Reviewerly/exHarmony huggingface dataset:
Section of rare and valuable publications of the library named after M. V. Lomonosov, branch of the municipal library “B. P. Hasdeu”

The rare book section appeared in the library named after M.V. Lomonosov 8 years ago. This fund is small - only a couple of hundred books. However, its value is measured not by size, but by uniqueness. The basis of the fund is made up of editions of the XIX–XX centuries. In terms of its content, the fund is universal. Most of it is fiction. In addition, there is popular science literature of past centuries, dictionaries, encyclopedias, newspapers. The collection of rare books includes: the collection “Poems and Prose Articles” by Mikhail Vasilyevich Lomonosov, published in 1886 and donated in 1984 by the poet Ion Odobescu; A. Glazunov, 1895 edition; I. Turgenev –1898; M. Gorky – 1901; Emilian Bukov – 1938; V. Zhukovsky – 1902; N. Leskov – 1903; P. Tchaikovsky – 1908; I. Brahms – 1873; M. Yu. Lermonotov – 1940; H. Wells – 1909 “Otechestvennye zapiski” – 1840; “Bulletin of Europe” – 1879; “Course of Geography of NonEuropean Countries” – 1905; “Niva” – 1899; K. Marx’s “Capital” – published in 1950 and much more. Our books are not museum pieces. They form part of the actively used collection of the library. Any interested reader can get acquainted with the collection of rare books on the website of the library M. V. Lomonosov in the “Rare Books” section.
Model Pembelajaran Scramble Didukung Permainan Teka-Teki Silang Terhadap Kemampuan Mengidentifikasi Tokoh-Tokoh Sejarah Hindu-Budha di Indonesia

The influence of the scramble learning model supported by crossword puzzle games on the ability to identify Hindu-Budhist historical figures in Indonesia. The purpose of this study was to determine the ability to identify Hindu-Budhist historical figures in Indonesia using a scramble learning model supported by a crossword game. This type of reseach using an experimental design, namely pre-test and post-test groups. Data collection instruments used tests and data analysis techniques using normality test, homogeneity test, and hypothesis testing (using t test). based on the results of the analysis, there is an effect of using scramble learning models supported by crossword puzzle on the ability to identify Hindu-Buddhist historical figures in Indonesia. It is evident from t-count 9.090 &gt; t-table 2.052 with a significance level of 0.00 &lt; 0.05. Therefore, H0 and Ha are accepted, which means that there is a significant influence using the scramble learning model supported by crossword puzzle games on the ability to identify Hindu-Buddhist historical figures in Indonesia. Thus the use of scramble llearning models supported by crossword puzzle games can create fun and challenging learning for students. In learning, students are enthusiastic and active because each student tries to compete with other friends. In addition, the use of scramble learning models supported by crossword puzzle games also makes it easier for students to understand the material of Hindu-Budhhist historical figure in Indonesia.
Cross-Domain Few-Shot Classification via Inter-Source Stylization

The goal of Cross-Domain Few-Shot Classification (CDFSC) is to accurately classify a target dataset with limited labelled data by exploiting the knowledge of a richly labelled auxiliary dataset, despite the differences between the domains of the two datasets. Some existing approaches require labelled samples from multiple domains for model training. However, these methods fail when the sample labels are scarce. To overcome this challenge, this paper proposes a solution that makes use of multiple source domains without the need for additional labeling costs. Specifically, one of the source domains is completely tagged, while the others are untagged. An Inter-Source Stylization Network (ISSNet) is then introduced to enhance stylisation across multiple source domains, enriching data distribution and model's generalization capabilities. Experiments on 8 target datasets show that ISSNet leverages unlabelled data from multiple source data and significantly reduces the negative impact of domain gaps on classification performance compared to several baseline methods.
The contribution of the Ukrainian Library Association to the development of library and information activities

The article deals with the role of library institutions in the current stage of information society formation. The importance of the Ukrainian Library Association in the development of library information activities is investigated, the tasks being solved due to the assets of the regional branches of the Association.&#x0D; It is noted that through the representations of the Ukrainian Library Association there are events for continuous education of librarians, motivating them to positive changes in work, strengthening the image of the profession, finding alternative ways to meet the information needs of users given the active use of modern information technology.&#x0D; Representatives of the Ukrainian Library Association are considered, where the main emphasis is on activities on continuous education of library workers and activities that motivate librarians to make positive changes in the future for the image of the profession, to find alternative directions to meet information needs of users, including more active use of modern information technologies.&#x0D; Membership of the Ukrainian Library Association in the International Federation of Library Associations made it possible to study and borrow progressive international experience in the development of library and information activities, to share best practices of national library theory and practice, telling foreign colleagues about Ukrainian realities and best practices, taking part in international projects.&#x0D; Attention is paid to the Library Strategy as a process of forming long-term goals and ways to achieve them to ensure development and effective changes in the quality aspect of its life.
Low-precision deep-learning-based automatic modulation recognition system

