Reviewerly/exHarmony huggingface dataset:
Testing Hypotheses

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.
Multi-Feature based Handwritten Script Identification at word level

SIFT and LBP are two popular techniques used for obtaining “feature description" of the object. SIFT identifies key points that are locations with distinct image information and robust to scaling and rotation whereas, LBP transforms an image into an array of integer labels describing small scale appearance of the image. In this paper, we present an efficient method wherein “feature description” of handwritten document images at word level are computed using SIFT and LBP. Identification of script type is done using KNN and SVM classifiers. Experimental results show that the performance of SVM is better over KNN. Further, the proposed method is compared with other methods in the literature to demonstrate the efficacy of the proposed method.
New Method of Solar Energy Representation for Environmental Analysis Software

Given the challenges to designing high-performance buildings, the use of Building Performance Simulation (BPS) tools during the early design phase is indispensable.There are many tools for evaluating solar impact on buildings, ranging from energy use to daylighting and renewable energy.However, no tool accurately reflects architects' needs; all lack clear communication and proper visualization methods.This research addressed energy consumption and production measures impacted by exposure to the sun.The goal was to create a new type of early design decision support tool that functioned without running BPS optimization and parametric simulations.This was accomplished by developing important solar algorithms that were then used in a new method of solar representation for building design.It is important that usability assessments of this type of tool be conducted, especially with regards to its ability to satisfy architects' needs during the design process.However, such research is not yet common in the field of BPS.Thus, in the present research, user experiments were conducted to evaluate the effectiveness of Eabbit 1.0's key interface.The results of these experiments illustrate the impact of the proposed method on annual heating and cooling consumption.The results show that tool will assist users in making significantly better decisions regarding the installation of photovoltaic panels and other issues related to solar energy.
Strategic responses and disclosures in a sporting drugs scandal: the case of the Essendon Football Club

This case utilises Oliver's (1991) framework of strategic responses to institutional processes to provide insights into the actions of an Australian football club faced with a prominent drugs scandal.It analyses public disclosures and statements by senior executives and coaching staff of the Club across the period 2012 to 2016 in the face of heavy media attention, as well as narrations and content through its annual reports.A defiance response strategy is identified and characterised using Oliver's framework.This case study presents lessons on how organisations may deal with these types of scandals, and how actors may alter their strategies to have their best interests served in a public sports scandal.
Theoretical Probability Models

Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.
Reconfigurable hardware architecture of public key crypto processor for VANET and wireless sensor nodes

This work proposes encryption of text and image data, embedding as elliptic curve point. Finite field arithmetic is utilised efficiently in this reconfigurable crypto system. Pre-computations for text data and image input conversion is done using MATLAB. This architecture is tailored for cryptographic applications and VANET using Xilinx Spartan-xc3s100e-4-fg320 FPGA with Verilog coding. Total encryption and decryption time results around 10.09021 microseconds for 100×100 images, 22.091 microseconds for 256×256 images and 0.029 microseconds for a message. The message size is varied with different stream size and dynamic mapping of input data and a cipher image with high randomness indicates good security i.e., less vulnerable to attacks. An entropy statistical analysis is performed on plain and encrypted images to assess the strength of the proposed method. An encryption throughput rate is 450 Mbps and area throughput 3.63, which is a good improvement over previous implementations.
Scaled Gradient Projection Methods for Astronomical Imaging

We describe recently proposed algorithms, denoted scaled gradient projection (SGP) methods, which provide efficient and accurate reconstructions of astronomical images.We restrict the presentation to the case of data affected by Poisson noise and of nonnegative solutions; both maximum likelihood and Bayesian approaches are considered.Numerical results are presented for discussing the practical behaviour of the SGP methods.
Talk About: E-Resources Librarian to the Rescue? Creating the Über Librarian: Turning Model Job Descriptions into Practical Positions

