Reviewerly/exHarmony huggingface dataset:
Design And Build A Practicum Equipment For The Law Of Conservation Of Mechanical Energy Of Rolling Objects

Using photo gate sensor technology and digital displays, the research team created a legal practicum for energy conservation. This study aims to replace manual practicum equipment that is prone to measurement errors during practicum. This practicum equipment is used to prove the law of conservation of mechanical energy in rolling objects. Practicum equipment is developed using a development research process that starts with identifying needs and continues through design, manufacturing, and testing. At the design stage, validation of design results is carried out, while at the manufacturing and testing stages, design specifications and performance (measurement of accuracy and precision) are carried out. Based on the results of design validation, a value of 0.85 with a valid category was obtained, while accuracy was 90.7%, with an average accuracy of 97.46%, and a relative error of 8.33%. The thoroughness of the practicum equipment for the law of conservation of mechanical energy of rolling objects is quite good. We are currently developing several pieces of practicum equipment with IoT-assisted smartphone displays.
Implementation of the Floyd-Warshall Algorithm and Bellman-Ford Algorithm to Determine the Shortest Path in the Distribution of LPG Gas

In this article, the method use to determine the shortest path in the distribution of LPG is the Floyd-Warshall algorithm and the Bellman-Ford algorithm. These two methods will be compared to find out methods that can provide the best result. The best result is the path that has the minimum distance of distribution of LPG. Both of these algorithms are focused on simulating calculations that begin by representing the map in the form of a graph. Finally, the results obtained from this study are that using the Floyd-Warshall algorithm produces a path with a total distance of 111.05 KM while the Bellman-Ford algorithm produces 332.3 KM.
Philosophical and aesthetic meaning of the image / title of O. Dovzhenko’s film story “Enchanted Desna”

The Subject of the study is the philosophical and aesthetic aspect of the title of the film story “Enchanted Desna”. The purpose is to find out the author’s intention, clarify the problem and idea of the work based on deciphering its title. The established interpretations of “Enchanted Desna” in Dovzhenko studies need to be revised. The widespread understanding of this text in a populist way contradicts or at least does not include the interpretive possibilities provided by the author’s poetics. The need for its re-reading in the context of today makes the study relevant. Linguistic, aesthetic-typological, comparative, and phenomenological methods were used in the research. The novelty of the research lies in the original interpretation of the key image of the famous work. The Results. The starting point in the interpretation of Dovzhenko’s title is the semantics of the passive participle “enchanted” in connection with the image of the mirror and the motive of self-discovery. This story cannot be adequately understood outside the context of H. Skovoroda’s philosophical heritage. Indirectly through Skovoroda, Dovzhenko learns and develops Plato’s aesthetic ideas and the concept of calokagatia. He modernizes the ancient idea of beauty in the realm of creativity. Knowledge turns out to be identical to self-knowledge, the discovery of beauty is its creation. In accordance with the latest utopias, man is assigned the role of demiurge. If we take into account that beauty in this concept is identical to truth, then man (humanity) is also recognized as the creator of truth. Dovzhenko’s new humanism was nourished by the atmosphere of national Renaissance, Marxist and Nietzschean ideas. It is claimed that the central problem of the film story is the role of art in the formation of personality. “Enchanted Desna” is an image of art that immerses the soul in the processes of purification, personalization, self-knowledge and knowledge of the world, comprehension / creation of beauty-truth. The artist’s mission was to awaken in each person their alternative self, or, according to Skovoroda, the inner man with the help of art. In the future, it is necessary to find other versions of the image of the mirror of self-recognition in Ukrainian and foreign writers and to trace the influence of the magnum opus of the novelist Dovzhenko in the 60s.
FORMATION OF DIGITAL LITERACY FOR TEACHERS IN THE SYSTEM OF POSTGRADUATE EDUCATION IN UKRAINE

The article discusses the problem of forming digital literacy for teachers in the system of postgraduate education in Ukraine. It is noted that digital literacy is important for teachers as it can contribute to enhancing the effectiveness of the educational process, teaching students to effectively use digital tools for educational purposes and in various areas of life. Digital literacy in the process of education implies knowledge and skills in using a wide range of digital tools, such as smartphones, tablets, computers, etc., for various educational goals. The idea of lifelong learning is one of the important directions of the educational process in Ukraine, the implementation of which is aimed at the development of society's digitalization, which requires teachers to have a high level of digital literacy. A teacher should be prepared to implement professional activities using digital technologies, utilizing the possibilities of these technologies to improve the quality of the educational process and prepare students for successful life in modern society. Digital literacy is crucial in the process of teachers' professional development, which is manifested in solving various tasks involving the use of digital technologies. Various ways of forming digital literacy can be introduced in the system of postgraduate education in Ukraine, namely: programs and courses aimed at forming digital literacy for teachers. The article presents the main directions of such programs and their content.
Survey on Secure Encrypted Data with Authorized De-duplication

