Reviewerly/exHarmony huggingface dataset:
A Novel Embedding Model for Knowledge Graph Entity Alignment Based on Graph Neural Networks

The objective of the entity alignment (EA) task is to identify entities with identical semantics across distinct knowledge graphs (KGs) situated in the real world, which has garnered extensive recognition in both academic and industrial circles. Within this paper, a pioneering entity alignment framework named PCE-HGTRA is proposed. This framework integrates the relation and property information from varying KGs, along with the heterogeneity information present within the KGs. Firstly, by learning embeddings, this framework captures the similarity that exists between entities present across diverse KGs. Additionally, property triplets in KGs are used to generate property character-level embeddings, facilitating the transfer of entity embeddings from two distinct KGs onto an identical space. Secondly, the framework strengthens the property character-level embeddings using the transitivity rule to increase the count of entity properties. Then, in order to effectively capture the heterogeneous features in the entity neighborhoods, a heterogeneous graph transformer with relation awareness is designed to model the heterogeneous relations in KGs in the framework. Finally, comparative experimental results on four widely recognized real-world datasets demonstrate that PCE-HGTRA performs exceptionally well. In fact, its Hits@1 performance exceeds the best baseline by 7.94%, outperforming seven other state-of-the-art methods.
Development and Evaluation of Digital Learning Tools Promoting Applicable Knowledge in Economics and German Teacher Education

Digital teaching interventions allow for tailor-made university teaching. This is especially relevant for teacher education, where applicable professional teaching knowledge needs to be promoted for later professional success. Digital teaching tools have been shown to be a promising supplement for this purpose. Even though the corresponding demands in teacher education have been increasing in recent years, the need to develop digital learning tools usable in instruction is still urgent. The TWIND project develops digital learning tools for teacher education and evaluates them in a quasi-experimental design. The present work investigates the usability and application of these newly developed tools. Sixty-three trainee teachers worked independently over four weeks with one of two digital learning tools, focusing on either ‘Multilingualism in Classrooms’ or ‘Professional Communication in Classrooms.’ This study includes a pre-post-test of pedagogical knowledge facets as well as student and instructor ratings on the digital tools. The digital learning tools led to a positive change in the respective target facets of pedagogical knowledge. The student and instructor feedback reflected positively on the usability and usefulness of the new digital tools. Based on these findings, the limitations of the study as well as implications for further research and teacher education practice have been outlined.
E-ReMI: Extended Maximal Interaction Two-mode Clustering

Abstract In this paper, we present E-ReMI, a new method for studying two-way interaction in row by column (i.e., two-mode) data. E-ReMI is based on a probabilistic two-mode clustering model that yields a two-mode partition of the data with maximal interaction between row and column clusters. The proposed model extends REMAXINT by allowing for unequal cluster sizes for the row clusters, thus introducing more flexibility in the model. In the manuscript, we use a conditional classification likelihood approach to derive the maximum likelihood estimates of the model parameters. We further introduce a test statistic for testing the null hypothesis of no interaction, discuss its properties and propose an algorithm to obtain its distribution under this null hypothesis. Free software to apply the methods described in this paper is developed in the R language. We assess the performance of the new method and compare it with competing methodologies through a simulation study. Finally, we present an application of the methodology using data from a study of person by situation interaction.
Expect to see rapid uptick in NIL enforcement

College athletics departments can expect to see a rapid uptick in name, image, and likeness enforcement actions stemming from the NCAA's new charging standard for NIL violations that went into effect on Jan. 1, 2023, which governs how the NCAA will investigate, charge, process, and punish NIL violations. Although the actual implications remain to be seen, there's little doubt these documents will greatly impact how the NCAA will carry out NIL enforcement. Here, we recommend practical, precautionary actions that institutions can take to avoid running afoul of the NCAA's NIL policies.
Surface-electrode ion trap design for near-field microwave quantum gates

