Reviewerly/exHarmony huggingface dataset:
Preparing Students For Teaching And Education: The Existential-Logotherapeutic Approach

The article is focused on the issues of professional training of students for teaching and education in postindustrial society: worldview uncertainty, unpreparedness for personal and professional development.Though the competence-based approach is prevalent in the system of higher pedagogical education, it is deficient.The necessity of its integration with approaches that can contribute to the harmonious socialization of the personality of the future teacher and educator is considered of significant importance.One of these approaches is the existential-logo therapeutic approach.Its essence is revealed in two interdependent aspects: a) facilitating the development of the existential sphere of the future teacher's personality; b) helping students' master the teaching profession as a meaningful purpose in life through striving for self-transcendence and self-realization in the work of love.In the educational process, the effectiveness of this approach can be achieved through implementation of methods and techniques of logo therapy within the framework of the pedagogy of life-meaning orientations.
Artificial Intelligence and Machine Learning in the STEAM classroom

This research aims in shedding light on international high school students’ perceptions, awareness, and prior knowledge about Artificial Intelligence and Machine Learning, as well as to investigate the effect of taking a relevant learning unit within a STEAM course on students’ dispositions toward and understanding about Artificial intelligence. The analysis of performance and reflection data from 62 individuals revealed low prior student engagement with Artificial Intelligence and Machine Learning content, a positive shift in the anticipated societal impact of Artificial Intelligence and an active engagement with online Artificial Intelligence applications during the unit, as well as no correlation between student performance and gender. It is suggested that the development and implementation of learning designs that focus on conceptual understanding of Artificial Intelligence and Machine Learning could benefit all students.
Research on Abnormal Behavior Recognition of Buses Based on Improved Support Vector Machine

The abnormal driving behavior of buses brings about greater security threat. How to effectively identify the abnormal driving behavior of buses has become one of the problems of cracking public transportation safety. This paper constructs an abnormal behavior recognition model of buses based on improved support vector machine. Through the verification, the model has a high recognition rate, which provides an important means for further improving the safety of public transportation operations.
Conversation Engine for Deaf and Dumb

Deaf and hard hearing people use linguistic communication to exchange information between their own community and with others. Sign gesture acquisition and text/speech generation are parts of computer recognition of linguistic communication. Static and dynamic are classified as sign gestures. Both recognition systems are important to the human community but static gesture recognition is less complicated than dynamic gesture recognition. Inability to talk is taken into account to be a disability among people. To speak with others people with disability use different modes, there are number of methods available for his or her communication one such common method of communication is linguistic communication. Development of linguistic communication recognition application for deaf people is vital, as they’ll be able to communicate easily with even people who don’t understand language. Our project aims at taking the fundamental step in removing the communication gap between normal people, deaf and dumb people using language.
Impact of Organizational Justice on Job Satisfaction in the Jordanian Pharmaceutical Companies Listed on the Amman Stock Exchange

The aim of this research is to identify organizational justice and its impact on job satisfaction in the Jordanian pharmaceutical companies listed on the Amman stock exchange, and the problem of the research was the impact of organizational justice dimensions on job satisfaction through the main hypothesis, which is that there is no statistically significant effect of organizational justice with its dimensions (procedural justice, distributive justice, transactional justice ) at the level of significance (α 0.05 ≥) on job satisfaction with its combined dimensions (training and development, Incentives, work environment) among Amman stock exchange employees.To achieve the goal, the researcher used the descriptive approach, and used the questionnaire as a tool for data collection.The research community consisted of ( 170) employees working at the biggest three companies of the Jordanian pharmaceutical companies listed on the Amman stock exchange, and (80) male and female employees were selected as a representative random sample of this community by using the SPSS statistical analysis program.The research found a set of results that there is a medium degree effect between organizational justice with its dimensions (procedural justice, distributive justice, transactional justice) on training and development, and there is a medium degree effect between organizational justice with its dimensions (procedural justice, distributive justice, transactional justice) on Incentives, and it was also found that there is a medium degree effect between organizational justice with its dimensions (procedural justice, distributive justice, transactional justice) on the work environment .The research also presented a set of recommendations, including: preparing a training plan based on the actual training needs of employees, striving to provide free or semi-free training programs or funded by other parties, taking into account the time factor of the training program, and allowing workers to object to decisions that are unfair to them equally and without discrimination Empowering all employees through participation in committees, internal and external courses, and work teams formed within these companies, for the purposes of transferring knowledge, strengthening ties and social communication between senior management and all employees at their various administrative levels through meetings, meetings, and national and international events, and taking improvement and Vol.11, No. 7, 2021, E-
Generative Adversarial Network-Based Edge-Preserving Superresolution Reconstruction of Infrared Images

