Reviewerly/exHarmony huggingface dataset:
Piecewise-defined function for effectively implementing the audio volume automation

Typically performed with the aid of media development software like digital audio workstations, audio volume automation requires the implementation of piecewise functions to customize the amplitude envelope of audio contents. The user sets various control points by imposing values for the audio volume at different playback positions and, in return, the application shapes the fades to be applied between each two neighboring control points. The adjustable fades, which act over the subintervals defined by each two back-to-back control points, are traditionally implemented by means of labored transcendental functions. In order to effectively construct the envelope of audio contents as well as to heighten the audio experience with a view to real-time computing, we advance a piecewise mapping with the sub-functions, depicting the adjustable fades, represented by rational functions only. A plain implementation in JavaScript is presented in the paper in order to highlight the real-time audio capabilities of the suggested procedure of performing audio volume automation.
Securing data using deep hiding selected least significant bit and adaptive swarm algorithm

The emphasis on data protection is improved in particular with respect to the transmission protocols utilized. Different research on numerous data protection areas such as authentication, encryption, hiding of data and validation were performed. In addition, a cybersecurity standard, such as IP-SEC, and secure sockets layer (SSL), were introduced to solve privacy infringement problems by applying encryption, authorization and protection to data exchanged and data stored in the cloud. This study suggests a new steganography algorithm, a data protection tool used to conceal massive amounts of data from graphic and statistic attacks in color images. The proposed algorithm is a multi-level steganography modified deep hiding/extracting technique (MDHET), which implements a selected least signified bit (SLSB) for color picture dispersal of the information. In addition, an accurate pixel location randomization feature has been applied. After MDHET, the predicted results will effectively conceal data up to 6 bpp (bit per pixel) with high safety levels by improving the quality of images. In addition, MDHET can be useful for encoding a deep series of images into one in which the testing procedure is carried out using regular reference images used in color image processing and compression analysis from different institutions.
Wearable Technology in Higher Education Reform

At present, China's higher education has entered a crucial stage of deepening reform in an all-round way.In this context, college physical education teaching is gradually moving towards the direction of combining diversification, flexibility and information.Through the functions of mobile Internet and cloud data collection, collation and analysis, the real-time data of students' learning activities can be effectively collected, and a good auxiliary reference system for the implementation and evaluation of physical education teaching can be established.Based on the existing research, this paper uses the wearable equipment to assist the teaching in the tennis elective course teaching activities, compares the traditional tennis elective course teaching methods, and tests the role and effect of wearable technology in tennis teaching.It provides valuable empirical support for the teaching of public sports tennis electives in the school.
Inclusive Leadership – International Perspectives and Examples

Book: &#x0D; Stefani, L. &amp; Blessinger, P. (Eds). (2018). Inclusive Leadership in Higher Education. International Perspectives and Approaches. Routledge Taylor &amp; Francis Group. New York and London. p. 214.
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with Smaller Factors

In the earlier work, Knuth present an algorithm to decrease the coefficient growth in the Euclidean algorithm of polynomials called subresultant algorithm. However, the output polynomials may have a small factor which can be removed. Then later, Brown of Bell Telephone Laboratories showed the subresultant in another way by adding a variant called 𝜏 and gave a way to compute the variant. Nevertheless, the way failed to determine every𝜏 correctly. In this paper, we will give a probabilistic algorithm to determine the variant 𝜏 correctly in most cases by adding a few steps instead of computing 𝑡(𝑥) when given 𝑓(𝑥) and𝑔(𝑥) ∈ ℤ[𝑥], where 𝑡(𝑥) satisfies that 𝑠(𝑥)𝑓(𝑥) + 𝑡(𝑥)𝑔(𝑥) = 𝑟(𝑥), here 𝑡(𝑥), 𝑠(𝑥) ∈ ℤ[𝑥]
Aspects of Social Engineering and Its Modalities to Countercharge and Prevent It

In an Information Age that has been subjected to the phenomenon of forced digitalization, human propensity towards false information and the susceptibility to social engineering attacks have become a major concern in the field of information security.Nowadays, information security is dependent on the recognition and the user's awareness of risks associated with social engineering attacks since, unlike the mainstream information threats, social engineering not only exploits the technological aspect of illicit information gathering, but emphasizes the manipulation of the human factor in order to accomplish the intended result.Therefore, the preconception that the victim of a cyber-attack is without culpability can be seen as a consequence of the mystification of cybersecurity and a lack of informedness on the part of the typical user.
Animal Detector System for Forest Monitoring Using OpenCV and Raspberry-pi

