Reviewerly/exHarmony huggingface dataset:
Heuristic and fair-queuing based VM load balancing strategy for cloud data centers: A hybrid approach

Cloud computing is an integrated service, which provides a new versatile of programming paradigm with a good number of features to dynamically migrate virtualized computing services between physical servers of various cloud data centers. This technology made the users and cloud service providers (C SPs) to manage the loads as per the demand. The effective management of loads in the cloud environment gives a rise to introduce new management methods, which are responsible for equally distributing the incoming workloads among all the available VMs in an effective way for a given period of time. To achieve this requirement various schemes have been proposed. However, many of them are not suitable to find the optimal information about required resources to fulfil the user demands. To accommodate this, in this paper, an approach named heuristic and fair-queuing based VM load balancing (HFQ-LB) has been introduced. With the help of fair-queuing, an efficient strategy has been designed. This configures the number of incoming loads for finding an appropriate VM for the assignment to satisfy the goal, i.e., it maximizes the utilization of resources according to the user demand. With the help of CloudSim, the proposed algorithm is validated in terms of makespan, waiting time, CPU utilization, and VM utilization by creating a multiple number of data centers. The proposed algorithm is also compared with other existing eight algorithms. Experimental results show that the proposed algorithm outperforms these existing algorithms in terms of makespan, waiting time, CPU utilization, and VM utilization.
Securing critical infrastructures with location based authentication blockchain

A smart infrastructure with many sensors becomes a complex hybrid system. Excluding external adversaries from infiltrating into the infrastructure is a key problem. With a formalized adversary model, we propose a decentralized architecture for smart infrastructure security. First, it effectively embeds information from the physical features of infrastructures into security. Second, our mechanism is managed through a decentralized blockchain architecture. Third, our approach is especially scalable to ever growing smart infrastructures. We prototyped an authentication blockchain for smart grids with a high penetration of distributed energy resources (DERs). Our implementation demonstrates the effectiveness of our mechanism compared to existing approaches.
Scaling Parallelism Under CPU - Intensive Loads in Node.js

An increasing number of applications are using Node.js, a framework for asynchronous I/O, event-driven, server-side JavaScript. The backbone of Node.js is the single-threaded event loop. Therefore, computationally intensive tasks are bound to the performance of a single core. Modules with different characteristics have been built to provide parallelism and scaling. We evaluate the performance of some representative Node.js multiprocess and multi-thread techniques focusing on their scaling behavior on different environments. We present computation metrics using a compute-intensive task as a constant. Finally, we use statistical analysis to identify similarities and differences in performance with the end goal of providing recommendations on deployment.
Deep neural network-based image copyright protection scheme

A deep neural network-based image copyright protection scheme is presented. Instead of modifying the images like traditional watermarking methods, the proposed scheme trains a neural network to extract the robust features from image blocks and then classify them to represent the copyright message. First, the original image and its multiple attacked images are divided into nonoverlapping blocks, a part of which will be selected as candidate blocks. Then, the copyright message bits, together with candidate blocks, constitute the training dataset for the network, enabling the trained network to serve as a copyright message extractor. With the proposed scheme, no quality loss will be caused, and moreover, superior robustness can be achieved due to the adaptive robust feature extraction. Our study also offers further insight into the rationalities and considerations in design. Extensive experiments on a wide range of images show that the proposed scheme possesses strong robustness to many attacks, including additive noise corruption, JPEG compression, filtering, and resizing.
Facial expression recognition based on weighted adaptive symmetric CBP-TOP

The changing process of facial expression contains dynamic texture information, and its accurate extraction is crucial to expression recognition. In this paper, a weighted adaptive symmetric center binary pattern from three orthogonal planes (ASCBP-TOP) is proposed for facial expression recognitio n. First, facial expression image sequences are partitioned into sub-blocks on different scales to establish a multi-scale space, and ASCBP-TOP is used to extract the dynamic texture features of facial expression image sequences in each scale space and obtain the corresponding feature histograms. Then, these feature histograms are connected in series with different weights to obtain an overall feature histogram for describing the dynamic texture features of facial expression sequences. Finally, support vector machine (SVM) is used to classify and recognize facial expressions. Experiments on Cohn-Kanade and JAFFE databases show that the proposed method is superior to the state-of-the-art methods, and it can extract the dynamic texture information more effectively. In addition, the proposed method is more robust to illumination, expression and pose variations, and it has higher expression recognition rate.
Framework for Feature Selection in Health Assessment Systems

