Reviewerly/exHarmony huggingface dataset:
Gift Inscriptions and Ex-librises on Books as Evidence of the Enrichment of the Funds of Book Collections and of Charity

In article gift inscriptions on books from the funds of the Lviv National Scientific Library of Ukraine named after V. Stefanyk, found in the process of preparation in the department of scientific bibliography of a multi-volume bibliographic index “Ukrainian book in Galicia, Bukovina, Transcarpathia, Volhynia and in exile, 1914–1939” are considered. The presence of inscriptions on books of this period indicates that gifts were one of the important sources of enrichment of public and private book collections, so they play an important role in studying the history of these libraries. A special role was played by the library of Shevchenko Scientific Society in Lviv and the book collection “Studion”, thanks to which Lviv National Scientific Library of Ukraine named after V. Stefanyk received the richest collection of Ukrainian. Some elements of the script play a bibliological role, in particular, help to supplement the bibliographic description in the absence of some of its components, especially the source information. They also contain additional information about the donor and the addressee, which allows the researcher to detail their biographies, reveal the range of family and friendly relations, scientific and social relationships. Keywords: Western Ukrainian and Ukrainian Emigration Book of 1914–1939, Lviv National Scientific Library of Ukraine named after V. Stefanyk, inscript, donor, addressee.
Quantum-holistic categories of social communications

Today, among specialists in social communications there is a lack of knowledge about the quantum-holistic categories of social communications, both the process of social and the process of social activity. The aimof our study was to identify and describe quantum-holistic categories of social communication processes. Through our study, we have chosen a transformed methodologyof holism, which argues that the integrity of the world is a consequence of creative evolution. Such evolution is guided by the intangible and unknowable «integrity factor»(in Jan Smuts's terminology). The transformation of the ideas of holism is based on the basic principles of quantum mechanics, which we have adapted to the social sciences. We also used in the study methodsof analysis and synthesis, deduction and induction, and the method of classification. The research methodologydetermined the differentiation of quantum-holistic categories of social communications into social and public; identification of three mandatory elements of the philosophy of knowledge of any subject of discussion, namely: identification, content, and functionality; analysis of known and little-known concepts of classification of philosophical categories in order to identify features that are consistent with our methodological conceptual concept of quantum-holistic categories of social communication processes; on the basis of the carried out analysis of isolation and nomination of ontological (metaphysical) and epistemological (epistemological) descriptive categories of processes of social communications. In conclusion, the author argues that the identification of quantum-holistic categories of social communication processes should be carried out both from a social and social point of view. The author proposes to consider as descriptive quantum-holistic categories of social communication processes «quantum (portion of energy)», «Whole», «Information», «living / inanimate», «action / peace», «society / non-society».
Longitudinal Development Changes of Positive and Negative Aspects of Creative Personality in Adolescent and Effects of Youth Activities

In this study, we analyzed adolescents’ creativity longitudinal development and how youth activities effect to the changes in creativity development using Korean Children and Youth Panel Survey(KCYPS) 2018 data. Large data on students' positive and negative aspects of creative personality of 7th to 9th grade were analyzed. Results revealed that the creative personality of middle school students tended to decrease in both positive and negative aspects, and the decrease in positive creative personality was greater. Youth activities related with culture/art, adventure, science/information, job/career were positively affected in the initial status of positive creative personality, and science/information and volunteer activities were negatively affected in the slope. Adventure and science/information activities had a positive effect on the initial status of negative creative personality, and adventure activities had a negative effect on the slope. Providing various experiences through extra-curriculum can have a positive effect on creative personality and can help prevent the decrease in creative personality in middle school. Finally, based on the results of this study, the direction of research on creativity development in adolescence and the strategy of creativity education were proposed.
Pengaruh Podcast (Siniar) Youtube terhadap Peningkatan Keterampilan Berbicara

