Reviewerly/exHarmony huggingface dataset:
System Identification of a Solar Maximum Power Point Tracking System for Dual Axis

The project is on Maximum efficiency of a solar panel. As the sun is not constant at one place and by fixing the solar array at one place, maximum power generation is not possible. This project develops a solar power generation system using maximum power point tracking system. More direct sun light on Photovoltaic (PV) modules leads to enhanced energy yield. Therefore, tracking systems are implemented to improve the performance of PV system by tracking sun trajectory. The current design of Maximum Power Point Tracking (MPPT) trackers are more efficient than conventional single state solar power generation. Then by comparing two designs initially produced, Maximum Power Point Tracking (MPPT) tracking devices are found to be at better efficiency than static models. The system locates maximum- power generating point using an MPPT controller. The base line results of this study were obtained via physical implementation of simulated system to analyze the various parameters. The system then uses a quadratic-equation based algorithm that calculates the quadratic function corresponding to the maximum power generation point. With the advent of different applications of PV solar power, system planners have been implementing different strategies and techniques to maximize the output of solar system with commonly available technology in market. Additionally, various mathematical techniques were utilized to analyze the results gathered from the physical implementation. The simulation results also implemented to compare between the hardware and software of the project. The comparison of static Photovoltaic output graphs with real time measured values for the systems and a Dynamic Photovoltaic Maximum power point Solar Tracker (MPPT) system was observed. Both PV systems were implemented practically to get appropriate results and mainly all the equipment and services utilized in installation are widely available in local market.
A Research on Computer Aided Detection System for Women Breast Cancer Diagnosis from Digital Mammographic Images

The Women breast cancer is the most critical cancer that are found in women. Its the second important cause of death in the world. Breast cancer has been ranked number one cancer in Indian females with rates occurrence of 25.8 per 1,00,000 females and death rate 12.7 among 1,00,000. Generally breast cancer is a malignant tumor that begins in the cells of the breast and eventually it spreads to the surrounding tissues. Early detection and diagnosis can reduce the mortality rate. Radiologist misdiagnosis the disease due to technical issues such as imaging quality and human error. Radiologists can improve the performance of Computer Aided Detection/Diagnosis (CAD) systems to finding and discriminating between the normal and abnormal tissues. Breast cancer diagnosis can applied are applied recent CAD systems on imaging modalities such as mammogram, ultrasound, MRI and biopsy histopathological images. CAD system have four stages for diagnosis which are pre-processing, segmentation, Feature Extraction and Classification. CAD system are developed to reduce the time taken to diagnose the breast cancer and reduce the death rate. This paper focus on the survey of CAD system to detect women breast cancer disease from the digital mammographic images to achieve high accuracy and low computational cost.
Bot Framework

A bot is an application that is able to interact with users conversationally [1]. It can be a very simple application that supports dialog and basic questions, or it can be sophisticated, capable of understanding language. In Microsoft Azure, it is possible to create a bot in C# or Node.js. You can create a bot using .NET SDK [2] and test it via such tools as Emulator [3]. In addition, some bot components help you to add more features [4]. In this chapter, a very simple bot using an Azure component will be presented. First, how to create a bot service in Azure will be shown, then how to create a simple bot for questions and answers will be presented, as well as a more complex one.
The Wide Trail Strategy

In this chapter we explain the strategy that underlies many choices made in the design of Rijndael and its related ciphers.
Convolutional Networks

Convolutional neural networks (CNN) are feedforward neural networks with shared weights and sparse interactions, that is, most weights are equal to zero. Given their fewer number of parameters, convolution networks are more efficient to train than any similarly sized fully-connected layer networks, with only minor negative consequences on their performance.
Effectual Resource Allocation using Auction Mechanism in Cloud Computing

Cloud computing(CC) is one of the fastest emerging technology. As we all know, cloud is a wide pool of resource which provides resources based on the users request. Any service can be provided as a service through cloud. As the cloud contains many resources, there may also wastage of resources. To reduce this wastage, cloud providers enter into auctioning of resources when the demand is high. Cloud computing includes distinct resources. Because of the complementary and supplementary effects between distinct assets, bidders have preferences not for just a single resource but also for a set of resources. Auctioning for a bundle of resources is called mergeable auction (MA). Dynamic resource allocation in on demand for a bundle of resource is proposed by using MA – PROVISION algorithm and the scenarios are simulated using Cloudsim, a simulator meant for cloud computing analysis.
CABARAN PDPR BAHASA TAMIL DI SEKOLAH LUAR BANDAR [CHALLENGES OF TAMIL LANGUAGE PDPR IN RURAL SCHOOLS]

