Reviewerly/exHarmony huggingface dataset:
Relationship between Training Effectiveness and Work Performance: Mediation of Workplace Environment

Background: There is widespread agreement that training and performance are positively associated in the prior studies. However, there is a lack of empirical evidence to establish the mediating role of the workplace environment in the relationship between training and performance.&#x0D; Objectives: The purpose of the study was to examine the relationship between training effectiveness and work performance and the mediating role of the workplace environment.&#x0D; Methods: We used consecutive sampling to select respondents, adopting a descriptive cross-sectional research approach with a sample of 205 Nepalese commercial bank employees across five different commercial banks. To assess the role of the workplace environment in the relationship between training effectiveness and work performance, a self-administered structured questionnaire with 12 items was developed. Data were analyzed using SPSS and analysis of moment structures. The characteristics of the respondents were examined using descriptive statistics such as frequency distribution, and the link between training effectiveness and performance was measured using mean value analysis. Exploratory factor analysis was used to identify the factor structure of the measure used in the study and examine internal reliability. Confirmatory factor analysis and structural equation modeling were used to demonstrate the link between the three components, test the hypothesis, and mediation effect. &#x0D; Results: The findings of training related data indicated that 45.4% of the respondents perceived that training was highly influential, 60.0% were satisfied with the training provided, 43.9% of the respondents reported that their organization focus on both on and off-the-job training, 57.6% felt that training is crucial and 82.4% perceive that training has improved their performance and respondents positively perceived the relationship between training effectiveness and work performance. The exploratory factor analysis revealed that items on work performance, workplace environment, and training effectiveness have higher internal reliability. The hypothesis test results showed a positive association between training effectiveness, job performance, workplace environment, and work performance, and training effectiveness has the highest impact on work performance. Similarly, the results also depicted that the workplace environment mediates training effectiveness and work performance. &#x0D; Conclusion: The study concluded that the more effective the training, the better the employees’ work performance. As a result, greater emphasis requires improving the context and process of training from the employees’ perspective. The study also concluded that the workplace environment affects training effectiveness and work performance. Therefore, it is essential to note that Nepalese organizations should provide practical training and create a favorable work environment for better work performance.
Induction Training for Developing Teachers’ Responsibility Acceptance

Teacher induction training is a vital aspect of formal education system to equip newly inducted teachers with necessary conceptual and pedagogical capacities to improve academic and professional proficiency of novice teachers as indicator of success of the activity.In professional journey, besides conceptual understating of the content, there are several emotions attached with the people and environment that transform a new graduate into a professional teacher.This study aims to explore the impact of induction training on Teachers Responsibility Acceptance regarding students' motivation, achievement, using suitable instructional tools and building relationship with students in academic setting.A sample of 200 newly inducted Public Elementary School Educators comprising of 143 female and 57 male educators was selected through multi stage sampling technique.Teacher Responsibilities Scale (TRS) was used to collect data on responsibility acceptance of teachers.The data were analysed by using paired samples t-test, and One-way ANOVA.The result indicated that induction training caused significant improvement in responsibility acceptance in newly inducted teachers and was beneficial of both male and female teachers with varied academic and professional qualification.
Discrete mixture representations of parametric distribution families: geometry and statistics

We investigate existence and properties of discrete mixture representations $P_\theta =\sum_{i\in E} w_\theta(i) \, Q_i$ for a given family $P_\theta$, $\theta\in\Theta$, of probability measures. The noncentral chi-squared distributions provide a classical example. We obtain existence results and results about geometric and statistical aspects of the problem, the latter including loss of Fisher information, Rao-Blackwellization, asymptotic efficiency and nonparametric maximum likelihood estimation of the mixing probabilities.
Analysis of Stability Covid19 Spread Mathematical Model Type SV1V2EIR Regarding Both Vaccinated and Not Vaccinated Human Population

The Covid19 case dated 11 November 2021 recorded that the human population died from Covid19 (143,595 people) with confirmed cases (4,249,323 cases) and active cases (9,537 cases). Based on these data, it can be concluded that Covid-19 is an acute and deadly disease. In addition to deaths, due to Covid-19, namely the increase in divorce cases, decreased income in the economy and tourism. In this study, the author made a mathematical modeling of Covid19 type as an effort to prevent the spread of Covid19. In the modeling there are human populations susceptible to Covid19 , human populations have been vaccinated , human populations have not been vaccinated , human populations are exposed , human populations are infected with Covid19 , and human populations recovered from Covid19 . The research objectives are 1) to build a mathematical model of Covid19, 2) to determine the fixed point and basic reproduction numbers, and 3) to analyze the stability of the fixed point. This type of research includes applied science research. The research procedure is 1) observing real phenomena, 2) searching literature, 3) determining variables, parameters, and assumptions in mathematical modeling, 4) building a mathematical model of Covid19, 5) analyzing the Covid19 mathematical model in the form of fixed points, basic reproduction numbers, and fixed point stability. The results of the analysis 1) the mathematical model type has a fixed point without disease and an endemic fixed point, 2) a fixed point without disease is stable for the condition, and the endemic fixed point is stable for the condition.
Decentralized Aggregation for Energy-Efficient Federated Learning via Overlapped Clustering and D2D Communications

