Reviewerly/exHarmony huggingface dataset:
The analysis of grammatical structures in written texts of ELF learners

This paper aims to analyze the grammatical structures used in the written texts of two English as a foreign language (EFL) learners. One of the EFL learners was from stage 2 and the other was from stage four. Therefore, both learners had different periods in learning English, and also grammatical knowledge of English. The learner in stage 2 has studied English for three terms and the learner in stage 4 has learnt English for two terms. The study was conducted using mixed-method approach. The results of the study showed that writing competence highly relied on the learners’ grammatical structure knowledge in order to express a clear message in writing. The learner of stage 4 was more capable of expressing his message in his writing because of his better ability to use grammatical knowledge in his sentences. The study concludes that the better English grammatical knowledge the learners have, the better expressions can be written by them.
BIOMETRIC AUTHENTICATION UTILIZING CONVOLUTIONAL NEURAL NETWORKS

Relevance. Cryptographic algorithms and protocols are important tools in modern cybersecurity. They are used in various applications, from simple software for encrypting computer information to complex information and telecommunications systems that implement various electronic trust services. Developing complete biometric cryptographic systems will allow using personal biometric data as a unique secret parameter instead of needing to remember cryptographic keys or using additional authentication devices. The object of research the process of generating cryptographic keys from biometric images of a person's face with the implementation of fuzzy extractors. The subject of the research is the means and methods of building a neural network using modern technologies. The purpose of this paper to study new methods for generating cryptographic keys from biometric images using convolutional neural networks and histogram of oriented gradients. Research results. The proposed technology allows for the implementation of a new cryptographic mechanism - a technology for generating reliable cryptographic passwords from biometric images for further use as attributes for access to secure systems, as well as a source of keys for existing cryptographic algorithms.
Testing of Fiber-Optic Lines for Quantum Key Distribution

The paper describes a technique for measuring an optical noise in a fiber-optic channel using quantum key distribution equipment. The equipment under investigation is placed on different sides of the communication channel and consisted of client and server. The client and server can distribute the quantum key using phase-time encoding protocol. They are controlled by computer via ethernet switch. A special software is developed to synchronize the client with server and measure the optic noise in cannel. The described technique allows, on the one hand, to determine the optical noise in units of a single-photon-detector counts, on the other hand, it allows to understand the applicability of this equipment on this optical communication channel.
Blockchain-Driven Cloud Service: A Survey

Cloud services run centrally and store data before making it available to customers through software and use large servers. When employing these centralized-based methods, organizations often have to make compromises in the areas of authorization, privacy, and security. Due to the fact that once data is recorded, it cannot be amended without also affecting the data in all preceding blocks, blockchains may restrict the modification of any data in any of the blocks. The key benefit of integrating blockchain into cloud services is increased data security. When cloud services and blockchain are merged, it is like having the best of both worlds in one convenient package. Cloud services powered by blockchain will be covered in this survey, along with their uses, difficulties, and prospects for the future.
The Review of Secret Image Sharing

Secret image sharing (SIS) is a significant research topic of image information hiding, which divides the image into multiple shares and distributes them to multiple parties for management and preservation. In order to reconstruct the original image, a subset with predetermined number of shares is needed. And just because it is not necessary to use all of the shares to make a reconstruction, SIS creates a high <i></i> which breaks the limitations of traditional image protection methods, but at the same time, it causes a reduce of safety. Recently, new technologies, such as deep learning and blockchain, have been applied into SIS to improve its <i></i> and <i></i>. This paper gives an overall review of SIS, discusses four important approaches for SIS, and makes a comparison analysis among them from the perspectives of pixel expansion, tamper resistance, etc. At the end, this paper indicates the possible research directions of SIS in the future.
Knowledge and Experience Guided Heterogeneous Multi-UAV Mission Planning Method

Heterogeneous multi-UAV mission planning, as the action guidelines of the team, can effectively improve the operational efficiency and mission revenue of the UAV team. Aiming at the mission planning problem of heterogeneous multi-UAV systems, a heuristic planning algorithm based on knowledge and experience guidance is proposed. Guiding the search direction of the algorithm through prior knowledge and optimizing experience, the optimization effect of the algorithm in complex decision space and large-scale mission planning problems can be improved. Finally, detailed simulation experiments are carried out to verify the effectiveness of the proposed algorithm.
Recent Advances in Artificial Autonomous Decision Systems and Their Applications

