scholarly journals Improving Performance and Mitigating Fault Attacks Using Value Prediction

Cryptography ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 27
Author(s):  
◽  
Keyword(s):  
Performance Optimization ◽  
State Of The Art ◽  
Present Value ◽  
Fault Attacks ◽  
Value Prediction ◽  
Data Value ◽  
Data Production ◽  
High Prediction ◽  
Design Perspective ◽  
Minimal Hardware

We present Value Prediction for Security (VPsec), a novel hardware-only framework to counter fault attacks in modern microprocessors, while preserving the performance benefits of Value Prediction (VP.) VP is an elegant and hitherto mature microarchitectural performance optimization, which aims to predict the data value ahead of the data production with high prediction accuracy and coverage. Instances of VPsec leverage the state-of-the-art Value Predictors in an embodiment and system design to mitigate fault attacks in modern microprocessors. Specifically, VPsec implementations re-architect any baseline VP embodiment with fault detection logic and reaction logic to mitigate fault attacks to both the datapath and the value predictor itself. VPsec also defines a new mode of execution in which the predicted value is trusted rather than the produced value. From a microarchitectural design perspective, VPsec requires minimal hardware changes (negligible area and complexity impact) with respect to a baseline that supports VP, it has no software overheads (no increase in memory footprint or execution time), and it retains most of the performance benefits of VP under realistic attacks. Our evaluation of VPsec demonstrates its efficacy in countering fault attacks, as well as its ability to retain the performance benefits of VP on cryptographic workloads, such as OpenSSL, and non-cryptographic workloads, such as SPEC CPU 2006/2017.

Performance Driven Development Framework for Web Applications

2017 ◽  
Vol 9 (1) ◽  
pp. 75
Author(s):  
K. S. Shailesh ◽  
P. V. Suresh
Keyword(s):  
Customer Loyalty ◽  
Performance Optimization ◽  
Web Search ◽  
Web Applications ◽  
State Of The Art ◽  
End User ◽  
Web Performance ◽  
Performance Engineering ◽  
Development Framework ◽  
Performance Patterns

The performance of web applications is of paramount importance as it can impact end-user experience and the business revenue. Web Performance Optimization (WPO) deals with front-end performance engineering. Web performance would impact customer loyalty, SEO, web search ranking, SEO, site traffic, repeat visitors and overall online revenue. In this paper we have conducted the survey of state of the art tools, techniques, methodologies of various aspects of web performance optimization. We have identified key web performance patterns and proposed novel web performance driven development framework. We have elaborated on various techniques related to different phases of web performance driven development framework.

scholarly journals FPL: fast Presburger arithmetic through transprecision

2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-26
Author(s):  
Arjun Pitchanathan ◽  
Christian Ulmann ◽  
Michel Weber ◽  
Torsten Hoefler ◽  
Tobias Grosser
Keyword(s):  
Performance Optimization ◽  
Memory Management ◽  
State Of The Art ◽  
Computational Cost ◽  
End User ◽  
Loop Optimization ◽  
Presburger Arithmetic ◽  
Benchmark Suite ◽  
Compilation Techniques ◽  
High Computational Cost

Presburger arithmetic provides the mathematical core for the polyhedral compilation techniques that drive analytical cache models, loop optimization for ML and HPC, formal verification, and even hardware design. Polyhedral compilation is widely regarded as being slow due to the potentially high computational cost of the underlying Presburger libraries. Researchers typically use these libraries as powerful black-box tools, but the perceived internal complexity of these libraries, caused by the use of C as the implementation language and a focus on end-user-facing documentation, holds back broader performance-optimization efforts. With FPL, we introduce a new library for Presburger arithmetic built from the ground up in modern C++. We carefully document its internal algorithmic foundations, use lightweight C++ data structures to minimize memory management costs, and deploy transprecision computing across the entire library to effectively exploit machine integers and vector instructions. On a newly-developed comprehensive benchmark suite for Presburger arithmetic, we show a 5.4x speedup in total runtime over the state-of-the-art library isl in its default configuration and 3.6x over a variant of isl optimized with element-wise transprecision computing. We expect that the availability of a well-documented and fast Presburger library will accelerate the adoption of polyhedral compilation techniques in production compilers.

