Reviewerly/exHarmony huggingface dataset:
Operation Analysis of Financial Sharing Center Based On Big Data Sharing Technology

With the advent of the information and the digital age, the company’s economic development scale is getting bigger and bigger, and its development is getting faster and faster. While the number of company businesses is gradually increasing, they are also becoming more and more complex. We can use information technology to integrate business and improve operational efficiency. Therefore, a new model of process reengineering of the financial management system-the financial sharing center came into being. According to statistics, more than 400 companies among the world’s top 500 companies have prepared or have run financial shared service centers. The financial sharing center can not only reduce costs for enterprises, but also create profits for enterprises. This chapter takes SF Financial Sharing Center as an example to introduce its operating mode.
Research on medical data protection based on differential privacy

With the rapid development of science and technology and medical informatization, medical data has emerged on a large scale. Although medical data can provide convenient conditions for data analysis, data mining and intelligent diagnosis, the huge medical data set involves a large amount of patient private information, and there is a risk of leakage. Many existing medical institutions use databases or cloud servers to store medical data, which are also vulnerable to attacks and data leakage. The second is that third-party service providers may illegally sell the original client data. Aiming at these problems, this paper proposes a differential privacy scheme based on Laplace and summary matrix, which solves the problems of large statistical information error, low accuracy and high time and space complexity of the algorithm in the current differential privacy model. And the scheme has higher availability and is suitable for data of different scales.
Deep Ensemble Model for Spam Classification in Twitter via Sentiment Extraction: Bio-Inspiration-Based Classification Model

Twitter Spam has turned out to be a significant predicament of these days. Current works concern on exploiting the machine learning models to detect the spams in Twitter by determining the statistic features of the tweets. Even though these models result in better success, it is hard to sustain the performances attained by the supervised approaches. This paper intends to introduce a deep learning-assisted spam classification model on twitter. This classification is based on sentiments and topics modeled in it. The initial step is data collection. Subsequently, the collected data are preprocessed with “stop word removal, stemming and tokenization”. The next step is feature extraction, wherein, the post tagging, headwords, rule-based lexicon, word length, and weighted holoentropy features are extracted. Then, the proposed sentiment score extraction is carried out to analyze their variations in nonspam and spam information. At last, the diffusions of spam data on Twitter are classified into spam and nonspams. For this, an Optimized Deep Ensemble technique is introduced that encloses “neural network (NN), support vector machine (SVM), random forest (RF) and convolutional neural network (DNN)”. Particularly, the weights of DNN are optimally tuned by an arithmetic crossover-based cat swarm optimization (AC-CS) model. At last, the supremacy of the developed approach is examined via evaluation over extant techniques. Accordingly, the proposed AC-CS [Formula: see text] ensemble model attained better accuracy value when the learning percentage is 80, which is 18.1%, 14.89%, 11.7%, 12.77%, 10.64%, 6.38%, 6.38%, and 6.38% higher than SVM, DNN, RNN, DBN, MFO [Formula: see text] ensemble model, WOA [Formula: see text] ensemble model, EHO [Formula: see text] ensemble model and CSO [Formula: see text] ensemble model models.
Research on disk failure prediction based on random forest algorithm

Aiming at the problem of low recall and precision of threshold-based disk failure prediction methods, this paper studies disk failure prediction through machine learning methods. By labeling the disk examples in the data set and balancing the number of examples with different labels, after selecting the features that have a greater impact on the results, the machine learning method of random forest is used to train the prediction model, and the performance of the model is verified. When the random forest method predicts the failure of the disk, the recall and the precision are both higher than those of the threshold-based disk failure prediction method.
Queuing-Based Federation and Optimization for Cloud Resource Sharing

Resource sharing can gain economies of scale and increase utilization of cloud infrastructure, a critical challenge of which is how to design efficient resource sharing solutions among self-interested cloud providers. Cloud federation can realize resource sharing, but the existing methods of forming federation need complex computation to guarantee the stability of federation. To address this shortcoming, after analyzing an optimal allocation approach of service requests among clouds, we propose a pareto optimal resource sharing solution named Cloud Light-Federation Sharing (CLFS), in which each cloud can choose its own optimal strategies individually and federation can be formed without complex computation for allocation of service requests and profits. In addition, an optimal resource sharing solution named Cloud Cooperative-Federation Sharing (CCFS) was also designed, in which cloud providers are fully cooperative and have fair profit allocation. The experimental results show that the two federation methods can significantly improve the total utility and decrease the number of dropped jobs. Although the federation rules of Cloud Light-Federation Sharing are simple, its performance is still very close to that of Cloud Cooperative-Federation Sharing.
Exploring translators’ perceptions of and editing actions on post-editing of machine translation

