Reviewerly/exHarmony huggingface dataset:
Donbas conductivity anomaly in the Karpinsky Swell

Donbas Foldbelt (DF) and Karpinsky Swell (KS) are contiguous parts of a system of elongated sedimentary basins forming lineament from Poland through Pripyat Trough in Belarus, the Dnieper-Donets Basin (DDB) and DF in Ukraine, KS in Russia, across the Caspian Sea, through Mangyshlak in Turanian plate in Asia. In DF, the Mesozoic-Cenozoic sediments were raised, and subsequent erosion exposed the Carboniferous coal-bearing strata. In contrast to DF, the Paleozoic rocks in KS are covered by 1—3 km thick sediments of Mesozoic-Cenozoic age; productive structures of the earlier age cannot be confidently studied in KS by geological methods. So, geophysical methods are the promising approach for the KS deep structure studies. This work is devoted to electromagnetic (EM) studies of the electrical conductivity of rocks by the methods of magnetic variation profiling (MVP) and magnetotelluric sounding (MTS). Previously [Rokityansky, Tereshyn, 2022], we described the results of the EM research on DF in detail. MVP reveals the intense Donbas electrical Conductivity Anomaly (DCA) running along the main anticline of the folded Donbas. DCA parameters: maximum possible depth of the anomalous currents center h=18±2 km. Frequency response maximum Т0≈3600 s yields the total longitudinal conductance G=(8±2)∙108 S∙m. 70 MTS at periods 0.1—3000 s yield two conductive stripes, with the upper edge varying from 0.3 to 5 km. The stripes are parallel to the DCA axis and are considered as part of DCA. A very large value of G suggests that the anomalous body extends to some considerable depth. The DCA axis spatially coincides with an intense (up to 90 mW/m2) deep heat flow anomaly. Thus, the nature of the DCA lower part can be a partial melting. Theoretical estimates show that intense anomalous fields of geomagnetic variations arise over highly elongated conductors. Therefore, there is reason to expect that the anomaly continues eastward. We found two MTS profiles crossing the Karpinsky swell, and under both profiles strong conductivity anomalies are clearly seen. We re-interpreted original data and presented parameters of all 3 anomalies in an identical style. The main conclusion: the anomaly parameters on the three profiles are approximately the same, and one can assume with a high probability the existence of a single anomaly of electrical conductivity in the Donbas and Karpinsky Swell with a common length of more than 500 km and longitudinal conductance G≈8×108 S×m.
Phase-shifting interferometry based on dynamic mode decomposition

A phase retrieval algorithm in phase-shifting interferometry is presented based on dynamic mode decomposition (DMD). The complex-valued spatial mode obtained from the DMD of phase-shifted interferograms allows the derivation of the phase estimate. At the same time, the oscillation frequency associated with the spatial mode provides the phase step estimate. The performance of the proposed method is compared to methods based on least squares and principle component analysis. The simulation and experimental results demonstrate the improvement in the phase estimation accuracy and noise robustness offered by the proposed method and thus substantiate its practical applicability.
Role of Quantum Computing for Healthcare

Quantum computing might accelerate diagnosis, personalize treatment, and optimize prices in healthcare. Quantum-enhanced machine learning is important. Quantum Computing and Healthcare are innovative partnerships. The healthcare sector advances with new technologies. Quantum computing was bound to revolutionize healthcare. With Quantum technology on the rise, a new age of computing is coming. Quantum technology and mechanics is an abstract technical subject, yet it might revolutionize healthcare and other sectors. Quantum computing is real. Quantum has great promise in healthcare. AI and other technologies are also significant in healthcare. Such technologies improve healthcare treatments, diagnoses, and assistance. Quantum Computing intends to change healthcare. Personalized healthcare hinges on genomes, physiology, and pharmacokinetics. Thus, more clinical data must be processed. Quantum Computing is the solution. This article explains quantum computing's influence on healthcare and its uses.
Survey on Encoding Binary Data within a Digital Image Using Deep Steganography and Multilayered Neural Network

