Reviewerly/exHarmony huggingface dataset:
Hybrid Encryption using Symmetric Block and Stream Cipher

Today’s digital world is entirely managed on the internet digitally. So to confirm the Confidentiality, Integrity and Availability of users data in transit and at rest using a hybrid encryption in place of using a particular encryption algorithm. In this paper we are going to propose the hybrid approach which is a combination of multiple symmetric block ciphers and stream ciphers primarily AES-GCM, Chacha20 Poly-1305, Multi Fernet and Fernet to assure more security in an acceptable time period.
Deep Neural Networks in Video Human Action Recognition: A review

&lt;p&gt;Currently, spatial-temporal behavior recognition is one of the most foundational tasks of computer vision. The 2D neural networks of deep learning are built for recognizing pixel-level information such as images with RGB, RGB-D, or optical flow formats, with the current increasingly wide usage of surveillance video and more tasks related to human action recognition. There are increasing tasks requiring temporal information for frames dependency analysis. The researchers have widely studied video-based recognition rather than image-based(pixel-based) only to extract more informative elements from geometry tasks. Our current related research addresses multiple novels proposed research works and compares their advantages and disadvantages between the derived deep learning frameworks rather than machine learning frameworks. The comparison happened between existing frameworks and datasets, which are video format data only. Due to the specific properties of human actions and the increasingly wide usage of deep neural networks, we collected all research works within the last three years between 2020 to 2022. In our article, the performance of deep neural networks surpassed most of the techniques in the feature learning and extraction tasks, especially video action recognition.&lt;/p&gt;
Federated Learning for Maritime Environments:Use Cases, Experimental Results, and Open Issues

&lt;p&gt;Maritime transportation is vital for economic growth, since it is responsible for the vast majority of global trade. However, optimizing maritime transportation, focusing on certain performance metrics may lead to non-convex problems due to the large number and heterogeneity of network nodes and vessels. Furthermore, the harsh propagation environment, and the long propagation distances might be prohibitive for the implementation of conventional optimization. Machine learning (ML) represents a viable way towards complexity minimization but still, it might not be feasible to fully exploit its potential, since error-free feedback channels are usually assumed while the overall centralized processing delay from numerous distributed sources might render real-time deployment infeasible, due to stringent latency requirements. Meanwhile, security and privacy concerns constitute key driving factors for decentralized ML solutions, since data locality is vital to protect sensitive information. Taking into consideration all the above, this paper discusses feasibility issues, regarding the deployment of federated learning (FL) solutions in maritime environments, via the presentation and analysis of various use cases. Moreover, experimental results using datasets from an enterprise specializing in the maritime industry are provided, showing the superiority of FL over traditional ML approaches, in terms of accuracy and complexity. Finally, open issues that must be addressed to pave the way for the wide adoption of FL in maritime applications are discussed.&lt;/p&gt;
A Detailed Evaluation of SQL Injection Attacks, Detection and Prevention Techniques

An SQL Injection attack is a database focused attack for programmes that utilise data. It is accomplished by inserting malicious lines of code into the SQL queryto alter and modify its meaning, allowing the attacker to gain access to the database or retrieve sensitive data. Many strategies for detecting and preventing such assaults have been developed and suggested. This study provides an in depth examination of 38 publications on approaches for detecting SQL Injection in web applications. This offers a foundation for designing and using efficient SQL Injection, detection and prevention techniques.
Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network

This paper proposes an innovative approach to improve the performance of 3SAT logic programming in the Hopfield neural network. The merged structures of the 3SAT and Hopfield network have specific weaknesses, one of which is that, at times, the system attained local minimum solutions rather than global minimum solutions. A new model of integration randomised alpha-cut fuzzy logic with 3SAT in the Hopfield network is built to convey information more effectively. 3SAT and fuzzy logic can work together to solve Hopfield networks' combinatorial optimisation issues. Procedures of fuzzifying and defuzzifying the neurons might ease the computational burden of determining the correct neuron states. Until the proper neuron state is established, unsatisfied neuron clauses will be modified using a randomised alpha-cut approach in the defuzzifier step. An incorporated design built a random approach to select the alpha-cut values of 0.1, 0.25, and 0.5. At this point, a fuzzy value switches into a crisp output back through the defuzzifier process. Based on the results obtained, the proposed hybrid strategy effectively improves the indicators of RMSE, SSE, MAE, MAPE, global minima and total computational time. A computer-generated data set was used to measure how well the hybridised techniques performed. The performance of the proposed network was trained and validated using Matlab 2020b. The results are significant because this model significantly affects how successfully Hopfield networks merged with fuzzy logic can tackle the 3SAT challenges. The obtained data and ideas will help to create novel approaches to data collection for upcoming logic programming exploration.
Parameter Estimation via Indefinite Causal Structures

