Reviewerly/exHarmony huggingface dataset:
3 Diminutive or singulative? The suffixes -in and -k in Russian

This paper investigates the semantics and distribution of two Russian suffixes, -in and -k, each of which has been treated both as singulative and diminutive. However, we argue that -in is singulative and -k diminutive. -in systematically applies to mass nouns and creates count ones with the meaning “a natural unit of N”, where the nature of the unit is lexically fixed and based on our conceptualization of the entity denoted by the stem. We propose that -in is a standardized partition operator ΠNU of type ≪e,t>,<e,t≫, which imposes division into natural units. In contrast, -k is a diminutive suffix whose main sub-meanings are smallness in size and affection. Under the semantic use, it maps the argument to a low degree on a size scale. Syntactically, it is the head of SizeP, located immediately above DivP. Finally, we analyze the suffix -ink, dividing it into compositional -in-k (goroš-in-k-a ‘a small/nice pea’) and non-compositional -ink (snež-ink-a ‘snowflake’; ✶snež-in-a does not exist). Compositional -in-k consists of two suffixes -in and -k, with the singulative and the diminutive functions, respectively. Noncompositional -ink is a single suffix imposing division into natural units and further contributing a presupposition that such units are inherently small.
Application of Augmented Reality on Chemistry Learning: A Systematic Review

The rapid development of technology in the world of education has many positive impacts, namely facilitating the learning process. Technological sophistication can be felt, one of which is augmented reality. This Augmented Reality concept collaborates the real world with the virtual world which can display more detailed information in 3D visuals. This review systematic analysis study was conducted to classify the application of augmented reality in chemistry learning. The main criteria for the selections of journals the implementation of augmented reality in the field of chemistry education with a period ranging from 2015 to 2022 from an international database. There were 74 journals were obtained that fit into the criteria. Journal studies focused on the application of AR chemistry materials at the high school and university levels. The studies analyzed focused on the form of applying AR in chemistry learning, applied chemistry, chemical material, and sample level. The results of the study show that the application of AR in chemistry learning is often presented in the form of android-based learning, organic chemistry is widely applied using AR. In addition, an explanation of the application of chemical concepts and sample groups is also discussed here.
A Parameters Estimation-Based Calibration Method for Rotational Axis of Line-Structured Light 3D Reconstruction

Rotation is a critical component in 3D reconstruction systems, where accurate calibration of rotation axis parameters is essential for 3D stitching. In this study, what we believe to be a novel parameters estimation-based method for calibrating rotation axis parameters using 2D planar targets is proposed. Compared to traditional circle fitting methods, this method takes both orientation and position information into account, resulting in better precision performance. By leveraging the transmission of spatial pose relationships, the parameters estimation-based calibration method also effectively mitigates the impact of noise for more accurate calibration of rotation axis parameters. Error validation and 3D reconstruction experiments proved the superior performance of the proposed method. The experiment results demonstrate the effectiveness and applicability of the approach in enhancing the calibration of rotation axis parameters for 3D reconstruction systems.
THE INFLUENCE OF STUDENT ATTITUDES ON LEARNING ACHIEVEMENT

This research is the first to examine the influence of attitude on learning outcomes among class X students of Senior high school 2 Jambi City. Attitude is one of the main indicators of current learning and learning outcomes are a benchmark for assessing student attitudes. The research aims to explore the relationship between student attitudes and their academic performance in various subjects. This research uses a quantitative research method by means of a survey with a purposive sampling technique. The analysis technique used is normality and linearity assumption testing and hypothesis testing using person correlation. The research subjects this time were 60 class X students of Senior high school 2 Jambi City. The results of this research show that the significance value is 0.042 which is smaller than 0.05, which shows that there is a sufficient relationship between student attitudes and student learning outcomes. The novelty of this research is that it introduces a new measure of student attitude based on a multidimensional scale that covers cognitive, affective, and behavioral aspects. The research also provides empirical evidence of the positive impact of student attitude on learning outcomes across different subjects, such as mathematics, science, and language. The research contributes to the literature on educational psychology and pedagogy by highlighting the role of student attitude in enhancing academic achievement and motivation.
A Study on the Relationship Between Risk Assessment and Safety Performance in Small- and Medium-Sized Businesses