Abstract: An image is the most popular media format amongst the current modern digital generation. Encoding binary data within an image is an easy way to hide the secret image. Broadly speaking, steganography is the practice of concealing a file, message, image, or video within another file, message, image, or video. Steganography helps us as the intended secret message does not attract attention to itself as an object of scrutiny. Plainly visible encrypted messages might be better protected but they arouse interest and may in themselves be incriminating in countries in which encryption is illegal. In other words, steganography is more discreet than cryptography when we want to send secret information while also being easier to extract. The usual implementations tend to significantly lose the image quality and are also easily detectable. However, this implementation makes efforts to overcome the existing problems of image steganography with the help of a deep neural network which results in the generation of a final image that is almost identical to the original image and isn’t detectable easily.
New Challenges in Exploring Solar Radiation: Influence, Consequences, Diagnostics, Prediction

The study of solar-influenced space weather and, consequently, extreme weather events, climate change, protection, and preservation are of particular interest in modern science because they are acknowledged as being important for sustainable development [...]
Research on Vehicle Re-Identification Algorithm Based on Fusion Attention Method

The specific task of vehicle re-identification is how to quickly and correctly match the same vehicle in different scenarios. In order to solve the problem of inter-class similarity and environmental interference in vehicle images in complex scenes, one fusion attention method is put forward based on the idea of obtaining the distinguishing features of details—the mechanism for the vehicle re-identification method. First, the vehicle image is preprocessed to restore the image’s attributes better. Then, the processed image is sent to ResNet50 to extract the features of the second and third layers, respectively. Then, the feature fusion is carried out through the two-layer attention mechanism for a network model. This model can better focus on local detail features, and global features are constructed and named SDLAU-Reid. In the training process, a data augmentation strategy of random erasure is adopted to improve the robustness. The experimental results show that the mAP and rank-k indicators of the model on VeRi-776 and the VehicleID are better than the results of the existing vehicle re-identification algorithms, which verifies the algorithm’s effectiveness.
Latent Dirichlet Allocation (LDA) Approximation Analysis of Financial-Related Text Messages

Topic modeling is one of the most widely used NLP techniques for discovering hidden patterns and latent relationships in text documents.Latent Dirichlet Allocation (LDA) is one of the most popular methods in this field.There are different approaches to tuning the parameters of LDA models such as Gibbs sampling, variational method, or expected propagation.This paper aims to compare individual LDA parameter tuning approaches with respect to their performance and accuracy on textual data from the financial domain.From our results, it can be concluded that for text datasets obtained from financial social platforms, stochastic solvers are the most suitable and especially less time consuming than approximation methods.
REFORMS IN EDUCATIONAL SECTOR IN INDIA- A COMPARATIVE APPROACH WITH OTHER COUNTRIES FOR STRATEGIC FORMULATION

The purpose of the research is to determine the impact and opportunities of educational reforms with special focus on comparative approach with other countries.This research is based on a thorough examination of a new set of educational reforms in India, which was conducted based on secondary data.The educational systems of several countries are compared to India's educational system in this research.It looks at which country in the world has the best educational system.Education is a fundamental human right that plays a crucial part in the growth of a country.India has a literacy rate of roughly 74 percent, making it the world's popular country.Although India's literacy rate is increasing, many states still have poor literacy rates due to various factors.This study throws lights on the strategies to implemented in the educational sector in India.
Teaching Narrative Texts in Young Learners’ Classroom During Pandemic Covid-19: An Analysis from A Multiliteracies Perspective

