Reviewerly/exHarmony huggingface dataset:
A Private Cloud Platform Supporting Chinese Software and Hardware

Abstract This paper designs and implements a private cloud platform deployed on an office system that supports domestic software and hardware. With the rapid development of cloud computing, more and more enterprises and users choose cloud platform as a vital Internet resource. At present, most private cloud technologies rely on mature foreign commercial applications and frameworks, and it isn’t easy to achieve compatibility between Chinese software and hardware. Therefore, it is urgent to design a private cloud platform that supports Chinese software and hardware. The key private cloud technology of the cloud platform designed in this paper is the key technology of private cloud that supports independent and controllable Chinese software and hardware. The cloud platform uses virtual computing, virtual storage, virtual network, and other technologies to complete the virtualization of computing resources, storage resources, and network resources. Users can centrally schedule and manage virtual resources.
Automatic Sketching of Planar Kinematic Chains

A method for the automatic sketching of kinematic chains is presented. First, a layout algorithm based on the move of inner edges is proposed to determine the optimal layouts of topological graphs. Then, these topological graphs are automatically converted into the corresponding kinematic chains via a correction algorithm.
Seismic Deformation Field Extraction and Fault Slip Rate Inversion Based on D-InSAR Technology

There are a large number of nearly north-south trending faults in the Qinghai Tibet Plateau, and there are great differences in the research and understanding of these normal faults. Using traditional geodetic data and spatial geodetic data to invert the dynamic parameters of faults is of great significance for understanding the dynamic process of fault sliding and exploring the relationship between crustal movement and earthquakes. This paper mainly analyzes and studies the seismogenic faults of the Dangxiong earthquake in Tibet. Using the C-band radar images of ENVISAT satellite before and after the earthquake, the co seismic deformation is extracted by using the two orbit difference method. Then, based on the Okada elastic half space model, the three-dimensional slip rate of the seismogenic fault is inversed by using the high-precision deformation information.
Distributed immutable ledger to maintain integrity and anonymity in health insurance – a blockchain approach

Health Insurance application and claim process involve doctors, hospitals, lab professionals and policy holders for approvals, making it slow and increasing the chances of anomalies. Previous research has made it possible to store medical reports on decentralised blockchain networks primarily by using private blockchain networks like Hyperledger fabric which doesn&apos;t assure no data leakage. Our blockchain&apos;s proposed solution will help in easing out the whole process and helping in maintaining integrity and establishing anonymity. We designed data structures for storing insurance payments and agreements, medical service records, and patient records within the ledger. We use smart contracts to automate the insurance claim process and to ensure privacy. This compliance system for the health insurance claim process will increase the privacy of patients&apos; records along with maintaining integrity and anonymity.
Crude Palm Oil Price Forecasting: Comparative Study of Hybrid GMDH Neural Network and ARIMA Model

This paper attempts to develop a more appropriate hybrid wavelet modified GMDH model and its comparative study with statistical ARIMA model for the Malaysia monthly crude palm oil (CPO) price forecasting. The complex data of monthly CPO price has decomposed by using discrete wavelet analysis into different sub series in such a way that error criteria is minimized. The new input of monthly CPO price data obtained from the wavelet analysis is linked to the modified GMDH model. The importance of modified GMDH neural network that has been used different transfer functions simultaneously into GMDH and has given the best fit for modeling in comparison with conventional GMDH model. Real time series data of monthly CPO price for the period of 37 years are collected from Malaysian Palm Oil Board (MPOB). The performance of hybrid GMDH type neural network, individual GMDH model and statistical ARIMA model are determined by using mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), correlation coefficient (R) and coefficient of determination (R2). The result of hybrid GMDH neural network have shown an improvement over ARIMA model with 25.35%, 19.16% and 22.50% reduction in MAE, RMSE and MAPE values respectively. The obtained results of the comparison show that the hybrid wavelet-modified GMDH model has performed better result than the ARIMA model and individual GMDH neural network. The hybrid wavelet modified GMDH neural network technique is imperative to the field of forecasting as it can be used in different forecasting applications.
Vector Learning: Digit Recognition by Learning the Abstract Idea of Curves

A method has been developed that solves digit recognition problem through the abstract idea of lines and curves. The human brain can easily classify digits because it learns from the formation of the digit, it learns from its geometry. Human brain will view the digits as combination of lines and curves and not just mere arrangement of pixel values. For the neuron network to exhibit human level expertise, it must also learn from the formation of digits. Traditional techniques learn from pixel values of the image and hence lack the sense of the geometry of the number. With the help the of this developed method the program begins to have a gist of the geometry and the shape of the number. It develops the idea that a combination of some lines and curves forms the digit. The curves can be easily represented by tangent vectors. The program uses this set of vectors instead of pixel values for training. Results show that the program, which is learning through the abstract idea of curves classifies digits far better than the program learning from mere pixel values and is even able to recognize hugely deformed digits. It also becomes capable of recognizing digits invariant to the size of the number.
Communications of Innovation for Supporting Farmer Empowerment and Productivity in The Digital Era