The purpose of this study was to conduct a study on the relationship between risk assessment signal and safety in small and medium-sized workplaces. While the Serious Accidents Punishment Act, which will be applied to places of business with five or more employees, will be effected in January 2024, the accident rate in small- and medium-sized businesses is still high. Promoting safety behaviors and preventing accidents in small- and medium-sized businesses require analyzing the role of risk assessment, an integral part of the self-regulatory safety and health management system.&#x0D; In this regard, this study is aimed at examining the relationship between risk assessment and safety performance in small- and medium-sized businesses. The subjects of this study were 211 employees, working at small- and medium-sized businesses that hired less than 300 workers. The collected data were analyzed using the IBM SPSS 29 program, through frequency analysis and independent samples t test. The results revealed significant differences in the domains of safety climate and compliance, accident rates, safety as a top priority, and the level of risk behavior management in small- and medium-sized businesses, based on whether risk assessment was conducted or not. The businesses that conducted risk assessment, demonstrated a higher level of safety performance than those that did not. These findings confirmed that risk assessment is an effective system to improve safety behaviors and prevent accidents at small- and medium-sized businesses. Further research should be conducted on the implementation of risk assessment in small- and medium-sized businesses and their resultant safety performance, to encourage more companies to adopt this practice. This will also facilitate risk assessment to develop into an effective measure for preventing industrial accidents and protecting the health and lives of workers.
Adaptive Patch Labeling and Multi-Label Feature Selection for 360-Degree Image Quality Assessment

Assessing the quality of 360-degree images based on individual regions presents a challenging task. The lack of ground truth opinion scores (MOS) for specific regions makes it difficult to evaluate image quality accurately. Existing datasets only provide MOS for entire 360-degree images, which limits the granularity of assessment. To overcome this challenge, we propose a novel framework that employs adaptive patch labeling techniques. We leverage a set of 2D-IQA methods to generate quality score distributions for each patch in the 360-degree images. These distributions, combined with the available MOS, serve as labels for individual patches, providing a more comprehensive characterization of patch quality. Furthermore, we use these labels to adaptively select and refine deep neural features. By selectively choosing label-specific features, we enhance the accuracy and effectiveness of patch-based 360-degree image quality assessment. This approach allows us to focus on the most relevant and informative features for each patch, resulting in improved assessment performance. The experimental results on two benchmark datasets demonstrate that adaptive patch labeling and feature selection achieve accurate and reliable performances, thus advancing the field of 360-degree image quality assessment.
INR-MDSQC: Implicit Neural Representation Multiple Description Scalar Quantization for Robust Image Coding

Multiple Description Coding (MDC) is an error-resilient source coding method designed for transmission over noisy channels. We present a novel MDC scheme employing a neural network based on implicit neural representation. This involves overfitting the neural representation for images. Each description is transmitted along with model parameters and its respective latent spaces. Our method has advantages over traditional MDC that utilizes auto-encoders, such as eliminating the need for model training and offering high flexibility in redundancy adjustment. Experiments demonstrate that our solution is competitive with autoencoder-based MDC and classic MDC based on HEVC, delivering superior visual quality.
Construction of a Video Inpainting Dataset Based on a Subjective Study

Video inpainting, the automated process of reconstructing missing or corrupted regions in video sequences, has gained significant attention in the fields of computer vision and image processing in recent years. However, a recurring remark in the literature has been the lack of a dedicated database specifically designed for video inpainting. As a result, existing inpainting studies have relied on locally created videos or datasets primarily intended for other applications. To address this limitation, this paper introduces the first publicly available video inpainting dataset accompanied by subjective scores, which closely aligns with real-world applications. The dataset covers three key inpainting scenarios: video hole completion, object removal, and post-stabilization inpainting. By providing this dataset, our goal is to facilitate the comparison and evaluation of both current and future video inpainting techniques. Moreover, we anticipate that it will serve as a solid foundation for the development of novel video inpainting assessment metrics in the future, thereby encouraging further advancements in this field. It is worth noting that, to the best of our knowledge, there is only one metric dedicated to video inpainting quality assessment, apart from those developed for image inpainting that can potentially be extended to videos. The dataset and the subjective scores are available on this link: https://github.com/rezkimed/VID-SS.
Towards Automatic Content Generation for Immersive Cinema Theater Based on Artificial Intelligence

