Reviewerly/exHarmony huggingface dataset:
Impact of professional self-improvement on the effectiveness of teachers in distance education

Distance education requires the teacher to conduct systematic classes with the student, methodologically competent use the developed content for distance learning, means of communication, and educational resources of the Internet, as well as implement technical support for the educational process. The purpose of the study is to develop educational and methodological support for the process of professional self-improvement of teachers in higher education institutions when working remotely. The study analyzes the role of a teacher in the distance learning system, identifies the types of teacher’s activities, as well as the requirements and conditions for their successful work. Working online expands and renews the teacher’s role, makes them a mentor-consultant who coordinates the cognitive process, constantly improving their course and increasing creativity and qualifications in accordance with innovations. Based on empirical research using the survey method, the dynamics of teachers’ readiness to work online before and after passing the interdisciplinary course “Distance Education Technologies in the Educational Process of the University” were determined. Positive results in the formation of teachers’ readiness to work online after completing the course were obtained. The study concluded that the readiness of teachers to work remotely can be increased if the appropriate educational and methodological support is developed and implemented in higher education institutions, which will lead to an increase in the effectiveness of teachers’ work.
Application of Continuous Wavelet Transform to Layer Boundaries Detection from Gamma Ray Log

Layer boundary analysis is an important process in determining the net pay value.Although the layer boundary can be determined manually through mud log data, it can also be determined automatically by signal analysis using wavelet transform.This analysis uses the gamma ray (GR) log data and the resistivity log.The type of wavelet used is Continuous Wavelet Transform (CWT).The CWT workflow begins with a blocking trace process to find inflection points in the GR log data and the resistivity log based on derivative analysis by convoluting the signal with the wavelet operator that matches the original frequency.Several parameters are required as input of the wavelet transform method such as number of layers detected and minimum thickness requirements which are 3 and 1.As for the input, the percentage of layer extract varies from 25%, 30% and 35%.The result of the comparison of layer boundaries from mud log data with CWT has the lowest difference in the yield of layer extract 35%.Where the results of the layer boundary on the mud log data on NP 1, NP 2, P 1, P 2 and P 3 wells are 155, 248, 29, 10 and 343, respectively, while the results of the bed boundary with 35% layers extract at NP 1, NP 2, P 1, P 2 and P 3 wells are 168, 192, 30,11 and 312 layers, respectively.The big difference of layer number identification in the NP1, NP2 and P3 is presumably because the CWT algorithm does not involve thin layer analysis (less than 1 ft).
Modelling and simulation of artificial neural networks using software SIMBRAIN

This paper is considered to be a continuation of a previous publication devoted to tendencies in the applications of advanced technology solutions to strengthen the cybersecurity of critical infrastructure (Yearbook Telecommunications, vol. 6, 2019). The specificity of the research is related to tracing the evolution of artificial neural networks (ANN) from their establishment to their modelling and simulation. The theoretical framework involves a well-supported rationale by some practical examples of advanced methods of design and simulation of ANN using SIMBRAIN. These methods are applicable in Cognitive science and Robotics because of their contribution to scientific researches related to study of perceptions and behaviors, abilities of decision making, pattern recognition and morphological analysis and etc.
Estrategias docentes para la enseñanza remota implementadas en la Licenciatura en Sistemas de Información de la Universidad Nacional del Nordeste [Not available in English]

The COVID-19 Pandemic scenario had repercussions on the traditional teaching modality, triggering the implementation of actions to guarantee the continuity of the educational process. This work intends to address different aspects related to the strategies implemented for the teaching of the subjects of the Licenciarura de Información de la Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, in order to elucidate which pedagogical models emerge from the development of remote teaching processes. It is a descriptive study with a quantitative-qualitative approach. In order to carry it out, different data sources and collection instruments applied in the faculty's environment were used, as well as from the UNNE Virtual Program. What was observed allowed to identify the strategies developed by the teachers of the LSI, to recognize some strengths and limitations and, based on that, to propose some future lines of work.
An Improvement in Digital Image Watermarking Scheme Based on Singular Value Decomposition and Wavelet Transform