The convolutional neural network has achieved good results in the superresolution reconstruction of single-frame images. However, due to the shortcomings of infrared images such as lack of details, poor contrast, and blurred edges, superresolution reconstruction of infrared images that preserves the edge structure and better visual quality is still challenging. Aiming at the problems of low resolution and unclear edges of infrared images, this work proposes a two-stage generative adversarial network model to reconstruct realistic superresolution images from four times downsampled infrared images. In the first stage of the generative adversarial network, it focuses on recovering the overall contour information of the image to obtain clear image edges; the second stage of the generative adversarial network focuses on recovering the detailed feature information of the image and has a stronger ability to express details. The infrared image superresolution reconstruction method proposed in this work has highly realistic visual effects and good objective quality evaluation results.
Shannon Entropy Loss in Mixed-Radix Conversions

This paper models a translation for base-2 pseudorandom number generators (PRNGs) to mixed-radix uses such as card shuffling. In particular, we explore a shuffler algorithm that relies on a sequence of uniformly distributed random inputs from a mixed-radix domain to implement a Fisher–Yates shuffle that calls for inputs from a base-2 PRNG. Entropy is lost through this mixed-radix conversion, which is assumed to be surjective mapping from a relatively large domain of size 2J to a set of arbitrary size n. Previous research evaluated the Shannon entropy loss of a similar mapping process, but this previous bound ignored the mixed-radix component of the original formulation, focusing only on a fixed n value. In this paper, we calculate a more precise formula that takes into account a variable target domain radix, n, and further derives a tighter bound on the Shannon entropy loss of the surjective map, while demonstrating monotonicity in a decrease in entropy loss based on increased size J of the source domain 2J. Lastly, this formulation is used to specify the optimal parameters to simulate a card-shuffling algorithm with different test PRNGs, validating a concrete use case with quantifiable deviations from maximal entropy, making it suitable to low-power implementation in a casino.
Team_KGP at SemEval-2021 Task 7: A Deep Neural System to Detect Humor and Offense with Their Ratings in the Text Data

This paper describes the system submitted to SemEval-2021 Task-7 for all four subtasks.Two subtasks focus on detecting humor and offense from the text (binary classification).On the other hand, the other two subtasks predict humor and offense ratings of the text (linear regression).In this paper, we present two different types of fine-tuning methods by using linear layers and bi-LSTM layers on top of the pre-trained BERT model.Results show that our system is able to outperform baseline models by a significant margin.We report F1 scores of 0.90 for the first subtask and 0.53 for the third subtask, while we report an RMSE of 0.57 and 0.58 for the second and fourth subtasks, respectively.
Application of the Apriori Algorithm and FP-Growth to find out the Association Rule between Gender, Education level on wages of SMEs workers in Palembang City

Small, Medium Enterprises (SMEs) are one of the supporting parts of the Indonesian economy by absorbing high labor and production value. One of the factors that greatly influences the development of SMEs in Indonesia is the workforce involved in SMEs business activities. Several factors that influence the workforces are gender, education level, marital status and wages level. The data mining used can help processing data into new knowledge that can be used in decision making. The purpose of this study was to analyze the data of SMEs workers in the Palembang city by comparing the Apriori algorithm and the FP-Growth algorithm to see the association between gender, education level, marital status and wages earned by SMEs’s workers in Palembang city. The sample used in this research are 400 SMEs’s workers who were randomly selected from 5 sub-districts in Palembang city. The results show that with a confidence level of 0.8, the Apriori Algorithm produces 25 association patterns while the FP-Growth Algorithm has 11 association patterns. In the Apriori Algorithm, it was found that 72% of associations with 1 level confidence while the rest is less than 1. As for the FP-Growth Algorithm, 9% of the association patterns have 1 confidence level and the rest is less than 1. These results indicate that the Apriori algorithm is able to explain more association between gender, education level, marital status and wages of SMEs’s workers in Palembang City. The results of this study can be used as a consideration for increasing the capacity of SMEs’s workers in Palembang City.
Pedagogical and Methodological Aspects of Non-Invasive Monitoring (case of Teaching Mathematics at School and University)

