Computer Science
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ISSN 1508-2806
e-ISSN: 2300-7036
Issue Date
2019
Volume
Vol. 20
Number
No. 3
Description
Reviewed by: Daniela Zaharie, Marcin Kurdziel, Lukasz Faber, Chris Gniady, Piotr Dziurzanski, Malgorzata Zajecka, Jerzy Pejas, Aleksander Byrski, Benedita Malheiro
Journal Volume
Computer Science
Vol. 20 (2019)
Projects
Pages
Articles
Exploring convolutional auto-encoders for representation learning on networks
(Wydawnictwa AGH, 2019) Nerurkar, Pranav Ajeet; Chandane, Madhav; Bhirud, Sunil
A multitude of important real-world or synthetic systems possess network structures. Extending learning techniques such as neural networks to process such non-Euclidean data is therefore an important direction for machine learning re- search. However, this domain has received comparatively low levels of attention until very recently. There is no straight-forward application of machine learning to network data, as machine learning tools are designed for i:i:d data, simple Euclidean data, or grids. To address this challenge, the technical focus of this dissertation is on the use of graph neural networks for network representation learning (NRL), i.e., learning the vector representations of nodes in networks. Learning the vector embeddings of graph-structured data is similar to embedding complex data into low-dimensional geometries. After the embedding process is completed, the drawbacks associated with graph-structured data are overcome. The current inquiry proposes two deep-learning auto-encoder-based approaches for generating node embeddings. The drawbacks in such existing auto-encoder approaches as shallow architectures and excessive parameters are tackled in the proposed architectures by using fully convolutional layers. Extensive experiments are performed on publicly available benchmark network datasets to highlight the validity of this approach.
Application of modified chebyshev polynomials in asymmetric cryptography
(Wydawnictwa AGH, 2019) Lawnik, Marcin; Kapczyński, Adrian
Based on Chebyshov polynomials, one can create an asymmetric cryptosystem that allows for secure communication. Such a cryptosystem is based on the fact that these polynomials form a semi-group due to the composition operation. This article presents two new cryptosystems based on modifications of Chebyshev's polynomials. The presented analysis shows that their security is the same as in the case of algorithms associated with the problem of discrete logarithms. The article also shows methods that allow for the faster calculation of Chebyshev polynomials.
Adapting text categorization for manifest based android malware detection
(Wydawnictwa AGH, 2019) Çoban, Önder; Özel, Selma Ayşe
Malware is a shorthand of malicious software that are created with the intent of damaging hardware systems, stealing data, and causing a mess to make money, protest something, or even make war between governments. Malware is often spread by downloading some applications for your hardware from some download platforms. It is highly probable to face with a malware while you try to load some applications for your smart phones nowadays. Therefore it is very important that some tools are needed to detect malware before loading them to the hardware systems. There are mainly three different approaches to detect malware: i) static, ii) dynamic, and iii) hybrid. Static approach analyzes the suspicious program without executing it. Dynamic approach, on the other hand, executes the program in a controlled environment and obtains information from operating system during runtime. Hybrid approach, as its name implies, is the combination of these two approaches. Although static approach may seem to have some disadvantages, it is highly preferred because of its lower cost. In this paper, our aim is to develop a static malware detection system by using text categorization techniques. To reach our goal, we apply text mining techniques like feature extraction by using bag-of-words, n-grams, etc. from manifest content of suspicious programs, then apply text classification methods to detect malware. Our experimental results revealed that our approach is capable of detecting malicious applications with an accuracy between 94.0% and 99.3%.
Approach to classifying data with highly localized unmarked features using neural networks
(Wydawnictwa AGH, 2019) Grzeszczuk, Rafał
To face the increasing demand of quality healthcare, cutting-edge automation technology is being applied in demanding areas such as medical imaging. This paper proposes a novel approach to classification problems on datasets with sparse highly localized features. It is based on the use of a saliency map in the amplification of features. Unlike previous efforts, this approach does not use any prior information about feature localization. We present an experimental study based on the Diabetic Retinopathy classification problem, in which our method has shown to achieve an over 20%-higher accuracy in solving a two-class Diabetic Retinopathy classification problem than a naive approach based solely on residual neural networks. The dataset consists of 35,120 images of various qualities, inconsistent resolutions, and aspect ratios.
Evolutionary multi-agent system with crowding factor and mass center mechanisms for multiobjective optimisation
(Wydawnictwa AGH, 2019) Różański, Mateusz; Siwik, Leszek
This work presents some additional mechanisms for Evolutionary Multi-Agent Systems for Multiobjective Optimisation trying to solve problems with population stagnation and loss of diversity. Those mechanisms reward solutions located in a less crowded neighborhood and on edges of the frontier. Both techniques have been described and also some preliminary results have been shown.