Reviewerly/exHarmony huggingface dataset:
Evolutionary Algorithm for Software Project Scheduling Considering Team Relationships

During software development, human relationships between team members have a significiant influence on development efficiency. Meanwhile, finishing tasks might require new skills that employees have not yet grasped due to changes of customer requirements or other reasons. However, such factors have not been taken seriously in existing software project scheduling work. To address this, a software project scheduling model considering the team relationships is established, which minimizes the duration and cost of software projects by finding the best employee-task allocation. The model introduces the communication cost factor of each team member, and its quantitative connections with changes in human relationships and the growth of skill proficiency are analyzed, respectively. In addition, a selection mechanism for external employees is designed to meet new skill requirements. To solve the model, a bi-population discrete evolutionary algorithm based on information feedback is proposed. Some initial individuals are generated by utilizing heuristic information of "employee suitability". Adaptive tuning of the subpopulation size is realized by the feedback of "evolutionary quality". The selection probabilities of different crossover operators are adjusted according to the "improvement rate". Additionally, a local search strategy is developed based on the "degree of team cooperation" and the ‘improvement rate’. The experimental results show that by considering human relationships under different communication cost factors, the scheduling performance is significantly improved. Compared with the state-of-the-art algorithms, the proposed algorithm can obtain a schedule with a higher quality in problems of different scales.
The Correlation Between Emotional Intelligence and Speaking Performance

This research aims to find out the correlation between emotional intelligence and speaking performance. This research is quantitative research that uses the correlation as a method. The subject in this research is the second semester English education students at Cokroaminoto Palopo University. The instrument of this research is questionnaire and speaking test, the questionnaire used to know students’ emotional intelligence and speaking test used to know students’ speaking performance. The result of this research shows the significance value Sig. (2-tailed) is 0.312, because the significantly is higher than 0.05 (0.312 &gt; 0.05), it can be concluded that there is no significant correlation between emotional intelligence and speaking performance (H0 is accepted and Ha is rejected) and also the researcher concluded that respondents have high emotional intelligence but very low correlation with speaking performance. It can be state that influential factor on speaking performance comes from linguistic problems.&#x0D;
Desain Pemrograman Visual Pengendalian Nomor Urut

Overvoltage is a voltage that can only be held for a very limited time. Overvoltage that occurs for a time that exceeds the equipment's capacity limit will damage the equipment connected to the network that is subjected to overvoltage. Lightning overvoltage is a periodic overvoltage caused by external overvoltage, both direct and indirect strikes. Lightning overvoltage only lasts a very short time but has a voltage that is so high that it exceeds the operating voltage of the equipment so it is potentially very damaging. Lightning Arrester is an installation safety equipment from overvoltage disturbances due to lightning strikes (Lightning Surge) or by switching surges (Switching Surge). Transformer / power transformer serves to distribute power / electrical power by increasing or decreasing the voltage at the substation. This research focuses on substation equipment, namely Lightning Arrester ASEA type XAR 170A3/144 which is connected to a Power I transformer (transformer) UNINDO 906007289. Good protection is obtained when the arrester is placed as close as possible to the transformer clamp. However, in practice the arrester must be located at a distance X from the transformer being protected. Therefore, the distance must be determined properly so that the resulting protection can be obtained properly. Arrester installation according to theory is 50 meters from the transformer, in practice at the TAMORA Substation the arrester distance is 3 meters from the temporary transformer based on calculations in this final project the best distance is 10.96 meters.
An Analysis Of Code-Mixing Used By A Lecturer In English Class

This research aims to find out the types of code-mixing used by the lecturer in English class. This research used both Qualitative methods. The subject in this research is the lecturer and the first yearof science students at CokroaminotoPalopo University. The instruments of this research are observation sheet and recorder. The result of this research shows that there are three types of code-mixing found in the lecturer’s utterances during the English teaching-learning process. They are Intra sentential, intra lexical, and involve a change of pronunciation and the results showed that the most frequent is intra sentential.&#x0D;
SPIRITUAL BASIS OF SUCCESSFUL PROFESSIONAL SOCIALIZATION OF YOUTH