This study is entitled Challenges of Tamil Language Pdpr in Rural Schools. This study uses a qualitative study with analysis of available journals from a variety of reliable sources. The objective of this study is to be a guide for students to follow PdPR optimally and a guide for teachers to implement PdPR in a structured and planned manner. There are several challenges in implementing PdPR, namely teachers are not prepared for the new norms of PdPR, students often get interrupted while implementing PdPR. The study found that PdPR is an implication of a holistic assessment. Several suggestions have been submitted for the improvement of PdPR, namely the improvement of the syllabus set by the MOE during the implementation period of PKP to better suit the needs of students.
Abstract Neurons

The abstract neuronabstract neuron is the building block of any neural network. It is a unit that mimics a biological neuron, consisting of an input (incoming signal), weights (synaptic weights), and activation function (neuron firing model). This chapter introduces the most familiar types of neurons (perceptron, sigmoid neuron, etc.) and investigates their properties.
Research on evaluation method of students' classroom performance based on artificial intelligence

In order to solve the problem of time-consuming and low reliability of the traditional evaluation method for students' classroom performance, an artificial intelligence-based evaluation method for students' classroom performance is proposed. This paper establishes the evaluation standard of students' classroom performance and quantifies the evaluation of students' classroom performance. Classroom monitoring device is driven by program code. The initial acquired image is processed by image enhancement and recognition, and the result is transmitted to the upper computer. This paper analyses the evaluation indexes of students' classroom performance, such as attendance, participation, attention and abnormal behaviour, and finally outputs the results of teachers' classroom performance. The experimental results show that compared with the traditional evaluation method, the artificial intelligence-based evaluation method saves 69 seconds on average and improves its reliability by 6.25%.
Research on integration method of AI teaching resources based on learning behaviour data analysis

Aiming at the problem of long running time and low precision of traditional artificial intelligence resource integration method, a new artificial intelligence teaching resource integration method based on learning behaviour data analysis is proposed. Analyse online learning behaviour data, extract relevant data of artificial intelligence teaching resources, and realise sample collection of artificial intelligence teaching resources. Random forest algorithm was used to classify them, reward and punishment factors were added, and mountain-climbing method was used to search the solution space, so as to realise the integration of artificial intelligence teaching resources. The experimental results show that this method can improve the efficiency and precision of the whole method, and obtain better integral results.
Decentralized Application on Voting System

Election is a significant job in our Elective Government. As technology progress with upcoming days, its impact becomes more optimistic. One such outcome is the Blockchain. It is possible to transform the process of voting system due to its decentralized property of immutability. Voting in most places are non-transparent and common with the corruption. In this paper we proposed the technology, is Blockchain technology. The concept of this paper is to develop a decentralized application for voting system. From there, the transaction votes are stored in the blockchain could be illustrated by the examining the block hashes. The outcome of the project shows the transaction of tokens from voter’s wallet into the candidate’s wallet. This application can deploy on a test platform using the Ethereum Virtual Machine (EVM) it provides the network to test the application. From there, the integrity of the blockchain technology is illustrated.
Adaptive Machine Learning Chatbot for Code-Mix Language (English and Hindi)

The humanoid assistant system can be described as the system resembling or imitating the human behaviour. These systems can be called as chatbots. There are a large number of conventional scripted types of chatbots. The problem with these chatbots is that they provide a monotonous type of communication i.e. they provide the user with a predefined set of options for any of its query. This scripted nature limits the scope of the chatbot systems, to provide smart and effective services to the users. This problem restricts the system efficiency. Efforts are being made to improve the scripted nature of chatbots and enable them to converse in a manner similar to the conversation between two humans. This makes the system more user-friendly, and provides better solutions to them. Chatbots providing health care services imitate the conversation between the doctor and the patients to give them general information about diseases, remedies, precautions, etc. and also provides a prediction of the diseases depending upon the symptoms provided by the user. Here, the chatbot behaves as a virtual doctor. This can be achieved by incorporating NLU, ML and NLG techniques in the system. Here, in this paper, we have briefed about the chatbot system architecture and adaptive self-learning algorithm for providing services in healthcare domain.
Towards more realistic values of elastic moduli for volcano modelling

