Reviewerly/exHarmony huggingface dataset:
A Novel Bayesian-Based Adaptive Algorithm Applied to Unobservable Sensor Measurement Information Loss for Underwater Navigation

In complex underwater environment, it is hard to acquire the accurate position estimation when the sensor measurement information is lost. Under this situation, the measurement loss is unobservable, and meanwhile the statistical information of system noise is unpredictable and time-varying, so existing algorithms have poor performances. To address the problem, the paper proposes a Bayesian-based adaptive Kalman filter with Gaussian-inverse-Wishart mixture distribution (BAKF-GIWM). By using BAKF-GIWM, the measurement loss is judged through the process of sensor measurement, then the system state vector and statistical information of system noise are together determined according to variational Bayesian method. Meanwhile, the estimation results of measurement loss status are regarded as evaluation criteria to improve efficiency of the BAKF-GIWM. The simulations and the real trials results illustrate that the proposed algorithm can efficiently improve the state estimation accuracy in complex underwater environment.
Evaluating the A* Algorithm Performance in Obstacle Avoidance Through Simulation

When trying to find a way from a starting point to a destination, an algorithm was needed to reach the destination while avoiding intermediate obstacles. Of course, it is also possible to determine the case where the destination cannot be reached during route finding.
METHODS OF IMPROVING THE QUALITY OF SPEECH-TO-TEXT CONVERSION IN BIOMETRIC AUTHENTICATION SYSTEMS

The article discusses the methods and algorithms of speech-to-text conversion, modern open and commercial systems for creating systems, as well as the use of these technologies in the field of cyber security. It is proposed to create a high-quality speech-to-text conversion system. An analysis of the mathematical algorithms used to reduce the error rate, which makes it possible to create unique voice prints and increase protection against forgery, has been carried out. The structure of modern speech-to-text conversion systems is described. By changing datasets, parameters of hidden Markov models, a high-quality dictionary of phonemes, and the use of language models, there is an opportunity to reduce the percentage of errors in language recognition, as well as the use of a system for multilingualism such as "surzhyk". The mathematical methods of assessing the quality of the system of speech to text (WER), as well as various methods of calculation, which is important for their further improvement and optimization, are considered. The structure of modern systems is considered, namely, signal pre-processing, feature extraction, acoustic modeling, speech modeling, decoding, post-processing. For each of the stages, study vectors have been proposed that can reduce the error rate of the system as a whole. Reducing speech recognition errors and the ability to fake a voice is achieved using various methods: deep neural networks, hidden Markov models, Baum-Welch algorithm, N-gram models, models with attention, creation of a high-quality phonemes dictionary, dataset, and fillers. Speech-to-text conversion technology can be used in biometric authentication systems to detect and analyze the unique features of the user's voice. However, modern speech-to-text conversion systems for Ukrainian, Russian, and "surzhyk" need improvement in acoustic and language units. Scientific works, which are devoted to research and optimization of these systems for biometric authentication, do not fully cover these issues. This became the reason for further research in this direction, so this work aims to create a speech recognition system with a minimum error rate.
Witnessing entanglement of remote particles with incomplete teleportation

Having common reference frames or aligned coordinate systems is one of the presumptions in witnessing entanglement in a two-party state possessed by two remote parties. This assumption may fail for many reasons. With an unlimited supply of singlet states, the two parties can first align their coordinate systems and then measure any entanglement witness. In this paper, we propose an alternative method which uses the same resource for incomplete teleportation of states between the two parties, enabling them to witness the entanglement of any shared state by local measurements without the need for prior alignment of the coordinate systems. The method works for any kind of witness and in any dimension. Beyond the context of entanglement witnesses, our method works also for remote measurements of observables of particles (entangled or not) in laboratories which may have deficiency in their resources.
DETERMINATION OF THE FACT OF TECHNICAL FORGERY OF A SIGNATURE IN THE ERA OF DIGITAL TECHNOLOGIES: PROBLEMS AND PERSPECTIVES