&lt;p&gt;Federated learning (FL) has emerged as a distributed machine learning (ML) technique to train models without sharing users' private data. In this paper, we propose a decentralized FL scheme that is called \underline{f}ederated \underline{l}earning \underline{e}mpowered \underline{o}verlapped \underline{c}lustering for \underline{d}ecentralized aggregation (FL-EOCD). The introduced FL-EOCD leverages device-to-device (D2D) communications and overlapped clustering to enable decentralized aggregation, where a cluster is defined as a coverage zone of a typical device. The devices located on the overlapped clusters are called bridge devices (BDs). In the proposed FL-EOCD scheme, a clustering topology is envisioned where clusters are connected through BDs, so as the aggregated models of each cluster is disseminated to the other clusters in a decentralized manner without the need for a global aggregator or an additional hop of transmission. Unlike the star-based FL, the proposed FL-EOCD scheme involves a large number of local devices by reusing the RRBs in different non-adjacent clusters. To evaluate our proposed FL-EOCD scheme as opposed to baseline FL schemes, we consider minimizing the overall energy-consumption of devices while maintaining the convergence rate of FL subject to its time constraint. To this end, a joint optimization problem, considering scheduling the local devices/BDs to the CHs and computation frequency allocation, is formulated, where an iterative solution to this joint problem is devised. Extensive simulations are conducted to verify the effectiveness of the proposed FL-EOCD algorithm over FL conventional schemes in terms of energy consumption, latency, and convergence rate.&lt;/p&gt;
Level of Effectiveness of In-House Production through Facility Management vs Outsourcing through Service Provider of Self-Learning Modules

The provision of learning resources at the onset of the COVID-19 pandemic has been the boiling point of the Department of Education's Basic Education Learning Continuity Plan (BE-LCP).This service should be geared toward achieving the strategic objectives of the organization, which largely aim at value creation.This study aims to compare and evaluate the level of effectiveness of in-house through facility management versus outsourcing through a service provider in the production of learning modules.The study employed a mixed method of research and was conducted in the four districts of DepEd Tandag City.Findings of the study revealed that teachers found the materials as a product of outsourcing to be more durable, appealing, and in conformance with the standards.It was also gleaned from the data that teachers were deeply engaged in the printing and sorting of materials during an in-house production.It was also found out that a significant relationship existed in the perception of teachers, school heads, and the Bids and Awards Committee members relative to the cost effectiveness of outsourcing versus in-house production.This cost ineffectiveness is attributed to the involvement of teachers in the in-house production instead of developing the modules or other teaching related tasks.
E-commerce Website Development Using Scrum Methods on Small Business

Websites are viewed primarily as a platform from which SMEs can portray a sophisticated image and advertise/market their products. One of the main factor affect the adoption of e-commerce by SMEs is the enviroment influence. On small business, the process of recording orders and sales transactions was done manually. These lead to customers complaints because they have to wait for a long time to get the product. In addition, the owners of small business find it difficult to know the popular products among customers. Moreover, the companys are eager to reach out to potential customers. Therefore, an e-commerce website is required not only as a solution to all these problems but also as a bridge between the business and their customers. The website was developed using the Scrum method. The application development process required three sprints to complete the seventeen product backlogs. The system testing proved the system is running according to its function and is accepted by the user.
Monitoring Social Distancing Violation

Abstract: At the end of December 2019, Wuhan city in China was hit by the coronavirus illness 2019 (COVID-19). At this point, the infection has spread all over. The majority of governments have adopted a variety of steps to halt the spread of the disease. To prevent the transmission of COVID-19, people should keep a safe distance from one another. SD is one of the most effective treatments. Monitor social separation using a model that incorporates the data from YOLOv4. Starting with a video or photo, the model generates warnings for SD violations. At universities, malls, railway stations, and other public places, we deployed a deep learning system called YOLOv4 to better analyse pedestrian behaviour. A warning sub-system is activated as soon as the SD threshold (SDTH) or violation index (VI) are breached, resulting in an immediate awareness action. In addition, the current literature on SD, object recognition methods, and SD monitoring is thoroughly investigated and discussed in this work. While COVID-19 is still a threat, the model offered is designed to keep track of people in the areas where it is most prevalent.
Choosing the Right Programming Language in Making a Website Backend Using the Waterfall Method