Food crisis has become the nation's severe problem due to a significant decrease in rice production.This decline was thought to be closely related to empowerment and farmers' access to innovation communication, especially with digital communication technology in agriculture.So we aimed to analyze the determinants of productivity levels and design a digital innovation communication strategy to increase farmer empowerment and productivity.We used a survey to extract qualitative information from related sources.The results showed that the level of farmer empowerment directly influenced farmer productivity and indirectly influenced positively by the use of innovation communication technology (ICT), individual characteristics, farmer innovative activities, and vice versa, indirectly negatively influenced by environmental support.The role of digital ICT was significant in increasing farmer empowerment and productivity.Environmental support has a negative effect on farmer empowerment, meaning that dominance in the intervention of farmers leads to dependency or helplessness.
Use&amp;nbsp;of&amp;nbsp;Machine Learning Models&amp;nbsp;To Predict In-Hospital Mortality in Patients with Acute Coronary Syndrome

Background: A number of models have been built to evaluate mortality risk in patients with acute coronary syndrome (ACS). However, limitations of existing models included limited variable sets and linear algorithms. This study sought to develop and validate a predictive model to predict in-hospital mortality among patients with ACS using an expanded variable set and non-linear algorithms.Methods: A total of 2414 ACS patients were enrolled in this study. All samples were divided into 5 groups for cross-validation. Logistic regression model and XGboost model were applied to predict in-hospital mortality. Results of two models were compared between the variable set by Global Registry of Acute Coronary Events (GRACE) score and the expanded variable set.Results: The in-hospital mortality rate was 3.5% in the dataset. Model performance on the expanded variable set was better than that on GRACE variables: 3% increase in AUC for logistic regression and 1.3% for XGBoost. The AUC of XGBoost is 0.914 (95% CI, 0.911-0.917), demonstrating a better discrimination ability than logistic regression(AUC = 0.905, 95% CI 0.903-0.907) on the expanded variable set. Almost perfect calibration was found in XGBoost (slope of predicted to observed events, 0.96; intercept, 0.023; P<0.001).Conclusions: XGboost modeling, an advanced machine learning algorithm, identifies new variables and provides high accuracy for the prediction of in-hospital mortality in ACS patients.
Optimized and adaptive dynamic load balancing in distributed database server

Modern large-scale and small-scale data applications and processing solutions depend primarily on the distributed database system. Distributed database servers are the most commonly used structures that give data availability, reliability, replication, and processing for both homogeneous and heterogeneous software and hardware systems. One of the significant challenges that will address by distributed file system is a parallel but not intersecting in the data load, which seems to be in the distribution of data items or data access from the nodes in the system. A distributed database system with high availability and scalability gives record offerings in terms of tolerance, availability, and reaction speed. Adaptive load balancing outperforms centralized and distributed load balancing. The technique considers restrictions such as network load, Input and output load, capacity and load, which were not taken into account in the previous method. The restriction satisfying hypothesis, which determines the best level for enhanced system productivity and response time, addresses the load balancing issue. According to experimental results, overall performance assessments in reading and writing operations are enhanced in response time.
Online Credit Card Fraud Detection and Anomaly User Blocking

The use of credit cards has expanded considerably as a result of rapid advancements in electronic commerce technologies. As credit cards become the most popular method of payment for both online and offline purchases, incidences of credit card fraud are on the rise. Transactions with Credit Cards continue to expand in number, capturing a larger share of the US payment system and resulting in a higher rate of stolen account numbers and bank losses. Improved fraud detection has thus become critical to the payment system's long-term stability in the United States. For several years, banks have deployed early fraud warning systems. Large-scale data mining tools have the potential to improve commercial practice. Scalable strategies for analysing enormous volumes of transaction data and efficiently computing fraud detectors in a timely way, especially for e-commerce, is a critical issue. Aside from scalability and efficiency, the fraud-detection task has technical issues such as skewed training data distributions and non-uniform cost per error, both of which have received little attention in the now-established ledge-discovery and data mining communities.
Noun Phrase Complexity in Independent and Source-Based Writing