Anomaly detection in health assessment systems has gained much attention in the recent past. Various feature selection techniques have been proposed for successful anomaly detection. However, these methods do not cater for the need to select features in health assessment systems. Most of the present techniques are data dependent and do not offer an option for incorporating domain information. This paper proposes a novel domain knowledge-driven feature selection framework named domain-driven selective wrapping (DSW) that can help in the selection of a correlated feature subset. The proposed framework uses an expert’s domain knowledge for the selection of subsets. The framework uses a custom-designed logic-driven anomaly detection block (LDAB) as a wrapper. The experiment results show that the proposed framework is able to select feature subsets more efficiently than traditional sequential selection methods and is very successful in detecting anomalies.
Research of Promoting the Performance of IaaS with Combined Clouds

The article studies IaaS service providers in combined clouds, including private clouds and public clouds, so as to improve the performance of IaaS services, and considers selective services provided through public clouds. Then, by providing selective services in combined clouds, it involves complicated processing, reliability, and other compatibility topics. The recommended system uses cloud ontology to ensure and rank selective cloud services to develop agent-based systems. Users can obtain the required services in case of failure or failure. This paper also uses transaction technology to ensure the performance of service adoption process. The recommended framework is evaluated by many kinds of experiments. They can prove that it improves the performance of services in combined clouds.
Assessing Ant Colony Optimization Using Adapted Networks Science Metrics

This paper presents a method to assess the state of convergence of Ant Colony Optimization algorithms (ACO) using network science metrics. ACO are inspired by the behavior of ants in nature, and it is commonly used to solve combinatorial optimization problems. Network Science allows studying the structure and the dynamics of networks. This area of study provides metrics used to extract global information from networks in a particular moment. This paper aims to show that two network science metrics, the Clustering coefficient and the Assortativity, can be adapted and used to assess the pheromone graph and extract information to identify the convergence state of the ACO. We analyze the convergence of the four variations of the ACO in the Traveling Salesman Problem (TSP). Based on the obtained results, we demonstrate that it is possible to evaluate the convergence of the ACO for the TSP based on the proposed metrics, especially the adapted clustering coefficient.
Improving the Annotation Efficiency and Effectiveness in the Text Domain

Annotated corpora are an important resource to evaluate methods, compare competing methods, or to train supervised learning methods. When creating a new corpora with the help of human annotators, two important goals are pursued by annotation practitioners: Minimizing the required resources (efficiency) and maximizing the resulting annotation quality (effectiveness). Optimizing these two criteria is a challenging problem, especially in certain domains (e.g. medical, legal). In the scope of my PhD thesis, the aim is to create novel annotation methods for an efficient and effective data acquisition. In this paper, methods and preliminary results are described for two ongoing annotation projects: medical information extraction and question-answering.
Distributed Ledger and Robust Consensus for Agreements

This work proposes the application of consensus processes to ensure the consistency of the data stored in distributed ledgers. Consensus allows a group of agents to reach agreements about the value of common variables or, in this case, data structures such as Merkle trees or chains of blocks. Nevertheless, the consensus algorithm requires for all the participants to apply the same equation. A malicious agent can interfere in the process just by introducing some deviation from the expected value. In this work, the authors propose a method to detect when the information has been modified and, under certain assumptions, it can recover the original data.
FURTHER PROFESSIONAL EDUCATION ROLE IN THE MAIN SOCIAL AND PROFESSIONAL PROCESSES

The social and professional reality of the modern society causes the following situation: having chosen a profession in his youth, a person doesn’t determine his labour and social trajectory as a whole, but only its initial stage under the conditions of not only vertical, but horizontal mobility as well. The article discusses the impact of further professional education on such social processes as secondary professional socialization and professional mobility. It reflects the interrelation of the socio-economic aspect of professional education and the socio-psychological issues of becoming a professional throughout one’s life.
Analogical Face Generation based on Feature Points

There are many ways to perform face recognition.The first step of face recognition is the face detection step.If the face is not found in the first step, the face recognition fails.Face detection research has many difficulties because it can be varied according to face size change, left and right rotation and up and down rotation, side face and front face, facial expression, and light condition.In this study, facial features are extracted and the extracted features are geometrically reconstructed in order to improve face recognition rate in extracted face region.Also, it is aimed to adjust face angle using reconstructed facial feature vector, and to improve recognition rate for each face angle.In the recognition attempt using the result after the geometric reconstruction, both the up and down and the left and right facial angles have improved recognition performance.
Multi-scale foreground extraction on graph cut

