Reviewerly/exHarmony huggingface dataset:
An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction

The original whale optimization algorithm (WOA) has a low initial population quality and tends to converge to local optimal solutions. To address these challenges, this paper introduces an improved whale optimization algorithm called OLCHWOA, incorporating a chaos mechanism and an opposition-based learning strategy. This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase, thereby enhancing the quality of the initial whale population. Additionally, including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations. The work and contributions of this paper are primarily reflected in two aspects. Firstly, an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed. Secondly, the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory (LSTM) networks. Subsequently, a prediction model for Realized Volatility (RV) based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically. To evaluate the performance of OLCHWOA, a series of comparative experiments were conducted using a variety of advanced algorithms. These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems. The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget. Additionally, the China Securities Index 300 (CSI 300) dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV. The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV. This further confirms that OLCHWOA effectively addresses real-world optimization problems.
Fusion of Hash-Based Hard and Soft Biometrics for Enhancing Face Image Database Search and Retrieval

The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade, owing to the continuing advances in image processing and computer vision approaches. In multiple real-life applications, for example, social media, content-based face picture retrieval is a well-invested technique for large-scale databases, where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of pictures. Humans widely employ faces for recognizing and identifying people. Thus, face recognition through formal or personal pictures is increasingly used in various real-life applications, such as helping crime investigators retrieve matching images from face image databases to identify victims and criminals. However, such face image retrieval becomes more challenging in large-scale databases, where traditional vision-based face analysis requires ample additional storage space than the raw face images already occupied to store extracted lengthy feature vectors and takes much longer to process and match thousands of face images. This work mainly contributes to enhancing face image retrieval performance in large-scale databases using hash codes inferred by locality-sensitive hashing (LSH) for facial hard and soft biometrics as (Hard BioHash) and (Soft BioHash), respectively, to be used as a search input for retrieving the top-k matching faces. Moreover, we propose the multi-biometric score-level fusion of both face hard and soft BioHashes (Hard-Soft BioHash Fusion) for further augmented face image retrieval. The experimental outcomes applied on the Labeled Faces in the Wild (LFW) dataset and the related attributes dataset (LFW-attributes), demonstrate that the retrieval performance of the suggested fusion approach (Hard-Soft BioHash Fusion) significantly improved the retrieval performance compared to solely using Hard BioHash or Soft BioHash in isolation, where the suggested method provides an augmented accuracy of 87% when executed on 1000 specimens and 77% on 5743 samples. These results remarkably outperform the results of the Hard BioHash method by (50% on the 1000 samples and 30% on the 5743 samples), and the Soft BioHash method by (78% on the 1000 samples and 63% on the 5743 samples).
The Effectiveness of Self-Regulation Learning Training on the Executive Functions of Male Elementary School Students with Reading Disabilities

Objective: The objective of the present research was to examine the effectiveness of self-regulation learning training on the executive functions of male elementary school students with reading disabilities in District 2 of Tehran during the 2022-2023 academic year. Methods and Materials: This study, considering its goals, was an applied study and, in terms of data collection method, was a quasi-experimental study employing a pre-test, immediate post-test, and delayed post-test design with a control group. The population comprised all male elementary students in District 2 of Tehran, totaling 6,515 students. The sample consisted of 40 male students (10 years old) from the fourth grade, selected through multi-stage cluster sampling. Within each cluster, participants were chosen through simple random sampling. At the end of the study, an educational package for teaching Persian language content, focusing on reading processes as outlined in the textbook, was prepared based on the resources mentioned in the research text. This package aimed at enhancing reading fluency and addressing reading difficulties. In each group, 20 students received either witness or self-regulation learning instruction. The self-regulation group underwent 16 sessions of 45-minute training focusing on executive functions. Following the training, an immediate test and a follow-up test two months later were administered to evaluate the intervention's sustainability. The research tools were the Stroop Test (2006), the Wisconsin Card Sorting Test (1948), the Continuous Performance Test (1956), and the N-back Test. Data were analyzed using a mixed ANOVA test with SPSS-V.24 software. Findings: The results indicated that self-regulation learning training effectively impacted the executive functions of students with reading disabilities (P = 0.01 &lt; 0.05). Conclusion: It can be concluded that educational interventions, especially self-regulation learning training, are efficient and effective strategies for enhancing performance and skills in students, particularly those with reading difficulties, in the realm of executive functions.
Representationalism in Knowledge Organization and Information Retrieval: Cheer or Criticism?

