Reviewerly/exHarmony huggingface dataset:
Impact of Financial Development and Resource Rents on Total Factor Productivity in Gulf Cooperation Council Countries

The present study estimates the impact of financial development and resource rents on total factor productivity in the Gulf Cooperation Council (GCC) countries by applying the GMM approach. The study takes the panel data from 1984 through 2019, while keeping population, corruption, and trade openness as control variables. The results reveal that financial development and resource rents affect total factor productivity positively in GCC countries. Moreover, the results show that improving trade openness contributes positively to total factor productivity. Though, raising corruption and population deteriorate productivity. Thus, this study emphasizes on the need to improve the quality of political institutions to minimize corruption and encourage contraceptives to control the fertility rate for intergenerational sustainability. Further, there is a need to promote trade and financial integration with developed countries, and to efficiently utilize natural resource rents for long-term growth and development.
La cucina delle parole

Abstract The Glossario Artusiano ( Artusian Glossary ) has been designed as a lexicographical tool, capable of gathering and studying the whole lexicographical wealth of Pellegrino Artusi’s La Scienza in cucina e l’Arte di mangiar bene (1 st edition in 1891; 15 th edition in 1911), a cornerstone of Italian gastronomic culture. By looking at some case studies, this article illustrates the criteria, tools and methodology which may have been used to compose the glossary. The purpose is to highlight how these results can establish a constructive dialogue with the Vocabolario Storico della Lingua Italiana della Gastronomia (VoSLIG), within the framework of the AtLiTeG Project .
Development of soft-skills as an integral part of secondary vocational education

The world is changing rapidly, what was relevant yesterday is no longer in demand in the modern labor market. The main task of secondary vocational education is to keep up with the times and prepare qualified specialists to meet the needs of the modern economy. The article examines the problem of the importance of soft-skills development in the structure of secondary vocational education, raises the question of closer attention to educational programs in terms of the development of personal qualities and human capital. The author reveals the concept of “soft” skills, and also draws attention to the abilities that are most in demand by modern employers. The definition of “hard skills” is given and it is shown why only complex possession of “soft” and “hard” qualities allows you to be a high-class specialist. The methods of developing “soft” skills in the process of obtaining secondary vocational education are proposed. The article summarizes the experience of the teaching staff of the Vologda Technical School of Railway Transport in the field of the formation of supra-professional skills: pedagogical technologies used by teachers in the classroom are presented, a variety of extracurricular activities that contribute to the harmonious development of the student is described. The article is written based on scientific literature, as well as regulatory and legal sources.
Subspace clustering via graph auto-encoder network for unknown encrypted traffic recognition

Abstract The traffic encryption brings new challenges to the identification of unknown encrypted traffic. Currently, machine learning is the most commonly used encrypted traffic recognization technology, but this method relies on expensive prior label information. Therefore, we propose a subspace clustering via graph auto-encoder network (SCGAE) to recognize unknown applications without prior label information. The SCGAE adopts a graph encoder-decoder structure, which can comprehensively utilize the feature and structure information to extract discriminative embedding representation. Additionally, the self-supervised module is introduced, which use the clustering labels acts as a supervisor to guide the learning of the graph encoder-decoder module. Finally, we obtain the self-expression coefficient matrix through the self-expression module and map it to the subspace for clustering. The results show that SCGAE has better performance than all benchmark models in unknown encrypted traffic recognization.
A Study on Knowledge Extraction Methods from Equipment Maintenance Documents

In enterprises operating large-scale equipment, such as plant enterprises, maintenance workers must quickly and accurately find and understand the information required in the equipment maintenance documents to perform maintenance tasks effectively. If the equipment maintenance documents exist in each file for each equipment and the sentence expression constituting each document is ambiguous, it will interfere with the effective performance of the maintenance, and it leads to loss of the company. In order to solve these problems, attempts have been made to efficiently manage equipment maintenance documents and fault documents and extract key information or maintenance knowledge. However, they have the limitations of not quantitatively presenting the effectiveness of the proposed method or considering the relationship between the entities. Therefore, in this paper, we propose a method for effective maintenance knowledge extraction by extracting entities for equipment, failures, and solutions from equipment maintenance documents through named entity recognition, and further building a set of relationships between individual entities using dependency parsing. Equipment maintenance documents used in domestic plant enterprises were used to show validation of the proposed approach, and 74.3% of correct relations were found for the test sentences.
The Reality of Governance Indicators and their Impact on Economic Growth in Iraq for the Period (1996 – 2020)