The need to keep a large amount of data in data centers is becoming a major issue, and research suggests that roughly 60% of the data in storage systems is the same. This is because data storage in industry is increasing. Keeping track of the ever-increasing volume of data is a major task. To make data management scalable, many methods of deleting duplicate data are used. It is a method of reducing the storage of storage space required by deleting duplicate copies of data that are saved more than once. It was a common strategy for conserving storage space and bandwidth. The benefit of de-duplication in terms of new security and privacy issues typically comes with high price. Data de-duplication is the new technology of data compaction that eliminates duplicates in the data. “Data de-duplication is the method of analyzing a sub-file level data set or I/O stream, and storing and/or sending only unique data”. It differs from the compression techniques by operating on sub-file level data where the data in the file is encoded as compression to minimize its storage requirement. We are proposing clouded up, a reliable and efficient storage service that simultaneously guarantees block-level de-duplication and data confidentiality.
COMMAS IN TEXTS OF STUDENTS FROM AGE 11 TO 15-YEARS-OLD: A LONGITUDINAL STUDY

ABSTRACT In this article, we investigate the presence and the absence of commas in a longitudinal sample of texts of the genre report written by 11 to 15 years-old students from a school in the countryside of São Paulo State. Our hypothesis is that the comma is used when syntactic boundaries match the prosodic ones. The prosodic constituents are formed from syntactic and phonological information, according to principles defined in the Prosodic Phonology theoretical approach. The characterization of the conventional commas and the non-conventional absences of commas was made from the identification of syntactic boundaries in which they should or could be placed, taking as reference a Brazilian Portuguese grammar. The results were that the conventional comma increases during the years of schooling, as expected. However, the presence and absence of comma rates changed depending on the type of syntactic structure and on the occurrence of intonational phrase boundaries. Comma occurred predominantly in positions where intonation phrase boundaries match major syntactic boundaries, as a sentence boundary. In turn, the comma systematically does not occur in positions where the syntactic and prosodic boundaries do not match.
A Novel Hybrid Approach for Dimensionality Reduction in Microarray Data

Researchers and engineers working in data mining and machine learning struggle with analysing data of high dimension. To address this issue, feature selection is a viable option. It eliminates redundant and unnecessary data, speeds up computation, improves training accuracy and makes learning models or data easier to understand. High-dimensional data creates more redundant, irrelevant and noisy data, leading to model overfitting and increasing the error rate of learning algorithms. A “dimensionality reduction” approach is used as part of the pre-processing step to solve these problems, which includes applications like feature selection and extraction. The paper targets on improving particle swarm optimization yield using features of grey wolf optimizer (GWO) that mitigate the dimensionality reduction problem. A microarray dataset was used in order to assess the HPSO-GWO algorithm's solution and work. From analytical and numerical outcomes, it was observed that the algorithm's performance is admirable in terms of stability, finding global optimums and rate of convergence, when both the algorithms were hybridized.
Transfer Learning of Pre-trained CNN Models for Meitei Mayek Handwritten Character Recognition

Many image classification models have been presented and studied for handwritten character recognition (HCR), the most recent and successful one being the convolutional neural network (CNNs). The success of CNNs has been proven in the famous ImageNet Large Scale Visual Recognition Challenge (ILSVRC) where different CNN models have been proposed and analyzed using the ImageNet dataset. Nevertheless, the task of HCR is challenged by the development of new character image datasets and the peculiarities found in those datasets. The present study aims to analyze the performance of five pre-trained CNN models, namely InceptionV3, ResNet50V2, DenseNet121, MobileNetV2 and EfficientNetB3 using transfer learning on a newly developed public image dataset of handwritten Meitei Mayek characters. Experiments show that EfficientNetB3 the best test accuracy and MobileNetV2 provides the least computational cost.
Target Tracking Method for Human Motion Image Based on Kalman Filtering Algorithm

