, they predict the mapping for a new unseen program across multiple input data sets. We evaluate our Approach by selecting parallelism mapping configurations for OpenMP programs on two representative but different multi-core platforms (the Intel Xeon and the Cell processors). Performance of our technique is stable across programs and architectures. On average, it delivers above 96% performance of the maximum available on both platforms. It achieve, on average, a 37% (up to 17.5 times) performance improvement over the OpenMP runtime default scheme on the Cell platform. Compared to two recent prediction models, our predictors achieve better performance with a significant lower profiling cost.
Klausrobert Muller - One of the best experts on this subject based on the ideXlab platform.
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risk estimation of sars cov 2 transmission from bluetooth low energy measurements
npj Digital Medicine, 2020Co-Authors: Felix Sattler, Patrick Wagner, David Neumann, Markus Wenzel, Ralf Schafer, Wojciech Samek, Klausrobert MullerAbstract:Digital contact tracing Approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic. In this work we propose a Machine learning based Approach to reliably detect subjects that have spent enough time in close proximity to be at risk of being infected. Our study is an important proof of concept that will aid the battery of epidemiological policies aiming to slow down the rapid spread of COVID-19.
Mauro Conti - One of the best experts on this subject based on the ideXlab platform.
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a Machine learning based Approach to detect malicious android apps using discriminant system calls
Future Generation Computer Systems, 2019Co-Authors: P Vinod, Akka Zemmari, Mauro ContiAbstract:Abstract The openness of Android framework and the enhancement of users trust have gained the attention of malware writers. The momentum of downloaded applications (app for short) from numerous app stores has stimulated the proliferation of mobile malware. Now the threat is due to the sophistication in malware being written to bypass signature-based detectors. In this paper, we investigate system calls to tackle mobile malware on Android operating system. To do so, we first employed Machine learning to extract system calls. We then performed the empirical estimation of system calls derived from diverse datasets employing human interaction and random inputs. After accomplishing intensive experiments on synthesized system calls with two feature selection Approach, namely Absolute Difference of Weighted System Calls (ADWSC) and Ranked System Calls using Large Population Test (RSLPT), we validated the results on five datasets. All classifiers generated in Area Under Curve of 1.0 with an accuracy exceeding 99.9% suggest the appropriateness and efficacy of the proposed Approach. Finally, we evaluated the effectiveness of classifier against adversarial attacks and found that the classifiers are vulnerable to data poisoning and label flipping attacks. Adversarial examples created by poisoning malware samples resulted in the significant drop of classifier performance on perturbing 12–18 prominent attributes. Moreover, we implemented class label poisoning attacks which brought down the classification accuracy by 50% on altering labels of 50 malicious training instances.
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Detecting crypto-ransomware in IoT networks based on energy consumption footprint
Journal of Ambient Intelligence and Humanized Computing, 2018Co-Authors: Amin Azmoodeh, Mauro Conti, Kim-kwang Raymond ChooAbstract:An Internet of Things (IoT) architecture generally consists of a wide range of Internet-connected devices or things such as Android devices, and devices that have more computational capabilities (e.g., storage capacities) are likely to be targeted by ransomware authors. In this paper, we present a Machine learning based Approach to detect ransomware attacks by monitoring power consumption of Android devices. Specifically, our proposed method monitors the energy consumption patterns of different processes to classify ransomware from non-malicious applications. We then demonstrate that our proposed Approach outperforms K-Nearest Neighbors, Neural Networks, Support Vector Machine and Random Forest, in terms of accuracy rate, recall rate, precision rate and F-measure.
Aziz Guergachi - One of the best experts on this subject based on the ideXlab platform.
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a systematic Machine learning based Approach for the diagnosis of non alcoholic fatty liver disease risk and progression
Scientific Reports, 2018Co-Authors: Sajida Perveen, Muhammad Shahbaz, Karim Keshavjee, Aziz GuergachiAbstract:Prevention and diagnosis of NAFLD is an ongoing area of interest in the healthcare community. Screening is complicated by the fact that the accuracy of noninvasive testing lacks specificity and sensitivity to make and stage the diagnosis. Currently no non-invasive ATP III criteria based prediction method is available to diagnose NAFLD risk. Firstly, the objective of this research is to develop Machine learning based method in order to identify individuals at an increased risk of developing NAFLD using risk factors of ATP III clinical criteria updated in 2005 for Metabolic Syndrome (MetS). Secondly, to validate the relative ability of quantitative score defined by Italian Association for the Study of the Liver (IASF) and guideline explicitly defined for the Canadian population based on triglyceride thresholds to predict NAFLD risk. We proposed a Decision Tree based method to evaluate the risk of developing NAFLD and its progression in the Canadian population, using Electronic Medical Records (EMRs) by exploring novel risk factors for NAFLD. Our results show proposed method could potentially help physicians make more informed choices about their management of patients with NAFLD. Employing the proposed application in ordinary medical checkup is expected to lessen healthcare expenditures compared with administering additional complicated test.
