Reviewerly/exHarmony huggingface dataset:
dracut

Put simply, dracut is a tool that creates the initramfs filesystem on Fedora-based systems. Debian- and Ubuntu-based systems use a similar tool called update-initramfs. If you want to generate, regenerate, or customize the existing initramfs, then you should know how to use the dracut tool. This chapter will explain how dracut works along with how to generate and customize initramfs. Also, you will learn some of the most common “can’t boot” issues related to initramfs.
Physics News

Physik in unserer ZeitVolume 50, Issue 3 p. 115-115 Treffpunkt Forschung Physics News First published: 02 May 2019 https://doi.org/10.1002/piuz.201970309Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume50, Issue3May 2019Pages 115-115 RelatedInformation
Performance evaluation of reformulated query for information retrieval using real estate ontology

The data over the internet is growing at an unprecedented rate daily. Hence efficient information retrieval has always been at stake. The scenario becomes more tedious in an e-government sector like real estate where fetching of legal documentation used for buying and selling of land property is unknown to novice users. The uncommon legal terminology also dilutes the keyword-based retrieval system. The proposed system thus insists on creating and further utilising ontology in this semantic web era. The performance of information retrieval is measured by calculating various parameters like mean average precision, precision@k, and normalised discounted cumulative gain for general user query first and then comparing it with the reformulated query after applying the real estate ontology. The experimental results are further statistically checked to depict an improvement in all the parameters thereby indicating that an initial user query, after ontological reformulation improves the efficiency of the information retrieval process.
Introduction

Change and motion are inherent features of reality. The ability to recognise patterns governing changes has allowed humans to thrive in a dynamic reality. Similarly, dynamics awareness can also improve the performance of robots. Dynamics awareness is an umbrella term covering a broad spectrum of concepts. In this chapter, we present the key aspects of dynamics awareness. We introduce two motivating examples presenting the challenges for robots operating in a dynamic environment. We discuss the benefits of using spatial models of dynamics and analyse the challenges of building such models.
An evolutionary algorithm for finding optimisation sequences: proposal and experiments

Evolutionary algorithms are metaheuristics for solving combinatorial and optimisation problems. A combinatorial problem, important in the context of software development, consists of selecting code transformations that must be utilised by the compiler while generating the target code. The objective of this paper is to propose and evaluate an evolutionary algorithm that is capable of finding an efficient sequence of optimising transformations, which will be used while generating the target code. The results indicate that it is efficient to find good transformation sequences, and a good option to generate databases for machine learning systems.
Behavioural analysis approach for IDS based on attack pattern and risk assessment in cloud computing

Cloud environments are becoming easy targets for intruders looking for possible vulnerabilities to exploit as many enterprise applications and data are moving into cloud platforms. The use of current generation of IDS have various limitations on their performance making them not effective for cloud computing security and could generate a huge number of false positive alarms. Analysing intrusion based on attack patterns and risk assessment has demonstrated its efficiency in reducing the number of false alarms and optimising the IDS performances. However, the use of the same value of likelihood makes the approach lacks of real risk value determination. This paper intended to present a new probabilistic and behavioural approach for likelihood determination to quantify attacks in cloud environment, with the main task to increase the efficiency of IDS and decrease the number of alarms. Experimental results show that our approach is superior to the state-of-the-art approaches for intrusion detection in cloud.
Medical Image Synthesis using Computer Vision Techniques

Magnetic Resonance Imaging (MRI) is a type of scan that produces comprehensive images of the inside of the body using a steady magnetic field and radio waves. On the other hand, Computed Tomography (CT) scans, is a combination of a series of X-ray images, which are a type of radiation called ionizing radiation. It can be harmful to the DNA in your cells and also increase the chances that they'll turn cancerous. MRI is a safer option compared to CT and does not involve any radiation exposure. In this paper, we propose the use of Generative Adversarial Networks (GANs) to translate MRI images into equivalent CT images. We compare it with past techniques of MRI to CT scan conversion and elaborate on why GANs produce more realistic CT images while modeling the nonlinear relationship from MRI to CT.
Chapter 9: Center-Based Clustering Algorithms