A growing body of scholars have explored noun phrase complexity according to the hypothesized noun modifier development sequence of Biber et al. (2011).Nevertheless, few researchers have investigated how this linguistic variable vary in different writing types.In response, the study has compared the noun modification by three Chinese EFL students of different writing proficiency in integrated writing (writing based on a source text while responding to a prompt) and independent writing (writing based on personal experience and knowledge, without reference to any reading materials).Following manual coding and descriptive analysis, the authors observed overall positive role of integrated writing for improving noun complexity.In the integrated writing, the highest-proficiency writer modified the nouns in a more varied way, the writer of intermediate level reduced frequent repetition of the same chunks or collocations, and the lowestproficiency writer employed certain advanced language features (of phrases as postmodifiers -abstract meanings and multiple prepositional phrases as postmodifiers) more frequently.The findings can inform writing test designers and instructors of the necessity to adopt integrated writing as a way to measure the phrasal complexity of students or test takers.
Rigorous Multi-Asset Optimal Execution With Bayesian Learning of the Drift

Liquidity issues have been increasingly addressed recently, especially with regards to optimal execution of large orders. In practice, agents are uncertain about the future dynamics of the assets, and face the risk of model misspecification, which could make the execution strategies non optimal. In this paper, we address the problem of uncertainty faced by an agent wishing to execute large orders on multiple assets. The agent only knows the distribution of the future drift of the assets composing her portfolio. We provide a solution to the problem of portfolio optimal execution where prices have drifted Bachelier dynamics with an unknown drift. The agent uses Bayesian learning to update her estimation of the drift, while she maximizes the expected exponential utility of her final wealth. We consider the specific case where the prior is Gaussian, and the costs are quadratic. We use stochastic optimal control tools to show how the problem simplifies into a matrix Riccati ODE with time-dependent coefficients, and use a-priori estimates to solve the control problem.
UAVs’ Dynamic Routing, Subject to Time Windows Variation

This paper presents a method for the multiple autonomous vehicles mission flight planning in changing weather conditions. We model UAVs fleet servicing spatially-dispersed customers in terms of declarative modelling framework. The considered problem boils down to a predictive and reactive planning of delivery missions within a specified timeframe. Due to the need to implement an emergency return of a UAV to its base, or to handle variations in delivery periods, conditions sufficient to allow eliminating unfeasible solutions, and thus allowing to speed up the calculations, have been developed. The results of numerous computer experiments have confirmed experiments these expectations.
A Hybrid Parallel Algorithm With Multiple Improved Strategies

This paper proposes a novel hybrid parallel algorithm with multiple improved strategies. The whole population is divided into three subpopulations and each sub-population executes butterfly optimization algorithm, grey wolf optimization algorithm, and marine predator algorithm respectively. Meanwhile, they share information through three different communication strategies. And in order to improve the performance of the algorithm, the text uses the cubic chaotic mapping mechanism in the initialization stage. At the same time, the idea of adaptive parameter strategy is also introduced, so that some hyperparameters are changed along with the iteration. The results show that the algorithm can provide very competitive results, and is superior to the best algorithm in the literature on most test functions.
A Comparative Study on Improving Straggler Tasks in Hadoop

Hadoop is a well-known parallel computing system for processing massive amounts of data, but there is a task in Hadoop called “Straggling task” that has a significant impact on Hadoop. Speculative Execution (SE) is a good technique to deal with “Straggling tasks” since it monitors the rate of running processes in real time. backing up the “Straggler” on another node to increase the opportunity of completing the backup task ahead of the original. This proposal tackles dealing with the “Straggling task” by creating a strategy able to deal with misjudgment, improper selection of backup nodes, and making speculative tasks start from the checkpoint, by leveraging the checkpoint of the original tasks. BY this work we can achieve comparing with common methods in this sector, such as LATE, ESAMR, and the real remaining time for Wordcount and Sort benchmarks, it was demonstrated to be capable of detecting straggler tasks and properly estimating execution time. It also allows for job execution to be sped up.
Investigating of Solar Energy Potential for Highest Intensity Radiation Over Egypt

Egypt having High solar energy potential and its utilisation are crucial for national sustainable development via efficient energy planning and gradual fossil-fuel independence. Energy access is a fundamental requirement for economic development and a critical element for accelerating economic growth. the area of study receives 3450 hours in the year of solar radiation (RS) with high intensity of solar radiation ranged from 5.28 to 6.22 KWh/m2. On average, Jul is the sunniest month with an average of 13.8 hours of sunshine. December has on average the lowest amount of sunshine with 10.3 hours. If the system's conversion efficiency is high enough 16.8%, it may be possible to install a PV generating capacity of 50,000 Megawatt on houses and buildings (roofs, walls). This research focuses on Egypt, using seven stations strategically placed across the country to analyze (RS) classified into three categories the first on is three station distributed over the western desert (ElGolf - Habatta â€“ Sewa) and the average annual of (RS) is 5.8, 5, 5.3 KWh/m2 and the second is two station over the eastern desert (Saffaga - Shiab-Elbanat) RS Recorded over these two station 5.5, 5.6 KWh/m2 and the last two station over Sinai (ElAgma - ElArish ) 5.2, 4.9 KWh/m2. Assuming that the system has a conversion efficiency of 16.8%.and its maximum power is 275W. The aim of this work is to deduce the cost of PV system over the selected sites to select the best location for solar energy application over Egypt. The result showed that the total cost ranged from 0.02$ /w in the south West side of Egypt to 0.05$/w in the north cost area.
Maximum Likelihood Estimation for Bangla–Odia Word Alignment

