Computer Science
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ISSN 1508-2806
e-ISSN: 2300-7036
Issue Date
2019
Volume
Vol. 20
Number
No. 4
Description
Reviewed by: Bogdan Kwolek, Boguslaw Rymut, Pawel Topa, Aleksander Byrski, Michal Wrzeszcz, Raed Hassan, Mostafa Mohammed, Saad Dheyab, Piotr Breitkopf, Krzysztof Banas, Samuel Sujith, Laounamer Lamri, Adel Khelifi, Wojciech Turek
Journal Volume
Computer Science
Vol. 20 (2019)
Projects
Pages
Articles
Current research opportunities for image processing and computer vision
(Wydawnictwa AGH, 2019) Gupta, Abhishek
Image processing and computer vision is an important and essential area in today’s scenario. Several problems can be solved through computer vision techniques. There are a large number of challenges and opportunities which require skills in the field of computer vision to address them. Computer vision applications cover each band of the electromagnetic spectrum and there are numerous applications in every band. This article is targeted to the research students, scholars and researchers who are interested to solve the problems in the field of image processing and computer vision. It addresses the opportunities and current trends of computer vision applications in all emerging domains. The research needs are identified through available literature survey and classified in the corresponding domains. The possible exemplary images are collected from the different repositories available for research and shown in this paper. The opportunities mentioned in this paper are explained through the images so that a naive researcher can understand it well before proceeding to solve the corresponding problems. The databases mentioned in this article could be useful for researchers who are interested in further solving the problem. The motivation of the article is to expose the current opportunities in the field of image processing and computer vision along with corresponding repositories. Interested researchers who are working in the field can choose a problem through this article and can get the experimental images through the cited references for working further.
Novel approach for big data classification based on hybrid parallel dimensionality reduction using spark cluster
(Wydawnictwa AGH, 2019) Ali, Ahmed Hussein; Abdullah, Mahmood Zaki
The big data concept has elicited studies on how to accurately and efficiently extract valuable information from such huge dataset. The major problem during big data mining is data dimensionality due to a large number of dimensions in such datasets. This major consequence of high data dimensionality is that it affects the accuracy of machine learning (ML) classifiers, it also results in time wastage due to the presence of several redundant features in the dataset. This problem can be possibly solved using a fast feature reduction method. Hence, this study presents a fast HP-PL which is a new hybrid parallel feature reduction framework that utilizes spark to facilitate feature reduction on shared/distributed-memory clusters. The evaluation of the proposed HP-PL on KDD99 dataset showed the algorithm to be significantly faster than the conventional feature reduction techniques. The proposed technique required >1 minute to select 4 dataset features from over 79 features and 3,000,000 samples on a 3-node cluster (total of 21 cores). For the comparative algorithm, more than 2 hours was required to achieve the same feat. In the proposed system, Hadoop’s distributed file system (HDFS) was used to achieve distributed storage while Apache Spark was used as the computing engine. The model development was based on a parallel model with full consideration of the high performance and throughput of distributed computing. Conclusively, the proposed HP-PL method can achieve good accuracy with less memory and time compared to the conventional methods of feature reduction. This tool can be publicly accessed at https://github.com/ahmed/Fast-HP-PL.
Certificate-less digital signature technology for e-Governance solutions
(Wydawnictwa AGH, 2019) Dhir, Shuchi; Devi, Sumithra
In spite of the fact that digital signing is an essential requirement for implementation of e-governance solutions in any organization, its use in large scale Government ICT implementation is negligible in India. In order to understand the reasons for low-level acceptance of the technology, authors performed a detailed study of a famous e-governance initiative of India. The outcome of the study revealed that the reasons are related to the challenges concerning the use of cryptographic devices carrying private key and the complicated process of generation, maintenance and disposal of Digital Signature Certificates (DSC). The solution, for the challenges understood from the case study, required implementation of a certificateless technology where private keys should be generated as and when required rather than storing them on cryptographic devices. Although many solutions which provide certificateless technology exist, to date there have been no practical implementation for using biometrics for implementing the solution. This paper presents the first realistic architecture to implement Identity Based Cryptography with biometrics using RSA algorithm. The solution presented in the paper is capable of providing a certificate-less digital signature technology to the users, where public and private keys are generated on-the-fly.
Detecting gaze direction using robot-mounted and mobile-device cameras
(Wydawnictwa AGH, 2019) Jarosz, Mateusz; Nawrocki, Piotr; Placzkiewicz, Leszek; Śnieżyński, Bartłomiej; Zieliński, Marcin; Indurkhya, Bipin
Two common channels through which humans communicate are speech and gaze. Eye gaze is an important mode of communication: it allows people tobetter understand each others’ intentions, desires, interests, and so on. The goal of this research is to develop a framework for gaze triggered events that can be executed on a robot and mobile devices and allows to perform experiments. We experimentally evaluate the framework and techniques for extracting gaze direction based on a robot-mounted camera or a mobile-device camera that are implemented in the framework. We investigate the impact of light on the accuracy of gaze estimation, and also how the overall accuracy depends on user eye and head movements. Our research shows that light intensity is important, and the placement of a light source is crucial. All the robot-mounted gaze detection modules we tested were found to be similar with regard to their accuracy. The framework we developed was tested in a human-robot interaction experiment involving a job-interview scenario. The flexible structure of this scenario allowed us to test different components of the framework in varied real-world scenarios, which was very useful for progressing towards our long-term research goal of designing intuitive gaze-based interfaces for human robot communication.
Track finding with Deep Neural Networks
(Wydawnictwa AGH, 2019) Kucharczyk, Marcin; Wolter, Marcin
High energy physics experiments require fast and efficient methods for reconstructing the tracks of charged particles. The commonly used algorithms are sequential and the required CPU power increases rapidly with the number of tracks. Neural networks can speed up the process due to their capability of modeling complex non-linear data dependencies and finding all tracks in parallel. In this paper, we describe the application of the deep neural network for reconstructing straight tracks in a toy two-dimensional model. It is planned to apply this method to the experimental data obtained by the MUonE experiment at CERN.