To improve the target tracking effect of the human motion image, a target tracking method of human motion image based on Kalman filtering algorithm is proposed. Using the prediction function of Kalman filter, the human motion image features are collected, the image motion features are analyzed, and the human motion image feature tracking algorithm and evaluation index are constructed, the target tracking of human motion image is realized. The results show that the target tracking method of human motion image based on Kalman filtering algorithm has practicability in the process of practical application.
Implementation of Business Data Sharing Based on Blockchain and Improvement of Consensus Algorithm

Due to different data categories, there may be great differences between data, and it is difficult to have a unified sharing model, so most of the research is on the sharing mechanism or sharing model of some specific data using blockchain. However, in the field of commercial data sharing, few people pay attention to commercial data sharing. At the same time, the PoW algorithm has very high security, but there are some problems at the same time, such as low efficiency, and the emergence of ASIC mining machines dedicated to mining, which wastes a lot of resources. Therefore, this paper improves the data-sharing model and implements it on the consortium blockchain. At the same time, based on the existing proof of learning (PoLe) consensus algorithm, the pre-training of the next task is carried out in the verification and reward process, and the block speed is increased under the premise of ensuring the security of the original algorithm.
Automatic Generation of Ancient Poetry Based on Generative Adversarial Network

In recent years, the technology of automatic generation of ancient poems has developed rapidly, but there are still problems of unclear themes and excessive deviation of generated contents from the themes. In this paper, based on seqGAN, we apply encoder-decoder to the generative model of seqGAN, and add the attention mechanism for a better generation of ancient poems. Through experimental validation, the scores of the improved model are improved compared with the original after manual and machine evaluation, which proves the effectiveness of the model.
The Effect of Colored Noise on Self-feedback Chaotic Neural Networks with Legendre Function

In the real world, any network model will inevitably be affected by noise. As a result, the academic community has gradually started to pay attention to various types of complex systems based on colored noise, but the application of colored noise to neural networks is still a blank field. The introduction of colored noise can make the operation of neural networks closer to the real application scenarios and avoid errors. Therefore, this article introduces four typical colored noises into Legendre function self-feedback chaotic neural networks, which are used to analyze the effects of colored noise on neural network models, and gives the maximum Lyapunov exponent and single neuron inverse bifurcation diagram of the network with colored noise. The Legendre function self-feedback chaotic neural network with colored noise is applied to solve the 10-city travelers problem. Simulation studies indicate that: with appropriate parameters, the Legendre function self-feedback chaotic neural network maintains excellent solution performance under colored noise interference at any signal-to-noise ratio, and the network model speeds up convergence due to noise interference on the basis of improved accuracy of the optimization algorithm. The findings provide theoretical support for the hardware implementation of this neural network in a realistic environment.
Portfolio Optimization Using Quantum-Inspired Modified Genetic Algorithm

Optimization of portfolios has an additional level of complexity and has been an area of interest for both financial leaders and artificial intelligence experts. In this article, a quantum-inspired version of an improved genetic algorithm is proposed for the task of portfolio optimization. An effort is made to implement two different genetic versions along with their extension in the quantum-inspired space. Improvements to the popular crossover techniques, viz. (i) arithmetic and (ii) heuristic crossover are proposed to reduce computational time.
Systematic Review of Automatic Arabic Text Summarization Techniques

In this article, the topic of Arabic automatic text summarization (AATS) techniques is discussed, which involves extracting significant information from lengthy texts. The vast amount of data available online and offline, as well as the large volume of information across different platforms, has made these techniques increasingly important. The article explains that there are three types of techniques used for automatic text summarization (ATS), which include extractive, abstract, and hybrid. However, the quality of summaries generated automatically using these techniques is still inferior to those created by human experts. Additionally, the article highlights the obstacles facing AATS, such as the structure and morphological diversity of the Arabic language, as well as the lack of standardized corpora. Finally, the article examines recent publications on novel and improved techniques for Arabic text summarization, with an emphasis on their characteristics, limitations, evaluation, and approaches.
Cooperative Strategy in Sociosystems

Today, Russia is facing severe economic and political conditions, thus affecting the overall standard of living of the population. There are steady trends of social disunity, internal conflict is brewing. Therefore, there is now a need to reassess the importance of the cooperative strategy as an effective mechanism for the development of sociosystems. Cooperation can be defined as an interdisciplinary category, since it is able to describe many mechanisms and processes at various levels of cognition. In turn, the cooperative strategy is a universal model for building all levels of sociosystems, as it is based on interaction and cooperation. Even with a certain confrontation and opposition, struggle and conflict between the elements of social systems, one way or another, when a certain crisis moment comes, they tend to unite through cooperation. This suggests that cooperation should be considered as a means of self-organization and self-development of individual and social being.
Dialectics of Systems Development