The purpose of this paper is to critically analyze the representational approach to language using the concept of ‘Representationalism’ in philosophy and a new framework referred to in this paper as Information Retrieval Action (IRA). IRA is a theoretical construct based on Information Retrieval (IR) theory. Therefore, the methodology employed in this study is philosophical and speculative. It examines and discusses representationalism in both the mental (cognitive) and linguistic dimensions, incorporating concepts from the philosophy of mind and the philosophy of language as methodological approaches. ‘Concept’ and ‘Meaning’ emerge as two critical semantic elements when considering language as a semiotic phenomenon. Representationalism in IRA is rooted in logical positivism, which serves as the foundation for interpreting meaning and concept based on mental representation. In response, the paper proposes an alternative non-representational approach to the semantic elements of language (meaning and concept) based on the philosophy of pragmatism within the IRA framework. While the emergence of new technologies like ontologies implicitly criticizes representationalism in IRA, previous research has not explicitly addressed the theoretical and philosophical criticism of representationalism within the IRA framework.
Multilingualism and Multiculturalism in Digital Libraries

Multilingualism and multiculturalism have become major preoccupations for Library and Information Science (LIS) which has to be as fair as possible to ensure and sustain knowledge diversity as a driver for development. This paper offers a broad overview of multilingualism and multiculturalism embedding into Digital Libraries (DL). We survey studies written in English over the past two decades (from 2002 to 2022) and indexed in one or more of the following databases: Science Direct, Google Scholars, Taylor and Francis, and Emerald Insight. We summarized studies to offer insights and outline opportunities in this area and classify them into five research themes: (i) studies describing Cross-Language Information Retrieval (CLIR) in Digital Libraires, (ii) studies describing building, enriching and using multilingual resources in digital libraries (iii) studies focusing on multilingual digital libraries initiatives, (iv) studies on collaboration, minority languages and ethics issues in digital libraries, and (v) studies on multilingual user needs. This survey aims to identify, collect and categorize the research literature on multilingualism and multiculturalism efforts in Digital Libraries.
ARKHANGELSK KOZULI AS A WAY OF PRESERVATION TRADITIONAL FOLK CULTURE AS PART OF THE USE OF THE METHOD OF JOINT CREATIVITY IN THE ADDITIONAL EDUCATION SYSTEM

The authors of the article consider the possibilities of applying an innovative approach to additional education for children, the purpose of which is to preserve and transfer the wealth of traditional folk culture in the process of joint creativity of children and parents. It is no coincidence that the theme of creative interaction was chosen: the creation of traditional Arkhangelsk gingerbread kozuli, since traditionally the creation of ceremonial cakes was done in the family circle. The article emphasizes the importance of involving parents not only in the creative process, but also in the process of creating and transmitting traditions, and also examines the practical aspects of implementing this method through the creation of author's gingerbread based on traditional recipes. The results of the study can serve as a basis for the development of educational programs aimed at preserving and popularizing traditional art forms in various regions.
Constraints and Syntactic-pragmatic Interface of Adversative Structure of the Cohesive Words

At the semantic level, cohesive words constitute a homogeneous class. For the case of coordinating conjunctions like but, yet, however, they form a distinct syntactic class, these cohesive words belong to the same semantic type and can mark interactivity relations or other species of relations. Through this paper, we try to find various aspects of structural constraints of a discourse via the relation of the hierarchical rule of insertion of the elements of a statement or a discourse. This hierarchical relationship provides us with a reference for corpus context analysis, namely the upstream and downstream of the cohesive word scope.
Research on the integration and utilization of library intelligence resources in the era of big data

