Reviewerly/exHarmony huggingface dataset:
The Field of Requirements Engineering Education

Requirements engineering (RE) is an essential part of the software development process. Good RE, among others, is the basis for high quality software, considerably reduces the risk for software projects to fail entirely or with budget-overspending and is crucial for coordinating systems and software engineering. Thus, RE education is a vital part of software engineering curricula. However, a central concept of what RE education comprise and how to best teach RE is lacking. Therefore, we conducted a systematic literature review of the field and provide a systematic map describing the state of the RE education field. Results for key trends in RE instruction of the past decade include involvement of real or realistic stakeholders, teaching predominantly elicitation as an RE activity, and increasing student factors such as motivation or communication skills.
Students’ perceptions of integrating a contribution measurement tool in software engineering projects

Measuring developers’ and teams’ contributions in project-based software engineering courses is challenging. Instructors require measurement tools to identify early improvement opportunities and simplify the assessment of students’ contributions to projects. Besides, students could appreciate detailed, timely, and valuable feedback that encapsulates the multi-faceted nature of their contribution to improve their skills. With the rise of software repositories, data-driven tools that mine and visualize repository data have been proposed to help assess and improve software engineering courses. Still, little is known about students’ perceptions of integrating these tools to measure project contributions. This paper analyzes students’ perceptions of integrating a contribution measurement tool in agile software engineering projects. We collected the perceptions of 65 undergraduate students developing projects through a mixed-method approach. Our results show several benefits of using these tools in software engineering courses, providing valuable feedback, intending to use such tools in projects, and facilitating timely and helpful feedback. Additionally, students described the benefits of nine agile practices, including coding standards, reviews and inspections, and planning games. These tools were perceived as useful, easy to use, and intended to be used in future projects. Six challenges for integrating these tools in a course setting related to adoption, learning curves, configuration issues, measurement completeness, team dynamics, and Goodhart’s law were found. Our results showed the acceptance and utility of such tools to aid students in software engineering projects.
Construction of Sports Training Evaluation System Based on Virtual Reality and Motion Capture

With the development of social economy and the improvement of living standard, people begin to seek more realistic simulation experience. As a new multi-channel information fusion technology, virtual reality (VR) is bringing people a brand-new human-computer interaction experience. The existing motion posture capture system needs to wear additional auxiliary equipment for the experimenter, which is inconvenient to operate; And some equipment can’t be applied to outdoor scenes, so the scope of use is limited; The method based on monocular estimation of human body’s three-dimensional posture has low accuracy and poor robustness. Virtual motion based on motion capture verifies the isomorphic motion between life ontology and virtual life, which provides unprecedented possibility for exploring and expressing the essence of life. This paper introduces VR technology and motion capture technology into the protection field of kungfu culture, develops a virtual system that gives consideration to both appreciation and learning, and solves the problem of protection and inheritance. The appreciation system tracks and identifies the user’s body through motion capture technology, uses VR engine to realize motion data driven character model, and realizes the visualization of characters in virtual environment and quantitative evaluation of results.
Basketball Training System Based on 3D Motion Intelligent Recognition Technology

With the rapid development in the fields of computer vision and deep learning, the combination of computer and camera has been used to replace the human eye to achieve the segmentation, classification and recognition of targets, especially in the detection, tracking and action analysis of motion images. The aim of this paper is to investigate the design and implementation of a basketball-assisted training system based on 3D motion recognition technology. This paper discusses the background and significance of basketball assisted training, provides an in-depth analysis of domestic and international research on shooting and assisted training, proposes a human motion recognition model using graphical neural networks to establish spatio-temporal graphical convolutional neural networks, and designs and develops an assisted system that can help basketball players in sports training. The platform for implementing the system and the technology used are introduced, and the system is verified. The implementation platform and the technology used are presented and the effectiveness of the system is verified.
Software Defect Prediction Method Based on RGB Image and Deep Learning

At present, the new software defect prediction method based on deep learning has problems such as large training sample size, low quality of training sample, low efficiency of software defect prediction. In this paper, the exe file is converted into RGB images preprocessed to generate a self-made training set. Then the residual neural network introduced compression excitation module is used to train it. The new software to be tested was classified by improved residual neural network. Comparing the similarity of classification, the optimal training set is obtained which bring a training set is highly similar to the software tested. After obtaining the optimal training set as the training sample, a hybrid Wolf Pack Algorithm is introduced to search the optimal hyperparameters that affect the efficiency of the deep neural network algorithm, which improves the software defect prediction performance and saves the prediction time. Experimental results show that the method proposed in this paper can predict the defects of new software faster in a totally prediction time and a better performance in AUC.
HedgeRank: Heterogeneity-Aware, Energy-Efficient Partitioning of Personalized PageRank at the Edge