An enormous growth of multimedia information in the internet has given rise to varied unauthorized use and modification. This authenticity issue is equally a limitation both in the defense data transmission and secured transmission. Digital watermarking is one of the legitimate solutions to the above problem since it makes possible validation and secure transmission of secret data. This paper presents a robust and secure digital image watermarking scheme that can be used for copyright protection. The scheme involves Lifting Wavelet Transform (LWT) and Singular Value Decomposition (SVD). The latest approach to wavelet transform is the Lifting Wavelet Transform and the significant transform technique for robust digital image watermarking is Singular Value Decomposition. The results are cross-validated by using inverse LWT and SVD. The digital signature mechanism is used to generate and embed a digital signature after the watermark is embedded, then the ownership is authenticated before extracting watermarks. In order to check the robustness of the method against various common image processing attacks like Mean, Median, Gaussian, Shear, Rotation and Crop, certain performance metrics such as peak to signal noise ratio (PSNR)and mean square error (MSE) is computed. To check similarity, normal cross-correlation (NCC) is used.
Research on Forecast Model of College Students Employment Structure and Trend Based on BP Artificial Neural Network

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
CONTRIBUTION DES FINANCEMENTS INTERNATIONAUX A LA REDUCTION DES INEGALITES DE REVENU EN AFRIQUE DE L’OUEST

The objective of this paper is to analyse the effect of external financing such as remittances, official development assistance and foreign direct investment on income inequality in West Africa. Using a study period from 1980 to 2019 and data from the World Bank and World Income Inequality Database, the econometric analysis was done with a dynamic panel of ECOWAS countries except Liberia and Sierra Leone. Using the Generalized System Moment Method developed by Blundell and Bond (1998), the results obtained revealed that among the three sources of external financing, only foreign direct investment contributes to significantly reducing income inequality income in West African countries.
Teaching and Explanation: Aligning Priors between Machines and Humans

Machine intelligence differs signficantly from human intelligence. While human perception has similarities to the way machine perception works, human learning is mostly a directed process, guided by other people: parents, teachers, ... The area of machine teaching is becoming increasingly popular as a different paradigm for making machines learn. In this chapter, we start from recent results in machine teaching that show the relevance of prior alignment between humans and machines. From here, we focus on the scenario when a machine has to teach humans, a situation more and more common in the future. Specifically, we analyse how machine teaching relates to explainable artificial intelligence, and how simplicity priors play a role beyond intelligibility. We illustrate this with a general teaching protocol and a few examples in several representation languages: feature-value vectors and sequences. Some straightforward experiments with humans indicate when a strong simplicity prior is --and is not-- sufficient.
Review of Incremental and Online Learning Methods for Network Anomaly Detection

The internet has emerged as one of the rapidly growing and transformative technologies over the past two decades. Due to the widespread availability of advanced network technologies, there is a serious concern about rise in threat in information and communication technology. The intrusion detection system (IDS) automates the monitoring process in computer networks and analyse the network packets and logs. Due to the static behavior of traditional data packets; the performance of the detection model reduces significantly. The intrusion detection system (IDS) automates the monitoring process in computer networks and analyse the network packets and logs. IDS should be updated timely in order to avoid the system degradation gradually. In order to adapt the model continually, incremental learning mechanism comes into picture in order to analyse network streams in real time. Incremental learning is a continuous or adaptive learning which emphasize on how a model acquire and fine-tune its knowledge. In this paper, various incremental approaches employed for detection of network anomalies are critically analysed in order to provide gist of how these techniques have influence the detection model while mitigating the effects of concept drift, noisy data, stability-plasticity dilemma, and complexity of the adaptive model. Various incremental learning classifiers along with challenges faced by incremental and online learning have also been discussed. In addition, this paper also focusses on study of anomaly detection techniques in high dimensionality and big data; that presents the comparative analysis of existing approaches to address some of the challenges of high dimensionality in large scale networks.
THE NAÏVE BAYES ALGORITHM FOR LEARNING DATA ANALYTICS

The field of data science analysis in artificial intelligence is being deeply interested by many scientists around the world, many techniques are proposed to improve accuracy from Naive Bayes model by reducing the problem of interdependence between its attributes.The new research of this paper, which is presented step by step by the Naive Bayes (NB) method, is the method of applying NB with a new set of attributes.It is worthy of consideration that using learning data analytics method is receiving increased attention, because of the importance of learning data analytics, in order to provide predictive learning results of learners or offer better solutions to support schools to strengthen educational measures or have optimal plans to increase student retention rates studying at the school, as well as help students succeed in their studies.Data related to student management and training activities are collected from softwares, student affairs and learning management systems are operating in practice such as Edusoft.Net software, Moodle and Microsoft Teams, student attendance system by FaceID face recognition… Research, evaluate and select a number of new data technologies for the purpose of building student digital profile, including document storage functions, specifically intelligent functions such as creating, processing and storing documents.
Teachers’ Techniques in Improving Students Pronouncing Words Loudly at the First Year of MAN Batam.