Abstract: Today, even without the help of technology, monitoring wildlife has become a challenging job. To address this problem, we have developed a solution for animal classification cameras that can detect and store data of these animals in SD card. The system can also be used to protect wildlife in zoos. The system is also able to identify new species. When a new species is discovered, it stores its data in a separate database for further study. It is also possible to control the system remotely if there is internet in the area. The system uses Python-based code that includes pretrained classification models. We have used the pi camera to feed live footage to the Raspberry Pi. It then uses the OpenCV module to segment the image into frames and compares the obtained frames with the pretrained module and gives labels accordingly. The system and stored data can also be accessed through the mobile application Rasp Controller and through computer using the software named VNC viewer. The system is also able to move from one place to another and also as it is a part of whole system it is also remotely monitored.
Research on Domain-Specific Knowledge Graph Based on the RoBERTa-wwm-ext Pretraining Model

The purpose of this study is to solve the effective way of domain-specific knowledge graph construction from information to knowledge. We propose the deep learning algorithm to extract entities and relationship from open-source intelligence by the RoBERTa-wwm-ext pretraining model and a knowledge fusion framework based on the longest common attribute entity alignment technology and bring in different text similarity algorithms and classification algorithms for verification. The experimental research showed that the named entity recognition model using the RoBERTa-wwm-ext pretrained model achieves the best results in terms of recall rate and F1 value, first, and the F value of RoBERTa-wwm-ext + BiLSTM + CRF reached up to 83.07%. Second, the RoBERTa-wwm-ext relationship extraction model has achieved the best results; compared with the relation extraction model based on recurrent neural network, it is improved by about 20%∼30%. Finally, the entity alignment algorithm based on the attribute similarity of the longest common subsequence has achieved the best results on the whole. The findings of this study provide an effective way to complete knowledge graph construction in domain-specific texts. The research serves as a first step for future research, for example, domain-specific intelligent Q&amp;A.
School policy design during the COVID 19 pandemic

The article provides a brief overview and comparison of school policy development trends during the pandemic in Kazakhstan. The relevance of the review is dictated by the need to identify problems and reduce the negative effects of the pandemic coronavirus (COVID-19). The partial or full transition to distance learning during the quarantine exposed many weaknesses in the education systems of the world, but at the same time provided new opportunities for their development. The work analyzes the legislative acts, orders and instructions of the competent authorities of the Republic of Kazakhstan in the field of education, aimed at the effective and inclusive transformation of school activities. On the topic of research an overview of the works of domestic and foreign scientists was conducted. The main requirements that should be taken into account when developing the educational policy of a particular school are considered. For practical application a model of the three-level stage of decision-making on the implementation and implementation of the educational policy of schools is proposed. The analysis of school policy changes was carried out in the directions of academic (educational), educational, human resources policy. The work offers theoretical justifications and practical recommendations for the development and introduction of changes to school policy documents in the period of the COVID-19 pandemic. &nbsp;&nbsp;&nbsp;&nbsp; Key words: world educational policy, state educational policy, school policy, policy, design. &nbsp
Modified BERT-based end-to-end Chinese named entity recognition model

In this paper, we present an end-to-end model based on modified Bidirectional Encoder Representations from Transformers (BERT) for Chinese named entity recognition (NER) in natural language processing. The model is composed of the SpanBERT layer and the Conditional Random Field (CRF) layer. By using combination, the model can express the input characters in the better form of "word embeddings", eliminating the steps of feature engineering or data processing in conventional approaches, and can be widely applied to the task of Chinese NER. Our experiments demonstrate that the SpanBERT-CRF model can effectively utilize the contextual data features and give more accurate recognition results. On our data set, the SpanBERT-CRF model had excellent performance with a recognition accuracy of 91.33%, outperforming the benchmark NER model BiLSTM-CRF (Bidirectional Long Short Term Memory, Conditional Random Field) and BERT-CRF model in performance and F1 score.
Advertising and Job Creations in High-tech Blockchain: A Bibliometrics Citation Analysis

The purpose of this paper is to explore the status of scholarly research on the review and bibliometrics citation analysis works on an overall journal related to research that engages advertising and job creations in blockchain technology. To execute the bibliometric analysis, data is extracted from the web of science (WOS) database. Data are selected through an inclusion and exclusion process called the PRISMA method. The final selected data are then assessed using the VOSviewer software to create a bibliometrics descriptive and an analytical citation analysis. The scarce number of blockchain bibliometric research; and the unavailability of information to researchers on citations, co-authorship, and co-occurrences of keywords in relation to advertising and job-creations incorporating high technology blockchain has motivated this research. Through a descriptive analysis, the study found that 2,215 article with 33,649 citations related to advertising and job creation in blockchain technology; and through the networking analysis showed that the highest citation counts by Journal of business research with 823 citations, Frishammar, Johan; Parida, Vinit; Westerberg, Mats as author with 339 counts, the Politecn Milan institution with 389 counts, and the U.S.A as country with 1,423 counts. This study has its added value as the analysis provides a bird’s eye view of citations and networking linkages globally benefitting researchers. This study also benefits young researchers to engage in developing novel and ground breaking empirical research ideas in the field of advertising, job creations and blockchain technology.
Path Planning for the Fragmented Learning of College Students Based on Artificial Intelligence

