scholarly journals KernelFaRer

2021 ◽  
Vol 18 (3) ◽  
pp. 1-22
Author(s):  
João P. L. De Carvalho ◽  
Braedy Kuzma ◽  
Ivan Korostelev ◽  
José Nelson Amaral ◽  
Christopher Barton ◽  
...  
Keyword(s):  
Deep Learning ◽  
Linear Algebra ◽  
Pattern Matching ◽  
Source Code ◽  
Matrix Multiplication ◽  
Fully Integrated ◽  
General Matrix ◽  
The Past ◽  
Compiler Framework ◽  
Alternative Solutions

Well-crafted libraries deliver much higher performance than code generated by sophisticated application programmers using advanced optimizing compilers. When a code pattern for which a well-tuned library implementation exists is found in the source code of an application, the highest performing solution is to replace the pattern with a call to the library. Idiom-recognition solutions in the past either required pattern matching machinery that was outside of the compilation framework or provided a very brittle solution that would fail even for minor variants in the pattern source code. This article introduces Kernel Find & Replacer ( KernelFaRer ), an idiom recognizer implemented entirely in the existing LLVM compiler framework. The versatility of KernelFaRer is demonstrated by matching and replacing two linear algebra idioms, general matrix-matrix multiplication (GEMM), and symmetric rank-2k update (SYR2K). Both GEMM and SYR2K are used extensively in scientific computation, and GEMM is also a central building block for deep learning and computer graphics algorithms. The idiom recognition in KernelFaRer is much more robust than alternative solutions, has a much lower compilation overhead, and is fully integrated in the broadly used LLVM compilation tools. KernelFaRer replaces existing GEMM and SYR2K idioms with computations performed by BLAS, Eigen, MKL (Intel’s x86), ESSL (IBM’s PowerPC), and BLIS (AMD). Gains in performance that reach 2000× over hand-crafted source code compiled at the highest optimization level demonstrate that replacing application code with library call is a performant solution.

scholarly journals Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion

2021 ◽  
Author(s):  
Zhenguang Liu ◽  
Peng Qian ◽  
Xiang Wang ◽  
Lei Zhu ◽  
Qinming He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Expert Knowledge ◽  
Source Code ◽  
Smart Contracts ◽  
Learning Approaches ◽  
Vulnerability Detection ◽  
Security Issues ◽  
The Past ◽  
Smart Contract ◽  
Local Expert

Smart contracts hold digital coins worth billions of dollars, their security issues have drawn extensive attention in the past years. Towards smart contract vulnerability detection, conventional methods heavily rely on fixed expert rules, leading to low accuracy and poor scalability. Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge. In this paper, we explore combining deep learning with expert patterns in an explainable fashion. Specifically, we develop automatic tools to extract expert patterns from the source code. We then cast the code into a semantic graph to extract deep graph features. Thereafter, the global graph feature and local expert patterns are fused to cooperate and approach the final prediction, while yielding their interpretable weights. Experiments are conducted on all available smart contracts with source code in two platforms, Ethereum and VNT Chain. Empirically, our system significantly outperforms state-of-the-art methods. Our code is released.

Implementasi Algoritma Hamming Distance dan Brute Force dalam Mendeteksi Kemiripan Source Code Bahasa Pemrograman C

2017 ◽  
Vol 8 (2) ◽  
Author(s):  
Andreas Budiman ◽  
Dennis Gunawan ◽  
Seng Hansun
Keyword(s):  
College Presidents ◽  
Hamming Distance ◽  
Source Code ◽  
Structural Type ◽  
Brute Force ◽  
Important Thing ◽  
The Past ◽  
Index Terms ◽  
Better Than

Plagiarism is a behavior that causes violence of copyrights. Survey shows 55% of college presidents say that plagiarism in students’ papers has increased over the past 10 years. Therefore, an application for detecting plagiarism is needed, especially for teachers. This plagiarism checker application is made by using Visual C# 2010. The plagiarism checker uses hamming distance algorithm for matching line code of the source code. This algorithm works by matching the same length string of the code programs. Thus, it needs brute will be matched with hamming distance. Another important thing for detecting plagiarism is the preprocessing, which is used to help the algorithm for detecting plagiarized source code. This paper shows that the application works good in detecting plagiarism, the hamming distance algorithm and brute force algorithm works better than levenstein distance algorithm for detecting structural type of plagiarism and this thesis also shows that the preprocessing could help the application to increase its percentage and its accuracy. Index Terms—Brute Force, Hamming Distance, Plagiarisme, Preprocessing.