Currently, podcasts (Siniar) are programs that are very popular with the public, not students. Inside the program , there is a speaking activity which is a fundamental aspect for students. The problem that occurs in learning speaking skills is that conventional methods are still often used. Through the broadcast, it is hoped that learning activities will be more innovative and able to improve students' speaking skills. Thus, the purpose of the study was to see the effect of YouTube podcasts on students' speaking skills. The quantitative method applied in this research is quasi-experimental. The population used were all fourth-semester students of the Pendidikan Bahasa dan Sastra Indonesia (PBSI) FKIP UHAMKA. D etermination of the sample with a non-probability sampling technique. The independent variable in this study is speaking skills, while the variable is the YouTube Podcast program. Data were collected through dissemination instruments in the form of questionnaires on Podcasts and speaking practice activities. The results showed that there was a constant regression coefficient score with a value of 0.296. This score means that if each student adds podcast activity, then speaking skills increase by 0.296. In other words, for every 1% increase in the value of the variable during podcast shows, the value of speaking skills increases by 0.296. Therefore, this study concludes that there is an effect between watching YouTube Podcasts on students' speaking skills. Abstrak Saat ini, podcast (siniar) menjadi acara yang banyak digemari oleh masyarakat, tidak terkecuali mahasiswa. Di dalam siniar ada kegiatan berbicara yang merupakan aspek yang fundamental bagi mahasiswa. Masalah yang terjadi dalam pembelajaran keterampilan berbicara adalah masih seringnya metode konvensional digunakan. Melalui siniar, diharapkan kegiatan pembelajaran lebih inovatif dan mampu meningkatkan kemampuan berbicara mahasiswa. Dengan demikian, tujuan pada penelitian untuk melihat pengaruh podcast YouTube terhadap keterampilan berbicara mahasiswa. Metode kuantitatif yang diterapkan di penelitian ini adalah quasi eksperimen. Populasi yang digunakan adalah semua mahasiswa semester IV Program Studi Pendidikan Bahasa dan Sastra Indonesia (PBSI) FKIP UHAMKA. Penentuan sampel dengan teknik nonprobability sampling . Variabel bebas pada penelitian ini adalah keterampilan berbicara, sedangkan variabel terikat yaitu acara Podcast YouTube. Data dikumpulkan melalui penyebaran instrumen berupa angket pada Podcast dan kegiatan praktik berbicara. Hasil penelitian menunjukkan terdapat konstanta skor koefisien regresi dengan nilai 0,296. Skor ini bermakna bahwa tiap-tiap mahasiswa melakukan penambahan aktivitas menonton acara podcast, maka keterampilan berbicara meningkat sebesar 0,296. Dengan kata lain, setiap penambahan sebesar 1% nilai variabel saat menonton acara podcast , maka nilai keterampilan berbicara bertambah sebesar 0,296. Oleh karena itu, simpulan penelitian ini yaitu ada pengaruh antara menonton acara Podcast YouTube terhadap keterampilan berbicara mahasiswa.
Comparative Analysis of Cryptocurrency Price Prediction Using Deep Learning

Cryptocurrency is branded as a digital currency, an alternative exchange currency system with significant ramifications for the economies of rising nations and the global economy. In recent years, cryptocurrency has infiltrated almost all financial operations; hence, cryptocurrency trading is frequently recognised as one of the most popular and promising means of profitable investment. Lately, with the exponential growth of cryptocurrency in-vestments, many Alternative Coins (Altcoins) resurfaced as to mimic the fiat currency. Altcoins prediction, as the name suggests the alternative coins from the traditional cryptocurrency which is Bitcoin (BTC). There are several methods to forecast cryptocurrency prices namely Technical Analysis and Fundamental Analysis which has been widely used in forecasting fiat and stock prices. With the emergence of Artificial Intelligence (AI), Machine Learning and Deep Learning algorithms provide a different perspective on how investors can estimate the trend or the movement of prices. In this thesis, as cryptocurrency price are time-dependent, Recur-rent Neural Network (RNN) is presented due to RNN’s nature that is well suited for Time Series Analysis (TSA). The topology of proposed RNN model consists of 3 stages which are model groundwork, model development and testing and optimisation. The RNN architecture are extended to two different models specifically Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU). There are 4 hyperparameters that will affect the accuracy of the deep learning model in predicting cryptocurrency price. Hyperparameters tuning set the basis of optimising the model to improve the accuracy of cryptocurrency prediction. Hyperparameters listed in this project are limited to number of epochs, adaptive optimisation algorithm, dropout rate, and batch size. Next, the models are tested with data of different coins listed in the cryptocurrency market with different input features to find out the effect on the accuracy and robustness of the model in predicting the cryptocurrency price. This research demonstrates that GRU has the best accuracy in forecasting the cryptocurrency prices based on the values of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Executional Time, scoring 2.2201, 0.8076 and 200s using intra-day trading strategy Open, High, Low, Close Price (OHLC) as input features.
On Sizes of Linear and Tree-Like Proofs for any Formulae Families in Some Systems of Propositional Calculus

The sizes of linear and tree-like proofs for any formulae families are investigated in some systems of propositional calculus: in different sequent systems (with quantifier rules, with the substitution rule, with the cut rule, without the cut rule, monotone) and in the generalization splitting system. The comparison of results obtained here with the bounds obtained formerly for the steps of proofs for the same formulas in the mentioned systems shows the importance of the size of proof among the other characteristics of proof complexities.
Furniture based interior design Application using Augmented reality

