Reviewerly/exHarmony huggingface dataset:
Application Research of Computer Bibliometric Analysis Based on Cluster Analysis

Bibliometric analysis is a branch of library and information science that uses mathematical statistics to quantitatively describe, evaluate, and predict academic status and forwarding trends. It usually contains certain characteristics of literature that can be quantified. Based on the basic information processing unit, this paper uses cluster analysis technology to study the development status of general music appreciation courses in comprehensive colleges and universities. Based on the principles of scientometrics and information visualization in computer programs, using Citespace information visualization software, Quantitative analysis was carried out on more than 350 literatures in the research field of general music appreciation courses in Chinese comprehensive universities provided by CNKI database, and corresponding knowledge maps were drawn. Through the analysis of knowledge map, the current situation of general music appreciation courses in comprehensive colleges and universities is further clarified, and some ideas are provided for the development and exploration of general music courses in the future.
What do students prefer - Use Cases, User Story or Design Thinking Techniques?

Teaching software requirements specifications can be challenging since students must learn the notations and representation of the problem domain in different software development contexts. So, one of the factors that impact the quality of requirements specifications can be the type of training received by software engineers. Given this context, In this paper, we present an exploratory study about the application of three different approaches for requirements specifications: in a traditional software context, applying UML Use Cases; in an agile context with User Stories; and a new technique proposed in Design Thinking (DT) context. We analyzed undergraduate students’ perceptions about the application of each one, including the ease, difficulty, usefulness, and adoption in future projects. The results present the participants’ perceptions about the techniques, and point out that the User Story is the easiest, and the most limited and most appropriate for the initial phases of a software process. The Use Case and the UJB shared a good part of the difficulties and facilities. But, the latter offers a wider range in relation to its use. The article shows SE teachers the benefits that each approach can offer not only for the specification of requirements, but for the entire software development process. Our research benefits instructors interested in understanding the students’ perceptions about software requirements specifications in such contexts, contributing to software quality teaching of them.
Research on the construction of multiple linear regression model based on satisfaction

Regression model is a mathematical model that quantitatively describes the statistical relationship and studies the relationship between the target and the predictor. Building a satisfaction model is of great significance for solving many practical problems. In view of this, this paper will use the method of multiple linear regression, take the satisfaction of innovation and entrepreneurship education as an example, through the fitting test of the collected 2000 people and nearly 100000 pieces of information big data, and constantly carry out computer simulation, Multivariate linear regression model based on satisfaction, and find out the factors affecting satisfaction.
Evaluating Approaches to Selecting Design Thinking Techniques : Quantitative and Qualitative Analysis

Context: Requirements Engineering (RE) is essential to software quality. Studies have shown that software engineers often make mistakes, such as insufficient or misunderstood requirements. Therefore, it is necessary to support all the RE phases, especially eliciting requirements. In this context, Design Thinking (DT) is commonly used to deal with these problems. DT aims at bringing quality to software development to achieve users’ needs. It has a set of techniques and methods that can help software engineers properly elicit requirements to achieve this goal. However, in the literature, there are several techniques and selecting an appropriate one is not a trivial task. Some approaches support the selection of techniques, including DTA4RE and Selection Universe. Objective: This paper aims to analyze the performance of these two approaches to selecting DT techniques in terms of accuracy. Method: We conducted a controlled experiment to obtain the data. We have applied quantitative and qualitative analysis to the data. Results: Regarding the quantitative results, we found no significant difference in accuracy between the Universe Selection approaches and the DTA4RE. Regarding the qualitative results, we found that grouping the techniques into categories presented by the Selection Universe allowed us to reduce the search time for the techniques from DT since the approach allowed us to associate the system features with the techniques that can be used. Conclusion: We found that the two approaches satisfactorily supported the selection of DT techniques for various requirements elicitation activities. In the participants’ perception, the selection universe was helpful because the approach is intuitive. Also, the categorization of techniques made it easier to find the appropriate techniques for the proposed scenarios.
An Intelligent Patient Admission Model of Day Surgery Using Heterogeneous Data with Semi-Supervised Learning

