Array Shadow State Compression for Precise Dynamic Race Detection (T)

2015 ◽  
Author(s):  
James R. Wilcox ◽  
Parker Finch ◽  
Cormac Flanagan ◽  
Stephen N. Freund
Keyword(s):  
Race Detection ◽  
Shadow State

Symbolic data race detection for Habanero programs

2019 ◽  
Vol 44 (4) ◽  
pp. 18-18 ◽  
Author(s):  
Egor Namakonov ◽  
Eric Mercer ◽  
Pavel Parizek ◽  
Kyle Storey
Keyword(s):  
Data Race ◽  
Data Race Detection ◽  
Race Detection ◽  
Symbolic Data

High-coverage, unbounded sound predictive race detection

2018 ◽  
Vol 53 (4) ◽  
pp. 374-389 ◽  
Author(s):  
Jake Roemer ◽  
Kaan Genç ◽  
Michael D. Bond
Keyword(s):  
High Coverage ◽  
Race Detection

scholarly journals Type-based race detection for Java

2000 ◽  
Vol 35 (5) ◽  
pp. 219-232 ◽  
Author(s):  
Cormac Flanagan ◽  
Stephen N. Freund
Keyword(s):  
Race Detection

Nature-inspired hybrid deep learning for race detection by face shape features

2020 ◽  
Vol 13 (3) ◽  
pp. 365-388
Author(s):  
Asha Sukumaran ◽  
Thomas Brindha
Keyword(s):  
Feature Extraction ◽  
Hybrid Algorithm ◽  
Shape Features ◽  
Error Performance ◽  
Color Feature ◽  
Content Type ◽  
Dragonfly Algorithm ◽  
Face Shape ◽  
Race Detection ◽  
Better Than

PurposeThe humans are gifted with the potential of recognizing others by their uniqueness, in addition with more other demographic characteristics such as ethnicity (or race), gender and age, respectively. Over the decades, a vast count of researchers had undergone in the field of psychological, biological and cognitive sciences to explore how the human brain characterizes, perceives and memorizes faces. Moreover, certain computational advancements have been developed to accomplish several insights into this issue.Design/methodology/approachThis paper intends to propose a new race detection model using face shape features. The proposed model includes two key phases, namely. (a) feature extraction (b) detection. The feature extraction is the initial stage, where the face color and shape based features get mined. Specifically, maximally stable extremal regions (MSER) and speeded-up robust transform (SURF) are extracted under shape features and dense color feature are extracted as color feature. Since, the extracted features are huge in dimensions; they are alleviated under principle component analysis (PCA) approach, which is the strongest model for solving “curse of dimensionality”. Then, the dimensional reduced features are subjected to deep belief neural network (DBN), where the race gets detected. Further, to make the proposed framework more effective with respect to prediction, the weight of DBN is fine tuned with a new hybrid algorithm referred as lion mutated and updated dragon algorithm (LMUDA), which is the conceptual hybridization of lion algorithm (LA) and dragonfly algorithm (DA).FindingsThe performance of proposed work is compared over other state-of-the-art models in terms of accuracy and error performance. Moreover, LMUDA attains high accuracy at 100th iteration with 90% of training, which is 11.1, 8.8, 5.5 and 3.3% better than the performance when learning percentage (LP) = 50%, 60%, 70%, and 80%, respectively. More particularly, the performance of proposed DBN + LMUDA is 22.2, 12.5 and 33.3% better than the traditional classifiers DCNN, DBN and LDA, respectively.Originality/valueThis paper achieves the objective detecting the human races from the faces. Particularly, MSER feature and SURF features are extracted under shape features and dense color feature are extracted as color feature. As a novelty, to make the race detection more accurate, the weight of DBN is fine tuned with a new hybrid algorithm referred as LMUDA, which is the conceptual hybridization of LA and DA, respectively.

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