&amp;lt;p&amp;gt;The accuracy of elastic analytical solutions and numerical models, widely used in volcanology to interpret surface ground deformation, depends heavily on the Young&amp;amp;#8217;s modulus chosen to represent the medium. The paucity of laboratory studies that provide Young&amp;amp;#8217;s moduli for volcanic rocks, and studies that tackle the topic of upscaling these values to the relevant lengthscale, has left volcano modellers ill-equipped to select appropriate Young&amp;amp;#8217;s moduli for their models. Here we present a wealth of laboratory data and suggest tools, widely used in geotechnics but adapted here to better suit volcanic rocks, to upscale these values to the scale of a volcanic rock mass. We provide the means to estimate upscaled values of Young&amp;amp;#8217;s modulus, Poisson&amp;amp;#8217;s ratio, shear modulus, and bulk modulus for a volcanic rock mass that can be improved with laboratory measurements and/or structural assessments of the studied area, but do not rely on them. In the absence of information, we estimate upscaled values of Young&amp;amp;#8217;s modulus, Poisson&amp;amp;#8217;s ratio, shear modulus, and bulk modulus for volcanic rock with an average porosity and an average fracture density/quality to be 5.4 GPa, 0.3, 2.1 GPa, and 4.5 GPa, respectively. The proposed Young&amp;amp;#8217;s modulus for a typical volcanic rock mass of 5.4 GPa is much lower than the values typically used in volcano modelling. We also offer two methods to estimate depth-dependent rock mass Young&amp;amp;#8217;s moduli, and provide two examples, using published data from boreholes within K&amp;amp;#299;lauea volcano (USA) and Mt. Unzen (Japan), to demonstrate how to apply our approach to real datasets. It is our hope that our data and analysis will assist in the selection of elastic moduli for volcano modelling. To this end, our new publication (Heap et al., 2019), which outlines our approach in detail, also provides a Microsoft Excel&amp;amp;#169; spreadsheet containing the data and necessary equations to calculate rock mass elastic moduli that can be updated when new data become available. The selection of the most appropriate elastic moduli will provide the most accurate model predictions and therefore the most reliable information regarding the unrest of a particular volcano or volcanic terrain.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Heap, M.J., Villeneuve, M., Albino, F., Farquharson, J.I., Brothelande, E., Amelung, F., Got, J.L. and Baud, P., 2019. Towards more realistic values of elastic moduli for volcano modelling. Journal of Volcanology and Geothermal Research, https://doi.org/10.1016/j.jvolgeores.2019.106684.&amp;lt;/p&amp;gt;
The Practice and Reform of Classroom Teaching Based on the Coordination of Online Environment and Deep Learning

By analyzing and reflecting on the characteristics of the network in teaching applications and some problems that arise in the traditional classroom teaching mode and classroom teaching in the network environment, some ideas and measures are proposed on how to strengthen and improve the organization and management of teaching in the network environment, and for the effective integration of teaching elements and resources. This paper proposes a teaching evaluation model based on a weighted plain Bayesian algorithm. According to the degree of influence of different attributes on the evaluation results, a method is proposed to determine the weights of each evaluation attribute using the correlation probabilities of class attributes and set the corresponding weights for each evaluation index. The experiment demonstrates that the accuracy of using the weighted plain Bayesian algorithm to construct the model for classification can reach 75%, and the average accuracy can be improved by about 3% compared with the traditional Bayesian classification algorithm.
The effects of prosodic training with logatomes and prosodic gestures on L2 spontaneous speech

Training L2 suprasegmental features benefits pronunciation accuracy and comprehensibility, especially through the use of hand gestures.However, studies have mainly looked at the effect of prosodic training in controlled tasks and less is known about spontaneous speech.The present study explores the effect of prosodic training with and without gestures depicting prosody on several dimensions of pronunciation in spontaneous speech.Fifty Catalan learners of French practiced oral reading and sentence-by-sentence imitation with short dialogues during three 30-minute sessions in one of three conditions: speechonly, repeating a selection of sentences from the dialogues; logatome, repeating logatome sequences (series of same consonant-vowel syllable leaving out lexical information) corresponding to the prosody of target sentences; and logatome+gesture, additionally mimicking gestures representing phrasal prosodic patterns.Perceptive evaluations of learners' spontaneous speech at pre-and posttest revealed that training prosody did not have any significant effect on participants' comprehensibility, accentedness, and suprasegmental accuracy in spontaneous speech.Acoustic analyses further showed null effects of prosodic training on fluency and the pronunciation of difficult vowel contrasts.Our findings suggest that L2 prosodic training should be better tailored for spontaneous speech.Alternatively, learners may need more time to transfer beneficial effects from controlled tasks to spontaneous speech.
Goffman's footnotes

Most academic books and research articles are full of these small – often relatively short and seemingly unimportant – snippets of text called ‘footnotes’, of which William James once said that they are those ‘little dogs yapping at the heels of the text’. The footnote has been known for centuries, but it seems as if it has become an important and almost indispensable part of the text, particularly within the world of science. Whereas some researchers hardly ever use footnotes, others do so frequently. In the work of Erving Goffman, one will find many footnotes, but as this chapter will show, his use of footnotes served many different purposes. Whereas some footnotes were short and reserved solely for providing references to the work of others, other footnotes contained much more insightful and important information about sociological and existential issues. In this chapter, we will excavate, analyse and discuss Goffman’s footnotes looking for hints and clues. It is the argument of the chapter that Goffman in his footnotes in fact several times revealed understandings that were too important to be ‘hidden away’ in footnotes and that thus risked being missed by the reader.
Tech Optimization in Cybersecurity Defenses by Advanced ML Methods: The Use Case of Volleyball Industry

