Reviewerly/exHarmony huggingface dataset:
Structural and functional model of formation of readiness of future specialists in computer technologies for professional activity by means of digital technologies

The article substantiates the structural and functional model of formation of the readiness of future specialists in computer technologies for professional activity by means of digital technologies. To achieve the goal of the article, the methodology is applied, which consists in conducting an analysis of the research of the problem of pedagogical modeling of the formation of the readiness of future specialists in computer technologies for professional activities in the field of digital technologies. On the basis of the conducted analysis, the concept of «structural-functional model», its structure and prospects of introduction into the educational process of future specialists in computer technologies are defined. It has been established that models in pedagogical science are successfully used for high-quality planning of the educational process, management of cognitive activity of students and optimization of the structure of educational material.&#x0D; In order to build a structural-functional model, its main constituent elements were determined, combined into logical blocks corresponding to the stages of the educational process (target, content, organizational, diagnostic-resultative) and complex application of modern digital technologies at all stages. The effectiveness of the implementation of the model was ensured by predefined organizational and pedagogical conditions.
VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graph

The availability of vast amounts of visual data with heterogeneous features is a key factor for developing, testing, and benchmarking of new computer vision (CV) algorithms and architectures. Most visual datasets are created and curated for specific tasks or with limited image data distribution for very specific situations, and there is no unified approach to manage and access them across diverse sources, tasks, and taxonomies. This not only creates unnecessary overheads when building robust visual recognition systems, but also introduces biases into learning systems and limits the capabilities of data-centric AI. To address these problems, we propose the Vision Knowledge Graph (VisionKG), a novel resource that interlinks, organizes and manages visual datasets via knowledge graphs and Semantic Web technologies. It can serve as a unified framework facilitating simple access and querying of state-of-the-art visual datasets, regardless of their heterogeneous formats and taxonomies. One of the key differences between our approach and existing methods is that ours is knowledge-based rather than metadatabased. It enhances the enrichment of the semantics at both image and instance levels and offers various data retrieval and exploratory services via SPARQL. VisionKG currently contains 519 million RDF triples that describe approximately 40 million entities, and are accessible at https://vision.semkg.org and through APIs. With the integration of 30 datasets and four popular CV tasks, we demonstrate its usefulness across various scenarios when working with CV pipelines.
[Research on the Progress of Applying Virtual Reality Technology in Preventing Falls in the Elderly].

The primary cause of injury and death in the elderly has been reflected in fall the elderly, so the application of reasonable and effective prevention strategies has great significance in reducing the risk of fall in the elderly. The research progress of virtual reality technology applied in preventing fall in the elderly at home and abroad over the years was systematically reviewed in this study. The mechanism of the technology in preventing fall in the elderly was mainly elaborated from five aspects of improving balance ability, gait disturbance, cognitive impairment, muscle strength and the fear psychology of falling. The purpose of this thesis is to broaden the research ideas of medical personnel on the prevention of fall of the elderly, provide more effective clinical practice plans, reduce the occurrence of fall, and guarantee the safety of the elderly.
Differentiating Content as a Way of Enhancing Students’ Self-Efficacy: A Study of Private Schools in Ruiru Sub-county

Research confirms that differentiated instruction has both academic and non-academic benefits for students. Individualized learning enhanced academic performance, and most importantly, a sense of self-efficacy are all associated with this mode of instruction. Teachers in Kenya, as in the rest of the world, hold the opinion that differentiation reflects the best professional comprehension of skilled and adaptable instruction. This study aimed to examine the effects of differentiated instruction on the self-efficacy of elementary school students. The investigation utilized the Theory of Multiple Intelligences and Self-Efficacy and a post-positivist methodology. In nine private primary schools in Ruiru Sub-County, Kiambu County, Kenya, 45 teachers and 1,022 pupils in upper primary levels (grades 6, 7, and 8) participated in the study. Using questionnaires, quantitative data was collected and analyzed using SPSS Student Version 14.0. The investigated aspect of differentiated instruction is content differentiation. It was discovered that differentiated content has a statistically significant impact on the self-efficacy of elementary school pupils. The establishment of comprehensive teacher professional development systems in private primary schools to equip teachers with the ability to effectively deliver differentiated instruction and build students' self-efficacy is one of the study's key recommendations. Among the areas for additional research is the conduct of a comparable study in public elementary schools. Additionally, longitudinal, and experimental studies can be conducted to examine the relationship between differentiated instruction and student self-efficacy in both private and public primary institutions. Additional recommendations include conducting research on other factors that contribute to the self-efficacy of elementary school students.
Regulating Crypto and Cyberware in the EU

