Reviewerly/exHarmony huggingface dataset:
ESL Lexical Inferencing for the Unknown Words in Newspaper Editorials at Advanced Level

The study aims at exploring the inferential behaviours of the ESL learners in generating the meanings of the unknown words faced in reading editorials of newspapers. It further notices the impact of instructional treatment on the use of knowledge sources and their relationship with success, the effect of syntactic property of the unknown words, and the difficulties faced in lexical inferencing while interacting with a text. The theoretical framework of knowledge sources adopted in the study was given by Bengeleil and Paribakht (2004). Data were collected through introspective verbal protocols and observation. The study used the texts of the editorials published in native newspaper The Washington Post. Flesch Reading Ease Measure was employed to compare readability of the editorials. The study is quasi-experimental which includes pretest, instructional treatment and posttest. It uses Winograd and Hare’s (1988) model of instruction due to its cognitive usefulness. The analysis of selected verbal protocols gave insight to the inferential behaviours of the subjects while using linguistic and non-linguistic knowledge sources in the pursuit of meaning of the unknown words. The instructional treatment had a significant effect on the sources in lexical inferencing. It was found out that the parts of speech of the unknown words had effect on ease or difficulty in deducing their meanings. The study sheds light on various reasons of incorrect inferences. &#x0D; Key words: ESL Reading, Lexical Inferencing, Editorials, Introspection, Linguistic and Non-Linguistic Knowledge Sources, Syntactic Property of Unknown Words
Application of Information Gain to Select Attributes in Improving Naïve Bayes Accuracy in Predicting Customer's Payment Capability

The customer is the main factor in the running of PT. XYZ. A good understanding of customers is very important for predicting the capability of customers to pay. The implementation of credit collectibility is used to determine the quality of customer credit, one of which is the customer's capability to pay interest and principal on time. While manually, it is very difficult to accurately predict the capability of customer credit payments. Data mining techniques with the Naïve Bayes algorithm were chosen to classify customers to be able to find patterns, analyze and predict, because they have good performance, are efficient, and simple. The Naïve Bayes algorithm has a weakness in terms of sensitivity to many attributes, so the accuracy is low. Based on the problem stated, his study will apply the Information Gain method to select the most influential attribute on the label in order to increase the accuracy of the Naïve Bayes algorithm. This research produces a new dataset with seven attributes: TENOR, SALARY, DOWN PAYMENT, INSTALLMENT, APPROVAL, OTR CLASS, AGE with Labels: Status and Id: Id number based on the Information Gain method. The dataset comparison process with 995 data records showed an increase in accuracy, precision, and AUC using the new dataset compared to the old dataset, but in the t-Test test with an alpha value = 0.05 there is a difference but not significant. In the evaluation process, performance experienced a significant increase in the use of new datasets with the following percentages of performance improvement: accuracy = 8%, precision = 18.42%, recall = 17.65% and AUC= 0.057%. The results of this study obtained AUC of 0.876, accuracy of 87.88%, precision of 61.90%, and recall of 76.47%, and classified into good classification.
SecVerifier: A Practical Memory-Security Verifier

Memory-related security vulnerabilities such as buffer overflow may cause severe consequences once exploited. Various technologies have been applied to discover them before a product is released, but they have significant false positives and false negatives. Formal verification has the reputation of mathematically reasoning about software properties, but it has limited scalability and is challenging to use. We have developed the novel manageable verification unit strategy to address the scalability issue and built a web-based verification service to lower the usage obstacles. With the system, we have verified 140K+ lines of real-world products and discovered 106 memory-related vulnerabilities that other technologies have missed.
EVALUATION IN MATHEMATICS USING SOFTWARES. METHODOLOGICAL REFLECTIONS

Computer-assisted evaluation in mathematics has been and remains a rather difficult activity. The experience, gained by mathematics teachers in the Republic of Moldova over the last two years in this regard, highlights aspects specific of the local educational process. The article analyzes data from a survey conducted among mathematics teachers in the country, the experience of the teachers themselves and the author's experience in using computer-assisted assessment. At the end some conclusions and suggestions are formulated about methodological conclusions and recommendations for the use of software-based evaluations both in distance learning and in direct contact.
Crops pests and diseases management system using android and GIS platform for the department of agriculture, province of samar

