Reviewerly/exHarmony huggingface dataset:
Research on School-enterprise Cooperation Education Practice of University Computer Major ‘Leading Flight’ Based on Big Data: Take UESTC as an example

With the information revolution in various industries, computer professional graduates have become a hot demand of major enterprises. However, at the same time, the number of computer professional graduates in colleges and universities has increased significantly, and major enterprises have higher requirements for the comprehensive ability of graduates. Enterprises hope that graduates not only need to master advanced theories and technologies, but also need to be driven by industrial innovation, with solid foundation, comprehensive knowledge, rich skills and outstanding innovative thinking. Therefore, many computer professional graduates cannot fully adapt to the needs of enterprises and cannot fully adapt to the current work after entering enterprises. In view of this problem, based on the background of big data, this paper puts forward a new mode of school-enterprise cooperation education for computer specialty named ‘Leading Flight’, carries out school-enterprise cooperation in depth, builds non-administrative classes, curriculum system, practice base and second classroom, and explores new comprehensive evaluation system. By using the method of big data analysis and questionnaire survey, this paper studies the improvement of student achievement, comprehensive ability and counterpart employment brought by the new mode of school-enterprise cooperation education of ‘Leading Flight’, which not only improves the comprehensive quality of computer specialty graduates, but also better guarantees the employment stability of computer specialty graduates, solves the employment adaptation problem, and provides new ideas for the school-enterprise cooperation of computer specialty in colleges and universities.
CHARACTERISATION OF GOLD-ASSOCIATED BASE METALS IN ITAGUNMODI, ILESHA SCHIST BELTS, NIGERIA, TO ASCERTAIN THEIR ORE BODY FORMATION TRENDS

The analysis of the discovered base metal elements in Itagunmodi, Southwest, Ife-Ilesha schist belts, Nigeria, with a case study of Amuta, was carried out to ascertain the likelihood of these elements forming ore bodies that might sustain mining activity in the area alongside gold. The aim is to determine the economic viability potentials of the detected base metals’ mineralisation to support mining activities in the study area. The geochemical analysis results indicated that the concentrations of these elements copper (Cu), nickel (Ni), lead (pb), and zinc (Zn) are substantial in the majority of the locations when compared to the area’s typical concentrations. When the copper 614.2, nickel 705.1, lead 47.1, and zinc 987.8 threshold values were compared to their recoverable concentrations, it was determined that these elements, with the exception of lead in location 4, are not anomalous in the area. When these elements’ concentration factors were compared to their usual crustal abundance values, it was determined that they are not enough concentrated to form ore bodies in the studied location. Electrical geophysical methods utilizing induced polarization were used to determine the locations of selected stream samples. The results from the five locations typically indicated a significant IP signature with chargeability values ranging from 8 to 18 msec, confirming the occurrence of base metals mineralization in the study region. This indicates that the ore deposit is uneven and distributed within fault/fracture zones. As a result, the detected base metals’ mineralisation potentials in the study region are not economically viable to support mining activities. Thus, it is concluded that, while the studied area is mineralized in base metals, its ore body formation propensity is extremely low, and thus cannot sustain economically viable mining activities.
ICT Based Research Support Services in Academic Libraries: A New Frontier to Enhancing Research Integrity and Productivity

This article focuses on the innovative approaches to delivery of research support services using emerging technologies in academic libraries. The emerging technologies rapidly change the research support system to enhance the research output and visibility in higher educational institutions with the aim of research data management and digital transformation of research output over the networks. Currently, Research Support Service (RSS) is one of the significant services delivered by libraries in the context of research support and data management at their institutions. Research support is a new service area for academic libraries to enhance and promote the research activities at their universities in the collaborative mission. To effectively delivery of the research support services by libraries in educational institutions, library professionals has formulated institutional research policy, research data lifecycle, redesign library space and services (digital repository, research advocacy, open data repository, e-resources discovery and accessibility, ETDs management, Web OPAC accessibility, virtual labs, research data management, reference management, plagiarism deterring, Current Awareness Service (CAS), Internet accessibility, wi-fi, email alerts, 3D printing, printing & scanning, bibliometrics, curation & preservation of data, e-documents delivery, data analysis support and open access publishing etc.) to 24x7 research support to the academicians and researchers in the digital age. These research support services help to enhance to usage of library collection & services and promote research-integrity at their instantiations. Research support services, namely the redesign of the library e-infrastructures and services to 24x7 research support to the researchers and academicians to enhance the research output in digital environment. The author’s noticed that delivery of research support services in Indian educational institutions are under progressive stage, only few universities are engaging in effectively delivery of such types of services in open research world. Also, this paper describes the advantage of delivery of research support services through the lens of the digital transformation of library services in academic institutions.
Hand Gesture Recognition Using ToF Camera and 3D Point Cloud Networks