Personalized PageRank (PPR) is a widely used graph processing algorithm used to calculate the importance of source nodes in a graph. Generally, PPR is executed by using a high-performance microprocessor of a server, but it needs to be executed on edge devices to guarantee data privacy and network latency. However, since PPR has a variety of computation/memory characteristics that vary depending on the graph datasets, it causes performance/energy inefficiency when it is executed on edge devices with limited hardware resources. In this paper, we propose HedgeRank, a heterogeneity-aware, energy-efficient, partitioning technique of personalized PageRank at the edge. HedgeRank partitions the PPR subprocesses and allocates them to appropriate edge devices by considering their computation capability and energy efficiency. When combining low-power and high-performance edge devices, HedgeRank improves the execution time and energy consumption of PPR execution by up to 26.7% and 15.2% compared to the state-of-the-art PPR technique.
MENINGKATKAN KETERAMPILAN MENYIMAK CERITA FABEL MELALUI MODEL PAIRED STORYTELLING MELALUI MEDIA WAYANG KARTUN DI KELAS II MI BPPI CISAAT DENGAN TEKNIK PROJECT BASE LEARNING (PJBL)

This study was conducted to overcome the problem of lack of enthusiasm and concentration of students when the teacher explains in front of the class in class II MI BPPI Cisaat. The purpose of the study was to improve listening skills to fable stories through the Paired Storytelling model using cartoon puppet media with the application of Project Based Learning (PjBL) techniques in the class.&#x0D; This study involved 26 students (12 boys and 14 girls) as subjects. Data were collected through observation methods and written tests. The results showed an increase in students' listening skills. In the first cycle, the average class score was 69 with a percentage of listening skills completeness of 70.91% (sufficient category) and learning outcomes completeness of 61.5% (unfinished category). In the second cycle, with the application of the Paired Storytelling model using cartoon puppet media and Project Based Learning (PjBL) techniques, the average class score increased to 82. The percentage of listening skills completeness increased to 81.73% (good category) with a percentage of learning outcomes completeness of 84.6% (complete category).&#x0D; Comparative analysis between the results of the first and second cycles shows a significant improvement in students' listening skills in dealing with fable stories in class II MI BPPI Cisaat. This research encourages a better and deeper understanding of listening skills through the implemented learning model.
Digital culture of the head of an educational institution: Methodological approaches to research

Introduction. The current contradictions between challenges of an intensively developing digital environment and the personal and professional values of the head of an educational institution, between theoretical knowledge of digitalization and its application in management practice require a detailed study of the phenomenon of ‘digital culture of the head of an educational institution’ and a methodological clarification of both the phenomenon itself and the structural components of digital culture, which influence the successfulness and efficiency of the headteacher in solving digital tasks. The aim of the article is to identify and clarify the methodological aspects of the phenomenon of ‘digital culture of the head of an educational institution’. Materials and Methods. The study is conducted through a logical analysis of scientific theories, system analysis and theoretical justification of the conclusions existing in science and practice about the application of methodological approaches to studying the category of digital culture of the head of an educational institution. The research methods of specification, modelling and formalization were used to substantiate the conceptual construct of the digital culture of the head of an educational institution. Results. The article explores the phenomenon of ‘digital culture of the head of an educational institution’, the significance of which is determined by the fact that it is represented as a new type of culture, which creates a new space of values and meanings, contributes to the emergence of other cultural practices and forms of human activities. The authors have identified methodological aspects of the phenomenon of ‘digital culture of the head of an educational institution’ based on the application of interrelated and complementary scientific approaches that provide a full and deep understanding of this phenomenon: system-activity, culturological, axiological, praxiological and setting incremental ones. According to the networking incremental approach, the authors justify the conceptual construct of the digital culture of the head of an educational institution, which includes the following elements: digital thinking, digital affiliation, digital adaptation, and digital experience. Conclusions. The article concludes that the considered methodological approaches are complementary, providing a wide range of methodological tools to study the phenomenon of ‘digital culture of the head of educational institution’. The combination of the approaches helps to deepen the understanding of different aspects of digital culture, their interconnection, and their influence on the behavior and activities of the head of an educational institution in the digital environment.
AWWA Awards