Augmented reality interior design application, is an application which is used to design the rooms. The room can be designed by placing furniture with the help of augmented reality. This application is built using Unity3D software. Usually, this software is based on Augmented Reality. In this software, various packages are in built. For this application, Visual studio code editor, Vuforia Engine AR has been installed. Using Augmented Reality technology, it is easy to place furniture in a room through Interior Design Application. Here, the user has a choice to choose the furniture that can be placed in a living room. The user can select furniture from database to place according to their room type. It is the main advantage of this application. Instead of buying furniture and then placing in a room, the user can choose their desired furniture within the application itself according to the room size. Hence this application is useful for the user while designing rooms for their house
Detection of Advertising Users Based on K-SMOTE and Ensemble Learning

Aiming at the problem of the unbalanced advertising user data of social networks leading to unsatisfactory prediction results, we propose a prediction model for advertising users based on the combination among K-Means, synthetic minority oversampling Technique (SMOTE), and Ensemble Learning. On the basis of the real user data provided by Scholat, we analyzed the data and extracted many key features from it to draw a portrait of advertising users. Our algorithm first clusters the minority class, and then processes the continuous and discrete features of each sample separately through the improved SMOTE to synthesize new minority samples, and finally constructs an integrated classifier using the ensemble learning. This method effectively avoids the problems of blurred positive and negative class boundaries caused by SMOTE and the inability of SMOTE to process discrete features. Meanwhile, ensemble learning enables the classifier to get more reasonable results and reduce overall errors. The experimental results show that our method improves the quality of the generated minority class samples and significantly improves the prediction performance of advertising users.
Zero Watermarking Scheme Based on Polar Harmonic Fourier Moments

Various research on digital watermarking has developed great significant progress in recent years but image copyright protection remains a major issue that must be addressed due to the growth and popularization of computer technology. In zero watermarking algorithms, the main tool is continuous orthogonal images for images. Ongoing orthogonal continuous activity is used, consistent in rotation and measurement, and highly improved. Polar Harmonic Fourier Moments (PHFMs) have the ability to define a solid image with excellent performance among the defined continuous orthogonal moments before. PHFM is therefore proposed to deal with images to make a robust image of zero watermarking image suitable for embedding watermark on this paper. Watermarking systems can help with identification and integrity; the importance of the medical image on the other hand should not be altered. Any changes within the cover image may have an impact on the process of decision-making. So, the features of medical images, collected as a set of data will be extracted. We use the Arnold modification of binary sequencing images to be generated for a selected moment to improve security objectives. Then the watermark is embedded in a special function between the scrambled logo image and the binary element of the cover image. A watermarked image abstracted from the PHFMs value without actively embedded to the host image.
The Digital Text Workshop Cloud, New Solutions for Super Calculation Environments

Quantum physics is the basis of scientific thinking and influences the mind. Wendt [1] in Quantum Mind and Social Science argues that the mind and social life are macroscopic phenomena of quantum mechanics and that the quantum consciousness hypothesis is the cognitive basis of a quantum social science. In these new scientific parameters of knowledge, our visualization matrix is essentially a holographic projection. Research innovation lies in seeking the point of convergence to describe a formal process that, as a multi-code process, goes from a phase of joined sentences to a data implementation phase, Planat of the Institute of Femto -ST Dep. of Micro Nano Sciences and Systems, Besançon, France. The goal is to find points of contact between quantum physics, computational linguistics, and quantum computing. Planat [2] states that the quality that the linguist must possess beyond courage is… Competence to transcribe mathematical concepts into grammatical structures. This assumption is at the basis of our study. The tools are 1) identifying new parameters for the production of fixed structures identified in the Planat model mathematical theories; 2) using Charles Baudelaire’s narrative text [3] to clarify the importance of the verb in the sentence through the research and experimentation method into a local grammar; 3) utilizing Max Silberztein's NooJ system [4], for the production of sentence analysis and paraphrases, tools for developing formal dictionaries and grammars, and NLP applications such as semantic annotators; 4) developing the implementation of advanced integrated Machine Learning algorithms for the creation of knowledge from obtained data; 5) applying the BuViTeMS (© 2020) AW Digital Intelligence model for the production of edited, reformulated and translated sentences using a mixed system. Thus, the entire formal process will make use of: 1 Lexicon-Grammatical to transfer the numerical quantum language into a lexicon-grammatical code; 2 The NooJ’s environment to generate statistical analysis and graphs.
Equilibrium Analysis of Block Withholding Attack: An Evolutionary Game Perspective