As the EU is finalizing measures for the regulation of cryptocurrencies and the digital resilience of financial institutions, this chapter describes the pros and cons of the new approach for regulating crypto, and assesses the framework for ICT oversight. Both regulations, Markets in Crypto-Assets (MICA) and Digital Operational Resilience Act (DORA), introduce a host of new provisions, and fundamentally change the regulatory framework for digital operations in finance in the EU. Crypto providers will be subject to a new set of rules in MICA, outside the existing rules for investment providers that are governed by the Markets in Financial Instruments Directive (MiFID). But the digital governance of all financial firms will be more strictly regulated under DORA, including the third-party ICT providers.
On The Symbolic 6-Plithogenic and 7-Plithogenic Rings

Symbolic n-plithogenic algebraic structures are considered as a direct application of fuzzy generalized systems in pure algebra, where the symbolic n-plithogenic set is used to generalize algebraic structures by adding logical generators. In this paper, we study the concept of symbolic 6-plithogenic rings and 7-plithogenic rings from an algebraic point of view, where the main substructures formed by them will be presented such as AH-ideals, AHS-isomorphisms, and AH-kernels. Also, many theorems that explain their algebraic behaviors and classifications will be proved and illustrated.
Real-time Monitoring of Activity Recognition in Smart Homes: An Intelligent IoT Framework

The rapid proliferation of the Internet of Things (IoT) has paved the way for transformative innovations, and this paper explores its profound impact on the realm of elderly care within smart homes. We present a pioneering IoT-based approach for human activity recognition, addressing the critical need for accurate and non-intrusive monitoring of elderly individuals. Our IoT-based approach begins with data preprocessing, where raw sensor data is refined using median filtering, reducing noise and ensuring high-quality inputs for our model. We apply the series_to_supervised transformation to convert the sensor data into a supervised learning format, which is critical for training the GRU-based activity recognition model. The heart of our approach lies in the federated distillation-based training strategy. Edge devices within the IoT network locally train their GRU models using their datasets while sharing knowledge with a central server and other edge devices. Knowledge distillation further enhances the model's performance by transferring knowledge from the global model to the edge devices. Experimental analysis demonstrated an impressive accuracy of 95% and an F1-score of 0.94, Our system excels in recognizing and classifying a wide range of human activities, from daily routines to emergencies.
Research on System of US Army Data Link

This paper summarizes the basic concept and development process of the U.S. army data link, analyzes the main data link systems such as the U.S. army situation awareness data link, weapon coordination data link and intelligence reconnaissance data link, studies the special data links such as satellite data link and UAV data link, and finally introduces the typical integrated applications of the U.S. army data link.
Secret data sharing through coverless video steganography based on bit plane segmentation

Recently information hiding is acquiring significance with the rising multimedia content over the web. Coverless steganography has been perceived as quite possibly the most solid way to deal with moving classified information and has turned into a research problem area. Considering video as a transporter, coverless steganography is expanding prominently because of its greater number of features than other transporters like text and images. Usually, the existing scheme hides confidential information based on single-frame features in the video. A secret data sharing through coverless video steganography technique based on bit-plane segmentation has been proposed. After extracting frames from the video, each frame is split into multiple bit-planes using BPCS(Bit-plane complexity segmentation). The hash sequences are obtained from these bit-planes by calculating the mean values of corresponding bit-plane sub-blocks. A retrieval database is constructed, including the relation between obtained hash sequences and bit-plane features. To begin with, the sender altered the secret data into a piece stream and divided it into equivalent lengths. The related retrieval information matching the fragmented secret information is transmitted to the recipient over the insecure channel. The experimental results show that the proposed technique achieves better robustness to various attacks, larger capacity, less time cost for extracting hash sequences, and a higher concealing success rate than existing coverless video steganography techniques.
Heuristics: The Tools of Intuition