Convolution Neural Network (CNN)-based deep learning models have recently been employed in Automated Modulation Classification (AMC) systems, with excellent results. However, hardware deployment of these CNN-based AMC models is very difficult due to their large size, floating point weights and activations, and real-time processing requirements in hardware such as Field Programmable Gate Arrays (FPGAs). In this study, we designed CNN-based AMC techniques for complex-valued temporal radio signal domains and made them less complex with a small memory footprint for FPGA implementation. This work mainly focuses on quantized CNN, low precision mathematics, and quantization-aware CNN training to overcome the problem of larger model sizes, floating-point weights, and activations. Low precision weights, activations, and quantized CNN, on the other hand, have a considerable impact on the accuracy of the model. Thus, we propose an iterative pruning-based training mechanism to maintain the overall accuracy above a certain threshold while decreasing the model size for hardware implementation. The proposed schemes are 21.55 times less complex and achieve at least 1.6% higher accuracy than the baseline. Moreover, results show that our convolution layer-based Quantized Modulation Classification Network (QMCNet) with pruning has 92.01% less multiply-accumulate bit operations (bit_operations), 61.39% less activation bits, and 87.58% less weight bits than the 8 bit quantized baseline model whereas the quantized and pruned Residual-Unit based model (RUNet) has 95.36% less bit_operations, 29.97% less activation bits and 98.22% less weight bits than the 8 bit quantized baseline model.
Network resource allocation for emergency management based on closed-loop analysis

The telecommunication system being a critical pillar of emergency management, intelligent deployment and management of slices in an affected area will help emergency responders. Techniques such as automated management of Machine Learning (ML) pipelines across the edge and emergency responder devices, usage of hierarchical closed-loops, and offloading inference tasks closer to the edge can minimize latencies for first responders in case of emergencies. This study describes the major results from building a Proof of Concept (PoC) for network resource allocation for emergency management using a hierarchical autonomous Artificial Intelligence (AI)/ML-based closed-loops in the mobile network, organized by the Internal Telecommunication Union Focus Group on Autonomous Networks (ITU FG-AN). The background scenario for this PoC included the interaction between a higher closed-loop in the Operations Support System (OSS) and a lower closed-loop in Radio Access Network (RAN) to intelligently share RAN resources between the public and the emergency responder slice. Representation of closed-loop "controllers" in a declarative fashion (intent), triggering "imperative actions" in the "underlay" based on the intent, setup of a data pipeline between various components, and methods of "influencing" lower layer loops using specific logic/models, were some of the essential aspects investigated by various teams. The main conclusions are summarised in this paper, including the significant observations and limitations from the PoC as well as future directions.
Multi-Valued Cognitive Maps: Calculations with Linguistic Variables without Using Numbers

A concept of multi-valued cognitive maps is introduced in this paper. The concept expands the fuzzy one. However, all variables and weights are not linearly ordered in the concept, but are only partially-ordered. Such an ap- proach allows us to operate in cognitive maps with partially-ordered linguis- tic variables directly, without vague fuzzification/defuzzification methods. Hence, we may consider more subtle differences in degrees of experts' uncer- tainty, than in the fuzzy case. We prove the convergence of such cognitive maps and give a simple computational example which demonstrates using such a partially-ordered uncertainty degree scale.
30 Years of Video Coding Evolution - What Can We Learn from it in Terms of QoE?

From the beginnings of ITU-T H.261 to H.265 (HEVC), each new video coding standard has aimed at halving the bitrate at the same perceptual quality by redundancy and irrelevancy reduction. Each improvement has been explained by comparably small changes in the video coding toolset. This contribution aims at starting the Quality of Experience (QoE) analysis of the accumulated improvements over the last thirty years. Based on an overview of the changes in the coding tools, we analyze the changes in the quantized residual information. Visual comparison and statistical measures are performed and some interpretations are provided towards explaining how irrelevancy reduction may have led to such a huge reduction in bitrate. The interpretation of the results in terms of QoE paves the way towards an understanding of the coding tools in terms of visual quality. It may help in understanding how the irrelevancy reduction has been improved over the decades. Understanding how the differences of the residuals relate to known or yet unknown properties of the human visual system, may enable a closer collaboration between perception research and video compression research.
Cyber attack risks to construction data management in the fourth industrial revolution era: a case of Gauteng province, South Africa

