Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models

2015 ◽  
Author(s):  
Katya Scheinberg
Keyword(s):  
Machine Learning ◽  
Complex Systems ◽  
Learning Models ◽  
Statistical Machine Learning ◽  
Derivative Free Optimization ◽  
Derivative Free ◽  
Machine Learning Models

scholarly journals Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1289 ◽  
Author(s):  
Sérgio Branco ◽  
André G. Ferreira ◽  
Jorge Cabral
Keyword(s):  
Machine Learning ◽  
Embedded Systems ◽  
Complex Systems ◽  
21St Century ◽  
Silk Road ◽  
The Internet ◽  
Learning Models ◽  
Future Directions ◽  
Machine Learning Models ◽  
Constrained Devices

The number of devices connected to the Internet is increasing, exchanging large amounts of data, and turning the Internet into the 21st-century silk road for data. This road has taken machine learning to new areas of applications. However, machine learning models are not yet seen as complex systems that must run in powerful computers (i.e., Cloud). As technology, techniques, and algorithms advance, these models are implemented into more computational constrained devices. The following paper presents a study about the optimizations, algorithms, and platforms used to implement such models into the network’s end, where highly resource-scarce microcontroller units (MCUs) are found. The paper aims to provide guidelines, taxonomies, concepts, and future directions to help decentralize the network’s intelligence.

scholarly journals Spatial Statistical Machine Learning Models to Assess Health Vulnerabilities in Children

2021 ◽  
Author(s):  
Wala Draidi Areed ◽  
Aiden Price ◽  
Kathryn Arnett ◽  
Kerrie Mengersen
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Physical Health ◽  
Spatial Variation ◽  
First Year ◽  
Learning Models ◽  
Statistical Machine Learning ◽  
Health And Development ◽  
Machine Learning Models

Abstract Background: The health and development of children during their first year of school is known to impact their social, emotional, and academic capabilities throughout and beyond early education. Physical health, motor development, social and emotional well-being, learning styles, language and communication, cognitive skills, and general knowledge are all considered to be important aspects of a child’s health and development. It is important for many organisations and governmental agencies to continually improve their understanding of the factors which determine or influence health vulnerabilities among children. This article studies the relationships between health vulnerabilities and educational factors among children in Queensland, Australia. In Queensland, the percentage of children who are developmentally vulnerable in at least one domain in 2018 was around 26%, and the overall percentage of attendance at preschool was around 75.4% These are the lowest rates among all states and territories of Australia. There is also substantial geographic variation in rates across the state. Methods: Spatial statistical machine learning models are reviewed and compared in the context of a study of geographic variation in the association between health vulnerabilities and attendance at preschool among children in Queensland, Australia. A new spatial random forest (SRF) model is suggested that can explain more of the spatial variation in data than other approaches.Results: In the case study, spatial models were shown to provide a better fit compared to models that ignored the spatial variation in the data. The SRF model was shown to be the only model which can explain all of the spatial variation in each of the health vulnerabilities considered in the case study. The spatial analysis revealed that the attendance at preschool factor has a strong influence on the physical health domain vulnerability and emotional maturity vulnerability among children in their first year of school. Conclusion: This study confirmed that it is important to take into account the spatial nature of data when fitting statistical machine learning models. A new spatial random forest model was introduced and was shown to explain more of the spatial variation and provide a better model fit in the case study of health vulnerabilities among children in Queensland. At small-area population level (statistical area level 2 (SA2)), increased attendance at preschool was strongly associated with reduced physical and emotional health vulnerabilities among children in their first year of school.

scholarly journals Detecting the Exploitation of Hardware Vulnerabilities using Electromagnetic Emanations

2021 ◽  
Author(s):  
Giancarlo Canales Barreto ◽  
Nicholas Lamb
Keyword(s):  
Machine Learning ◽  
Experimental Results ◽  
Raspberry Pi ◽  
Learning Models ◽  
Statistical Machine Learning ◽  
Machine Learning Models

We present a cache attack monitoring methodology that leverages statistical machine learning models to detect n-day hardware attacks by analyzing the electromagnetic emanations of a device. Experimental results from a Raspberry Pi 4 hosting Linux and a Jetson TX2 development board running a Linux guest hosted by seL4 demonstrate that our approach can sense Spectre attacks with a concordance statistic of 97% and 95%.

Detecting the Exploitation of Hardware Vulnerabilities using Electromagnetic Emanations

2021 ◽  
Author(s):  
Giancarlo Canales Barreto ◽  
Nicholas Lamb
Keyword(s):  
Machine Learning ◽  
Experimental Results ◽  
Raspberry Pi ◽  
Learning Models ◽  
Statistical Machine Learning ◽  
Machine Learning Models

We present a cache attack monitoring methodology that leverages statistical machine learning models to detect n-day hardware attacks by analyzing the electromagnetic emanations of a device. Experimental results from a Raspberry Pi 4 hosting Linux and a Jetson TX2 development board running a Linux guest hosted by seL4 demonstrate that our approach can sense Spectre attacks with a concordance statistic of 97% and 95%.

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