The recent development in information and computer technology has offered diverse and far-reaching possibilities for translators and the language service industry. Post-editing of machine translation is the most common solution at present applied by translation agencies to balance cost, speed and quality to meet the future localization paradigm. With the output quality offered by machine translation improving, machine translation has been widely used. Post-editing has been adopted to revise the output of machine translation to bridge the quality gap between that of professionals within a tight project period. This paper investigates translators’ perceptions of and editing actions on post-editing through quantitative method and concludes that post-editing of machine translation is becoming a widely accepted practice and will play a significant role in translation industry.
Design and implementation of a decision tree classification algorithm for radar signal recognition

In order to classify and identify the countries of radar signals, this paper analyzes the characteristics and current situation of radar signal recognition, adopts the decision tree classification algorithm in machine learning, studies the data preprocessing and the construction, evaluation and optimization of decision tree model, and realizes the program design and practical application of decision tree classification algorithm, which provides effective support for improving the efficiency of radar reconnaissance and early warning. The research shows that the decision tree model can effectively classify and recognize radar signals after training and optimization, with an accuracy of more than 96% and an AUC value of more than 0.98.
Realization of garbage classification system based on WeChat mini program

In view of that people need to spend a lot of time in studying garbage classification knowledge, garbage classification system based on cloud development had been designed, and the system includes functions such as text search, photo identification, common sense of garbage classification and green news, which will help people improve the cognition of garbage classification, and promote work development of garbage classification.
Implementation of Bayesian high dimensional data analysis with SIR model program

In order to solve the problem of large mean square error of regression coefficient in traditional Bayesian high-dimensional data analysis, sir model program is applied to realize Bayesian high dimensional data analysis. This paper capture Bayesian high dimensional data, and use sir model program to set super parameters. The prior probability of Bayesian high dimensional data analysis is calculated, and the topological structure of Bayesian high dimensional data analysis is constructed to realize Bayesian high dimensional data analysis. The results show that the mean square error of regression coefficient in the experimental group is significantly lower than that in the control group, which can solve the problem of large mean square error in traditional analysis.
Research on blockchain-based crowdsourcing mechanism for knowledge system

In the era of informationization, knowledge acquisition has penetrated into people's daily life. Although traditional knowledge service platforms have a rapid knowledge update speed and cover a wide range of fields, they have problems such as low professional threshold, lack of clear logical knowledge structure and difficulty in knowledge rights maintenance and protection. Although knowledge graph can effectively manage knowledge, it faces challenges when it comes to expanding the knowledge base. Based on this, a knowledge system crowdsourcing expansion method is proposed to enrich and improve the knowledge base. The method is integrated with blockchain technology to ensure the openness, transparency and fairness of the crowdsourcing process through smart contracts. In addition, it also improves the authentication and transaction process of knowledge contribution on the blockchain, which solves the problem of difficulty in maintaining knowledge rights in traditional knowledge service platforms and further protects the legal rights of knowledge contributors.
Distance education through the eyes of teachers and students: key issues

The article analyses the experience of Russian education digitalisation in the context of restricting the social contacts due to the COVID-19 pandemic. The results of a survey of teachers and students of the State University of Management on the quality of education during the pandemic and the difficulties that the participants of the educational process overcame in connection with the emergency transition to the online format are presented. The problems of psychological, pedagogical, didactic, socio-psychological and technical nature that the participants of the educational process encountered when organising online learning activities have been identified by the methods of expert analysis. As a result of the analysis of respondents’ opinions and their comparison with the opinions of education researchers, a generalisation was made: despite the fact that e-education has firmly entered our lives, in the near future, at the bachelor’s level, the online learning format will not be able to replace the traditional format, since it does not provide a high quality of knowledge transfer and assimilation. At the same time, it is emphasized that the indisputable advantages of learning using electronic educational environments should not be overlooked, which aims to develop the principles and technologies of a “hybrid” bachelor’s education system, providing for a combination of online and offline formats in accordance with the requests and specific conditions of educational activities, depending on the orientation and profile of training.
Application of ASTER images for identification and localization of iron minerals in red mud dumps of the Ural aluminum plant

