<abstract><title style='display:none'>Abstract</title>The paper presents an overview of the current methodology and practice in modeling and control of the grinding process in industrial ball mills. Basic kinetic and energy models of the grinding process are described and the most commonly used control strategies are analyzed and discussed.</abstract>

Information and Communication Technologies, 1113 Sofia

Institute of Information and Communication Technologies, 1113 Sofia

Grinding in Ball Mills: Modeling and Process Control

<abstract><title style='display:none'>Abstract</title>Data mining methods are often implemented at advanced universities today for analyzing available data and extracting information and knowledge to support decision-making. This paper presents the initial results from a data mining research project implemented at a Bulgarian university, aimed at revealing the high potential of data mining applications for university management.</abstract>

Predicting Student Performance by Using Data Mining Methods for Classification

<abstract>
<title style='display:none'>Abstract</title>
Domain-Specific Languages (DSLs) are programming languages designed specifically to express solutions to problems in a particular domain. It is said they foster productivity and quality. In this work we describe CRUDyLeaf, a DSL focused on the generation of Spring Boot REST APIs from entity CRUD operations. Spring Boot is an open source Java-based framework used to implement the REST architecture style. It has gained popularity among developers mainly because it allows to build stand-alone and production ready software applications (avoiding the use of an application server). Through seven proposed stages (domain immersion, golden application implementation, syntax definition, meta model generation, code generator implementation, deployment, and refinement) we describe the development of this DSL. We also exemplify and evaluate the proposed DSL. Our findings suggest a yield automation rate of 32.1 LOC (Lines Of Code) for each LOC written in this DSL, among other observed benefits.
</abstract>

CRUDyLeaf: A DSL for Generating Spring Boot REST APIs from Entity CRUD Operations

<abstract><title style='display:none'>Abstract</title>A shadow appears on an area when the light from a source cannot reach the area due to obstruction by an object. The shadows are sometimes helpful for providing useful information about objects. However, they cause problems in computer vision applications, such as segmentation, object detection and object counting. Thus shadow detection and removal is a pre-processing task in many computer vision applications. This paper proposes a simple method to detect and remove shadows from a single RGB image. A shadow detection method is selected on the basis of the mean value of RGB image in A and B planes of LAB equivalent of the image. The shadow removal is done by multiplying the shadow region by a constant. Shadow edge correction is done to reduce the errors due to diffusion in the shadow boundary.</abstract>

Shadow Detection and Removal from a Single Image Using LAB Color Space

<abstract><title style='display:none'>Abstract</title> Big Data becameabuzz word nowadays due to the evolution of huge volumes of data beyond peta bytes. This article focuses on matrix multiplication with big sparse data. The proposed FASTsparse MULalgorithm outperforms the state-of-the-art big matrix multiplication approaches in sparse data scenario.</abstract>

School of Computer Science & Engineering, VIT University, Vellore,

School of Advanced Sciences, VIT University, Vellore,

Fast Matrix Multiplication with Big Sparse Data

<abstract>
<title style='display:none'>Abstract</title>
HPC clouds may provide fast access to fully configurable and dynamically scalable virtualized HPC clusters to address the complex and challenging computation and storage-intensive requirements. The complex environmental, software, and hardware requirements and dependencies on such systems make it challenging to carry out our large-scale simulations, prediction systems, and other data and compute-intensive workloads over the cloud. The article aims to present an architecture that enables HPC workloads to be serverless over the cloud (Shoc), one of the most critical cloud capabilities for HPC workloads. On one hand, Shoc utilizes the abstraction power of container technologies like Singularity and Docker, combined with the scheduling and resource management capabilities of Kubernetes. On the other hand, Shoc allows running any CPU-intensive and data-intensive workloads in the cloud without needing to manage HPC infrastructure, complex software, and hardware environment deployments.
</abstract>

Serverless High-Performance Computing over Cloud

<abstract>
<title style='display:none'>Abstract</title>
Knowledge-based recommender systems have always had their privileged place among all Decision Support Systems (DSS), given their advantage on several points over other techniques. Our paper presents a framework implementing a hybrid form of Rule-Based Reasoning and Case-Based Reasoning (RBR-CBR), to address the rarely discussed domain of educational planning. The system has been tested and presented outstanding results with a high accuracy, which will benefit educational planners’ decision support. We have also developed a dedicated application for this project to visualize the results obtained.
</abstract>

