Reviewerly/exHarmony huggingface dataset:
Research on Speech Communication Enhancement of English Web-based Learning Platform based on Human-computer Intelligent Interaction

This study presents a novel web-based learning platform that leverages human-computer intelligent interaction to enhance English communication skills. The platform integrates cutting-edge technologies to create an immersive learning experience, combining natural language processing, speech recognition, and interactive exercises. Learners engage in real-time conversations with virtual tutors, receive personalized feedback, and access a vast repository of educational resources. The platform not only facilitates language acquisition but also encourages self-paced learning, making it a valuable tool for both educators and students. By harnessing the power of artificial intelligence, this web-based platform represents a significant advancement in the realm of English language education. To overcome these issues this paper proposed SVM with an improved satin Bower bird optimization algorithm (SVM-ISBBO). SVM-ISBBO uses fog computing services that minimize the latency and speeds up the process, effectively handling huge wearable devices. In this proposed work SVM-ISBBO monitors the students communication, vocal parameters, blood pressure, etc, and these values are obtained from wearable sensor devices and their notifications are sent back to teachers. Teachers diagnosed the student information and sent back the alert notifications to the students for taking proper medications. All this information is stored in fog-based cloud storage in a secure manner. The accuracy rate of KNN got 78.56%, NB got 81.74%, SVM got 85.15% and the proposed work of SVM-ISBBO got 92.34%.
Artificial Intelligence in “Ivory Tower”

This article is devoted to the problem of adapting the higher education system to the new realities of introducing artificial intelligence into everyday life. The author proposes new constructs in communication between teachers and students, based on the use of modern research and development tools (neural networks, big data, artificial intelligence, etc.), which make it possible to improve the mastery of the material with minimal losses due to obsolescence and a decrease in the relevance of the proposed studying courses. Thus it maximizes the competitive advantages of university graduates who acquire competencies that are in high demand in the modern world.
Text Summarization for Online and Blended Learning

Online learning text summarization is vital for managing the constant influx of online information. It involves condensing lengthy online content into concise summaries while retaining the original meaning and information. While several online summarization tools are available, they often fall short in preserving the underlying semantics of the text. In this paper, we introduce an innovative approach to online text summarization that strongly emphasizes capturing and preserving the semantics of the text. Our automatic summarizer leverages distributional semantic models to extract and incorporate semantics, producing high-quality online summaries. To evaluate the effectiveness of our online summarization system, we conducted experiments on a diverse range of online content. We employed ROUGE metrics, a popular evaluation method for text summarization, to assess our system's performance. Additionally, we compared our results with those of four state-of-the-art online summarizers. The outcome of our study demonstrates that our online summarization approach, which integrates semantics as a fundamental feature, outperforms other reference summarizers. This conclusion underscores the significance of leveraging semantics in the context of online learning text summarization. Furthermore, our system's ability to reduce redundancies in online content makes it a valuable tool for managing information overload in the digital age.
Patriotic Agenda in the Information Flows of Russian Universities: Key Areas of Efficiency Improvement

The article presents the results of an empirical study of the patriotic agenda in the information flows of the largest Russian universities. The relevance of the study is determined by the set of global socio-political challenges facing the state at the current stage of development. Based on modern methods and tools of social media analytics, the authors determine the basic characteristics of thematic information flows in social media and make a comprehensive assessment of their effectiveness in interacting with target groups of students. Based on the results of the study the authors differentiate contexts and discourses regarding the level of engagement and personal involvement of young people in patriotic activities, as well as achievement practical results in such work. The main directions for improving the information work of higher educational institutions as subjects of formation and promotion of patriotic information flows in the new media should be focused on systemic activities that simultaneously consider the current external contexts and the needs of student youth.
Efficient ground state preparation in variational quantum eigensolver with symmetry-breaking layers

A variational quantum eigensolver (VQE) solves the ground state problem of a given Hamiltonian by finding the parameters of a quantum circuit Ansatz that minimizes the Hamiltonian expectation value. Among possible quantum circuit Ansätze, the Hamiltonian variational Ansatz (HVA) is widely studied for quantum many-body problems as the Ansatz with sufficiently large depth is theoretically guaranteed to express the ground state. However, since the HVA shares the same symmetry with the Hamiltonian, it is not necessarily good at finding symmetry-broken ground states that prevail in nature. In this paper, we systematically explore the limitations of the HVA for solving symmetry-broken systems and propose an alternative quantum circuit Ansatz with symmetry-breaking layers. With extensive numerical simulations, we show that the proposed Ansatz finds the ground state in depth significantly shorter than the bare HVA when the target Hamiltonian has symmetry-broken ground states.
Network Traffic Monitoring and Real-time Risk Warning based on Static Baseline Algorithm

