Reviewerly/exHarmony huggingface dataset:
Ultra-Lightweight Fast Anomaly Detectors for Industrial Applications

Quality inspection in the pharmaceutical and food industry is crucial to ensure that products are safe for the customers. Among the properties that are controlled in the production process are chemical composition, the content of the active substances, and visual appearance. Although the latter may not influence the product’s properties, it lowers customers’ confidence in drugs or food and affects brand perception. The visual appearance of the consumer goods is typically inspected during the packaging process using machine vision quality inspection systems. In line with the current trends, the processing of the images is often supported with deep neural networks, which increases the accuracy of detection and classification of faults. Solutions based on AI are best suited to production lines with a limited number of formats or highly repeatable production. In the case where formats differ significantly from each other and are often being changed, a quality inspection system has to enable fast training. In this paper, we present a fast method for image anomaly detection that is used in high-speed production lines. The proposed method meets these requirements: It is easy and fast to train, even on devices with limited computing power. The inference time for each production sample is sufficient for real-time scenarios. Additionally, the ultra-lightweight algorithm can be easily adapted to different products and different market segments. In this work, we present the results of our algorithm on three different real production data gathered from food and pharmaceutical industries.
Artificial intelligence capabilities in the context of the author's conception of creativity

The study provides some philosophical reflections on the creativity of artificial in-telligence. The study supposes that artificial intelligence can be considered creative only if it creates something new with help of imagination (or its equivalent) and ap-pealing to the so-called “background” (background and general knowledge, biases, competencies, experience, habits, intuition, prejudices, political preferences, skills, stereotypes, values, and others), and its creative activity must be either necessary or arbitrary. Necessary creative activity is related to the solution of specific tasks, for example, within the framework of technical invention or scientific discovery. Arbitrary creative activity is associated with spontaneous, aimless and inexpedient human activity. This type of creative activity takes place when a creative doer has free time, leisure, enthusiasm (hobby), plays a game or is bored. Based on the two types of creative activity, two types of creative artificial intelligence can be distinguished: a weak creative artificial intelligence that makes necessary creative activities related to the tool nature of artificial intelligence, specifically to effectively solving specific problems and tasks, and a strong creative artificial intelligence that makes arbitrary creative activities, that is, creates for the sake of creating alone. The strong creative artificial intelligence can be possible only if the artificial intelligence is given autono-my, the freedom to manage that autonomy, and learns to manage its freedom.
Official Opponent's Review of the dissertation by Yuriykov Oleksandr Oleksandrovych, "Criminal Liability for Intentional Damage or Destruction of a Telecommunications Network"

The relevance of the dissertation topic by Yuriykov Oleksandr Oleksandrovych, "Criminal Liability for Intentional Damage or Destruction of a Telecommunications Network" has been considered. The degree of substantiation of scientific provisions, conclusions, and recommendations formulated in the dissertation, as well as their reliability, has been illuminated. The scientific novelty and practical significance of the obtained results in the dissertation have been established.
Catching up with missing particles

The implementation of particle-tracking techniques with deep neural networks is a promising way to determine particle motion within complex flow structures. A graph neural network-enhanced method enables accurate particle tracking by significantly reducing the number of lost trajectories.
Task execution flow management in the software development process under the minor change event

In modern project management methodologies, insufficient attention is devoted to the process of promptly responding to minor changes during task execution, which necessitate adjustments to the priorities of ongoing tasks. The existing approaches are not sufficiently detailed for a fundamental reassessment of priorities while such changes significantly impact project execution. The available materials and approaches do not provide ready-made solutions. This article proposes a task planning model during project execution. The model comprises the following key elements: Executor, Task Set, Task Execution Progress, and Calculation of Task Execution Quality Indicators. The Executor element contains information for identifying the developer and allocating their working time. It is anticipated that under exceptional conditions, a portion of non-working time may be scheduled for task execution. The Task Set element represents planned temporal characteristics and the priority of each task. The Task Execution Progress element contains information about actual dates, hours, and durations of segments during which the task was executed. The calculations of task execution quality indicators enable obtaining operational information about the progress of specific projects and assessing the effectiveness of process management. Basic algorithms for managing task sequences have been developed. The “Addition of a New Task” algorithm implements a task queue based on priority and start and end dates. The “Task Priority Change” algorithm envisages the possible repositioning of a task, as well as cases of task transfer to another executor or rescheduling tasks during non-working hours. Additionally, algorithms for notification of critical planning changes for dependent tasks (“Notification of Critical Planning Change for Dependent Tasks”) and critical deprioritization of dependent tasks (“Notification of Critical Deprioritization for Dependent Tasks”) have been developed. The proposed model and algorithms allow for accommodating micro-changes in the project and responding to their occurrence. The validation of research results in a real project demonstrated the effectiveness of the proposed model and algorithms while concurrently revealing a certain range of open questions requiring further consideration. Future research directions include the classification of micro-change scenarios, analysis of possible scenarios for suspending the execution of current tasks, and the development of scenarios and algorithms for selecting executors.
La Inteligencia Artificial en el aula y el desafío educativo ante los avances en los dispositivos y las nuevas formas de comunicación