Gesture recognition is achieved by using ToF camera and deep neural networks based on 3D point cloud. An average recognition accuracy of the networks is &gt;99%. PointNet++ (MSG) network demonstrates better performance than other networks.
To the Documentary History of the Journal “Voprosy filosofii”

The article deals with the issues of expanding the source base for studying the history of philosophical journalism in Russia, primarily the history of the lead­ing philosophical journals of the 19th–21st centuries: “Voprosy filosofii i psikhologii”, “Pod znamenem marksizma”, “Voprosy filosofii”. Author pro­vides information about the funds of various state and departmental archives, which contain materials on the documentary history of the journal “Voprosy filosofii”. The main types of sources are determined. Information about already available publications of sources on the history of the journal is given. The ap­pendix contains a transcript of the meeting of the editorial board of the journal “Voprosy filosofii” in 1953 to discuss the publication of articles by physicist, academician V.A. Fock and philosopher, corresponding member of the USSR Academy of Sciences A.A. Maksimov (Archive of the Russian Academy of Sci­ences. Fund 1515. Inventory 2. File 128. Sheets 1–13). The controversy of V.A. Fock and A.A. Maksimov is an essential moment in the history of the philo­sophical struggle on the theory of relativity in the USSR. The most acute and significant phase of this struggle fell on the period after the successful imple­mentation of the Soviet atomic project, as a result of which it was decided not to subject Soviet physics to an ideological defeat. The discussion on the philosophi­cal issues of the theory of relativity, which was held on the pages of the journal “Voprosy filosofii” in 1951–1955, also played a role in this question. V.A. Fock defended the theory of relativity from nihilistic assessments and accusations from A.A. Maksimov, who was known as an ideological worker.
A Combined Method for Document Image Enhancement Using Image Smoothing, Gray-Level Reduction and Thresholding

Document digitization is becoming popular with its enhanced portability, efficient storage, processability and easy retrieval. Document images acquired using the scanning process are filled with additional noise. These noises in document images are associated with document paper quality, the typing machine or printer, or the scanner during the scanning process. Aging, folded corners, stains, shadow-through, and bleed-through noises are also present in this process. During digitization, these noises may get amplified and make the digital representation further noisy. Noise removal methods, techniques, or algorithms refer to the process of removing noises from digital images utilizing image processing, image analysis, or filtering approaches. The transmission, scanning, and aging processes individually or in combination could lead to introducing noise in images. So, here speckle noise is considered for modeling the noise during transmission, Gaussian noise during scanning procedures considering the thermal radiations of the scanning mechanism, and the salt and pepper noise (impulse valued noise) for representing the aging phenomenon. To eliminate a certain kind of noise, a particular noise removal technique uses a special kind of filter. Based on the aforementioned noises, a combined method for noise reduction from scanned document images is proposed. The result of the proposed method is presented considering the resultant image quality. The metrics like Mean Square Error, Signal-to-Noise Ratio, Peak Signal to Noise Ratio, and Structural Similarity Index Metrics are used to evaluate the quality of resultant image.
Question Answering Based on Entity-Aware Self-attention

With the advent of the Big Data era, computers and mobile networks are growing rapidly. We have ushered in the 5g network, more and more people have access to information, and hundreds of millions of information remains on the Internet every day. The wealth of information resources on the Internet has built a huge knowledge base and satisfied people’s thirst for unknown information. The huge amount of information makes it difficult for people to obtain valuable information from it, and how to quickly filter this valuable information is the key to the intelligent question and answer system. In recent years, BERT [1] pre-trained models have shone on major natural language processing tasks with their huge semantic expressiveness. The model in this paper is based on the BERT model pre-trained using a large corpus of entity annotations obtained from Wikipedia, using an entity-aware self-attention mechanism, which is an extension of the self-attention mechanism. This model obtains desirable results on intelligent question and answer systems.
Design of Foreign Investment Risk Model Based on Computer Big Data Technology

The location of foreign direct investment is one of the frontier issues of modern location theory research.Using discrete-time survival analysis methods that can control for multiple lifetimes and unobserved heterogeneity, this paper examines whether foreign-invested enterprises defined in different ways in China have "free-flow" characteristics.Then this paper establishes a multi-objective optimization model of venture capital.At last, this paper proves its feasibility and effectiveness with an example.This model can comprehensively and comprehensively evaluate the economic benefit and risk size of the venture capital scheme and help to make correct decisions.
Efficient NIZKs from LWE via Polynomial Reconstruction and “MPC in the Head”

