Reviewerly/exHarmony huggingface dataset:
Advantages and disadvantages of developing and producing educational multimedia content

The article discusses current trends in the field of distance education services. There are three main types of multimedia content, such as webinar, video lesson, and video conference. The main characteristics are disclosed in detail, as well as algorithms that must be followed during their development and production. The analysis of the proposed algorithms for the development and production of the specified multimedia content revealed a number of advantages and disadvantages inherent in each of the three types of content. In the order of discussion, new trends in the multimedia context were considered, such as webinar in the form of an interview, webinar (video lesson) in the form of a master class and networking. It is concluded that a qualitative and quantitative leap is now taking place in the field of educational services provided remotely.
Designing course “Differential Geometry” based on the LMS Moodle platform for bachelor - future teachers of Mathematics

The authors describe the qualification of a graduate in differential geometry for the preparation of bachelors - future higher mathematicians. The article substantiates the choice of the LMS Moodle platform as a course development environment.
Path of the Engineer: Logic + Common Sense or Physics + Mathematics?

The study provides a detailed analysis of the conflicting requirements for the “digital” competencies of an engineer, their comparison with the real requirements of a Russian employer. Numerous normative, organizational, methodological, educational documents and materials (Federal State Educational Standards of Higher Education, recommendations of Academic Methodological Associations, professional standards, educational and methodical publications, programs of disciplines, etc.) are considered. The author's experience in teaching natural sciences and special disciplines, supervising young scientists and specialists, their certification, organization of the educational process at a university, and the formation of a system of practical training at an industrial enterprise are used. The author sees obvious distortions in the current system of training engineering personnel and notes the formation of a gap between the natural science base and the “digital” competencies of an engineer. The article develops some recommendations for redesigning educational standards and programs for technical specialties, which, ultimately, should help to better prepare university graduates for the requirements of the labor market.
Introduction to the 38th International Conference on Logic Programming Special Issue

ICLP was a part of the Federal Logic Conference (FLoC) 2022 (https://floc2022.org/).Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming.
Collective Learning of Low-Memory Matrix Adaptation for Large-Scale Black-Box Optimization

The increase of computing power can be continuously driven by parallelism, despite of the end of Moore's law. To cater to this trend, we propose to parallelize the low-memory matrix adaptation evolution strategy (LM-MA-ES) recently proposed for large-scale black-box optimization, aiming at further improving its scalability (w.r.t. CPU cores) in the modern distributed computing platform. To achieve this aim, three key design choices are carefully made and naturally combined within the multilevel learning framework. First, to fit into the memory hierarchy and reduce communication cost, which is critical for parallel performance on modern multi-core computer architectures, the well-known island model with a star interaction network is employed to run multiple concurrent LM-MA-ES instances, each of which can be effectively and serially executed in each separate island owing to its low computational complexity. Second, to support fast convergence under the multilevel learning framework, we adopt Meta-ES to hierarchically exploit the spatial-nonlocal information for global step-size adaptation at the outer-ES level, combined with cumulative step-size adaptation, which exploits the temporal-nonlocal information in the inner-ES (i.e., serial LM-MA-ES) level. Third, a set of fitter individuals at the outer-ES level, represented as (distribution mean, evolution path, transformation matrix)-tuples, are collectively recombined to utilize the desirable genetic repair effect for statistically more stable online learning. Experiments in a clustering computing environment empirically validate the parallel performance of our approach on high-dimensional memory-costly test functions. Its Python code is available at https://github.com/Evolutionary-Intelligence/D-LM-MA .
Gene-pool Optimal Mixing in Cartesian Genetic Programming

Genetic Programming (GP) can make an important contribution to explainable artificial intelligence because it can create symbolic expressions as machine learning models. Nevertheless, to be explainable, the expressions must not become too large. This may, however, limit their potential to be accurate. The re-use of subexpressions has the unique potential to mitigate this issue. The Genetic Programming Gene-pool Optimal Mixing Evolutionary Algorithm (GP-GOMEA) is a recent model-based GP approach that has been found particularly capable of evolving small expressions. However, its tree representation offers no explicit mechanisms to re-use subexpressions. By contrast, the graph representation in Cartesian GP (CGP) is natively capable of re-use. For this reason, we introduce CGP-GOMEA, a variant of GP-GOMEA that uses graphs instead of trees. We experimentally compare various configurations of CGP-GOMEA with GP-GOMEA and find that CGP-GOMEA performs on par with GP-GOMEA on three common datasets. Moreover, CGP-GOMEA is found to produce models that re-use subexpressions more often than GP-GOMEA uses duplicate subexpressions. This indicates that CGP-GOMEA has unique added potential, allowing to find even smaller expressions than GP-GOMEA with similar accuracy.
Runtime Analysis of Unbalanced Block-Parallel Evolutionary Algorithms

