Reviewerly/exHarmony huggingface dataset:
Analysis of Human Development, Economic Growth and Income Inequality in SAARC Country

The improvement of product novelty calls for learning in interorganizational networks with partners.Learning new knowledge and information based on innovation enhances the performances of modern organizations.This study is conducted to find out the relationship of inter-organizational learning with innovation and organizational performance.A total number of 58 construction organizations of Islamabad and Rawalpindi were taken as population of the study.The total sample size was 403.Data was analysed though SPSS.The results are authentic and all the hypothesis were accepted.The results show that independent variable drives the mediating variable that ultimately effect the dependent variable.A significant implication for researchers and business practitioners is the familiarization of the key variables inter-organizational learning, innovation and performance.
The influence of self-defense jamming on the coordinates conversion error of radar

Aiming at the problem of insufficient analysis of the influence of self-defense jamming on the radar measurement to determine the target position, the mean and standard deviation of the conversion error under different jamming are derived. The angle and distance measurement errors of the radar to the target under different jamming are analyzed, and the coordinate conversion equation is used to derive the mean and standard deviation of the conversion error under different jamming. The simulation results show that the self-defense noise jamming makes the conversion error more random and the error standard deviation increases; the self-defense deception jamming makes the coordinate conversion produce a fixed error. It shows that under the influence of different self-defense jamming, the positioning of the target by the radar produces different fixed error and random error, the performance of the radar is greatly affected. The target penetration success probability is guaranteed.
Towards energy-efficient neural network calculations

Nowadays, the problem of creating high-performance and energy-efficient hardware for Artificial Intelligence tasks is very acute. The most popular solution to this problem is the use of Deep Learning Accelerators, such as GPUs and Tensor Processing Units to run neural networks. Recently, NVIDIA has announced the NVDLA project, which allows one to design neural network accelerators based on an open-source code. This work describes a full cycle of creating a prototype NVDLA accelerator, as well as testing the resulting solution by running the resnet-50 neural network on it. Finally, an assessment of the performance and power efficiency of the prototype NVDLA accelerator when compared to the GPU and CPU is provided, the results of which show the superiority of NVDLA in many characteristics.
Scaffolding and sequencing

This article is the fifth in a series of myth-busters on the topic of planning in the moment. In this piece, I explain how logical sequencing occurs in the practice of planning in the moment.
Perspectives for the implementation of blockchain technology in the supply chain: research, trends and future

The rise of new technologies is pushing companies to a new digital era. Among all technologies, one that has been gaining prominence is blockchain, which has potential for improving processes and enhance business models in supply chain. The implementation of blockchain technology in the supply chain is promising as it can bring benefits to everyone involved in the chain, among them, highlight for reliability, security, transparency, traceability and authenticity of information. Nonetheless, its application is still in the initial phase and this paper aims to shed light on the current research, trends and future related in the literature. Through an RSL (Systematic Literature Review) is identified how recent and innovative are studies published about the subject, as well as how researchers and universities are working on that around the world. This research contributes to the development of Blockchain theory as it identifies and analyzes the works currently available in the literature. The conclusion is that the issue has been attracting attention from academics, with researches dispersed in several countries and universities around the world, with growth potential.
Improved Ephemeral Search and Nepenthes Algorithms for Diminution of True Power Loss

