Reviewerly/exHarmony huggingface dataset:
Heterogeneous Graphs Embedding Learning with Metapath Instance Contexts

Embedding learning in heterogeneous graphs consisting of multiple types of nodes and edges has received wide attention recently. Heterogeneous graph embedding is to embed a graph into low-dimensional node representations with the goal of facilitating downstream applications, such as node classification. Existing models using metapaths, either ignore the metapath context information which describes intermediate nodes along a metapath instance, or only using simple aggregation methods to encode metapath contexts, such as mean or linear methods. To address the problems, we propose Metapath Instance Contexts based Graph neural Network (MICGNN). Specifically, MICGNN includes four components: Node transformation, projects the input different types of features into the same feature space. Context aggregation, incorporates the node embeddings along a metapath instance context. Instance aggregation, combines context embeddings obtained from each metapath instance. Semantic aggregation, fuses together the semantic node embeddings derived from different metapaths. Extensive experiments results on two real-world datasets show our model exhibits superior performance in node-related tasks compared to existing baseline models.
An Entity Alignment Method Based on Graph Attention Network with Pre-classification

Entity alignment is the process of identifying entities that point to the same object in different knowledge graphs. Entity alignment is a key step in building knowledge graphs, and the result of entity alignment directly affects the quality of the knowledge graphs. Most of the current entity alignment methods learn the feature vectors of entities or entity attributes based on representation learning. While the study of the interaction learning between entity attributes and entity relationship features is not sufficient, and the data division is not specific enough to confuse the feature information of different categories of entities. That reduces the quality of the finally learned entity vectors. To solve that problem, we propose an entity alignment method that uses graph attention mechanism after entity classification. Firstly, we classify the entities according to their semantics of the source data, and then classify the entities with different entity attributes to complete the dual entity classification. Based on the dual entity classification, the graph attention mechanism is used to complete the aggregation of the feature information of different categories of entities, which is used to learn the final entity vector representation for the calculation of similarity in the entity alignment task. On the general dataset for entity alignment, DBP15K, our model achieves hits@1 scores 80.82, 80.17 and 93.13 on its three subdatasets. The results show our method are better than the compared methods.
A study on the application of non-fiction writing in writing courses in local universities based on a decentralized Internet model

Abstract Along with the development of Chinese journalism and the continuous exploration of journalistic genres in the journalism industry, non-fiction writing has been shuttling between the two genres of literature and journalism. The development background of the times traditional non-fiction writing course teaching mode is difficult to adapt to the needs of college students, this paper around the decentralized Internet-based model of non-fiction writing in local college writing course application research, firstly, to build a non-fiction writing platform mathematical model, Markov random field, through the Bayesian network algorithm to process the characteristics of college students in the classroom data, the results show that students within the platform The results show that as the number of tests increases, the score trend from the three aspects of writing ideas and writing style and writing skills all show an upward trend, and the average score basically stays between 80-90, which is beneficial to understand the students’ writing level and performance, provide different users with suitable learning content for themselves, and contribute to the cause of Chinese literature by improving the students’ ability of non-fiction writing. It is of great value in changing the closed mode of writing courses, enhancing the practicality of writing content, and training college students to think logically and critically.
Arithmetic optimizer algorithm: A comprehensive survey of its results, variants, and applications

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Pravin Dhawale, Vikram Kumar, S. K. Bath; Arithmetic optimizer algorithm: A comprehensive survey of its results, variants, and applications. AIP Conf. Proc. 8 September 2023; 2800 (1): 020063. https://doi.org/10.1063/5.0162724 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
The construction of a localized teaching model of the Orff music teaching method based on big data analysis

Abstract In the era of the big information wave and rapid development of economic globalization, the traditional music teaching mode in China has failed to completely develop the times. Here focuses on constructing a localized teaching mode of the Orff music teaching method in the case of cloud information analysis, firstly, understanding the definition of cloud information, using the MOOC platform design and offline traditional Orff music teaching method to build the localized teaching mode. The system is designed with SSH structure as the framework and BP-related calculation methods, with basic theory as the key point adopted by related calculation method, implementation process adopted for break-in effect inspiring rules for evaluating the actual use, analyzing the accuracy and stability of the three algorithms through experiments of three cases, and the final from experimental analysis accuracy of BP neural network algorithm is between 77% and 86%, which is higher than the accuracy and stability of the other two algorithms. Furthermore, stability is high, thus building a set of localized and innovative teaching models suitable for Chinese music education, which is conducive to improving students’ aesthetic and artistic level and bringing Chinese music to the world stage.
The Fit of Constructivist Teaching Theory and Foreign Language Teaching Reform in the Background of the Internet Era