Often we are wrong in choosing a good programming language to create a website backend to process the data we have so that the impact is that our website is not able to handle so many requests with a lot of data so users feel uncomfortable because our application takes a very long time to process. processing each user's request. So in this journal, we will try to discuss the selection of the right programming language in making application backends according to the function of the programming language. In this journal, we will compare the programming languages ​​PHP, Python, Java and C# for the manufacture of an application backend that must be able to process very large population data
Action recognition using deep learning

Several recent studies have shown action recognition to be a popular research topic. Traditionalapproaches have been surpassed by using deep learning approaches. For a more robust recognition,multimodal action recognition systems combine numerous picture modalities (RGB, Depth,Skeleton, and InfraRed). We can distinguish three families of approaches based on the fusion levelin the action recognition pipeline: early fusion, in which the raw modalities are combined beforefeature extraction; intermediate fusion, in which the features for each modality are concatenatedbefore classification; and late fusion, in which the modality-wise classification results arecombined. After analysing the literature, we found the major flaws in each category, which weattempt to overcome by focusing on early-stage fusion, which has received little attention in theliterature.
A Hybrid Proof of Stake-Trust Block Chain Model in Pervasive Social Networking for E-voting System

Technological advancements in block chain (BC)-based frameworks have empowered scientists to create innovative inventions such as e-casting ballots.The traditional agreement models utilized proof-of-work (PoW) in the Bitcoin that affected energy utilization and bargained the adaptability for the ballot framework.The existing works evaluates the trust basically only on the centralized party as it was not feasible because of the dynamic changes in the pervasive social networking (PSN) topology and their characteristics.The present research work proposes a block chain based trust evaluation model for the PSN based BC.The proposed hybrid proof of stake-trust (PST) BC is based on the proof-of trust (PoT) and also the proof of stake (PoS) overcomes the issues that are occurring in the e-vote casting.The trust evaluation is performed for public verification and becomes transparent for each node of PSN.The advantage of the proposed method is that a new block will be designed for trust evaluation during the block generation.The process of sharding erases the workload when the network works fast for the individual nodes provides the sum of their alternative parts.Therefore, the model utilizes the agreements for generating the safe process that guarantee the precision to vote it from the time of the election results.The present research work utilizes the proof of stake-trust based BC resulted in security improvement.The model improves the adaptability and execution of the BC based on the ballot framework provided a secured voting system for the government.The proposed PST-BC model showed better results in terms of latency as 15/s when compared with the existing models merkle hash tree -bloom filter that obtained 107.3/s and performance constraints based electron of 18/s.
The Correlation between Knowledge Level and Cariogenic Animated Video Playback in 10 to 12-year-old Children

Recently, technology has developed massively. The utilization of technology has benefited all aspects of human life. One field has benefitted from technology as a tool for innovation in education. Health education, including dental and oral health, makes the use of audio-visual media as an impact of technology development for teaching tools. The teaching tool is used for children of the school-age (10-12 years old) by utilizing videos that describe cariogenic food. It is conducted since children are likely to consume food causing dental caries. This study aims to identify animated video effects on knowledge enhancement about cariogenic food in children of 10-12 years old. This study is quasi-experimental with a pre-test and post-test design. The study sample included 32 students of 10-12 years old. The students filled out the pre-test sheet, watched animated videos, and completed an online post-test sheet. Data analysis was carried out using the statistical test of the Wilcoxon signed-rank test. The mean pre-test score was 79.17, while the post-test was 82.03. The difference between pre and post-test was 2.86. A normality test of data distribution was conducted using the Shapiro Wilk test and showed that the data distribution was not normal. Wilcoxon test results showed a significance value of 0.274 (p0.05). As a result, an animated video showed no significant increase in knowledge level about cariogenic food in children of 10-12 years old.
Quantum-Enhanced Selection Operators for Evolutionary Algorithms