The task of establishing the authenticity of document details, primarily signatures and handwritten notes by their executor, becomes relevant in connection with the development of copying and reproduction equipment and printing devices associated with software and computer tools that allow editing original images or copying and change the content of records made on behalf of other persons and pass them off as genuine. Traditional methods of forging a technical signature are becoming increasingly obsolete, and there is a growing need for the development of new methods and technologies for protection against forgery. One such technology is the reproduction of signatures using reproductions, which are produced using a plotter that uses a ballpoint pen as a printing device. This technology makes it possible to reproduce signatures with high accuracy and quality and to convey small signs of any handwritten object, which, in turn, makes it extremely difficult for an expert to establish the fact and method of its technical imitation. In connection with the use of modern techniques for reproducing handwritten objects, publications on this topic began to appear in the scientific literature, but these works are clearly not enough. This fact arouses interest in a deeper study of the problems of using new technologies for the forgery of documents, including from experts who conduct forensic examinations in this field and need scientific development of methods of performing handwriting objects using new technical means. The result of the presented review was practical recommendations that allow us to discover a new technique of reproduction of signatures, namely, the technology of reproduction of a signature with the help of reproduction, which is carried out with the help of a plotter. The developed recommendations can be used by forensic experts in the field of technical forensic examination of documents and will prevent possible expert errors.
DETECTION OF TRACES OF THE USE OF SOFTWARE SUCH AS «STEALER» IN THE MEMORY OF THE STORAGE DEVICE

The purpose of this work is to use an experimentally way to determine the characteristic features of the use of the software «Mars Stealer», which is positioned as a software product of the «Stealer» type, which is contained in the memory among the available and deleted data of the information storage device. The following conclusions can be drawn on the basis of the conducted experiment: Autopsy» software is a good tool for trying to reproduce and trace the events and processes that took place on the storage device; By studying the events and processes in the memory of the information storage, it was possible to establish the sequence of actions of the expert to detect the executable file(s) of third-party software that may be malicious; In the case when the research of the detected executable build file is not carried out immediately after receiving the research objects, but after a certain time, for example, within 90 calendar days, there is a high probability of «losing» communication with the C2 server, which the build – file calls. But, in order to establish the IP address to which the file-initiator try to connect on the affected computer, it is recommended to repeatedly run the build-file (this may increase the chances of detecting the IP address of the C2 server) while simultaneously monitoring the Internet traffic, which will go through a virtual machine; The executing file-initiator on the affected PC by the «Mars Stealer» software, even if there is an error in its work, will be «forced» to save the information collected on the affected PC in the root directory of the location of the Build file itself; Based on the results of the work carried out, it is possible to establish the characteristic features of the use of the «Mars Stealer» software, which is positioned as a «stealer» type of software, which include logging the number of launches of the executing Build-file, and interaction with the folders of WEB browsers installed in the memory storage of information, as well as interaction with a large number of libraries. The obtained results of the experiment given in this paper can be used when conducting examinations in the expert speciality 10.9 «Research of computer equipment and software products», in the study of information storage devices, among the available and deleted data of which information about samples of executable files of the «Mars Stealer» software, which is confirmed by the evidence collected during the examination.
How Do Institutional Frameworks Shape Financial Development in Developing Countries?

While the economic effects of financial development have been widely studied,less attention has been paid to the sources of such development, particularly indeveloping countries. Existing studies that have identified institutions as a driverof financial development have not taken into account the different institutionalframeworks, such as democracy, political stability and social policies in Africa,where institutional and financial development is a challenge to trigger the economictake-off of countries. This paper aims to fill this gap by studying the effect of differentinstitutional frameworks on financial development in a sample of North andsub-Saharan African countries using a panel ARDL analysis. In contrast to previousliterature that supports a positive effect between institutions and finance, we findmore nuanced results. While less corruption, expropriation, political instability andautocracy favor the financial sector, a higher level of social policies can undermineits development. These results validate the “hierarchy of institutions”-hypothesisand reveal a “social-financial” dilemma in African countries.
Nic Cheeseman, Gabrielle Lynch, and Justin Willis. The Moral Economy of Elections in Africa: Democracy, Voting, and Virtue. Cambridge: Cambridge University Press, 2020. xv + 259 pp. List of Figures. List of Tables. List of Abbreviations and Glossary. Appendix. Bibliography. Index. $31.65 Paper. ISBN: 978-1108404723.

