Reviewerly/exHarmony huggingface dataset:
Architecture of intelligent SIEM for detecting cyber incidents in databases of military information and communication systems.

The article looks at the current supply of cyber defense to the databases of information and communication systems. The effectiveness of continuous visits to databases is analyzed. The analysis indicates that existing systems are not immune to security, which is a critical aspect in the field of cybersecurity. A new approach to the architecture of the SIEM system is being introduced, which is aimed at different parts of the protection circuit of the information and communication system. The fragmented architecture makes it possible to effectively detect and respond to cyber attacks at all levels of protection, from the operating system to databases. A new approach is being introduced to ensure cyber security of databases with the benefit of the intelligent capabilities of the SIEM system. The main aspect of this architecture is rich database protection, which allows you to effectively detect and respond to cyber attacks. The registration approach includes the addition of data from different levels to the security circuit of the information and communication system, the module for analyzing data about categories in the database, which operates on the basis of The use of fuzzy multiplicity theory methods, fuzzy logic inference and rule correlation module to improve the detection of cyber incidents. And also the integration of OLAP technologies to provide a deep and analytical view of the database security system. The architecture for identifying cyber incidents has been designed to improve the efficiency of identifying cyber incidents related to the functioning of the database of information and communication systems and. The result of the investigation is a reduction in the capabilities of the SIEM system in identifying and responding to cyber identities in the database of the military information system. Further, we directly monitor the operational model of the cybersecurity system of the information and communication system database.
Pengaruh Penampilan Dan Fitur Robot Quality Check Apilastik Terhadap Tingkat Kepuasan Pengguna

Rapid advances in technology, particularly in the Internet of Things (IoT), are enabling the development of devices and robots that can be operated remotely. This research explores the effects of APILASTIK Quality Check Robot Appearance and Features on User Satisfaction Level. This tool, developed by Computer Engineering Technology students at IPB University's Vocational School, aims to detect defects in production products. The robot attracted great attention when it was presented at IT FEST 2023, showing the potential for wide use. Evaluations of the robot's appearance and features showed a positive and significant influence on the level of user satisfaction, with each variable contributing 66.4% and 25.4%, respectively. Hypothesis testing confirmed the overall positive and significant impact, with the coefficient of determination (R2) reaching 82.7%. The results of multiple linear regression analysis and t-test show that the robot's appearance and features individually contribute significantly to user satisfaction. With these findings, we can understand the factors that influence user satisfaction with damage-scanning robots. The recommendation is to focus on feature development or appearance enhancement to improve overall user satisfaction.
EXPRESS: The Caring Machine: Feeling AI for Customer Care

Customer care is important for its role in relationship-building. This role has traditionally been performed by human customer agents, given the less mature feeling intelligence of AI. The emergence of interactive generative AI (GenAI) shows the potential for using AI for customer care in such emotionally charged interactions. Bridging practice and the academic literatures in marketing and computer science, this paper develops an AI-enabled customer care journey, beginning from accurate emotion recognition to empathetic response, emotional management support, and finally, the establishment of an emotional connection. Marketing requirements for each of the stages are derived from in-depth top manager interviews and a CMO survey. By juxtaposing these requirements against the current feeling capabilities of GenAI, the technological challenges that need to be tackled by engineers are highlighted. This paper wraps up with a set of marketing tenets for implementing and researching the caring machine. These marketing tenets encompass verifying emotion recognition accuracy using marketing emotion theories through multiple emotion signals and methods, utilizing prompt engineering to let customers reveal their thinking and feeling to enhance emotion understanding, employing “response engineering” for knowledge of customer preferences to personalize emotion management recommendation, and strategically deploying GenAI for emotional connection to simultaneously enhance customer emotional well-being and customer lifetime value.
Arab National Identity in Online Humour: Sociolinguistic Perspective

