Reviewerly/exHarmony huggingface dataset:
Authorship Attribution via Occupancy-problem-type Indices

In this paper, we propose a new methodology for authorship attribution based on a profile of indices related to the occupancy problem, called occupancy-problem indices. The occupancy problem has a long history and is an important example in standard textbooks like Feller (1971). We base our methodology on function words. We establish a testing procedure by constructing a confidence band of the occupancy-problem indices using the sampling distribution of the number of distinct function words. We validate our proposed methodology using controlled and constructed writing samples whose authorship is known. We then apply this methodology to explore the question of who wrote the 15th Oz book, which has a disputing authorship between Lyman Frank Baum (1856–1919) and his successor Ruth Plumly Thompson (1891–1976) on the Oz series.
Classification of Patients with Diabetes Mellitus Using Neural Networks

The work is devoted to the actual problem of determining the diagnosis of a patient according to his biological data. The paper examines the relationship between biological data and the incidence of diabetes. An automated decision support system for analyzing and processing data based on a neural network has been developed. A neural network has been built for the classification of patients with diabetes mellitus. The simulation results show that the use of a software product based on the proposed neural network architecture will improve the accuracy of the diagnosis of diabetes mellitus by doctors.
Sand-dust image enhancement based on color correction and haze removal

This article presents a dust image enhancement method based on color correction and haze removal. For color correction, a novel image prior---histogram similarity prior is proposed. With this image prior, image color is recovered by histogram matching. For contrast enhancement, a haze removal method based on boundary condition constraints and Retinex theory is developed, where boundary condition constrains is used for initial transmission map estimation, and Retinex is employed for reducing the effect of the incident light on output images. Considerable evaluations on both synthetic and real dust images show that this method performs favorably against other existing similar methods.
PREDICTION OF BREAST CANCER USING MACHINE LEARNING

In order to support and supervise patients, the key detection and estimation of cancer type should establish a compulsion in the cancer research. Many research teams from the biomedical and bioinformatics fields have been advised to learn and evaluate the use of machine learning (ML) methods because of the relevance of classifying cancer patients into high or low risk clusters. To predict breast cancer, the logistic regression method and many classifiers have been proposed to generate profound predictions about breast cancer data in a new environment. This paper discusses the various approaches to data mining using classification to create deep predictions that can be applied to Breast Cancer data. In addition, by testing datasets on different classifiers, this analysis predicts the best model that delivers high efficiency. In this paper, the UCI machine learning repository has 699 instances with 11 attributes collected from the Breast cancer dataset. First, the data set is pre-processed, visualized and fed to different classifiers such as Logistic Regression, Support Vector Classifier, K-Nearest Neighbour, Decision Tree and Random Forest. 10-fold cross validation is implemented and testing is carried out in order to create and validate new models. Effective analysis shows that Logistic Regression generates the deep predictions of all classifiers and obtains the best model delivering strong and precise outcomes, followed by other methods: Support Vector Classifier, K-Nearest Neighbour, Decision Tree and Random Forest. Most models were less reliable compared to the approach of logistic regression.
A fuzzy control method of nano material forming temperature based on PLC

In order to overcome the problem of low temperature monitoring accuracy and forming temperature control accuracy of nano materials, a new nano material forming temperature control method based on PLC is proposed in this paper. Firstly, the steady-state gain of nano material forming temperature is calculated, and the first step response curve of forming temperature is calculated. Secondly, particle swarm optimisation algorithm is introduced to optimise the control parameters of PLC controller. Finally, the fuzzy control algorithm is used to discretise the nano material forming temperature control, and the nano material forming temperature control is completed. The experimental results show that compared with the traditional control method, the moulding temperature control result of this method is basically consistent with the actual moulding temperature, and the maximum control error is only 0.1°C.
Connecting and Using Peripherals and Hardware

Do you still have a printer attached to your PC? These venerable devices are becoming less and less common, but many people still have one at home, and they’re still pretty essential in the workplace. Let’s face it, we might hardly use them these days, but there are still times when you really can’t do without one.
Online Product Review Monitoring System Using Machine Learning