There is an urgency in education to situate literacy instruction in practices that are responsive to the globalized, multilingual world we live in. The New London Group (1996) has predicted the situation and conceptualized multiliteracies, which view literacy as supplemental, continual, and modifying established literacy teaching and learning rather than replacing traditional teaching and learning practices (Rowsell, Kosnik, Beck, 2008). With the pandemic Covid-19 outbreaks, all schools were forced to be online, encompassing the principles of more than just a traditional literacy pedagogy. Thus, this study aims to see types of teaching and learning resources and pedagogical acts conducted in the classroom seen from multiliteracies perspective during the online classroom. This qualitative study explored the teaching and learning resources and the pedagogical acts based on the multiliteracy pedagogy perspective. The data was collected from classroom observation, interview with the teacher and document analysis. The results showed that the teacher in this study mostly focused on Situated Practice and Overt Instruction in teaching narrative texts in an online classroom. This indicated that the teaching and learning practices only focused on the knowledge process of experiencing and conceptualising. The teacher did not really promote the process of analysing-both functionally and critically- and applying creatively. Keywords: Multiliteracies, Narrative texts, COVID-19, English for Young learners
Organizational citizenship behavior and employee performance during the Covid-19 pandemic

The study aims to identify factors influencing the organizational citizenship behavior of office employees and how those factors impact their performance during theQualitativequalitative research was conducted with twelve experts to evaluate and improve the scale. Quantitative research with the participation of 249 employees from service enterprises was carried out to measure and examine the above-mentioned correlation. The research findings indicate that job satisfaction, organizational justice, and organizational support during the pandemic have a positive impact on employees’ organizational citizenship behavior, and this behavior also positively affects their performance. Managerial implications, limitations and recommendation for future research have been also discussed in this paper.
RELATION BETWEEN THE IMPLEMENTATION FREQUENCY OF REFLEXIVE METHODS AND SELF-EFFICACY OF LOWER SECONDARY SCHOOL TEACHERS

The aim of this paper is to analyse the relations between the frequency of reflexive method implementation by teachers and their self-efficacy. To find out the frequency of reflexive method implementation by teachers our own scale questionnaire was used. Its internal structure was analysed using exploratory factor analysis which showed the existence of two dimensions (traditional and non-traditional reflective methods). Reliability of dimensions was evaluated using Cronbach’s alpha and the result varied between 0.71 and 0.73. To map self-efficacy of teachers, OSTES (The Ohio State Teacher Efficacy Scale) was used; and it was adapted to Slovak conditions by Gavora (2012). In this case, exploratory factor analysis demonstrated that it is appropriate to consider two dimensions (teacher efficacy in using teaching strategies and manage class). Their reliability varied between 0.84 and 0.86. 345 lower secondary school teachers – ISCED 2 participated in the research. The average length of work experience of male and female respondents (n=45) was 12.80 years (SD=10.70) and (n=300) 16.20 years (SD=9.88) respectively. Statistically significant moderate positive relation was identified between the implementation frequency of traditional reflective methods and self-efficacy of teachers in using teaching strategies. Similar relation was proved between the implementation frequency of non-traditional reflective methods and the mentioned component of self-efficacy of teachers. A weak direct dependence proved to exist between implementation frequency of both reflective method groups and self-efficacy of teachers in class management.
From Speech to Text: Enhancing Descriptive Paragraph Writing with Unjuk Tutur‘s Learning Model

This study fills a gap in the literature by examining effectiveness of one specific speaking project named Unjuk Tutur (Show and Tell) learning model in improving the ability of seventh-grade students at SMPN 01 Simpang OKU Selatan to write descriptive paragraphs. The study used a one-group pretest-posttest design with a sample of 16 out of 119 students due to social restrictions from the government in response to COVID-19. Data was collected through a descriptive paragraph writing test, and the data was analyzed using statistical techniques. The results showed a significant improvement in students' ability to write descriptive paragraphs after using the Speaking Project learning model, with an average score increase from 66.67 on the pre-test to 78.13 on the post-test. The findings suggest that the Speaking Project learning model could be an effective tool for improving students' writing skills in descriptive paragraphs. The assessment criteria used to evaluate the students' descriptive paragraphs included vocabulary, sentence structure, and spelling. The implications of this study are significant for educators seeking to enhance students' writing abilities. The Speaking Project learning model, which focuses on simple communication skills, could be an effective approach to teaching writing skills to seventh-grade students. Moreover, the findings of this study are relevant to the wider context of Indonesian language learning in the field, where there is a lack of variety in learning methods and models. In conclusion, this study highlights the effectiveness of the Speaking Project model in improving students' descriptive writing skills in Indonesian language learning. Educators can benefit from this approach to enhance students' writing abilities.
READING ABILITY OF SENIOR AIR TRAFFIC CONTROL CADETS AS MEASURED BY ICAO ENGLISH LANGUAGE PROFICIENCY TEST AT CASEA OF MAKASSAR