A Recommender System for Educational Planning

<abstract>
<title style='display:none'>Abstract</title>
In the present work, the maximal generalized network flow, often referred to as the profit-loss flow, is examined, considering the motivation in the decision-making systems built on this flow. The general description of the features of motivation as a psychological process actively involved in decision-making systems through the generalized network flow is given. A method is proposed to embed motivation in a generalized network flow through the motivation coefficients on different network sections (arcs). It is shown that the proposed method offers more possibilities than the classical network flow. It is proven that the initial resource in the source does not match the final resource in the consumer. This has been suggested to be due to the influence of motivation. The theoretical and experimental results convincingly prove the possibilities of considering motivation in decision-making systems when managing the transportation of resources in an arbitrary transport network.
</abstract>

Maximal Generalized Network Flow Accounting for Motivation

<abstract>
<title style='display:none'>Abstract</title>
This paper focuses on the development and deployment of a dApp (decentralized Application) for Smart Crop Production Data exchange (SCPDx) that runs on Antelope blockchain/IPFS infrastructure. The paper emphasizes practical approaches to dApp design and deployment, analyses architectural patterns of dApps, and underlines the role of smart contracts in implementing complex functionality. The paper’s contribution is the detailed description of the main smart contracts and the practical knowledge provided on the architecture and implementation of dApps, emphasizing the challenges and solutions in the development process, especially in the context of smart contract implementation. Future developments of the application towards additional data types processing, and design of an interface for leveraging, testing, and evaluating the performance of open source Large Language Models (LLMs) on specific datasets are commented on.
</abstract>

Decentralized Application (dApp) Development and Implementation

<abstract>
<title style='display:none'>Abstract</title>
Radio Frequency IDentification (RFID) Network Planning (RNP) is the problem of placing RFID readers in a working area where a tag is interrogated by at least one reader and at the same time satisfies some constraints such as minimum number of placed readers, minimal interference, and minimal outside coverage. The RNP optimization has been proven NP-hard; thus, natural-inspired approaches are often used to find the optimal solution. The paper proposes an energy-efficient RNP approach in which the positions of placed readers are optimized by a Hopfield network, and the energy efficiency is achieved through a placement area restriction technique. A mechanism of redundant reader elimination is also added to minimize the number of placed readers. Simulation results show that the Hopfield network-based energy-efficient RNP approach achieves the maximum tag coverage and energy efficiency by reducing interference, outside coverage, and the number of placed readers.
</abstract>

An Approach to Hopfield Network-Based Energy-Efficient RFID Network Planning

<abstract>
<title style='display:none'>Abstract</title>
This work presents an improved parallel biobjective hybrid real-coded genetic algorithm (MORCGA-MOPSO-II). The approach is based on the combined use of the parallel Multi-Objective Real-Coded Genetic Algorithm (MORCGA) and the Multi-Objective Particle Swarm Optimization (MOPSO). At the same time, clustering-based selection techniques are used to form subpopulations of parent individuals. Using well-known clustering algorithms (e.g., k-Means, hierarchical clustering, c-means, and DBSCAN) in combination with the proposed clustering-based mutation (the CL-mutation) directed toward the obtained cluster centers allows for improving the quality of the Pareto fronts’ approximations. The results of the MORCGA-MOPSO-II were compared with other well-known multi-objective evolutionary algorithms (e.g., SPEA2, NSGA-II, FCGA, MOSPO, etc.). Moreover, the MORCGA-MOPSO-II was integrated with the previously developed agent-based model of a goods exchange through the objective functions. As a result, the Pareto fronts have been obtained for the agent-based model of a goods exchange in different configurations of the initial distribution of agents.
</abstract>

An Improved Parallel Biobjective Hybrid Real-Coded Genetic Algorithm with Clustering-Based Selection

<abstract>
<title style='display:none'>Abstract</title>
Despite the wide acceptance of blended learning in Higher Education Institutions (HEIs) worldwide, the issue of monitoring its implementation has been little addressed in the literature. The paper presents the results of the first stage of the study for the development and implementation of tools for monitoring the degree of use of blended learning courses within the learning process in HEIs. The tool introduced here extracts data from the database of the e-learning environment and visualizes the results of the data analysis in dashboards that provide valuable insights to decision-making for improving the quality of blended learning implementation. The tool allows governing bodies to track trends in the user registration, development, and updating of blended learning courses, the number of learners, and the usability of the courses by users for a selected period. Based on the results of tool experimental testing, goals for its further development are set.
</abstract>