This article presents the methodological background and an overview of recent applications of artificial autonomous decision systems (AADS), endowed with intelligent coordination algorithms. The research results comprise modelling the decision-making processes in autonomous anticipatory systems, specifically the decisions made in teams composed of robots and humans. These can be modelled as timed anticipatory networks. We present selected real-life applications of anticipatory decision support methods, such as industrial safety management systems and coordination of ground unmanned autonomous robot teams. New algorithms have been proposed to solve multicriteria combinatorial optimization problems that occur when selecting safety strategies, such as AADS evacuation from endangered areas. Decision analytics, based on reference sets, is proposed to solve optimal supervisory control problems related to coordinated autonomous robot deployment. Finally, we discuss the scenarios of future research on AADS and their applications in developing decision algorithms for teams of autonomous harvesting robots or robotic inspection of large industrial plants.
Prolog for Scientific Explanation

Scientific explanations play an important role in most academic disciplines because they give us a fundamental understanding of why things happen or do not happen. In this paper we argue that Prolog has certain properties that make the language ideal for generating scientific explanations. We illustrate these properties in relation to a scientific theory.
Displacement driven simulation and material calibration based on digital image correlation Part I - Theory

Abstract Standard material characterization methods for subsequent finite element analysis rely on the simulation utilizing ideally discretized specimen that represent its geometrical characteristics as in the real experiment. Since many material models hold a set of parameters which is dependent on the discretization, e.g. the mesh size, especially when failure prediction for crash analysis is relevant, various mesh sizes are utilized during this calibration process. Optical measurement systems accompanying the experimental tests have become a standardized procedure, at least for a 2-dimensional (or specimen top-surface) measurements. Investigated calibration methods relying on the results of these optical measurements either include the derived strain field into the characterization process (full-field calibration), determine the crack path and iteratively derive plasticity curves (stepwise-modelling approach) or establish virtual fields based on the principal of virtual work allowing to solve for the scalar unknowns of the underlying chosen constitutive equation (virtual fields method). In the presented work, we directly use displacements measured using a GOM/ARAMIS system, mapped as boundary conditions for the nodal displacements of differently discretized specimen to establish a material characterization scheme being as close to the experiment as possible.
Online Child Predator Detection Using ML

Professionals in the field need a comprehensive understanding of the risks and practices associated with online sex grooming to safeguard young individuals from online sex offenders. While the Internet offers numerous positive aspects, one of the most detrimental issues is its potential for facilitating online sexual exploitation. Originally designed as a communication tool, the Internet inadvertently provides access to promiscuous content for countless children, often in a covert manner. The objective of our task is to identify and flag potential predators through analysis of comments and online media accounts, with the intention of reporting such instances to the appropriate cyber cell administrator. Recent public surveys indicate that approximately one in five young people actively search for sexual content online each year (Finkelhor, Mitchell, &amp; Wolak, 2000; Mitchell, Finkelhor, &amp; Wolak, 2001). This task report outlines our progress in developing a framework to address this issue. Through the implementation of this framework, accounts associated with predatory behaviour are identified, and reports are promptly submitted to the administrator for further action.
End-to-End Multi-dialect Malayalam Speech Recognition Using Deep-CNN, LSTM-RNN, and Machine Learning Approaches

Research in Malayalam speech recognition is constrained by the scarcity of speech data. Accent variation poses the greatest challenge for automatic speech recognition (ASR) for any language. Malayalam, spoken by the people in the southernmost state of India, has a wide range of accents that reflect regional, cultural, and religious differences. Malayalam is a low-resource language; there are not many works proposed in the ASR of the language which makes this work more significant and challenging at the same time. The majority of the experiments done in the ASR for Malayalam use the traditional HMM methods. No benchmark dataset for accented data is available for doing research. The authors have constructed accent-based data for doing this experiment. The proposed methodology comprises three distinct stages: dataset preparation, feature engineering, and classification using machine learning and deep learning approaches. A hybrid approach is adopted for the feature engineering process. Different feature extraction techniques are considered for extracting features from the inputted accent-based speech signals for the best representation of the data. Mel frequency cepstral coefficient (MFCC), short-term Fourier transformation (STFT), and mel spectrogram techniques are adopted for the feature engineering process. The features are then used to build machine learning models using multi-layer perceptron, decision tree, support vector machine, random forest, k-nearest neighbor, and the stochastic gradient descent classifiers. In the deep learning approach, the feature set is first fed to LSTM-RNN architecture to construct the accented ASR system. The next approach is to plot the spectrograms of the speech signals and hence represent the speech data as images. The features are then extracted from these spectrograms and fed into deep convolutional network architecture to build a deep learning model. Finally, a hybrid ASR system has been constructed from all the independent models. The result of each experiment is compared against each other to find the better approach for modeling the end-to-end accented ASR (AASR).
New Voices: Your Words, Your Voice