The article reveals the spiritual foundations of successful professional socialization of student youth in the modern conditions of the information society. The author has defined the essence of the following concepts of the research: «spirituality» as a category of human existence that expresses their ability to create culture and self-creation; «professional socialization»‘ as a person’s entering a profession, acquiring specific role knowledge, when the professional role is connected with the division of labor; «institution of higher education» as an institution of professional socialization of an individual, connected with their choice of profession, deepening of worldview and final consolidation of professional social role; «religious socialization» as the process which is performed by society to transmit a system of religious knowledge from generation to generation, orientations, guidelines acquired during the life-creating period of humanity, nation, individual.&#x0D; The conditions of professional socialization of student youth in the educational space of a higher education institution are highlighted. They are the following: the inclusion of student youth in a creative activating environment and developmental situations, achieving openness of youth in the process of communication and formation of professional competence, active living by an individual of a new personal experience, involving young people in self-analysis and self-evaluation of their own activities, teaching youth self-regulation of their experiences and activities, personal orientation in the process of professional socialization, the system of continuous education.&#x0D; Eight levels of spiritual development of the individuality of each representative of student youth, which is significant for their professional socialization, are distinguished, i.e., the level of the biological individual; the level of the psycho-physiological individual; the level of the mental individual; the level of the social individual; the level of the individual as a subject of volitional action; the level of the continuous intuitive mind, freedom of will; the level of compassion and empathy, emotional sensitivity and love; the level of transcendence.&#x0D; Attention is focused on the main concepts of determining the spiritual values of the professional socialization of student youth in the direction of humanistic psychology (A. Maslow, G. Allport, K. Rogers, etc.).
Tackling new and emerging threats

The threat landscape is never static. If you are to stay one jump ahead of attackers, you need to evolve your defences – with threat awareness and education being among our most important defences.
Word-to-Grammar Index

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Captionizer: Automated Caption Suggestions

More than 3.78 billion people use Social Media and every day millions of images are posted on these platforms. Many users need quotes and phrases for their respective images for which they surf on the internet even for hours which consumes time and energy. So our project will provide automatic quotes and phrases based on your image provided using machine learning and database searching.
Improving state estimation through projection post-processing for activity recognition with application to football

Abstract The past decade has seen an increased interest in human activity recognition based on sensor data. Most often, the sensor data come unannotated, creating the need for fast labelling methods. For assessing the quality of the labelling, an appropriate performance measure has to be chosen. Our main contribution is a novel post-processing method for activity recognition. It improves the accuracy of the classification methods by correcting for unrealistic short activities in the estimate. We also propose a new performance measure, the Locally Time-Shifted Measure (LTS measure), which addresses uncertainty in the times of state changes. The effectiveness of the post-processing method is evaluated, using the novel LTS measure, on the basis of a simulated dataset and a real application on sensor data from football. The simulation study is also used to discuss the choice of the parameters of the post-processing method and the LTS measure.
THE STATES OF DISTANCE TRAINING IN THE UNIVERSITY EDUCATION SYSTEM

Today's education faces modern challenges in the context of profound methodological and content changes in the world. Global realities have a fundamental impact on education processes, research and projected outcomes. As a result of the COVID-19 pandemic and thanks to the development of information technologies, the learning process of higher education is currently organized in a hybrid manner, with combination of face-to-face and distance learning modalities. We were somewhat prepared for it, because yet in 2010, the Government of Armenia had adopted a decision to approve the procedure for distance learning of Higher, Postgraduate and Vocational education specialties (Government of the RA – Hayastani Hanrapetutyun karavarutyan voroshumy. 2010). &#x0D; In order to understand the current issues of distance education and aiming to help to improve the process in the context of hybrid education, we conducted research and explored the process of organization of distance learning from different perspectives, tested its effectiveness and identified various problems at Khachatur Abovian Armenian State Pedagogical University?
Comparison of Adaboost Application to C4.5 and C5.0 Algorithms in Student Graduation Classification

Students become a benchmark used to assess quality and evaluate college learning plans. Therefore, students who graduate not on time can have an effect on accreditation assessment. The characteristics of students who graduate on time or not on time in determining student graduation can be analyzed using classification techniques in data mining, namely the C4.5 and C5.0 algorithms. The purpose of this study is to compare the application of the Adaboost Algorithm to the C4.5 and C5.0 Algorithms in the classification of student graduation. The data used is the graduation data of students of the Statistics Study Program at Tanjungpura University Period I of the 2017/2018 Academic Year to Period II of the 2022/2023 Academic Year. The analysis begins by calculating the entropy, gain and gain ratio values. After that, each data was given the same initial weight and iterated 100 times. Based on the classification results using the C5.0 Algorithm, the attribute that has the highest gain ratio value is school accreditation, meaning that the school accreditation attribute has the most influence in the classification of student graduation. The application of the Adaboost Algorithm to the C5.0 Algorithm is better than the C4.5 Algorithm in classifying the graduation of students of the Untan Statistics Study Program. The Adaboost algorithm was able to increase the accuracy of the C5.0 Algorithm by 12.14%. While in the C4.5 Algorithm, the Adaboost Algorithm increases accuracy by 10.71%.
Supply Chain Resources and Economic Security Based on Artificial Intelligence and Blockchain Multi-Channel Technology