In this fast moving world, having major proportion of people depending on online websites for purchase of their day to day necessities, their only scope of trust is the reviews available in the about the particular product. Counterfeit audit discovery and its disposal from the given dataset utilizing distinctive Natural Language Processing strategies is significant in a few perspectives. These reviews can either be authentic made by loyal buyers or even it may be forged for marketing purposes. According to survey, almost sixty percent of the reviews present in amazon are fake and nearly fifteen percent of companies as sellers, wage individuals for creating fake reviews. In order to settle the imbalance and forgery a study has been made to identify the fake reviews and filter it out from the user’s sight, such that they can’t be manipulated as well as the particular company’s reputation won’t be at stake. This system majorly works on the basis of identifying fake reviews by user extracting the multiple review of single user from reviewers.
Streaming of Music and Audiovisual Works

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Analysis of Revenue Accounting Information Systems at PT. Pusaka Sunny Jaya

This study aims to analyze the income accounting information system that has been applied at PT. Sunny Jaya's heritage is in accordance with the theory of accounting information systems which in the future can be used as material for improving company systems. The method used in this research is descriptive method. Data collection methods used are observation, interviews, and documentation. The data analysis technique used is to analyze the income accounting information system at PT. Sunny Jaya's heirloom. Accounting information system at PT. In practice, the Sunny Jaya heritage has been going well because each part has been managed by each staff so that there is very little cheating that can be done. The use of the system in the company here as a whole has helped to meet reporting needs so that reports and inputs can be updated, besides that the use of programs/systems has provided good internal control.
Quantify and Alleviate OAuth Approach Token System Exploiting by Conspiracy Lattice

We exposed a flourishing structure of high process eminence supertendence services on face book that influence fundamental of encounter. Collusion networks accumulate OAuth get admission to tokens from colluding contributors and abuse them to provide fake likes or feedback to their individuals. We completed a complete size examine to apprehend how these collusion networks exploited popular 0.33-celebration face book programs with susceptible security settings to retrieve OAuth get admission to tokens. We infiltrated famous collusion networks the use of honeypots and identified more than one million colluding face book debts by way of 'draining' these collusion networks. This determines outcome for face book and associated through the process of different alleviations which terminates open authorisation to get expression maltreatment externally giving up function stage convinces by third party engineers.
Pendampingan Penyusunan Dokumen KTSP Paud

This service activity aims to provide assistance to the PAUD Santa Fransiska Asisi unit in compiling the KTSP document because the managers and teachers of the PAUD Santa Fransiska Asisi do not understand and have sufficient competence in compiling the KTSP document. The method used in this service activity is discussion and mentoring. The partners of this service activity are the managers and teachers of PAUD Santa Fransiska Assisi, totaling 3 people. The activity stages consist of (1) Studying the KTSP document; (2) Conduct discussions and assist the preparation of the PAUD KTSP document, (3) Observe the progress of the PAUD KTSP document preparation. The results of the service activities show that the managers and teachers of PAUD Santa Fransiska Assisi are able to prepare their own KTSP and produce a final KTSP document.
Industrial Control System Attack Detection Model Based on Bayesian Network and Timed Automata

The current industrial control system attack detection methods are single, the detection results are fuzzy and cannot be applied to the domestic industrial environment. In response to the above problems, an industrial control system attack detection model based on Bayesian network (BN) and Timed automata (TA) theory is proposed. First, collect the real industrial purification data of the aluminum factory, that is, the sensor and actuator signals, and preprocess the signals through time compression, segmentation, and queue division; secondly, establish Timed automata and Bayesian network models respectively, using probability time automatization The computer simulates the regular behavior of the time series, and at the same time uses the Bayesian network to build the dependency relationship between the sensor and the actuator; finally, the model’s detection result of the attack data is calculated. Theoretical analysis and experimental results show that compared with Deep Neural Network (DNN) and Support Vector Machine (SVM), the model in the article has improved time and accuracy.
Learning Prepositions Using Augmented Reality Smartphone Apps

The paper is about augmented reality-based learning apps which were developed and demonstrated to enhance the learning potential of kindergarteners, grade 1 and 2 (K-2). The apps have covered different aspects of learning words and formation of short sentences using prepositions. This helps students enhance their vocabulary and communication. The paper is mainly divided into two sections where the first section is about the description of apps, working and methodology and the second section is about the reviews and results of the students and teachers who used these apps. Further, the paper has a small portion which marks the observations made with the usage of these mobile apps during the period of total lockdown due to COVID-19.
Methods for bypassing the check of final qualifying works for the amount of borrowed text and ways to identify them. Inserting large amounts of third-party text. Part 1

The article is devoted to the issues of checking student papers for the amount of borrowed text and to the problems of controlling the circumvention of this check. The article considers the implementation of a number of methods of the widely used method of bypassing validation associated with the insertion of a large amount of third-party text. Shows various ways to detect this workaround in MS Word documents.
Using clinical decision support systems in breast cancer treatment: a critical review