According from the Department of Agriculture, there were 9 types of pests and 8,000 types of diseases currently existing in the province of Samar. The farmers cannot identify the types of pests and diseases that affects their crops. At present, the provincial agriculturists and scientists used surveillance forms and damage reports and manually processed in analyzing and interpreting the results for decision making. The proponent decided to conduct a scientific research and develop a customized mobile application with GIS platform that adequately helped the hitches in the department that adequately solved the present problem. The customized mobile application with GIS platform intensify the data monitoring and analysis of crops pests and diseases in the province of Samar. The research design used was fact-finding techniques that provides reliable and accurate results through mixed methods. In the development of the software, Agile model and Hersh Iterative Four Stages Approaches were used. Furthermore, for the development of GIS map, the proponent used thematic mapping and manifold system. The customized software with GIS map was evaluated under ISO 9126 in terms of Effectiveness, Reliability and Functionality. The results understood that the target users perceived the advantage of customized software with a very high rating or 94.3%.
THE PSYCHOLOGICAL ASPECT OF THE SPECIFIC CHARACTERISTICS OF TEACHING MATHEMATICS IN HIGH SCHOOL

In the digital age, the integration of information and communication technologies in the teaching of matematics in high school is a necessity. The correct integration of ICT, as a tool to ensure the efficiency of learning, requires a complex approach to the phenomenon from a psychological and pedagogical point of view, in order to identify the optimal conditions for integrating ICT in teaching mathematics in high school. This article is aimed at identifying the specific characteristics of teaching mathematics in high school from a psychological point of view, in order to outline an overview of the benefits of using ICT, but also the risks posed by the incorrect use of ICT in the mathematics instruction process.
The Design of a Monitoring Application System for The Production of Foam Products Using the UML And Waterfall Methods

The development of information technology, which is followed by a higher level of competition in the foam product industry, encouraging companies to manage their company's resources properly and to plan effective, systematic and mature activities within the company. As a company with a variety of products, the most dominant problem is in the productivity process. Production is the most important part of a manufacturing company, where in carrying out its production activities this company produces based on orders from customers (Job Orders). And the problems that often occur are planning revisions in the midst of production and changing production schedules between groups (lines), delays in production planning in terms of prioritizing planning, and still being done manually in making daily reports. By implementing monitoring, which is the supervision and control of an activity where measurements and evaluations are completed repeatedly from time to time, monitoring is carried out for the purposes of the company and to maintain ongoing management. Monitoring will provide information about the status and trend of production activities towards the company's goals. The solution to this production problem is to build a web-based foam product production monitoring system application using the Waterfall method which is integrated with UML the method used is use case diagrams, activity diagrams, sequence diagrams, class diagrams and component diagrams and software development with PHP and MySQL technology. With Black box testing, it is proven that the design of this foam production monitoring system application can assist the company's foam product production activities in fulfilling customer orders and accurate reports so that it becomes effective and efficient. in improving the productivity and performance of the company.
Control of Eye-And-Hand robot arm by visual space based visual servoing

We propose control method of Eye-And-Hand robot arm by visual space based visual servoing which directly generates command values from visual space coordinate by using jacobian of between velocity of feature points on visual space coordinate and velocity of robot arm on 3D coordinate. Visual space based visual servoing can suppress unexpected movement in space because it is control method which minimizes deviation on visual space coordinate which deeply relates to spatial recognition of human beings and is derived from image coordinate of stereo camera, camera angles and camera focal length. On the other hand, image based visual servoing which is conventional method of visual servoing sometimes causes unexpected movement in space because it is control method which minimizes deviation on image coordinate by using jacobian of between velocity of feature points on image coordinate and velocity of robot arm on 3D coordinate. And we show that visual space based visual servoing suppresses unexpected movement in space by comparing it with image based visual servoing by simulations. First simulation shows that difference of hand trajectory between visual space based visual servoing and image based visual servoing is little. Second simulation shows that the difference becomes larger because image based visual servoing causes unexpected movement by making deviation of posture between first hand posture and target posture larger than first simulation.
Identifying User's Smartphone Possession Location and Behavior Including Untrained Classes by Using Deep Metric Learning