This research is aimed to know the reading ability of Senior Air Traffic Control Cadets through ICAO English Language Proficiency Test. This research applied descriptive study. The population of the research was 87 cadets of D.III SATC CASEA Makassar which are divided into 4 classes, they are D.III SATC VI, D.III SATC VII, D.III SATC VIII A dan D.III SATC VIII B. The sample of the research took through their registration number consists of 24 cadets. They are cadets of D.III SATC VIII B. Procedure of collecting data that the researcher has applied was gave the reading test to cadets, and each cadet should answer the questions that given. The result of the research shows that the senior air traffic control cadets still have low ability in reading
ADVANCED STUDENTS’ SPEAKING ANXIETY AND THEIR STRATEGIES TO REDUCE ANXIETY DURING ONLINE LEARNING

Anxiety becomes one of obstacles which is probably experienced by advanced-level students when they are speaking class. Nevertheless, the students should deal with this situation. This study examined the students’ anxiety level and their solutions in online speaking classes. Descriptive quantitative and qualitative were used in this study. In collecting the data, a questionnaire consisting fifteen statements and interview with five questions were employed. The subject was 33 English-speaking learners in the fourth English semester at the Department of English Education, University of Islam Malang. They were required to fill out a questionnaire about speaking anxiety through Google Form. To analyze the data, descriptive analysis was used. The results obtained from this study were; the fourth-semester students tended to feel confident during online learning without feeling significant anxiety; the students revealed their solutions to cope with their anxiety such as by applying memory, metacognitive, social, affective, compensation, and cognitive strategies so that they could feel confident practicing speaking during online learning. Furthermore, the most strategies utilized was the affective strategy. This strategy focused on the way the students learnt to speak and become more confident. It can be concluded that students could feel confident, and they needed to apply one of the speaking strategies to reduce speaking anxiety.
LITERACY STRATEGY FOR POST-PANDEMIC “LEARNING LOSS" RECOVERY AT SD NEGERI WONOKERTO 2 PROBOLINGGO

Learning loss is a situation where learners lose knowledge and skills either general or special or decline academically, which occurs due to prolonged gaps or discontinuity of the educational process. This study aims to examine and analyze literacy strategies for learning loss recovery in post-pandemic students at SD Negeri 2 Wonokerto. The method used is the observation method, this observation is done by directly observing the learning process in elementary school after re-entry to school and the implementation of limited face-to-face learning. Observation was conducted on Grade 6 students in SD Negeri 2 Wonokerto. From the observations made by the school literacy team (TLS) to create a literacy innovation program for the recovery of learning loss in post-pandemic students with the JIBAKU (required to read books) and SIGASA (language Guardian Students) programs, both literacy programs are considered effective in the recovery of learning loss in Grade 6 students at SDN 2 Wonokerto.
How Working from Home has Affected South African Industries? A Cybersecurity Culture Perspective

The notion of working from home is not a new concept. However, it has become a buzzword amidst the COVID19 pandemic. Some scholars were already engaging in the work-from-home (WFH) concept a few years before the pandemic. However, contextualization of this concept from a South African perspective only became compelling during COVID19 times, when various industries had to shift their focus and adjust rapidly. This paper aims to investigate through literature reviews, and to understand the cybersecurity challenges that emerged because of this adjustment. Moreover, to investigate South Africa’s readiness and the lessons learned from this significant shift.
Learning spatial regularized correlation filters with response consistency and distractor repression for UAV tracking

