<abstract><title style='display:none'>Abstract</title> Modeling of the object interaction is one of the core tasks during system analysis and design, because it gives developer an ability to define responsibilities of class objects and to sketch general architecture of software components. In this task an ability of automatic generation of the UML sequence diagram becomes one of the most important activities. The twohemisphere model contains enough information to define operations to perform by classes and therefore is investigated in this paper in the context of the UML sequence diagram generation.</abstract>

UML Sequence Diagram: Transformation from the Two-Hemisphere Model and Layout

<abstract><title style='display:none'>Abstract</title>Virtualization has enabled the commoditization of cloud computing, as the hardware resources have become available to run different environments and share computing resources amongst different enterprises. Two technology types are commonly used in virtualization of hardware, namely, hypervisor and container. The present paper concerns only container technologies.A comprehensive overview of the container technologies for virtualization and the dynamics of their popularity have not been available yet. Without such an overview, the informed fast choice of technologies is hindered. To fill this knowledge gap, a systemic literature review was conducted to reveal the most popular container technologies and the trends in their research.</abstract>

Containers for Virtualization: An Overview

<abstract><title style='display:none'>Abstract</title> Kinematic model is the basic aspect in robot design and motion planning. Kinematic models are idealized, however there exist certain specific aspects of particular robot or environment, so that during navigation, the robot can significantly deviate from the planned trajectory. To increase the accuracy of motions, kinematic model can be improved and to achieve that the artificial intelligence methods can be used. In case of fixed base robots different approaches are used to train kinematics, at the same time, for the mobile base robots it proves to be a more complicated task. The reason is that a mobile robot can move unbound with respect to environment thus it is difficult to control the platform without deviation from the target position, which leads to inaccuracy in the position estimate. This paper presents the method meant for improvement of the accuracy of motion of differential drive platform. Genetic programming is used to obtain the wheel velocity function, from which the coefficient, which describes different factor influence on motion, is obtained. As a result, the kinematic model of a particular platform for a particular task is obtained. This method is effective because the developed kinematic model is more specific than the general one.</abstract>

Use of Learning Methods to Improve Kinematic Models

<abstract><title style='display:none'>Abstract</title>Since a past decade, social media networking has become an essential part of everyone’s life affecting cultural, economic and social life of the people. According to internetlivestats.com, in March 2019 the Internet users reached 4 168 461 500, i.e., 50.08 % penetration of world population. According to Statista, in 2019 there are 2.22 billion social media networking users worldwide, i.e., 31 % of global social media networking penetration and it is expected that in 2021 this number will reach 3.02 billion. These social networking sites are attracting users from all walks of life and keeping these users’ data in the cloud. Today’s big challenge is related to an increase in volume, velocity, variety and veracity of data in social media networking, and this leads to creating several concerns, including privacy and security; on the other hand, it also proves as a tool to prevent and investigate cybercrime, if intelligently and smartly handled. The law enforcement agencies are putting their utmost efforts to prevent cybercrime by monitoring communications activities over the Internet. In this paper, the authors discuss recommendations and techniques for preventing cybercrime.</abstract>

Social Media-Related Cybercrimes and Techniques for Their Prevention

<abstract>
<title style='display:none'>Abstract</title>
In recent years, various domains have been influenced by the rapid growth of machine learning. Autonomous driving is an area that has tremendously developed in parallel with the advancement of machine learning. In autonomous vehicles, various machine learning components are used such as traffic lights recognition, traffic sign recognition, limiting speed and pathfinding. For most of these components, computer vision technologies with deep learning such as object detection, semantic segmentation and image classification are used. However, these machine learning models are vulnerable to targeted tensor perturbations called adversarial attacks, which limit the performance of the applications. Therefore, implementing defense models against adversarial attacks has become an increasingly critical research area. The paper aims at summarising the latest adversarial attacks and defense models introduced in the field of autonomous driving with machine learning technologies up until mid-2021.
</abstract>

Adversarial Attacks and Defense Technologies on Autonomous Vehicles: A Review

<abstract>
<title style='display:none'>Abstract</title>
MEMS gyroscopes are widely used as an alternative to the more expensive industrial IMUs. The instability of the lower cost MEMS gyroscopes creates a large demand for calibration algorithms. This paper provides an overview of existing calibration methods and describes the various types of errors found in gyroscope data. The proposed calibration method for gyroscope constants provides higher accuracy than datasheet constants. Furthermore, we show that using a different constant for each direction provides even higher accuracy.
</abstract>