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The Rising Tide of Financial Crime: A Ponzi Scheme Case Analysis

Ponzi scheme is one of the most common types of fraud perpetrated by con artists. The reason for this is that the mode is relatively easy to conceal. In this regard, criminal law plays a significant role in countering fraudulent practices. It serves as an initial warning to individuals who intend to engage in such behavior that they may face harsh penalties if they do so. The study aims to compare and analyze the Ponzi and pyramid schemes in the framework of criminal law enforcement. It also thoroughly investigates the criminal rules and their application to specific cases associated with Ponzi and pyramid schemes in Indonesia. This normative or doctrinal legal research employs statutory, conceptual, and case approach. The study reveals that there is some confusion in the community as well as the legal enforcement officers on the practice of Ponzi and pyramid schemes. As a result of this discrepancy, law enforcement officers may be incorrect in ensnaring an allegation against the offenders, or they may even consider that offenders are innocent. It is also exacerbated by the lack of a single rule that particularly governs the issue of Ponzi schemes, which may result in a legal vacuum. This circumstance might therefore be used by irresponsible persons to create such business companies that use Ponzi schemes and conceal their actions with crypto investment or robot trading software, as recently occurred in Indonesia.
Protecting controversial speakers plays key role in protecting free speech

Colleges and universities commonly host controversial speakers on campus, typically invited by a student organization. Student protests of controversial speakers are almost just as common. Such protests often involve students shouting down, interrupting, or engaging in disruptions that are aimed at canceling the event. These disruptions are commonly referred to as a “heckler's veto.”
Knowledge Management and Sustainable Competitive Advantage in the Kingdom of Bahrain

Knowledge management (KM) has become one of the most exciting topics in today's fast-paced and changing business world. More and more business organizations see KM as a sustainable competitive advantage (SCA) source. This paper looks at how business organizations in the Kingdom of Bahrain use KM and SCA together. The paper adopts a cross-sectional survey design. The KM initiatives are categorized into knowledge creation, knowledge storage, knowledge transfer, and knowledge application. Employees' attitudes towards workplace knowledge resources are derived from their perceptions of their importance, usefulness, and ease of use. Organizations sustain competitiveness by improving product or service quality, performing efficiently, or innovating. Data is collected via an electronic questionnaire developed using Google Forms. Purposive sampling used a list of 122 subjects from randomly selected business organizations. Ninety responses were received and taken into consideration for data analysis. Spearman correlation analysis and Partial Least Square Structural Equation Modeling (PLS-SEM) revealed a positive association between KM adoption and SCA. The study reflected a positive association between employees' attitudes toward knowledge resources and sustaining organizations' competitive advantages. The study recommends the effective launch and adoption of KM to achieve SCA. Also, the study suggests organizations and academics explore the factors that improve employees' attitudes towards knowledge resources at their workplace to realize the potential gains on firms' SCA.
Big Data Analytics and Mental Health: Would Ethics Be the Only Safeguard Against the Risks of Identifying “Potential Patients”?

Despite all the prospects for data growth, sharing, and processing, and all the benefits that big data can bring, this revolution is not exempt from risks. Even if, at some point, computers may be able to provide diagnoses with greater accuracy than medical professionals, would ethics be the only safeguard against the possible risks? In this article, we implement a pragmatic approach to answer this question by focusing on possible outcomes concerning “potential patients.” We define a “potential patient” as an individual who has not yet shown obvious or early signs of disease. Through the outcomes and inferences derived from big data analysis, such a patient could potentially require early treatment, more accurate diagnoses, and medications that are better adapted to the patient’s conditions.
Texture features analysis technique to detect mass lesion in digitized mammogram images