We revisit the analysis of the ( $$1$$ + $$\lambda $$ ) EA in a parallel setting when the offspring population size is significantly larger than the number of processors available. If the workload is not balanced across the processors, existing runtime results do not transfer directly. We therefore consider two new scenarios that produce unbalanced processors: (1) when the computation time of the fitness function is variable and depends on the structure of the individual, and (2) when processing is interrupted as soon as a viable offspring is found on one of the machines. We derive parallel execution times for both these models as a function of both the population size and the number of parallel machines. We discuss the potential trade-off between communication overhead and execution time, and we conduct some experiments.
Evolutionary Algorithms for Cardinality-Constrained Ising Models

The Ising model is a famous model of ferromagnetism, in which atoms can have one of two spins and atoms that are neighboured prefer to have the same spin. Ising models have been studied in evolutionary computation due to their inherent symmetry that poses a challenge for evolutionary algorithms. Here we study the performance of evolutionary algorithms on a variant of the Ising model in which the number of atoms with a specific spin is fixed. These cardinality constraints are motivated by problems in materials science in which the Ising model represents chemical species of the atom and the frequency of spins is constrained by the chemical composition of the alloy being modelled. Under cardinality constraints, mutating spins independently becomes infeasible, thus we design and analyse different mutation operators of increasing complexity that swap different atoms to maintain feasibility. We prove that randomised local search with a naive swap operator finds an optimal configuration in $$\varTheta (n^4)$$ expected worst case time. This time is drastically reduced by using more sophisticated operators such as identifying and swapping clusters of atoms with the same spin. We show that the most effective operator only requires O(n) iterations to find an optimal configuration.
Scientific and methodological interaction of school and university: organization of school research conferences

The paper considers the interaction between the university and general education organizations (schools) in the implementation of teaching students to take part in project and research conferences. Each student is required to write and defend a project in accordance with the FSES SVE. The expected result is the possession of universal ways of knowing. The research type of socialization, developed in modern society, sets a person to treat the world as a volatile new, requiring search methods of thinking. The teacher often acts as the mentor in group the pupil the teacher the teacher of higher education institution. The author selected two institutions in Samara as general education organizations. Project and research works performed with the curatorial support of the teacher and the scientific leadership of the university teacher provide an opportunity for the students self-realization and self-education. The choice of subject areas for research among students corresponds to their major and further professional interest. An important factor determining the success of research activities is the research and training motivation of students. Project and research works performed with the curatorial support of the teacher and the scientific leadership of the university teacher provide an opportunity for the students self-realization and self-education.
A model of environmental consciousness development among university students in the framework of extracurricular activities: structure, features of implementation in the pedagogical process

The paper contains results of the development and implementation of a pedagogical model for the environmental consciousness development among students in the framework of extracurricular activities on the example of organization and functioning of the Ecoclub in Samara State University of Social Sciences and Education. Within the framework of solving the set tasks and during a comprehensive analysis of psychological, pedagogical, sociological literature, the ecological consciousness of students is considered to be a system of interaction of figurative-sign concepts, ideas, views and judgments of an individual, undergoing comprehension in the course of mental processes that find their expression through its interaction with the environment habitations regulated by means of cognitive functions and emotional-personal attitudes. The structural components of ecological consciousness are: 1) a conceptual and sign component contains concepts and ideas of the individual about the habitat and the patterns that make up its main features; 2) an emotional-personal component contains individual characteristics and the emotional component of the relationship to the natural environment, compliance with certain norms of human relations with nature; 3) a cognitive-activity component contains meaningful representations of the individual aimed at observing environmental standards in the course of the implementation of personal attitudes; 4) a coordinating-behavioral component determines the behavior corresponding to the scientific picture of the world and decision-making aimed at minimizing environmental harm, based on the knowledge and ideas received.
The potential of a personal educational environment in the development of information and communication competence of a university teacher

