scholarly journals Automatic Emotion Recognition for the Calibration of Autonomous Driving Functions

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 518 ◽  
Author(s):  
Jacopo Sini ◽  
Antonio Costantino Marceddu ◽  
Massimo Violante
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Social Acceptance ◽  
Autonomous Driving ◽  
Recognition System ◽  
Machine Learning Techniques ◽  
Objective Method ◽  
Proof Of Concept ◽  
Complex Activity ◽  
Learning Techniques

The development of autonomous driving cars is a complex activity, which poses challenges about ethics, safety, cybersecurity, and social acceptance. The latter, in particular, poses new problems since passengers are used to manually driven vehicles; hence, they need to move their trust from a person to a computer. To smooth the transition towards autonomous vehicles, a delicate calibration of the driving functions should be performed, making the automation decision closest to the passengers’ expectations. The complexity of this calibration lies in the presence of a person in the loop: different settings of a given algorithm should be evaluated by assessing the human reaction to the vehicle decisions. With this work, we for an objective method to classify the people’s reaction to vehicle decisions. By adopting machine learning techniques, it is possible to analyze the passengers’ emotions while driving with alternative vehicle calibrations. Through the analysis of these emotions, it is possible to obtain an objective metric about the comfort feeling of the passengers. As a result, we developed a proof-of-concept implementation of a simple, yet effective, emotions recognition system. It can be deployed either into real vehicles or simulators, during the driving functions calibration.

scholarly journals Analysis of Teeline Shorthand Recognition using Machine Learning and Deep Learning Techniques.

2020 ◽  
Vol 9 (4) ◽  
pp. 2133-2138
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Recognition System ◽  
Machine Learning Techniques ◽  
Handheld Devices ◽  
Original Text ◽  
Language Recognition ◽  
Learning Techniques ◽  
Learning Technique ◽  
Short Time

In order to take notes of the speech delivered by the VIPs in the short time short hand language is employed. Mainly there are two shorthand languages namely Pitman and Teeline. An automatic shorthand language recognition system is essential in order to make use of the handheld devices for speedy conversion to the original text. The paper addresses and compares the recognition of the Teeline alphabets using the Machine learning (SVM and KNN) and deep learning (CNN) techniques. The dataset has been prepared using the digital pen and the same is processed and stored using the android application. The prepared dataset is fed to the proposed system and accuracy of recognition is compared. Deep learning technique gave higher accuracy compared to machine learning techniques. MATLAB 2018b platform is used for implementation of the experimental setup.

scholarly journals Human-like Speed Modelling for Autonomous Vehicles during Car-Following at Intersections

2021 ◽  
Author(s):  
Mostafa H Tawfeek
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Machine Learning Techniques ◽  
Behavioral Measures ◽  
Car Following ◽  
Learning Techniques ◽  
Proposed Model ◽  
Intersection Control

This study aims at modelling drivers’ speed in car-following during braking situations at intersections to estimate a safe comfortable human-like speed at the minimum car-following distance for Autonomous Vehicles (AV). Several car-following behavioral measures at different times before reaching the minimum following distance and the intersection control type (signalized or unsignalized) were extracted to train the model using three machine learning techniques. The results showed that the XGBoost model is superior when compared to other techniques with R-squared values of 0.99 and 0.97 for training and testing datasets. The results also indicated that the control type impact driver speed at the minimum following distance. The modelled speed will provide more comfortable speed to the AV riders and will not violate the expectations of the surrounding traditional vehicle drivers. Also, the proposed model can be adopted to enhance current car-following models by considering the effect of intersections and its control type.

Performance evaluation of 2D face recognition techniques under image processing attacks

2018 ◽  
Vol 32 (19) ◽  
pp. 1850212 ◽  
Author(s):  
Sahil Sharma ◽  
Vijay Kumar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Face Recognition ◽  
Recognition System ◽  
Two Dimensions ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ensemble Techniques ◽  
Learning Techniques ◽  
Face Recognition System

Face recognition is a vastly researched topic in the field of computer vision. A lot of work have been done for facial recognition in two dimensions and three dimensions. The amount of work done with face recognition invariant of image processing attacks is very limited. This paper presents a total of three classes of image processing attacks on face recognition system, namely image enhancement attacks, geometric attacks and the image noise attacks. The well-known machine learning techniques have been used to train and test the face recognition system using two different databases namely Bosphorus Database and University of Milano Bicocca three-dimensional (3D) Face Database (UMBDB). Three classes of classification models, namely discriminant analysis, support vector machine and k-nearest neighbor along with ensemble techniques have been implemented. The significance of machine learning techniques has been mentioned. The visual verification has been done with multiple image processing attacks.

Machine Learning Techniques in Handwriting Recognition

2012 ◽  
pp. 12-29 ◽  
Author(s):  
Hong Lee ◽  
Brijesh Verma ◽  
Michael Li ◽  
Ashfaqur Rahman
Keyword(s):  
Machine Learning ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Research Area ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Handwritten Text ◽  
Learning Techniques ◽  
Active Research ◽  
Offline Handwriting Recognition

Handwriting recognition is a process of recognizing handwritten text on a paper in the case of offline handwriting recognition and on a tablet in the case of online handwriting recognition and converting it into an editable text. In this chapter, the authors focus on offline handwriting recognition, which means that recognition system accepts a scanned handwritten page as an input and outputs an editable recognized text. Handwriting recognition has been an active research area for more than four decades, but some of the major problems still remained unsolved. Many techniques, including the machine learning techniques, have been used to improve the accuracy. This chapter focuses on describing the problems of handwriting recognition and presents the solutions using machine learning techniques for solving major problems in handwriting recognition. The chapter also reviews and presents the state of the art techniques with results and future research for improving handwriting recognition.

scholarly journals Better Not to Use Vulnerability’s Reference for Exploitability Prediction

2020 ◽  
Vol 10 (7) ◽  
pp. 2555 ◽  
Author(s):  
Heedong Yang ◽  
Seungsoo Park ◽  
Kangbin Yim ◽  
Manhee Lee
Keyword(s):  
Machine Learning ◽  
Prediction Performance ◽  
Machine Learning Techniques ◽  
Proof Of Concept ◽  
Release Date ◽  
Software Companies ◽  
Reference Information ◽  
Learning Techniques ◽  
Test Sets ◽  
The Difference

About half of all exploit codes will become available within about two weeks of the release date of its vulnerability. However, 80% of the released vulnerabilities are never exploited. Since putting the same effort to eliminate all vulnerabilities can be somewhat wasteful, software companies usually use different methods to assess which vulnerability is more serious and needs an immediate patch. Recently, there have been some attempts to use machine learning techniques to predict a vulnerability’s exploitability. In doing so, a vulnerability’s related URL, called its reference, is commonly used as a machine learning algorithm’s feature. However, we found that some references contained proof-of-concept codes. In this paper, we analyzed all references in the National Vulnerability Database and found that 46,202 of them contained such codes. We compared prediction performances between feature matrix with and without reference information. Experimental results showed that test sets that used references containing proof-of-concept codes had better prediction performance than ones that used references without such codes. Even though the difference is not huge, it is clear that references having answer information contributed to the prediction performance, which is not desirable. Thus, it is better not to use reference information to predict vulnerability exploitation.

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