Quality of Blended Learning Implementation in HEIs: Tool for Monitoring the Use of e-Learning Management Systems

<abstract><title style='display:none'>Abstract</title>Nowadays, being in digital era the data generated by various applications are increasing drastically both row-wise and column wise; this creates a bottleneck for analytics and also increases the burden of machine learning algorithms that work for pattern recognition. This cause of dimensionality can be handled through reduction techniques. The Dimensionality Reduction (DR) can be handled in two ways namely Feature Selection (FS) and Feature Extraction (FE). This paper focuses on a survey of feature selection methods, from this extensive survey we can conclude that most of the FS methods use static data. However, after the emergence of IoT and web-based applications, the data are generated dynamically and grow in a fast rate, so it is likely to have noisy data, it also hinders the performance of the algorithm. With the increase in the size of the data set, the scalability of the FS methods becomes jeopardized. So the existing DR algorithms do not address the issues with the dynamic data. Using FS methods not only reduces the burden of the data but also avoids overfitting of the model.</abstract>

A Review of Feature Selection and Its Methods

<abstract><title style='display:none'>Abstract</title>Learning is a goal driven social activity determined by motivational factors. To be able to efficiently gamify learning for improved student motivation and engagement, the educators have to understand the related aspects studied in games, motivational psychology and pedagogy. This will help them to identify the factors that drive and explain desired learning behaviors. This paper presents a survey of the main approaches employed in gamification and the emerging new directions in the context of the relevant motivational psychology and pedagogy. The focus is on the motivational factors that impact learning and understanding of behavior change. The purpose of the paper is two-fold: on one side, to provide analysis and guide to relevant works related to gamification along with outlining the emerging trends, and on the other, to provide foundation for evaluation and identification of the areas of possible improvements.</abstract>

Winston Salem State University, Winston Salem, NC, USA

Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia

From Gamification to Gameful Design and Gameful Experience in Learning

<abstract><title style='display:none'>Abstract</title> Playing computer games is a predominantly emotional than rational process. Video games with affect-based adaptation measure player’s behavior signals and recognize player’s emotional states in order to adapt specific game features in a dynamic manner and to improve player’s engagement, immersion, excitement, and challenge. The present review deals with models for the presentation of emotions, techniques for measuring behavioral signals, emotion recognition and adaptation mechanisms applied in video games with affective feedback including methods for their assessment and validation. Studies using self-reports, observational methods and psychophysiological measurements of both autonomic and central nervous systems including processing and interpretation of signals are systematically reviewed with regard to their results concerning emotional adaptation in games. Next, the article provides a comparative analysis of affect-based adaptation methods and techniques applied in 14 adaptive video games developed in last ten years. Based on this comparison, we summarized future directions in research, design and evaluation of video games with affective adaption.</abstract>

Adaptation in Affective Video Games: A Literature Review

<abstract><title style='display:none'>Abstract</title>This article proposes bit flipping method to conceal secret data in the original image. Here a block consists of 2 pixels and thereby flipping one or two LSBs of the pixels to hide secret information in it. It exists in two variants. Variant-1 and Variant-2 both use 7th and 8th bit of a pixel to conceal the secret data. Variant-1 hides 3 bits per a pair of pixels and the Variant-2 hides 4 bits per a pair of pixels. Our proposed method notably raises the capacity as well as bits per pixel that can be hidden in the image compared to existing bit flipping method. The image steganographic parameters such as, Peak Signal to Noise Ratio (PSNR), hiding capacity, and the Quality Index (Q.I) of the proposed techniques has been compared with the results of the existing bit flipping technique and some of the state of art article.</abstract>

Digital Image Steganography Using Bit Flipping

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Cybernetics and Information Technologies

The Journal of Institute of Information and Communication Technologies of Bulgarian Academy of Sciences

Editor-in-ChiefDanail Dochev, IICT - BAS, Bulgaria Institute of Information and Communication Technologies of Bulgarian Academy of Sciences - <A...

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 Editor-in-ChiefDanail Dochev, IICT - BAS, Bulgaria  Institute of Information and Communication Technologies of...

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Volume 13 (2013): Issue 4 (December 2013) The publishing of the present issue (Volume 13, No 4, 2013) of the journal “Cybernetics and Information Technologies” is financially supported by FP7 project “Advanced Computing for Innovation” (ACOMIN), grant agreement 316087 of Call FP7 REGPOT-2012-2013-1.