Individual and team performance can be improved by utilizing “smart” devices and applications that are connected through networks. In sports, the Internet of Things (IoT) refers to all of the “smart” devices and applications linked through networks to reduce injuries to the bare minimum, develop advanced training techniques, and apply analytical advanced sports improvement methodologies to improve sports performance in general. The Internet of Things (IoT) in sports is closely related to the objective of both security and privacy in sports, which has become a topic of crucial concern for the sports business in recent years, as evidenced by the adoption of IoT in sports years. For this reason, security flaws can have catastrophic consequences, including the disclosure of personal data, the manipulation of statistical findings, the harming of organizations’ reputations, and enormous financial losses for the sporting organization. One or more of the consequences, as previously mentioned, is related to sports organizations and the athletes who are members of those organizations, and they have a direct impact on the corresponding set of sports-related, medical-related, and paramedical enterprises, specifically those that provide specialized sports equipment and associated services. A critical need to detect and quantify threats has long been recognized to better support decision-making when adopting or constructing a safe and reliable sports Internet-of-Things infrastructure, which is becoming increasingly common. Using advanced machine learning algorithms, this research provides a methodology for technology optimization in cybersecurity defenses that is then used in a unique case study utilizing volleyball players to demonstrate its effectiveness. In conjunction with a Monte Carlo optimization technique, an upgraded variant of fuzzy cognitive maps (FCM) is presented in greater detail. This model is utilized for a specific scenario of risk identification of volleyball industry, assessment, and optimization for IoT sports networks.
Analysis of Photovoltaic Panel Temperature and Photovoltaic Electric Power at Chuncheon Meteorological Station using Intensive Observation Period Data

In this study, photovoltaic (PV) electricity power and PV panel temperature for operation and monitoring of PV power plant were calculated and analyzed.A PV panel temperature sensor was installed at the Chuncheon Meteorological Observatory solar power plant for intensive observation from May 1 to August 19, 2018.When the calculated PV panel temperature was analyzed using the measured PV panel temperature, the calculated PV panel temperature was overestimated at a higher temperature compared to the measured PV panel temperature, which was overestimated at a lower temperature; however, the determination coefficient (R 2 ) was 0.88 or more.The bias was -0.33°C and RMSE was 3.43°C when the ground observation data were used.However, when the Local Data Assimilation and Prediction (LDAPS) model were used, the bias was 0.22°C and RMSE was 4.27°C.The PV electricity power generation by ground meteorological observation data (Korea Meteorological Administration, KMA), LDAPS model prediction data (LDAPS), and Communication Ocean and Meteorological Satellite (COMS) data using the PV module temperature were compared with those of the Chuncheon PV power plant.The determination coefficient (R 2 ) of PV power generation was the highest for KMA (0.91) followed by COMS (0.88) and LDAPS (0.84).The slope of the linear regression, (1.05) for KMA, and the smallest bias (2.24 kWh) and RMSE (3.38 kWh) were similar to the measured
Construction of Evaluation Index System of Physical Education Teaching Environment in Colleges and Universities Based on 5G Technology

A top notch actual training climate has an indispensable main impetus for the physical and mental improvement of youths and kids and the nature of actual instruction. This paper proposes an assessment file arrangement of actual instruction showing climate in schools and colleges in light of 5G innovation, planning to fabricate an assessment list arrangement of actual training educating in the climate of 5G data innovation in the coordination stage. The technique for this paper is to concentrate on the organization energy effectiveness model, layout a framework model and 5G list demonstrating, and afterward propose a showing climate assessment review and examination strategy. The job of these strategies is to plan sensible assessment norms based on hypothetical examination. It utilizes 5G data innovation and organization effectiveness and lastly constructs a school sports ecological assessment framework. Through the poll overview, this paper comprehends what is going on of actual schooling showing climate in Chinese universities and colleges and tracks down the purposes behind its disparities and issues. This paper develops actual training climate assessment file framework, researches the game climate assessment and actual exercise direction, and afterward proposes actual instruction and assessment framework under 5G innovation. The outcomes show that in the assessment list arrangement of actual instruction showing climate in schools and colleges, the heaviness of material climate is 0.6796, demonstrating that the most dire errand for schools and colleges to further develop the actual training climate is to work on the actual climate.