In this paper Improved Ephemeral Search Algorithm and Nepenthes Algorithm are used for solving the power loss lessening problem. Ephemeral Search Algorithm is physics-based algorithm that mimics the ephemeral actions of switching circuits which made by one or more intermediary switching expedients (like inductor and capacitor circuits). Actions of inductor and capacitor circuits are mathematically formulated to design the algorithm. To improve the convergence rate of the algorithm and Improved Ephemeral Search Algorithm has been designed by integrating chaotic opposition learning approach (to engender superior preliminary populations). Logistic chaos possesses arbitrary, ergodic, and systematic characteristics and chaotic variables used for optimization exploration which features the algorithm to evade local optimum, endorse the population multiplicity. Subsequently an adaptive inertia weighting approach is used (to balance exploration and exploitation capability with good convergence speed) then a neighbour learning (dimension) approach is utilized (to uphold the population assortment with every iteration of weight pursuing). Learning between neighbours (dimensional), modernizing the coordinates of the present entity by means of some data (dimensional) of adjacent entities and the data (dimensional) of an entity is arbitrarily chosen from the total population. Then in this paper Nepenthes Algorithm for solving power loss lessening problem. Nepenthes Algorithm is moulded based on the deeds of Nepenthes plant. Certain plants enthral the prey for reproduction deprived of killing them and quite a few restrain or kill the intruders for protection obstinacies conversely do not digest the physical bodies. Although several plants do engross the nutrients from the numb faunas, hitherto they do not have the competence to kill it. They simply eat the physical bodies found in the topsoil or on the surface of leaf. Every entity is structured rendering to its fitness value in uphill order mode. The highest p Nepenthes plant solutions of the systematized population are measured as the "p Nepenthes plant" plants, NP; however, the left-over solutions (p prey) are the victim (prey). The method of combination is obligatory to simulate the atmosphere of each Nepenthes plant and its victim (prey). In the course of the combination method, the Victim (prey) with the outstanding fitness is allotted to the Rank one Nepenthes plant. In the same way, the consequent preys are allotted to consecutive Nepenthes plants, consistently. When fascination rate is mediocre to the produced capricious value, the prey is successful to spurt from the ploy and Nepenthes plant withstands to propagate. Authenticity of the Ephemeral Search Algorithm, Improved Ephemeral Search Algorithm and Nepenthes Algorithm are substantiated in IEEE 30 bus system. Actual power loss lessening is reached. Proportion of actual power loss lessening is augmented
An Introduction to Data Encryption and Future Trends in Lightweight Cryptography and Securing IoT Environments

This paper presents an overview of the basic concepts of cryptography and encryption. The work aims at presenting the main concepts and concerns of encryption on a high-level of abstraction, allowing non-domain expert readers to navigate through these topics. Less traditional arguments are also shown, from the relevance of Key Management Services with its usage in Envelope Encryption, to Zero Knowledge proofs and their innovative applications. The crucial importance of securing communications between IoT devices and widely used algorithms to do so, are also discussed.
Color light field image encryption algorithm based on DNA coding and self-controlled chaotic system

Color light field images encryption algorithm based on frequency domain DNA coding and integer wavelet transform (IWT) is introduced. First, the light field image is decomposed into sub-view images in the angular domain plane. Subsequently, a low-frequency coefficient matrix and three high-frequency coefficient matrices are obtained in the frequency domain using the two-dimensional IWT. The low-frequency coefficient matrix is encrypted using DNA coding and a self-controlled chaotic system based on PWLCM. The high-frequency coefficient matrix is scrambled, and then the reconstructed intermediate encrypted image is obtained by a two-dimensional integer wavelet inverse transform. Finally, in the spatial domain, a diffusion operation is performed to obtain the final encrypted image. The simulation shows that this algorithm has the advantages of large key space, high operation efficiency, good encryption security and strong resistance to various attacks.
Quiet or loud? Hand position matters when it comes to voice directivity

First systematic study demonstrates hand postures affect voice radiation more than differences between phonemes
NPV analysis &amp; prediction of SRES with python tools

This paper highlights a Solar energy generation plant as Renewable energy source (SRES) on the criterion of cost involvement using net present value (NPV) method and its payback period. The Homer app is also used for analyzing a selected SRES system. The various big data analysis tools of python are discussed,which could be utilized effectively to analyze RES based on predicting the outcomes, like maximum generation capacity, optimum generation month, weather conditions and performance and many more. System integrity and SRES economic considerations are critically evaluated for an educational institution. The cost analysis using NPV method is included with the existing MAHADISCOM as one of the projects and SRES as another project. Both the projects are analyzed by assuming the lifespan of 11 years. The payback period of the SRES system is 3.3 years and hence after that, the energy will be available for free of cost through the mentioned system. This analysis is done for the site located in the state of Maharashtra, India, and can be used as a case study for engineering students for future perspectives. The tools used for data analysis of a RES project are found very useful for predictive analysis. In the present work Matplotlib and Seaborn tools of python are used for prediction of seasonal effect on generation of power. The new innovative method of big data analysis using python programming is applied to a case study and results are presented successfully.
Comparative study of mathematical optimization methods with computer simulation by Python

Because of the lack of the raw material for some essential purposes, using them more efficiently has already become a necessary question. We used optimization to find out what is the best way to allocate the resources. In this essay, the definition is covered, and there are several ways to solve these problems and some ideas about combining these solutions. We also suggest how we could make a more efficient, cost-effective process to solve these problems. We will explain and introduce the way people usually used before, and use the data to show the advantages and disadvantages of using our method. Ultimately, it is shown that among all the five methods that are presented in this essay, the method of UCM is the rapidest way to reach the optimal value within a given precision.
The Influence of Metacognition Strategy on Learning Attitude and Academic Achievement in Students from Cosmetology School