This study aims to know the impact of governance in achieving economic growth, because corporate governance has become one of the main factors of economic growth models in current studies and research.This study came to show two things: where the first was concerned with identifying the reality of governance indicators as one of the expressive concepts of institutional reforms in Iraq and their role in economic growth through descriptive analysis of data expressing governance.As for the second, it was limited to the empirical evaluation of the impact of these indicators on economic growth through the use of time series data for the period 1996-2020 and based on some econometric tools represented by the Autoregressive Distributed Lags (ARDL), and the study relied on global governance indicators issued by The World Bank, consisting of (government effectiveness, quality of legislation, rule of law, voting and accountability, control of corruption, political stability, and absence of violence).The study achieved the following results: -According to the reality of governance indicators in Iraq, it was found that all indicators fell within the negative performance area, and the worst of them were the indicators of political stability and the rule of law.-In terms of the econometrics model, the study concluded that the two indicators of government effectiveness, voting and accountability have an important and positive impact on economic growth in the short and long terms, while the study showed that the political stability indicator has a negative impact in the long term.One of the reasons that have led to instability is foreign interference, and therefore, work must be done to create harmony between the components of the society of various nationalities and religions, as well as to strengthen the status of security and military institutions and work on their independence from political pressures by the influential political parties in Iraq.
The Role of Monetary Policy Indicators in Achieving Monetary Stability in Iraq and The Kurdistan Region - Iraq for the Period (1988-2019)

The monetary policy used by the Central Bank has an important and prominent role in the economic activity of the country because it is based on monitoring the money supply through which the bank seeks to achieve its final goals of monetary stability, so the aims of this research is to Clarify the role of monetary policy indicators and its impact in achieving Monetary stability in Iraq for the period using the )Co-integration( tests and the Autoregressive Distributed Time Gaps (ARDL) model, based on the premise that monetary policy has a major role in achieving monetary stability, and the research reached some conclusions, the most important of which is that there are Positive and negative effects of monetary policy indicators through their effects on the economic variables related to monetary stability, and accordingly, it is necessary to work on achieving monetary stability by activating and diversifying these indicators so that they become more appropriate to achieve their goals of monetary and economic stability, through building a banking system A sophisticated that contributes to the development of the local money markets by enhancing competition between commercial banks and building the confidence of individuals in them .
Marketing Innovation and its Contribution to Promoting Customer Confidence-An Exploratory Study of the Opinions of A Sample of Workers in Singular Stores in the City of Mosul

The research aims to identify the contribution of marketing innovation to enhancing customer confidence.Marketing innovation focuses on marketing activities and methods of marketing work, especially those related to the elements of the marketing mix, as organizations of all kinds have been interested in marketing innovation to achieve their marketing objectives through the increasing role of marketing innovation, which is the main means and tool.For the work of many organizations, including organizations operating in the city of Mosul, the problem of the study is summarized in an attempt to determine marketing innovation in enhancing customer confidence.The questionnaire consisted of a sample of workers in single stores in the city of Mosul, with a total of (51) forms, and (43) valid forms were retrieved for analysis and testing some hypotheses with the correlation and influence relationships between the research variables using many statistical methods based on a program.The research reached conclusions them.There is a correlation and impact relationship between marketing innovation and customer confidence, and in light of this, it has been A proposal that organizations use marketing innovation to practice their activities by developing the products offered by the organization and keeping pace with modern and rapid developments in the needs and desires of customers and maintaining the trust that customers give to the store
Real-Time CNN-Based Face Mask Detection System

At a time when the coronavirus pandemic is at its peak and a third wave could be right around the corner, the importance of wearing a mask in public spaces cannot be emphasized enough. Existing research shows that wearing a mask in public reduces the spread of infection by 70% yet only 44% of the population reportedly wears a mask in public. Hence, we found it imperative that a real-time system should be designed to detect whether a person is wearing a mask or not. This chapter presents a simplified approach to achieve this purpose using some basic deep learning concepts like the creation of neural networks and data flow graphs (TensorFlow), data preprocessing (Keras, NumPy), to plot the training loss and accuracy (Matplotlib), to detect objects from our camera (OpenCV), and max pooling (MobileNet). Our software can be used in public spaces to catch violators of government safety protocols without requiring human intervention and should attain good accuracy on different datasets.
Processing of Multi-valued Attributes Based on Sparse Matrix