Abstract Quantum Fisher information is the principal tool used to give the ultimate precision bound on the estimation of parameters for quantum channels. In this work, we present analytical expressions for the quantum Fisher information with three noisy channels for the case where the channels are in superposition of causal orders. We found that the quantum Fisher information increases as the number of causal orders increases for certain combinations. We also show that certain combinations of causal orders attain higher precision on bounds than others for the same number of causal orders. Based on our results, we chose the best combinations of causal orders with three channels for probing schemes using indefinite causal structures.
An effective knowledge graph entity alignment model based on multiple information

Entity alignment refers to matching entities with the same realistic meaning in different knowledge graphs. The structure of a knowledge graph provides the global signal for entity alignment. But in the real world, a knowledge graph provides insufficient structural information in general. Moreover, the problem of knowledge graph heterogeneity is common. The semantic and string information can alleviate the problems caused by the sparse and heterogeneous nature of knowledge graphs, yet both of them have not been fully utilized by most existing work. Therefore, we propose an entity alignment model based on multiple information (EAMI), which employs structural, semantic and string information. EAMI learns the structural representation of a knowledge graph by using multi-layer graph convolutional networks. To acquire more accurate entity vector representation, we incorporate the attribute semantic representation into the structural representation. In addition, to further improve entity alignment, we study the entity name string information. There is no training required to calculate the similarity of entity names. Our model is tested on publicly available cross-lingual datasets and cross-resource datasets, and the experimental results demonstrate the effectiveness of our model.
A PLATFORM TO PROMOTE A MORE ACTIVE LIFESTYLE BETWEEN STUDENTS

Due to the current lifestyle, people in general and adolescents in particular are not physically active enough which put at risk their health. Immediate actions are needed to promote a more active lifestyle. This paper introduces a platform that aims to encourage students to have a more active life. The platform suggests for each user to perform specific physical activities/exercises based on the physical activities that the user had during the day, the duration of the sleep and his/her profile (preferences, performance history) as well as some health parameters (heart rate, blood pressure, weight). All these tasks are realized through a game with an avatar that is controlled by the user by physical exercises (e.g. the avatar is placed in an environment and he/she must collect different objects by jumping, running). The exercises performed by the user are acquired using an RGB camera. Images are processed and the skeleton of the user is obtained using the OpenPose library. To increase motivation, the platform gives different rewards (e.g. more points) for executing physical tasks and suggests some collective physical activities based on the profile of each user. The platform is multilingual, it supports Romanian and English language, with the possibility to extend it to other languages. The platform integrates a multimodal interface that enables the users to interact through voice and touch-based commands as well as touch-free gestures. During the execution of a physical exercise, the platform tracks the performance of the user. The platform sends visual and phonetic notifications to the users' phones to suggest some the execution of some physical activities/exercises.
USAGE OF ARTIFICIAL INTELLIGENCE IN QUALITY IMPROVEMENT OF EDUCATIONAL PROGRAMS.

In the field of education, we are faced with a wide range of absorbability degrees of educational materials in various educational programs and courses. We assume that part of this problem is the style of delivery of information in the associated training materials. The described software product is designed to assess the complexity of the material based on the analysis of various criteria received from the subject during the study of the material. Taking into account the research in psychology, we can conclude that such criteria as the direction of the gaze and the length of time of attention on certain elements of the exposition (text, images, interface elements), by the subject (student), tells us about the importance of these expositions and their elements, as well as the characteristics specifically for the subject. In particular, such matters as interest in new information, the necessity of additional information related to the main theme of the exhibition, complexity of understanding or absorption of complex data (schemes, graphs, interfaces) can be considered. To study such parameters as the direction and length of the view, a firmware and software complex is used, which is based on the following technologies: digital video processing, computer vision for processing video information in the required formats, neural networks for identifying areas where the subject's face and eyes are located, and a software subsystem for performing the necessary calculations. Binding the exposition contexts and data on the current parameters of the subject's sight, this firmware and software complex is able to add special metrics to the exposition and its elements. For example, in addition to the exposition consisting of text and graphs, we also add metrics of interest, importance of this information, complexity of understanding. For general exposure research average values of metrics unique for each subject are used. This allows us to evaluate not the subjective attitude to the exposition, but the parameters of the quality of the exposition itself (digestibility of the material, complexity, thematic comprehensiveness). In the field of education, based on the obtained metrics, training courses, teaching methods and testing methods can be analyzed. Having this analysis, we can be more objective about the quality of the material and training programs, as well as to find and designate their most problematic areas for further improvement processing.
Does non-linguistic segmentation still predict literacy in an L2 education? Statistical learning in Ivorian primary schools