Genetic algorithms have unique properties which are useful when applied to black box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without the need to calculate a gradient. In this work, we study results obtained from using quantum-enhanced operators within the selection mechanism of a genetic algorithm. Our approach frames the selection process as a minimization of a binary quadratic model with which we encode fitness and distance between members of a population, and we leverage a quantum annealing system to sample low energy solutions for the selection mechanism. We benchmark these quantum-enhanced algorithms against classical algorithms over various black-box objective functions, including the OneMax function, and functions from the IOHProfiler library for black-box optimization. We observe a performance gain in average number of generations to convergence for the quantum-enhanced elitist selection operator in comparison to classical on the OneMax function. We also find that the quantum-enhanced selection operator with non-elitist selection outperform benchmarks on functions with fitness perturbation from the IOHProfiler library. Additionally, we find that in the case of elitist selection, the quantum-enhanced operators outperform classical benchmarks on functions with varying degrees of dummy variables and neutrality.
MANIPULATIVE NATURE OF MEDIA-POLITICAL DISCOURSE

At the present moment, media saturation provides us with a reasonably objective criterion available for examination. In a loose sense, the Internet has become a parallel reality where people live to pursue constantly updated news at the repeated click of a button, which maintains the reader's immediate interest in every sense of the word. In perusing news articles, placing a particular emphasis on their manipulative nature, we can plunge deeply into such language layers as that of the lexical, grammatical, stylistic, etc. In this paper we place our central interest on the lexical aspect of language manipulation because words are the foundation of meaning in speech. A functional-communicative view of the lexical material of the discourse of news articles reveals the effect of a journalist's choice and arrangement of words on the reported news and how the manipulative potential of language unfolds.
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions

Recent machine reading comprehension datasets include extractive and boolean questions but current approaches do not offer integrated support for answering both question types. We present a multilingual machine reading comprehension system and front-end demo that handles boolean questions by providing both a YES/NO answer and highlighting supporting evidence, and handles extractive questions by highlighting the answer in the passage. Our system, GAAMA 2.0, is ranked first on the Tydi QA leaderboard at the time of this writing. We contrast two different implementations of our approach. The first includes several independent stacks of transformers allowing easy deployment of each component. The second is a single stack of transformers utilizing adapters to reduce GPU memory footprint in a resource-constrained environment.
Bio-inspired ensemble feature selection (biefs) and kernel extreme learning machine classifier for breast cancer diagnosis

Breast cancer is a major disease identified in women, affecting 2.1 million women every year, and is the reason for most cancer-related mortality in women, as per the World Health Organization (WHO). For cancer researchers, accurately forecasting the life expectancy of breast cancer patients is a serious challenge. Machine Learning (ML) has acknowledged much interest in the hope of providing correct results, but due to irrelevant features, its modelling methodologies and prediction performance are still a difficulty. To solve this issue, Feature Selection (FS) was also done to verify whether comparable accuracy can be achieved even with lesser number of features or not. Bio-Inspired Ensemble Feature Selection (BIEFS) algorithm is introduced aimed at selecting a subset of features that increase the prediction performance of subsequent classification models while also simplifying their interpretability. BIEFS algorithm uses three feature selection methods such as Adaptive Mutation Enhanced Elephant Herding Optimization (AMEHO), Adaptive Mutation Butterfly Optimization Algorithm (AMBOA), and Adaptive Salp Swarm Algorithm (ASSA) and integrates their normalized outputs for getting quantitative ensemble importance. BIEFS algorithm depends upon the aggregation of multiple FS techniques by Pearson Correlation Coefficient (PCC).This BIEFS algorithm can improve the accuracy of analysis (benign and malignant).
A Classification of Improper Inference Rules

In the natural deduction system for classical propositional logic given by G. Gentzen, there are some inference rules with assumptions discharged by the rule. D. Prawitz calls such inference rules improper as opposed to proper ones. Improper inference rules are more complicated than proper ones and more difficult to understand. In 2022, we provided a sequent system based solely on the application of proper rules. In the present paper, on the basis of our system from 2022, we classify improper inference rules.
Demystifying the Base and Novel Performances for Few-shot Class-incremental Learning

Few-shot class-incremental learning (FSCIL) has addressed challenging real-world scenarios where unseen novel classes continually arrive with few samples. In these scenarios, it is required to develop a model that recognizes the novel classes without forgetting prior knowledge. In other words, FSCIL aims to maintain the base performance and improve the novel performance simultaneously. However, there is little study to investigate the two performances separately. In this paper, we first decompose the entire model into four types of parameters and demonstrate that the tendency of the two performances varies greatly with the updated parameters when the novel classes appear. Based on the analysis, we propose a simple method for FSCIL, coined as NoNPC, which uses normalized prototype classifiers without further training for incremental novel classes. It is shown that our straightforward method has comparable performance with the sophisticated state-of-the-art algorithms.