Abstract We present a design study into an ion trap electrode geometry for applying near-field microwave two-qubit gates. This design features an ‘S’-shaped meander electrode to passively null the microwave field. It has ground planes separating the meander electrode from all of the DC and single-qubit microwave electrodes, which should reduce the sensitivity of the microwave field distribution to the boundary conditions of these electrodes. We show that it is possible to design a single-layer trap with this geometry such that the simulated microwave field null overlaps with the RF field null, and that the positions of these nulls can be simulated to a precision of 100 nm with moderate computing resources. We also show that such a trap can be designed such that ion chains can be trapped, transported and split with feasible DC and RF voltages. While this particular design is optimized for $$^{43}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow /><mml:mn>43</mml:mn></mml:msup></mml:math> Ca $$^{+}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow /><mml:mo>+</mml:mo></mml:msup></mml:math> ions, our approach could be applied to other ions by changing the microwave frequency to match the corresponding qubit transition frequency.
Research on the Relevant Methods and Technologies of Digital Watermarking

With the diversification of recording equipment and the rapid development of Internet technology, it is easier to steal and spread movies, which makes the copyright protection of movies face great challenges. The technology of tracing the origin of movie piracy in cinemas provides strong technical support for film digital copyright protection by extracting the watermark information hidden in the film carrier, tracking and locating the playback server where the piracy flows out and the theater where the piracy occurs. In addition, the emergence of digital technology has led to the rapid spread and growth of electronic resources, such as photos, music and movies. It is convenient to access online information, but it also poses a challenge to the technical protection of intellectual property. Image watermarking, including watermark embedding and extraction, is a typical technology of copyright protection and ownership identification. In addition, because many software (such as modern browsers and video players) can easily display all these contents, adding image watermarks on the screen and avoiding information leakage in screenshots or screen recording has become an important application scenario of image watermarking. In this paper, we propose and discuss digital watermarking pipeline with some.
Blockchain as a service environment: a dependability evaluation

Blockchain has become an important processing paradigm in recent years. The blockchain supports financial transactions and validates contracts, documents and data. However, the evolution of blockchain has become viable for many applications. The servers' availability and reliability (dependence) are required in the data processing. The contract will only be signed if there are enough components to form the blockchain blocks. This paper analyses the dependency between project components that use blockchain. We present a model based on stochastic Petri net (SPN) for evaluating the dependency of the blockchain architecture. The Design of Experiments (DoE) method was used to analyse this model's factors, seeking to know which ones had the higher impact on the system. The sensitivity analysis showed that the MongoDB component has a greater impact on the system dependency and the need to upgrade such a component. Also, for reliability, making component improvements is unnecessary if the system has fewer than 36,000 h of runtime.
Proofs, Generalizations and Applications of Fermat’s Little Theorem

This paper introduces Fermat’s little theorem (FLT), which says that any integer raised to power is congruent to modulo . This paper will give several proofs of FLT, using methods including number theory and group theory, together with generalizations of FLT in different directions. FLT is an important result in number theory and group theory. It has multiple generalizations and corollaries, and one of its corollaries is the foundation of RSA cryptography. The effort made trying to prove FLT stimulated researches in many fields in mathematics, and FLT is crucial and fundamental in research of modern cryptography.
Research and Analysis of Glass Weathering Based on Ergodic K-Prototype Algorithm

When making glass products, adding fluxes with different chemical compositions can form different glass types. Affected by the burial environment, the composition proportion of ancient glass products will change over time, thus affecting the correct judgment of their types. Based on decision tree, traversal K-prototype algorithm, grey correlation analysis and other methods, this paper studies and analyzes the classification and identification related to glass weathering and the statistics of chemical composition laws, the relationship between the surface weathering of glass relics and the glass type, decoration and color is analyzed. In this paper, after data pretreatment, the surface weathering and the other three indicators are preliminarily observed through contingency analysis, and then the correlation between the variables is further clarified by using the chi-square test. It is found that the surface weathering is significantly related to the overall glass type, and lead barium glass is more prone to surface weathering than high potassium glass; There is no obvious correlation between surface weathering and decoration and color. Then, according to the glass types, the glass relics are divided into four glass types: lead-barium weathering/non-weathering, high-potassium weathering/non-weathering, and the statistical analysis is established after the pretreatment of the chemical composition of these four types. Then, combined with the independence t-test, we can further obtain the specific element differences, and give a ranking table of the degree of influence. Finally, based on the above statistical exploration, the model of chemical composition law of glass relics before weathering is built according to the mechanism analysis of the four composition laws of high-potassium glass Si and its chemical composition that significantly affects the weathering, the chemical composition that significantly affects the weathering of lead-barium glass, and the chemical composition that has no effect on the weathering of glass. According to the test data of weathering point, the prediction table of chemical composition content before weathering is given. The interval test of the model results shows that the failure rate of high potassium prediction is high. The revised model of chemical composition law of glass relics before weathering is obtained by analyzing and considering the modification of the value rule of high potassium Si.
The effects of foreign bank presence on financial development in Africa: The role of institutional quality