Statistical learning (SL) is a learning mechanism that does not directly depend on knowledge of a language, but predicts language and literacy outcomes for children and adults. Research linking SL and literacy has not addressed children who first learn to read in their second language (L2), common in primary schools worldwide. Several studies have linked SL with childhood literacy in Australia, China, Europe, and the U.S., and we pre-registered an adaptation for Côte d’Ivoire, where students are educated in French and speak a local language at home. Recruiting 117 sixth- graders from primary schools in several villages, we tested for correlations &amp;gt;0.3 between SL and literacy with 80-90% power. We found no evidence for these correlations, but visual SL was correlated with L2 phonological awareness. Although this finding may suggest a role of SL in emergent L2 skills, it underscores the need to include L2 acquisition contexts in literacy research.
Morphotectonic Analysis of Wadi Al-Batin Alluvial Fan, South of Iraq, Using Remote Sensing and GIS Techniques

This research aimed to know the tectonic activity of the Wadi Al-Batin alluvial fan using hydrological and morphotectonic analyses. Wadi Al-Batin alluvial fan is deposited from Wadi Al-Rimah in Saudi Arabia, which extended to Iraqi and Kuwait international boundaries. The longitudinal and transverse faults that characterize this region were common. The Abu- Jir-Euphrates faults have a significant impact on the region. The faults zone consists of several NW- SE trending faults running from the Rutba in western Iraq to the south along the Euphrates through Kuwait and meeting the Al-Batin fault to the Jal Al-Zor fault. The Hydromorphometric analysis of the present fan shows five watersheds having asymmetry shapes, more elongated and activity from the elongation ratio and asymmetry factor values. In contrast, transverse topographic symmetry and mountain front sinuosity factor have moderate to low activity from the stream length gradient index. The morphotectonic analysis suggests that the Wadi Al-Batin alluvial fan has a moderate tectonic activity due to the tectonic activity of the subsurface lineaments and the oil field activities found in the region.
Education of pedagogical university students on media competence under the conditions of the transformation of the educational process

Introduction. The article deals with the problems of information literacy education, the systematic management of media education and the media literacy of future teachers. The purpose of the study is to identify the specifics of developing media literacy of pedagogical university students under the conditions of transformation of the educational process. Materials and Methods. The study was carried out in the logic of an educational experiment. The development of favorable conditions contributing to the formation of media literacy was based on the analysis of successful educational practices. The empirical data were analyzed and summarized. The sample consisted of 256 undergraduate students studying Education. Criteria and corresponding level indicators of media competence formation were developed in order to check the validity of research findings. In the development of criteria for the formation of media literacy of future teachers, adequate and proven methods were used for validity and reliability. The development of favorable conditions to facilitate the development of media literacy was based on the analysis of successful pedagogical practices. The obtained data were interpreted and analyzed. In order to calculate the reliability of statistical data and the accuracy of the results, the formulas of Ryulon, Spearman-Brown and Kronbach were used. Results. Theoretical analysis of scientific literature made it possible to clarify the concept of media literacy, to determine the main educational mechanisms and conditions for the formation of media literacy. The proposed definition of media literacy made it possible to designate it as one of the professional competencies of a future teacher. The formative experiment involved the revision of the teaching content and the creation of effective and relevant educational conditions for the formation of students’ media literacy. It is noted that the indicators of media literacy formation are observed within the framework of interdisciplinary educational events in extracurricular activities. Student associations and activities allowed students to put media literacy skills into practice. Within the framework of student associations, students' perception of the importance of the ability to critically perceive information, the need for verification, the significance of the ability to work with various sources of information has changed. Conclusions. The author concludes that the conditions implemented within the framework of the educational process ensure the successful formation of media literacy in pedagogical university students.
Development of Tringo Based Multiliteracy Learning Model Design as an Effort to Improve Student Writing Skills