The development of information technologies makes it possible to record a large amount of data in the learning process, both on the results of performing educational operations and on the psychophysical characteristics of students. In this regard, a number of ethical, pedagogical and methodological problems arise. Among them are the disproportionate control actions to the volume and properties of information retrieved during monitoring and problems associated with ensuring the information security of the student. In contrast to natural monitoring, monitoring the management of teaching has as an object of training a person, who can be considered, from the point of view of management, as a highly organized information system. The article is devoted to the study of the problem of non-invasive monitoring, involving the redirection of most of the monitoring information to the student himself. As a definition of non-invasive monitoring, the following is proposed: non-invasive monitoring is a type of pedagogical feedback that is used exclusively for correcting the student’s actions and is prohibited from using to control the effectiveness and transfer the results outside class. The article analyzes 15 different teaching paradigms, which are explicitly or implicitly adhered to by teachers, organizing the educational process, from the point of view of compliance with the tasks of non-invasive monitoring. The methodological aspects of the implementation of non-invasive monitoring from the point of view of computer support of the learning process are also analyzed. It is shown how non-invasive monitoring can be supported by software that provides active interaction of the student with the subject environment and gives the teacher more freedom in planning and achieving strategic goals of managing the educational process.
Multi-Party Private Set Intersection Protocols for Practical Applications

Multi-party Private Set Intersection (MPSI) is an attractive topic in research since a practical MPSI protocol can be deployed in several real-world scenarios, including but not limited to finding the common list of customers among several companies or privacy-preserving analyses of data from different stakeholders.Several solutions have been proposed in the literature however, the existing solutions still suffer from performance related challenges such as long run-time and high bandwidth demand, particularly when the number of involved parties grows.In this paper, we propose a new approach based on threshold additively homomorphic encryption scheme, e.g., Paillier, which enables us to process the bit-set representation of sets under encryption.By doing so, it is feasible to securely compute the intersection of several data sets in an efficient manner.To prove our claims on performance, we compare the communication complexity of our approach with the existing solutions and show performance test results.We also show how the proposed protocol can be extended to securely compute other set operations on multi-party data sets.
An Empirical Investigation of the Relationship between Bitcoin and Developed and Developing Country Stock Markets

Purpose: This study aims to investigate the causal relationships between Bitcoin prices and developed and developing country stock markets.Design/methodology/approach: In the analysis part of the study, the causality test developed by Hacker and Hatemi (2006) was used to identify the causality relationship between Bitcoin and developed and developing country stock markets.Findings: As a result of the analysis, a two-way causality was found between BTC and DJI, among the developed country stock markets.On the other hand, there was a causality relationship from FCHI to BTC, while there was no causality from BTC to FCHI.There was a causality relationship from BTC to N225, while there was no causality from N225 to BTC.Finally, no causality relationship was found between DAX and BTC.Looking at the developing country stock markets, however, there was no causality relationship from BIST to BTC, there was a causality relationship from BTC to BIST.There was no causality relationship from BVSP to BTC, but there was a causality relationship from BTC to BVSP.There was no causality from MOEX to BTC, but there was a causality relationship from BTC to MOEX.There was no causality from BSE to BTC, but it was found that there was a causality relationship from BTC to BSE.As can be seen from the results, it is seen that Bitcoin prices are the cause of the stock markets of developing countries.It has been determined that Bitcoin historical values are effective on BIST, BOVESPA, MOEX Russia and BSE Sensex 30.The findings of the study were discussed in the results section.Originality/value: It is of great importance for investors to follow the developments in the stock market indices subject to research simultaneously with the Bitcoin prices.It is important that investors who will invest in these markets do not ignore the relationship between these markets in portfolio diversification.
A Proposal: Interactively Learning to Summarise Timelines by Reinforcement Learning

