<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> 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>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>
Facial Expression Recognition (FER) is a fundamental component of human communication with numerous potential applications. Convolutional neural networks, particularly those employing advanced architectures like Densely connected Networks (DenseNets), have demonstrated remarkable success in FER. Additionally, attention mechanisms have been harnessed to enhance feature extraction by focusing on critical image regions. This can induce more efficient models for image classification. This study introduces an efficient DenseNet model that utilizes a fusion of channel and spatial attention for FER, which capitalizes on the respective strengths to enhance feature extraction while also reducing model complexity in terms of parameters. The model is evaluated across five popular datasets: JAFFE, CK+, OuluCASIA, KDEF, and RAF-DB. The results indicate an accuracy of at least 99.94% for four lab-controlled datasets, which surpasses the accuracy of all other compared methods. Furthermore, the model demonstrates an accuracy of 83.18% with training from scratch on the real-world RAF-DB dataset.
</abstract>

Efficient DenseNet Model with Fusion of Channel and Spatial Attention for Facial Expression Recognition

<abstract><title style='display:none'>Abstract</title>
Continuous Software Process Improvement (SPI) is essential for achieving and maintaining high-quality software products. Web-based software enterprises, comprising a substantial proportion of global businesses and forming a cornerstone of the world’s industrial economy, are actively pursuing SPI initiatives. While these companies recognize the critical role of process enhancement in achieving success, they face challenges in implementing SPI due to the distinctive characteristics of Web-based software projects. This study aims to identify, validate, and prioritize the sustainability success factors that positively influence SPI implementation efforts in Web-based software projects. Data have been meticulously gathered through a systematic literature review and quantitatively through a survey questionnaire. The findings of this research empower Web-based software enterprises to refine their management strategies for evaluating and bolstering SPI practices within the Web-based software projects domain.
</abstract>

Success Factors for Conducting Software-Process Improvement in Web-Based Software Projects

<abstract><title style='display:none'>Abstract</title>
In this research, we propose two new image steganography techniques focusing on increasing image-embedding capacity. The methods will encrypt and hide secret information in the edge area. We utilized two hybrid methods for the edge detection of the images. The first method combines the Laplacian of Gaussian (LoG) with the wavelet transform algorithm and the second method mixes the LOG and Canny. The Combining was performed using addWeighted. The text message will be encrypted using the GIFT cipher method for further security and low computation. For the effectiveness evaluation of the proposed method, various evaluation metrics were used such as embedding capacity, PSNR, MSE, and SSIM. The obtained results indicate that the proposed method has a greater embedding capacity in comparison with other methods, while still maintaining high levels of imperceptibility in the cover image.
</abstract>

Hybrid Edge Detection Methods in Image Steganography for High Embedding Capacity

<abstract><title style='display:none'>Abstract</title>
In today’s world, Electronic Health Records (EHR) are highly segregated and available only within the organization with which the patient is associated. If a patient has to visit another hospital there is no secure way for hospitals to communicate and share medical records. Hence, people are always asked to redo tests that have been done earlier in different hospitals. This leads to monetary, time, and resource loss. Even if the organizations are ready to share data, there are no secure methods for sharing without disturbing data privacy, integrity, and confidentiality. When health data are stored or transferred via unsecured means there are always possibilities for adversaries to initiate an attack and modify them. To overcome these hurdles and secure the storage and sharing of health records, blockchain, a very disruptive technology can be integrated with the healthcare system for EHR management. This paper surveys recent works on the distributed, decentralized systems for EHR storage in healthcare organizations.
</abstract>

A Review on State-of-Art Blockchain Schemes for Electronic Health Records Management

<abstract><title style='display:none'>Abstract</title>
An Intrusion Detection System (IDS) is essential to identify cyber-attacks and implement appropriate measures for each risk. The efficiency of the Machine Learning (ML) techniques is compromised in the presence of irrelevant features and class imbalance. In this research, an efficient data pre-processing strategy was proposed to enhance the model’s generalizability. The class dissimilarity is addressed using k-Means SMOTE. After this, we furnish a hybrid feature selection method that combines filters and wrappers. Further, a hyperparameter-tuned Light Gradient Boosting Machine (LGBM) is analyzed by varying the optimal feature subsets. The experiments used the datasets – UNSW-NB15 and CICIDS-2017, yielding an accuracy of 90.71% and 99.98%, respectively. As the transparency and generalizability of the model depend significantly on understanding each component of the prediction, we employed the eXplainable Artificial Intelligence (XAI) method, SHapley Additive exPlanation (SHAP), to improve the comprehension of forecasted results.
</abstract>

Enhancing Intrusion Detection with Explainable AI: A Transparent Approach to Network Security

<abstract><title style='display:none'>Abstract</title>
Modern organizations are currently wrestling with strenuous challenges relating to the management of heterogeneous big data, which combines data from various sources and varies in type, format, and content. The heterogeneity of the data makes it difficult to analyze and integrate. This paper presents big data warehousing and federation as viable approaches for handling big data complexity. It discusses their respective advantages and disadvantages as strategies for integrating, managing, and analyzing heterogeneous big data. Data integration is crucial for organizations to manipulate organizational data. Organizations have to weigh the benefits and drawbacks of both data integration approaches to identify the one that responds to their organizational needs and objectives. This paper aw well presents an adequate analysis of these two data integration approaches and identifies challenges associated with the selection of either approach. Thorough understanding and awareness of the merits and demits of these two approaches are crucial for practitioners, researchers, and decision-makers to select the approach that enables them to handle complex data, boost their decision-making process, and best align with their needs and expectations.
</abstract>

Integration Approaches for Heterogeneous Big Data: A Survey

<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.