Abstract The development of the fit between constructivist teaching theory and foreign language teaching is explored to improve learners’ language proficiency and comprehensive application skills effectively. This paper discusses a constructivist teaching model in the Internet era, using the fruit fly algorithm to optimize the kernel function of support vector machines and then constructing an FOA-SVM classification analysis model. The FOA-SVM model was used to analyze the fit data of the foreign language culture introduction teaching model based on constructivist teaching theory, including teaching principles and teaching practices. Regarding teaching principles, 55.69% of the students thought that the appropriateness principle was the best for the constructivist teaching theory to fit foreign language teaching. In terms of teaching practice, students’ learning attitude, learning initiative, learning ability, language ability, and application ability increased by 65.55%, 71.94%, 62.23%, 62.22%, and 78.42%, respectively. This shows that the integration and development of constructivist teaching theory and foreign language teaching should be based on the principle of appropriateness, and the use of a foreign language culture introduction mode of teaching is conducive to improving students’ learning attitudes and initiatives.
The theory of innovation and entrepreneurship education practice with the integration of modern information technology and new engineering

Abstract In the context of integrating modern information technology and new engineering, this paper first sets up new alternative controller nodes and data nodes in the whole blockchain network, which no longer requires administrators to maintain the platform and eliminates the problem of centralized trust crisis. Then the data nodes are optimized through RP-PBFT consensus according to blockchain technology, and the data nodes pack and encrypt the transaction data based on an elliptic encryption algorithm to store it on the new block while distributing the data to all other data nodes. Finally, from college students themselves, with the essential purpose of improving college students’ innovation ability as the research, the effect of blockchain technology on the development of college students’ innovation and entrepreneurship practice community is analyzed. The results show that the average values of innovation spirit, innovation consciousness, innovation thinking, innovation learning ability, and innovation practice ability are 3.76, 3.75, 3.71, 3.80, and 3.76, respectively, which can be seen in the community members’ feelings of the improvement of innovation and entrepreneurship ability. This study applies blockchain technology to the innovation and entrepreneurship information data service platform of college students not only can solve the trust crisis of administrators based on the centralized working model and accelerate the transmission of entrepreneurship information.
Research on cloud-CRITIC-PDR method for hybrid multi-criteria decision making

In order to solve the problems of insufficient uncertainty information measure, inaccuracy of weight calculation and incommensurability of indices in hybrid multi-criteria decision making, this paper introduces the Cloud-CRITIC weight calculation method and Cloud-CRITIC-PDR method, which combine cloud model, CRITIC method and Probabilistic Dominance Relation (PDR). In these two methods, the cloud model is used to characterize uncertainty, the Comprehensive information of CRITIC method has been modified in order to adapt to uncertain situation, the PDR method is used to compare schemes. A case study concerning supplier evaluation is given to demonstrate the merits of the cloud-CRITIC and cloud-CRITIC-PDR. The effectiveness and superiority of the developed methods are further illustrated through method comparison and sensitivity analysis. These combined methods can be used for dealing with decision-making problems with complex index types and strong data uncertainty, such as supplier evaluation and risk assessment. There are few papers about combining the cloud model, CRITIC method, and PDR method under hybrid indices decision-making situation at present, so this paper can provide a new perspective on hybrid MCDM.
Una nazione, molti tipi di politeness. Il complimento come esempio di pragmatica variazionale in Italia

Framed in variational pragmatics, this work aims at comparing the compliment management in three different regional varieties of Italian. In order to fully understand the different facework dynamics in the compliments in Piemonte Orientale, Cancello ed Arnone (Caserta) and among the Sardinian community (Biella), the compliments have been analyzed having recourse to three different models of politeness: Brown and Levinson (1987), the “Face Constituting Theory” (Arundale 2006), Mursy and Wilson’s “Social Contract of Values” (2001).
-ata nominalisations in Maltese: a language contact perspective