SUMMARY. Libraries are assessing the need for a separate electronic resource librarian and identifying where managing e-resources fits into the organizational structure. If such a position is warranted, what does it look like? Feedback from the 2004 conference evaluations suggested having a topical discussion session to hear many ideas and find out what has and has not worked. Questions pointed to the need for a better understanding of the responsibilities of these positions and the need to identify the knowledge required to perform at a high level. Salary concerns were also expressed, as w'as the prevalence of trial and error training. A good 174mix of librarians from various types of institutions participated in two li vely discussions exploring these and other issues related to the successful management of electronic resources. [A nick copies available for a fee from The Haworth Document Delivery Service: 1-8QQ-HAWORTH. E-mail address: &lt;docdelivery@lummlhpress.com &gt; Website: &lt; https://www.HaworthPress.com &gt;]
Classification of Glandular Cells using a Pre-trained Convolutional Neural Network

Tissues can be divided into glandular structures (cells) and non-glandular when we magnify them with a microscope. To classify the two types, we performed experiments using convolutional neural network. We cropped regions of glandular cells and non-glandular in 10 x magnification images. The size of cropped images was between 80 and 100 pixels in width and height. We prepared 932 glandular cells and 1000 non-glandular for the train and test. Of these, 1468 were used for learning and 532 were used for testing. We trained and tested the dataset using a slightly modified pre-trained VGG16. The inside of glandular cells consists of nucleus, lumen and cytoplasm. Normal glandular cells and abnormal glandular cells that we call tubular adenoma have different texture features. But both types of glandular cells have distinct boundaries and specific shapes. In the case of cancer, as the nucleus grows excessively, the boundaries of the glandular cells become unclear and disappear. We trained three types of glandular cells, which is normal, tubular adenoma, and cancer. Experimental results using the pre-trained VGG16 classification showed a high classification accuracy of 99.44%. Only three non-glandular out of the 532 test data were misclassified into glandular cells. The classification method presented in the paper can be used to eliminate false positives that produced by an automatic segmentation system for the pathology image. The performance of the segmentation can be improved by eliminating segmented objects that are false positives.
Mammogram Image Retrieval using Ipso Optimized Anfis Classifier

Content-based image retrieval (CBIR) is an research area over the past years that has attracted research. In various medical applications like mammogram analysis CBIR techniques helps the medical team to get similar set of images from a large medical records to help in diagnosis of a disease. This paper proposes an efficient Content-Based Mammogram Image Retrieval method by using an Optimized Classifier. Initially, the input dataset is preprocessed, in which noise removal and contrast enhancement are done. Next, pectoral muscles of the mammogram images are removed using Single Sided Edge Marking (SSEM). Now, feature extraction is done, in which GLCM features, Gabor features and the Local Pattern with Binary features are being removed. The features that are being removed are classified into three classes namely benign, malignant and normal. An optimized classifier named as Adaptive Neuro Fuzzy Inference System (ANFIS), which is optimized by using the Improved Particle Swarm Optimization (IPSO) technique, is used for classification purpose. Finally, similarity is assessed between the trained feature distance vectors and the feature distance vectors of the input query image. Similarity assessment is done using Euclidean Distance metric and the image that has the lowest distance compared with the query is retrieved. The experimental results are obtained for the proposed system and they are compared with the existing techniques.
An Integrated Digital Pathology Platform for Tumors Using Artificial Intelligence Analysis