Nic Cheeseman, Gabrielle Lynch, and Justin Willis. The Moral Economy of Elections in Africa: Democracy, Voting, and Virtue. Cambridge: Cambridge University Press, 2020. xv + 259 pp. List of Figures. List of Tables. List of Abbreviations and Glossary. Appendix. Bibliography. Index. $31.65 Paper. ISBN: 978-1108404723.
An Evolutionary Review on Resource Scheduling Algorithms Used for Cloud Computing with IoT Network

Abstract: The goal of the distributed computing paradigm known as "cloud computing," which necessitates a large number of resources and demands, is to share the resources as services delivered over the internet. Task scheduling is a very significant stage in today's cloud computing. While lowering the makespan and cost, the task scheduling method must schedule the tasks to the virtual machines. Various academics have proposed many scheduling methods for organizing work in cloud computing environments. Scheduling has been considered the most important for cloud computing since it might directly impact a system's performance, including the efficiency of resource utilization and running costs. This paper has compared all the already used algorithms that work on different parameters. We have tried to give better solutions for resource allocation and resource scheduling. In this study, various swarm optimization, evolutionary, physical, evolving, and fusion meta-heuristic scheduling methods are categorized according to the environment of the scheduling problem, the main scheduling goal, the task-resource mapping pattern, and the scheduling constraint. More specifically, the fundamental concepts of cloud task scheduling are addressed without difficulty.
Multilevel Thresholding-based Medical Image Segmentation using Hybrid Particle Cuckoo Swarm Optimization

Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.
Crisis Detection in the Age of Digital Communication: The Power of Social Listening as a Method to Identify Corporate Events in Time Series Data

The increased usage of digital media to exchange information has increased the speed in which corporate crises become known. This has increased the necessity to react to a crisis as quickly as possible. As a result, social listening – i.e. listening to and analysing digital communication – is establishing itself as an instrument for companies to control their own representation in the media. Against this background, different methodological approaches in crisis detection (e.g. outlier detection, t-test and Chow test) were tested regarding their quality. For that, we used a data set created by an AI crawling online sources and analysing the results using a neural network. The findings of this study suggest that crises can be identified quite reliably using existing econometric methods. A simple outlier detection in a time series of the total number of fragments that uses a time frame of one month on each side of a crisis seems to be the best method so far with the method by Chen and Liu being a close second. The results of this study provide a foundational contribution to this field of research and can help companies detect crises as early as possible allowing the management to react appropriately.
Research on the Method of Hypergraph Construction of Information Systems Based on Set Pair Distance Measurement

As a kind of special graph of structured data, a hypergraph can intuitively describe not only the higher-order relation and complex connection mode between nodes but also the implicit relation between nodes. Aiming at the limitation of traditional distance measurement in high-dimensional data, a new method of hypergraph construction based on set pair theory is proposed in this paper. By means of dividing the relationship between data attributes, the set pair connection degree between samples is calculated, and the set pair distance between samples is obtained. Then, on the basis of set pair distance, the combination technique of k-nearest neighbor and ε radius is used to construct a hypergraph, and high-dimensional expression and hypergraph clustering are demonstrated experimentally. By performing experiments on different datasets on the Kaggle open-source dataset platform, the comparison of cluster purity, the Rand coefficient, and normalized mutual information are shown to demonstrate that this distance measurement method is more effective in high-dimensional expression and exhibits a more significant performance improvement in spectral clustering.
Nurturing Young Minds and Bodies: Exploring Critical Media Literacy Through Healthy Food Adventures

This study aimed to help young children understand media better and develop healthy eating habits through fun and engaging activities that encourage critical thinking about media content. These activities were designed for children aged 60 to 72 months and were part of an action research project. Eleven children participated in the study, and the results showed that initially, they had limited knowledge about healthy eating and how media works. However, after taking part in the media literacy activities, they became better at analyzing media content and realized that not everything they see in advertisements is accurate. As a result, their awareness of healthy eating improved significantly. This research highlights the potential benefits of teaching media literacy to young children, which can positively influence their overall development and enhance their critical thinking skills on real-world issues.
An Analysis of Handwriting Legibility of First Grade Students by School Starting Age

