Selected Papers from the First International Symposium on Future ICT (Future-ICT 2019) in Conjunction with 4th International Symposium on Mobile Internet Security (MobiSec 2019)

The International Symposium on Future ICT (Future-ICT 2019) in conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019) was held on 17–19 October 2019 in Taichung, Taiwan. The symposium provided academic and industry professionals an opportunity to discuss the latest issues and progress in advancing smart applications based on future ICT and its relative security. The symposium aimed to publish high-quality papers strictly related to the various theories and practical applications concerning advanced smart applications, future ICT, and related communications and networks. It was expected that the symposium and its publications would be a trigger for further related research and technology improvements in this field

Multi-sensor fusion for human-robot interaction in crowded environments

For challenges associated with the ageing population, robot assistants are becoming a promising solution. Human-Robot Interaction (HRI) allows a robot to understand the intention of humans in an environment and react accordingly. This thesis proposes HRI techniques to facilitate the transition of robots from lab-based research to real-world environments. The HRI aspects addressed in this thesis are illustrated in the following scenario: an elderly person, engaged in conversation with friends, wishes to attract a robot's attention. This composite task consists of many problems. The robot must detect and track the subject in a crowded environment. To engage with the user, it must track their hand movement. Knowledge of the subject's gaze would ensure that the robot doesn't react to the wrong person. Understanding the subject's group participation would enable the robot to respect existing human-human interaction. Many existing solutions to these problems are too constrained for natural HRI in crowded environments. Some require initial calibration or static backgrounds. Others deal poorly with occlusions, illumination changes, or real-time operation requirements. This work proposes algorithms that fuse multiple sensors to remove these restrictions and increase the accuracy over the state-of-the-art. The main contributions of this thesis are: A hand and body detection method, with a probabilistic algorithm for their real-time association when multiple users and hands are detected in crowded environments; An RGB-D sensor-fusion hand tracker, which increases position and velocity accuracy by combining a depth-image based hand detector with Monte-Carlo updates using colour images; A sensor-fusion gaze estimation system, combining IR and depth cameras on a mobile robot to give better accuracy than traditional visual methods, without the constraints of traditional IR techniques; A group detection method, based on sociological concepts of static and dynamic interactions, which incorporates real-time gaze estimates to enhance detection accuracy.Open Acces

Multi-Class Classification for Identifying JPEG Steganography Embedding Methods

Over 725 steganography tools are available over the Internet, each providing a method for covert transmission of secret messages. This research presents four steganalysis advancements that result in an algorithm that identifies the steganalysis tool used to embed a secret message in a JPEG image file. The algorithm includes feature generation, feature preprocessing, multi-class classification and classifier fusion. The first contribution is a new feature generation method which is based on the decomposition of discrete cosine transform (DCT) coefficients used in the JPEG image encoder. The generated features are better suited to identifying discrepancies in each area of the decomposed DCT coefficients. Second, the classification accuracy is further improved with the development of a feature ranking technique in the preprocessing stage for the kernel Fisher s discriminant (KFD) and support vector machines (SVM) classifiers in the kernel space during the training process. Third, for the KFD and SVM two-class classifiers a classification tree is designed from the kernel space to provide a multi-class classification solution for both methods. Fourth, by analyzing a set of classifiers, signature detectors, and multi-class classification methods a classifier fusion system is developed to increase the detection accuracy of identifying the embedding method used in generating the steganography images. Based on classifying stego images created from research and commercial JPEG steganography techniques, F5, JP Hide, JSteg, Model-based, Model-based Version 1.2, OutGuess, Steganos, StegHide and UTSA embedding methods, the performance of the system shows a statistically significant increase in classification accuracy of 5%. In addition, this system provides a solution for identifying steganographic fingerprints as well as the ability to include future multi-class classification tools

