Reviewerly/exHarmony huggingface dataset:
Improving Software Development Effort Estimation with a Novel Design Pattern Model

A U.S. Midwestern Fortune 500 financial services firm develops software capabilities in-house and requires predictions of project needs for efficient resource allocation decisions across the many projects operating simultaneously. The company develops a novel prediction tool based on the projects’ required software development tasks as described by firm-specific design patterns. The firm provides these predictions within a set of estimates based on industry standard function count methods as well as firm-specific predictive models based on function points and on initial labor assignments. Company management is thus equipped with predictions from multiple methodologies and multiple information sources, enhancing the firm’s ability to predict project needs. Managers aggregate the forecasts, with prediction performance estimated to improve by 35%–49%, measured relative to estimates of the absolute percentage error of the prior method. The improved predictions provide a significant advantage to planning decisions and efficient internal operations. Insights to how managers aggregate the set of forecasts and insights to how the models contribute to the scaled value of information are discussed and further illustrate the benefits of the approach. History: This paper was refereed. This paper was accepted for the Special Issue of INFORMS Journal on Applied Analytics—Decision Analysis.
Evaluation Method of Music Teaching Effect Based on Fusion of Deep Neural Network under the Background of Big Data

Music is an art course that is different from traditional subjects. It not only needs to teach basic music knowledge but also needs to show the timbre contained in music itself, the form of music creation, or the effects expressed by music. The teaching effect evaluation model for traditional subjects can no longer be applied to the teaching effect evaluation of music subjects. If the traditional subject teaching effect evaluation model is used to evaluate the music subject teaching effect, it will easily lead to the inaccuracy of the effect evaluation. Because there is a strong subjectivity in the evaluation of the teaching effect of music subjects, this study combines deep neural network technology to study the feasibility and accuracy of the convolutional neural network (CNN) and long short-term memory neural network (LSTM) method in the evaluation of music teaching effects. It mainly reflects the evaluation under the three characteristics of music basic knowledge, music expression effect, and music innovation effect. The research results show that the CNN and LSTM methods can better extract the three features in the evaluation of music teaching effect, and the maximum prediction error is only 2.23%. Hybrid CNN-LSTM with LSTM neural network has higher accuracy in predicting music teaching effect than single neural network technique. The linear correlation coefficients also all exceeded 0.955.
Locating Faulty Source Code Files to Fix Bug Reports

Open source software is usually released while it still contains bugs. In order to fix a reported bug during maintenance phase, the developer has to search the source code files to identify the faulty ones; this process is called bug localization (BL). Automating BL is a necessity to boost the developer's productivity and enhance the software quality. The paper proposes an information retrieval based approach for retrieving and ranking a list of suspicious faulty source files relevant to a submitted bug report (BR). The proposed approach leverages textual features of the BRs and source files, which are parts-of-speech tagging, lexical and semantic similarity between the source files and BRs, in addition to the source file change history. The effectiveness of the proposed approach was evaluated over three open-source software repositories. Experimental results showed the superiority of the proposed approach over eight previous approaches in terms of top@N and MAP metrics.
Content-Based Collaborative Filtering With Predictive Error Reduction-Based CNN Using IPU Model

Recommender systems (RS) are strong tools for addressing the internet networking overload problems by considering past user ratings on multiple items with auxiliary data and suggests the better item to the end user. Traditional collaborative filtering (CF) and content-based methods were identified the interaction or correlation between users and the items. But they have failed to identify the join user-item interactions and suffering from incomplete cold start (ICS) and complete cold start (CCS) issues. To address the deficiencies of CF-based approaches, this article offers a novel deep learning based error predictions method along with CF based user-item interactions. Initially, incentivized/penalized user-based content-based collaborative filtering (IPU-CBCF) method is introduced for learning low-dimensional vectors of users and items, separately. The simulation results shows that IPU-CBCF using PER-CNN resulted in better performance as compared to the conventional approaches for all performance metrics like F1-score, recall, and precision, respectively.
Deep Convolutional Real Time Model (DCRTM) for American Sign Language (ASL) Recognition

Sign language is a kind of communication rich of expressions, and it has the same properties as spoken languages. In this paper, the authors discuss the use of transfer learning techniques to develop an intelligent system that recognizes American Sign Language. The idea behind was that rather than creating a new model of deep convolutional neural network and spend a lot of time in experimentations, the authors used already pre-trained models to benefit from their advantages. In this study, they used four different models (YOLOv3, real-time model, VGG16, and AlexNet). The obtained results were very encouraging. All of them could recognize more than 90% of images.
E-wallet Adoption, Innovative Behaviour and Technology Readiness in Jordan: The Mediating Effect of Customer Awareness