In this paper, the mathematical function Maximum Likelihood Estimation (MLE) used to measure Bangla–Odia word alignment performance is used to provide better results and good accuracy. This MLE technique helps to find out the maximum likelihood probability value with the collaboration of the ‘argmax function’ that follows the mapping between two or more words of source and target language sentences. The lexical relationship among the words between two parallel sentences knows after calculating some mathematical values and those values indicate which word of the source language (SL) is aligned with which word of the target language (TL). Find MLE or MAP, the maximum a posterior parameter in the probability model, which depends on the unobserved latent model or hidden variables. Keeping all these issues in mind, it is described the nature of lexical problems that arise at the time of analyzing bilingual translated texts between Bangla (source language) and Odia (target language). The basic challenges lie in the identification of the single word units of the source text which are converted to multiword units in the target text and vice versa. The experimentally, there are thousands of parallel sentences are taken as training set and out of these sentences only hundreds of parallel sentence pairs are considered for test data. The accuracy of the proposed model is giving better performance as compared to other model and the accuracy which is more than expectation.
Transfer Learning and Clustering Analysis of Epileptic Eeg Signals on Riemannian Manifold

The automatic epileptic seizure detection based on electroencephalogram (EEG) is of great significance in the clinical diagnosis of epilepsy. Since the EEG signal are nonlinear and non-stationary, it is more efficient to analyze it on the manifold. Nevertheless, problems of epileptic seizure data and labeling scarcity result in insufficient data available for training. The feature distribution difference in cross-subject EEG signals is notable, which affects the generalization of models. Therefore, a manifold transfer learning method based on divergence alignment (MTLDA) is proposed to solve the few-shot problem. According to the definition of the domain divergence, the objective function is constructed on the manifold, and the transformation matrix is obtained to narrow the feature distribution difference via transforming the source and the target domains. Moreover, there are only few clustering methods on the manifold. Hence, the Riemannian manifold fuzzy clustering (RMFC) is devised to realize unsupervised clustering on the manifold after sample transferring. Riemannian distance and Riemannian mean are utilized in MTLDA and RMFC rather than the classical Euclidean distance and mean. The MTLDA and RMFC do not require any label information from the subjects. We conduct the experiments on two public datasets and one real dataset. Experimental results indicate that the MTLDA and RMFC are effective, and outperform the Riemannian alignment and the fuzzy C-means clustering, respectively.
Sarcasm Detection Using SVM

Many industries need a sentiment analysis for identifying customer reviews. The main hurdle in sentiment analysis is the existence of sarcasm. The presence of sarcasm can diminish the accuracy of the sentiment analysis. Sarcasm can be described as mocking in convenient ways. Sarcasm can occur in two forms, numerical and contextual. Numeric sarcasm is extreme numerical values that show a contrary sentiment, while contextual can be displayed because of different sentiment words. There are many approaches to deal with the problem. Approaches like SVM and neural network are most applied to solve the problem. Purpose of the study is to increase the preceding accuracy by SVM by feature engineering. Features used in the project are delta TF-IDF, word2vec, and pattern related features. The SVM model surpassed the previous SVM model by 7% in F-score for the same dataset.
Trackable and Personalized Shortcut Menu Supporting Multi-user Collaboration

Large interactive displays play an important role in multi-user collaboration scenarios, such as scheme discussion, situation plotting and brainstorming. Users in multi-user collaboration scenarios may have diverse task allocations and usage preferences. Moreover, users’ positions in the interaction process is not fixed and they may move back and forth, because of the larger display and interactive areas. In order to promote the working efficiency and interactive experience of multi-user collaboration for large interactive displays, we have designed and added trackable and personalized shortcut menus to the large multi-touch desktop system——consultation platform. We use a Kinect to capture the skeletal information of different people to achieve multi-user recognition and real-time motion tracking. “Trackable” means that the shortcut menu could track the hand positions of different users, and the users could wake up the shortcut menu with touch gestures from anywhere on the screen. “Personalized” means that the users can set their shortcut menu content according to their usage frequency and preference of common functions. After testing, the shortcut menu received good feedback, and users said that this design could well balance the “shareability” and “independence” of large desktop systems. Our attempt to combine motion tracking with touch interaction in the consultation platform can provide ideas for human-computer interaction design for multi-user collaboration.