In order to improve Grab Cut implementation effect for real images, we propose a novel improvement which extends the Grab Cut in three aspects: 1) a series of edge-preserved components are generated via the TV smoothing model; 2) the number of sub-regions is estimated by histogram shape analysis to remove the negative effects on the unreasonable number of the sub-regions; 3) a segmentation termination condition is constructed by integrating the multi-scale components. The experiment result indicates that this method performs well compared to other methods based on graph cut and is insensitive to sub-regions.
Education Standards of Foreign Countries and Their Features

The education system in any state is aimed at solving the strategic tasks of its development. The content of education in many respects must meet the needs of the customer — the state, the region, the specifi c organization. For a more systematic and meaningful understanding of the standards of education in the Russian Federation, it is necessary to navigate and know the features of the educational standards of foreign countries. This paper presents a vision of the features of the standards of both European and Eastern states, as well as the USA, Canada.
Types of Syntactic n-grams According to their Components

So, we have already learned how to obtain syntactic n-grams (although, at the moment, we are considering only continuous syntactic n-grams). Now let’s discuss what types of syntactic n-grams exist depending on the elements they are formed of, i.e., what kind of elements (components) can be parts of syntactic n-grams. In fact, the considerations to be discussed are the same for any type of n-grams.
Study on Infrastructure Construction of Higher School

In this paper, the author fully recognized and grasped the opportunity brought by now economic trend to higher school. Based on infrastructure construction present situation of higher school, the significance and effect of development in line with its national conditions and the requirement of times to higher school infrastructure construction has been elaborated and the ideas of infrastructure construction to higher school have been proposed.
The Implementationof Digital Text Coding Algorithm Through A Three Dimensional Mapping Derived From Generalized ΔΔ-mKdV Equation Using Mathematica

Abstract Encryption-decryption algorithm using a mapping can be done for encoding a digital text, such as two dimensional mapping ΔΔ-sine Gordon equation. In this article, we will be given an encryption-decryption algorithm to a digital text through a three dimensional mapping that derived from the generalized ΔΔ-mKdV equation. Implementation of Encryption-decryption algorithm in this article using MATHEMATICA.
Extracting True Number of Clusters for Segmenting Image through Adaptive Finite Gaussian Mixture Model

Knowing exact number of clusters in a digital image significantly facilitates in precisely clustering an image. This paper proposes a new technique for extracting exact number of clusters from grey scale images. It analyzes the contents of the input image and adaptively reserves one distinct cluster for one distinct grey value. The total count of the grey values found in an image determines the exact number of clusters. Based on the contents of image, this number of clusters keeps on changing from image to image. After obtaining this number, it is given as an input to Gaussian Mixture Model (GMM) which clusters the image.GMM works with finite number of clusters and forms mixture of various spectral densities contained in that image. The proposed method facilitates GMM to adapt itself according to the changing number of clusters. Therefore, the proposed model along with the inclusion of GMM, is named as Adaptive Finite Gaussian Mixture Model (AFGMM). The clustering performance of AFGMM is evaluated through Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). Both of these performance measuring methods confirmed that exact number of clusters is essentially important for reliably analyzing an image.
A correction for the guidelines for validation of qualitative real-time PCR methods from Broeders et al. 2014

Population time series data from field studies are complex and statistical analysis requires models that describe nonlinear population dynamics and observational errors. State-space formulations of stochastic population growth models have been used to account for measurement error caused by the data collection process. Parameter estimation, inference, and prediction are all sensitive to measurement error. The observational process may also result in detection errors and if unaccounted for will result in biased parameter estimates. We developed an N-mixture state-space modeling framework to estimate and correct for errors in detection while estimating population model parameters. We tested our methods using simulated data sets and compared the results to those obtained with state-space models when detection is perfect and when detection is ignored. Our N-mixture state-space model yielded parameter estimates of similar quality to a state-space model when detection is perfect. Our results show that ignoring detection errors can lead to biased parameter estimates including an overestimated growth rate, underestimated equilibrium population size and estimated population state that is misleading. We recommend that researchers consider the possibility of detection errors when collecting and analyzing population time series data.
THE COMPUTATIONAL TOOLS INTERNET OF THINGS, BIG DATA, DATA MINING AND THE ENERGY EFFICIENCY