Legibility, which can be defined as the format of the text, is one of the key elements in teaching writing. School starting age can affect students’ learning and writing, and determining it can give an idea about what type of work to be undertaken to ensure the legibility of the handwriting in the early reading and writing processes. The possible effect of the school starting age on the legibility of handwriting was the basis of this study. As such, the study aimed to examine the straight basic writings of primary school first grade students in terms of legibility based on the school starting age. The study was designed as a survey in the quantitative research method. The study group consists of 513 primary school first grade students. The convenience sampling method was chosen to determine the study group. The students who participated in the study voluntarily consisted of primary school first-year students who passed into literacy in May of the 2022-2023 academic year. Data were obtained using the Multidimensional Legibility Scale. The Mann-Whitney U test was used in the analysis of the data. Results revealed that the handwriting legibility of the students was moderate regarding the dimensions of slant, spacing, size, and line awareness, but not legible in terms of ‘form’. While the results of this study show that the average handwriting legibility of girls is higher than that of boys, there is no statistically significant difference between the age of school entry and handwriting legibility.
Predictive Modeling of Jakarta's Social Cohesion: GBDT Leads Comparative Analysis

In this study, we address the challenge of predicting the Social Cohesion Index in Jakarta through a comprehensive analysis of machine learning models. Finding the most accurate and effective predictive model for this crucial urban evaluation task is the primary goal of our research. We use a variety of machine learning algorithms, comparing their performance using metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and computational cost. These algorithms include Gradient Boosted Decision Trees (GBDT), Polynomial Regression, Random Forest, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). It should be noted that GBDT stands out as a top performer, regularly displaying outstanding accuracy with a competitive MAE of 0.692, RMSE of 0.887, and MAPE of 25.59%. The computational efficiency of GBDT is also impressive, with predictions taking only 0.05 seconds. These results underscore the potential of GBDT as a practical and precise tool for real-time assessments of social cohesion in large urban environments like Jakarta. The findings offer a data-driven way to guide policy decisions and community development activities, with important implications for urban planning and governance. Overall, this research emphasizes the promise of GBDT in boosting social cohesion evaluation approaches and increases our understanding of the application of machine learning in addressing complex urban difficulties.&#x0D;
Applying Federated Learning on Decentralized Smart Farming: A Case Study

In the field of Smart Agriculture, accurate time series forecasting is essential for farmers to gather and evaluate relevant information about various aspects of their work, such as the management of harvests, livestock, crops, water and soil. One commonly used method for trend forecasting in time series is the Long Short Term Memory (LSTM) Recurrent Neural Network (RNN) model, due to its ability to retain context for longer periods and enhance performance in context-intensive tasks. To further improve the results, the use of Federated Learning (FL) can be implemented, allowing multiple data providers to simultaneously train on a shared model while preserving data privacy. In this study, a Centralised Federated Learning System (CFLS) is leveraged, that implements and evaluates the efficacy of FL in smart agriculture through the use of datasets produced by such infrastructures. The system receives data from multiple clients and creates an optimised global model through model federation. Consequently, the federated approach is compared with the conventional local training to explore the potential of FL in real-time forecasting for the Smart Farming sector.
CLMD:Detection and Prevention of Poisoning Attacks for Federated Learning in Maritime Communication Network

As maritime communication networks become more developed and widely used, an increasing number of end devices are being connected to these networks. Edge computing is an effective way to meet the realtime computing needs of these devices. However, with federated learning becoming more prevalent in edge computing decision-making, resource-constrained end devices are becoming more vulnerable to security threats. In particular, poisoning attacks are a significant security concern in the federated learning training process as local data is invisible to the outside world, enabling malicious participants to easily tamper with it. To address this issue, this paper proposes a Common Layer Mean Detection method for poisoning attacks that reduces their impact on the accuracy and resource consumption of the federated model while ensuring its security. The proposed method identifies poisoning attacks by comparing the mean distribution differences between attackers and honest clients in the common layer. Aggregation weights are then set based on the detection results to eliminate the impact of spurious parameters of malicious participants on the overall model. The effectiveness of this approach is demonstrated by comparing it with related schemes in terms of security, communication overhead, and computational overhead. Overall, the proposed method is shown to be both secure and efficient, making it a valuable addition to the field of federated learning for maritime communication networks.