Mass lesions are one of the breast cancer tumors. Mammogram images are the first screening tool to detect tumors in the women breast, but due to radiologist fatigue, number of false positive (FP) and false negative (FN) rates are increased. The main objective of this paper is to develop an intelligent computer aided diagnosis (CAD) system that can accurately detect mass lesions in digitized mammogram images. The proposed method has three stages. The first stage is a preprocessing stage, where the mass lesion is enhanced using a customized Laplacian filter. Then, multi-statistical filters are implemented to detect a potential mass lesion in the mammogram images. In the final stage, the number detected FP regions are reduced using five texture features. The proposed algorithm is evaluated using 45 mammogram images and the algorithm achieved an accuracy rate of 97% in detecting mass lesion with 83% sensitivity rate and 98% specificity rate.
ORGANIZATION OF A STUDENT'S OWN WORK ON A PHYSICS COURSE IN A TECHNICAL UNIVERSITY

The article outlines ways to improve the organization of independent work of university students. Innovative education is the use of the latest information technologies in the entire didactic complex of information support for the subject. The innovative model of teaching physics in higher education includes methodologically structured information presented in the form of one of the types of the infosphere - a system of interrelated and interdependent textbooks and other means of educational activity (modular programs, electronic lectures, various methodological aids). In the new conditions of increasing the volume and strengthening the role of students' independent work in the educational process, students receive and master only methodological information at the appropriate level. To master the material on a regular basis, a student needs to work for months, and only a qualified teacher who knows the psychological characteristics of the learning process and owns the teaching methodology will help save the student's time by transforming the professional knowledge of teachers into educational knowledge. The paper shows the types and stages of performing classroom, extracurricular IWS in physics, as well as ways to improve the efficiency and quality of students' educational work.
The main stages of theatrical directorial activity of Nicolai Merzlikin

The purpose of the article is to study the main stages of the theatrical directorial activity of Nicolai Merzlikin. The methodology is based on historical, systemic, and informational approaches, methods of analysis and synthesis. Scientific novelty. The material analyzed in the article gives a general idea of the main stages of theatrical directorial activity of an outstanding Ukrainian theater director Nicolai Merzlikin. The article also deals with the circumstances of the formation of Merzlikin as a theater director, shows the influence of the school of L.Kurbas, the school of M. Verhatsky, as well as the experience of staging other creative spectacular forms. Conclusions. The article provides a general systematic review of the main periods of theatrical directorial activity of the outstanding Ukrainian theater director, actor and educator Nicolai Merzlikin and highlights the following important stages.1958-1965 – studied at the Kyiv Institute of Theater Arts, gained experience of staging the first plays.1964-1967 – worked as the director of the Zhdanovsky (Mariupol) Russian Drama Theater and practiced in stanging non-theatrical art shows. 1967-1970 – worked as a director of the Kyiv Youth Theater.1970-1977 – worked in the cinema and in the Theater-Studio of a film actor.1977-1985 – worked as the main director of the Kyiv Theater of a young spectator and the main director of the Kyiv Youth Theater. 1985 – 2006 - worked as the main director of the State Musical Theater for Children and Youth in Kyiv. Each stage of N. Merzlikin’s activity was accompanied by another search, a transition to a qualitatively new level of creative directorial work due to the accumulation of experience and the discovery of new faces of the directorial profession.
Applications of AI and NLP to advance Music Recommendations on Voice Assistants

This paper builds on the popular use case of music requests on voice assistants like Siri, Google Assistant, Alexa, and others and explores the different AI and NLP techniques. The paper particularly focuses on how each of these techniques can be applied in the context of musical recommendations and experiences on voice assistants. It enumerates specific problems in the space of music recommendations and illustrates how specific techniques like multi-armed bandits can be applied.
A Spanning-tree-based Method for Accelerating Graph Classification

Graph neural network (GNN) shows strong processing ability on graph model data. Compared with node-level tasks, the GNN-based graph pooling model for graph-level tasks pays more attention to the overall features of the graph and is often used for protein function prediction, friend network classification, and other practical problems. Recently, many researchers have introduced graph structure information into the graph pooling model to improve the model's accuracy. However, there is still a problem with the long training period, and the influence of structure information on node features is not considered in graph convolution. Therefore, this paper proposes a graph-level feature learning acceleration method based on a spanning tree. This method can construct a spanning tree containing the overall structure information on the undirected graph according to the greedy strategy, called Graph-units, and use the graph attention network to learn the node features containing the graph structure information in the graph convolution process, then input the node features into the graph pooling process to learn the graph level features. Finally, the model can achieve the highest accuracy with fewer iterations. This method is applied to multiple graph pooling models on multiple graph classification task datasets, and an experimental comparison is made to verify the effectiveness of the proposed method.