Reviewerly/exHarmony huggingface dataset:
The Concept of Originality and Creativity in the Humanities, the Social Sciences and the Natural Sciences

The study was conducted to determine how 21 experts in the discipline clusters of humanities, the social sciences and the natural sciences defined originality and creativity in terms of how these concepts are understood and realized in the production of works in their respective disciplines, what related copyright issues emerge related to these concepts, and how these two concepts are related.Through thematic qualitative analysis of in-depth interview data, findings suggest that while there may be converging ideas about originality and creativity among the discipline clusters, the way this concept is operationalized by each discipline significantly vary.The study is deemed useful because apart from clarifying vague notions about original works, the study can serve as reference for legislators in amending the Copyright law, and for law practitioners in contextualizing cases involving original and derivative works, plagiarism and other related concepts.In this connection, the study provides the implications for the evaluation of the originality and creativity in works produced from the various discipline clusters.
USAGE OF A GRAPHICS PROCESSOR TO ACCELERATE COREFERENCE RESOLUTION WHILE USING THE RoBERTa MODEL

"The problem of finding coreferential objects (coreference resolution) in Ukrainian-language texts is considered, examples of co-referentiality in the Ukrainian language are given. Coreference in texts means the relationship between syntactic units that point to the same object (referent) in a given context. The differences between English-language and Ukrainian-language texts in solving the problem of searching for coreferenced objects and the difficulties that arise in finding coreferenced objects are noted. In particular, there are differences in the order of words in sentences: strict word order in English and arbitrary word order in Ukrainian. It is also pointed out the need to take into account the relationship between objects that are not part of the co-reference groups (predicates and secondary parts of sentence). Created an application that searches for reference objects in Ukrainian-language texts using the Transformers library and the RoBERTa language model. The use of a graphics processor to speed up the search for referential objects by the algorithm is considered. The work of the algorithm was tested on a set containing 2500 texts. 21.3 times acceleration reached due to the use of a graphics processor (GPU) in comparison to the performance of CPU. The resulting acceleration has greatly facilitated the development and testing of the application."
FACE RECOGNITION TECHNOLOGY IMPROVING USING CONVOLUTIONAL NEURAL NETWORKS

The work is devoted to the study of methods of construction and algorithms of convolutional neural networks in the context of facial recognition to identify ways to improve existing models. The purpose of the study is to find and implement approaches to improve the face recognition system, which allows to recognize objects with increased recognition accuracy without slowing down. The object of research in this work is the process of face recognition using convolutional neural networks. The subject of research is the architecture and capabilities of convolutional neural networks to solve the problem of face recognition in two-dimensional images. The result are modifications of neural networks trained on various datasets, analysis and comparison of the obtained models on the parameters of recognition quality and learning time on CPU processors, as well as processors with accelerators – GPU and TPU. The scientific novelty is to obtain a more efficient architecture of the convolutional neural network, which improves the accuracy of face recognition. The practical importance is the accumulation of recommendations for the development and training of convolutional neural networks for use in face recognition systems based on the analysis of modern architectures and the principles of existing models.
UPAYA MENINGKATKAN KEAKTIFAN SISWA PADA PEMBELAJARAN ONLINE PAI MELALUI METODE TANYA JAWAB PADA SISWA KELAS VIII3 DI SMP NEGERI I UJUNG PADANG TAHUN PELAJARAN 2020/2021

This research is a Classroom Action Research (CAR) which aims to increase student activity by applying the question and answer method in PAI online learning for class VIII3 SMPN 1 Ujung Padang in 2020/2021. The results showed that student activity had increased, in cycle 1, meeting 1, the score of student activity was 15.38%, at meeting 2 it was 22.11%, at meeting 3 it was scored 28.85%. In cycle 2, meeting 2, the score of student activity was 36.59%, at meeting 2 it was 41.34%, and at meeting 3 it was 77.88%. Based on these results, it can be concluded that the question and answer method can improve Indonesian language learning achievement. The conclusions in this study are, among others, (1) the use of the question and answer method can increase student activity in class VIII3 (2) The effect of the question and answer method for students in class VIII3 more active in learning, many of them ask questions or express opinions, (3) With the application of the question and answer method, Class VIII3 students can actively participate in online learning and think more critically than before, so that the class feels more alive, students give a lot of positive and positive responses. enthusiasm in online KBM, Provide experience and input for teachers and students.
Mushroom Diversity in the River Flow in the TAHURA Area of Palu City and its Utilization as a Learning Media