&#x0D; Most education platforms attempt to plan reasonable learning paths for college student users, but they have generally ignored differences in their learning time distribution preferences, learning habits, and learning requirements, and haven’t taken the dynamic development trends of their learning states into consideration. To make up for these shortcomings, this paper aims to study the learning path planning for the fragmented learning of college students based on artificial intelligence. At first, this paper proposed a fragmented learning path planning model for college students, and introduced a fragmented knowledge concept map method to mine the recommended fragmented knowledge structure and the association between concepts, so as to fragment knowledge concept into small knowledge pieces and realize fragmented knowledge recommendation. Then, this paper introduced the regularization constraints, and adopted the sparse auto-encoder to predict the missing values in the interaction matrix, so as to solve the problem of data sparsity. After that, hybrid recommendation of fragmented knowledge was made based on the multi-layer perception network model, and the accuracy of the personalized recommendation of fragmented knowledge had been effectively improved. At last, experimental results verified the effectiveness of the proposed recommendation model.
Data-Driven Learning and Low-Level Learners

It has been claimed that data-driven learning (DDL) has many benefits for second language acquisition. However, due to the difficulty of the language included in corpora and the complexity of concordancing software, the suitability of DDL for low-level learners has been questioned. This study investigated the appropriateness of DDL through direct access to a corpus in high school lessons and compared the use of a graded and a non-graded corpus. Through parametric testing, results showed that there was no statistically significant difference between improvements made by learners accessing either a graded or a non-graded corpus. However, analysis of a post-study questionnaire suggests that a non-graded corpus was more positively perceived by learners. The study has identified some interesting areas for future, larger-scale research into DDL. Despite the challenges of DDL, directly accessing corpora can be an effective and meaningful way for learners to notice and discover elements of authentic English in use. データ駆動型学習 (DDL) は、第二言語習得に多くの利点があるといわれているが、コーパスに含まれる言語の難しさやコンコーダンスソフトの複雑さから、初級学習者にとってDDLがふさわしいかどうかは疑問視されている。本研究では、高校の授業において、コーパスへ直接アクセスするDDLが適切かどうかを調査し、語彙制限コーパスと非制限コーパスの使用を比較した。パラメトリック検定の結果、語彙制限コーパスと非制限コーパスの間には統計的に有意な差はみられなかったが、学習後のアンケート調査では、非制限コーパスの方が学習者に肯定的に受け入れられていることが示唆された。本研究により、DDLに関する今後の大規模な研究に向けて興味深い領域がいくつか明らかになった。DDLの難しさにもかかわらず、コーパスに直接アクセスすることは、学習者が生きた英語の要素に気づき、発見するための効果的で有意義な方法であると考えられる。
Benchmark of 2D quality metrics for the assessment of 360-deg images

Omnidirectional or 360-degree images are becoming very popular in many applications and several challenges are raised because of both the nature and the representation of the data. Quality assessment is one of them from two different points of view: objectively or subjectively. In this paper, we propose to study the performance of different metrics belonging to various categories including simple mathematical metrics, humand perception based metrics and spherically optimized metrics. The performance of these metrics is measured using different tools such as PLCC, SROCC, KROCC and RMSE based on the only publically available database from Nanjing university. The results show that the metric that are considered as optimized for 360 degrees images are not providing the best correlation with the human judgement of the quality.
About Some Data Precaution Techniques For K-Means Clustering Algorithm

Clustering is a technique of creating groups of objects such that each group contains similar and unique objects. One of the most popular clustering techniques is the k-means clustering algorithm. Conventional k-means techniques may not work well for high-dimensional datasets, due to the noise, discrepancies, and outliers associated with the original dataset. However, some form of transformation is required to organize the data for clustering. Four different data pre-processing methods are applied before the clustering algorithm to make the data clean, noise-free and consistent. The impact of data pre-processing on the basic k-means clustering algorithm was tested on real-life data using some normalization techniques such as z-score, mean-max, decimal scaling, and mean absolute deviation. We find that the pre-processing before clustering yields good clustering results and significantly reduces the running time compared to the traditional techniques. We can also conclude that the mean absolute deviation is the best among the four normalization methods as it captures all clustering points.
GALGO: Scalable Graph Analytics with a Parallel DBMS

