Reviewerly/exHarmony huggingface dataset:
Performance of Service-Oriented Architecture (SOA)

A service-oriented architecture is combination of services having different platforms for implementation. These services are combined, used by, and communicate with each other. The communication is done by massage or data passing. Communication is done by interacting with each other based on different platforms. Chronic diseases are long-term illness that require observation with heavy treatments by the doctors and special attention by family members. Chronic diseases are Alzheimer's disease, Addiction, Autoimmune diseases, Blindness, Rheumatoid arthritis, Chronic renal failure, Chronic Kidney Disease, Deafness and hearing impairment, Hypertension, Mental illness, Thyroid disease, Blood Pressure abnormalities.
Undergraduate Students Perform Successful Cogeneration Study for University

Abstract Undergraduate Students Perform Successful Cogeneration Study for University Heating and cooling the buildings at the University of ________ power generation is entirely from renewable resources. The primary source is biomass. The largest secondary source is hydroelectric, supplemented by wind and solar. Emergency backup is natural gas that can be fed directly to the university’s two boilers. In this process, too much of the steam provided for heating and for chilled water gets throttled down. Too much of its energy is lost. The university facilities director proposed adding a turbine to replace the throttling process, capturing some energy as electrical cogeneration in the process. Four undergraduate students were assigned this project, to provide a pilot study to determine the feasibility of such an idea. The students began by studying the energy levels involved. It soon became apparent that there was substantial energy to be captured. It would save about 10% on the energy portion of the university’s electrical bill. The more significant result of the study is the finding that these proposed turbines would decrease the demand portion of the university’s electrical bill by more than 80%. Therefore, the students proposed a set of alternatives. These were categorized into solutions involving two or three turbine generators to capture energy from the steam. An economic study showed that three turbines would capture more energy, as expected. Three turbines would also provide a degree of security to this resource and would enable substantial peak shaving. They would enable the electrical utility to more efficiently schedule energy to the surrounding urban area, providing a controllable draw of energy from each of the university’s two portals on the public utility electrical grid. The students gained the confidence of turbine manufacturers. They were able to do far more in gaining information and design assistance than previous teams on similar projects. The result was a fully formed study ready to be presented to the university facilities engineers and, with little modification, to the public utility and state regulators. There are important legal issues involved as well because the university is forbidden by state law from generating more electrical energy than it consumes. Implementation of the students’ recommendations is expected before the ASEE Conference in 2019. This paper will summarize how the students performed the study and what their results were. The paper will also assess the effectiveness of the methods employed in the undergraduate senior project course that provided the framework for the studies that were performed.
Synopsis to Artificial Intelligence and the Nature of a Firm: Implications to Strategy and Strategy Implementation

This book examined how various forms of AI could shape the firm’s strategy. The book confines itself to AI forms such as machine learning (ML), natural language processing (NLP) and robotic process automation (RPA). Following the introductory chapter, the book discussed a high-level overview of artificial intelligence in Chap. 2 . It discussed the strategy diagnosis and formulation in Chaps. 3 and 4 discussed strategy implementation. Chapter 5 discussed machine learning as a form of artificial intelligence. It sought to provide an understanding of machine learning as a starting point. It further considered how machine learning (ML) could be used to shape strategy and its implementation. Chapter 6 discussed natural language processing as a form of artificial intelligence. It sought to provide an understanding of natural language processing (NLP). It further hypothesised on how the NLP could be used to shape strategy and strategy implementation. Chapter 7 discussed robotic process automation as a form of artificial intelligence. It sought to determine how robotic process automation (RPA) could be used to shape strategy and strategy implementation.
Differential evolution with adaptive subpopulation size for multimodal optimization problems

Multimodal optimization problems arise commonly in real-world applications. The main difficulty is to find all the optimal solutions. Niching techniques are considered as the effective methods for locating multiple optimal solutions. However, niching-based algorithms introduce additional parameters and are sensitive to parameters. To solve this problem, a niching technique based on adaptive subpopulation size is proposed. In addition, due to the different sizes and shapes of the basins of attraction, the convergence situation of algorithms is different in different search regions. An optimal solution recognition technique is proposed, which is used to remove the subpopulations that have converged to the optimal solutions. Experimental results show the proposed algorithm has a superior or at least competitive performance.
An Extensive Demographic Inquiry of HDT Distinction for Localizing and Reestablishing Impulsive Irregularity Illustrations