Journal AWWAVolume 115, Issue 7 p. 80-85 AWWA Awards AWWA Awards First published: 31 August 2023 https://doi.org/10.1002/awwa.2148Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Volume115, Issue7September 2023Pages 80-85 RelatedInformation
E-commerce User Recommendation Algorithm Based on Social Relationship Characteristics and Improved K-Means Algorithm

Abstract In the era of the Internet, information data continue to accumulate, and the explosive growth of network information explosion leads to the reduction of the accuracy of users’ access to information. To enhance the user experience and purchasing desire of e-commerce users, a e-commerce user recommendation algorithm based on social relationship characteristics and improved K-means algorithm is proposed. It combines the Automatic Time Division Dynamic Topic Model based on adaptive time slice division for building a strength calculation model in view of the characteristics of social relations. Then, it proposes an e-commerce user recommendation algorithm in view of the improved K-means algorithm to improve the accuracy of topic feature extraction and user recommendation. The experiment illustrates that there is no fluctuation in the clustering function of the improved K-means algorithm, and the highest, lowest, and average accuracy remain consistent under the three datasets, with average accuracy of 78.9%, 84.5%, and 95.9%, respectively. The community discovery-based friend recommendation algorithm presented in the study has the highest accuracy, illustrating that improving the K-means algorithm can further improve recommendation accuracy. The accuracy of the feature extraction method in view of alternative cost is 0.63, which improves the accuracy by about 9%. The results indicate that this study can provide technical support for user recommendations on e-commerce platforms.
Modeling and optimization of regular multi-stage multi-product gravity classifiers

The issues of processing bulk materials are of great importance in the energy, pharmacology, and chemical industries. New technologies have new requirements for the granulometric characteristics of individual components of powders and their mixtures. Often, the components of a mixture differ in their properties and grain sizes, thus, multi-stage processing of these mixtures is required to obtain powders with desired technological properties. Optimal control of the structure and operating modes of complex multi-stage apparatus to obtain powders with desired properties is a topical task. Methods of probability theory and fractional mass balance equations are used to model multistage gravitational classifiers. Mathematical programming methods are applied to solve optimization problems of powder classification. A generalized method of combinatorial matrix description of an open stage of regular multiproduct classifying elements for an arbitrary number of finished products is proposed. The proposed model of the considered stage is used for the matrix description of a closed system of devices with an arbitrary flow structure. The authors have determined the best structures and operating modes of individual stages to obtain powders with specified technological properties. The developed approach makes it possible to optimally control the structure of the cascade of classifiers and the modes of operation of its individual stages to obtain powders with desired technological properties, as well as to design systems for predictive diagnostics of the state of these installations in the energy, pharmacological and chemical industries.
Optimization of Information System Resources Integration in Port Clusters

With the rapid development of information science and technology and the Internet.
Development of Engklek Game Media Based on Higher Order Thinking Skills in Learning Science of Class V Elementary School

The students still have a limited understanding of science learning, particularly the topic of food chains, due to the lack of diverse media, resulting in less engaging lessons that fail to spark the students' enthusiasm to actively participate in the learning process. This research aims to determine the feasibility of an engklek game medium based on higher-order reasoning skills in science learning in the V grade elementary school. This research uses the ADDIE model development research, which includes five phases: analysis, development design, implementation and evaluation. Data collection using questionnaires and observations. The research sample consisted of teachers and 5th grade students of one of the public elementary schools in Bogor regency. Media validation through media expert tests achieved a score of 84% with a very good category, material validation achieved a score of 83% with a very good category. After conducting tests, students and teachers also give ratings on the game media created. Based on the results obtained, it shows that the engklek game media based on higher-order thinking skills are suitable for instilling higher-order thinking skills in the participants.
Fine-Grained Cooperative Coevolution in a Single Population: Between Evolution and Swarm Intelligence