Abstract Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was used to identify and mapping of industrially and environmentally important iron oxides and hydroxides in red mud dumps. In paper we were focused on specified region of interests - one of the largest boxite processing manufactory in Russia Ural Aluminum Plan (UAP). Initially we use ASTER L1T images. The graph of processing includes band relationship, principal component analysis and spectral angle mapper procedures for identifying spatial distribution of targeted minerals. Several minerals were mapped within red mud dumps No 2 and No 3: Hematite, Goetite, Jarosite and Magnetite. At the next stage the Scanning Electronic Microscopy (SEM) of red mud mineral samples was carried out. The results show a good correlation between remote sensing techniques and standard mineralogical analysis (SEM) method. In both cases the principal iron oxides and hydroxides were observed. Combination of remote sensing and traditional mineralogical analysis methods allows to increase the reliability of assessing ore potential and efficiency of planning utilization technologies for wastes in technogenic deposits. In addition, processing of several ASTER images from different time gap (2004 and 2015) allows to trace in dynamics the transformation of mineral composition in red muds.
Semi-global shape-aware attention network for image segmentation and retrieval

Non-local operations are usually used to capture long-range dependencies via aggregating global context to each position recently. However, most of the methods cannot preserve object shapes since they only focus on feature similarity but ignore proximity between central and other positions for capturing long-range dependencies, while shape-awareness is beneficial to many computer vision tasks. In this paper, we propose a Semi-Global Shape-aware Network (SGSNet) considering both feature similarity and proximity for preserving object shapes when modeling long-range dependencies. A hierarchical way is taken to aggregate global context. In the first level, each position in the whole feature map only aggregates contextual information in vertical and horizontal directions according to both similarity and proximity. And then the result is input into the second level to do the same operations. By this hierarchical way, each central position gains supports from all other positions, and the combination of similarity and proximity makes each position gain supports mostly from the same semantic object. Moreover, we also propose an efficient algorithm for the aggregation of contextual information, where each of rows and columns in the feature map is treated as a binary tree to reduce similarity computation cost. Experiments on semantic segmentation and image retrieval show that adding SGSNet to existing networks gains solid improvements on both accuracy and efficiency.
Heterogeneous domain adaptation by Features Normalization and Data Topology Preserving

Transfer Learning (TL) algorithms are effective methods for utilizing the source domain knowledge to improve classifier learning in the target domain. These algorithms use labeled source domain instances to reinforce learning in the new target domain. The challenging model of these algorithms, i.e. heterogeneous domain adaptation, is characterized by the source and target domains, which have different features spaces, different data distributions and labels. Two effective factors for improving the performance of TL algorithms are reducing the difference in feature space and distribution between domains. Recently, some TL methods have focused on reducing the difference in distribution and some on reducing the difference in feature space between domains, and a few number of methods have considered the two issues, simultaneously. However, these methods usually use complex computational structures, such as deep neural networks and optimization methods, to adapt the feature space and domain distribution. Another important factor for increasing the efficiency of TL algorithms is preserving topology of data during transfer between domains that have not been studied in the existing TL algorithms. Simultaneous use of these three factors in a single framework improve the performance of TL algorithms. To this end, this paper proposes a novel method for solving heterogeneous domain adaptation problems based on Feature Normalization and Data Topology Preserving (FN-DTP). FN-DTP employs the feature normalization technique in the source and target domains. Hence, the feature spaces of the source and target domains closes together and reduces the difference in the distribution of domain data, while simultaneously preserve topology of data during transfer between domains. This method, without using complex computational structures, employs the mentioned three factors to improve the performance of TL algorithms in a unified framework. Experimental evaluation by using the Office-Caltech and PIE benchmark datasets demonstrates the effectiveness and efficiency of the proposed method compared with the state-of-the-art method in improving learning in semi-supervised classifications.
Laser simulator logic: A novel inference system for highly overlapping of linguistic variable in membership functions

A new approach for Fuzzification and Defuzzification processes of a high degree of overlapping between the linguistic variables through proportional and relative-dynamic-scale membership functions is presented in this paper. Most of related works that utilized type-1 and 2 fuzzy logic algorithms are confused and questionable from two points of view: (1) the overlapping between linguistic variables values starts almost from the midst of each other; so, what will be happened if it starts before the midst i.e., increasing the degree of overlapping, - (2) the linguistic variables are commonly represented by symmetrical membership functions form. It has been found that the current type 1 and 2 fuzzy logic are no longer suitable to deal with highly inference between linguistic variables. Thus, a novel algorithm, called laser simulator logic, is introduced in this paper to deal with the issue of highly inference of linguistic variables membership functions, which depends mainly on a proportional and relative-dynamic scale of membership function that will change the scale of membership continuously based on the crisp input/output. Results show that the laser simulator performs better than both type-1 and 2 fuzzy logic when there is a high degree of overlapping between the linguistic variables.
An Advanced Unscented Kalman Filter and Fuzzy-Based Approach for GPS Position Estimation Real-Time Applications