To develop a scientific perspective on intuition, we first need to dispense with the old and misleading dualistic opposition of intuition and reason. Rather, intuition and reason go hand in hand: where a doctor feels that something is wrong with a patient, intuition comes first, followed by a deliberate search for what is wrong. Even abstract disciplines like mathematics, need both intuition and reasoning. As George Pólya emphasized, finding a problem or discovering a proof requires intuition and heuristics; checking whether the proof is correct requires logic and analysis. The theoretical framework to understand the nature of intuition is that of ecological rationality, the study of how mental processes are adapted to their environments, based on Herbert Simon’s notion of bounded rationality, i.e., how people make decisions under uncertainty – where the best action cannot be calculated. Good intuitions rely on adaptive heuristics that are not logically, but ecologically, rational. The fluency heuristic, the recognition heuristic, and satisficing exemplify tools of the adaptive toolbox. Under uncertainty, they can lead to better decisions than complex algorithms.
Alignment-free genome analysis and comparison

This chapter shows how to perform analysis and comparison of genomes without assuming a reference genome to be available. The bidirectional BWT index turns out to be essential here, and the chapter covers a comprehensive set of techniques to manipulate this data structure. The algorithms covered include computing maximal exact/unique matches, substring kernels, matching statistics, and Jaccard similarity.
Hidden Markov models

Analysing the content of a biological sequence can often be modeled as a segmentation problem. For example, one may wish to segment a genome in coding and non-coding regions, where only the former are translated to proteins. Statistical features of what genes usually look like can be used to derive an optimization framework. This process can be formalized through hidden Markov models, and the underlying segmentation problem can be solved using dynamic programming. This chapter introduces the key methods related to such optimization.
Classical indexes

A full-text index for a string T is a data structure that is built once and that is kept in memory for answering an arbitrarily large number of queries on the position and frequency of substrings of T. Such queries can be used for speeding-up dynamic programming algorithms tailored for mapping reads to a reference genome – a fundamental task in the analysis of high-throughput sequencing data. This chapter covers the classical full-text indexes and the like, including k-mer indexes, suffix arrays, and suffix trees. Linear-time algorithms for suffix sorting and for basic genome analysis tasks, such as finding maximal exact matches, are also presented.
Burrows–Wheeler indexes

Classical index structures like suffix trees are powerful, but they occupy much more space than the data they are built on. Many space-efficient alternatives exist that occupy space close to the input data. This chapter covers such data structures based on the Burrows–Wheeler transform (BWT). A special emphasis is given to the bidirectional BWT index, which can be used for solving basic genome analysis tasks by simulating suffix tree exploration without any sacrifice in run time. Space-efficient representations of de Bruijn graphs are also covered.
Compression of genome collections

A pragmatic problem arising in the analysis of biological sequences is that collections of genomes, and especially collections of read sets consisting of material from many species, occupy too much space. This chapter explores techniques to efficiently compress such collections. Several algorithms related to Lempel–Ziv factorization are covered, as well as the prefix-free parsing technique to run-length encode the Burrows–Wheeler transform of a collection of genomes.
Grammatical consequences of the Digital on the Learning of English at Senegalese High School: The Case of WhatsApp

The present work refers to the analysis of grammatical consequences of digital technology on the learning of English language in the Senegalese high schools. In other words, this article deals with the grammatical consequences of WhatsApp in the learning of English in a context dominated by the implementation, by the State of Senegal, of the "Digital Senegal" strategy and the concretization of certain programs in the building of an “inclusive digital society”, in order to promote a learning society. WhatsApp is a cross-platform instant messaging application that allows users to exchange text, image, video, and audio messages for free through phones. Thus, the work aims to analyze the grammatical consequences of this cross-platform application in the study of English as a foreign language. However, to collect data in order to have a corpus to treat and analyze, we have done some research, organized interviews with teachers and pupils at high school. We have also exchanged messages through WhatsApp with other pupils. This approach has helped us to point out that this method of communication has affected the learning of English at high school.
Path Planning Algorithm for Long-Range Autonomous Underwater Vehicles Based on Environmental Features