A comparison of machine learning surrogate models for net present value prediction from well placement binary data

2021 ◽  
pp. 109208
Author(s):  
João Roberto Bertini ◽  
Sérgio Ferreira Batista Filho ◽  
Mei Abe Funcia ◽  
Luis Otávio Mendes da Silva ◽  
Antonio Alberto S. Santos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Binary Data ◽  
Net Present Value ◽  
Surrogate Models ◽  
Present Value ◽  
Well Placement ◽  
Value Prediction

scholarly journals A Novel Interaction-based Methodology Towards Explainable AI with Better Understanding of Pneumonia Chest X-ray Images

2021 ◽  
Author(s):  
Shaw-Hwa Lo ◽  
Yiqiao Yin
Keyword(s):  
State Of The Art ◽  
Image Data ◽  
Prediction Performance ◽  
Data Set ◽  
X Ray ◽  
Real World Application ◽  
Explainable Ai ◽  
Chest X Ray ◽  
High Prediction ◽  
Visualization Techniques

Abstract In the field of eXplainable AI (XAI), robust “black-box” algorithms such as Convolutional Neural Networks (CNNs) are known for making high prediction performance. However, the ability to explain and interpret these algorithms still require innovation in the understanding of influential and, more importantly, ex-plainable features that directly or indirectly impact the performance of predictivity. A number of methods existing in literature focus on visualization techniques but the concepts of explainability and interpretability still require rigorous definition. In view of the above needs, this paper proposes an interaction-based methodology – Influence Score (I-score) – to screen out the noisy and non-informative variables in the images hence it nourishes an environment with explainable and interpretable features that are directly associated to feature predictiv-ity. We apply the proposed method on a real world application in Pneumonia Chest X-ray Image data set and produced state-of-the-art results. We demonstrate how to apply the proposed approach for more general big data problems by improving the explainability and in-terpretability without sacrificing the prediction performance. The contribution of this paper opens a novel angle that moves the community closer to the future pipelines of XAI problems.

scholarly journals Bioinformatics Resources for Plant Abiotic Stress Responses: State of the Art and Opportunities in the Fast Evolving -Omics Era

Plants ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 591
Author(s):  
Luca Ambrosino ◽  
Chiara Colantuono ◽  
Gianfranco Diretto ◽  
Alessia Fiore ◽  
Maria Luisa Chiusano
Keyword(s):  
Abiotic Stress ◽  
Stress Responses ◽  
Abiotic Stresses ◽  
Climate Changes ◽  
State Of The Art ◽  
Limiting Factors ◽  
Abiotic Stress Response ◽  
Data Production ◽  
Molecular Aspects ◽  
Plant Abiotic Stress

Abiotic stresses are among the principal limiting factors for productivity in agriculture. In the current era of continuous climate changes, the understanding of the molecular aspects involved in abiotic stress response in plants is a priority. The rise of -omics approaches provides key strategies to promote effective research in the field, facilitating the investigations from reference models to an increasing number of species, tolerant and sensitive genotypes. Integrated multilevel approaches, based on molecular investigations at genomics, transcriptomics, proteomics and metabolomics levels, are now feasible, expanding the opportunities to clarify key molecular aspects involved in responses to abiotic stresses. To this aim, bioinformatics has become fundamental for data production, mining and integration, and necessary for extracting valuable information and for comparative efforts, paving the way to the modeling of the involved processes. We provide here an overview of bioinformatics resources for research on plant abiotic stresses, describing collections from -omics efforts in the field, ranging from raw data to complete databases or platforms, highlighting opportunities and still open challenges in abiotic stress research based on -omics technologies.

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