With the rapid growth of network traffic, in order to monitor network traffic, the author proposes a baseline based traffic inspection method. The main objective is to develop a global system for identifying malicious traffic, rather than a precise method for detecting the types of worms produced by malicious traffic. Although traffic is caused by the causes, network administrators can use this international search technique to detect malicious traffic data. The system based approach mainly includes designing time based on the traditional traffic model, detecting various equipments and network traffic process, and configuring the traffic flow according to each time frame. This method uses Cisco's NetFlow Collector, a NetFlow Collector (NFC), to collect raw NetFlow data transmitted by the device through UDP every 5 minutes. the Then, three-dimensional data such as communication port, communication time, and traffic flow (bytes or packets) is used to filter, remove the different values, calculate the base values, and compare the real-time results with the base values to check the traffic defects in the current network. If there are differences between the monitoring data and the system configuration at the same time, the system will issue an abnormal warning, and as time accumulates, the alarm level will gradually escalate.
New Model of Inclusive Higher Education Practices in Megacities’ Universities

This study analyzes the problem of higher professional education for people with disabilities in multinational megacities from different countries. The aim of this paper is to investigate the emerging changes and perspectives of educational inclusion in the universities of Moscow, Jakarta, Barcelona, and Cairo and to develop a new model of inclusive educational practice for universities based on an inclusive ideology of understanding the position of people with disabilities in the education system and society overall. To perform this study and verify the new model proposed by the authors, participants were selected from ten universities that indicated parameters such as accessibility, participation, quality, students’ education results, and government funding. The survey aimed to collect participants’ perspectives and experiences from diverse elements of the higher education community in the aforementioned locations. 751 participants were selected for the survey with a balanced gender distribution: 48.6% men and 51.4% women. Age distribution was rather diverse: the age group of 18–24 years was 25,7%, the age group of 25–34 years was 27,3%, the age group of 35–44 years was 25,6%, and the age group of 45 years 21,4%. Another distribution was people without disabilities (N=250) and with disabilities (N=551). The reliability of the research was achieved using several methods, including ANOVA. The results of this research show similarities and differences in implementing inclusive education practices across selected locations, providing a detailed picture of the current state and future perspectives of higher inclusive education. The scientific novelty of this research lies in the theoretical-methodological rationale of the new model of inclusive higher education practices in universities of major megacities and in forming the propositions on this model’s implementation in higher education institutions. Doi: 10.28991/ESJ-2024-SIED1-03 Full Text: PDF
Maximizing Penetration Testing Success with Effective Reconnaissance Techniques Using ChatGPT

Background/Objective: The study investigates the integration of ChatGPT, a generative pretrained transformer language model into the reconnaissance phase of penetration testing. The research aims to enhance the efficiency and depth of information gathering during critical security assessments offering potential improvements to traditional approaches.&#x0D; Research Problem: The research study addresses the challenge of optimizing the reconnaissance phase in penetration testing. It seeks to provide a solution by exploring the capabilities of ChatGPT in extracting valuable data, such as various aspects of the digital footprint or infrastructure of a system or an organization. The scope of the research relies in demonstrating how ChatGPT can contribute to the planning phase of penetration testing, guiding the selection of tactics, tools, and techniques for identifying and mitigating potential risks that could be used to assist with securing Internet accessible assets of a system or an organization.&#x0D; Methodology: The research adopts a case study methodology to assess the effectiveness of ChatGPT in reconnaissance. Tailored questions are formulated to extract specific information relevant to penetration testing. The study highlights the importance of prompt engineering emphasizing the need for carefully constructed questions to ensure usable results.&#x0D; Results: The research showcases the ability of ChatGPT to provide diverse and insightful reconnaissance information. The extracted data includes IP address ranges, domain names, vendor technologies, SSL/TLS ciphers, and network protocols. The information gathering improves efficiency of the reconnaissance phase aiding penetration testers in planning subsequent phases of the assessment.&#x0D; Discussion: The research study extends to the broader field of cybersecurity where artificial intelligence language models can play a valuable role in enhancing the success of reconnaissance in penetration testing. The research suggests that integrating ChatGPT into penetration testing can bring about positive changes in the efficiency and depth of information obtained during reconnaissance.&#x0D; Conclusion: The results of the study determine that incorporating ChatGPT in the reconnaissance phase significantly benefits penetration testers by offering valuable insights and streamlining subsequent assessment planning. The results affirm ChatGPT as a pivotal tool in maximizing success in penetration testing, contributing to ongoing advancements in cybersecurity practices.
Investigating delay of the media access control protocols for IoT‐RoF using quantum entanglement