In modern conditions, significant changes are taking place in the functional components of the university teachers activity, due to the influence of many factors of the transforming digital educational reality. The paper actualizes the problem of detailed study at the theoretical and practical level of the process of developing information and communication competence of a teacher, as a personal quality manifested in the willingness and ability to work with information, the main types of software and hardware and to carry out productive interaction in the digital educational space, due to the capabilities and resources of personal educational environment. The author of the paper defines the possibilities (filling with various structural components depending on the goals, objectives, content of activities and planned results; adaptation to the abilities, needs, interests of the teacher; providing conditions for achieving the necessary level of personal self-organization of the individual; creating a space for effective online communication; integration of formal education and education outside the formal system; implementation of individual models of pedagogical activity) and resources (functional technical and software tools: gadgets, services, programs, platforms, as well as their interrelations; electronic information and educational resources used to solve educational and professional-pedagogical tasks, as well as contributing to effective personal and professional development) of the personal educational environment in the development of information and communication competence of a university teacher.
ML-CookGAN: Multi-Label Generative Adversarial Network for Food Image Generation

Generating food images from recipe and ingredient information can be applied to many tasks such as food recommendation, recipe development, and health management. For the characteristics of food images, this paper proposes ML-CookGAN, a novel CGAN. This network enables the generation of food images based on recipe and ingredient labels. The generator of ML-CookGAN, Multi-Label Fusion Generator, converts recipe and ingredient labels into different granularity features and generates corresponding food images. The discriminator of ML-CookGAN, Multi-Branch Discriminator, implements discrimination and classification with a multi-branch structure. In addition, we propose two training strategies, Region-Wise Pooling and Image Style Distillation, to better the network performance. Region-Wise Pooling handles region-wise features with the discriminator. Image Style Distillation aims at extracting image latent features to assist image generation by an unsupervised method. The experiments conducted on VIREO Food-172 databases validate the proposed method to generate high-quality Chinese food images. And Region-Wise Pooling and Image Style Distillation are proven to enhance the diversity and realism of generated food images.
THE RELATIONSHIP OF PARENTS' MOTIVATION AND STUDENT'S LEARNING INTEREST TOWARDS READING ACTIVITIES OF THE QURAN(SMP IT Robbani Sintang Kalimantan Barat)

This research includes correlational research. This research was carried out at the IT Robbani Sintang Middle School, West Kalimantan in 2021. This study contained three variables, namely two independent variables and one dependent variable. Data collection techniques used in this study were questionnaires and documentation. Based on the results of research on the relationship between parents' motivation and students' interest in learning to read Al-Quran at SMP IT Robbani Sintang, West Kalimantan, it can be concluded as follows: (1) There is a significant relationship between parents' motivation (X1) on reading Al-Quran activities. Qur'an (Y) students of Class VIII SMP IT Robbani Sintang West Kalimantan where the contribution is 39.9%. (2) There is a significant relationship between interest in learning (X2) on the activity of reading the Koran (Y) students of Class VIII SMP IT Robbani Sintang West Kalimantan where the contribution is 46.3% (3) There is a significant relationship between parents' motivation (X1) and interest in learning (X2) together, with the activity of reading the Koran (Y) of Class VIII students of SMP IT Robbani Sintang, West Kalimantan, where the contribution is 30.8%
Adaptive content suppression based on shared‐hidden‐layer convolutional autoencoder for blind image forensics

Summary Double JPEG (DJPEG) compression detection is one of the crucial issues in detecting the presence of image forgery or tampering in blind image forensics. To address this challenge, this article investigates DJPEG compression detection in aligned and nonaligned tampering cases using adaptive image content removal based on a shared‐hidden‐layer convolutional autoencoder for preprocessing. Then the suppressed content images are classified with two convolutional neural networks (CNNs) in a parallel structure. These two networks are responsible for separating the small 64 × 64 single JPEG compression images from the DJPEG ones. The results indicate appropriate performance in the aligned and especially nonaligned cases. The detection possibility in the absence of alignment was investigated and favorable results were achieved. Since it is not possible to determine the exact values of the quality factor (QF) for optimal detection in some cases, we examined the network sensitivity to these QFs. The experimental results were satisfactory, especially in the aligned case. Comparing state‐of‐the‐art methods, we achieved a relative error reduction of about 69% in some cases. In addition, we determined the manipulated locations with high accuracy.
Applying Time-Inhomogeneous Markov Chains to Math Performance Rating