This paper deals with the peculiarities of language code choice as a means of national self-expression in the Arabic social media humour (in particular, in memes and jokes). In the globalized online communities, humour can play a pivotal role for the rapprochement of in-group members based on a certain type of their identity (national, social, gender, etc.). In this study, the notion of Arab national identity refers to the entire Ummah and, on the other hand, to the representatives of different Arab countries as well. It is reflected on the Arabic language variation online and beyond. This research revealed diverse language attitudes conveyed within the “self&nbsp;– other” binary opposition. The analysis of over 1,000&nbsp;online posts (Egyptian, Iraqi, Moroccan, and other) showed that the commonality with the in-group members is expressed with the language code which is considered the closest for the recipients. It is indicated to the predominance of colloquial varieties (territorial dialects, sociolects, youth slang) with the variation in their written transmission. Modern Standard Arabic is used as well, sometimes with minor deviations from the norm or with the colloquial lexis insertions. Both explicit and implicit lexical tools (colloquial and slang words, precedent lexis, etc.) are utilized to stress the uniqueness of “self”. Different codes are used to create the contrast between “self” (all Arabs&nbsp;/ a certain Arab country) and “other” (all non-Arabs&nbsp;/ the West&nbsp;/ a certain Arab or non-Arab country, etc.). The studied varieties were also affected by the sociocultural context, topics of posts, communicants’ situational roles, the technology, and so forth. The results of this investigation ascertained that the Arabic language form used in the humorous posts is an integral part of the communicants’ identity as it demonstrates their belonging to a national group with its specific sociocultural, political, and historical background.
Transformation of the educational paradigm in the context of the formation of academic leadership and the development of transprofessionalism

The study substantiates the need to change educational paradigms in accordance with the new challenges of the time and the emergence of the phenomenon of academic leadership and the advent of the era of transprofessionals. The purpose of this work is related to the study of the phenomena of academic leadership and transprofessionalism, their role in the development of the modern educational paradigm. The article analyzes mainly the university sector, in which the development of academic leadership and transprofessionalism is taking place. The results obtained allow us to identify the main directions of development of the existing education system and the role of academic leadership and transprofessionalism in it. The scientific novelty of the study lies in the totality of the results obtained, revealing the features of academic leadership and transprofessionalism, their influence on the change of educational paradigms. It is concluded that academic leadership can be achieved subject to both the introduction of innovative technologies into the educational process and the implementation of a set of measures aimed at preserving the fundamental knowledge that a graduate must master. Therefore, it is necessary to consider academic leadership in the context of the strategic perspective of maintaining Russia’s status as the largest scientific power in the modern world. The phenomenon of transprofessionalism acts as an adequate response from the point of view of leading educational institutions to the challenges of technological change characteristic of the modern era
Enhancing Semantic Image Synthesis: A GAN-Based Approach with Multi-Feature Adaptive Denormalization Layer

Semantic image synthesis, a pivotal task in image-to-image translation, has been widely addressed using generative adversarial network (GAN) models. However, existing GAN-based approaches often suffer from inadequate incorporation of structural and spatial information, resulting in unsatisfactory quality of the synthesized images and a pronounced disparity between photo-realistic and generated images. In this paper, we propose a novel GAN-based methodology to address these limitations, enabling the generation of high-resolution images from semantic label maps while bridging the quality gap and preserving detailed information in the generated outputs. The proposed approach leverages a two-step process, starting with a local binary pattern convolutional generator that produces a local binary pattern feature map. Subsequently, a global convolutional generator is fed with the segmentation map and the feature map through a learned modulation scheme facilitated by a multi-feature adaptive denormalization layer (MFADE) during the training process to generate photo-realistic images. Extensive experiments using Cityscapes, ADE20K, and COCO-stuff datasets validate the performance of our proposed method and showcase its accuracy and robustness in addressing semantic image synthesis tasks, thereby paving the way for its potential applications in enhancing urban sensing and data analytics in Smart Cities. The source code is available at https://github.com/karimmagdy/ULBPGAN .
Factors Affecting on Students’ Preference on Choosing Academic Track for Higher Education in Pokhara

The students joining higher education undergo various confusions while selecting academic track for their further studies. Students who pass the tenth grade are supposed to join the subject of their interest in grade eleven or intermediate level which will be the fundamental course for their higher education ahead. Grade eleven is the main entry point for students to establish their future in the higher education. Higher education in Nepal starts from bachelor level but students are supposed to identify their major subject starting from grade eleven. It seems that SEE graduates tend to select the major subject while joining higher education depending on various aspects like intelligence of a person, the interest towards that subject, family, peer pressure, social inputs, guidance from teachers , etc. Family (57.47%) is the major agent to assist students to take decisions for choosing the academic track while joining grade eleven. 92.52% students were not forced but 7.4% students were forced by the parents to choose the major subjects for higher education. It is surprising that 59.19% students wish to go abroad for university level education. The children joining senior high school need to be provided career guidance and counseling before choosing and handling major academic track for their higher education.
A Brief Review of Text Summarization Approaches