This study aims to analyze the diversity of mushroom species in the TAHURA watershed in Palu City and to make media posters that can be used as learning media. This type of research is descriptive research with the belt transect method. Sampling is divided into 3 stations in the TAHURA area of Palu City, along the river flow 10 Belt Transects will be made and the distance between transects is 20 m. The results of the study found 16 species belonging to 2 divisions, where in the Basidiomycota division there were 15 types of fungi and in the Ascomycota division there was 1 type of fungus. medium category or value H' = 2.57. The results of the research were used as learning media in the form of posters about the diversity of mushroom species in the TAHURA watershed area of Palu City, after going through validation by the lecturer, media validation and due diligence by 24 students who were divided into 2 groups, with an average score of the large group of 86.8 % and small group 88.3%, the results obtained are classified as suitable for use as learning media.
Hubungan Motivasi Belajar Dengan Hasil Belajar Mata Pelajaran Tata Hidang Kelas XI Di SMK Negeri 3 Solok

This research is motivated by the low student learning outcomes in the Subjects in Class XII Catering Services. This study aims to 1) Describe the learning motivation of class XI students in state Vocational High School 3 Solok, 2) Describe the learning outcomes of class XI students in State Vocational High School 3 Solok, 3) Describe the relationship of motivation with student learning outcomes in Subjects in State Vocational School 3 Solok. This type of research is quantitativedescriptivewithacorrelationalapproach.Thepopulationinthisstudywereallstudents of catering services, namely class XII catering services at SMK Negeri 3 Solok, amounting to 60 people with a sample of 60 people using total sampling techniques. The instrument used in this studywasaquestionnaireusingalikerscalethathadtesteditsvalidityandreliability.Thenthedata is analyzed through analysis of descriptions and correlations using formulas and calculated manually and SPSS. The results showed that overall student motivation was in the moderate category.Thereisastrongandsignificantpositiverelationshipbetweenmotivationandlearning outcomesintheSubjectsofClassXIICateringServicesatSMK Negeri3Solok
Correlation between reading interest and news text learning outcome of eighth graders SMP Negeri 4 Satu Atap Mananggu, Indonesia

What is the relationship between reading interest and learning outcomes of news texts in Class VIII students at SMP Negeri 4 Satu Atap Mananggu? is the issue with this study. In this study, eighth-grade students at SMP Negeri 4 Satu Atasp Mananggu were asked to describe the relationship between reading interest and news text learning outcomes. This study employed a descriptive methodology. This kind of study uses 133 student samples to determine a correlation. Random sampling is the method used for sampling. Techniques for gathering data include surveys, interviews, and documentation. Data normality checks, construct validity checks from a group of subject matter experts serving as question validators, content validity checks using the product moment correlation method, and reliability checks utilizing the alpha formula were all prerequisite tests in this study. The findings indicated a connection between class VIII students at SMP Negeri 4 Satu Atas Mananggu's reading interest and learning outcomes for news texts. Accordingly, the effective contribution of reading interest, which is very minor at 13.20%, has a positive correlation with learning outcomes for news texts in Class VIII students at SMP Negeri 4 Satu Atap Mananggu.
The Impact of Blockchain on Digital Identity Management

Abstract: This journal explores the profound influence of blockchain technology on the realm of digital identity management. As traditional identity systems face significant challenges in terms of security, privacy, and interoperability, blockchain emerges as a promising solution with its decentralized and immutable nature. This paper delves into the key aspects of blockchain technology that contribute to enhancing digital identity management, including decentralization, transparency, security, privacy, and interoperability. The potential benefits and challenges of implementing blockchain-based identity solutions are discussed, along with notable use cases and ongoing initiatives. Ultimately, this journal aims to provide a comprehensive understanding of how blockchain is transforming digital identity management and shaping the future of trusted online interactions.
The Role of Machine Learning in Natural Language Understanding

Abstract: This paper shows deeply the algorithms used in Natural Language (NLU) using Machine Learning (ML) in order to develop Natural Language applications like sentimental analysis, text classification and question answering. The paper thoroughly investigates the diverse applications, inherent challenges, and promising future prospects of machine learning in NLU, providing valuable insights into its revolutionary influence on language processing and comprehension.
Accelerating Software Quality: Unleashing the Power of Generative AI for Automated Test-Case Generation and Bug Identification

Abstract: This paper explores the benefits, challenges, and real-world applications of automated test-case generation and bug identification using generative artificial intelligence (AI). In today's software development and testing landscape, ensuring code quality and minimizing bugs are crucial. However, manual testing methods can be time-consuming and error-prone. Generative AI algorithms, such as generative models, can automatically generate test cases based on inputs, specifications, or system behavior. These algorithms employ machine learning techniques to analyze codebases, uncover test scenarios, and produce comprehensive test cases with broad coverage. The advantages of automated test-case generation include amplified test coverage, supercharged efficiency and time savings, and seamless scalability. Generative AI models also excel in bug identification by scrutinizing codebases, execution traces, and test results. They can detect coding mistakes and identify anomalous patterns indicating potential bugs, memory leaks, or security vulnerabilities. However, challenges such as data quality and bias, domain specificity, and the need for human expertise must be addressed. Real-world applications of automated test-case generation and bug identification using generative AI include software development and security testing. By leveraging generative AI, organizations can enhance test coverage, improve efficiency, and ensure the quality of software products. To successfully implement this approach, challenges related to data quality, domain specificity, and human expertise must be navigated. Generative AI has the potential to revolutionize software testing and contribute to the development of robust and reliable systems in the complex digital landscape.
Design of Poetry Text Teaching Material Modules Based on Higher Order Thinking Skills (HOTS) for Middle School Students