With the rapid growth of social economy and the improvement of people's living standards, the logistics industry not only shows a trend of rapid development, but also faces various business risks. Therefore, supply chain management becomes particularly important. Strengthening supply chain management has become a general trend. The traditional cost-oriented supply chain management model has been unable to meet the requirements of modern enterprise development. Given that the changes in customer demand, production, and sales have not been considered, enterprise resources are easily wasted. How to build a new supply chain model driven by value and customer demand has become one of the problems that large enterprises must solve. The combination of artificial intelligence (AI) and blockchain technology (BT) can realize information sharing, risk sharing, data interaction, and other functions. Through smart contracts, the division of responsibilities among all roles in the supply chain is controlled, which makes the supply chain safer and more efficient. In this paper, the authors designed a new supply chain financial system model that can effectively reduce financial risks, improved the efficiency of capital use, and enhanced the competitiveness of enterprises. The authors compared the traditional supply chain management mode with the management mode under the combination of AI and BT. The supply chain resource and economic security management based on AI and blockchain multichannel technology was more efficient, the system security index was higher, and the internal employee satisfaction of the enterprise also increased by 5.98%. The simulation application system for the internet of things and edge computing has the characteristics of high real-time and scalability. By analyzing the differences between the internet of things and edge computing technologies in terms of device perception, communication mode, data processing, and processing performance, product manufacturing efficiency can be improved to a certain extent.
Similarity Contrastive Capsule Transformation for Image-Text Matching

Image-text matching plays a critical role in bridging the vision and language, which attracts a lot of attention. Most prior works are devoted to making progress through global alignment between image and text or local alignment between words and regions. However, the scalar-based score is not sufficient to infer the complex relationship between image and text. In this paper, we propose a novel Similarity Contrastive Capsule Transformation (SCCT) network to infer the image-text similarity with capsule clustering. Specifically, we can through capsule network to encode the relationship between word-region score vector and phrase-region score vector, then get more accurate predictions with a dynamically changing number of capsules. We conduct experiments on Flickr30K and MSCOCO, which shows our superiority over state-of-the-arts methods.
Extreme Learning Machine for Mass Spectrometry Data Analysis

In this work, we introduce the use of a weighted extreme learning machine (ELM) to give an automated predictive value to mass spectrometry data. In specific, the data obtained with Matrix-Assisted Laser DesorptioMonization-Time of Flight (MALDI-TOF) technique are explored for balanced and unbalanced dataset scenarios, and compared with benchmarking machine learning algorithms (Naive Bayes, Support Vector Machine, Random Forest, and Logistic Regression). Finally, the evaluation of the performance of the proposed weighted ELM was realized in order to determine the most efficient technique in terms of predicting diseases. In the training phase, the weighted ELM reaches the 100% of accuracy, sensitivity and specificity, which are 25% and 30% higher than the rest of benchmarking machine learning algorithms. Meanwhile, in the testing phase results, the ELM observations highlight the scarce bias to predict positive and negative classes in unbalanced datasets.
Real-time considerations for a source-time dominant auralization scheme

A well-designed recording system can capture a moving source without risk of distortions, knowledge of the source or path, or transmission of information back to the source (i.e., a smartphone can reasonably record a plane flying overhead). This necessarily happens in real time. It would be good if signal processing schemes for auralization possessed these properties. Recent work on the NoTAP method of auralization proposed an asynchronous sample rate conversion scheme that keeps track of the (nonuniform) rate of incoming samples to formulate an effective incoming sampling frequency. This value allows the method to predict what frequency regions at the receiver are vulnerable to aliasing or imaging artifacts. Strategies of oversampling and filtering can be used to eliminate these problem regions while preserving as much of the original content as possible given the desired receiver sampling frequency. This approach creates a situation where the receiver processing can run independently of the source/path processing making it attractive for real-time implementation. This presentation discusses the challenges associated with producing a truly real-time scheme. A three-way tradeoff emerges between an interpolation mechanism that generates decorrelated noise, the computational burden, and the nearness to absolute real-time with which one wants the scheme to run.
Motivating housekeeping staff in the lodging industry in Jordan