The 4IR or the digital revolution refers to a collective term for a value chain organizational concepts and technologies that together build the Internet of Things, Internet of people, Cyber-Physical Systems (CPS), Internet of Services and the Internet of Energy. While this digital revolution has helped the construction industry to prevent cost and time overruns and enable efficiency and good work’s quality, it also has disadvantages and risks such as cyberattacks and loss of jobs. This study set out to determine the risks associated with data management (cyberattacks) on construction projects in the fourth industrial revolution era. The research study will address the questions of “what are the cyberattacks risk attributed to construction data management in the fourth revolution era?” and “What are the differences in the opinion of respondents concerning the identified cyberattacks?”. This research work used a quantitative method and gathered information from different construction professionals in the South African construction industry precisely in Gauteng province via a well-structured questionnaire through online platforms. These professionals involve quantity surveyors, architects, civil, mechanical, and electrical engineers practising under a firm, company, organisation, or institution within the Gauteng province, South Africa. Findings revealed that viruses, hacking, and password cracking are the most frequent risk to data management encountered in the construction industry. It was also indicated that construction project stakeholders need a strong knowledge of how attackers operate to address, avoid, and stop the different risks rising when executing a project. The study contributes to the body of knowledge by highlighting the various risks encountered in managing data in the construction industry which will assist professionals in the industry to pay attention to means of mitigating the identified risks. This will keep stakeholders abreast of how simple negligence from their side can deeply affect the project data thereby affecting project delivery. It was concluded based on findings that construction professionals need to avoid the occurrence of these risks to enhance satisfactory project delivery and protect their project information. The study recommended that all construction project parties require full training sessions on risks to data to prevent any types of intrusion into the company’s information system.
Knowledge base on the health status of students and a client application for user interaction

Monitoring can be useful in controlling the state of a student's physical health and is carried out in order to obtain the information necessary to make management decisions to improve the health of students involved in physical culture and sports. The article describes the data processing process on the state of physical health of students, used in an au-tomated expert system based on the "Student's Health" ontology and created in Fluent Editor. The developed ontology and client application for interaction with the user are described. The purpose of the work is to take into account data on the physical development and preparedness of the student, including the organization automated preparation of output documents. The Python language and its libraries were used to create an application and ensure interaction with the ontology. The interaction of the created ontology with the client application is a novelty of the applied approach.
DeSMP: Differential Privacy-exploited Stealthy Model Poisoning Attacks in Federated Learning

Federated learning (FL) has become an emerging machine learning technique lately due to its efficacy in safeguarding the client's confidential information. Nevertheless, despite the inherent and additional privacy-preserving mechanisms (e.g., differential privacy, secure multi-party computation, etc.), the FL models are still vulnerable to various privacy-violating and security-compromising attacks (e.g., data or model poisoning) due to their numerous attack vectors which in turn, make the models either ineffective or sub-optimal. Existing adversarial models focusing on untargeted model poisoning attacks are not enough stealthy and persistent at the same time because of their conflicting nature (large scale attacks are easier to detect and vice versa) and thus, remain an unsolved research problem in this adversarial learning paradigm. Considering this, in this paper, we analyze this adversarial learning process in an FL setting and show that a stealthy and persistent model poisoning attack can be conducted exploiting the differential noise. More specifically, we develop an unprecedented DP-exploited stealthy model poisoning (DeSMP) attack for FL models. Our empirical analysis on both the classification and regression tasks using two popular datasets reflects the effectiveness of the proposed DeSMP attack. Moreover, we develop a novel reinforcement learning (RL)-based defense strategy against such model poisoning attacks which can intelligently and dynamically select the privacy level of the FL models to minimize the DeSMP attack surface and facilitate the attack detection.
IMPLEMENTASI DAN PERANCANGAN KAMERA NIRKABEL MENGGUNAKAN RASPBERRY PI

The design of a camera becomes smarter by using a raspberry pi device so that it can monitor and control a location or place and provide mitigation reports through the telegram social media application to a smartphone that has been set to the device. So that with the Raspberry Pi device and this camera module, it can be more comfortable, safer and faster to know and control the situation in the places we want. So from the explanation above, the title of this research is the Implementation and Design of the Raspberry Pi Wireless Camera.
Specific Skills and Its Assessment in A New Institutional Context: Discussions, Challenges and Prospects