This article primarily explores the amalgamation and use of library intelligence resources in today's big data age. Initially, it delves into the distinct attributes of both the big data era and library intelligence resources, and signifies the hurdles faced when integrating and utilizing these resources due to big data phenomena. Additionally, this manuscript delves into the amalgamation and application methods of library intelligence resources, which encompasses data collection and merging, data preservation and management, and data examination and use. Then, through case studies, this paper expounds the integration and application of library information resources in other industries under the background of big data era. In conclusion, implementing strategies and proposals such as enhancing data acquisition and administration skills, fortifying the capacity for data analysis and application, as well as broadening collaborative and sharing frameworks, is recommended to bolster the effective blending and utilization of library intelligence resources.
The influencing factors of intelligent technology in teaching activities

Under the new vision of education modernization 2035, schools, teachers and students are facing the challenge of educational and teaching environment reform, and it is urgent to explore new educational and teaching modes. As an important carrier to put forward higher requirements for education informatization, intelligent education and intelligent classroom emerge at the historic moment. In this context, teachers' intelligent classroom teaching skills become the backbone of implementing the reform of education and teaching mode. Therefore, this paper aims to explore the characteristics of junior high school teachers' behavior towards intelligent classroom teaching and the important factors affecting teachers' use of intelligent classroom teaching, which is of great practical significance for promoting the achievement transformation of education informatization.
Regulation and Crypto on a Cliff Edge

The collapse of FTX exchanges, in November 2022 put a capstone on a terrible year of failures and lost market value in the world of digital assets. Its profound implications included not only the change of institutional investors’ attitude to crypto-assets, but also the rapid rise of global regulations on the crypto markets. This chapter focuses on the regulatory approaches from the three biggest markets: China, USA, the EU. While China develops its own sovereign digital currency and put a full stop on the private crypto markets, in April 2023, the European Parliament gave its final blessing to the new Markets in Crypto-Assets Regulation, the world’s first comprehensive framework for crypto regulation, to both support and regulate the crypto space. The USA is accelerating its regulatory development. With global regulation rising, the crypto community must pay close attention to such developments and adapt to the “new normal.”
The number of young teachers and the indicators of socio-economic development of the regions

The article discusses the influence of socio-economic characteristics of the regions on the effectiveness of attracting and professional adaptation of young teachers in educational institutions. Based on the analysis of theoretical and methodological approaches, empirical research, and the regulatory framework, it is concluded that there are several features of the analysis of professional adaptation: the problems of professional adaptation are closely related to the key categories of sociological theory and have a long history of analysis at the theoretical and empirical levels; there is noted an increased attention of state and society to the problems of attracting young teachers to the educational system along with an increased state support in its various forms and growing research on various aspects of professional adaptation of novice teachers. Nevertheless, at the moment there is a gradual increase in personnel tension in organizations of general education, associated with a number of factors of a psychological, professional, and social nature. There is also noted a significant difference in the share of young teachers among the regions of Russia. As a source of empirical material, statistical data are used on the number of teachers in the regions of Russia, their distribution according to the criteria of age, length of service, type of settlement, as well as information on the socio-economic condition of the constituent territories of the federation (indicators of unemployment and the average level of wages, the number of population younger than able-bodied age, development indicators of science-intensive sectors of the economy). Based on the correlation analysis carried out, the author concludes about the specific nature of the process of young teachers’ professional adaptation in urban and rural areas as well as the specifics of the relationship between the proportion of teachers in different age groups and indicators of the socio-economic development of the region.
Energy Consumption and Carbon Emissions of Modern Software Video Encoders