This paper intends to inspect the extensive inquiry of the irregularity suppression scheme established on HDT (Hard Decision Threshold) distinction, which is one of the most functional and impressive irregularity identification schemes for confining and reestablishing the impulsive irregularity illustration elements, especially FVIN (Fix Value Impulsive Noise). The primary contribution of this demographic inquiry paper is the extensive simulation of the refined window size and optimal HDT threshold of the irregularity suppression scheme established on HDT for FVIN. By using large number of simulated illustrations, an extensive demographic inquiry delineates that the HDT distinction is able to categorize between regular and irregular illustration elements. In addition, the stochastic interrelation of HDT distinction and the cluster diameter (3x3, 5x5 or 7x7) is extensively simulated for estimate the refined cluster size from achievement perspective. At last, the stochastic interrelation between the de-irregular achievement and the Hard Decision Threshold (HDT) is extensively simulated on large number simulated illustrations on FVIN for estimating the refined threshold.
Cryptographic Cloud Computing Environment as a More Trusted Communication Environment

Cloud Communication Environment is an internet-based computing, where shared resources, software, and information are provided with computers and devices on-demand. They guarantee a way to share distributed resources and services that belong to different organizations. In order to develop cloud computing applications, security and trust to share data through distributed resources must be assured. This paper offers a study of the different mechanisms used in open cloud environments such as keys generation and management, and encryption/decryption algorithms. In addition, the paper proposes a new cryptographic environment, annotated as “CCCE” that deploys the combination between quantum key distribution mechanisms (QKD) and advanced encryption standard (AES), and demonstrates how quantum mechanics can be applied to improve computation.
Assessment of National Crime Reporting System: Detailed Analysis of the Desktop Application

Numerous standards and codes of best practice recommend the implementation of security practices since the early stages of the application development process. Commonly known as ‘security-as-default’, these practices consist of coding while keeping security in mind to start addressing threats and vulnerabilities before integrating security measures becomes too laborious. Hence, these best practices lead to cleaner and safer code. An effective way to find out whether the application is secure is by performing source code analyses. These analyses report weaknesses in the code once run against a predefined set of rules. Consequently, developers can detect and correct these issues, preventing the application from future exploits. A static source code analysis was performed to assess a National Crime Reporting System for a Latin American country’s National Police. The system is comprised of three modules: the mobile app, the desktop app, and the backend service. This paper focuses on the assessment of the desktop module.
Displaying Hidden Information in Glossaries

Requirements engineering is frequently seen as the activity of the software engineering process with fewer tools. Usually there are only available graphic and text editing aids. This is supported by the perception that it is a human-being-intensive task. This chapter is based on the understanding that such perception is just partially true. Models used along the requirements engineering process have underlying structures holding semantic information difficult to be seen by the reader. In fact, models created with well-defined objectives were designed to maximize their expressiveness for that objective. However, they may hold some useful shadowed information. Here is where a specialized tool may become valuable. From an epistemological point of view, this situation is similar to what happens in data mining. In this chapter, a tool able to make visible any clustering existing in universe of discourse glossaries is described. It is based on the automatic constructions of graphs using references embedded in the glossary itself.
Model‐based Product Line Engineering–Enabling Product Families with Variants

ABSTRACT Product Lines are a group of related products manufactured or produced within or between collaborating organizations. Effectively managing a product line requires understanding the similarities and differences between products, optimizing the development life cycle to leverage the similarities, and concentrating development on the differences. ISO 26550:2013 Software &amp; Systems Engineering–Reference Model for Product Line Management &amp; Engineering provides a standard for defining similarities, differences, and the choices between them. Model‐Based Systems and Software Engineering (MBSE) using the Systems Modeling Language (SysML) and the Unified Modeling Language (UML) provides a means of modeling systems and software. Bringing the two together allows users to model product lines in industry standard formats. These standards provide Model‐Based Product Line Engineering (MB‐PLE). Combining these with an execution engine means product models can be created for specific products, whilst maintaining the original product line model, providing significant return on investment (ROI) for aerospace companies.
A New Approach in Cloud Computing User Authentication

Cloud computing provides the full scalability, reliability, high performance and relatively low cost feasible solution as compared to dedicated infrastructure. These features make cloud computing more attractive to users and intruders. It needs more and complex security measures to protect user privacy and data centers. The main concern in this chapter is security, privacy and trust. This chapter will give a discussion and a suggestion for using cloud computing to preserve security and privacy. The malicious hacker and other threats are considering the major cause of leaking security of the personal cloud due to centralized location and remote accesses to the cloud. According to attacks, a centralized location can be easier target rather than several goals and remote access is insecure technologies which offer a boundary of options for attackers to infiltrate enterprises. The biggest concern is attackers that will use the remote connection as a jumping point to get deeper into an organization.
Crown-lengthening