This research wass motivated by the problems faced in learning to write because of the low literacy of students.These problems also stem from the complexity of the writing process, an ineffective learning process, and the challenges of the times that require students to have various literacy skills.Writing is important because it is able to maintain the understanding that has been obtained.Writing is an activity that is considered difficult by students because it requires practice with a long process.Therefore, the purpose of this research is to produce an appropriate and effective learning model design in order to improve students' writing skills.The subjects of this study were students of the Senior High Scohol Nasional Malang.The method used in this research is research and development using modified Borg and Gall research procedures.The approach in this development adopts the Tringo concept from Ki Hadjar Dewantara.With the idea put forward by this researcher, it is hoped that the research subject can experience an increase in writing skills.
Use of Federated Learning and Blockchain towards Securing Financial Services

&lt;p&gt;This paper presents a survey of FL and blockchain for financial security. &lt;/p&gt;
Application of Traditional Machine Learning Models for Quantitative Trading of Bitcoin

Bitcoin, the most popular cryptocurrency around the world, has had frequent and dramatic price changes in recent years. The price of bitcoin reached a new peak, nearly $65,000 in July 2021. Then, in the second half of 2022, the bitcoin price begins to decrease gradually and drops below $20,000. Such huge changes in the bitcoin price attract millions of people to invest and earn profits. This research focuses on the predictions of bitcoin price changes and provides a reference for trading bitcoin for investors. In this research, we consider a method in which we first apply several traditional machine learning regression models to predict the Changes of Moving Average in the bitcoin price, and then based on the predicted results, we set labels for bitcoin price changes to get the classification results. This research shows that the method of transforming regression results to the classification analysis can achieve higher accuracy than the corresponding machine learning classification models and the best accuracy is 0.81. Besides, according to this method, this research constructs a Machine Learning Trading Strategy to compare with the traditional Double Moving Average Strategy. In a simulation experiment, the Machine Learning Trading Strategy also has a better performance and earns a 68.73% annualized return.
Artificial Intelligence and the Sustainable Development Goals: GPT-3`s Reflections on the Society Domain

Artificial Intelligence (AI) experienced significant advancements in recent years, and its impact is already recognized across various industries. Yet, the rise of AI has led to a growing concern about its impact on meeting the Sustainable Development Goals (SDGs). The aim of this paper was to evaluate contributions and the potential impact of AI to sustainable development in the society domain. Furthermore, the study analyzed GPT-3 responses, as one of the largest language models developed by OpenAI, descriptively. We conducted a set of queries on the SDGs to gather infor-mation on GPT-3`s perceptions of AI impact on sustainable development. Analysis of GPT-3&amp;rsquo;s contribution potential towards the SDGs showcased its broad range of capabilities for contributing to the SDGs in areas such as education, health, and communication. The study findings provide valuable insights into the contributions of AI to sustainable development in the society domain and highlight the importance of proper regulations to promote the responsible use of AI for sustainable development. We plan subsequent research for effects on ecologic and economic domains of SDGs. We highlighted the potential for improvement in neural language processing skills of GPT-3 by avoiding imitating weak human writing styles with more mistakes in longer texts.
Dimension Reduction and Redundancy Removal through Successive Schmidt Decompositions

Quantum computers are believed to have the ability to process huge data sizes, which can be seen in machine learning applications. In these applications, the data, in general, are classical. Therefore, to process them on a quantum computer, there is a need for efficient methods that can be used to map classical data on quantum states in a concise manner. On the other hand, to verify the results of quantum computers and study quantum algorithms, we need to be able to approximate quantum operations into forms that are easier to simulate on classical computers with some errors. Motivated by these needs, in this paper, we study the approximation of matrices and vectors by using their tensor products obtained through successive Schmidt decompositions. We show that data with distributions such as uniform, Poisson, exponential, or similar to these distributions can be approximated by using only a few terms, which can be easily mapped onto quantum circuits. The examples include random data with different distributions, the Gram matrices of iris flower, handwritten digits, 20newsgroup, and labeled faces in the wild. Similarly, some quantum operations, such as quantum Fourier transform and variational quantum circuits with a small depth, may also be approximated with a few terms that are easier to simulate on classical computers. Furthermore, we show how the method can be used to simplify quantum Hamiltonians: In particular, we show the application to randomly generated transverse field Ising model Hamiltonians. The reduced Hamiltonians can be mapped into quantum circuits easily and, therefore, can be simulated more efficiently.