The need for Text Summarization is growing significantly because a huge amount of data is present on the internet. Manual text summarization takes effort and time, therefore, automating text summarization is desirable. Much research work has already been carried out in this area and is still progressing. This paper briefly reviews different methods, datasets, and evaluation metrics for text summarization. The main focus is on neural networks and pre-trained transformer language models.
Designing a Deep Learning Model for Video Anomaly Detection-Based Surveillance

Anomaly detection in surveillance videos is a crucial task for ensuring public safety and security. Traditional methods rely on rule-based or handcrafted feature-based approaches, which are often limited in their ability to detect complex and subtle anomalies. In recent years, deep learning models have shown promising results in detecting anomalies in video data. In this paper, we propose a deep learning model for video anomaly detection in surveillance videos. Our model utilizes a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The CNNs are used to extract spatial features from individual frames, while the LSTM networks are used to capture the temporal dependencies between frames. We propose to use a two-stage training process to train our model. In the first stage, we pretrain the CNNs on a large dataset of unlabeled images to learn generic features. In the second stage, we fine-tune the CNNs and train the LSTM networks on a smaller dataset of labeled surveillance videos. To evaluate our model, we will use the UCSD Pedestrian dataset, which contains video sequences of pedestrians walking in a busy street. We will compare our model's performance with proposed hybrid methods and demonstrate its ability to detect various anomalies, such as sudden changes in the number of pedestrians or the presence of unusually detected various anomalies in the scene. Our proposed model shows great potential for detecting anomalies in surveillance videos, which can greatly improve public safety and security in various settings.
Discovering Relationships Between Heterogeneous Declarative Mappings for RDF Knowledge Graph

Nowadays, Knowledge Graphs (KGs) are extensively used in companies, they are created using different techniques, mapping languages among them, and involve various designers teams. Mappings languages have been proposed to explicitly define mappings assertions between heterogeneous (semi)-structured datasets and the KG. The wide variety of mapping languages and their associated systems and the lack of a generic mapping language that covers all the existing languages, lead each expert/design team to use the mapping language they master and that fits their requirements, to enrich the company’s KG. This situation creates new sources of heterogeneous mapping assertions. A straightforward analysis of these mappings is very complex because of the strong differences of their syntaxes. In this article, we propose an approach to detect relationships between heterogeneous mappings from the analysis of their generated graphs, following a reverse ETL approach. This analysis is relevant for analyzing and maintaining the mappings, and may provide relevant insights in the datasets usage and design patterns defined within the global KG project, where the mappings play a central role. A set of experiments is provided to show the feasibility of the approach.
Strategies and Methods for Developing Practical Skills in Film and Media Education

This study aims to explore the strategies and methods for cultivating practical skills in film and media education. Key strategies include: first, teachers should involve students in practical projects, such as film production or media planning, so that they can apply theoretical knowledge to practice and develop relevant technical and creative skills; second, schools should organize regular seminars and workshops, invite industry professionals to share their experiences, provide practical activities, and interact with students; third, learning should include field trips and internships, allowing students to experience industry operations first-hand and have opportunities to collaborate with professionals; fourth, schools should encourage teamwork projects involving film production or media work, cultivating teamwork, communication, and problem-solving skills; fifth, teachers should allow students to showcase their work and provide regular feedback assessments, motivating students to apply the knowledge and skills they have learned to practical projects and continuously enhance their practical abilities and professional qualities. These strategies and methods effectively cultivate students' practical skills, laying a solid foundation for their future career development.
The value of education and educational values of Ukrainian high school students: factor determination