With the prolongation of the COVID-19 pandemic, an untact system has been quickly adopted across many aspects of society, including homes, schools, and businesses. The trend has caused many changes in the education environment, and cosmetology education is not exempt from these. Therefore, it is time to make further efforts to improve the quality of education by solving the problems in training and enhance learning attitudes and academic achievement. For this, learners are required to understand the entire curriculum and make a plan. For effective implementation, a ‘metacognition strategy’ needs to be developed to build the ability to check, evaluate, edit, and judge themselves in such learning courses. The purpose of this study is to help students become very aware of what they know and do not know while promoting the building of a metacognition strategy to adapt to new educational environments. For this, a questionnaire survey was performed among students from cosmetology academies in Seoul and Gyeonggi-do from May 3 to June 24, 2021. A total of 386 out of 400 copies were used for final analysis, and the results found the following: The metacognition strategy-based courses positively influenced learning attitudes and course satisfaction with statistical significance. In addition, students with high metacognition also revealed high academic achievement. In the Untact Era, it is important to build a metacognition strategy for better adaptation to new educational environments and social changes. This study analyzed how to help students develop their learning ability through customized courses and check their capabilities through monitoring. It is anticipated that there should be further studies on diverse dependent variables and training courses.
Spatio-Temporal Analysis of Dry Spell for Agricultural Decision Support in North-Central Nigeria

This study aims to examine the trend and geospatial variability of dry spells for agricultural decision support. Daily precipitation data spanning from 1981 to 2020 was analyzed using the Instat climatic software to calculate rainfall onset, onset plus 50, length of the growing season, cessation, dry spell 1, and dry spell 2. Multiple regression analysis was used to analyze the trend in maximum dry spell length. Kriging method in Surfer 13 software was used to interpolate and plot dry spell distribution into a spatial grid in the study region. Based on records of daily rainfall data obtained from eight meteorological stations, the spatial distribution of dry spells was generated for the beginning and end of the season. The results obtained revealed a decreasing trend for maximum dry spell length during onset, meaning its recurrence is declining except in Bida, Minna, and Jos that show a positive trend. While the trend for maximum dry spell length at end of the season appears to be positive except for Lafia and Minna that shows a negative trend. The result further indicates that dry spells towards the end of the season are more severe than dry spells at beginning of the season.
Toward Automatic Discovery of Diverse Perspectives Sihao Chen, Daniel Khashabi, and Dan Roth

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Credit card fraud detection using supervised machine learning methods

Nowadays, people are using credit cards more frequently, especially for online shopping. Credit card fraud has a negative influence on individuals, merchants, and financial institutions. This paper focuses on credit card fraud detection by using three supervised machine learning methods: decision tree, random forest, and AdaBoost algorithm. These three methods are widely used in the medical field, chemical field, visual identification, and finance field. We use the confusion matrix and receiver operating characteristic curve to interpret our model. The random forest has the highest accuracy of 99.6% in predicting non-fraud. The AdaBoost has the highest accuracy of 77% in predicting fraud transitions, while it has the lowest accuracy of 95% in predicting non-fraud. Therefore, the random forest algorithm is the best model to apply in credit card fraud detection.
Chen Yi, Energetic Duo for two violins (2015)

This passage consists of three thirteen-measure periods, each divided into four small sections with a duration of thirteen quarter notes. Each of these smaller thirteen-beat sections uses a consistent rotation of three contrasting types of material. These three types of material give an initial impression of maximum distinctiveness and contrast, but as the piece progresses, subtle affinities are emphasized and the musical materials show a surprising ability to morph into each other.
Privacy and security issues of big data

Due to the reasons such as rapid growth, spread of mobile devices, network services, and online users on the internet leading to a remarkable increase in the amount of data. nearly every industry is struggling to survive with a large amount of data. Large data occurrence has started to attract the significance. Although reserving and examining of large amount of data with conventional applications is not tough, but in this situation, we face some challenging issues concerned with security and privacy. This article discusses large information, territory, and huge information concerns and gives a relative perspective on immense data of protection and well-being approaches to system applications and data. To handle privacy and security issue in big data, is recommended by assembling all these applications.
First Encounter with ReLU Networks