Timeline Summarisation (TLS) aims to generate a concise, time-ordered list of events described in sources such as news articles.However, current systems do not provide an adequate way to adapt to new domains nor to focus on the aspects of interest to a particular user.Therefore, we propose a method for interactively learning abstractive TLS using Reinforcement Learning (RL).We define a compound reward function and use RL to finetune an abstractive Multi-document Summarisation (MDS) model, which avoids the need to train using reference summaries.One of the sub-reward functions will be learned interactively from user feedback to ensure the consistency between users' demands and the generated timeline.The other sub-reward functions contribute to topical coherence and linguistic fluency.We plan experiments to evaluate whether our approach could generate accurate and precise timelines tailored for each user.
USING EDMODO AND STUDENTS’ READING MOTIVATION ON STUDENTS’ WRITING ABILITY IN NARRATIVE TEXT

Writing ability is the power of students to write something with a good way. In writing there are some aspects influences in good writing. The aspects are organization, content, grammar, mechanic, and vocabulary. Besides that, having ideas or opinion also influent in writing activity. The population of the research consisted of all the eighth-grade students of SMP 35 Palembang in the academic year of 2020-2021, and there were 32 students of them chosen as the samples of the research. The samples were selected by using proportional random sampling technique, because all of individuals in the population have the same opportunity to be chosen as the sample. In this research, the researcher investigates the correlation among the the use of edmodo, students’ reading motivation and writing ability. In analyzing the data, it was used multiple correlation in SPSS 22.0 Program for Windows. The data analysis shows that Ryx1x2 = 0,663 at the significant level of 0,05. And for the criteria of significant test is gotten Fcount 9,798 &gt;Ftable (0,05) (4,02). The result of hypothesis test showed that there is a significant correlation among the use of edmodo, students’ reading motivation and writing ability.
Implementing a System for Diagnosing Lung Cancer based on Active Contour Algorithms

Lung cancer is a very common cancer, but it only becomes noticeable relatively late in the form of symptoms and complaints. To date, the possibilities of early detection are limited - the examinations tested so far are stressful and not without risk. In particular, people who are at high risk of developing lung cancer should be aware of important warning signs. Due to late detection, often in therapeutically overdosed stages, only 14% of the diagnosed patients are able to survive for a longer period of time (5 years). Once lung cancer begins to cause symptoms, it is usually visible on an X-ray and next, CT scan of the chest may be ordered for a more detailed exam. In this paper we aimed by image processing to develop a system for lung cancer recognition and detect the stage of the disease. In this study, some ultrasonography images with different types of lung cancer and with no lung cancer were tested. Active contour algorithms were designed and applied to the CT images in order to segment the image without clearly defined boundaries. Next, the resulted images have been interpreted. The proposed method for diagnosing lung cancer is useful, because it helps doctors to diagnose if it is the case of lung cancer or other disease.
CLULEX at SemEval-2021 Task 1: A Simple System Goes a Long Way

This paper presents the system we submitted to the first Lexical Complexity Prediction (LCP) Shared Task 2021. The Shared Task provides participants with a new English dataset that includes context of the target word. We participate in the single-word complexity prediction sub-task and focus on feature engineering. Our best system is trained on linguistic features and word embeddings (Pearson’s score of 0.7942). We demonstrate, however, that a simpler feature set achieves comparable results and submit a model trained on 36 linguistic features (Pearson’s score of 0.7925).
Extendability of Causal Graphical Models: Algorithms and Computational Complexity

Finding a consistent DAG extension for a given partially directed acyclic graph (PDAG) is a basic building block used in graphical causal analysis. In 1992, Dor and Tarsi proposed an algorithm with time complexity $O(n^4)$, which has been widely used in causal theory and practice so far. It is a long-standing open question whether an extension can be computed faster and, in particular, it was conjectured that a linear-time method may exist. The main contributions of our work are two-fold: Firstly, we propose a new algorithm for the extension problem for PDAGs which runs in time $O(n^3)$; secondly, we show that, under a computational intractability assumption, our cubic algorithm is optimal. Thus, our impossibility result disproves the conjecture that a linear-time method exists. Based on these results, we present a full complexity landscape for finding extensions in various causal graphical models. We extend the techniques to recognition problems and apply them to design an effective algorithm for closing a PDAG under the orientation rules of Meek.