Abstract One of the significant challenges of the Radio over fibre (RoF) Medium Access Control (MAC) protocol is the propagation delay. This delay can lead to serious issues, such as higher propagation delay resulting in collisions and unnecessary retransmissions. Quantum entanglement is an excellent candidate to overcome the propagation delay of the RoF MAC protocol. A new quantum MAC protocol is proposed, named the Quantum Entanglement‐based MAC protocol (QE‐MAC), in which Quantum Teleportation is utilised to address the propagation delay. Four entanglement states are employed to represent the control packets of the classical MAC protocol, and data is transmitted over the classical channel. Instead of using control packets such as acknowledgement, request to send, and Clear to send, state transitions are employed. This approach avoids the delay and collision issues associated with control packets, resulting in a significant improvement in network performance. The delay, duty cycle (DC), and power consumption of the proposed QE‐MAC protocol are formulated and derived. The protocol is evaluated in terms of delay, DC, and power consumption, demonstrating superior performance compared to the classical RoF MAC protocol. In comparison to published works, our proposed approach has successfully reduced both delay and power consumption by 35%.
Determining mammalian cells state by fractal micromotion

We applied four fractal dimension estimation algorithms on the temporal electrical impedance signal of normal MDCK type II cell cultures monitored by ECIS technique and showed that the fractal dimension due to micromotion allows discriminating processes not sensed by the spectral impedance of the culture. In this work we subjected cell cultures to electric current damage and drug exposure to analyze the changes in the fractal structure of the temporal signal. Among the changes presented and detected are the differentiation between a healthy monolayer and one exposed to a drug, as well as the distinction between a seeding process and a wound-healing process performed by electric current. The four algorithms used were validated by applying them on topological functions of known fractal dimension, a study that determined the necessary conditions for a correct estimation.
MAPPING RADIOACTIVITY IN CONTINENTAL TERMINAL 3 (CT3) FORMATIONS: THE CASE OF THE THREE SISTERS, NIAMEY REGION (EASTERN EDGE OF THE WEST AFRICAN CRATON, ILLUMMEDEN BASIN)

The Continental Terminal 3 (Ct3) formations, which rest in major unconformity on the Birimian basement of the West African Craton, correspond to the last filling deposits of the Iullemmeden basin in the Niamey region (West, Niger). The general objective of this study is to produce a surface radiometric map of the Niamey Three Sisters Hills area. The methodology used included field and laboratory work, with radiometric data processed using Surfer software. Radioactivity values in the Three Sisters area ranged from 5 to 25 shocks per second (C/s). High radioactivity values (15 to 25 c/s) are recorded in the upper terms, corresponding to the breastplates. Low values of radioactivity (5 to 15 c/s) are measured in the basal layers, corresponding to clays and alluvium. This radioactivity is thought to be due to trace amounts of uranium in these outcrops.
Shallow unitary decompositions of quantum Fredkin and Toffoli gates for connectivity-aware equivalent circuit averaging

The controlled-swap and controlled-controlled-not gates are at the heart of the original proposal of reversible classical computation by Fredkin and Toffoli. Their widespread use in quantum computation, both in the implementation of classical logic subroutines of quantum algorithms and in quantum schemes with no direct classical counterparts, has made it imperative early on to pursue their efficient decomposition in terms of the lower-level gate sets native to different physical platforms. Here, we add to this body of literature by providing several logically equivalent circuits for the Toffoli and Fredkin gates under all-to-all and linear qubit connectivity, the latter with two different routings for control and target qubits. Besides achieving the lowest cnot counts in the literature for all these configurations, we also demonstrate the remarkable effectiveness of the obtained decompositions at mitigating coherent errors on near-term quantum computers via equivalent circuit averaging. We first quantify the performance of the method in silico with a coherent-noise model before validating it experimentally on a superconducting quantum processor. In addition, we consider the case where the three qubits on which the Toffoli or Fredkin gates act nontrivially are not adjacent, proposing a novel scheme to reorder them that saves one cnot for every swap. This scheme also finds use in the shallow implementation of long-range cnots. Our results highlight the importance of considering different entangling gate structures and connectivity constraints when designing efficient quantum circuits.
Quo vadis higher education? Post-pandemic success digital competencies of the higher educators – a Hungarian university case and actions

