Reviewerly/exHarmony huggingface dataset:
Statistical Model for the Quality of Panoramic Images of 2D artifacts

The field of digital imaging emphasizes the quality of 2D artifact images, often facing challenges when capturing large images due to their wide field of view. A successful technique for addressing this is panoramic image creation, which involves merging overlapping segments from a larger image. Research in this domain focuses on understanding the visual quality aspects of panoramic images. This study aims to achieve two main objectives: firstly, to identify the key visual quality attributes associated with panoramic images, and secondly, to propose predictor variables for a statistical model that assesses the quality of 2D artifact panoramic images. To accomplish this, the researchers conducted a case study centered on generating panoramic images of mural paintings found in Sri Lankan temples. Through their investigation, they pinpointed color balance and noise &amp; distortion as the most significant factors influencing the overall quality of these images. The researchers employed three methods to create the panoramas: an innovative technique, alongside two established methods—Photoshop and Hugin. Experts in Visual Arts evaluated the resulting images using a four-point Likert scale. Color balance and noise &amp; distortion were used as predictor variables, while overall quality was the response variable. The gathered data underwent analysis using ordinal logistic regression within the Minitab statistical package. The outcomes underscored the pivotal roles of color balance and noise &amp; distortion in determining the quality of panoramic images. Moreover, the findings showcased the model’s high accuracy in fitting the data, reinforcing its effectiveness in assessing panoramic image quality.
Comparative Analysis of the Implementation of Projects for the Modernization of Libraries in the Krasnoyarsk Territory

Libraries of the Krasnoyarsk Territory take part in the regional project “Libraries of the Future” and the national project “Culture” (the federal project “Cultural Environment” in terms of creating model municipal libraries). In 2015—2022, a network of 60 modern municipal libraries was launched in the Krasnoyarsk Territory (the regional project “Libraries of the Future” — 45 institutions, the national project “Culture” — 15). Both projects enable participants to create new conditions to carry out their activities. At the same time, they are an effective tool for the realization of the state cultural policy. The article describes in detail the stages of project implementation. A comparative analysis of the features and advantages of the ongoing projects (competitive conditions, methodological support, funding volumes, main items of expenditure) is given. The importance of the activity of libraries within the framework of the regional project “Libraries of the Future” and the national project “Culture” for creating the necessary conditions for the development of man and society was noted. Participation in the presented projects contributes to the improvement of library services, as evidenced by the growth of the main indicators of the institutions’ activities. The study presents the performance indicators of libraries before and after modernization, provides a comparative analysis of the efficiency of libraries (conventionally called “modernized” and “model”). The projects studied in this work are based on such concepts as “model” and “modernization”. The authors analyzed both directions of innovative development of libraries in the Krasnoyarsk Territory and concluded that the processes of creating model and modernized libraries, despite certain differences, affect each other.
Neural networks in nonlinear time series: a subsampling model selection procedure

In this chapter, the problem of model selection in neural networks for nonlinear time series data is addressed. A systematic review and an appraisal of previously published research on the topic are presented and discussed with emphasis on a complete strategy to select the topology of the model. The procedure attempts to explain the black box structure of a neural network by providing information on the complex structure of the relationship between a set of inputs and the output. The procedure combines a set of graphical and inferential statistical tools and allows to choose the number and the type of inputs, considered as explanatory variables, by using a formal test procedure based on relevance measures and to identify the hidden layer size by looking at the predictive performance of the neural network model. To obtain an approximation of the involved statistics, the approach heavily uses the subsampling technique, a computer-intensive statistical methodology. The results on simulated data show the good performance of the overall procedure.
Vladimir Ivanovich Vernadsky as a Librarian, Bibliographer and Document Historian

The outstanding scientist-naturalist, philosopher, public figure and organizer of science V.I. Vernadsky (1863—1945) dealt with the issues of setting up librarianship in Russia. He was one of the founders of the circle on the reform of people’s libraries (1884), which included undergraduates and graduates of the Imperial St. Petersburg University, most of whom later became prominent scientific and public figures of the country. The participants of the circle considered it necessary to reform the people's libraries. V.I. Vernadsky was one of the compilers of Moscow home reading programs with bibliographic lists of recommended literature for self-educational study. He selected natural science literature for them. The lists were designed for people with secondary education who want to broaden their horizons to the level of university education. V.I. Vernadsky left his name in the history of the creation of the National Library of Ukraine: in 1918, he was chairman of the commission on its establishment; since 1988, this library deservedly bears his name. In the university course of lectures on the world history of science, V.I. Vernadsky devoted a significant place to the history of the origin of printing, the invention of geographical maps, atlases, compass, sextant, bussoli and other devices, which in terms of documentology are sources of information, i.e. documents. As a biographer and bibliographer, V.I. Vernadsky took part in the compilation of a unique 25-volume Russian biographical dictionary, the last volume of which was published in 1917. In the year of the 160th anniversary of the birth of Academician V.I. Vernadsky, these facts from his biography deserve the attention of the library and bibliographic community.
Shaping the Country’s Cultural Sovereignty: Murmansk Hosted the All-Russian Library Congress