Abstract Correlation filter-based trackers have made significant progress in visual object tracking for various types of unmanned aerial vehicle (UAV) applications due to their promising performance and efficiency. However, the boundary effect remains a challenging problem. Several methods enlarge search areas to handle this shortcoming but introduce more background noise, and the filter is prone to learn from distractors. To address this issue, we present spatial regularized correlation filters with response consistency and distractor repression. Specifically, a temporal constraint is introduced to reinforce the consistency across frames by minimizing the difference between consecutive correlation response maps. A dynamic spatial constraint is also integrated by exploiting the local maximum points of the correlation response produced during the detection phase to mitigate the interference from background distractions. The proposed appearance model can optimize the temporal and spatial constraints together with a spatial regularization weight simultaneously. Meanwhile, the proposed appearance model can be solved effectively based on the alternating direction method of multipliers algorithm. The spatial and temporal information concealed in the response maps is fully taken into consideration to boost overall tracking performance. Extensive experiments are conducted on a public UAV benchmark dataset with 123 challenging sequences. The experimental results and analysis demonstrate that the proposed method outperforms 12 state-of-the-art trackers in terms of both accuracy and robustness while efficiently operating in real time.
Research on Algorithm of Video Analysis System Based on Text Error Correction

When making a video, if the video has a language organization error, it needs to be re-recorded. It is not possible to remove inappropriate or unnatural pronunciation parts of the recording more effectively. In response to this problem, this paper studies the speech extraction, error correction and synthesis of video, which is divided into three parts: (1) Speech segmentation and speech-to-text of video; (2) Text recognition error correction; (3) Text-to-speech and video speech synthesis. For the first part, we applied the staged and efficient algorithm based on (Bayesian Information Criterion) BIC &amp; (Statistical Mean Euclidean Distance) MEdist to segment the video voice, and then, the segmented audio is subtracted to reduce noise, and finally converted to text using the iFLYTEK interface. For the second part, we apply the (Double Automatic Error Correction) DAEC algorithm to text error correction. For the third part, we use the (Improved Chinese Realtime Voice Cloning) I-Zhrtvc for text-to-speech. Then merge the voice into the video. The simulation result shows that the staged and efficient algorithm based on BIC &amp; MEdist, which accurately segmented by sentences, can identify audio with dialect accents, and has high accuracy in translating to text, up to an average of 95.8%. DAEC algorithm has a high error correction rate. The audio prosody accuracy after synthesis is high. ZVTOW text-to-speech (Mean Opinion Score) MOS up to 4.5.
IoETTS: A Decentralized Blockchain-based Trusted Time-stamping Scheme for Internet of Energy

&lt;p&gt;As a new-style smart grid, Internet of Energy (IoE) is important and how to provide its trusted time-stamping service becomes a hit. For example, an energy provider needs to prove he/she transferred some energy to a consumer at some time. Nevertheless, traditional trusted time-stamping scheme with a central service provider is not suitable for IoE. Some researchers try to solve this problem via blockchain, due to its decentralization, traceability and tamper-proof. However, there are still chal&amp;shy;lenges when using blockchain. Some have to introduce another kind of central participant. Some have to face the problem of accuracy and availability when using the Bitcoin blockchain. Some have to generate too many extra transactions. To address the aforementioned problems, we propose a fully decentralized trusted time-stamping scheme without any central participant and fulfill six design goals. Compared with the state-of-the-art blockchain-based time-stamping scheme named Chronos, our scheme enjoys less cryptographic operations. We then tested our scheme in the development (local) network and two live networks of the Ethereum. The experiment shows that we have implemented a simple, effective, accurate and low-cost decentralized trusted time-stamping scheme.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;