This chapter starts by introducing the key concepts attached to neural networks, such as architecture, weights, biases, and activation function. It proceeds with the specific choice of the rectified linear unit (ReLU) as activation function. In this case, neural networks generate continuous piecewise linear (CPwL) functions. It is then shown that, in the univariate setting, any CPwL function can generated by a shallow ReLU network. This is no longer true in the multivariate setting, for which it is nonetheless shown that any CPwL function can generated by a deep ReLU network.
Learnability for Binary Classification

This chapter contains the proof of the fundamental theorem of PAC-learning for binary classification. In particular, the so-called uniform convergence property is introduced and used to show that finite VC-dimension implies PAC-learnability. Conversely, the so-called no-free-lunch theorem is used to show that PAC-learnability implies finite VC-dimension.
Simple random walk and Pólya’s recurrence theorem

The essay puts focus on the properties of the random walk, especially the simple random walk. In the properties, recurrence and transience is the most important one to cover with the comprehensive deduction from Pólya random walk theorem. Moreover, simulation in one- and two-dimension in python is used to verify it, and the essay finally gets a more straightforward statement of this theorem.
Auto insurance fraud prediction model based on improved particle swarm optimization BP neural network

With the booming of the insurance industry, the number of insurance frauds has increased gradually, among which the fraud in the auto insurance industry is the most serious. In order to improve the BP neural network prediction is easy to form local minimal value and not the global optimum, convergence speed is slow and the prediction accuracy is low, etc., we propose an improved particle swarm optimization (PSO) algorithm based on the dynamic linear decreasing adjustment of the weights in the particle swarm algorithm, and then the BP neural network weights and thresholds are optimized to establish the auto insurance fraud prediction based on the improved particle swarm optimization BP neural network model. The 400 sets of sample data of 13 main influencing variables of auto insurance historical claims of an insurance company were selected for PSO-BP and BP neural network model training, and 50 sample data were used for the prediction accuracy evaluation of the optimized model. The results show that based on the improved PSO-BP neural network algorithm its mean square error between the predicted and true values is significantly reduced and the prediction accuracy is greatly improved, which can effectively predict auto insurance fraud.
Geology Today Index 2021

Geology TodayVolume 38, Issue 2 p. i-viii Index Geology Today Index 2021 First published: 21 April 2022 https://doi.org/10.1111/gto.12387 Figures in Italic, Tables in Bold Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume38, Issue2March/April 2022Pages i-viii RelatedInformation
Digital competencies in the activities of libraries

The trends in the development of information technologies in the Russian Federation imply intensive digitalization of all spheres of public life. In this regard, the problem of training personnel with the competencies that are necessary to work in the digital environment becomes relevant. The library as a social institution is no exception. One of the key trends in the development of professional development is the formation of a library of digital credentials for specialists, their subsequent improvement, which will ensure the future of the profession. To accomplish the tasks set, it is required to study «digital competence» and use the terms: digital literacy, digital competence, digital skills, and for further research it is important to determine its essential characteristics, their differences from other synonymously used terms. The problem of forming a circle of rights for specialists in the library and information sphere is relevant both at the world and at the Russian level
Marked social networks: A new model of social networks based on dynamic behaviors

Social networks are electronically information sharing systems, due to this case, there are many studies on social networks. When studying social networks, text-based solution methods can be used; this type of study is outside the scope of this paper. Some studies have used mathematical models such as graphs, and graphs are mathematical models to represent many things, and social networks are one of them. However, graphs are static models whose structure cannot match the behaviors of social networks. To get rid of this case, Petri nets have been used in some recent studies , however, they have some deficiencies (obtained models are not complete and sound). Because of this case, we modeled social networks by using Petri nets. The resulting model is called Marked Social Network. The marked social network has two types such as Concurrent Marked Social Network and Parallel Marked Social Network. The obtained models were analyzed in case of behavioral and structural properties, and the major properties of the model were determined. All these properties are described in this study.
Research on the Measurement Method of Conducted Emissions of Surge for the Protection of Substation Facilities by Switching Surge

As substations are digitized, electromagnetic suitability for digital facilities is emerging. In general, surge has a great influence on facilities, especially in the case of substation facilities, it is greatly affected by switching surge and lightning surge. In this paper, we design a system that can measure and analyze switching surges that can be easily accessed by indoor substations. Through this, it will be used in research that can reduce interference with land on protective devices.