The search for new drivers of global socio-economic development in the face of a slowdown (partially due to the COVID-19) put the issues of skills development, including institutional solutions of the human capital formation, at the front line. Despite significant progress in understanding the importance of general skills and their contribution to economic growth, professional skills as the pivot of specific human capital are commonly analyzed through the vague categories like formal qualification and years of tenure. The lack of an institutionalized and commonly accepted practice for measuring professional skills restrains research in economics dealing with returns to skills, as well as institutional studies. The present study aims to partially fill this gap by analyzing the discussion about professional skills in broader discourse about skills and systematizing the existing approaches to professional skills’ assessments, as well as by mapping the prospects in the context of emerging digital technologies and institutional change attributed to them. The analysis was conducted on academic papers and reports published in 2013–2020. The research showed that although the number of academic papers focusing on professional skills is high, the discussion (especially, about assessment) is fragmented reflecting industries’ specifics. The mainstream expert discussion on skills and their assessment tends to focus on general skills, overlooking professional skills, with partial exception of certain digital skills. Discussion largely grounds on traditional approaches, which cannot produce scalable and comparable results for further economic analysis. At the same time, new digital assessment tools are not yet widely disseminated. There is a need for further improvement and expansion of traditional skills assessments via exams or tests, alongside with searching for novel approaches to measuring skills acquired and being used on the workplace. This could also contribute to the institutional studies dealing with the research of transformations happened in corporate, national, and international level.
Children’s Literature and Its Relevance for Character Formation and Developments

The prominence of literature to children and their being is colossal. Literature, to some, is a path to knowledge; to others, a means of empowerment. Despite its roles, it is disheartening that little or no attention is paid to the content of children’s literature. How relevant are these texts to the children? How far are they dated? Does it empower or inform them? The questions above provided the impetus for this study. This study explores children’s literature and its implication for character formation and development. A descriptive research design was adopted for the study. Through a simple and random sampling, forty-three pupils and 20 principals were selected in Ondo, Ondo state, Nigeria. The instrument for the study was: The Children’s Literature Questionnaire (CLQ). Three research questions were raised, and a null hypothesis was tested. Data were analyzed using descriptive and inferential statistics. The study revealed that literary texts recommended for pupils were few [`x=2.02]; head teachers chose literary texts that appealed to them [`x=3.4]; recommended literary texts did not project the struggle of the readers [`x=1.9]. The study indicated significant differences in the head teachers’ responses based on selected texts. The study recommended that keen attention should be paid to children’s literature. Literary texts for children should be censored before filling them on shelves.
Bottom-Up Strategy in Teaching Listening for Academic Purposes for EFL in Pesantren-Based University

Receiving information begins with listening. EFL learners at universities should master listening for academic purposes with appropriate strategies. This article examined the strength of the bottom-up strategy in teaching listening for the academic purpose of the third-semester female students in the Department of English Language Education, the University of Darussalam Gontor, which is a pesantren -based university. This research utilized a quasi-experimental with one group pretest-posttest design. The participants involved 14 who were regarded in English language teaching. They were tested by pre-and post-treatment listening for academic purposes. The data were analyzed by paired sample t-test. The results showed a significant inequality between the mean score of the pre-test and post-test. The mean score of the post-test of learners was improved than theirs in the pre-test. Thus, the female learners in this research achieved well in promoting teaching listening for academic purposes after being treated using a bottom-up strategy.
Japan

Japan is one of the 14 OECD countries where at least half of 25-34 year-olds have a tertiary education). Between 2000 and 2021, the share of 25-34 year-olds with tertiary attainment increased by 17 percentage points.
Korea

Educational attainment has been increasing throughout the OECD, in particular at tertiary level. Between 2000 and 2021, the share of 25-34 year-olds with tertiary attainment increased on average by 21 percentage points. In Korea, the share increased at an even faster pace, by 32 percentage points (from 37% in 2000 to 69% in 2021) (Figure 1). Korea is one of the 14 OECD countries where at least half of 25-34 year-olds have a tertiary education.
Application of pest detection on vegetable crops using the cnn algorithm as a smart farm innovation to realize food security in the 4.0 era

Pests and diseases are one of the factors that become obstacles in the cultivation of vegetables because they can cause a decrease in the quality and quantity of production. The more varied types of pests have different impacts on crops, so if farmers incorrectly identify the class of pests, the treatment will be ineffective. Therefore, we need a technology that can classify the types of pests on vegetable crops to maintain the quality and quality of the product as well as the abundant harvest. The classification model of pests on vegetables using the deep learning method using the Convolutional Neural Network (CNN) algorithm with a high level of accuracy is the solution to this problem. The application of artificial intelligence in the agricultural sector also supports smart agriculture in Indonesia. Based on the research that has been carried out, the application of pest classification on vegetable crops made by applying the CNN model using the Inception V3 - k-fold cross-validation method has a test accuracy rate of 99%, meaning that the application can perform pest classification correctly.