Immersive display systems like the one proposed by ICE® technology aims to enhance visual immersion by widening the field of view. However, creating immersive content while maintaining immersion integrity is a challenging task due to the sensitivity of human peripheral vision to flickering and movement. Moreover, identifying elements in videos that may disrupt immersion and determining whether they can be expanded into an immersive context is a complex and time-consuming process due to the lack of automatic methodologies. In this paper, we propose a pipeline for automatically generating content for lateral displays from movies. The pipeline consists of several steps. Firstly, the input content is divided into cinematic shots, and then further segmented into snippets. Next, domain-specific features are extracted using dedicated video deep learning models. Additionally, handcrafted features are computed to provide task-specific information. These extracted features are utilized to predict the required processing steps for generating lateral content that aligns with ground-truth annotations provided by cinema experts. The results obtained from our pipeline show promising accuracy and demonstrate the potential for this specialized application.
QUALITY OF TRAINING OF SPECIALISTS OF THE "SECONDARY EDUCATION (NATURAL SCIENCES)" EDUCATIONAL PROGRAM AT THE POLTAVA NATIONAL PEDAGOGICAL UNIVERSITY NAMED BY V. G. KOROLENK: PROBLEMS AND PERSPECTIVES

The article examines the state of higher science education at V.G. Korolenko National Pedagogical University. Scientific developments within the scope of the investigated problem are analyzed. The peculiarities of the formation of bachelor's and master's levels of higher education in the specialty 015.15 "Secondary education (Natural sciences)" are highlighted. The key aspects of ensuring the process of quality training of specialists in natural sciences are analyzed. It was found that natural sciences are the leading specialty for entrants, and V.G. Korolenko National Pedagogical University is the only educational institution in the Poltava region that implements training under the OP "Secondary Education (Natural Sciences)". During teaching at this OP, modern didactic tools and various forms of pedagogical interaction are used, which are necessary for solving complex problems in professional activity. Auditory classes of students are held in specialized offices in compliance with safety rules. Students combine studies with research, participate in scientific and scientific-practical conferences, have the right to academic mobility, and are regular participants in excursions. The training of education seekers is provided by financial, material and technical resources and educational and methodological support. The main source of information support for the educational process and scientific activity of the applicants is the library named after M. A. Zhovtobryukha of V.G. Korolenko National Pedagogical University. Participants of the educational process have free access to international scientometric databases. The safety of the educational environment of V.G. Korolenko National Pedagogical University is ensured by the activities of the University's divisions: the labor protection service, the civil protection headquarters, the economic department, psychological and legal services, and an interesting student life is ensured by student self-government - the "Student Council of V.G. Korolenko National Pedagogical University" .About 20 amateur art groups and sports sections function for students to realize their creative potential in extracurricular time. Continuous professional development of scientific and pedagogical workers takes place, and priority tasks are self-education and self-improvement. In addition, the article provides prospects for improving the educational process at V.G. Korolenko National Pedagogical University.
The study for word prediction based on Skip-gram and CBOW model

In the modern world, when people use their texting apps to communicate with each other, they find their phones are trying to predict what they want to type. The process of this word prediction is actually not a new topic, previous researchers have involved the Natural Language Processing (NLP) word prediction algorithms in many studies. Thus, in this article, a project implementing the Skip-gram model and the Continuous Bags of Words (CBOW) model is created in this study to find out whichever of the two newest and reversed word prediction models would be better to utilize. By comparing the efficiency items including total training time, total effective words, training time per epoch, and effective words per epoch in the experimental results, the project discovered that the CBOW model could be better utilized than the Skip-gram model, which means that the CBOW model can be considered more under this kind of task.
Classification of key human factors in the area of information resources security management in small enterprises in Poland

This paper attempts to classify major errors affecting the security of information resources made by employees. Human factors that can be a direct cause of these errors were enumerated. The empirical part presents surveys conducted among small businesses in 2019 and 2022 related to the human factors discussed in the theoretical part. The study compared the results obtained before and after the COVID-19 pandemic. A discussion related to the factors and the results was presented. The main aim of the present research, conducted twice on a group of 287 small businesses in Poland, was to show changes in attitudes toward the security of intangible resources and how the COVID-19 pandemic has transformed the way of thinking of people responsible for information security in terms of behavioral security.
Adaptation of the Functionality of the Primo VE Search Engine to the Needs of Users of the University of Maria Curie-Skłodowska Library Catalog

The article discusses configuration changes of the default settings of the Primo Ve search engine, using tools available from the Alma system. It presents search solutions tailored for users of the catalog of the Maria Curie Skłodowska University Library in Lublin. transparent access to the information about the traditional and electronic collections held was the main assumption of the work related to the configuration of the Primo Ve search engine. During the necessary configurations and adjustments, efforts were made to ensure that the presented system functions provided the user and all agendas of the UMCS library and information system with consistent information.
The Work of Informal Environment in Language Learning