Literature survey of deep learning-based vulnerability analysis on source code

IET Software ◽  
2020 ◽  
Vol 14 (6) ◽  
pp. 654-664
Author(s):  
Abubakar Omari Abdallah Semasaba ◽  
Wei Zheng ◽  
Xiaoxue Wu ◽  
Samuel Akwasi Agyemang
Keyword(s):  
Deep Learning ◽  
Source Code ◽  
Vulnerability Analysis ◽  
Literature Survey

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Register-based implementation of the sparse general matrix-matrix multiplication on GPUs

2018 ◽  
Vol 53 (1) ◽  
pp. 407-408
Author(s):  
Junhong Liu ◽  
Xin He ◽  
Weifeng Liu ◽  
Guangming Tan
Keyword(s):  
Matrix Multiplication ◽  
General Matrix

scholarly journals Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3046
Author(s):  
Shervin Minaee ◽  
Mehdi Minaei ◽  
Amirali Abdolrashidi
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Visualization Technique ◽  
Convolutional Network ◽  
The Past ◽  
Multiple Datasets ◽  
The Face ◽  
Traditional Approaches

Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier’s output. Through experimental results, we show that different emotions are sensitive to different parts of the face.

scholarly journals Feature Extraction for Finger-Vein-Based Identity Recognition

2021 ◽  
Vol 7 (5) ◽  
pp. 89
Author(s):  
George K. Sidiropoulos ◽  
Polixeni Kiratsa ◽  
Petros Chatzipetrou ◽  
George A. Papakostas
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Methods ◽  
Identity Recognition ◽  
Learning Framework ◽  
Finger Vein ◽  
The Past ◽  
Biometric Systems ◽  
Vein Recognition ◽  
Finger Vein Recognition

This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

Beyond Deep Learning: An Econometric Example

2020 ◽  
Vol 28 (Supp01) ◽  
pp. 31-38 ◽  
Author(s):  
Ruofan Liao ◽  
Paravee Maneejuk ◽  
Songsak Sriboonchitta
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Prediction Models ◽  
Original Data ◽  
Parametric Model ◽  
Parametric Models ◽  
The Past ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Linear And Nonlinear

In the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learning-based predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural networks with parametric model: namely, to train neural networks not on the original data, but on the differences between the actual data and the predictions of the parametric model. On the example of predicting currency exchange rate, we show that this idea indeed leads to more accurate predictions.

scholarly journals Laplacian networks: bounding indicator function smoothness for neural networks robustness

2021 ◽  
Vol 10 ◽  
Author(s):  
Carlos Lassance ◽  
Vincent Gripon ◽  
Antonio Ortega
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Supervised Learning ◽  
Indicator Function ◽  
Training Data ◽  
Theoretical Justification ◽  
The Past ◽  
Noisy Examples

For the past few years, deep learning (DL) robustness (i.e. the ability to maintain the same decision when inputs are subject to perturbations) has become a question of paramount importance, in particular in settings where misclassification can have dramatic consequences. To address this question, authors have proposed different approaches, such as adding regularizers or training using noisy examples. In this paper we introduce a regularizer based on the Laplacian of similarity graphs obtained from the representation of training data at each layer of the DL architecture. This regularizer penalizes large changes (across consecutive layers in the architecture) in the distance between examples of different classes, and as such enforces smooth variations of the class boundaries. We provide theoretical justification for this regularizer and demonstrate its effectiveness to improve robustness on classical supervised learning vision datasets for various types of perturbations. We also show it can be combined with existing methods to increase overall robustness.

scholarly journals Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 52
Author(s):  
Thomas Lee ◽  
Susan Mckeever ◽  
Jane Courtney
Keyword(s):  
Deep Learning ◽  
Research Work ◽  
Learning Approaches ◽  
Clear Definition ◽  
Top Down ◽  
Research Activity ◽  
Comprehensive Overview ◽  
The Past ◽  
Computer Vision Applications ◽  
Definition Of

With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the Society of Automotive Engineers, to specific drone tasks in order to create a clear definition of autonomy when applied to drones. A top–down examination of research work in the area is conducted, focusing on drone navigation tasks, in order to understand the extent of research activity in each area. Autonomy levels are cross-checked against the drone navigation tasks addressed in each work to provide a framework for understanding the trajectory of current research. This work serves as a guide to research in drone autonomy with a particular focus on Deep Learning-based solutions, indicating key works and areas of opportunity for development of this area in the future.

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