This study aims to develop a module design for poetry text teaching materials based on Higher Order Thinking Skills (HOTS) for junior high school students intended to provide solutions to problems at school including students who often have difficulty understanding poetry content, limited vocabulary, lack of examples of poetry, limited literary facilities and the limited poetry books in schools.The formulation of the problem of this study, particularly (1) How to design a module for teaching materials for poetry texts based on higher order thinking (HOTS) for junior high school students, (2) What is the result of implementing the module design for teaching materials for poetry texts based on higher order thinking (HOTS) for junior high school students.This research uses the Sukmadinata 2020 Research and Development (RnD) research method which consists of preliminary studies, product development and trials.The design of this module contains an introduction, material, practice questions, answer keys and bibliography.The results of the validation of the experts show that the material expert gets a score of 80, the linguist gets a score of 82 and the graphics expert gets a score of 65.Based on the data and analysis, it is included in the Good category.This means that the Design of Poetry Text Teaching Materials Modules Based on Higher Order Thinking Skills (HOTS) is able to improve the ability to identify elements in text poetry including physical elements and mental elements.
Innovative Methods and Technologies for the Formation of Research Skills of Students in Teaching Geography

Currently, innovations are being implemented through dialogue learning, diagnostic, distance learning, project method, collaborative learning, problem-based learning, collective learning, brainstorming and other learning technologies and methods. Among these innovative methods, methods that were effective, formed research skills of schoolchildren and gave positive results are presented in the article. Barriers to the formation of students ' research skills are identified and solutions are presented using new methods. The reasons for changing the paradigm of students ' attitudes towards learning were also revealed. In the course of teaching geography, various methods and techniques have been used so far. And in teaching a child of a rapidly changing society, there was a need to form research skills, influencing the thinking of students. That is why new methods, new classifications are needed. The great scientific and practical significance, effectiveness of this classification, its function as a guide for design, selection and evaluation are analyzed by the authors in the article. The analysis of school experience on the topic of the study made it possible to identify a number of stages in the development of students ' educational and research skills with a new methodology.
APPLICATION OF THE STELLAR NETWORK IN THE MANAGEMENT OF CRYPTO ASSETS

During the active development of Blockchain technology and the growing popularity of cryptocurrencies, users have a need for effective management of their assets. The work presents the process of creating a cryptoasset control system with a minimal set of functions, a clear interface and achieving the main goal of users - systematization and promotion of the effective growth of invested investments. One of the popular platforms for conducting blockchain operations is the Stellar network (Stellar Network). Thanks to the flexibility of the network and its features, a cross-platform system has been created, the communication with which is carried out by means of Telegram, and the main functionality has been developed using the Horizon API and SDK in the Python programming language
Graph Energy Change on Edge Deletion

The energy E(G) of a graph G is the sum of the absolute values of all eigenvalues of G.An interesting and "hard-to-crack" problem about graph energy was mentioned by Gutman in [The energy of a graph: old and new results]: characterize the graphs G and their edges e for which E(G -e) < E(G).In this paper, we give a new sufficient condition for E(G-e) < E(G) where e is not necessarily to be a cut-edge set or a cut edge.This work can be used to generalize some well-known results.
MATHEMATICAL MODELING OF THE PROCESS OF FORECASTING THE TIME SPENT ON THE IMPLEMENTATION OF A TYPICAL TASK FOR SOFTWARE DEVELOPMENT

The article is devoted to the creation, justification and verification of a mathematical model for forecasting time labor costs for solving typical problems of a software development project. Forecasting is carried out on the basis of information about the duration of each typical task by an individual employee from the development team. The constructed model takes into account information about the time frame for the employee to complete the task with a certain number of repetitions of its implementation. The work was done using the cftool service tools of the MatLab package. The adequacy of the parameters of the mathematical model was checked experimentally, a comparative assessment of the results obtained with the indicators calculated by the theoretical method was carried out.
On the structural and combinatorial properties in 2-swap word permutation graphs

