Abstract In the modern online world, users are often asked for a permission to track their actions as a permission to “allow cookies”. The gathered information could be very valuable for a potential advertiser. However, online tracking is not only a benefit for a user but also a threat to the user’s privacy. This information combined with a targeted advertisement on a mass scale has potential to alter behaviour of large groups. This study summarises previous academic work on online user tracking and anti-tracking measures. As a result, it describes the current mechanisms used to track a user, as well as some methods that can be applied to reduce tracking. The study concludes that government legislation and open dialog between Internet users and advertisers might be the only way to ensure online privacy.

Abstract The world is heading towards more modernized and digitalized data and therefore a significant growth is observed in the active number of social media users with each passing day. Each post and comment can give an insight into valuable information about a certain topic or issue, a product or a brand, etc. Similarly, the process to uncover the underlying information from the opinion that a person keeps about any entity is called a sentiment analysis. The analysis can be carried out through two main approaches, i.e., either lexicon-based or machine learning algorithms. A significant amount of work in the different domains has been done in numerous languages for sentiment analysis, but minimal research has been conducted on the national language of Pakistan, which is Urdu. Twitter users who are familiar with Urdu update the tweets in two different textual formats either in Urdu Script (Nastaleeq) or in Roman Urdu. Thus, the paper is an attempt to perform the sentiment analysis on the Urdu language by extracting the tweets (Nastaleeq and Roman Urdu both) from Twitter using Tweepy API. A machine learning-based approach has been adopted for this study and the tool opted for the purpose is WEKA. The best algorithm was identified based on evaluation metrics, which comprise the number of correctly and incorrectly classified instances, accuracy, precision, and recall. SMO was found to be the most suitable machine learning algorithm for performing the sentiment analysis on Urdu (Nastaleeq) tweets, while the Roman Urdu Random Forest algorithm was identified as the best one.

Abstract Deconvolutional neural networks are a very accurate tool for semantic image segmentation. Segmenting curvilinear meandering regions is a typical task in computer vision applied to navigational, civil engineering, and defence problems. In the study, such regions of interest are modelled as meandering transparent stripes whose width is not constant. The stripe on the white background is formed by the upper and lower non-parallel black curves so that the upper and lower image parts are completely separated. An algorithm of generating datasets of such regions is developed. It is revealed that deeper networks segment the regions more accurately. However, the segmentation is harder when the regions become bigger. This is why an alternative method of the region segmentation consisting in segmenting the upper and lower image parts by subsequently unifying the results is not effective. If the region of interest becomes bigger, it must be squeezed in order to avoid segmenting the empty image. Once the squeezed region is segmented, the image is conversely rescaled to the original view. To control the accuracy, the mean BF score having the least value among the other accuracy indicators should be maximised first.

Abstract Uniform multi-dimensional designs of experiments for effective research in computer modelling are highly demanded. The combinations of several one-dimensional quasi-random sequences with a uniform distribution are used to create designs with high homogeneity, but their optimal choice is a separate problem, the solution of which is not trivial. It is believed that now the best results are achieved using Sobol’s LPτ-sequences, but this is not observed in all cases of their combinations. The authors proposed the creation of effective uniform designs with guaranteed acceptably low discrepancy using recursive Rd-sequences and not requiring additional research to find successful combinations of vectors set distributed in a single hypercube. The authors performed a comparative analysis of both approaches using indicators of centred and wrap-around discrepancies, graphical visualization based on Voronoi diagrams. The conclusion was drawn on the practical use of the proposed approach in cases where the requirements for the designs allowed restricting to its not ideal but close to it variant with low discrepancy, which was obtained automatically without additional research.