Purpose – The objective of this research study is to examine the complicated relationship between work motivation, job satisfaction, job dissatisfaction, and turnover intention of the housekeeping staff in selected four and five-star hotel properties in Jordan by using Herzberg’s Two-Factor theory. Design – A survey design was implemented in this research study. The sample for this research study consists of individuals working in the housekeeping departments of four and five star international hotels in Jordan. Methodology – Cluster analysis, correlation, and t-test were utilized for data analysis. The two-step clusters method was used to cluster groups based on the mean values of job dissatisfaction, as it is a unique concept that explains Herzberg’s Two-Factor Theory and also the work-related behaviour of employees. Approach – The validity of Herzberg’s Two-Factor Theory will be examined as well as examining the complicated relationship among work motivation, job satisfaction, job dissatisfaction, and turnover intention. Findings – The results of this study revealed that there is no relationship between job dissatisfaction of housekeeping staff and job satisfaction, and between job dissatisfaction and job motivation. However, there was a positive relationship between their job satisfaction and work motivation and also between their job dissatisfaction and turnover intention. No relationship was found between their work motivation and turnover intention and between their job satisfaction and turnover intention. Among all fourteen motivational factors, work itself (4.25 out of 5.00) and achievement (4.24 out of 5.00) were ranked at the top, which is consistent with Herzberg’s findings. Salary was the least satisfied/preferred factor in this study. Originality of the research – Having a separate job dissatisfaction construct in a research study is a very unique concept that Herzberg’s Two-Factor Theory (1959) developed and which also contributes to the uniqueness and originality of this study.
A Meta-Summary Study of Qualitative Research on Good Teacher Characteristics

This study synthesizes the primary qualitative research findings on the views of students, teachers, school administrators, and parents about the characteristics of a good or effective teacher in the Turkish context.Therefore, a meta-summary method of qualitative research synthesis was employed to aggregate knowledge from the primary qualitative studies published in peer-reviewed journals between 2000 and 2022 in Turkey.Thirty-one research reports meeting the inclusion criteria were included in the analysis.All these reports were assessed based on a set of quality assessment criteria for qualitative studies.Then, the qualitative findings were extracted from the primary research reports, edited, grouped, and abstracted, and the frequency effect sizes were computed for each conceptualized statement of findings.The analyses yielded 42 abstracted statements regarding good teacher characteristics with frequency effect sizes of 19.4-80% clustered under three dimensions, namely personal characteristics, professional characteristics, and characteristics related to teacher-student relationships.The personal characteristics underlined the key personality traits of teachers and their skills.Their professional characteristics were addressed under professional attitudes and professional skills.The findings also highlighted practical measures or characteristics to consider for healthy teacher-student relationships.The study concludes by discussing major findings, limitations, and implications for education and future research.
Real-Time FPGA Implementation of the Semi-global Matching Stereo Vision Algorithm for a 4K/UHD Video Stream

In this paper, we propose a real-time FPGA implementation of the Semi-Global Matching (SGM) stereo vision algorithm. The designed module supports a 4K/Ultra HD ( $$3840 \times 2160$$ pixels @ 30 frames per second) video stream in a 4 pixel per clock (ppc) format and a 64-pixel disparity range. The baseline SGM implementation had to be modified to process pixels in the 4ppc format and meet the timing constrains, however, our version provides results comparable to the original design. The solution has been positively evaluated on the Xilinx VC707 development board with a Virtex-7 FPGA device.
Early Experiences with a Photonic Quantum Simulator for Solving Job Shop Scheduling Problem

Quantum computing is a rapidly developing technology that, in theory, can solve complex computational problems practically intractable for classical computers. Although the technology offers promising breakthroughs, it is only in the early stages of development, and various quantum computer architectures are emerging. One such new development is the photonic quantum computer. Since the work on discrete optimization using different quantum computer architectures is well studied, in this paper, we experiment with solving a toy instance of the Job-Shop Scheduling problem using a hybrid learning algorithm on a photonic quantum computer simulator. The promising results, combined with some highly desirable properties of photonic quantum computers, show that this new architecture is worth considering for further development and investment in the quantum technology landscape.