Background: This study aimed to develop a pathologist-annotated and artificial intelligence (AI)-empowered platform for integration and analysis of whole-slide images (WSIs) data and molecular detection data in tumors, which is called PAI-WSIT2.0 (http://www.paiwsit.com).Methods: The standardized data collection process was used for data collection in PAI-WSIT2.0, and in combination, a multifunctional annotation tool was developed and a user-friendly search engine and a web interface were integrated for database accessing. Furthermore, deep learning frameworks were applied in two tasks to detect malignant regions and classify phenotypic subtypes in colorectal cancers (CRCs), respectively.Findings: PAI-WSIT2.0 recorded 8633 WSIs of 1772 tumor cases in total, of which, emphasis was put on the WSIs for CRCs from four regional hospitals in China which dominated in the records and The Cancer Genome Atlas (TCGA), others were WSIs for cancers in breast, lung, prostate, bladder, and kidneys from two Chinese hospitals. A total of 1298 WSIs with high-quality annotations were evaluated by a panel of 8 pathologists. Molecular-level information including immunohistochemistry and gene detection reports of 582 tumor cases were collected. Clinical information of all tumor cases was documented. Besides, we reached an overall accuracy of 0.933 in WSI classification for malignant region detection of CRC, and an area under curves (AUC) value of 0.719 on colorectal subtype dataset.Interpretation: Collectively, the clinical data curation, annotation function, and AI analysis of PAI-WSIT2.0 may serve as an excellent source for WSI analysis researchers and pathologists, providing the clinical support for related computational pathology tasks.Funding: This work was financially supported by the Double-Class University project (CPU2018GY19), the National Natural Science Foundation of China (81473274), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX_19_0667). This study was supported by the High-Performance Computing Center, China Pharmaceutical University.Declaration of Interest: The authors declare that they have no conflict of interests.Ethical Approval: The study protocol was approved by the Ethical Committee of Nanjing First Hospital, the First Affiliated Hospital of Zhejiang University, the First Affiliated Hospital of Soochow University, and Nanjing Drum Tower Hospital
Prelims

Citation (2020), "Prelims", Sengupta, E., Blessinger, P. and Cox, M.D. (Ed.) Designing Effective Library Learning Spaces in Higher Education (Innovations in Higher Education Teaching and Learning, Vol. 29), Emerald Publishing Limited, Bingley, pp. i-x. https://doi.org/10.1108/S2055-364120200000029014
Identifikasi Penderita COVID-19 Berdasarkan Chest X-Ray Menggunakan Algoritma Jaringan Syaraf Tiruan Backpropagation

Corona Virus Disease 2019 (COVID-19) is an infectious respiratory disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV2). This disease first appeared in Wuhan, China and spread throughout the world. COVID-19 has had a major impact on public health around the world. On March 9, 2020, the World Health Organization (WHO) declared COVID-19 a pandemic. Early identification of people with COVID-19 can help limit the wider spread. One of the factors behind the rapid spread of the disease is the long clinical trial time. Rapid clinical testing is a challenge facing the spread of COVID-19. Most countries, including Indonesia, face the problem of lack of detection equipment and experts in diagnosing this disease. Chest X-Ray is one of the medical imaging techniques and also an alternative to identify the symptoms of pneumonia caused by COVID-19. This study aims to identify pneumonia caused by COVID-19 and other diseases based on Chest X-Ray. 107 Chest X-Ray images used as material for this study were obtained from the General Hospital of Ibnu Sina Padang Indonesia, which consisted of 27 images of pneumonia caused by COVID-19, 51 images with other diseases and 29 images of normal lungs. Then pre-processing is carried out as an initial stage and then feature extraction is carried out. Furthermore, the learning and identification process is carried out using the Backpropagation Artificial Neural Network (ANN) algorithm. In this study, 92 images were used as training data, and 15 images were used as test data. The results of calculations carried out using a network with a pattern of 16-100-100-100-2 obtained an accuracy value of 73%. The results of the identification prediction can be used as consideration in establishing a diagnosis of COVID-19 sufferers, but cannot be used as an absolute reference.
Modeling Future Professional Activities of A Specialist in A Vocational Educational Institution

The process of future graduates’ professional activities development is a complex multi-stage process that requires continuous improvement. Preparation of students in construction majors requires special attention, since this area is constantly changing as technical progress is developing. The purpose of the article is to develop a model for future specialists’ professional activities development in the field of construction and operation of buildings and structures. This article analyzes the college curriculum for the major "Construction and maintenance of buildings and structures." Based on the data obtained, the objective necessity of improving the process of students’ future professional activity development was established. The authors developed a model for future specialists’ professional activities development in the field of construction and operation of buildings and structures, the target component of which is mastery of professional activities and development of professional competencies. This model includes necessary elements that are aimed at a more perfect development of students' professional activities. The model includes targeted, meaningful and effective components. The content component reveals the documents on which the process of modeling future specialists’ professional activities development should be based; methodological approaches ensuring implementation of modern educational paradigm; main functions of professional activity, its principles and stages of model implementation. Conclusion: the process of professional development of students requires constant improvement. The model developed by the authors is able to update it and improve the dynamics of professional competencies development.
Software Design and Development for Optimizing Quality Assurance Assessments