Decision-making in breast cancer treatment is a complex process. There is no certainty of the effects of a treatment regimen in a specific patient and the consequences of erroneous decisions can be serious or even fatal for the patient. Clinical decision support systems (CDSSs) are technology aids that have been progressively introduced in clinical practice to reduce decision errors and improve the quality of clinical decisions.This article presents a critical review of the empirical literature on the use of CDSSs in breast cancer treatment decision-making. Three articles were included and two salient challenges identified in the design and research of CDSSs. First, there is a lack of standardisation in the tumour and patient factors and the health goals used to predict the benefit of different treatment options, as well as in the methods used for the evaluation of these. Second, the design of CDSSs follows a biomedical, disease-focused approach to healthcare, and this reductionist approach also prevails in the related scholarly literature.
Interview with Manfred Broy and Walter Brenner About Design Thinking and Requirements Engineering

In the age of digitalization, user-friendly software that solves real human problems becomes increasingly important. Walter Brenner and Manfred Broy, two of the editors of this book, interview each other about the motivation to combine Design Thinking and requirements engineering. They discuss the current state of requirements engineering and how selected elements of Design Thinking can support current practices. The authors emphasize the importance to make greater use of human-centered methods when developing innovative applications and software-intensive solution. The interview shows how the combination of requirements engineering and Design Thinking can succeed and demonstrates how companies can use the potential of digitization more efficiently and effectively in the future.
Desktop Development

I often get the question if there is still use in learning desktop technologies like WPF or WinForms. My answer is always the same: of course there is! There has been a big movement toward the Web the past few years, which definitely makes sense in regard to ease of deployment and installation. No need to juggle MSI files around, making sure every client computer has the correct version of .NET installed, finding out what version of the software a customer is using, and so on. With web applications, we install the software on a server, and all of our users just use their browsers to use the application. So why are native apps (both desktop and mobile apps) still a thing? The answer is simple, performance and capabilities. The Web, for now, does not have the same level of access to peripherals and operating system as a native app does. As for performance, let’s look at the way an operating system renders its applications. Rendering happens in steps, layer by layer. A WPF application, for example, will render its main window followed by the controls on that specific window; the whole rendering of an application is optimized to draw the user interface as fast as possible to not make the user feel like the application is hanging. If we apply that same logic to a web browser, you’ll understand that the browser’s main window and controls like the back and forward buttons, favorite bar, extension icons, and so on are rendered first. Once everything is on the screen, the browser will start interpreting and rendering the HTML, so the actual user interface of your web application is last to render. Another major difference is threading, JavaScript is single-threaded, so it’s not possible to schedule heavy work on a separate thread. The advice there is to have a server handle the heavy lifting and make your frontend wait for the response, which is a very valid argument, except for applications that need real-time heavy processing, like the software stock brokers use, for example. Every millisecond of delay caused by a request going over the network can cost them money. A native desktop application, running on a powerful client computer, can handle these calculations with ease.
The Art of Statistics: Learning from Data

At the outset of the book, the author notes two modern challenges to the traditional view of statistics as a basic ‘bag of tools’: the inappropriate application of statistical methods, resulting in the crisis of reproducibility among scientific publications, and the emergence of data science, in which large datasets are analysed by increasingly sophisticated algorithms but with diminished human oversight. As the Winton Professor of the Public Understanding of Risk at the University of Cambridge and former President of the Royal Statistical Society, David Spiegelhalter is extremely well placed to offer a remedy to these developments. To this end, Spiegelhalter has produced a highly readable and engaging introduction to an impressive array of statistical concepts, emphasizing common pitfalls at each step. Eager to avoid the litany of mathematical formulae which often pepper introductory statistical texts, Spiegelhalter motivates each concept with compelling questions derived from real-world case studies, such as calculating...
Sentiment analysis of an epidemic: a case of Nipah virus in India

Data in social media and other news media can have an impact on the decision-making process of the government and the citizen if properly examined. The mode and pace of dissemination in both media leads to an increase in the delivery of misinformation. This affects the economy of the country and people's mental health. The government must formulate the required measures to counter the proliferation of fake messages and disinformation in the media, which would otherwise lead to an unnecessary burden. Regulation of health communication during the period of epidemic is important, as it has an effect on the mental health of users of the media. The study assesses the emotions of health communication in social media and online news media in the context of the Nipah epidemic in India during 2018.
Evaluation of a blockchain-enabled multi-energy transaction system for district energy platforms