Field Programmable Gate Arrays (FPGAs) II

This Edited Volume Field Programmable Gate Arrays (FPGAs) II is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Computer and Information Science. The book comprises single chapters authored by various researchers and edited by an expert active in the Computer and Information Science research area. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on Computer and Information Science, and open new possible research paths for further novel developments

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Use of Pattern Classification Algorithms to Interpret Passive and Active Data Streams from a Walking-Speed Robotic Sensor Platform

In order to perform useful tasks for us, robots must have the ability to notice, recognize, and respond to objects and events in their environment. This requires the acquisition and synthesis of information from a variety of sensors. Here we investigate the performance of a number of sensor modalities in an unstructured outdoor environment, including the Microsoft Kinect, thermal infrared camera, and coffee can radar. Special attention is given to acoustic echolocation measurements of approaching vehicles, where an acoustic parametric array propagates an audible signal to the oncoming target and the Kinect microphone array records the reflected backscattered signal. Although useful information about the target is hidden inside the noisy time domain measurements, the Dynamic Wavelet Fingerprint process (DWFP) is used to create a time-frequency representation of the data. A small-dimensional feature vector is created for each measurement using an intelligent feature selection process for use in statistical pattern classification routines. Using our experimentally measured data from real vehicles at 50 m, this process is able to correctly classify vehicles into one of five classes with 94% accuracy. Fully three-dimensional simulations allow us to study the nonlinear beam propagation and interaction with real-world targets to improve classification results

Lip print based authentication in physical access control Environments

Abstract: In modern society, there is an ever-growing need to determine the identity of a person in many applications including computer security, financial transactions, borders, and forensics. Early automated methods of authentication relied mostly on possessions and knowledge. Notably these authentication methods such as passwords and access cards are based on properties that can be lost, stolen, forgotten, or disclosed. Fortunately, biometric recognition provides an elegant solution to these shortcomings by identifying a person based on their physiological or behaviourial characteristics. However, due to the diverse nature of biometric applications (e.g., unlocking a mobile phone to cross an international border), no biometric trait is likely to be ideal and satisfy the criteria for all applications. Therefore, it is necessary to investigate novel biometric modalities to establish the identity of individuals on occasions where techniques such as fingerprint or face recognition are unavailable. One such modality that has gained much attention in recent years which originates from forensic practices is the lip. This research study considers the use of computer vision methods to recognise different lip prints for achieving the task of identification. To determine whether the research problem of the study is valid, a literature review is conducted which helps identify the problem areas and the different computer vision methods that can be used for achieving lip print recognition. Accordingly, the study builds on these areas and proposes lip print identification experiments with varying models which identifies individuals solely based on their lip prints and provides guidelines for the implementation of the proposed system. Ultimately, the experiments encapsulate the broad categories of methods for achieving lip print identification. The implemented computer vision pipelines contain different stages including data augmentation, lip detection, pre-processing, feature extraction, feature representation and classification. Three pipelines were implemented from the proposed model which include a traditional machine learning pipeline, a deep learning-based pipeline and a deep hybridlearning based pipeline. Different metrics reported in literature are used to assess the performance of the prototype such as IoU, mAP, accuracy, precision, recall, F1 score, EER, ROC curve, PR curve, accuracy and loss curves. The first pipeline of the current study is a classical pipeline which employs a facial landmark detector (One Millisecond Face Alignment algorithm) to detect the lip, SURF for feature extraction, BoVW for feature representation and an SVM or K-NN classifier. The second pipeline makes use of the facial landmark detector and a VGG16 or ResNet50 architecture. The findings reveal that the ResNet50 is the best performing method for lip print identification for the current study. The third pipeline also employs the facial landmark detector, the ResNet50 architecture for feature extraction with an SVM classifier. The development of the experiments is validated and benchmarked to determine the extent or performance at which it can achieve lip print identification. The results of the benchmark for the prototype, indicate that the study accomplishes the objective of identifying individuals based on their lip prints using computer vision methods. The results also determine that the use of deep learning architectures such as ResNet50 yield promising results.M.Sc. (Science