In this paper, we study the graph induced by the $\textit{2-swap}$ permutation on words with a fixed Parikh vector. A $2$-swap is defined as a pair of positions $s = (i, j)$ where the word $w$ induced by the swap $s$ on $v$ is $v[1] v[2] \dots v[i - 1] v[j] v[i+1] \dots v[j - 1] v[i] v[j + 1] \dots v[n]$. With these permutations, we define the $\textit{Configuration Graph}$, $G(P)$ defined over a given Parikh vector. Each vertex in $G(P)$ corresponds to a unique word with the Parikh vector $P$, with an edge between any pair of words $v$ and $w$ if there exists a swap $s$ such that $v \circ s = w$. We provide several key combinatorial properties of this graph, including the exact diameter of this graph, the clique number of the graph, and the relationships between subgraphs within this graph. Additionally, we show that for every vertex in the graph, there exists a Hamiltonian path starting at this vertex. Finally, we provide an algorithm enumerating these paths from a given input word of length $n$ with a delay of at most $O(\log n)$ between outputting edges, requiring $O(n \log n)$ preprocessing.
Ambitropical geometry, hyperconvexity and zero-sum games

Shapley operators of undiscounted zero-sum two-player games are order-preserving maps that commute with the addition of a constant. We characterize the fixed point sets of Shapley operators, in finite dimension (i.e., for games with a finite state space). Some of these characterizations are of a lattice theoretical nature, whereas some other rely on metric or tropical geometry. More precisely, we show that fixed point sets of Shapley operators are special instances of hyperconvex spaces: they are sup-norm non-expansive retracts of $\R^n$, and also lattices in the induced partial order. Moreover, they retain properties of convex sets, with a notion of ``convex hull'' defined only up to isomorphism. This provides an effective construction of the injective hull or tight span, in the case of additive cones. For deterministic games with finite action spaces, these fixed point sets are supports of polyhedral complexes, with a cell decomposition attached to stationary strategies of the players, in which each cell is an alcoved polyhedron of $A_n$ type. We finally provide an explicit local representation of the latter fixed point sets, as polyhedral fans canonically associated to lattices included in the Boolean hypercube.
Model based clustering of multinomial count data

Abstract We consider the problem of inferring an unknown number of clusters in multinomial count data, by estimating finite mixtures of multinomial distributions with or without covariates. Both Maximum Likelihood (ML) as well as Bayesian estimation are taken into account. Under a Maximum Likelihood approach, we provide an Expectation–Maximization (EM) algorithm which exploits a careful initialization procedure combined with a ridge-stabilized implementation of the Newton-Raphson method in the M-step. Under a Bayesian setup, a stochastic gradient Markov chain Monte Carlo (MCMC) algorithm embedded within a prior parallel tempering scheme is devised. The number of clusters is selected according to the Integrated Completed Likelihood criterion in the ML approach and estimating the number of non-empty components in overfitting mixture models in the Bayesian case. Our method is illustrated in simulated data and applied to two real datasets. The proposed methods are implemented in a contributed package, available online.
Transformed Protoform Reconstruction

Protoform reconstruction is the task of inferring what morphemes or words appeared like in the ancestral languages of a set of daughter languages. Meloni et al. (2021) achieved the state-of-the-art on Latin protoform reconstruction with an RNN-based encoder-decoder with attention model. We update their model with the state-of-the-art seq2seq model: the Transformer. Our model outperforms their model on a suite of different metrics on two different datasets: their Romance data of 8,000 cognates spanning 5 languages and a Chinese dataset (Hou 2004) of 800+ cognates spanning 39 varieties. We also probe our model for potential phylogenetic signal contained in the model. Our code is publicly available at https://github.com/cmu-llab/acl-2023.
FREEDOM: Target Label &amp; Source Data &amp; Domain Information-Free Multi-Source Domain Adaptation for Unsupervised Personalization

From a service perspective, Multi-Source Domain Adaptation (MSDA) is a promising scenario to adapt a deployed model to a client's dataset. It can provide adaptation without a target label and support the case where a source dataset is constructed from multiple domains. However, it is impractical, wherein its training heavily relies on prior domain information of the multi-source dataset -- how many domains exist and the domain label of each data sample. Moreover, MSDA requires both source and target datasets simultaneously (physically), causing storage limitations on the client device or data privacy issues by transferring client data to a server. For a more practical scenario of model adaptation from a service provider's point of view, we relax these constraints and present a novel problem scenario of Three-Free Domain Adaptation, namely TFDA, where 1) target labels, 2) source dataset, and mostly 3) source domain information (domain labels + the number of domains) are unavailable. Under the problem scenario, we propose a practical adaptation framework called FREEDOM. It leverages the power of the generative model, disentangling data into class and style aspects, where the style is defined as the class-independent information from the source data and designed with a nonparametric Bayesian approach. In the adaptation stage, FREEDOM aims to match the source class distribution with the target's under the philosophy that class distribution is consistent even if the style is different; after then, only part of the classification model is deployed as a personalized network. As a result, FREEDOM achieves state-of-the-art or comparable performance even without domain information, with reduced final model size on the target side, independent of the number of source domains.