In recent years, day surgery has established a strong reputation and popularity. It has a number of advantages over traditional surgery, including a shorter hospital stay, a lower risk of hospital-associated infections, and a higher cost efficiency. However, the patient admission criteria for day surgery were primarily dependent on manual guidelines defined by specialists, which lacked data driving from the real world and potentially wasted a lot of medical resources. In this paper, we proposed a day surgery patient admission algorithm, which is built by semi-supervised learning that combines both structured data and unstructured diagnoses to help surgeons make speedy admission decisions. We test this algorithm with the clinical data of day surgery patients who underwent laparoscopic cholecystectomy at West China Hospital and achieve an accuracy of 0.85 and an f1-score of 0.83, as well as reaching 0.97 on the precision score. The result is potentially broadly applicable to more day surgery types.
Teaching software processes from different application domains

In a current application development scenario in different environments, technologies and contexts, such as IoT, Blockchain, Machine Learning and Cloud Computing, there is a need for particular solutions for domain-specific software development processes. The proper definition of software processes requires understanding the involved teams and organization’s particularities and specialized technical knowledge in Software Engineering. Although it is an essential part of Software Engineering, many university curricula do not dedicate as much effort to teach software processes, focusing more on the basic principles of Software Engineering, such as requirements, architecture and programming languages. Another important aspect of software processes is modeling. The modeling of a software process provides a basis for managing, automating and supporting the software process improvement. In this context, teaching software process modeling becomes challenging, mainly due to the great emphasis on theory and few practices. This work presents an experience report teaching the definition and modeling of software processes in different domains. We applied in the discipline of software processes a practice for defining and modeling processes in various application domains, such as: IoT, cloud, mobile, critical systems, self-adaptive systems and games. The processes were modeled in the Eclipse Process Framework (EPF) Composer tool based on references from the literature for each domain. In the end, we evaluated the process modeling practice with the students. We concluded that the modeling tool and the maturity in the domain are essential for the good performance of the process.
An Ontology to support Knowledge Management Solutions for Human-Computer Interaction Design

Developing interactive systems is a challenging task that involves concerns related to the human-computer interaction (HCI), such as usability and user experience. Therefore, HCI design is a core issue to the quality of such systems. HCI design often involves people with different backgrounds (e.g., Arts, Software Engineering, Design). This makes knowledge transfer a challenging issue due to the lack of a common conceptualization about HCI design, leading to semantic interoperability problems, such as ambiguity and imprecision when interpreting shared information. Ontologies have been acknowledged as a successful approach to represent domain knowledge and support knowledge-based solutions. Hence, in this work, we propose to explore the use of ontologies to represent structured knowledge of HCI design and improve knowledge sharing in this context. We developed the Human-Computer Interaction Design Ontology (HCIDO), which is part of the Human-Computer Interaction Ontology Network (HCI-ON) and is connected to the Software Engineering Ontology Network (SEON). By making knowledge related to the HCI design domain explicit and structured, HCIDO helped us to develop KTID, a tool that aims to support capturing and sharing knowledge to aid in HCI design by allowing HCI designers to annotate information about design choices in design artifacts shared with HCI design stakeholders. Preliminary results indicate that the tool can be particularly useful for novice HCI designers.
Space and Time as A Priori Forms in the Works of Hermann Cohen and Ivan Lapshin

In the late nineteenth and early twentieth centuries the need to rethink the status of space and time which Kant considered to be a priori forms of sensibility was prompted by the emergence of new approaches to the methodology of scientific cognition. In neo-Kantian interpretation these cognitive forms acquire a special epistemological status, manifesting themselves in theoretical research as “pre-given” foundations of knowledge. It seems necessary to conduct a comparative analysis of two interconnected neo-Kantian concepts, of Hermann Cohen and Ivan Lapshin. Studying Kant’s philosophy since his student days, Lapshin gradually came to the conclusion that the need to clarify and develop Kant’s transcendental method was dictated by the development of scientific knowledge. Indeed, the works of the Russian neo-Kantian contain echoes and polemical adjustments of Cohen’s spatio-temporal ideas. Our study has revealed common epistemological attitudes in Cohen and Lapshin: the wish to improve elements of Kantian philosophy, adjusting them to prove the possibility of scientific-theoretical cognition and of overcoming psychologism and developing the logicistic approach to the critique of cognition. Each of the two authors developed their own “mechanism” of reducing space and time to a range of intellectual procedures for the construction of the object of knowledge. In Cohen’s account space and time pre-establish the language of observation and found all scientific-theoretical work. Lapshin, on the other hand, in discussing the formal and substantive features of these categories (the introduction of “axioms” of time, the need to specify the concepts of time and space through other categories), notes that their use in scientific judgment implies an epistemological givenness of the concept of the object. This I see as a variant of solving one and the same epistemological task. I submit that Lapshin worked out an independent concept of space and time as cognitive forms that are congruent with the spirit of European neo-Kantianism and contemporary science.
Ya. M. Lisovoy’s collection destiny: historical and library aspects