Abstract Despite the momentous rise in concentration and market share of foreign banks in overall bank assets in Africa, rigorous empirical work(s) examining their effect on financial sector development in Africa is hard to find. In view of this, the study seeks to investigate the moderating role of institutions/governance on the foreign bank presence‐financial development nexus. Employing the two‐step system generalized method of moment for a panel dataset of 28 economies in Africa over the period, 2000–2018, the results show that foreign bank presence has a positive effect on financial development. Also, the study finds evidence that foreign banks in host countries increase financial development in the presence of quality institutions. The study recommends improvement in institutional quality, which will help foreign banks to promote financial development.
Augmented storytelling: effects of making on students’ interest in literacy

Creating technological artefacts through hands-on practice has gained traction as a means to rethink educational approaches. This study reports the effectiveness of the maker pedagogy on students’ interest in literacy. An evaluation study was conducted involving 112 primary school children, aged 11, in two primary schools in Malaysia with the aim of improving students’ reading interest. The study involved an experimental group of participants creating augmented storybooks using paper-based circuitry and programming. Participant results reveal a significant increase in interest while learning by making augmented storybooks in comparison to just reading using augmented storybooks. The study demonstrates how learning by making can be implemented in primary school settings and highlights key challenges and opportunities for applying the approach in everyday teaching and learning contexts.
Application of Bayes Theorem to Accident Analysis and Classification

In today's social context, "data" is a very common term. Data Science, Data analysis and other applications on different levels of Data have become very popular topics and even jobs. The main topic to be analyzed in this paper is Bayesian theorem. Bayesian theorem is a basic theorem in statistics, but with sufficient data support, Bayesian theorem can be applied in many fields. In this paper, this paper will analyze the application of Bayes' theorem in these two aspects: Bayes Theorem to Accident Analysis and Bayes Theorem to Classification. In terms of Bayes Theorem to Accident Analysis, this paper will analyze three papers that use Bayesian theorem to analyze accidents. In these papers, the authors have used Bayesian theorem to analyze different accidents. These accidents are: The causes and analysis of engineering accidents in mining areas, the analysis of causes of roof accidents in coal mines, and the analysis of the causes of highway traffic accidents. way of accident. In terms of Bayes Theorem to Classification, most of the authors use naive bayes to classify different data sets. The benefits of this classification are that whenever new data enters, researchers can easily classify new data sets. Incoming data is categorized.
Future Trend of Supply Chain Being Exposed to the Metaverse

Due to the skyrocketing development of the metaverse, there is an inevitable trend that the supply chain will one day be exposed to the metaverse environment. The supply chain system supported by metaverse can solve the problems that traditional blockchain platforms are separated and have different standards, and governance of them is not effective, etc. This system can also coordinate with other fields of industry, bringing up digital collections and other industries to develop consistently, helping to make food and medicine safer to eat and take. This study has recovered how the metaverse affects supply chain management and has created a conceptive model of blockchain under the metaverse. It also gives the solution to the existing problem in traditional supply chain systems supported by blockchain technology. The study shows how the supply chain can work more efficiently under the metaverse and guides the researchers afterward to complete the supply chain system more flexibly and scientifically.
Application of in Information Visualization and Generative Adversarial Network in the Review of Style Transfer

Style transfer is the task that aims to adopt the appearance or visual style of an image to another image. For example, we can transfer the style of a normal landscape photo into the style of a Monet's painting, in which objects are generally drown in an impressionism way. Nowadays, style transfer has become one of the most important application in photo editing. It has attracted artists and designers from around the globe.
Construction of military English formative assessment model based on intelligent teaching platforms

The integration of formative assessment and summative assessment is the dominant method and reform direction of contemporary teaching assessment. In view of the current lack of scientific nature of the formative assessment system, a foreign language teaching platform, an oral training apps, a writing evaluation system and rain class are integrated to construct the formative assessment model of military English. The model covers listening, speaking, reading and writing skills in the phases of before, during and after classes, providing reference for military and college English teaching reforms in China.
Method to Facilitate E-Commerce Buying Power by Using Machine Learning Techniques