The objective of this paper is to recognize all different aspects of E-wallet, increase customer awareness and increase E-wallet adoption in Jordon.This study identifies how people in Jordan react to technological changes and how they deal with them.This study analyzes the personal impact of technical aspects for welcoming a new mode of transaction.Methodology: This study has employed a quantitative research method using a descriptive research design.The targeted population for the present study was 660,000 users of orange money in Jordan in 2020.A sample of 370 orange money user was selected based on probability simple random sampling technique.A self-administrated survey questionnaire was designed to adapt and adopt theories from previous studies.A questionnaire was used as a research instrument for collecting data and validated using expert review and pre-testing.Results: The findings demonstrated that there is a significant and direct relationship between innovative behaviour and customer awareness, a significant and direct relationship between technological readiness and customer awareness, and a significant and direct relationship between customer awareness and E-wallet adoption.On the other hand, findings proved that customer awareness mediates the relationship between innovative behaviour and E-wallet adoption and technological readiness and E-wallet adoption.The results show that due to rising awareness related to benefits, E-wallet users are expected to grow if a direct experience with E-wallet impacts future behaviour.
Classroom Behavior Analysis and Evaluation in Physical Education by Using Structure Representation

Behavior analysis plays a critical role in physical education. This paper resorts to computer vision technology to establish a classroom behavior analysis system for physical education. First, the behavior video is collected by a Kinect camera. Then, the behavior is recognized based on the symbiotic relationship and geometric constraints between human posture and interactive objects. The human skeleton is used to describe the behavior subject and the local area boundary boxes are divided with each node in the skeleton as the center. The human posture features are used to learn a structural classification model to recognize human behavior sequence. Finally, the behavior recognition results are used to analyze physical education. The experimental results show that the proposed behavior analysis framework can accurately recognize human behavior during physical education classes.
341.11: Online Perspectives of Deemed Consent Organ Donation Legislation in Nova Scotia, Canada: A Thematic and Content Analysis of Commentary in Facebook Groups

Background: The Canadian province of Nova Scotia recently became the first jurisdiction in North America to implement deemed consent organ donation legislation. Changing the consent models constituted one aspect of a larger provincial program to increase organ and tissue donation and transplantation rates. Deemed consent legislation can be controversial among the public, and public participation is integral to the successful implementation of the program. Objective: Social media constitutes key spaces where people express opinions and discuss topics, and social media discourse can influence public perceptions. This project examined how the public in Nova Scotia were responding to the legislative changes in Facebook groups. Methods: This project analyzed 2,337 comments on 26 relevant posts in 12 different public Nova Scotia-based Facebook groups. We conducted thematic and content analysis on the comments to determine how the public was responding to the legislative changes and how the participants interacted with one another in the discussions. Results: Our thematic analysis revealed principle themes which supported and critiqued the legislation, which raised specific issues, and which reflected on the topic from a neutral perspective. Subthemes showed individuals presenting perspectives through a variety of themes which included compassion, anger, frustration, mistrust, and a range of argumentative tactics. Comments included personal narratives, beliefs about government, altruism, autonomy, misinformation, and reflections on religion and death. Content analysis revealed that Facebook users react to popular comments with “likes” more so than other reactions. Comments with the most reactions included both negative and positive perspectives about the legislation. Personal donation and transplantation success stories as well as attempts to correct misinformation were some of the most “liked” positive comments. Conclusions: The findings provide key insights into Nova Scotian perspectives on deemed consent legislation as well as organ donation and transplantation broadly. The insights derived from this analysis can contribute to public understanding, policy creation, and public outreach efforts that might take place in other jurisdictions considering the enactment of similar legislation. The authors thank and acknowledge Health Canada, Genome Canada, Genome Alberta, and the Canadian Institutes for Health Research for their generous support of Legislative Strategies to Improve Deceased Organ Donation in Canada: A Special Focus on Evaluating the Impact of Opt-Out Legislation in Canada LEADDeR and Precision Medicine CanPREVENT AMR: Applying Precision Medicine Technologies in Canada to Prevent Antibody Mediated Rejection and Premature Kidney Transplant Loss.
THE EFFECT OF THE READING CORNER PROGRAM ON INCREASING READING INTEREST