Particle Swarm Optimisation (PSO) and Evolutionary Algorithms (EAs) differ in various ways, in particular with respect to information sharing and diversity management, making their scopes of applications very diverse. Combining the advantages of both approaches is very attractive and has been successfully achieved through hybridisation. Another possible improvement, notably for addressing scalability issues, is cooperation. It has first been developed for co-evolution in EA techniques and it is now used in PSO. However, until now, attempts to make PSO cooperate have been based on multi-population schemes almost exclusively. The focus of this paper is set on single-population schemes, or fine-grained cooperation. By analogy with an evolutionary scheme that has long been proved effective, the fly algorithm (FA), we design and compare a cooperative PSO (coPSO), and a PSO-flavoured fly algorithm. Experiments run on a benchmark, the Lamp problem, show that fine-grained cooperation based on marginal fitness evaluations and steady-state schemes outperforms classical techniques when the dimension of the problem increases. These preliminary results highlight interesting future directions of research on fine-grained cooperation schemes, by combining features of PSO and FA.
Foundations of Intellectual Freedom. Emily J. M. Knox. Chicago: ALA Neal-Schumann, 2023. 144 p. Paper, $54.99 (978-0-8389-3783-9).

Foundations of Intellectual Freedom is an introduction to the concept of intellectual freedom, encompassing its history and intersections with concepts including freedom of expression, censorship, privacy, and copyright. The book follows the outline of the eight-week course Intellectual Freedom and Censorship, taught by author Emily J. M. Knox at the School of Information Sciences at the University of Illinois at Urbana-Champaign. While this text is an excellent accompaniment to that course, it will also be useful for any information professional wanting to develop a foundational understanding of information freedom and recent conversations in the field. In addition to the discussion within each chapter, each chapter ends with an annotated bibliography of recent or important related works a reader may want to review. The titles in the annotated bibliography come at the discussion from a variety of angles and viewpoints. Each chapter also includes a bibliography of cited references. While Knox notes that the focus of the text is intellectual freedom in the context of the United States, she also includes information about internationally focused organizations. She notes that the conversation about intellectual freedom at the time of publication is influenced by the realities of an ongoing pandemic, the insurrection of January 6, 2021, and an increasing number of book challenges.
From Traditional Data Models to Blockchain Technology: A Polyglot Persistence Approach to Store the Electronic Health Record

The health sector has demanded computational solutions from academia and the software industry that ensure data security, interoperability, and reliability in software systems. Blockchain technology has been used in healthcare, and numerous studies have used its foundations and characteristics to provide solutions for the ecosystem of healthcare organizations. However, there is a lack of studies investigating the combination of Blockchain technology with different database models to meet the data management demands in the healthcare sector. This article presents a service-based software architecture to enable electronic health record (EHR) storage in different database models. The proposed approach uses the concept of polyglot persistence, whose characteristics of referential data integrity, data schema flexibility, data immutability, and authorized access were considered in the development of a software architecture that meets the demands of data management in the healthcare industry. To guarantee the characteristics of immutability and authorized access to EHR data, database management systems with Blockchain technology resources and platforms that automate the execution of smart contracts in Blockchain networks were added to the proposed software architecture. To evaluate the proposed solution, the application of outpatient care in a health organization in Brazil was remodeled using the concept of polyglot persistence. The results showed that the combination of DBMS with Blockchain resources and platforms that run DApps is a viable alternative to represent data heterogeneity and reduce the financial cost of using Blockchain technology in the healthcare sector.
CENTAL at TSAR-2022 Shared Task: How Does Context Impact BERT-Generated Substitutions for Lexical Simplification?

Rodrigo Wilkens, David Alfter, Rémi Cardon, Isabelle Gribomont, Adrien Bibal, Watrin Patrick, Marie-Catherine De marneffe, Thomas François. Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022). 2022.
CILS at TSAR-2022 Shared Task: Investigating the Applicability of Lexical Substitution Methods for Lexical Simplification

Lexical simplification — which aims to simplify complex text through the replacement of difficult words using simpler alternatives while maintaining the meaning of the given text — is popular as a way of improving text accessibility for both people and computers. First, lexical simplification through substitution can improve the understandability of complex text for, for example, non-native speakers, second language learners, and people with low literacy. Second, its usefulness has been demonstrated in many natural language processing problems like data augmentation, paraphrase generation, or word sense induction. In this paper, we investigated the applicability of existing unsupervised lexical substitution methods based on pre-trained contextual embedding models and WordNet, which incorporate Context Information, for Lexical Simplification (CILS). Although the performance of this CILS approach has been outstanding in lexical substitution tasks, its usefulness was limited at the TSAR-2022 shared task on lexical simplification. Consequently, a minimally supervised approach with careful tuning to a given simplification task may work better than unsupervised methods. Our investigation also encouraged further work on evaluating the simplicity of potential candidates and incorporating them into the lexical simplification methods.