All existing works building non-interactive zero-knowledge (NIZK) arguments for $$\textsf{NP}$$ from the Learning With Errors (LWE) assumption have studied instantiating the Fiat-Shamir paradigm on a parallel repetition of an underlying honest-verifier zero knowledge (HVZK) $$\varSigma $$ protocol, via an appropriately built correlation-intractable (CI) hash function from LWE. This technique has inherent efficiency losses that arise from parallel repetition. In this work, we show how to make use of the more efficient “MPC in the Head” technique for building an underlying honest-verifier protocol upon which to apply the Fiat-Shamir paradigm. To make this possible, we provide a new and more efficient construction of CI hash functions from LWE, using efficient algorithms for polynomial reconstruction as the main technical tool. We stress that our work provides a new and more efficient “base construction” for building LWE-based NIZK arguments for $$\textsf{NP}$$ . Our protocol can be the building block around which other efficiency-focused bootstrapping techniques can be applied, such as the bootstrapping technique of Gentry et al. (Journal of Cryptology 2015).
Research on Reading Proficiency Training Informatization Based on NeuMOOC Blended Teaching Platform

Based on NeuMooc blended teaching platform, an online teaching platform developed by Dalian Neusoft University of Information, the author implemented a supplementary blended teaching mode in the course of Business English Reading. By informatizating core educating factors, including teaching materials, before-class task and discussion, test, after-class assignment and score analysis, the author has real-time observation of students study performance with the help of blended teaching platform. With this paper, the author wishes to present a new way of reading proficiency training.
The Differences in English Learning Outcomes Using Flashcards and 'Snake-Ladder' Game Learning Media in Elementary School

This study was to determine the differences in learning outcomes of English using Flashcards and Snake and Ladders learning media in elementary schools. This research is a type of comparative research using a quasi-experimental research design. This research was conducted at Tanjung Balai State Elementary School using two classes. Test the instrument by testing the validity and reliability. Hypothesis testing using t-test, obtained tcount &gt; ttable (6.63 &gt; 2.00), with a significant level = 0.05. The results of this study indicate that there are differences in English learning outcomes between students who are taught using Flashcards as learning media. taught using snake and ladder learning media, fourth grade elementary school students.Keywords: Learning Outcomes, media, Flashcards, Snake and ladders game , English
The Benefits and Challenges of Group Discussion in Syntax Project

In the frame of qualitative research, this study aims to know the benefits and challenges of group discussion in Syntax project for students in EFL context in accomplishing Syntax project. The participants of this study consist of 25 students from the students of Syntax class in one of the higher educations in Kendari. The qualitative data were analyzed by questionnaire and students’ interview. The results of the research revealed that the students’ responded positively to the group discussion activity. It indicated this activity can improve students’ confidence and communication skills. Nevertheless, the students found it challenging in terms of the time allocation to finish the project as they cannot manage their time to finish the project well.
Machine Learning Based News Text Classification

With the rapid development of the Internet era, the explosive growth of news data volume and the lack of effective management are gradually becoming serious problems, and it is increasingly difficult for readers to obtain valuable information quickly.How to quickly search for valuable information from the large amount of news text information is a meaningful task in text classification.Existing research methods still have some problems, such as directly combining the headline with the text, thus neglecting the importance of the headline, and the single model of classification, which leads to low classification results.For this reason, the main objective of this paper is to investigate the classification of news texts based on machine learning.This paper examines the current state of deep learning-based text classification and, in combination with the characteristics of news texts, chooses a machine learning-based text classification method to further explore and study news texts.Through the response time and core algorithm accuracy test of this system, the system better reflects the excellent performance of the system and meets the actual performance requirements of the system.The proposed news system can not only process system requests quickly, but also has excellent accuracy rate, which can better assist users to filter information and improve the user's experience of reading news.
In-Vehicle Speech Text Classification based on Multiple Machine Learning Algorithms

With the rapid development of modern society, especially the popularity of the Internet and the rapid development of computer technology, people's lifestyles are undergoing fundamental changes.The rapid development of the Internet has led to an explosion of data and how people use this data has become one of the most popular research topics in modern society.In the past, the limitations of computer technology made it difficult to manage large amounts of data effectively, but the rapid development of computer technology has now made it possible to recognise that it is possible to manage and analyse such large amounts of data.The main objective of this paper is to develop a study of in-vehicle speech TC based on a variety of machine learning algorithms.In this paper, after acquiring the features of the text, a classification model is trained and this tagged data is learned by the model to obtain a classifier.Plain Bayesian classification, nearest-neighbour classification and decision trees are introduced and their advantages and disadvantages are analysed.The results of experiments on text from in-vehicle speech devices reveal that text classification (TC) using support vector machines has good results; natural language understanding can be achieved by combining a rule-based approach by first performing classification and information extraction operations on the text; and the feasibility of architectural modifications is demonstrated through functional verification.
Learning From Events