We present GALGO, a system for large scale graph analytics. GALGO provides complex graph analytics in a parallel cluster, exploiting a parallel database system as a computation engine. In this demonstration we show that fundamental graph algorithms including all pairs shortest path, single source shortest path, PageRank, triangle counting, connected components and reachability can be solved completely with queries, dynamically generated by our system. Our system presents performance that is very competitive to state-of-the-art graph systems. Furthermore, our out-of-core graph computation can process graphs larger than available main memory, without compromising performance.
Rethinking Large-scale Pre-ranking System

Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, including matching, pre-ranking, ranking and re-ranking. As a critical bridge between matching and ranking, existing pre-ranking approaches mainly endure sample selection bias (SSB) problem owing to ignoring the entire-chain data dependence, resulting in sub-optimal performances. In this paper, we rethink pre-ranking system from the perspective of the entire sample space, and propose Entire-chain Cross-domain Models (ECM), which leverage samples from the whole cascaded stages to effectively alleviate SSB problem. Besides, we design a fine-grained neural structure named ECMM to further improve the pre-ranking accuracy. Specifically, we propose a cross-domain multi-tower neural network to comprehensively predict for each stage result, and introduce the sub-networking routing strategy with $L0$ regularization to reduce computational costs. Evaluations on real-world large-scale traffic logs demonstrate that our pre-ranking models outperform SOTA methods while time consumption is maintained within an acceptable level, which achieves better trade-off between efficiency and effectiveness.
Collection Management in Libraries of Rohilkhand Region

The amount of valuable electronic resources that may be included in a library's collection is expanding at an exponential rate, which presents a challenge for librarians.’ (Maheswarappa) Several methods and related standards have been established in recent years that enable the creation and distribution of documents in electronic form. The procedure involves selecting and de-selecting present-day and historical resources, planning acquisition and preservation methods, and assessing collections to ascertain how effectively they meet operator requirements. The construction of collections is now more difficult than ever in an increasingly digitized age.
Transitional signal error on xtudents’ paragraph writing

This research aims to know error analysis in using transition signal on students’ Paragraph Writing. The subjects of this research were sixth semester students of Institut Agama Islam Negeri Sultan Amai Gorontalo. English Education Department, in academic year 2018/2019, consisted of 20 students. The method used in this research was a qualitative method with a descriptive design. Technique of collecting data was carried out through interview. Data analysis was performed through data reduction, data presentation, and drawing conclusions. The results showed that the types of error in using transition signal that existed in Paragraph Writing namely; punctuation for transition signal, sentence and clause connectors, types of transition signal, placement of transition signal and repetitive transition signal. The implication of this research was students did not have good competence in using transition signal according to their function and grammar, but some transition signals were still used inappropriately.
Synthetic Data for Anonymization in Secure Data Spaces for Federated Learning

Federated learning implies the integration of shared data. Privacy-enforcing platforms should be implemented to provide a secure environment for federated learning. We are proposing the integration of real world data from local data lakes and the generation and use of general synthetic data to simplify, eventually avoid, encryption or differential learning and use general architectures for data spaces.
A Study on the Utilization of the National Occupational Competency Standards for the Development of Security Field

Purpose: In Korea's industrial sites, the state standardizes the skills required to perform duties and supports to foster the field-oriented talents, yet the use of the National Occupational Competency Standards in the security industry is very inadequate. Method: In this study, the current status of the security industry and the operation of the National Occupa-tional Competency Standards are reviewed, and the National Occupational Competency Standards for the devel-opment of the security industry are conducted based on the interviews with the experts applying to the security industry using the National Occupational Competency Standards towards the efficient use of the Standards. Results: In the security industry, the National Occupational Competency Standards are not functioning properly given the dual contents provided by the existing Security Services Industry Act and the new National Occupational Competency Standards. Accordingly, it is required to change the legislative system which can more actively utilize the Security Services Industry Act and National Occupational Competency Standards and further improve the National Occupational Competency Standards. Conclusion: For the development of the security industry, this study presents the following conclusions. First, in the security industry and the NCS-based education system, the curriculum of the Security Services Industry Act and the education system of the NCS must be unified to reestablish the curriculum. Second, the classification of the duties of the Security Services Industry Act and the level of the NCS according to the occupational rating must be reestablished in a systematic and progressive direction by comprehensively examining the industrial reality and foreign cases. Third, it must be redeveloped as a comprehensive and efficient model by collecting the opin-ions of experts in each field for the inspection of the security industry and the NCS-based system.