With the advancement of blockchain technology, blockchain-based digital cryptocurrencies, like Bitcoin, have received broad interest. Due to the permissionless environment, the blockchain is vulnerable to different kinds of attacks, such as the block withholding (BWH) attack. BWH attack is one common selfish mining attack, by which the attacking pool infiltrates the attacked pool, and the infiltrating miners withhold all the blocks newly discovered in the attacked pool. Therefore, the attacking pool benefit by withholding blocks, damaging the benefits of victim pools. In this paper, we introduce the reward reallocation mechanism by paying additional rewards to the miners who successfully mine blocks, and propose an evolutionary game model for BWH attack among pools to study the strategy selection of pools. By constructing the replicator dynamic equations, the evolutionary stable strategies of pools are explored based on different levels of additional rewards. Our results provide enlightening significance to mitigate the negative influence from BWH attacks in practice.
Domain Generalization on Constrained Platforms: On the Compatibility with Pruning Techniques

The wide deployment of Machine Learning models is an essential evolution of Artificial Intelligence, predominantly by porting deep neural networks in constrained hardware platforms such as 32 bits microcontrollers. For many IoT applications, the deployment of such complex models is hindered by two major issues that are usually handled separately. For supervised tasks, training a model requires a large quantity of labelled data which is expensive to collect or even intractable in many real-world applications. Furthermore, the inference process implies memory, computing and energy capacities that are not suitable for typical IoT platforms. We jointly tackle these issues by investigating the efficiency of model pruning techniques under the scope of the single domain generalization problem. Our experiments show that a pruned neural network retains the benefit of the training with single domain generalization algorithms despite a larger impact of pruning on its performance. We emphasize the importance of the pruning method, more particularly between structured and unstructured pruning as well as the benefit of data-agnostic heuristics that preserve their properties in the single domain generalization setting.
Knowledge of Self and Other Within a Broader Commonsense Setting

We examine some formal-reasoning aspects of Theory of Mind (ToM) from the perspective of an agent teaming with others, in a broad (active logic) setting that includes a commonsense-reasoning context. Our emphasis is on how to represent time-evolving inferences by an agent about what it and other agents do or don’t know. Specifically, what sorts of knowledge representation and reasoning are needed for an agent in a team to capture ToM in a formal time-evolving commonsense-reasoning setting, especially with regard to introspection, presence of direct contradictions, inference about another’s knowledge or lack (e.g., on their asking a question, and on being told an answer), and quotation mechanisms for representing beliefs about beliefs.
Advanced Intuitive Model for Digital Forensics Collection Methods in the Context of Cloud Computing and IoT

Abstract: With the wide scale adoption of cloud computing paradigms and Internet of Things implementations, the arena of service delivery has changed significantly for the world of technology-oriented services. While these new modes of service delivery have provided innovations that were not fathomable earlier, they not only create new non-traditional pathways for evidence generation, nevertheless, they have also been seen to carry a potential of creating and exposing a whole new set of security vulnerabilities that malicious actors can exploit. Traditional forensic investigation process falls short in many ways including the way it aims to identify and link crucial pieces of evidence in these scenarios due to a myriad of internal and external factors. Challenges and inadequacies have been identified in the community in relation to these specific sets of advancing technologies that hinder the efficient identification, extraction and processing of evidence generated in crimes involving them. In this paper we wish to surmise the current forensic collection methods, how they pose challenges when put together with the latest cloud and IoT technologies and then attempt to propose an advanced, more intuitive, and theoretically validated digital forensic collection process guided by zero trust principle and enforced with artificial intelligence and machine learning methodologies with the aim to fill the gaps and provide a more reliable, robust and intuitive model to perform digital evidence collection in the given context.
IMPLEMENTATION OF 21ST CENTURY LEARNING PRINCIPLES ON LOCAL CONTENT OF JAVANESE ELEMENTARY SCHOOLS IN EAST JAVA

This research needs to be carried out to find answers to the problem of local content of elementary school Javanese language learning in the East Java region. This study aims to collect information related to teacher competence, understanding of learning content, media use, learning design, and perspectives on information technology in Javanese language learning in elementary schools in East Java. This study used mixed/collaborative research methods (descriptive qualitative) and quantitative, in a sample of 194 elementary school teachers in the East Java region. This study used instruments in the form of structured observation sheets and questionnaires whose dissemination procedures used google forms. Observation data is processed descriptively and qualitatively and questionnaire data is processed with the help of SPSS software. The results showed that the implementation of Javanese language learning in elementary schools 80% has not implemented the principles of 21st-century learning, which among others require pedagogical, professional, and information technology elements. These results will be used as a basis for decision-making, to meet the needs of providing services for teachers in elementary schools, as well as in adobe technology in the practice of learning quality and useful Javanese local content as academic studies and for practical purposes for elementary school teachers.