June 10—17 in Murmansk, the Library Capital of Russia 2023, the All-Russian Library Congress: XXVII Annual Conference of the Russian Library Association (RLA) took place. The event was supported by the Government of the Murmansk Region. The Congress was attended by 957 delegates from 68 regions of Russia. The professional programme included two plenary sessions and 57 sectional and special events with 634 presentations. The Congress was held under the sign of two significant dates — the 85th anniversary of the Murmansk Region and the 85th anniversary of the Murmansk State Regional Universal Scientific Library. The grand opening and the first plenary session took place on June 12 2023 on the Day of Russia. On June 13 and 14 2023 the work continued at the breakout sessions. The results of the congress were summed up at the final plenary session on June 15 2023. The relevance of the main theme of the Congress “Library — the cultural code of Russia” is connected with the state cultural policy and the tasks set for libraries. The definition of the cultural code of Russia is conditioned by the need to consolidate the citizens of the country on the basis of a common system of values, the formation of cultural sovereignty. The issues of implementation of one of the core documents of the library industry — “Strategy for the librarianship development in the Russian Federation for the period up to 2030” were raised. The work of libraries in new social and geopolitical conditions was analyzed. The “Model Regulations on the Organisation of Research Activities of the Central Library of the Subject of the Russian Federation” was adopted.
POSSIBILITY OF USING MACHINE LEARNING ALGORITHMS TO MODERNIZE AGRICULTURAL RESEARCH IN INDONESIA

Even though many researchers and campuses have agricultural technology study programs, various technological and agricultural research in Indonesia still needs to be fully integrated. The author believes that technological advancements, particularly in Artificial Intelligence and Machine Learning, provide substantial advantages in various industries, including agriculture. This paper will examine the opportunities for using machine learning algorithms in precision agricultural research to inspire Indonesian agricultural researchers.
Deep Learning Techniques in Radar Emitter Identification

In the field of electronic warfare (EW), one of the crucial roles of electronic intelligence is the identification of radar signals. In an operational environment, it is very essential to identify radar emitters whether friend or foe so that appropriate radar countermeasures can be taken against them. With the electromagnetic environment becoming increasingly complex and the diversity of signal features, radar emitter identification with high recognition accuracy has become a significantly challenging task. Traditional radar identification methods have shown some limitations in this complex electromagnetic scenario. Several radar classification and identification methods based on artificial neural networks have emerged with the emergence of artificial neural networks, notably deep learning approaches. Machine learning and deep learning algorithms are now frequently utilized to extract various types of information from radar signals more accurately and robustly. This paper illustrates the use of Deep Neural Networks (DNN) in radar applications for emitter classification and identification. Since deep learning approaches are capable of accurately classifying complicated patterns in radar signals, they have demonstrated significant promise for identifying radar emitters. By offering a thorough literature analysis of deep learning-based methodologies, the study intends to assist researchers and practitioners in better understanding the application of deep learning techniques to challenges related to the classification and identification of radar emitters. The study demonstrates that DNN can be used successfully in applications for radar classification and identification.&#x0D; &#x0D;
Playing the Snake Game with Reinforcement Learning

When it is necessary to make several decisions to solve a problem, reinforcement learning works well. In this project, we demonstrate the outcomes of playing with snakes using a Deep Q-Network (DQN). Unfortunately, DQN has a tendency to overfit, which results in a deterioration in terms of scores that are subpar after numerous training sessions. To solve this issue, we enhanced the reward setting and provided the snake with more information about its surroundings in order to raise our score and prevent some overfitting issues. Also, we went through the differences between DQN and conventional evolutionary algorithms as well as possible avenues for DQN optimization.
Short Time Series Forecasting Method Based on Genetic Programming and Kalman Filter