Distance Sensor and Wheel Encoder Sensor Fusion Method for Gyroscope Calibration

<abstract>
<title style='display:none'>Abstract</title>
A method of heuristically solving large and extremely large traveling salesman problems is suggested. The solver is a specific genetic algorithm producing approximately shortest routes the fastest of known heuristics without losing much in accuracy. The method consists in parallelizing the problem by clustering its nodes and finding the best route passing through the centroids of the clusters. The open-loop subroutes of the clusters are connected via specific nodes. These specific nodes referred to as connectors are determined as those for which the distance to the depot is maximal and the distance to the cluster of the following subproblem is minimal. Thus, a bunch of smaller open-loop problems is solved instead of solving the whole (closed loop) problem. Extremely large problems should be clustered manually by imposing a mesh of rotated square cells. In this case, the connectors should be determined manually as well. A connector can also be approximated by a node which is the closest to the line connecting the centroids of the two clusters. The suggested parallelization can produce a very significant speedup depending on how many processor cores are simultaneously available. The factual speedup by the parallelization depends on the availability of processor cores, memory, and the processor clock frequency. The efficiency of the parallelization is maintained for a few hundred to a few million nodes by any number of clusters being less than the size of the average cluster.
</abstract>

Parallelization of the Traveling Salesman Problem by Clustering its Nodes and Finding the Best Route Passing through the Centroids

<abstract>
<title style='display:none'>Abstract</title>
In the present era of the internet and social media, the way of information dissemination has changed. However, due to rapid growth in the amount of news generated regularly and the unsupervised nature of social media, fake news turns out to be a big problem. Fake news can easily build a false positive or negative perception about a person, or an event. Fake news was also used as a tool by propagandists during the Coronavirus (COVID-19) pandemic. Thus, there is a need to use technology to tag fake news and prevent its dissemination. Previously, different algorithms were designed to detect fake news but without considering the semantic meaning and long sentence dependence. This research work proposes a new approach to the detection of fake news in the context of COVID-19. The suggested approach uses a combination of Bidirectional Encoder Representations from Transformers (BERT) for extracting context meaning from sentences, SVM for pattern identification to detect fake news in a better way from the COVID-19 dataset, and an evolutionary algorithm called Non-dominated Sorting Genetic Algorithm II (NSGA-II) to distribute text for Support Vector Machine (SVM) classification. The suggested approach improves accuracy by 5.2 % by removing a certain amount of ambiguity from sentences.
</abstract>

An Improved FakeBERT for Fake News Detection

<abstract>
<title style='display:none'>Abstract</title>
Source code constitutes the static and human-readable component of a software system. It comprises an array of artifacts and features that collectively execute a specific set of tasks. Coding behaviours and patterns are formulated through the orchestrated utilization of distinct features in a specified sequence, fostering inter-dependencies among these features. This study seeks to explore into the presence of specific coding behaviours and patterns within Java, which could potentially unveil the extent to which developers endeavour to leverage the facilities and services that exist in the programming language aggregatively. In pursuit of investigating behaviours and patterns, 436 open-source Java projects are selected, each having more than 150 Java files (Classes and Interfaces), in a semi-randomized manner. For every project, 39 features have been chosen, and the frequency of each individual feature has been independently assessed. By employing linear regression, the interrelationships among all features across the complete array of projects are scrutinized. This analysis intends to uncover the manifestation of distinct coding behaviours and patterns. Based on the selected features, preliminary findings suggest a notable collective incorporation of diverse coding behaviours among programmers, encompassing Encapsulation and Polymorphism. The findings also point to a distinct preference for using a specific commenting mechanism, JavaDoc, and the potential existence of Code-Clone and dead code. Overall, the results indicate a clear tendency among programmers to strongly adhere to the fundamental principles of Object -Oriented programming. However, certain less obvious attributes of object-oriented languages appear to receive relatively less attention from programmers.
</abstract>

Source Code Features and their Dependencies: An Aggregative Statistical Analysis on Open-Source Java Software Systems

<abstract>
<title style='display:none'>Abstract</title>
Comparable corpora are the right resources for extracting parallel data due to their abundant availability. It is of great importance where parallel data are scarce. In this study, the focus is placed on building of parallel data for Punjabi and English language pair. The raw data were collected from web contents of “Mann Ki Baat”, which is a collection of textual speeches of Prime Minister of India Mr. Narendra Modi broadcasted every last Sunday of the month. Data were cleaned and pre-processed using a natural language toolkit. An alignment model using BERT was built that aligned two textual files on a sentence level. Furthermore, extraction of noun forms with the help of NLTK library in Python programming was performed. The noun aligned dataset was built for English-Punjabi language pair and made available at Mendeley data repository.
</abstract>

Building English – Punjabi Aligned Parallel Corpora of Nouns from Comparable Corpora

<abstract>
<title style='display:none'>Abstract</title>
Content-based image retrieval system (CBIR) is a well-known and widely used system for image retrieval. Most of the current CBIR systems are either command-based or specific to applications. However, due to the availability of a good computing facility, a graphical way of retrieving images may prove to be very useful for both industrial and research purposes. This paper proposes a generic and user-friendly graphical user interface (GUI) for CBIR framework. With the proposed GUI, any user with or without knowledge of CBIR can operate and retrieve images of their choice among a huge number of images. The GUI gives a vast range of facilities for selecting options. The proposed GUI is implemented and verified on a well-known image database.
</abstract>