Based on the identification of the problem, many Indonesian people, especially school children, are still not interested in reading activities. One of the efforts to increase interest in reading promoted by the government is to promote a reading corner program in schools. This study aims to determine the effect of the reading corner program in terms of the quality of the reading corner and the teacher's role in using the reading corner to increase the reading interest of fourth grade elementary school students in the Cijantung-East Jakarta area. By using survey research methods, the research instrument consists of 23 Questionnaire statement items were distributed to 4 schools consisting of 2 public elementary schools and 2 private elementary schools with a total sample of 233 students. Confirmatory Factor Analysis and Structural Equation Modeling (SEM) were performed to analyze instrument validation and test hypotheses. The results of the factor analysis showed that of the 23 instruments tested, there were 6 items that did not meet the requirements because they had an unqualified loading factor so that only 16 statements were used for analysis. The results of the SEM analysis show that the increase in reading interest is not dominantly influenced by the quality of the reading corner itself, but must be accompanied by the role of the teacher. These results indicate that the implementation of the reading corner program in schools is not only physically present but also must be supported by the role of the teacher.
Filter Design

In this chapter, a positive $$\mathscr {L}_1$$ filter is designed for positive nonlinear phase-type semi-Markov jump systems (S-MJSs). By means of the supplementary variable and the plant transformation technique, phase-type S-MJSs are transformed into Markov jump systems (MJSs). Next, according to the normalized membership function, the associated nonlinear MJSs are transformed into the local linear MJSs with specific T-S fuzzy rules.
Impacts of teachers’ Training on secondary school students’ Learning Attitude and performance

The study was carried out in order to examine the impacts of Teaches’ Training on secondary school students’ learning attitude and performance. The study was carried out in the secondary schools of district Loralai. It was hypothesized that Teachers’ training enhances students’ cognitive and physical abilities and encourages the students for the adoption of deep achievements. Students who learn from trained teachers show different and better attitude and performance in academic and social activities than those who do not learn from trained teachers. The sample of this study comprised of the students of secondary schools of district Loralai. A questionnaire regarding the impacts of trained and untrained teachers on the learning attitude and performance of the students was done. Two hundred students were taken as sample from thirty five schools of the twelve union councils of tehsil Bori and one hundred students were taken as sample from twelve schools of the five union councils of tehsil Makhter of district Loralai. It was concluded that trained teachers had much better impacts on students than those of untrained teachers both in rural and urban areas.
Integration and Sharing Mechanism of Vocational Education Resources from the Perspective of Industry-Education Integration

Teaching resource platform construction is a systematic project that needs to be constantly enriched and improved in use. The improvement process should always adhere to the principles of school-enterprise cooperation and joint construction and sharing. To solve the problems of weak service content and imperfect sharing mechanism during the construction of the current vocational education teaching resource library, a general framework for the construction of a shared teaching resource platform is designed. To address the problems of low platform resources differentiation and few sharing paths, an intelligent recommendation method based on coupled collaborative filtering and portrait analysis technology is proposed. By introducing attention mechanism and portrait analysis technology into the coupled collaborative filtering model, the platform users and resources are accurately matched to achieve intelligent sharing of vocational education resources.
Generic Structure Construction of 3D Assembly Model Based on Conjugate Subgraph

As an important resource under the background of digital design technology, 3D assembly model plays a great role in many complex product design occasions. Based on the existing 3D model database, how to find and reuse the effective information that meets their own needs in many models is one of the important ways to provide design efficiency. The research content of this paper is to build a general assembly model according to the requirements of 3D assembly model. Based on the common structure, the general structure aims to integrate as much model information as possible to meet the design needs of designers in different periods and conditions. Due to the large number of components in the 3D assembly model, blindly improving hardware cost is the root of the problem. To improve quality and efficiency from the source, we need to start with the source of input. This paper simplifies the analysis of 3D assembly model information, which can not only improve the construction efficiency of the general structure of 3D assembly model but also highlight the assembly characteristics of 3D assembly model and facilitate the matching of assembly features during the construction process. Firstly, based on the attributes of assembly features and matching relationship, combined with the idea of conjugate, the matching problem between two features in the model is solved by using the idea of conjugate subgraph matching. Secondly, based on the Ullmann algorithm, combined with the definition of conjugate subgraph and related optimization operations, a conjugate subgraph matching algorithm based on vertex screening is proposed. Finally, the construction process of the general structure is proposed, and the general structure of 3D assembly model is established.
Geochemical study of the Ashele Copper-Zinc Deposit using portable X-ray fluorescence spectrometry based on matrix effect correction, Northwest China