The study of the unique bibliophile heritage of writers, historians, philosophers, scientists and artists who happened to be far from Russia is important from the point of view of understanding how the best representatives of Russian emigration sought to preserve the collections that they managed to bring out of Russia and multiply over the years of living abroad. Fortunately, many collections were returned to their homeland, but many rare priceless materials remained abroad forever. The problem of book collections existence, which for one reason or another and at different times fell into the category of “prohibited”, acquired special significance at the end of 1980s – the beginning of 1990s, after the abolition of all censorship restrictions in the country, and, as a result, the opening of library special collections and archives. The purpose of the article is – on the basis of a brief source review of emigrants’ collections which entered the leading libraries of Russia from abroad, and the collection gathered by the White Guard emigrant officer and politician, historian, publicist, bibliographer Ya. M. Lisovoy – to illustrate the need to move from the analytical stage of research activity in this subject field to the synthetic one aimed at accumulating research intentions on a general cultural scale. The fate of the collection in the period from 1915 to 1992 is traced, its structure is presented, and the analysis of Ya. M. Lisovoy publishing activity is undertaken.
Determinants of Income Diversification among Famers in the Kano River Irrigation Project (KRIP), Nigeria

Income diversification is examined in two ways: as a change in agricultural activity, or as an increase in the number of income sources. The primary source of income for rural Nigerian is agriculture. This study is conducted to access the level of income diversification among beneficiaries and non-beneficiaries of Kano River irrigation Project. The result indicated that crop production account for 65% of the beneficiaries’ household income, with other agricultural activities accounting for 18% of the total. While non- agricultural activities account for only 18%. For the non-beneficiaries the result indicated that crop production accounts for 44% of their income other agricultural activities (Fishing, livestock, poultry,) account for 13%, while Non-agricultural activities account for 43 percent of their income. Income diversification is found to be influenced by (age, household size, cultivated area, educational attainment, credit constraints, and annual income. The results show that these elements need to be examined by decision makers in the development of agricultural and non-agricultural projects in this study area.
A Study to Analyze the Students’ Intentions to use the Digital Library provided by Higher Education Commission (HEC) at the Post-Graduate Level

Digital libraries allow users to have access to diverse resources of international information. Intentions of the users regarding the use of digital libraries vary among different groups. This study investigated the students’ intentions to use HEC digital library at the post-graduate level in Pakistan. All the students enrolled in the M. Phill Education (2nd and 4th semester) were the population of research. The sample size of 112 was determined by using online available software i.e; www.surveysystem.com. The data was collected through a questionnaire and analyzed by using a statistical package for social sciences (SPSS). Students perceived the digital library as useful for reading books, accessing online magazines, newspapers and previously conducted thesis. Students had positive intentions toward the effective use of digital libraries for educational development. Teachers and supervisors should take responsibility and promote the use and effectiveness of digital libraries among students.
Compositional PLS biplot based on pivoting balances: an application to explore the association between 24-h movement behaviours and adiposity

Abstract Movement behaviour data are compositional in nature, therefore the logratio methodology has been demonstrated appropriate for their statistical analysis. Compositional data can be mapped into the ordinary real space through new sets of variables (orthonormal logratio coordinates) representing balances between the original compositional parts. Geometric rotation between orthonormal logratio coordinates systems can be used to extract relevant information from any of them. We exploit this idea to introduce the concept of pivoting balances, which facilitates the construction and use of interpretable balances according to the purpose of the data analysis. Moreover, graphical representation through ternary diagrams has been ordinarily used to explore time-use compositions consisting of, or being amalgamated into, three parts. Data dimension reduction techniques can however serve well for visualisation and facilitate understanding in the case of larger compositions. We here develop suitable pivoting balance coordinates that in combination with an adapted formulation of compositional partial least squares regression biplots enable meaningful visualisation of more complex time-use patterns and their relationships with an outcome variable. The use and features of the proposed method are illustrated in a study examining the association between movement behaviours and adiposity from a sample of Czech school-aged girls. The results suggest that an adequate strategy for obesity prevention in this group would be to focus on achieving a positive balance of vigorous physical activity in combination with sleep against the other daily behaviours.
Character Recognition System Based on Deep Learning Networks

