<abstract xmlns="http://www.w3.org/1999/xhtml">The paper presents general guidelines for designing affective multi-agent systems (affective MASs). The guidelines aim at extending the existing agent-oriented software engineering (AOSE) methodologies to enable them to design affective MASs. The reason why affective mechanisms need specific attention during the design is the fact that the way how both rational tasks and interactions are done differ based on the affective state of the agents. Thus, the paper extends the traditional design approaches with the design of affective mechanisms and includes them in the design of the system as a whole.</abstract>

General Guidelines for Design of Affective Multi-Agent Systems

<abstract xmlns="http://www.w3.org/1999/xhtml">Game-based learning as a learning approach has been popular for ages; however, game-based assessment as a trend started to evolve only few years ago. Since knowledge assessment is more associated with negative emotions, systems intended to assess knowledge should take into consideration emotions as well. The analysis of existing studies shows that systems with integrated game-based assessment seldom utilise learner’s emotions for provision of adaptation. The main aim of the present paper is to introduce an adaptation approach for to affective game-based assessment.</abstract>

The Adaptation Approach for Affective Game-Based Assessment

<abstract xmlns="http://www.w3.org/1999/xhtml">The paper explores geophysical methods of wells survey, as well as their role in the development of Kazakhstan’s uranium deposit mining efforts. An analysis of the existing methods for solving the problem of interpreting geophysical data using machine learning in petroleum geophysics is made. The requirements and possible applications of machine learning methods in regard to uranium deposits of Kazakhstan are formulated in the paper.</abstract>

Branch Office “Geotechnocentr” of JSC Volkovgeologija

Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

<abstract xmlns="http://www.w3.org/1999/xhtml">In the present paper, a new and improved visual sensor data fusion method is proposed that uses visible and far-infrared light sensors. Additionally, lux meter data are used for decision level fusion of beliefs of recognised target classes. The database consisting of 4 ambient light condition images is created using Canon and FLIR cameras.The developed approach has been tested using database images, neural network training and classification, particularly for low light level conditions. Enhancements of target identification precision are proved by practical implementation and testing of the proposed method.</abstract>

Norwegian University of Science and Technology

Target Identification Using Sensors of Different Nature

<abstract xmlns="http://www.w3.org/1999/xhtml">Model-Driven Software Development (MDSD) is a trend in Software Development that focuses on code generation from various kinds of models. To perform such a task, it is necessary to develop an algorithm that performs source model transformation into the target model, which ideally is an actual software code written in some kind of a programming language. However, at present a lot of methods focus on Unified Modelling Language (UML) diagram generation. The present paper describes a result of authors’ research on Two-Hemisphere Model (2HM) processing for easier code generation.</abstract>

Workflow Generation from the Two-Hemisphere Model

<abstract xmlns="http://www.w3.org/1999/xhtml">Implementation of an emulator of MIX, a mythical computer invented by Donald Knuth, is used as a case study of the features of the Scala programming language. The developed emulator provides rich opportunities for program debugging, such as tracking intermediate steps of program execution, an opportunity to run a program in the binary or the decimal mode of MIX, verification of correct synchronisation of input/output operations. Such Scala features as cross-compilation, family polymorphism and support for immutable data structures have proved to be useful for implementation of the emulator. The authors of the paper also propose some improvements to these features: flexible definition of family-polymorphic types, integration of family polymorphism with generics, establishing full equivalence between mutating operations on mutable data types and copy-and-modify operations on immutable data types. The emulator is free and open source software available at <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://www.mix-emulator.org">www.mix-emulator.org</ext-link>.</abstract>

Implementation of a MIX Emulator: A Case Study of the Scala Programming Language Facilities

<abstract xmlns="http://www.w3.org/1999/xhtml">A technique of DropOut for preventing overfitting of convolutional neural networks for image classification is considered in the paper. The goal is to find a rule of rationally allocating DropOut layers of 0.5 rate to maximise performance. To achieve the goal, two common network architectures are used having either 4 or 5 convolutional layers. Benchmarking is fulfilled with CIFAR-10, EEACL26, and NORB datasets. Initially, series of all admissible versions for allocation of DropOut layers are generated. After the performance against the series is evaluated, normalized and averaged, the compromising rule is found. It consists in non-compactly inserting a few DropOut layers before the last convolutional layer. It is likely that the scheme with two or more DropOut layers fits networks of many convolutional layers for image classification problems with a plenty of features. Such a scheme shall also fit simple datasets prone to overfitting. In fact, the rule “prefers” a fewer number of DropOut layers. The exemplary gain of the rule application is roughly between 10 % and 50 %.</abstract>

Appropriateness of Dropout Layers and Allocation of Their 0.5 Rates across Convolutional Neural Networks for CIFAR-10, EEACL26, and NORB Datasets

AHEAD OF PRINT

Volume 28 (2023): Issue 2 (December 2023)

Volume 28 (2023): Issue 1 (June 2023)

Volume 27 (2022): Issue 2 (December 2022)

Volume 27 (2022): Issue 1 (June 2022)

Volume 26 (2021): Issue 2 (December 2021)

Volume 26 (2021): Issue 1 (May 2021)

Volume 25 (2020): Issue 2 (December 2020)

Volume 25 (2020): Issue 1 (May 2020)

Volume 24 (2019): Issue 2 (December 2019)

Volume 24 (2019): Issue 1 (May 2019)

Volume 23 (2018): Issue 2 (December 2018)

Volume 23 (2018): Issue 1 (May 2018)

Volume 22 (2017): Issue 1 (December 2017)

Volume 21 (2017): Issue 1 (May 2017)

Volume 20 (2016): Issue 1 (December 2016)

Volume 19 (2016): Issue 1 (May 2016)

Volume 18 (2015): Issue 1 (December 2015)

Volume 17 (2015): Issue 1 (May 2015)

Volume 16 (2014): Issue 1 (December 2014)

Volume 15 (2014): Issue 1 (July 2014)

Volume 14 (2013): Issue 1 (June 2013)

Volume 13 (2012): Issue 1 (October 2012)

Applied Computer Systems

 The Journal Applied Computer Systems is an open access scientific journal published biannually in May and December. The journal serves for experience generation in computer science and information technologies use in different applications and is a source of information for the industry practitioners, for academic personnel and students.  The journal publishes research papers describing new results, original ideas and applications or reports on the development and maintenance of actual systems addressing issues concerning theory, development, management and applications of advanced applied computer science.  The topics covered by the journal are:   Artificial Intelligence  Computer Applications  Data  Information Technology and Systems  Programming Technologies  Software Engineering   Besides, the reader will find papers on conception of framework for automated creation of graph visualisation systems, theoretical basis and practical usage of simulation for creating solutions for data management systems, modelling of software systems and business processes, comparison of programming languages, intelligent tutoring systems, semantic web development, applications of cloud-based services, issues of dynamic wireless networks, as well as surveys and papers focused on different other specific topics.  The aim of the publishers is to encourage the applied research by granting free publication possibilities to professionals in the software field, young scientists and doctoral students.  The authors of articles are researchers from various educational and research institutions and companies not only from different European countries, but also from Africa, North and South America, Asia and Australia.  Rejection Rate   47%   Publication History 　  Currently known as Applied Computer Systems 2012 – current, ISSN 2255-8683 (print), ISSN 2255-8691 (online).Formerly known as Scientific Journal of Riga Technical University, Series Computer Science. Applied Computer Systems 2000–2011, ISSN 1407-7493 (print).In 2012 the journal Applied Computer Systems separated from the RTU journal series Computer Science and the present volume numbering reflects the number of journals published within the series since 2000.   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 