Generic Graphical User Interface for CBIR Framework

<abstract>
<title style='display:none'>Abstract</title>
Worldwide websites publish enormous amounts of text, audio, and video content every day. This valuable information allows for the assessment of regional trends and general public opinion. Based on consumers’ online behavioural habits, businesses are showing them their chosen ads. It is difficult to carefully analyse these raw data to find valuable trends, especially for a language with limited resources like Urdu. There have not been many studies or efforts to create language resources for the Urdu language and analyse people’s sentiment, even though there are more than 169 million Urdu speakers in the world and a sizable amount of Urdu data is generated on various social media platforms every day. However, there has been relatively little research on sentiment analysis in Urdu. Researchers have primarily performed studies in English and Chinese. In response to this gap, we suggest an emotion analyser for Urdu, the primary language of Asia, in this research study. In this paper, we propose to assess sentiment in Urdu review texts by integrating a bidirectional long short-term memory (BiLSTM) model with a convolutional neural network (CNN). We contrast the CNN, LSTM, BiLSTM, and CNN-LSTM models with the CNN-BiLSTM model. With an accuracy rate of 0.99 %, the CNN-BiLSTM model performed better than the other models in an initial investigation.
</abstract>

Sentiment Analysis Based on Urdu Reviews Using Hybrid Deep Learning Models

<abstract><title style='display:none'>Abstract</title>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>

Blockchain Use Cases and Their Feasibility

<abstract><title style='display:none'>Abstract</title>Predicting the stock market remains a challenging task due to the numerous influencing factors such as investor sentiment, firm performance, economic factors and social media sentiments. However, the profitability and economic advantage associated with accurate prediction of stock price draw the interest of academicians, economic, and financial analyst into researching in this field. Despite the improvement in stock prediction accuracy, the literature argues that prediction accuracy can be further improved beyond its current measure by looking for newer information sources particularly on the Internet. Using web news, financial tweets posted on Twitter, Google trends and forum discussions, the current study examines the association between public sentiments and the predictability of future stock price movement using Artificial Neural Network (ANN). We experimented the proposed predictive framework with stock data obtained from the Ghana Stock Exchange (GSE) between January 2010 and September 2019, and predicted the future stock value for a time window of 1 day, 7 days, 30 days, 60 days, and 90 days. We observed an accuracy of (49.4–52.95 %) based on Google trends, (55.5–60.05 %) based on Twitter, (41.52–41.77 %) based on forum post, (50.43–55.81 %) based on web news and (70.66–77.12 %) based on a combined dataset. Thus, we recorded an increase in prediction accuracy as several stock-related data sources were combined as input to our prediction model. We also established a high level of direct association between stock market behaviour and social networking sites. Therefore, based on the study outcome, we advised that stock market investors could utilise the information from web financial news, tweet, forum discussion, and Google trends to effectively perceive the future stock price movement and design effective portfolio/investment plans.</abstract>

Predicting Stock Market Price Movement Using Sentiment Analysis: Evidence From Ghana

<abstract><title style='display:none'>Abstract</title> The paper focuses on the real-world usage of IEEE 802.11 wireless network encryption and Wi-Fi Protected Setup (WPS) function. A brief history on the development of encryption methods and WPS is given. Wireless scanning of 802.11 networks in a capital city has been performed, and the results of it have been analysed. To ascertain the knowledge about the security of wireless networks of the average user, an online survey has been conducted. To test the security of encryption methods and WPS function, practical attacks against private test wireless networks have been made. The authors conclude that the safest way to set up 802.11 network with a pre-shared key is to use Wi-Fi Protected Access 2 (WPA2) encryption without support for WPS function. Statistics in Riga shows that networks are often configured otherwise and thus vulnerable to attacks. Survey results prove that respondents are not well informed regarding the security of wireless networks.</abstract>

Security Evaluation of Wireless Network Access Points

<abstract><title style='display:none'>Abstract</title><italic> - </italic>This paper focuses on the experimental analysts of Contract NET protocol for Multi-Robot task allocation. The problem domain consists of multiple vacuum cleaning robots that need to cooperate for cleaning an area that is beyond the capabilities of a single robot. A robot simulator has been used to experiment with various area and robot locations, and the summary of the effort required to process the tasks has been recorded. Experimental results show that using Contract NET protocol alone is not sufficient to achieve optimal results in task allocation. A more advanced strategy with or without involving the Contract NET protocol is required. Possible strategies are outlined and their analysis is the subject of the future work. </abstract>

Experimental analysis of contract net protocol in multi-robot task allocation

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Applied Computer Systems

The Journal of Riga Technical University

 The Journal Applied Computer Systems is an open access scientific journal published biannually in May and December. The journal serves for experience...

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 The Journal Applied Computer Systems is an open access scientific journal published biannually in May and...

Applied Computer Systems

The Journal of Riga Technical University

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