This research has three research questions. The first research question is “What techniques the teacher use in improving students pronouncing words loudly at the first year of MAN Batam?” the second is “Which technique frequently used by the teacher in improving students pronouncing words loudly at the first year of MAN Batam ?” and third is what might made the teacher prefer to the technique? Moreover, the subject of the study are two English teachers. Meanwhile, the object of the study is the teachers’ techniques in improving students pronouncing words loudly at the first year of MAN Batam .&#x0D; In this research, the researcher took two teachers as the respondent. The techniques of collecting data are observation and interview. Observation is used to know the teacher technique in teaching pronounciation. And interview is used to know the effectiveness of technique in teaching pronounciation. The researcher uses observations as main data collection and the interview is as to support the data needed. In addition to the data analysis, the researcher uses descriptive quantitative which applies statistical data. To know the percentage, the researcher used formula as follow:&#x0D; P&#x0D; As for the result of this research, the researcher takes some conclusions. They are: English teacher uses drilling technique how to pronounce the word, English teacher uses repetition technique to practice and improve students pronouncing, English teacher uses backward building up technique to improve students pronouncing, and End the repetition technique is the most frequently used by the teacher.&#x0D; &#x0D; Based on the analysis of data, the researcher concludes that The Teachers’ Techniques in Improving Students Pronouncing Words Loudly at the first year of MAN Batam is categorized into “less”.&#x0D;
Keyword Categorization using Statistical Methods

Keyword analysis is a way to gain insight into market behaviour. It is a detailed analysis of words and phrases that are relevant to the selected area. Keyword analysis should be the first step in any search engine optimization, as it reveals what keywords users enter into search engines when searching the Internet. The keyword categorization process takes up almost half of the total analysis time, as it is not automated. There is currently no known tool in the online advertising market that facilitates keyword categorization. The main goal of this paper is to streamline the process of keyword analysis using selected statistical methods of machine learning applied in the categorization of a specific example.
Challenges Preventing Academic Staff from using Information and Communication Technology (S) for Teaching in the Nigerian Public Universities and the way Forward

Information and Communication Technology(s) is one of the major resources adopted by the universities across the world for delivering teaching and conducting researches. The academic, non-academic staff and students use information and communication technology (s) for providing academic service and administrative functions. In Nigerian public universities, academic staff have not using information and communication technologies effectively for teaching and learning processes due to many problems. The article discussed the challenges preventing academic staff from using ICT(s) for teaching in the Nigerian public universities. Secondary data were used to provide empirical support to the point raised in the article. The secondary data were sourced from print material and online publication from libraries and internet. The article identified: inadequate funding of ICT programme, inadequate ICT facilities, poor internet services, unstable electricity, high cost of ICT facilities, poor implementation of ICT educational policies, poor ICT literacy of academic staff and institutional corruption as the challenges preventing academic staff from using ICT(s) for teaching in the Nigerian public universities. To address this challenges, the article recommends as follow: adequate funding of ICT programme, provision of ICT infrastructural facilities, training and retraining programme for academic staff, subsiding ICT facilities for academic staff, ensure stable internet services, stable electricity supply, fight institutional corruption and implement all ICT policies in the Nigerian higher institutions.
Understanding SDLC using CI/CD Pipeline

Development of complex and quality software necessitates the use of a development model, so that the development process is efficient, reliable and faster. Software development life cycle (SDLC) is a well-defined and wellorganized process used to plan, develop, deploy and maintain high quality software systems. DevOps is one recent addition to SDLC that ensures that the development and operations team collaborate to accelerate the deployment and delivery of higher quality software products. This paper throws a light on how development processes are accelerated using DevOps tactics like continuous integration and deployment (CI/CD) pipelines. however, there are several factors that prevent the organizations from using these approaches. Discovering the evolution of DevOps and its continuous practices, gives a thorough understanding of the importance of the DevOps culture. Manual deployment and testing increase the feedback time of a commit operation. The paper discusses various tools available in the DevOps community that can be used to automate various stages of continuous integration and deployment pipeline, so that the feedback time is reduced.
Lightweight TEM and VLF systems for low-altitude UAV-based geophysical