Nowadays, researchers are trying to improve and find new ways and methods to mitigate climate change and increase energy efficiency. Areas of study include smart grid, smart grid, distributed energy systems, accumulative and renewable energy sources. Renewable energy integration, system operation, transparent spending system and data integrity are the common challenges facing distribution network providers. The overall, holistic view of the technologies listed above about delivering and consuming any kind of energy has many aspects that need to be studied and optimized. Good compatibility with distribution systems is characterized by modern blockchain technology and multi-agent system. Blockchain has very good data protection, immutability and transparency properties in transactions. Its decentralized nature and automatic calculation system can bring great potential in terms of financing and calculation in the energy sector. In this thesis article a novel process to coordinate, allocate and settle energy transactions in a district multi-carrier energy system. The process operates in a decentralized way, fully on-chain. The design leaves producers the freedom to choose their preferred pricing strategy for profit maximization. The price-availability-based allocation system guarantees consumers the lowest possible cost.
Deploying Azure Arc-enabled PostgreSQL Hyperscale

While Chapter 7 was handling Azure Arc-enabled SQL Managed Instance, this chapter will guide us through the necessary steps when it comes to working with PostgreSQL Hyperscale instead.
Quantifying the Quantumness of an Ensemble of Quantum States

The characterization of the quantum ensemble is a fundamental issue in quantum information theory and foundations. The ensemble is also useful for various quantum information processing. To characterize the quantum ensemble, in this article, we generalize the coherence measure of a state to the quantum ensemble. Exploiting the fidelity and affinity between the ensemble, we propose a quantumness quantifier for the quantum ensemble. It is shown that the proposed quantifier satisfies the necessary axioms of a bonafide measure of quantumness. Finally, we compute the quantumness of a few well-known ensembles.
A Case Study of Pre-service Teachers’ Educational Practicum

This study investigated the common difficulties and conflict factors experienced in educational practicum based on the actual cases of a total of 230 pre-service teachers, and confirmed what is necessary to prepare and proceed with the school-based practicum.<BR> The four-week on site training consists of observation practicum, teaching practicum, and practical work education. This paper focused on observation practicum and teaching practicum.<BR> The difficulties that prospective teachers experienced in field training were the preparation and progress of lessons, the establishment of relationships with school members, and the response to university and secondary school requirements.<BR> In order to overcome the three difficulties, prospective teachers must behave in a way that suit the secondary school environment. And they must also improve their specialized subject knowledge, lesson planning ability, and communication ability. During on-site training, prospective teachers must learn the know-how of in-service teachers, while observing the lessons of other teachers and apply it to their own lessons. In addition, prospective teachers should maintain good relationships with their students for effective lessons.<BR> Finally university education needs to expand simulated lessons, and it is necessary to create an environment where prospective teachers can concentrate on the educational practicum during the training period.
FLight: FPGA Acceleration of Lightweight DNN Model Inference in Industrial Analytics

Deep Neural Networks (DNNs) have been recently in the focus of interest owing to their high-quality results in various application domains of big data analytics. The lack of end-to-end toolchains that can automatically optimize and translate a DNN algorithm, usually implemented in high-level languages, on FPGA-based platform hinders software developers to exploit such platforms. A few available toolchains, namely Xilinx DPU, have been optimized for computational expensive applications, e.g., real-time image and video processing, and therefore they introduce large performance overhead on lightweight applications, e.g., sensor-driven decision analytics. In this paper, we introduce the FLight framework to fully automatize the acceleration of lightweight DNN algorithms on FPGA-based embedded platforms. The framework takes a trained model in TensorFlow/Keras as input and generates an optimized synthesizable C++ version and maps it on the target platform using HLS tools. FLight is an easy-to-use framework and does not require deep knowledge of embedded system design since it automatically performs all the required mapping steps. FLight supports the acceleration of various deep learning algorithms, e.g., Feed-Forward Neural networks (FFNNs), Convolutional Neural Networks (CNNs), and sequential models like Recurrent Neural Networks (RNNs). We evaluated the applicability of FLight with various DNN models on both academic and industrial datasets. As a case study, we exploited the FLight framework for wind turbine condition monitoring using an industrial dataset provided by Weidmüller Monitoring Systems GmbH. The experimental results revealed the 40 times speedup compared to the Xilinx DPU framework.