The modern period of historical development is replete with oversaturation of information, which entails a number of consequences associated with its uncritical processing, inadequate conclusions, cognitive distortions, manipulative influence, kaleidoscopic consciousness, defragmentation of the integrity of the personality. The process of information interaction proceeds in conditions of ignorance in the presence of colossal technical resources for information transmission. Such an overload is associated with the unformed system that ensures the implementation of ideological strategies. The goals set by the authors predetermined the use of the dialectical approach as the basis of the research. The specifics of solving the problems that have arisen led to the appeal to methods of analysis and synthesis to comprehend the result. Within the framework of the study, the authors considered the problems of the development of the cognitive system in the emerging mixed virtual-objective reality. Some particular problems inherent in the educational system and its implementation in the dialectical unity of cooperation and competition have received a new interpretation, namely: the inevitability of the synthesis of rationality and “mythologization” that affects the miscalculation of risks, the growth of self-awareness of participants in the educational process, the pretentiousness of the actor of the educational process. The essential problem of realizing the purpose of education is the process of forming the personal qualities of a successful future specialist. Within the framework of this work, we have separately identified the cornerstone problem areas of the dialectics of cooperation and competition, considered in the aspect of the socio-philosophical approach. In this contextual field, the act of choice, which occurs on many levels of personality functioning and is determined by natural and socio-cultural conditions, is especially vividly manifested.
Non-Interactive Privacy-Preserving Frequent Itemset Mining Over Encrypted Cloud Data

Frequent itemset mining is a data mining technique widely used on massive datasets. In cloud computing, the dataset may be encrypted for privacy protection. Therefore, frequent itemset mining over encrypted data is a crucial application in secure cloud computing. In this paper, we propose an effective privacy-preserving framework where the cloud server can directly perform data mining on the encrypted database without interacting with other cloud servers. We first design three security primitives to implement subset determination, accumulation, and comparison in the encrypted domain for frequent itemset mining. Based on the proposed framework, we then propose two secure protocols that allow the cloud server to perform frequent itemset mining on encrypted cloud data with these security primitives. The first protocol leaks no information to the cloud and the second protocol has the advantage of more efficient mining performance. We then present two strategies with parallel algorithms and GPU computing to accelerate the running time. We also analyze the security of our protocols and the computational complexities. Experimental results show that our serial-based protocols achieve shorter running times and higher levels of privacy than previous solutions. Our multi-CPU (or GPU) based parallel protocol can further reduce the practical running time.
Towards More Reliable Confidence Estimation

As a task that aims to assess the trustworthiness of the model's prediction output during deployment, confidence estimation has received much research attention recently, due to its importance for the safe deployment of deep models. Previous works have outlined two important characteristics that a reliable confidence estimation model should possess, i.e., the ability to perform well under label imbalance and the ability to handle various out-of-distribution data inputs. In this work, we propose a meta-learning framework that can simultaneously improve upon both characteristics in a confidence estimation model. Specifically, we first construct virtual training and testing sets with some intentionally designed distribution differences between them. Our framework then uses the constructed sets to train the confidence estimation model through a virtual training and testing scheme leading it to learn knowledge that generalizes to diverse distributions. Besides, we also incorporate our framework with a modified meta optimization rule, which converges the confidence estimator to flat meta minima. We show the effectiveness of our framework through extensive experiments on various tasks including monocular depth estimation, image classification, and semantic segmentation.
Learned Lossless Image Compression With Combined Channel-Conditioning Models and Autoregressive Modules

Lossless image compression is an important research field in image compression. Recently, learning-based lossless image compression methods achieved impressive performance compared with traditional lossless methods, such as WebP, JPEG2000, and FLIF. The aim of the lossless image compression algorithms is to use shorter codelength to represent images. To encode an image with fewer bytes, eliminating the redundancies among the pixels in the image is highly important. Hence, in this paper, we explore the idea of combining an autoregressive model for the raw images based on the end-to-end lossless architecture proposed to enhance the performance. Furthermore, inspired by the successful achievements of Channel-conditioning models, we propose a Multivariant Mixture distribution Channel-conditioning model (MMCC) in our network architecture to boost performance. The experimental results show that our approach outperforms most classical lossless compression methods and existing learning-based lossless methods.