Appropriate algorithms and embedded compact mobile cameras have become the mainstream of current vision applications. The purpose of this paper is to solve the problem of book serial number recognition based on computer vision. Through supervised learning of specific book serial numbers, the characters are collected and stored in the Open CV library in the form of pictures, and the results of the files are manually marked. It is trained to recognize. For the target character picture, the serial number recognition is completed through image segmentation, character segmentation, and splicing. The recognition of characters by the model trained by the convolutional neural network algorithm is the research direction of this paper. After testing, the recognition time for a unit based on the camera installed on the Jetson Nano B01 (4b) motherboard is 0.05 s, and the recognition accuracy rate is as high as 94%. In reality, it has certain research value and application prospect.
Digital Technologies in the Educational Process and the Effectiveness of Their Use

The article discusses modern tools that serve as the basis for digitalization of the educational environment of the university, as well as the features of their application in the educational process. The primary goal of digitalization of the educational environment of the university is to increase the efficiency of the educational process, expand opportunities to make it more adapted to modern training conditions and more interesting for students. As practice shows, the more interesting the educational process is for the student, the more educational information the student learns and remembers. The modern generation cannot imagine life without a variety of gadgets. Such a hobby is not always useful, but this interest can bring tangible benefits if modern digital devices and inventions are made an obligatory part of the educational process. The article evaluates the effectiveness of digital educational technologies in the educational process and their application. The assessment includes a statistical analysis of the results of a survey of teachers and students about the usefulness of using basic digital elements. The representativeness of the sample and the randomness of the selection of respondents allowed us to judge with enough confidence about the objectivity of the results we got. Digitalization of the educational environment makes it more accessible, safe and interesting. But, there are obvious disadvantages to using digital technologies. They are also noted in the article. Solutions to cut the identified shortcomings are proposed. The practical significance of the study lies because the results got are further used to optimize using digital devices and services in the educational process.
Teaching Strategies of Chinese Characters as a Foreign Language: A Corpus-Based Analysis

As a typical representative of ideograms, Chinese characters are very different from Latin characters, which consist of strokes and components. Due to the unique nature and construction, the recognition and writing of Chinese characters are the main obstacles to learning Chinese and Chinese culture. The paper aims to focus on Chinese character writing errors of international students, and offer the corresponding teaching strategies to improve students’ character writing ability. Based on the 390 high-frequency wrongly written Chinese characters summarized from 240 Chinese test papers of international students in Wuhan University of Technology, the paper presents a corpus-based study of Chinese character writing errors of foreign students via software AntConc. Through survey and statistic, the paper reveals that there are three main types errors in Chinese character writing of foreign students: stroke error, component error and structure error, accounting for 70%, 12% and 18% respectively, and each type contains several minor categories. Consequently, the paper proposes targeted teaching strategies: systematic explanation of Chinese character theory, the repeated writing exercises in various forms, and applying multimedia and software are considered to be effective to improve the efficiency of Chinese character learning for international students.
Arabic Sentiment Classification on Twitter Using Deep Learning Techniques

Sentiment analysis is one of the biggest challenges for mining textual online content generated by users. This paper concentrates on reviews and feedback from customers, which are relevant content types representing opinions. The aim of this work is to identify the semantic orientation of each sentence (e.g. positive or negative). Traditional methods for classifying sentiments often involve significant human efforts, e.g. construction of lexicons, feature engineering, etc. In recent years, deep learning has emerged as a powerful way of solving the problem of classifying sentiments. In this paper, a novel deep learning framework is proposed for sentiment classification on different sizes of four binary balanced Arabic datasets; Arabic Twitter Dataset (collected dataset), ArSenTD, Arabic Sentiment Analysis Dataset, and Arabic 100k Reviews dataset. The proposed framework overcomes the limitations of current Arabic sentiment analysis, and consists of using different types of deep neural networks: The densely connected neural network (Basic Neural Network), Convolutional Neural Network (CNN), Long Short Term Memory Network (LSTM), which is a variant of Recurrent Neural Networks (RNN), Bidirectional LSTM (Bi-LSTM), and CNN + LSTM. Experiments on different datasets prove the effectiveness of the proposed framework and its superiority over the traditional methods for datasets of large sizes approaching 100% of accuracy.