What can be learned from the successful production of large-scale real-world arts events that is useful in the classroom? Through practical examples, this paper attempts to make some connections. We start with a short story of how the same software came to be used to deliver university-level classes on AI and also co-ordinate the international
Paraphrase Patterns of Expert Academic Writers: Implications for Writing Development, Writing Pedagogy, and Plagiarism Policies

This study offers a linguistic analysis of language borrowing in expertly produced paraphrases. Within the context of higher education, paraphrase writing, an essential skill for source-based writing tasks across the curriculum and a key component in the development of disciplinary expertise, represents a challenge for students and teachers because 1) there is no precise and generally accepted definition of acceptable academic paraphrase, 2) discussions of paraphrase are more often framed in terms of plagiarism than effective writing, and 3) little is known about the linguistic (or rhetorical dimensions) of paraphrases in writing published by experts. The present study analyzes five corpora of paraphrase/source passage pairs (n = 233) produced by expert writers. Corpora 1 and 2 contain exemplar paraphrase/source passage pairs drawn from writing guides and handbooks designed for college students. The remaining three corpora of paraphrase/source passage pairings were assembled using award-winning published articles in three disciplines. Using Keck’s (2006) taxonomy for classifying paraphrases, the study found that about 27% of the average paraphrase of the expert writers analyzed here is made up of language found in the source passage. Paraphrase patterns of expert academic writers point toward a potential continuum of acceptable language borrowing practices likely driven by disciplinary differences. Implications for writing development, writing pedagogy, plagiarism policy, and further research are discussed.
Sagnac interferometer-type nondegenerate polarization entangled two-photon source with a Fresnel rhomb

Telecommunication wavelength-entangled photon sources (EPS) are indispensable systems for a fiber-based quantum network. We developed a Sagnac-type spontaneous parametric down conversion system adopting a Fresnel rhomb as a wideband and reasonable retarder. This novelty, to the best of our knowledge, enables the generation of a highly nondegenerate two-photon entanglement comprising the telecommunication wavelength (1550 nm) and quantum memory wavelength (606 nm for Pr:YSO) with only one nonlinear crystal. Quantum state tomography was performed to evaluate the degree of entanglement, and the fidelity with a Bell state |Φ+⟩ with a maximum of 94.4% was obtained. Therefore, this paper shows the potential of nondegenerate EPSs that are compatible with both telecommunication wavelength and quantum-memory wavelength to be installed in quantum repeater architecture.
Cluster Analysis of Open Research Data: A Case for Replication Metadata

Research data are often released upon journal publication to enable result verification and reproducibility. For that reason, research dissemination infrastructures typically support diverse datasets coming from numerous disciplines, from tabular data and program code to audio-visual files. Metadata, or data about data, is critical to making research outputs adequately documented and FAIR. Aiming to contribute to the discussions on the development of metadata for research outputs, I conducted an exploratory analysis to determine how research datasets cluster based on what researchers organically deposit together. I use the content of over 40,000 datasets from the Harvard Dataverse research data repository as my sample for the cluster analysis. I find that the majority of the clusters are formed by single-type datasets, while in the rest of the sample, no meaningful clusters can be identified. For the result interpretation, I use the metadata standard employed by DataCite, a leading organization for documenting a scholarly record, and map existing resource types to my results. About 65% of the sample can be described with a single-type metadata (such as Dataset, Software orReport), while the rest would require aggregate metadata types. Though DataCite supports an aggregate type such as a Collection, I argue that a significant number of datasets, in particular those containing both data and code files (about 20% of the sample), would be more accurately described as a Replication resource metadata type. Such resource type would be particularly useful in facilitating research reproducibility.
Content moderation issues on social media are evolving

Significance The fast pace of technological innovation, and the growing scale of user content, have made content moderation on social media increasingly difficult. Types of content under scrutiny include hate speech, misinformation and terrorist content, and an ever-expanding list of harmful online content. Impacts Better moderation on big platforms will drive more harmful content to smaller platforms, and linking of content between the two will rise. Growing use of AI filters will increase false-positives and removal of legitimate content, triggering civil society criticism. Extremist groups will produce content just below the threshold of removal, with a view to enticing users to unmoderated online spaces. A broader range of online platforms such as podcast hosting platforms and e-commerce sites will need to prepare for content moderation.