Computer science is a tool that has allowed great advances for the development of humanity in recent decades, leading to well-being, productivity and various types of gains. In turn, with the increasing consumption of electric energy, energy efficiency has been used by more areas and sectors of society. This work aims to investigate the literatures and terminologies of Internet of Things, Big Data, Data Mining and the relationship with energy efficiency through publications and application examples these terms. A literature review was carried out based on the search for works in the area of ​​computing and computing and its relation with energy efficiency. Considering that these themes are contemporary to the new millennium, even though the themes are recent and although several items still need to be deepened, there is a great development involving energy efficiency and the terms internet of things, great data and data mining.
Transformation Mode of Job Research Results of Hubei Universities

This paper discusses the practice of transformation of Wuhan’s universities’ job research results. The research shows that the transformation mode of universities in Wuhan focus on the reform of income distribution rights, and has not put the reform of mixed ownership into effect. However, Sichuan province has implemented the mixed ownership of job research results and the practice of Southwest Jiao Tong University proves that the advantages of mixed ownership are clear. It has clear property rights and stronger incentives. It reduces transaction costs and promotes the transformation of scientific research marketization. It can not only drive innovation, but also drive development. This article starts from the transformation of Hubei’s job research results. We summarize the experience and problems, and suggest that colleges and universities in Hubei Province emancipate their minds and boldly implement the reform of mixed ownership.
Research on the Teaching Practice of Java Program Design Based on OJ System

In order to improve students’ Programming ability, improve the quality of teaching, and enable students to strengthen and consolidate the understanding of teaching content, a teaching System based on Online Judge based Java Programming Training System (ojb-jpts) was designed, and the platform was used to carry out extended Training and stimulate students’ potential. Students release and complete a certain number of programming questions through the OJ system, and timely and effectively get feedback information, teachers can complete student performance statistics more efficiently. In addition, the OJ system is basically intelligent and the student score judgment is more fair and just. Compared with the traditional course platform system, the teaching platform has achieved more significant teaching results in the process of practice.
Ellipsis In Natural Language

Abstract This chapter introduces the general topic of the handbook: the linguistic study of elliptical phenomena in natural language. In addition, it outlines the general structure of the handbook, with a subdivision into four parts (the theory of ellipsis; ellipsis as a diagnostic tool; the taxonomy of elliptical constructions; case studies exploring the elliptical inventory of a single language), and explains the philosophy behind and rationale for this structure. Finally, the chapter also highlights (a selection of) the main results and generalizations that emerge from contributions to the handbook, and briefly reflects on the future of linguistic research into ellipsis.
A Survey on Analysis of Twitter Opinion Mining Using Sentiment Analysis

In recent years, there is a rapid growth in online communication. There are many social networking sites and related mobile applications, and some more are still emerging. Huge amount of data is generated by these sites everyday and this data can be used as a source for various analysis purposes. Twitter is one of the most popular networking sites with millions of users. There are users with different views and varieties of reviews in the form of tweets are generated by them. Nowadays Opinion Mining has become an emerging topic of research due to lot of opinionated data available on Blogs &amp; social networking sites. Tracking different types of opinions &amp; summarizing them can provide valuable insight to different types of opinions to users who use Social networking sites to get reviews about any product, service or any topic. Analysis of opinions &amp; its classification on the basis of polarity (positive, negative, neutral) is a challenging task. Lot of work has been done on sentiment analysis of twitter data and lot needs to be done. In this paper we discuss the levels, approaches of sentiment analysis, sentiment analysis of twitter data, existing tools available for sentiment analysis and the steps involved for same. Two approaches are discussed with an example which works on machine learning and lexicon based respectively.
Route Optimization of Construction Machine by Deep Reinforcement Learning

After it was reported that an AI player scored higher in Atari2600 games than skilled human players by using deep reinforcement learning techniques, many researchers were inspired to apply deep reinforcement leaning in various fields. This paper focuses on the autonomous ground leveling work by a bulldozer, which is expected to optimize the action of the bulldozer. In a previous work, we implemented a deep Q learning method by giving the images as the input data for the network. However, when learning the image using the convolution layer as the input using deep reinforcement learning, it requires a large computational cost for the learning process. If the size of the neural network is shrunken by contriving the data to be supplied to the input, the learning time (duration) will be reduced. This paper describes the comparison results for different orders of input data. the transition of the learning sequence is also evaluated.
Formalization in Computational Linguistics

Computational linguistics is an important area within the field of linguistics. Computational methods used in computational linguistics originate from computer science, or, to be more specific, from artificial intelligence. In fact, large part of modern computational lingusitics consists in application of machine learning methods to large textual datasets.