Quality assurance (QA) in one part to assest standard quality in the management, product, environment or other.QA in organization controlled by QA staff. This staff work to prepare all requirement in process of internal audit . The requirement like making checklist from QA standard like ISO, OHSAS and others, arrengement scheduling for audit, until make summary from audit result is the job of them. The problem is all requirement to prepare internal audit still used manual job, like using excel and distributed it one by one to each section in organization. This is make waste of time just to prepare the requirement until to make summary of the audit result. So, how to reduce the time of the job QA staff from weekly to just a minute. This can do by implementing quality assurance using software. By implementing this system can reduce significantly, the step of work of QA assessment from 15 step, can be 5 step only. And some of work in QA assessment can be done just a minutes, that before need a day until weekly just to make the job done.
JavaScript Strings

This chapter will focus on working with strings, the JavaScript String object, and the String object’s built-in functions. You will learn how to access, compare, decompose, and search strings for commonly used real-life purposes. In addition, the chapter will explore string encoding, decoding, encryption, and decryption. By the end of this chapter, you will understand how to effectively work with JavaScript strings and have a fundamental understanding of string encoding and encryption.
Adopt: A Context-Aware Domain Ontology-Based Framework For Public Transportation

Understanding dynamic environments is a problem that many developers face when developing mobile applications for a particular domain. Dynamic environments can contain large sets of objects, people, and places that have varying characteristics and attributes that are constantly changing. Therefore, understanding all of the domain concepts and their relationships is a challenge for many application developers. To solve this problem an ontology based framework, ADOPT (context-
A path of structural transformation for the automotive and insurance industries toward autonomous vehicles

This chapter outlines the upcoming disruption and structural transformation of the automotive and insurance industries on the road to autonomous driving. Through the introduction of the concept of Industry 4.0 and technological revolution in the automotive industry, we explore the major car innovations and major pillars that lead to autonomous systems. In this context, we delve into the structural transformation that these technological breakthroughs impose to the automotive and insurance business models and elaborate on the reconstruction of the traditional automotive value chain to an emerging mobility value chain that introduces new competitors and an imminent change of traditional business for the automotive and insurance sectors.
Penentuan Aspek yang Berpengaruh Terhadap Produk Smartphone Berdasarkan Ulasan Berbasis Tekstual

With the rapid development of new technologies in smartphones, understanding market trends has become an increasingly difficult task. In these circumstances, online product reviews that can reflect consumer sentiment about the product has been a concern for now. Online review analysis can help sellers understand consumer interests and desires prior to launching a new product. The author wants to contribute to seeing the state of the smartphone market by creating a method to find important aspects of a smartphone. The data source comes from the Amazon e-commerce web with 4 predefined smartphone brands. In this study, the authors used topic modeling with the LDA algorithm and sentiment analysis with VADER to find aspects of a smartphone and its sentiment classification. From the 15 scenarios made in this reserach, it is found 3 aspects that always appear, namely the screen, camera, and battery aspects, so it is concluded that these 3 aspects are the most important of a smartphone based on textual reviews.&#x0D; Keywords— Topic Modeling; Sentiment Analysis; LDA; ecommerce
Clustering-based word segmentation from off-line handwritten Uyghur text-line images

For the word segmentation of handwritten Uyghur text images, this paper proposes a segmentation method based on clustering algorithm. In this paper, firstly, the pre-processed text line images are projected to the vertical direction, which can get the initial probable segmentation points and record the blank spaces and text length between connected domains. By using clustering algorithm, the blank spaces are classified into two categories: 'within word' gap and 'between words' gap. Then the first mergence is completed according to the clustering results. For the existed phenomenon of over segmentation, one merging method based on threshold is proposed through the combination of text region length and blank space length so that the final segmentation points are obtained. And the experimental results show that this method can effectively solve the word segmentation problem in the handwritten text images.