Abstract The purpose of this paper is to show the necessary competence sets for Higher Education (HE) lecturers in the framework of the COVID and post-COVID. A COVID-situated competence survey was carried out among university lecturers. Exploratory and confirmatory factor analyses (EFA, CFA) were applied to show the significant digital education competence factors. The representative online survey of 322 respondents shows that three big competence sets can be identified: Awareness, Professional, and Digital. Those having higher scores in Professional competencies foresee more digital programs and communications in the future, and not only the digital competencies but also the professional ones should be developed to meet the requirements of the digital education transformation process. The findings emphasize that the forced and drastic changes in the application of digital education to the intensification of COVID-19 should become sustainable and find its proper place and role in the future HE. The structured and closely managed use of the results was followed by a set of digital and professional competence development initiatives carried out within the framework of the Digital Education and Learning Support Centre, founded in 2020 at the University of Pécs.
UNVEILING THE LIVED EXPERIENCES OF 5-6S MONEY LENDERS: A PHENOMENOLOGICAL INQUIRY

The purpose of this study was to uncover and understand the experiences of 5-6 money lenders in Kapalong, Davao del Norte. In-depth interviews were done with 10 purposively chosen money lenders. The gathered information was analyzed thematically. The experiences of the participants encompass the struggle of collecting debt amortization and lack of working capital. Hence, they stressed the need for financial agreements, additional investment, efficiency &amp; amiability, and aid through training, capital, and tax incentives. The results imply that government agencies may craft a program to improve the lending experiences of the money lenders and enhance the lending industrys overall quality. KEYWORDS: Business management, micro-money lenders, plight, qualitative-phenomenological inquiry, thematic analysis, Philippines
Orman yangınları çalışmalarında uygulanan makine öğrenmesi (ML) tekniklerine genel bir bakış

With the increasing frequency of forest fires globally, causing substantial environmental and economic damages, there is an imperative need for early fire prediction and detection. This study aims to examine the utility of machine learning techniques in predicting and identifying forest fires. A comprehensive review was conducted on various technologies and techniques proposed for forest fire prediction. Particular emphasis was placed on understanding the pros and cons of each machine learning algorithm, with an aim to identify the most effective approaches. It was observed that while numerous machine learning methods exist for forecasting forest fires, each possesses unique strengths and limitations. Some techniques, when tailored to specific forest characteristics, displayed enhanced predictive capabilities. Machine learning (ML) plays a pivotal role in advancing the field of forest fire studies. Identifying and utilizing the most suited ML technique, based on forest characteristics and the nature of data, can significantly augment prediction accuracy.
Iterative Ensemble Learning over High Dimensional Data for Sentiment Analysis

For sentiment analysis in particular, the problem of processing and analyzing high-dimensional data becomes more prominent in recent past. This is where the IEL-HDDSA model, which aims to increase accuracy and performance in complex, high-dimensional data streams sentiment analysis comes into play. Iterative approach in ensemble learning; a contribution to the field. It integrates preprocessing techniques such as tokenization, stop word removal, lemmatization and the collection of sentiment-related features. Then the training corpus is divided by label, and features with high mutual information are selected. Highly replicated points of data for model training can also be identified at this point. First a Naive Bayes model is trained, then later it's placed in an ensemble as part of bagging. Its major advantage over earlier methods is that IEL-HDDSA can iteratively train on selected subsets of data until the performance in sentiment analysis for high-dimensional objects reaches an optimum level. A 10-fold cross validation method was used to rigorously evaluate the performance of this model, which showed consistently high levels of operation with almost no variation across different measures. IEL-HDDSA's precision ranged from 0.9359 to 0.9492, and its specificity was between 0. Its accuracy differed from 0.93 to around 0.95, and its F1-measure fluctuated between the values of about 0.94 and above; so here too balance was well maintained in a manner that satisfied both precision and recall requirements equally. The false alarming rate fell from 0.056 to 0.1, a fairly low ratio of incorrect positive classifications; Moreover, MCC quantities ranged from 0.8668 to 0. These results testify to the IEL-HDDSA model's stable effectiveness and high reproducibility in sentiment analysis applications, especially for massive data flows.