Abstract A problem of single image super-resolution is considered, where the goal is to recover one high-resolution image from one low-resolution image. Whereas this problem has been successfully solved so far by the known VDSR network, such an approach still cannot give an overall beneficial effect compared to bicubic interpolation. This is so due to the fact that the image reconstruction quality has been estimated separately by three subjective factors. Moreover, the original VDSR network consisting of 20 convolutional layers is apparently not optimal by its depth. This is why here those factors are aggregated, and the network performance is deemed by a single estimator. Then the depth is tried to be decreased (truncation) along with adjusting the learning rate drop factor. Finally, a plausible improvement of the VDSR network is confirmed. The best truncated network, performing by almost 3.2 % better than bicubic interpolation, occupies less memory space and is about 1.44 times faster than the original VDSR network for images of a medium size.

Abstract The article shows the relevance of the application of discrete models of rod structures of radio-electronic means (REM) for the study of their behaviour under transient loading. A discrete model of the propagation of harmonic waves in the rod and the study of standing waves are proposed. Computational experiments using the proposed model are conducted. The results show that the model accurately reflects qualitative dynamics of the physical processes in the elastic rod while the waves of elastic deformations are passing through. The proposed models are used for software implementations of systems of mechanical simulation of the behaviour of rod structures.

Abstract The relevance and nature of a new technology for measurement of vibrational displacement of a material point through normal toward the object plane are stated in the article. This technology provides registration and processing of images of a round mark or a matrix of round marks, which are applied to the surface of a control object. A measuring signal here is the module of radius increment of the round mark image at vibrational blurring of this image. The method for calculation of the given error of measurements, as a function of a number of pixels of the round mark image, has been developed and proven in the present research. The results of pilot studies are given. Linearity of transformation of the measured size into a measuring signal has been proven. The conditions of a technical compromise between the field of view area of a recording device during distribution measurement of vibrational displacements along the surface of a control object, and the accuracy of this measurement are determined. The results are illustrated with numerical examples of calculations of the given error of measurements in the set field of view and the one at the given maximum set error of measurements.

Abstract A complex classification task as scene recognition is considered in the present research. Scene recognition tasks are successfully solved by the paradigm of transfer learning from pretrained convolutional neural networks, but a problem is that the eventual size of the network is huge despite a common scene recognition task has up to a few tens of scene categories. Thus, the goal is to ascertain possibility of a size reduction. The modelling recognition task is a small dataset of 4485 grayscale images broken into 15 image categories. The pretrained network is AlexNet dealing with much simpler image categories whose number is 1000, though. This network has two fully connected layers, which can be potentially reduced or deleted. A regular transfer learning network occupies about 202.6 MB performing at up to 92 % accuracy rate for the scene recognition. It is revealed that deleting the layers is not reasonable. The network size is reduced by setting a fewer number of filters in the 17th and 20th layers of the AlexNet-based networks using a dichotomy principle or similar. The best truncated network with 384 and 192 filters in those layers performs at 93.3 % accuracy rate, and its size is 21.63 MB.

Abstract The goal of the paper is to provide a vague summary of currently existing blockchain use cases in the information technology industry. Respective use cases have been examined in already existing scientific papers, Master Theses, industry white papers and blogs of industry experts. The paper also contains a description of blockchain main technological aspects and working principles, which allows making the assessment of the presented use cases. For each use case respective companies or organisations are added that are applying or testing the given solution. Due to research limitations the paper should not be considered an exhaustive blockchain use case description. The paper also provides short introduction into a feasibility analysis of specific blockchain use case. The authors describe the basic steps of potential idea evaluation with regards to blockchain main aspects. It helps understand the necessity for development of a detailed blockchain feasibility model.

Abstract The paper describes the implementation of organic benchmarks for Java EE and ASP.NET Core, which are used to compare the performance characteristics of the language runtimes. The benchmarks are created as REST services, which process data in the JSON format. The ASP.NET Core implementation utilises the Kestrel web server, while the Java EE implementation uses Apache TomEE, which is based on Apache Tomcat. A separate service is created for invoking the benchmarks and collecting their results. It uses Express with ES6 (for its async features), Redis and MySQL. A web-based interface for utilising this service and displaying the results is also created, using Angular 5.