The -ata derivational suffix is attested frequently in nominalisations in Italo-Romance languages and its functions related to ‘event/single instance’ nouns have been documented extensively. This affix with these functions is also present in the derivational morphology of Maltese, in which diachronic and synchronic developments of -ata nominalisations reflect the evolutionary paths of this language, characterised by contact. By investigating the etymology of verbal and nominal bases, this research provides a classification of forms which take the -ata suffix in Maltese to refer to events and single instances within them, through derivation involving contact with Italian, English and Sicilian base forms. -ata nominalisations resulting from etymologically Arabic bases will also be included in the classification.
Global Nuclear Explosion Discrimination Using a Convolutional Neural Network

Abstract Using P‐wave seismograms, we trained a seismic source classifier using a Convolutional Neural Network. We trained for three classes: earthquake P‐wave, underground nuclear explosion (UNE) P‐wave, and noise. With the current absence of nuclear testing by countries that have signed the Comprehensive Test Ban Treaty, high quality seismic data from UNEs is limited. Even with limited training data, our model can accurately characterize most events recorded at regional and teleseismic distances, finding over 95% signals in the validation set. We applied the model on holdout datasets of the North Korean test explosions to evaluate the performance on unique region and station‐source pairs, with promising results. Additionally, we tested on the Source Physics Experiment events to investigate the potential for chemical explosions to act as a surrogate for nuclear explosions. We anticipate that machine‐learning models like our classifier system can have broad application for other seismic signals including volcanic and non‐volcanic tremor, anomalous earthquakes, ice‐quakes or landslide‐quakes.
Credit Card Fraud Detection using Random Forest

The project undertaken is extremely engrossed on Credit Card Fraud Detection. The rise within the number of card transactions in every sector had been resulting in many fraudulent transactions. The aim is to get goods without paying, or to get unauthorized funds from an account. This way of implementing the efficient method of detecting the fraud credit cards has been the most crucial for every bank which provides the credit cards in order to reduce their loses. The crucial &amp; foremost challenge in construction of business is that neither the card holder nor the card has to be present when some purchase is being made. So, this makes it impossible for the merchant to verify whether the customer is an authentic cardholder or not. With the proposed scheme, the accuracy of detecting the fraudulent transactions is improvised using random forest algorithm. The Classification process of random forest algorithm is to investigate data set and user current dataset. At last, the result obtained is further gone for optimizing the accuracy. And also, this is chosen to be the best as this technique’s performance is best in accuracy, precision and thus can be easily evaluated and supported
Actual problems of social inclusion of students in institutions of higher professional education, the graduates of orphanages

Introduction. At the present stage of the dynamic development of society, the problem of socialization and adaptation of children, primarily orphans, remains unchanged. The skills acquired within the walls of orphanages do not always allow move effectively in obtaining a higher professional education. In this regard, it becomes relevant to study the problems of social inclusion of orphans. Research aim: study of topical problems of social inclusion and ways to overcome them among students, the graduates of orphanages. Materials and methods: Based on the analysis of theoretical data, an empirical study was conducted, in which graduates of orphanages (students and graduates of various Russian universities, as well as future applicants) took part. The study used the focus group method. At the second stage, an express survey was conducted in the form of an online survey of graduates of orphanages in order to find out their attitude towards entering a higher educational institution. Results. The study found that orphanage graduates are highly motivated to get a higher education and a good job after graduation, despite certain problems of social inclusion. This contradicts the generally accepted opinion that orphans are not ready to receive a higher professional education. In addition, problems of a domestic and psychological nature are not considered by respondents as serious obstacles to achieving the goal. Rather, they are willing to try new things and take risks. The above requires a rethinking of approaches to working with orphans, both in orphanages and in universities. Scientific novelty of the study is in specifying the reasons why graduates of orphanages do not want to enter higher educational institutions. First of all, it is the need for additional efforts that complicate life, selfdoubt and the lack of a positive experience of entering an institution of higher professional education among acquaintances. Among the ideal conditions for the formation of a professional career are the presence of a foster family, more significant adult support and information. Practical significance is in the possibility of using the data obtained to focus on motivating and informing orphans, providing support and more active search for foster families for them. Conclusion. This study allows taking a closer look at the problems of graduates of orphanages who entered universities and suggests focusing on supporting the independence of students and passing on a positive experience of studying at a university to their peers.
Distributed Privacy-Preserving Data Analysis in NFDI4Health With the Personal Health Train