Multi-value attribute has always been a difficult problem to deal with in machine learning. Most models are unique for data format matching. When it is multi-value, most models cannot be used directly. At the same time, a large number of multi-valued attributes will be encountered in the construction of medical model. These attributes often represent that patients have multiple symptoms.. The processing methods of multivalued attributes can be roughly divided into two categories, one is through data preprocessing, the other is through algorithm pattern matching. The solution to medical multi-valued attributes in this paper is mainly through preprocessing, from the perspective of multi-valued attribute representation and projection. The process is to use sparse matrix to represent multivalued data, convert it to high-dimensional space, and then project it back to one dimension to complete the processing of such data.
Survey On Task Scheduling in Cloud Computing Environment

This pasper introduces the characteristics and structural framework of cloud computing and the process of task scheduling. On this basis, various typical task scheduling algorithms of cloud computing are introduced, including traditional scheduling strategies, heuristic-based intelligent algorithms, emerging swarm intelligence algorithms and hybrid algorithms in cloud computing scheduling. The characteristics of various algorithms are compared and analyzed based on optimization indicators such as time, cost, and energy consumption. Finally, the main problems existing in the current research are put forward, and the future research direction is prospected.
Speech recognition and hardware accelerator design based on hummingbird core

Abstract In recent years, with the rapid development of artificial intelligence, speech recognition has irreplaceable advantages in human-computer interaction. In human-computer interaction, we hope that speech recognition has the characteristics of fast recognition speed, high recognition accuracy, and strong flexibility. This paper develops a hardware accelerator based on a programmable gate array, in which the nucleic acid detection systems utilize voice to complete machine control. We use a traditional BP neural network to train the parameters of the speech recognition model, and it is successfully deployed on FPGA to realize speech recognition. The acceleration effect is significantly improved, realizing that the cycles used before acceleration are almost four times those used after acceleration.
Application of the laws to compose fractal computation algorithms when modeling and analyzing their growth

Abstract Generalized analysis of the methods to construct fractals let to find algebraic patterns of fractal algorithm, fractal operators fi formalizing basic operations to construct the fractal structures of a specific type being the major concept. The fi are introduced with multiplication and addition operations together with the concepts of singular, zero and inverse operator that allow defining periodic and quasi-periodic fractal structures. An endless fractal length - basic operator which can give a possibility to obtain a limit fractal synthesis idea as a structure without self-similarity but with a regularly decreasing scale. The connection between fractal growth computations and widely examples of application in the nature is considered. Methods to analyze growth processes are offered, their basic characteristics are shown. The mathematical description of the fractal growth characteristics and their dependence on specifically defined parameters of algorithm are given. The analysis of fractal point growth trajectory is made. The algorithms applied and the methods to analyze growth processes are shown to be modernized and adapted for wide spectrum of tasks.
A Fault Diagnosis Method of 25Hz Phase-Sensitive Track Circuit Based on BP Neural Network Optimized by Genetic Algorithm

In this paper, considering the actual situation of the track circuit, aiming at the fault diagnosis of the 25Hz Phase-Sensitive track circuit, because the BP neural network (BPNN) model possess the characteristics of succinct construction and powerful nonlinear fitting ability as well as good fault tolerance performance, A fault diagnosis system of 25Hz Phase-Sensitive track circuit based on BPNN is designed. In addition, considering that the BPNN is greatly affected by the initial weight. Different results can be obtained when BPNN fault classifier is initialized with different weights, and it is easy to converge to the local minimum, so the genetic algorithm is selected to optimize the initial weight and threshold of the BPNN. The research results show that the BPNN optimized by genetic algorithm has fewer iteration steps, lower average error, higher classification accuracy, and is simple and easy to handle, which can better deal with nonlinear system problems such as track circuit fault diagnosis.
An Introduction to IoT-Based Posture Detection and Underlying Sensor Technology

Analysis of human posture has many applications in the field of sports and medical science including patient monitoring, lifestyle analysis, elderly care etc. Many of the works in this area have been based on computer vision techniques. These are limited in providing real-time solutions. Thus, Internet of Things based solutions are being planned and used for human posture recognition and detection. The data collected from sensors is then passed to machine learning or deep learning algorithms to find different patterns. In this chapter, an introduction to IoT based posture detection is provided with an introduction to underlying sensor technology, which can help in selection of appropriate sensors for posture detection.
A Large-Scale Study of Activation Functions in Modern Deep Neural Network Architectures for Efficient Convergence