Accurate forecasting of the baccalaureate admission statistics is a crucial step towards an improvement of the educational system in Mauritania and its responsiveness to the economical needs. Since an available historical information is collected only over last ten years, an accurate forecasting technique for short time series is required. Addressing this issue, the presented paper proposes a tool based on the genetic programming and Kalman filter. This tool allows to make accurate short term prediction for short time series and easily set up experiments. A tool validation on different data sets is presented, where the provided tool provides more robust forecasting results comparatively with the state-of-the-art techniques. It is expected that this tool can help to make a qualitative jump in the improvement of Mauritanian education system.
Fuzzy Pattern Trees with Pre-classification

Fuzzy Pattern Trees evolved using Grammatical Evolution have been shown to be a robust Explainable Artificial Intelligence technique. We trained a black-box classifier, XGBoost, to identify and remove instances our evolved Fuzzy Pattern Tree model was seen to struggle to classify correctly. This framework mimics a human-computer interactive approach, where the removed instances would be classified by the human and not the Fuzzy Pattern Tree model. We investigated a range of fitness functions to ascertain which is most suitable for use with pre-classification by examining their accuracy, errors and how well they perform with the pre-classifier. We show that Fuzzy Pattern Tree classifiers, on each benchmark and using every fitness function, improved when used with a pre-classification method. Fuzzy Pattern Tree models with pre-classification found better performance than any other black-box classifier on all the benchmarks considered and routinely removed less than 10% of test data for human inspection.
Automation of User Interface Testing by Reinforcement Learning-Based Monkey Agents

The complexity of Graphical User Interfaces (GUIs) in consumer applications such as home appliances has significantly risen in recent years. For example, the number of different views in the GUIs has increased from simple selection views to complex sub-menu structures. Alongside the development, both testing complexity and cost have risen drastically. A way of handling this increase is test automation by the use of machine learning algorithms. This work focuses on reinforcement learning-based autonomous grey-box monkey testing for consumer GUIs. As a monkey tester, a Deep Q-Network is interacting with the device under test. Experiments are performed on an oven GUI as well as on a desktop training environment. A known feature representation for monkey testing is compared to three alternative representations, as well as a random agent. A careful selection of the feature representation can improve the exploration performance. Empirical results for the autonomous exploration of GUIs show the usefulness of reinforcement learning-based monkey testing over pure random testing on consumer GUIs. This can lead to an efficiency advantage in practice, as pure random testing is often the status quo in many well-known GUI test-frameworks such as Squish.
Performance Upgrade of Sequence Detector Evolution Using Grammatical Evolution and Lexicase Parent Selection Method

Quickly designing correct and efficient digital circuits is a crucial need for the electronics industry. Several Electronic Design Automation tools are used for this task. Still, they often lack the diversity of designs that search-based techniques can offer, such as our system producing three different designs for a 5-bit ‘11011’ Sequence Detector. Sequence Detectors are some of the most crucial digital sequential circuits evolved in this work using Grammatical Evolution, a Machine Learning technique based on Evolutionary Computation. Compared to the literature, a reasonably small training data set is used to generate diverse solutions/circuits. A comparison is delivered of the results of the evolved circuits using two different parent selection techniques, tournament selection and lexicase selection. It is shown that the evolved circuits using a small training data set have shown a hundred percent test accuracy on a vast amount of test data sets, and the performance of lexicase selection is much better than tournament selection while evolving these circuits.
Exploration of the Innovation Path in Radio Music during the New Media Era

The rise and rapid development of radio music programs during the 1940s and 1950s represented a monumental shift in twentieth-century music. From their inception, radio music programs have been intimately intertwined with media technology, serving as a medium that harmonizes humanistic care, artistic expression, scientific advancements, and entertainment. Over the past few decades, these programs have evolved in tandem with the community, keeping pace with the continuous innovation and development of media platforms and channels. In today's era of new media, technology advances at an unprecedented rate, profoundly impacting traditional media such as radio, music programs, and print media. These traditional media outlets face dual challenges and opportunities within the digital economy and new media. This article analyzes media development during the new media era, delves into the inherent advantages of radio music programs, and explores the current challenges they encounter. Emphasis is placed on describing the prevailing issues faced by radio music programs. Additionally, the article highlights an innovative trajectory for radio music programs within the new media era. This entails leveraging value leadership to foster listener development, ensuring content quality as a sustainable source of captivating material, innovating approaches to enhance the music experience, achieving diversified communication through exploring novel communication carriers, and cultivating the innovative growth of radio music programs within the new media landscape.
Research on the Concept and Innovation Path of Science and Education Integration