To solve the time optimal path planning problem of long-range autonomous underwater vehicle (AUV) in the spatially non-uniform ocean current environment, a path planning algorithm based on environmental features is studied. Firstly, a method for analyzing the distribution of ocean current velocity is proposed to pre analyze the ocean current environment. According to the analysis results, an environmental features based particle swarm optimization algorithm (EFPSO) is proposed for path planning. This algorithm consists of two layers. The first layer is a reference path search algorithm based on environmental features, which is used to identify and quickly locate the region where the global optimal path is located based on environmental features. The second layer is a path search algorithm for key region, which is used to further optimize the path in the region where the reference path is located. Simulation experiments show that EFPSO can avoid falling into local optima and stably obtain low-cost paths while increasing a small amount of computation time.
Topology Inference for Low-Resource Non-Cooperative Cluster Networks Based on Deep Learning

Swarm target networks play an important role in modern battlefields. Encrypted countermeasure technology makes it difficult to collect enemy information. Resource data acquisition is limited in non-cooperative situations that cannot participate in cluster target communication. The existing topology inference work scenarios are very different, and there is a lack of cluster network communication data sets. Therefore, this paper uses the network simulation software NS-3 to construct a more general communication network data set, realize the analysis and comparison of the same scene based on the model-driven algorithm. Then picture the communication data, and using the convolutional neural network, in the case of limited training data, the data-driven low-resource target on-off reasoning is realized. Experimental results on this dataset show that data-driven methods are more accurate for Ad hoc networks, while star networks are more suitable for model-driven algorithms.
Development of innovative forms of education with the combination of distant forms in higher educational institutions

In the process of implementing educational reforms, it is necessary to introduce the latest form of education that would meet the demands and needs of innovative education and guarantee the effective implementation of fundamental educational programs. Distance learning has a significant potential to meet the high needs of modernity in obtaining digital literacy, spreading innovative, more flexible forms of learning.
Using Physical Models and Image Processing Techniques Efficient Dehazing Algorithm Prior

To improve this human production and living environment and Currently, there are various dehazing techniques available, but they are limited in their effectiveness. But these methods have certain limitations in the dehazing effect. In this paper, we provide an excellent image dehazing method, by introducing a weighting function to optimize and minimize the scattering coefficient, obtain a more accurate scattering map, and complete the dehazing of the image. The distortion in the image is caused by a combination of clear images and environmental factors, so a technology is developed that can remove the color veil of hazy images. Specific comparisons have shown that these studies have improved dehazing performance in several ways, making the recovered images more realistic.
Deep Learning-Based Human Activity Recognition Algorithms: A Comparative Study

Human activity recognition is essential for developing personalized healthcare, fitness tracking, and fall detection systems. This study focuses on developing an accurate deep learning-based algorithm for recognizing human activities using accelerometer and gyroscope data. The proposed model is based on a 1D-CNN-BiLSTM architecture and is trained on a public dataset. In this study, six different human actions-walking, walking upstairs, walking downstairs, sitting, standing, and laying-are identified to examine how well the proposed model performs compared to a baseline model and to assess how well the model recognizes these activities. According to the results, the suggested model performs better than the baseline model at identifying human activity and is more accurate overall. All six activities can be recognized by the suggested model with high accuracy, with walking, walking downstairs, and walking upwards having the best performance. The model's success is attributed to its ability to capture both spatial and temporal dependencies in the data, allowing it to effectively recognize human activities based on accelerometer and gyroscope data. The study's findings have significant implications for developing activity recognition systems, especially for real-time health monitoring, fall detection, and fitness tracking applications. The proposed model's high accuracy can improve the performance of such systems, leading to better outcomes for patients and individuals. The study's limitations are also discussed, including the relatively small size of the dataset and the lack of variability in the activities performed. This study provides evidence of the effectiveness of a deep learning-based algorithm for recognizing human activities using accelerometer and gyroscope data. It demonstrates the potential of such models for developing accurate and reliable activity recognition systems with important applications in healthcare and fitness tracking.