.
Central Bank Digital Currencies: A Comprehensive Study of Characteristics, Implications and Future Perspectives

The advent of the modern digital age has changed the way money is used and payments are made. A groundbreaking financial innovation created in response to the changing digital economic environment is the Central Bank Digital Currency (CBDC). The primary objective of this paper is to examine the novel features of CBDC and consider the far-reaching implications for the current financial system. This paper first examines the nature and objectives of CBDC. The distinctiveness of CBDC in the currency issuance and payment systems, as well as its potential impact on the current payment industry, is highlighted in our comparisons of CBDC with conventional currencies, cryptocurrencies, and online payment methods, respectively. However, the digital aspect of CBDC also raises issues of digital divide and privacy. Finally, we highlight the critical role of the government in the creation and oversight of CBDCs. In conclusion, this article offers a thorough viewpoint that explores the ideas, advantages, and disadvantages of CBDC and provides crucial insights into the future growth of CBDC and its influence on the future financial system.
Integrating Internet of Things (IoT), Machine Learning (ML), and the Cloud Infrastructure to Monitor Driving Behavior for Usage-based Insurance in the Indian Context

Although Usage-Based-Insurance has a wide range of applications, it is practically nonexistent in India. We have done research to help design a system that combines IoT, ML, and cloud to help us overcome this problem as a next step. The goal is to create a machine learning model for the cloud that will allow us to calculate driver and customer safety scores based on where and how the car has been driven. We were able to do this by evaluating information from our smartphone's sensors. Utilizing this data and classification system, problems like Usage-Based-Insurance can subsequently be resolved in the real world (UBI). Usage-based insurance has proven to be efficient and appealing to both insurers and the ones insured in a wide range of countries. The use of this method is sparse to nonexistent in India, but it is on the horizon. As a result, there is a lot of potential for our research and suggested works on UBI in India.
Using XR to Transform Todays Short-form Video Content Consumption

The purpose of this research is to investigate how Extended Reality (XR) technologies can help address the effects of consuming short form content, on social media platforms. With the rise in popularity of video apps its important to understand their impact on users, younger generations. While these platforms cater to peoples entertainment and news needs during their time they can have effects such as shorter attention spans, changes in cultural appreciation and even social disconnection. This research proposes an approach by combining Extended Reality technology with short form content to enhance user engagement while minimizing negative consequences. The proposed system includes experiences created by users that can be accessed individually or by viewers through an interactive feature called The Hall. Additionally the system includes features that encourage world connections and a transparent content recommendation mechanism that prioritizes user privacy. By prioritizing physical health well as privacy this Extended Reality based application has the potential to offer an effective solution to the challenges posed by current 2D short form content platforms. The findings are based on a survey conducted among 30 high school students in Beijing, which indicates the feasibility of this XR based application to transform short-form content consumption.
Identificiranje prilika za inoviranje upravljanja projektima u obrazovanju u digitalnom dobu

This paper focuses on innovative learning methods (serious games, simulation games, virtual and augmented reality, the learning-by-doing concept, and Massive Online Open Courses) used in project management education. The use of these learning methods in project management education is crucial, as this subject needs to be practiced in real life. This paper aims to examine the current state of research in this area to determine which learning methods are most commonly used in project management education, what impact they have on the ability to learn, and whether these methods are beginning to transfer to the online environment. To this end, a systematic literature review was conducted in the Web of Science database, which resulted in 53 papers. The results show that game-based learning is the most frequently researched method (43 times). The impact of the analysed methods on the ability to learn was found to have been confirmed 23 times, especially in game-based learning research (18 times). On the other hand, the online environment is not predominant, and these methods are mostly taught on-site (28 times).
Colours of the upper Neogene “Poznań Clays” in the light of sedimentological, mineralogical and nuclear methods