In this paper, we present a case study in collaboration with mathematics education and probability theory, with one providing the use case and the other providing tests and data. The application deals with the reason and execution of automation of difficulty classification of mathematical tasks and their users' skills based on the Elo rating system. The basic method is to be extended to achieve numerically fast converging ranks as opposed to the usual weak convergence of Elo numbers. The advantage over comparable state-of-the-art ranking methods is demonstrated in this paper by rendering the system an inhomogeneous Markov Chain. The usual Elo ranking system, which for equal skills (Chess, Math, ...) defines an asymptotically stationary time-inhomogeneous Markov process with a weakly convergent probability law. Our main objective is to modify this process by using an optimally decreasing learning rate by experiment to achieve fast and reliable numerical convergence. The time scale on which these ranking numbers converge then may serve as the basis for enabling digital applicability of established theories of learning psychology such as spiral principal and Cognitive Load Theory. We argue that the so further developed and tested algorithm shall lay the foundation for easier and better digital assignment of tasks to the individual students and how it is to be researched and tested in more detail in future.
An Explainable Multimodal Fusion Approach for Mass Casualty Incidents

During a Mass Casualty Incident, it is essential to make effective decisions to save lives and nursing the injured. This paper presents a work in progress on the design and development of an explainable decision support system, intended for the medical personnel and care givers, that capitalises on multiple modalities to achieve situational awareness and pre-hospital life support. Our novelty is two-fold: first, we use state-of-the-art techniques for combining static and time-series data in deep recurrent neural networks, and second we increase the trustworthiness of the system by enriching it with neurosymbolic explainable capabilities.
Case Study on a Session Hijacking Attack: The 2021 CVS Health Data Breach

The CVS medical data breach in March of 2021 was a source of anxiety, fear, and anger in many users, leading to lower customer loyalty. Our study found that their websites used misconfigured databases, allowing an adversary to steal healthcare data through session hijacking attacks. Customers’ search metadata containing email addresses, prescriptions, and other medical search queries, were stored in cloud-hosted log files. Although no concrete evidence of data misuse was uncovered, the research found that over a billion confidential search queries were potentially exposed to adversaries. This paper analyzes the data breach methodology and impact in detail and provides possible defense strategies against such attacks. It violates the security and protection regulations mandating proper confidentiality of users’ private medical and healthcare information. This paper also outlines possible defense measures against healthcare data session hijacking attacks, including having policies in place, such as an incident response plan, preserving evidence of the breach, and isolating the data breach.
An Untold Tale of Scientific Collaboration: SCCH and AC$$^2$$T

In the last two decades, the application of artificial intelligence (AI) in different fields has increased significantly. As an interdisciplinary field, AI methods are improving toward efficiency and applicability due to a vast number of high-quality research and adopting themselves with multiple use cases. Therefore, the industry is investing in AI-based companies to overcome their use cases efficiently. This paper discusses the collaboration between two research-based companies with different expertise, one in the fields of Data and Software Science and the other in tribology. We show how both companies benefit from such collaboration to tackle the problem at hand.
Battling the Bullwhip Effect with Cryptography

In real-world supply chains it is often observed that orders placed with suppliers tend to fluctuate more than sales to customers and that this deviation builds up in the upstream direction of the supply chain. This bullwhip effect arises because local decision-making based on orders of the immediate customer leads to overreaction. Literature shows that supply chain wide sharing of order or inventory information can help to stabilize the system and reduce inventories and stockouts. However, sharing this information can make a stakeholder vulnerable in other areas like the bargaining over prices. To overcome this dilemma we propose the usage of cryptographic methods like secure multiparty computation or homomorphic encryption to compute and share average order/inventory levels without leaking of sensitive data of individual actors. Integrating this information into the stylized beer game supply chain model, we show that the bullwhip effect is reduced also under this limited information sharing. Besides presenting results regarding the savings in supply chain costs achieved, we describe how blockchain technology can be used to implement such a novel supply chain management system.
A Table Extraction Solution for Financial Spreading

Financial spreading is a necessary exercise for financial institutions to break up the analysis of financial data in making decisions like investment advisories, credit appraisals, and more. It refers to the collection of data from financial statements, where their extraction capabilities are largely manual. In today's fast-paced banking environment, inefficient manual data extraction is a major obstacle, as it is time-consuming and error-prone. In this paper, we, therefore, address the problem of automatically extracting data for Financial Spreading. More specifically, we propose a solution to extract financial tables including Balance Sheet, Income Statement and Cash Flow Statement from financial reports in Portable Document Format (PDF). First, we propose a new extraction diagram to detect and extract financial tables from documents like annual reports; second, we build a system to extract the table using machine learning and post-processing algorithms; and third, we propose an evaluation method for assessing the performance of the extraction system.