In our previous study,(1) we confirmed that it was possible to classify the user’s smartphone possession location and behavior simultaneously using Mdk-ResNet.(2) However, there was a problem that when untrained classes were given as input, it was impossible to output them as unknown classes using normal classifications as in our and other’s previous studies. Therefore, this time, we aimed to output untrained data as an unknown class by learning the distance between images using deep metric learning, and not only classifying the learned classes. In this study, we used a smartphone’s accelerometer to acquire the data of experimental participants and created a trained model by learning through deep metric learning. We classified the user’s smartphone possession location and behavior, including the unknown class of unlearned data, by calculating the cosine similarity between the image vector prepared in advance and the image vector output from the trained model during inference using the trained model. As a result, we confirm not only that the untrained data can be output as the unknown class, but also that the accuracy is comparable to the previous study.
Optimizing usage of remaining blood from Interferon Gamma Releasing Assay (IGRA) to search for immune signatures and biomarkers for Latent Tuberculosis Infection (LTBI)

Introduction: Tuberculosis (TB) is a serious public health problem, killing more than 1.5 million people a year.The WHO estimates that 1 ⁄ 4 of the global population is infected with M. tuberculosis (Mtb).To end the TB epidemic, in addition to treating symptomatic cases, it is important to diagnose and treat latent TB (LTBI).LTBI is characterized by a persistent immune response to Mtb antigens without clinical evidence of TB.Interferon Gamma Release Assays (IGRA) are used to detect LTBI, one of them is the QuantiFERON-TB-Gold-Plus (QTF).In addition to diagnosing and treating LTBI, it is important to define biomarkers that characterize the risk of developing TB.Reusing blood remaining from clinical diagnostic tests to increase knowledge in TB is useful and avoids wasting the sample and recalling patients.Objective: Optimize the use of IGRA remnant blood and define new biomarkers for Latent TB, correlating the expression of T cell activation molecules with IFN-g production in response to QTF Mtb antigens.Methodology: Pheripheral blood samples from LTBI suspected subjects (n=38) was collected in tubes QTF-TB-Gold-Plus, incubated at 37°C, overnight.Plasma was collect and stored between 2-8 o C until perform the IFN-g.The remaining blood in the tubes was stained with monoclonal antibodies to CD45, CD3, CD4, CD8, CD69, CD71 and HLA-DR, lysates and evaluated in flow cytometer. Results:We immunophenotyped the remaining blood cells used in the QTF test.Our preliminary results show that, as expected, most of the cells present in the lymphocyte gate are CD45+/CD3+ with a median of 77.8% (Tube N, Nill); 82.5% (Mitogen, M); 74.03% (TB1) and 76.75% (TB2).Considering CD4+ T cells, we detected 56% (N), 44% (M), 56% (TB1) and 55% (TB2).On the other hand, we showed that there were 28% (N), 25% (M), 31% (TB1) and 29% (TB2) of CD8+ T cells in the tubes.When we looked for activation markers (CD69, CD71 and HLA-DR) we showed that when T cells are stimulated with mitogen there are higher percentages of T cells, TCD4+ TCD8+ expressing these markers.For the antigen-specific tubes (TB1 and TB2) there were not enough QTF positive samples to conclude the correlation of IFN-g production with the expression of these activation markers. Conclusion:We conclude that it is possible to better characterize the immune phenotype of remaining blood from IGRA.
Management of the Development of a Modern Teacher’s Digital Competencies

Motivation is the basis for managing the development of teachers' competencies in the modern education system.The motivational component in the study is considered as a set of internal and external motives of the teacher to use modern digital technologies, accompanied by the processes of forming an internal desire to achieve success and a sincere and personally significant desire to improve themselves in this area.The purpose of the study is to study the possibility of managing the development of teachers' digital competencies with different motivations.The article examines the features of teachers' motivation in mastering digital competence, revealed the nature of teachers' awareness of the possibilities of using information and communication tools in teaching schoolchildren, and identified the main problems that teachers with different motivations have that prevent the formation and deepening of their digital competencies.Features of motivation were studied using A.A. Rean's methodology "Motivation for success and fear of failure" and a questionnaire about teachers' ideas about the use of information and communications technologies in teaching their subject.About 30% of teachers from the experimental sample focus on achieving success in the development of digital competencies, although the proportion of teachers without a pronounced motivational orientation and focus on avoiding failure is approximately the same.This indicates an insufficiently effective methodological and psychological support for teachers who cannot independently cope with the urgent task of modern educationthe formation of digital competence.This happens for various reasonsfear of mastering new technologies, age composition, inability to take advanced training courses, etc.Thus, the strategy and tactics of developing digital competencies of teachers who avoid failure and are motivated to achieve success have fundamental differences.Methodological and psychological-pedagogical support for specialists with low and high levels of motivation is built within the framework of an individual trajectory of their professional development and self-improvement.The results of the study can be used both in the design of higher education programs and programs for professional training and retraining of teaching staff.
Acquisition of value-based competencies for effective management of training of specialists