In today's digital landscape, video streaming holds an important role in internet traffic, driven by the pervasive use of mobile devices and the surge in streaming platform popularity. In light of this, the imperative to gauge energy consumption takes center stage, paving the way for eco-conscious and sustainable video streaming solutions with a minimal Carbon Dioxide (CO2) footprint. This paper meticulously examines the energy consumption and CO2 emissions of five popular open-source and fast video encoders: x264, x265, VVenC, libvpx-vp9, and SVT-AV1. These encoders are optimized software implementations of three video coding standards (AVC/H.264, HEVC/H.265, VVC/H.266) and two video formats (VP9andAV1).Toensureafaircomparison,wealsoassesscodingefficiencyacross these encoders at four distinct presets, applying three objective quality metrics. Additional factors like computing density and memory usage are considered. Our experiments employ the JVET-CTC video dataset, encompassing video sequences of diverse content and resolutions. Encoding is executed on an Intel x86 multi-core processor, while CO2 emissions are computed based on the energy mix data from a server situated in France, reflecting an average emission rate of 101 g/kWh. Our findings underscore that the x264 and SVT-AV1 encoders, especially at fast and faster presets, exhibit the lowest energy consumption and CO2 emissions. Notably, x264 boasts the most energy-efficient performance, yielding CO2 emissions of 0.28 g, 0.91 g, 2.07 g, and 9.74 g when encoding videos using faster, fast, medium, and slower presets, respectively. Furthermore, SVT-AV1 and VVenC encoders operating at a slower preset demonstrate superior coding efficiency, albeit at the cost of higher computational complexity and CO2 emissions of 60.5 g and 406 g, respectively. A salient observation from our study is that resolution and encoder presets serve as crucial parameters for curbing energy consumption and CO2 emissions, albeit with an inherent trade-off in video quality. Comprehensive results from this research are publicly accessible <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://chachoutaieb.github.io/encoding</uri> energy co2.
Blockchain-Based Self-Certified Key Exchange Protocol for Hybrid Electric Vehicles

How to safely and anonymously interact with fog nodes’ charging stations is a big deal for hybrid electric vehicles in fog-based vehicular networks. Although there exist key exchange protocols, which tried to cover this critical concern, most of them are certificate-based. In addition, they do not support key revocation option or cannot totally resist advanced cyber attacks such as key compromise impersonation attack. As a result, this paper, by means of Blockchain, proposes a highly-secure self-certified key exchange protocol with an exceptional level of privacy. By the proper employment of distributed ledger, the suggested protocol can also support the authentication token revocation and immutability. Formal security and performance analyses as well as comparison with top scholarly articles demonstrate the distinct security features and applicability of the proposed protocol.
Data-Driven Adaptive Distributed Localization of Multi-Agent Systems With Sensor Failure

This work solves the localization estimation of dynamic multi-agent systems (MASs) with sensor multiplicative failures, which is more general yet challenging to address than static sensor networks with ideal conditions. Barycentric coordinate is introduced to characterize the relative positions between agents. A new linear data model is constructed to represent the relationship between barycentric coordinates and relative distance. Based on the linear model, an adaptive parameter estimation algorithm is designed, and then it is applied to solve the relative distance compensation problem of the MASs with sensor multiplicative failures. Using the estimated parameter, a data-driven adaptive distributed localization estimation scheme based on iterative learning is proposed, in which only the measured relative distance data are available instead of the system model information. A key to obtaining accurate localization is overcoming the difficulties from inaccurate relative distance variables due to sensor failure via the data-driven adaptive relative distance compensation method. The numerical examples and experimental results verify the effectiveness of the proposed methods.
Efficient Provably-Secure Authentication Protocol for Multi-Domain IIoT Using a Combined Off-Chain and On-Chain Approach

The industrial internet of things (IIoT) has developed into a promising technology that raises the level of productivity and automation for smart manufacturing. Due to the growing importance of cross-domain (e.g., factory) collaboration in manufacturing, it is now commonplace for IIoT devices from different domains to communicate with one another, raising significant privacy and security risks. Recent studies have utilized blockchain in their authentication scheme to build trust across multiple domains. This integration, however, resulted in substantial communication, computation, and storage overheads, as well as vulnerability against distributed denial of service (DDoS) attacks. To overcome these issues, in this paper, we propose an efficient and highly secure blockchain-assisted authentication scheme using a combined off-chain and on-chain approach. The suggested protocol just employs one domain server for authentication of both local and foreign domain devices. The distinctive security features and applicability of the proposed protocol are demonstrated by formal security proof and performance analysis as well as comparisons with leading research works.