Data sharing is often met with resistance in medicine and healthcare, due to the sensitive nature and heterogeneous characteristics of health data. The lack of standardization and semantics further exacerbate the problems of data fragments and data silos, which makes data analytics challenging. NFDI4Health aims to develop a data infrastructure for personalized medicine and health research and to make data generated in clinical trials, epidemiological, and public health studies FAIR (Findable, Accessible, Interoperable, and Reusable). Since this research data infrastructure is distributed over various partners contributing to their data, the Personal Health Train (PHT) complements this infrastructure by providing a required analytics infrastructure considering the distribution of data collections. Our research have demonstrated the capability of conducting data analysis on sensitive data in various formats distributed across multiple institutions and shown great potential to facilitate medical and health research.
Dream India Dream:Re-kindling a Post Colonial Perspective Through Manas Bakshi's Vision of India

&#x0D; &#x0D; &#x0D; &#x0D; In an era when colonial legacies are being abandoned and national identities are being re-claimed, literature, rather than history, is recognized as the most reliable source of information on monumental events, epochs, and upheavals that mark the evolution of various civilizations. Since both literary works and historical events are influenced by social dynamics, they can both point to watershed moments and spark transformation. Poetry, in a reasonable sense, also qualifies as an authentic record of times and a way of looking at life with an attempt at drawing greater meanings from one's circumstances of existence than may be apparent through other modes of expression. Poetry, with its ability to evoke emotions, communicate complex ideas, encapsulates the essence of human experiences in a manner that is both subjective and universal. Apart from enabling the quest for self-knowledge and self-fulfillment, poetry has also helped humanity survive against demons of destruction and annihilation. It has aided as a sure and steady toolforsocialcriticism,provingitslastingsignificancenotonlyfor humanitybut to other lifeforms and the planet as a whole. The latest compilation of poems brought out by Manas Bakshi, titled Dream India Dream,published in 2022, constitutes a significant addition to his artistic oeuvre.&#x0D; &#x0D; &#x0D; &#x0D;
Cloud Computing Applications In Business Development

A crucial technology with significant consequences for company growth is cloud computing. This magazine examines how cloud computing is used in the context of company expansion and assesses how it affects several facets of organizational development. This study offers important insights into the advantages, difficulties, and best practices related to incorporating cloud computing in corporate development processes by completing an extensive literature review and looking at actual case studies. The research technique comprises a thorough examination of the body of knowledge regarding cloud computing and its function in organizational transformation. In order to pinpoint useful applications and results, case studies of businesses that effectively incorporated cloud computing into their operations are also looked at. The results reveal a number of significant benefits of using cloud computing for corporate growth. First off, cloud computing makes it simple for businesses to access a vast collection of computational resources, enabling them to grow their operations effectively and shorten the time it takes to market goods and services. With less upfront investment required for infrastructure and hardware, organizations may more efficiently allocate resources and concentrate on their core capabilities. Second, cloud computing's elasticity and scalability allow businesses to adapt swiftly to changing demand, assuring efficient resource use and low costs. Businesses may take advantage of market opportunities, adjust to shifting trends, and experience sustainable growth because to this agility. Additionally, better data security features including encryption, access controls, and data backup systems are provided by cloud computing. And finally, the use of cloud computing for corporate growth results in significant cost reductions.
Public opinion evaluation on social media platforms: a case study of High Speed 2 (HS2) rail infrastructure project

Public opinion evaluation is becoming increasingly significant in infrastructure project assessment. The inefficiencies of conventional evaluation approaches can be improved with social media analysis. Posts about infrastructure projects on social media provide a large amount of data for assessing public opinion. This study proposed a hybrid model which combines pre-trained RoBERTa and gated recurrent units for sentiment analysis. We selected the United Kingdom railway project, High Speed 2 (HS2), as the case study. The sentiment analysis showed the proposed hybrid model has good performance in classifying social media sentiment. Furthermore, the study applies latent Dirichlet allocation topic modelling to identify key themes within the tweet corpus, providing deeper insights into the prominent topics surrounding the HS2 project. The findings from this case study serve as the basis for a comprehensive public opinion evaluation framework driven by social media data. This framework offers policymakers a valuable tool to effectively assess and analyse public sentiment.