Crown lengthening refers to procedures intended to increase the amount of clinical tooth crown visible and available above the gum line. This may be necessary to improve the periodontal prognosis for an undererupted tooth, to aid in the management of subgingival fractures or caries, or to increase the surface area available for the retention of a prosthetic tooth crown. This chapter outlines the indications for crown lengthening surgery, the essential anatomy, and the surgical techniques for type II surgical crown lengthening of a maxillary canine tooth a mandibular canine tooth and a maxillary fourth premolar tooth.
Deep Chessboard Corner Detection Using Multi-task Learning

Camera calibration is an indispensable step in the fields of robotics and computer vision, which includes augmented reality, 3D reconstruction, and camera motion estimation. Before camera calibration, detecting matching correspondence is necessary to understand the structure of the world from multiple images. For an accurate result, a calibration object, such as a chessboard, is used. Existing handcrafted feature methods precisely detect chessboard corners but are weak against blurs, noises, and severe lens distortion. Conversely, neural network-based methods can detect corners regardless of noises in the image. Both methods do not utilize the information of camera priors, which are lens distortion and intrinsic parameters, affecting the location of chessboard corners. Learning of lens distortion and intrinsic parameters enables the proposed network to understand the alignment of corners more precisely. Therefore, in this paper, we propose a novel multi-task learning framework to detect chessboard corners and simultaneously estimate lens distortion and intrinsic parameters. In order to train these three tasks, synthetic images of the chessboard are generated with ground-truth labels corresponding to each task. Hence, by learning the camera priors, the proposed network can more precisely locate the corners than other state-of-the-art corner detection methods while robust to noises, blurs, and distortion.
Basic Descent Methods

We turn now to a description of the basic techniques used for iteratively solving unconstrained minimization problems. These techniques are, of course, important for practical application since they often offer the simplest, most direct alternatives for obtaining solutions; but perhaps their greatest importance is that they establish certain reference plateaus with respect to difficulty of implementation and speed of convergence. Thus in later chapters as more efficient techniques and techniques capable of handling constraints are developed, reference is continually made to the basic techniques of this chapter both for guidance and as points of comparison.
Enhancing Multi-Body Mechanisms With Learning-Aided Cues in an Augmented Reality Environment

Augmented reality (AR) is a potential area of research for education, covering issues such as tracking and calibration, and realistic rendering of virtual objects. The ability to augment real world with virtual information has opened the possibility of using AR technology in areas such as education and training as well. In the domain of computer-aided learning (CAL), researchers have long been looking into enhancing the effectiveness of the teaching and learning process by providing cues that could assist learners to better comprehend the materials presented. Although a number of works were done looking into the effectiveness of learning-aided cues, none has really addressed this issue for AR-based learning solutions. This chapter discusses the design and model of an AR based software that uses visual cues to enhance the learning process and the outcome perception results of the cues.
Progressive Ordinal Regression for Long-tailed Age Estimation

In this paper, we propose a progressive ordinal regression (POR) framework for robust facial age estimation. Unlike the conventional class-balanced approaches which lead to bias prediction under long-tailed data distribution, our approach incorporates asymmetric cost-sensitive with progressive learning to address the long-tailed age classification problem. Specifically, the asymmetric cost-sensitive method aims to exploit the nonlinear ordinal correlation of age labels. Accordingly, the progressive learning scheme is adopted to decouple the learning procedure into representation learning and age classification. These two processes jointly learn robust feature representation and address class imbalanced problem progressively. Experimental results on three public benchmarks demonstrate the effectiveness of our proposed method.
Enterprise Security Framework for Enterprise Cloud Data Centres

Enterprise security is the key to achieve global information security in business and organisations. Enterprise Cloud computing is a new paradigm for that enterprise where businesses need to be secured. However, this new trend needs to be more systematic with respect to Enterprise Cloud security. This chapter has developed a framework for enterprise security to analyze and model Enterprise Cloud organisational security of the Enterprise Cloud and its data. In particular, Enterprise Cloud data &amp; Enterprise Cloud storage technologies (Amazon s3, Drop Box, Google Drive, etc.) have now become a normal practice for almost every computing user's. Therefore, building trust for Enterprise Cloud users should be the one of the main focuses of Enterprise Cloud computing research. This chapter has developed a framework for enterprises which comprises of two models of businesses: Enterprise Cloud provider enterprise model and Enterprise Cloud consumer enterprise model.
Cut To Size: A Lightweight Design for STR via Channel Pruning