With the continuous development of technology, culture and economy, higher expectations have been placed on higher education institutions by the nation. The goal of higher education is to cultivate talents with a broad knowledge base and practical abilities. Scientific research and teaching are crucial issues in the field of education, and to maximize their roles in the educational process, it is essential to promote their integration and mutual influence. However, there is currently a disconnect between research and teaching in the education sector, where higher education institutions often prioritize research outcomes over teaching quality, teachers overly focus on research projects to the detriment of their students' education, and a lack of coherence between research and teaching persists. Therefore, it is necessary to comprehensively motivate teachers, enhance students' creativity, and achieve the concurrent development of research capabilities and teaching effectiveness in higher education institutions.
The influence of home literacy environment on children’s literacy and language development: A systematic review

Children’s literacy and language development has been found to share an intimate link with their home literacy environment (HLE) by many researchers. However, as HLE is not a unitary construct and children’s literacy and language development also includes multifaceted sets of skills, most existing studies focus on one or only a few aspects of HLE and literacy and language skills. Therefore, listerature in this area lacks a coherent approach and theoretical integration. One potential problem is that, while some aspects of HLE and their associations with language and literacy skills are frequently studied, some other aspects tend to be ignored. This systematic review selects 15 related empirical studies through rigorous screening and examines the variables used to measure HLE along with the correlations between different aspects of HLE and specific domains of children’s literacy and language development across studies. The results indicate that parent-child activities (not direct teaching behaviours) are the most frequently discussed aspect of HLE while child-led literacy activity is one of the least measured items. Regarding the correlation between HLE and literary and language development, oral language skills, such as receptive vocabulary and expressive vocabulary, are more related to parental demographics and attitudes towards reading and informal literacy activities. Meanwhile, decoding skills, such as letter knowledge and phonological development, are more linked with parental direct teaching behaviours. Based on the findings, implications for future HLE research are discussed.
Multimodal Entity Alignment

Abstract In various tasks related to artificial intelligence, data is often present in multiple forms or modalities. Recently, it has become a popular approach to combine these different forms of information into a knowledge graph, creating a multi-modal knowledge graph (MMKG). However, multi-modal knowledge graphs (MMKGs) often face issues of insufficient data coverage and incompleteness. In order to address this issue, a possible strategy is to incorporate supplemental information from other multi-modal knowledge graphs (MMKGs). To achieve this goal, current methods for aligning entities could be utilized; however, these approaches work within the Euclidean space, and the resulting entity representations can distort the hierarchical structure of the knowledge graph. Additionally, the potential benefits of visual information have not been fully utilized. To address these concerns, we present a new approach for aligning entities across multiple modalities, which we call hyperbolic multi-modal entity alignment (). This method expands upon the conventional Euclidean representation by incorporating a hyperboloid manifold. Initially, we utilize hyperbolic graph convolutional networks() to acquire structural representations of entities. In terms of visual data, we create image embeddings using the model and subsequently map them into the hyperbolic space utilizing . Lastly, we merge the structural and visual representations within the hyperbolic space and utilize the combined embeddings to forecast potential entity alignment outcomes. Through a series of thorough experiments and ablation studies, we validate the efficacy of our proposed model and its individual components.
Recent Advance of Alignment Inference Stage

Abstract In this chapter, we introduce recent progress of the alignment inference stage.
Recent Advance of Representation Learning Stage

Abstract Over the last few years, there are a pile of research devoted to learning better KG representations to facilitate entity alignment. Thus, in this chapter, we summarize recent progress in the representation learning stage of EA and also provide a detailed empirical evaluation to reveal the strengths and weaknesses of current solutions.
Large-Scale Entity Alignment

Abstract In this chapter, we focus on the concept of entity alignment at scale and present a new method for addressing this task. The proposed solution is capable of handling vast amounts of knowledge graph pairs and delivering high-quality alignment outcomes. First, to manage large-scale KG pairs, we develop a set of seed-oriented graph partition strategies that divide them into smaller subgraph pairs. Next, within each subgraph pair, we employ existing methods to learn unified entity representations and introduce a novel reciprocal alignment inference strategy to model bidirectional alignment interactions, which can lead to more accurate outcomes. To further enhance the scalability of reciprocal alignment inference, we propose two variant strategies that can significantly reduce memory and time costs, albeit at the expense of slightly reduced effectiveness. Our solution is versatile and can be applied to existing representation learning-based EA models to enhance their ability to handle large-scale KG pairs. We also create a new EA dataset that comprises millions of entities and conduct comprehensive experiments to verify the efficiency of our proposed model. Furthermore, we compare our proposed model against state-of-the-art baselines on popular EA datasets, and our extensive experiments demonstrate its effectiveness and superiority.