Activation functions play an important role in the convergence of learning algorithms based on neural networks. Theyprovide neural networks with nonlinear ability and the possibility to fit in any complex data. However, no deep study exists in theliterature on the comportment of activation functions in modern architecture. Therefore, in this research, we compare the 18 most used activation functions on multiple datasets (CIFAR-10, CIFAR-100, CALTECH-256) using 4 different models (EfficientNet,ResNet, a variation of ResNet using the bag of tricks, and MobileNet V3). Furthermore, we explore the shape of the losslandscape of those different architectures with various activation functions. Lastly, based on the result of our experimentation,we introduce a new locally quadratic activation function namely Hytana alongside one variation Parametric Hytana whichoutperforms common activation functions and address the dying ReLU problem.
Research on the Visual Servoing method based on semantic segmentation results of metal objects in a complex lighting environment

Abstract The problem of feature recognition and servo tracking for semantic segmentation results of metal parts under complex and variable lighting conditions is addressed. In this paper, we propose an algorithm that incorporates template matching and image-based visual servoing based on semantic segmentation. The method adopts a geometry-based template matching method to stably identify the image features of the pixel region obtained from semantic segmentation, introduces a feature pyramid dimensionality reduction method to improve the processing speed, and then introduces the extracted image moment features into the image-based visual servoing to operate the robotic arm to perform the servo process on the target.
The Impact of Transparency on Achieving Effective Procurement in the Syrian Telecommunication Sector

The purpose of this paper is to investigate the impact of transparency on achieving effective procurement in the Syrian Telecommunication Sector. In addition, it aims to assessing the level of awareness and understanding the concepts of transparency and effective procurement in the Syrian context. The research methodology involved the adoption of a survey as a research strategy and quantitative approach, utilized a self-administered questionnaire, to arrive at the major findings of the study. The type of research is a single cross-sectional design in which the collection of data from the respondents was carried out only once. Data was analyzed using the statistical package for social sciences (SPSS). The paper revealed that there is an impact of transparency on achieving effective procurement in the Syrian Telecommunication Sector. It also revealed that there is a high level of awareness among the respondents about the concepts of transparency and effective procurement. As far as the researcher is aware, this paper is the first to investigate the impact of transparency on achieving effective procurement in the Syrian telecommunication sector; thus its finding will be an original contribution to the field of transparency and effective procurement. In addition, as there has been a shortage of research in the field of transparency, generally in the Middle East countries, the researcher hopes that this paper will establish a foundation for further research in the region.
Web page prediction using adaptive deer hunting with chicken swarm optimization based neural network model

The world wide web acts as the dominant tool for data transmission due to access such as data retrieving and data transactions. The retrieval of data from the web is a complex procedure due to the large volume of web domain. The basic uses of the websites are described through web usage mining, which mines the weblog records to identify the pattern of accessing the web pages through the user. The web page prediction assists the web users in finding the plot and obtains the information as to their requirements. Several effective algorithms have been developed to mine association rules that make the predictive model more appropriate for web prediction. They can be commonly revised to ensure the changing feature of web access patterns. The Apriori algorithm involves extracting the recurrent itemset and interrelation rule that learns the relational data is commonly utilized for web page prediction. The Apriori algorithm remains the standard model for deriving the patterns and rules from the datasets in co-operative rule extraction. The Apriori algorithm thus generates large mines associated rules for web page prediction. Hence, to select the best rule, the proposed deer hunting rooster-based chicken swarm optimization algorithm is used by integrating the cockerel search agents’ dominating social search creatures’ hunting habits and their traits of looking for food. Further, the neural network (NN) is employed in this research for the prediction of web pages with minimum error. The trained NN is a technique of unsupervised learning that analyzes a dataset of input to produce the desired result, in which the effectiveness of the NN is enhanced by optimal tuning of weight by the adaptive deer hunting rooster-based chicken swarm optimization algorithm. The experimental analysis illustrates that the proposed adaptive deer hunting rooster-based chicken swarm optimization frameworks inherit lower error measures such as mean deviation = 139.89 and symmetric mean absolute percentage error[Formula: see text]0.45579 for the FIFA dataset. The proposed web page prediction models’ L2 norm and infinity norm are 58.017 and 14, respectively, for the MSNBC_SPMF dataset.