Summary To create a complete set of UAV-based aerogeophysical methods, two lightweight UAV-electromagnetic systems were developed. The first one (VLF-type) is designed to first-stage geophysical mapping over large areas; it can be performed simultaneously with magnetic and gamma surveys on one UAV. In this case, the receiver with mutually orthogonal ferrite antennas provides measurement of the vertical and horizontal components of the electromagnetic field in the audio frequency range (10 – 48 kHz), at the far field region of the electromagnetic field of navigation radio stations. The second variant is the UAV-TEM, where the time-domain transmitter located on the ground and connected to a galvanically grounded line several kilometers long, and the receiver with coil is towed by the multirotor UAV. From one line placement, it is possible to perform surveys in areas up to several square kilometers. This variant is optimal for detailed electromagnetic soundings in local sites identified at the previous stage of geophysical mapping, it provides the possibility of data inversion to a depth more than 300 meters. Together with UAV-magnetic prospecting and gamma-spectrometry, the created systems provides a solution to a wide range of geological tasks in any complexity of landscape conditions.
The effect of multi-stage mobile training based on Gagne instructional design on learning, retention, and satisfaction of health volunteers

BACKGROUND: One of the most important ways to acquire knowledge in the present age is education based on new technologies, including mobile learning. The purpose of this study was to investigate the effect of a multi-stage mobile training based on Gagne instructional design on learning, retention, and satisfaction of health volunteers. MATERIALS AND METHODS: In this interventional study, 112 health volunteers a Health Centre in Mashhad during 2019-2020 were enrolled. The participants were randomly divided into two groups: mobile education based on Gagne model (n = 56) and conventional lecture (n = 56). A researcher-made questionnaire was completed by the eligible subjects before, after, and 2 months after the intervention to assess their learning, retention and satisfaction in educational methods. Face validity, content validity, and reliability of the data collection tool and model fitness indices were confirmed. Content validity of data collection tools verified by experts and its reliability was calculated to be 0.78. The collected data were analyzed in SPSS-25, using descriptive (mean and standard deviation SD]) and analytical statistical tests (independent t-test). RESULTS: The results showed that 2 days and 2 months after the intervention, learning, and retention rate was significantly higher in the mobile learning based on Gagne model group in comparison to conventional lecture group (P < 0.001). Furthermore, the intervention group was generally satisfied with the mobile training method. CONCLUSION: Mobile learning based on Gagne model is recommended for improving learning, retention among health-care workers
Turkish Named Entity Recognition for Search Engine Queries

Recognizing named entities in short search engine queries is a difficult task due to their weaker contextual information compared to long sentences. Standard named entity recognition (NER) systems that are trained on grammatically correct and long sentences fail to perform well on such queries. In this study, we share our efforts towards creating a cleaned and labeled dataset of real Turkish search engine queries. A NER system is trained by applying the state-of-the-art deep learning method BERT to the collected data and its high performance on search engine queries is reported.
Artificial Intelligence and Human Society

Artificial intelligence is the latest stage of technological revolutions, through which mankind is undergoing at present. We have crossed the four phases of first agricultural, second 18th century industrialrevolution, third 20th century industrial revolution and fourth 1970’s scientific revolution. We have entered in to fifth era of information technology revolution when&#x0D; &#x0D; Machines are talking to machines&#x0D; Machines are talking to humans and&#x0D; Machines have gained the power of reasoning.&#x0D; &#x0D; Sophia robot is the latest example of such a revolution. Previous stages of technological revolutions have affected economic environment more than the social environment. Machines had brought changes in economic productivity and we have seen it very recently, but with the introduction of intelligent machines, we are bound to see drastic changes in the human society itself. The fact is reflected in the citizenship for the robot ‘Sophia’. We see that machines have started becoming humans. The paper proposes to study the effect of these changes on human psychology and then as later effect on the society and the units of the society of the country. Psychological changes in the mankind have hardly been addressed by previous scholars. Path of research will be&#x0D; Artificial intelligence Impact on human psychology Impact on Indian society&#x0D; Primary and secondary data will be collected to study these impacts. Survey tool will be used for data collection. The analysis and interpretation of the data will give us research outcome which can prove helpful in decision making for the future development of artificial intelligence techniques and their acceptance in the society. Such researches can surely contribute in giving correct direction to innovationsin science and their adaptation in the society.&#x0D; &#x0D;