The Miocene–Pliocene overbank facies “Poznań Clays” are particularly well exposed in large lignite outcrops in central Poland, for example, in the Jóźwin IIB opencast mine. During their accumulation the climate fluctuated from moderately warm and humid to cool and dry. In general, the dark grey and the greyish-violet colours come from macroscopically visible organic matter and the absence of hematite and/or goethite. The organic matter also affects the Fe 3+ /Fe 2+ ratio and, consequently, the redox conditions. When the study area was poorly drained, there were favourable conditions for plant vegetation, resulting in Histosols (hydromorphic palaeosols). In a reducing environment, other sediments with “cold” colours (greenish to bluish shades) formed that included pyrite and/or gypsum, though with few or no pigments such as hematite, goethite or jarosite. At that time, elemental sulphur could also crystallize, occasionally giving a yellowish shade to the sediment mottles. When the area was well drained, most of the organic matter decayed, and the Fe 3+ /Fe 2+ ratio was relatively high due to progressive weathering. Under such conditions, Vertisols developed with “warm” colours (from yellow to cherry-red), enriched in hematite and goethite, and also containing gypsum and native sulphur.
Ontology Based Information Retrieval By Using Semantic Query

The volume of data is increasing quickly in the modern day. Effective information retrieval techniques are needed to extract important facts from such a large collection of information. As a result, retrieval of information is the process of gathering valid data from a variety of sources. The majority of the time, information is retrieved from the internet using search queries. The aim of this research is to explore various issues existing in information retrieval techniques and to propose new techniques to overcome existing challenges in the field of Information retrieval. Modern information retrieval methods have been examined, and it was discovered that they do not take semantic keyword knowledge into account when returning results. The semantic web is a development of the internet that enables computers to comprehend human inquiries in terms of their intent and produce pertinent responses. This research mainly focuses on Ontology-Based Information Retrieval which can support semantic similarity and retain the view of an approximate search in a document repository using machine learning techniques. Further, this research works explores an adaptive update model for retrieving the information and proposes a semantic search model for the given user query. The objective of ontology-based semantic web information search is to increase the accuracy, precision and recall of user queries.
A New Fusion Feature Selection Model (FFSM) based Feature Extraction System for Hand Gesture Recognition

The research presented here proposes a unique approach for exact feature extraction that makes use of the Fusion Feature Selection Model (FFSM). The method encompasses various stages to preprocess and extract crucial features from first-person hand action (FHPA) images. Preprocessing involves video-to-frame conversion, RGB to grayscale conversion, an improved median filter, and Gaussian blur-based image smoothing. Segmentation is achieved using the Improved SwinNet to identify meaningful regions within the images. Feature extraction employs the Gabor Line Derivative (GLD) method, Active Shape Model (ASM), and Histogram of Oriented Gradients (HOG) to capture texture, edge, and shape information, respectively. Extensive experimental evaluations demonstrate the effectiveness of our proposed approach, achieving remarkable performance in accurate feature extraction tasks.
Ideas and Ideals of Open Education

This article examines the phenomenon of open education, its infl uence, and prospects within the context of contemporary education. An in-depth exploration of this pedagogical approach offers invaluable insights for educators, researchers, policymakers, and other stakeholders intrigued by advanced methodologies and ideas in the educational realm. The primary objective of the research is to ascertain how the principles of open education facilitate the transformation of traditional educational frameworks and how they can promote educational equity. To achieve this aim, the author employs a method of analyzing diverse sources and practical examples. It is posited that open education represents a signifi cant stride towards creating a more inclusive and accessible educational milieu, anchored in the principles of democracy, self-organization, and knowledge equality. The article’s key fi ndings encompass the understanding that open education, rooted in principles of accessibility, inclusivity, self-organization, and knowledge democratization, offers novel opportunities for the transformation of conventional education and the realization of educational fairness. Challenges in implementing open education, including digital disparity, content quality, accreditation, and student motivation, are also illuminated. Open education introduces pedagogical innovations, infusing elements of fl exibility and individualization. Novelty contributions of the research encompass delineating the impact of open education on transforming educational frameworks, a profound comprehension of open education principles and their application for ensuring educational equity, and the formulation of new methodological approaches to studying the open education phenomenon. In conclusion, the study offers valuable perspectives on the role and position of open education in the current educational landscape and its potential for further exploration. This work will be of interest to those seeking to understand and probe the possibilities that open education presents for today’s educational environment.
The World of Human Happiness and Its Philosophical Origins

