Computer Science
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ISSN 1508-2806
e-ISSN: 2300-7036
Issue Date
2018
Volume
Vol. 19
Number
No. 3
Description
Reviewed by: Oris Sulaiman, Marcin Kurdziel, Janusz Bobulski, Rosa Perez, Emil Dumic, Tomasz Kryjak, Filip Gralinski, Alina Wroblewska, Marcin Kuta, Barbara Glut, Michal Slaski, Tamas Kozsik
Journal Volume
Computer Science
Vol. 19 (2018)
Projects
Pages
Articles
Generation of cryptographic keys with algorithm of polygon triangulation and catalan numbers
(Wydawnictwa AGH, 2018) Saračević, Muzafer; Selimi, Aybeyan; Selimović, Faruk
In this paper, a procedure for the application of one computational geometry algorithm in the process of generating hidden cryptographic keys from one segment of a 3D image is presented. The presented procedure consists of three phases. In the first phase, the separation of one segment from the 3D image and determination of the triangulation of the separated polygon are done. In the second phase, a conversion from the obtained triangulation of the polygon in the record that represents the Catalan key is done. In the third phase, the Catalan-key is applied in the encryption of the text based on the balanced parentheses combinatorial problem.
Lifelogging system based on averaged Hidden Markov Models: dangerous activities recognition for caregiver support
(Wydawnictwa AGH, 2018) Postawka, Aleksandra; Rudy, Jarosław
In this paper, a prototype lifelogging system for monitoring people with cognitive disabilities and elderly people as well as a method for the automatic detection of dangerous activities are presented. The system allows for the remote monitoring of observed people via an Internet website and respects the privacy of the people by displaying their silhouettes instead of their actual images. The application allows for the viewing of both real-time and historical data. The lifelogging data (skeleton coordinates) needed for posture and activity recognition are acquired using Microsoft Kinect 2.0. Several activities are marked as potentially dangerous and generate alarms sent to caregivers upon detection. Recognition models are developed using Averaged Hidden Markov Models with multiple learning sequences. Action recognition includes methods for dierentiating between normal and potentially dangerous activities (e.g., self-aggressive autistic behavior) using the same motion trajectory. Some activity recognition examples and results are presented.
FPGA implementation of procedures for video quality assessment
(Wydawnictwa AGH, 2018) Wielgosz, Maciej; Karwatowski, Michał; Pietroń, Marcin; Wiatr, Kazimierz
The video resolutions used in a variety of media are constantly rising. While manufacturers struggle to perfect their screens, it is also important to ensure the high quality of the displayed image. Overall quality can be measured using a Mean Opinion Score (MOS). Video quality can be affected by miscellaneous artifacts appearing at every stage of video creation and transmission. In this paper, we present a solution to calculate four distinct video quality metrics that can be applied to a real-time video quality assessment system. Our assessment module is capable of processing 8K resolution in real time set at a level of 30 frames per second. The throughput of 2.19 GB/s surpasses the performance of pure software solutions. The module was created using a high-level language to concentrate on architectural optimization.
Building semantic user profile for polish web news portal
(Wydawnictwa AGH, 2018) Misztal-Radecka, Joanna
The aim of this research is to construct meaningful user profiles that are the most descriptive of user interests in the context of the media content that they browse. We use two distinct state-of-the-art numerical text-representation techniques: LDA topic modeling and Word2Vec word embeddings. We train our models on the collection of news articles in Polish and compare them with a model built on a general language corpus. We compare the performance of these algorithms on two practical tasks. First, we perform a qualitative analysis of the semantic relationships for similar article retrieval, and then we evaluate the predictive performance of distinct feature combinations for user gender classification. We apply the algorithms to the real-world dataset of Polish news service Onet. Our results show that the choice of text representation depends on the task - Word2Vec is more suitable for text comparison, especially for short texts such as titles. In the gender classification task, the best performance is obtained with a combination of features: topics from the article text and word embeddings from the title.
Using erlang in research and education in a technical university
(Wydawnictwa AGH, 2018) Petrov, Iurii; Alexeyenko, Andrey; Ivanova, Galina
This paper addresses the problem of using functional programming (FP) languages for research and educational purposes. In order to identify the problems associated with the use of FP languages such as Erlang, an experiment consisting of two surveys was performed. The first survey was anonymous and aimed at establishing whether the participants prefer object-oriented or functional coding. The second one was a survey made after the students finished an Erlang course. The results of these two surveys demonstrate that functional programming is underrated with no apparent reasons. Possible steps to address this problem are suggested.