Because the Ashele Cu–Zn deposit is located deep below and core samples are difficult to obtain, portable X-ray fluorescence spectroscopy (pXRF), which can be used for rapid and non-destructive analysis in the field, was chosen as the testing instrument. However, the testing accuracy and precision of elements are significantly affected by matrix effects. In this study, 170 samples from the deposit were measured, and the data was corrected using the matrix effect correction (MEC) method instead of the simple linear regression (SLR) method to enhance the data quality, followed by geochemical and correspondence analysis of the data. The results show that calibration data corrected by the MEC method was of greater quality than those corrected by the SLR method, particularly for elements such as Co, Cr, Mo, Sn, Ba, and W. The Ashele Cu–Zn deposit is formed in multi-phase hydrothermal superposition mineralization and is rich in Mg, Fe, and S but deficient in Al, Si, K, and Ca. The main mineralized zones are located at 400–500 m and 200–300 m. Cu, Zn, and Pb are significantly enriched in the mineralization, with the axial zoning sequence of the primary halo (Pb–Zn–Mn–As–Cu–Cd–Sn–Ba–Bi–Sb–Co–W–Mo) showing inverse zoning. The correspondence analysis results also show a clear distinction between non-metallogenesis and metallogenesis. Also, the application of pXRF based on MEC in the Ashele Cu–Zn deposit is effective and can be extended to other types of deposit studies.
Coefficient correction of Ångström–Prescott equation for China and its influence on solar radiation and reference crop evapotranspiration at different temporal and spatial scales

In recognition of the lack of solar radiation (Rs) monitoring data at some meteorological stations, FAO56 recommends the Ångström–Prescott (A–P) equation for estimating Rs and gives recommended coefficient values (recommended values), which are widely used in the estimation of the reference crop evapotranspiration (ETo). Compared with the corrected coefficient values (corrected values), the temporal and spatial distribution characteristics of the average relative errors (MRE) of Rs and ETo in China caused by the recommended values are studied. The MRE of the estimated annual Rs and ETo all tend to increase from the northwest inland to the southeast coast. However, the monthly Rs and the monthly ETo have an MRE that presents a double-valley curve and a unimodal curve, respectively, of the year, leading to inconsistency between the inter-monthly spatial distribution of the MRE of the monthly Rs and the monthly ETo. The recommended values have considerable influence on the daily Rs in the year and the daily ETo during the critical crop growth period (April–September). Therefore, the suitability of the recommended values should be scrutinized by taking into account the influence on Rs and ETo at different temporal and spatial scales. For the lack of Rs monitoring data in China, this paper can support whether the recommended values can be used in estimating Rs and ETo by using A-P equation and its error.
The Correlation between Self-Regulation Learning and Listening Comprehension of the Eleventh Grade Students

This research aimed to describe the correlation between self-regulation and listening comprehension. The method which was used in this research was correlational research. The population of the research was the eleventh grade students of SMA Muhammadyah 2 Palembang. There were 105 students from four classes as the population in this research. The sample was taken by using a total sampling which consists of 99 students. Furthermore, there were two variables in this research. The first one was self-regulation (variable X) and the second one was listening comprehension (variable Y). The self-regulation score was taken from the MSLQ questionnaire whereas the student’ comprehension in listening was taken from TOEFL Junior descriptive statistics for the MSLQ and listening comprehension. Based on Pearson Product Moment Correlation Coefficient, the result indicated that the pattern of correlation between self-regulation and listening comprehension was negative. The correlation coefficient or the r-obtained (.300) was higher than r-table (.0.1663). Then, the level of probability (p) significance (sig.2-tailed) was .003. It means that p (0.03) was lower than .05. Thus, there was a significant correlation between the self-regulation and listening comprehension.
Estimation of predictive performance in high-dimensional data settings using learning curves

In high-dimensional prediction settings, it remains challenging to reliably estimate the test performance. To address this challenge, a novel performance estimation framework is presented. This framework, called Learn2Evaluate, is based on learning curves by fitting a smooth monotone curve depicting test performance as a function of the sample size. Learn2Evaluate has several advantages compared to commonly applied performance estimation methodologies. Firstly, a learning curve offers a graphical overview of a learner. This overview assists in assessing the potential benefit of adding training samples and it provides a more complete comparison between learners than performance estimates at a fixed subsample size. Secondly, a learning curve facilitates in estimating the performance at the total sample size rather than a subsample size. Thirdly, Learn2Evaluate allows the computation of a theoretically justified and useful lower confidence bound. Furthermore, this bound may be tightened by performing a bias correction. The benefits of Learn2Evaluate are illustrated by a simulation study and applications to omics data.