The purpose of this study is to examine the features of the acquisition of value-based competencies for effective management of training of specialists. Higher education institutions are tasked with implementing educational programs that allow to achieve the set goals and form the necessary competencies to provide quality training of specialists. An analysis of recent research and publications has shown that the use of the competency approach in higher education institutions has become possible owing to the new standards approved by the Ministry of Education and Science. Methodology: General scientific and special methods of system-structural analysis and synthesis, frequency analysis, quantitative distribution, grouping were used in the research process. Competencies which the applicants for higher education are supposed to acquire after mastering the educational and professional programs were analyzed in each specialty. The research findings show that the main components of professional education are 11 core competencies, which account for 35% of all competencies studied and form 68% of universal skills in all specialties, while 22 competencies are unique to educational and professional programs and provide performing professional functions by type of activity. It has been found that in the specialties of natural sciences, engineering and information technologies, the main emphasis is placed on the acquisition of purely professional competencies, while in humanities, economic, managerial and law specialties, professional competencies are supplemented with intercultural and social competencies. The grouping of competencies according to the opportunity to obtain learning outcomes has been given, the understanding of the grouping allows to provide effective management of quality training of specialists. JEL Classification: A22, I20.
Blind estimation of noise level based on pixels values prediction

Noise parameters estimation is required in various stages of digital image processing.Many efficient algorithms of noise estimation were proposed during last two decades.However, most of these algorithms are efficient only for a specific type of noise for which they are designed.For example, methods of variance estimation of additive white Gaussian noise (AWGN) will not work in the case of additive colored Gaussian noise (ACGN) or, in general, in the case of a noise with non AWGN distribution.In this paper, a totally blind method of noise level estimation is proposed.For a given image, a distorted image with a discarded portion of pixels (around 10%) is generated.Then an inpainting (or impulse noise removal) method is applied to recover those discarded pixels values.The difference between the true and recovered pixel values is used to robustly estimate image noise level.The algorithm is applied for different image scales to estimate a noise spectrum.In this paper, we propose a convolutional neural network called PIXPNet for effective prediction of values of missing pixels.A comparative analysis confirms that the proposed PIXPNet provides smallest error of recovered pixel values among all existing methods.A good efficiency of application of the proposed method in both AWGN and spatially correlated noise suppression is demonstrated.
Interdisciplinary issues in education

The fundamental importance of education is to unlock, reveal or make evident the unique potential of students. It is often regarded as a synonym for learning and is a product of experience. Education is also considered a restructuring or reorganization of experience to build new knowledge.As a lifelong learning process, education lasts from birth to death, and schools are not the only institutions that provide education. The role of educational institutions in providing quality education is by no means sufficient. Continued research and development activities are always relevant in this context.Learning involves continuous and constant changes in experience, ideas, knowledge, attitudes and skills, all complemented by homes, communities, religions, the media, etc., but educators innovate in this regard by considering development that can do a lot to come up with ideas. The present sixth volume, Issue 2 of Interdisciplinary Research in Education includes fourteen original articles. This is an attempt to address ongoing changes in the education sector, including both empirical research and desk reviews that help expand existing knowledge and understanding, especially in the areas of curriculum, pedagogy and education management.
Future Research Directions

From Chaps. 2–5, we have shown the existing research status of machine learning driven privacy preservation in IoTs, especially focus on three leading directions using GAN, federated learning, and reinforcement learning. Several advanced technologies and theories are also integrated, for example, differential privacy, game theory, blockchain, etc. Nevertheless, there are still plenty of significant and prospective issues worthy investigating. The popularization of blockchain, digital twin, and artificial intelligence offers a mass of opportunities for researches on machine learning driven privacy preservation in IoTs, but meanwhile they raise new challenges such as unsatisfying data utility and limited communication and computing resources. In addition, there are various other research topics that desiderata consideration in machine learning driven privacy preservation in IoTs. To pave the way for readers and forthcoming researchers, we outline several potentially promising research directions that may be worthy of future efforts.