Currently, the necessity for GPS is evolved in each stage throughout several applications, due to the increasing number of applications related to GPS, the need for GPS receiver positioning is increasing in almost every field. This process is a bit like a nonlinear process. To get the exact position of the GPS receiver, the received signal is corrupted due to the many factors that must be rectified. Also, the error of satellite orbit is very important to determine the exact position of GPS device. These errors are minimized statistical signal processing and Adaptative filtering techniques are commonly applied to estimate the GPS receiver position. In this work, estimation of the receiver position is done through the Extended Kalman filter (EKF). The result of this study projects the efficiency of the Unscented Kalman filter (UKF) method which is better than EKF in tracking the GPS receiver position than the EKF.
Fourier Series Nonparametric Regression Modeling in the Case of Rainfall in West Java Province

The Fourier series is a trigonometric polynomial that has flexibility, so it adapts effectively to the local nature of the data. This Fourier series estimator is generally used when the data used is investigated for unknown patterns and there is a tendency for seasonal patterns. This study aims to determine the results of the best Fourier series nonparametric regression model and the level of accuracy of the Fourier series nonparametric regression model on rainfall data by month in West Java Province in 2015-2019. This research is about a nonparametric regression model of Fourier series which is estimated using Ordinary Least Square method. Nonparametric regression using the Fourier series approach was applied to Rainfall data in West Java Province in 2015-2019. The independent variables used were the average air humidity, air pressure, wind speed, and air temperature. The model used to model the amount of rainfall in West Java Province is a nonparametric Fourier series. The nonparametric regression model is the best Fourier series with K =13 values obtained Generalized Cross Validation, Mean Square Error, and R2 respectively at 549.92; 462.09; and 97.30%. The results showed that the variables of air humidity and air pressure had a significant effect on rainfall.
Supervised Learning for Abrupt Change Detection in a Driven Eccentric Wheel

Event detection is often a predominant challenge in processing non-stationary signals. In engineering mechanics, events may result from non-smoothness in the form of loss of contact, impact, or the onset of sliding-friction. An interesting example of such a mechanical system is a wheel whose center of mass does not coincide with its geometric center. An eccentric wheel may evolve in three distinct phases: roll without slip, roll with slip, and hop. Therefore, this paper seeks to explore and compare supervised learning methods for phase identification (i.e., roll, slip, and hop) in simulated data from a driven eccentric wheel. The mechanics of a torque driven wheel on a flat surface are derived through an augmented Lagrangian formulation and Coulomb friction is adopted to model transverse contact forces. To accommodate for non-smoothness, the system is broken down in complementary sub-problems and the simulation is conducted using event-based methods. The simulated data is then used to train a Naive Bayes classifier, a Support Vector Machine (SVM), and an Extreme Gradient Boosting (XGBoost) classifier. Lastly, the methods as well as their performance, merits, and drawbacks are discussed in detail.
How to Improve University-Business Cooperation in Postgraduate Education: Effects on Students’Satisfaction

The purpose of university-business cooperation (UBC) is to enrich students’ practical knowledge, and the improvement of UBC quality is of great significance. This study proposes a research model that examines the determinants of students’ satisfaction on UBC in postgraduate education, based on work-based learning and ecological systems theory. The data is collected from questionnaires distributed among postgraduate students of universities of applied sciences in China and the multivariate statistical analysis is used to validate the measurement and hypotheses, including canonical correlation analysis, partial correlation analysis, principal component analysis (PCA) and cluster analysis methods. The results provide insight into those factors that are likely significant antecedents for implementing UBC in postgraduate education to improve the quality of UBC in postgraduate education and enhance students’ satisfaction. The results indicate that teaching, tutor supervision, practical relevance and individual development are the primary determinants of students’ satisfaction with UBC in postgraduate education. The results show that correlation exists between teaching and tutor supervision, between teaching and practical relevance, between teaching and individual development, between tutor supervision and practical relevance, between practical relevance and individual development, which is basically consistent with the research model proposed. As a result, this study has put forward some implications regarding the UBC of universities of applied sciences.