The subject of this research is various ideas about happiness in the history of philosophy. Happiness is a concept of a special kind, the importance of which in motivating human activity is extremely great. The subject arouses a constantly renewed interest among philosophers, psychologists, educators, sociologists and other specialists throughout the foreseeable intellectual history of mankind from Antiquity to the present day. All people strive for happiness, understanding it as a guarantee of a prosperous existence, but they put the most diverse meaning into this concept. Happiness has always been the most important motive of human actions, therefore it is necessary to clarify the actual content of this phenomenon in the conditions of the modern spiritual situation of the time of the confrontation of cultures and civilizations. The purpose of the work is an attempt to analyze from the historical and philosophical positions the intellectual and emotional-spiritual state of a person in which he feels happy. Each historical epoch put forward its own ideals, so intellectuals offered new formulas of happiness. The philosophical origins of the concept of happiness investigated in the article allow us to consider and substantiate its substantive aspect from various positions (theological, social, psychological). The methodological basis is the general scientifi c methods of cognition (historical, system-structural and dialectical, due to the complexity and internal inconsistency of the subject) in combination with the philosophical and anthropological approach. The main method of research is the historical-deductive method, a method of comparative analysis of philosophical approaches to solving the problem of happiness in various historical epochs. In conclusion, the authors assert that happiness is a deeply experienced complex integral physical, mental, intellectual, social and spiritual state that depends on the person himself. A person’s happiness is himself, his life created by him, in which all the acquired values of culture fi nd their true place and meaning. The results of the study can be used in the process of educating young people and teaching philosophy and humanities in various educational institutions.
The Impact of China's Virtual Currency Policy on the Bitcoin Market

This thesis aims to understand and analyze the impact of China's virtual currency policy on the Bitcoin market. This paper will summarize the impact of digital technology on cryptocurrencies, including the development path, technological development, process, and regulation through literature search, data analysis, and government policies to comprehensively analyze the impact of the virtual currency policy on cryptocurrency circulation. Regulators and governments must formulate appropriate policies and measures to ensure compliance and stability in the cryptocurrency market. This paper analyzes the Bitcoin market as an example by using a mathematical model to reflect the trend and direction of the market before and after the release of the policy and using the model to predict future changes in the Bitcoin market. The Chinese government's regulatory measures have led to the closure of several Bitcoin miners, which has affected Bitcoin mining activity. Second, the decline in liquidity in the Chinese market has led to short-term volatility in the price of bitcoin. In addition, some investors may view Bitcoin as a safe-haven asset against an uncertain policy environment. Overall, China's virtual currency policies have had a multifaceted impact on the Bitcoin market, and these impacts are still evolving to some extent. This research provides valuable insights into the dynamics of the global Bitcoin market and the impact of virtual currency regulation.
MENINGKATKAN PRESTASI BELAJAR MEMBACA AL QURAN MELALUI MODEL PEMBELAJARAN TUTOR SEBAYA DI KELAS VII B SMP NEGERI 1 BATURETNO KABUPATEN WONOGIRI

As the results of the initial test for reading the Al-Quran, learning achievement to read the Al-Quran in class VII B SMP Negeri 1 Baturetno is still low with an average score of 62.30. While the learning completion score has only reached 22%. In general, the learning process is still dominated by teachers. This research aimed to improve learning achievement in reading the Al Quran through optimizing learning activities. This research consists of three cycles, each cycle including the following steps: 1) Planning: creating a learning scenario using a peer tutor model, 2) Acting: carrying out learning according to the scenario, 3) Observing: taking data through triangulation as data validation, 4) Reflecting: the observation data is reflected collaboratively to determine improvements in achievement and plan further action. The results of this research show that there is a quite large increase in learning achievement in reading the Al Quran as follows: in cycle I there was an increase in the average score of 4.30 points (from 62.30 to 66.60) and an increase in learning completeness of 15% (from 22% to 37 %), in cycle II there was an increase in the average score of 8.20 points (from 66.60 to 74.80) and an increase in learning completeness of 29% (from 37% to 66%), and in cycle III there was an increase in the average score of 1.80 points (from 74.80 to 76.60) with an increase in learning completeness of 25% (from 66% to 91%). The same actions from cycle I to cycle III show an increase in student achievement in learning to read the Quran.