This study examines the work of informal environment in language learning. This study is an ex post facto research, employing quantitative approach. The students at English Department of Madura University are as parts of population in the current research. A sample is decided by using proportional stratified random sampling technique. The data are analyzed by simple regression technique. The result of the study shows that the work of informal environment in language learning is not particularly high, 0.43 (43%). The informal environment operates on students’ proficiency level in English. It enriches the students’ input to communicate in English as well as makes the input of students becoming intake. It implies that the teachers and members of academic society should assist students with natural communication in order to meaningful input for the students.
General monogamy and polygamy relations of arbitrary quantum correlations for multipartite systems

Monogamy and polygamy of quantum correlations are the fundamental properties of quantum systems. We study the monogamy and polygamy relations satisfied by any quantum correlations in multipartite quantum systems. General monogamy relations are presented for the $$\alpha $$ th $$(0\le \alpha \le \gamma $$ , $$\gamma \ge 2)$$ power of quantum correlation, and general polygamy relations are given for the $$\beta $$ th $$(\beta \ge \delta $$ , $$0\le \delta \le 1)$$ power of quantum correlation. We show that these newly derived monogamy and polygamy inequalities are tighter than the existing ones. By applying these results to specific quantum correlations such as concurrence and the square of convex-roof extended negativity of assistance (SCRENoA), the corresponding new classes of monogamy and polygamy relations are obtained, which include the existing ones as special cases. Detailed examples are given to illustrate the advantages of our results.
Ushoshi2023 at BLP-2023 Task 2: A Comparison of Traditional to Advanced Linguistic Models to Analyze Sentiment in Bangla Texts

This article describes our analytical approach designed for BLP Workshop-2023 Task-2: in Sentiment Analysis. During actual task submission, we used DistilBERT. However, we later applied rigorous hyperparameter tuning and pre-processing, improving the result to 68% accuracy and a 68% F1 micro score with vanilla LSTM. Traditional machine learning models were applied to compare the result where 75% accuracy was achieved with traditional SVM. Our contributions are a) data augmentation using the oversampling method to remove data imbalance and b) attention masking for data encoding with masked language modeling to capture representations of language semantics effectively, by further demonstrating it with explainable AI. Originally, our system scored 0.26 micro-F1 in the competition and ranked 30th among the participants for a basic DistilBERT model, which we later improved to 0.68 and 0.65 with LSTM and XLM-RoBERTa-base models, respectively.
Is the Answer in the Text? Challenging ChatGPT with Evidence Retrieval from Instructive Text

Generative language models have recently shown remarkable success in generating answers to questions in a given textual context. However, these answers may suffer from hallucination, wrongly cite evidence, and spread misleading information. In this work, we address this problem by employing ChatGPT, a state-of-the-art generative model, as a machine-reading system. We ask it to retrieve answers to lexically varied and open-ended questions from trustworthy instructive texts. We introduce WHERE (WikiHow Evidence REtrieval), a new high-quality evaluation benchmark of a set of WikiHow articles exhaustively annotated with evidence sentences to questions that comes with a special challenge: All questions are about the article’s topic, but not all can be answered using the provided context. We interestingly find that when using a regular question-answering prompt, ChatGPT neglects to detect the unanswerable cases. When provided with a few examples, it learns to better judge whether a text provides answer evidence or not. Alongside this important finding, our dataset defines a new benchmark for evidence retrieval in question answering, which we argue is one of the necessary next steps for making large language models more trustworthy.
Let’s Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models

*Data Synthesis* is a promising way to train a small model with very little labeled data. One approach for data synthesis is to leverage the rich knowledge from large language models to synthesize pseudo training examples for small models, making it possible to achieve both data and compute efficiency at the same time. However, a key challenge in data synthesis is that the synthesized dataset often suffers from a large distributional discrepancy from the *real task* data distribution. Thus, in this paper, we propose *Synthesis Step by Step* (**S3**), a data synthesis framework that shrinks this distribution gap by iteratively extrapolating the errors made by a small model trained on the synthesized dataset on a small real-world validation dataset using a large language model. Extensive experiments on multiple NLP tasks show that our approach improves the performance of a small model by reducing the gap between the synthetic dataset and the real data, resulting in significant improvement compared to several baselines: 9.48% improvement compared to ZeroGen and 2.73% compared to GoldGen, and at most 15.17% improvement compared to the small model trained on human-annotated data.