In natural scenes, text full of rich and prominent information contributes to understand the scene. Scene text recognition (STR) is commercially viable in the wave of big data, which comes to be the center of attention. The existing STR methods based on deep learning have make significant progress, but for the complexity and size of model, it is difficult to be deployed in embedded devices. Therefore, a lightweight design for STR via synergistic pruning is proposed. To score the value of channels, it evaluates not only the convolution layer weights, but also the BN(Batch Normalization) layer scaling factors jointly. It can delete redundant channels which slightly influences the ability of the model. Meanwhile, some strategies are presented for fine-tuning model performance or contracting the model size. All models are trained and tested on public datasets. The experimental results on CCPD2019 well prove the effectiveness of this work, which can cut 87.60% params of CRNN and 78.31% params of Rosetta, respectively, and only reduce a little accuracy.
Data Analytics in Industry 4.0

The new form of future generation machines and automated systems could be synchronized by IoT adaptation. By this way, a very large size data can be carefully stored in data repositories and have to be analyzed for extracting knowledge. Thus, optimization techniques are becoming invaluable tools for finding patterns from parallel distributed machines. On the other hand, statistical methods and optimization models could not be utilized efficiently due to excessive dimension of data. Additionally, data analytics should be applied and results should be gathered by using practical approaches especially for security, access control and fault detection issues. In this study, optimization techniques are evaluated in the perspective of big data analytics and both mathematical and statistical methods will be extensively analyzed for different versions of problem solving and decision making in Industry 4.0 era.
The Universal Invariant-Based SLA Theory

SLA is a broad multilateral realm of theoretical and applied projections. The discipline being topical for the world community, its coterminous issues are rather summarily thrown together, but actually spread out or split up of the field originally meant as a more concentrated and closely-knit nucleus. The research mainstream branches out into numerous aspects of language acquisition, most of which are ‘cross-sectional'. The heterology of research approaches hinders the progress towards the development of a well-balanced unified SLA theory relying on the basics inherent in science at large. A theory like that is aimed at the elimination of any ambiguity and confusion, so that anyone could similarly interpret it. Although the idea sounds like a utopian goal so far, a number of steps could be taken for SLA integrity to get closer and ultimately to transpire. A holistic theoretical model of SLA requires that its modules be represented on the basis of the same property, or radix. In the model developed, the radix is identified as a minimal predicative unit being formed. The unit takes shape in the process of predication, which can be referred to as the act of joining initially independent objects of thought expressed by self-determining words—predicate and argument—in order to convey any idea. Predication is a most important function of language cognition due to which the real and individualized worlds converge in the learner's mind. Hence, predication is not just a common fundamental of language, social intercourse, and individual inner thought activity but actually a medium creating the environment in which all three spheres mentioned function cohesively. The SLA Universal Invariant-Based Binary Predication Theory is identified in terms of its domain, content and procedural phenomena, principles, rules and regularities, binary opposition logic. and idealized object.
Bayesian Classification

This chapter covers the first of four image classification methods in the book. It begins with a brief introduction of Bayesian theorem and demonstrates how Bayesian theorem works using two examples. The rest of the chapter focuses on applications of Bayesian theorem on image classification. It begins with describing how images can be classified using a simple Naïve Bayesian classifier and its variants. The NB methods give readers the opportunity to relate image data with Bayesian theorem before contacting with other sophisticated Bayesian methods. After these preparations, three more powerful and non-parametric Bayesian methods are described in algorithmic forms and illustrations. In the next, more advanced methods such as parametric method and Gaussian process are discussed with illustrations.
Introduction

Introduction of the Arm Cortex-M23 and Cortex-M33 processors and how they are related to microcontroller devices. This chapter also gives some background about Arm processors.
Realistic Image-to-Image Translation with Enhanced Texture

In the image-to-image translation field, most researchers tend to achieve the overall translation of images without paying too much attention to the texture details of images. However, it is also of great importance to have enhanced and more realistic textures for synthesized images, which could bring better impressions. Therefore, in this work, we propose a method based on CycleGAN and the texture of output images is highly improved. The presented generator involves dilated convolutions which are conducive to processing image texture details. Furthermore, an improved cycle consistency loss is proposed for stable and effective training. The experiments demonstrate that our proposed method is able to generate images that contain more details and better meet the visual perception of humans. Our code will be publicly available at GitHub soon.