The data analysis given in this article made it possible to find out that for high school students in Kharkiv, Odesa, and Cherkasy, the value of education is not terminal but rather instrumental, expressed in various instrumental values. First and foremost, it is the value of high intelligence and abilities. This value is shared the most by respondents from all three Ukrainian cities under study, who see it mainly as a means to achieve material well-being in the future, an opportunity to make a career both in Ukraine and abroad.The hypotheses about correlations between the values of education and high intelligence, abilities with gender, parental education level, the feeling of going to school, and the overall schooling assessment were not confirmed for Kharkiv and Odesa high school students, leading to factor analysis.It was concluded that high school students in the three cities have much in common in their value consciousness; the most crucial terminal values are the same. However, in choosing this value, high school students in Kharkiv and Odesa are guided by their other values (terminal ones), while Cherkasy students are guided by the conditions and circumstances of the external environment, namely the level of education of their parents and their experience at school. In addition, for Cherkasy high school students, their parents’ desire for them to enter a higher education institution is significantly more important than for Kharkiv and Odesa students.
A Hybrid Model for Predicting Classification Dataset based on Random Forest, Support Vector Machine and Artificial Neural Network

Machine Learning offers a rich array of algorithms, and the performance of these algorithms can vary significantly depending on the specific task. Combining these traditional algorithms can lead to the development of innovative hybrid structures that outperform individual models. One such novel hybrid model is the Hybrid Support Random Forest Neural Network (HSRFNN), which is designed to deliver enhanced performance and accuracy. HSRFNN represents a fusion of Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN) to leverage their respective strengths. This hybrid model consistently outperforms the individual models of Random Forest, SVM, and ANN. In this study, ten diverse datasets sourced from UCI and Kaggle data repositories were considered for evaluation. The accuracy of the HSRFNN model was meticulously compared with the three traditional algorithms, namely Random Forest, Support Vector Machine, and Artificial Neural Network. Various accuracy metrics, such as Correctly Classified Instances (CCI), Incorrectly Classified Instances (ICI), Accuracy (A), and Time Taken to Build Model (TTBM), were used for the comparative analysis. This research strives to demonstrate that HSRFNN, through its hybrid architecture, can offer superior accuracy and performance compared to individual algorithms. The choice of datasets from different sources enhances the generalizability of the results, making HSRFNN a promising approach for a wide range of machine learning tasks. Further exploration and fine-tuning of HSRFNN may unlock its potential for even more challenging and diverse datasets.
Custom Convolution Neural Network for Breast Cancer Detection

Breast cancer remains a serious global health issue. Leveraging the use of deep learning techniques, this study presents a custom Convolutional Neural Network (CNN) framework for the detection of breast cancer. With the specific objective of accurate classification of breast cancer, a framework is made to analyze high-dimensional medical image information. The CNN's architecture, which consists of specifically developed layers and activation components tailored for the categorization of breast cancer, is described in detail. Utilizing the BreakHis dataset, which comprises biopsy slide images of patients in a range of cancer stages, the model is trained and verified. Comparing our findings to conventional techniques, we find notable gains in sensitivity, specificity, and accuracy. Gray-Level Co-Occurrence Matrix (GLCM) features extracted from the BreakHis dataset was used to analyze the performance on sequential neural network, transfer learning and machine learning models. After analysis, we have proposed hybrid models of CNN-SVM, CNN-KNN, CNN-Logistic regression and achieved accuracy of about 95.2%.
Yolo Versions Architecture: Review

Deep learning techniques are used across a wide range of fields for several applications. In recent years, deep learning-based object detection from aerial or terrestrial photos has gained popularity as a study topic. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed. A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles andclassification probabilities for an image. In layman's terms, it is a technique for instantly identifying and recognizing items in images.This article, will be focusing on comparing the main differences among the YOLO version's Architecture, and will discuss its evolution from YOLO to YOLOv8, its network architecture, newfeatures, and applications. Itstarts by looking at the basic ideas and design of the first YOLO model, which laid the groundwork for the following improvements in the YOLO family. In additionally, this article will provide a step-by-step guide on how to use the YOLO version architecture, Understanding the primary drivers, feature development, constraints, and even relationships for the versions is crucial as the YOLO versions advance.Researchers interested in object detection, especially beginning researchers, would find this paper useful and enlightening
MENTORING AS A STRATEGY FOR DEVELOPING STUDENTS' INTEREST IN FUTURE PROFESSIONAL ACTIVITIES

The article dwells on the issues that arise while young professionals are being onboarded at ecologically oriented organizations. A training system that allows students to acquire professional skills transferred to them by experienced mentors, to find and eliminate a problem, is proposed in the article. Plans developed for teacher education students based on the results of job-training programmes implemented in 2023 include all job functions of environmental engineers in the waste management sector. This experience is recommended to be used in environmental organi-zations in order to develop interest of recent graduates in their future professional activities and to train competent personnel.