The incremental internet usage triggers the rising of e-commerce, a burgeoning shopping mode. Unlike other papers which focus primarily on the technical construction of a sentiment classification model, this paper combines machine learning techniques with business strategies. It aims to determine how sentiment analysis facilitates businesses’ improvement of offerings on e-commerce platforms, increasing customers’ buying power. First, the paper defines consumer sentiment analysis, summarizes the methods different scholars used when classifying sentiment on aspect level, and points out how sentiment analysis is valuable to both businesses and customers. Second, the paper describes an e-commerce notebook, which covers how sentiment analysis can be carried out using data from Olist online retailing store in Brazil. Naïve Bayes and Logistic Regression are utilized when implementing sentiment classification. Finally, according to the word cloud for positive and negative words in reviews, the paper gives some coming-up suggestions for tackling with the most frequently appeared complaint - the delivery time. Businesses can decompose the supply chain into six sub-systems, and adopt computer vision and GIS system in the packaging management system and delivery management system respectively to squeeze the delivery time.
Research on Marketing Methods based on Machine Learning Model

Machine learning is an interdisciplinary discipline, involving many fields, from probability theory to algorithms, to statistics, and other things, all of which enable computers to simulate human learning methods. Machine learning can also greatly enhance the efficiency of learning by dividing the original content into knowledge structures. Turing proposed to build a learning machine and promoted the progress of practical application, which is a great progress in machine learning. Although banks have a large amount of information data, the traditional marketing methods can not fully mine the value of these large amounts of data. Through the use of machine learning technology to establish a precision marketing system, use the machine learning model on the big data platform to conduct in-depth analysis of user behavior, needs, preferences, etc., and then carry out the implementation of the marketing plan for the mined potential customers. This paper analyzes the relevant content and application of machine learning, and gives a more comprehensive analysis of the system.
Improvements of portfolio performances by bitcoin based on Markowitz Model and Index Model

Contemporarily, with the rapid development of society, investors are noticing the importance of finance and asset allocation and actively participating in the market. Achieving greater returns with controlled risk is the common goal of the vast majority of investors, and thus there has been a lot of researches focusing on portfolio optimization. As Bitcoin has become known and its high volatility has captivated people who want to achieve high returns, it seems feasible to add it to the investment portfolios to enhance the performance. However, it is crucial to investigate the impacts of Bitcoin on portfolio. On this basis, this study uses weekly data of five companies from different industries and Bitcoin to construct two portfolios with and without Bitcoin based on Markowitz model and index model to calculate annual return, annual volatility and Sharpe ratio. According to the analysis, the implementation of Bitcoin has boosted both the Sharpe ratio and annual return of the portfolio for the optimal parameter, i.e., indicating there are positive impacts for the Bitcoin. These results shed light on guiding further exploration of improving the performances of portfolios based on different types of underlying assets.
The Application of ‘Suggestopedia’ in English Teaching

Suggestion teaching method was put forward by psychotherapist George Lozano in the 1960s, which provides a new idea for the development of English reading teaching. Suggestive media points out that people are suggestive and positive suggestions can put people's bodies in a relaxed state and minds in an active state. This good physical and mental state can enable learners to give full play to their potential and improve the efficiency of foreign language learning. This paper studies the application of Suggestopedia in English teaching. Suggestopedia puts forward eight suggested strategies, such as a relaxed and intuitive environment, positive unconscious cues, double communication and other strategies. It found that there are some problems with the suggested teaching method. The suggested teaching method has high requirements for conditions and pays too much attention to form. The education administrators should pay attention to teaching reform, and improve their own quality and other methods.
Dynamic entanglement for continuous variables in an electric field background

Abstract Quantum entanglement is a typical nonclassical correlation. Here, we use this concept to analyze quantum entanglement for continuous variables generated by the Schwinger pair production for constant and pulsed electric fields. An initial two-mode entangled state evolves into a three-mode entangled state through a Gaussian channel of the Schwinger effect, which encodes the information about the Schwinger effect. By detecting the entanglement of the output three-mode state, we obtain the optimal parameters for easier to generate particle–antiparticle pairs. We find that the generated 1 → 2 entanglement is more sensitive to the parameters than the generated 1 → 1 entanglement. Therefore, we should choose the generated 1 → 2 entanglement to extract information. We argue that extracting the optimal parameters from quantum entanglement may guide future experiments.