Compared to other types of clustering algorithms, center-based algorithms are very efficient for clustering large databases and high-dimensional databases. Usually, center-based algorithms have their own objective functions, which define how good a clustering solution is. The goal of a center-based algorithm is to minimize its objective function. Clusters found by center-based algorithms have convex shapes, and each cluster is represented by a center. Therefore, center-based algorithms are not good choices for finding clusters of arbitrary shapes. In this chapter, we shall present and discuss some center-based clustering algorithms and their advantages and disadvantages. We should mention that the expectation-maximization (EM) algorithm can be treated as a center-based algorithm, but we will defer the introduction of the EM algorithm to the chapter on model-based algorithms (Chapter 14).
Align-Filter &amp; Learn Video Super Resolution using Deep learning (AFLVSR)

In this article, we focused on the super-resolution technique in computer vision applications. During last decade, image super-resolution techniques have been introduced and adopted widely in various applications. However, increasing demand of high quality multimedia data has lead towards the high resolution data streaming. The conventional techniques which are based on the image super-resolution are not suitable for multi-frame SR. Moreover, the motion estimation, motion compensation, spatial and temporal information extraction are the well-known challenging issues in video super-resolution field. In this work, we address these issues and developed deep learning based novel architecture which performs feature alignment, filtering the image using deep learning and estimates the residual of low-resolution frames to generate the high-resolution frame. The proposed approach is named as Align-Filter &amp; Learn Video Super resolution using Deep learning (AFLVSR). We have conducted and extensive experimental analysis which shows a significant improvement in the performance when compared with the state-of-art video SR techniques.
Some Results Independent of Specific Distributions

We could start out and derive some of the standard probability distributions. However, some very important and deep results are independent of individual distributions. It is very easy to think that many results are true for normal distributions only when in fact they are generally true. We will discuss multiple scattering, the \(\sqrt{N}\) law, propagation of errors, errors when changing variables, correlation coefficients, and some generally useful inequalities.
An Insight Framework for Content based Medical Image Retrieval using Deep Convolutional Neural Network

Content Based Image Retrieval system is essentially proven techniques for retrieving the DICOM based images. DICOM format is use to acquire, store and shared medical images. Typically, retrieval of image under query upon the medical image databases is performed by fusing the low-level and high-level descriptors along with DCNN features. In this paper, DICOM image meta data are extracted by using dicom function and create the local database for reserve the information. To accomplish the initial search and retrieve the images by using extraction of Semantic information. The DICOM tags are extracted from the DICOM images and use DCNN feature to build a feature vector database. Subsequently prediction is done based on the by leveraging the convolution layers based on the meta data along with DCNN image features. This paper attempts to implement pre filter method to all DICOM images which further decrease the searching no of images, searching time and ultimately gives fast processing. DCNN based prediction model was constructed and finest results are accomplished. Average accuracy of precision and recall up to0.80 and 0.87 respectively is achieved based on precision value which would be suitable for high quality image retrieval based on semantic information confined in the image.
D. Deductive, Inductive, and Retroductive Reasoning

This chapter looks at deduction, induction, and retroduction, which are three forms of reasoning that explain observations or develop new explanations from observations, by connecting sentences to a logical structure. Deduction explains individual occurrences of a phenomenon based on general sentences (laws) and respective circumstances. Induction derives general sentences (laws) from repeated observations of similar events. Retroduction, also often referred to as ‘abduction’, is an educated guess about the likely explanation for an observation, which can then be tested. The purpose of applying these forms of reasoning to observational studies is to make logic an explicit tool that applies extant knowledge, or develops new knowledge. While deduction applies extant knowledge, induction and retroduction develop new knowledge. The basic structure of all three forms of reasoning is derived from classical syllogisms (arguments), i.e. a structure in language that combines sentences (premises) to a conclusion. The chapter then considers examples of scientific work that applies the three forms of reasoning.
Thank You to Reviewers

Journal of Adolescent & Adult LiteracyVolume 64, Issue 6 p. 727-731 Thank You To Reviewers Thank You to Reviewers First published: 20 May 2021 https://doi.org/10.1002/jaal.1162 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume64, Issue6May/June 2021Pages 727-731 RelatedInformation
Real World Applications

The analysis of face images has been extensively applied for the recognition of individuals in several application domains. Most notably, faces not only convey information about the identity of the subject, but also a number of ancillary information, which may be equally useful to anonymously determine the characteristics of an individual. Even though the first applications of face recognition have been related to security and access control, nowadays the analysis of human faces is related to several applications including law enforcement, man-machine interaction, and robotics, just to mention a few. This chapter explores the analysis of face images.
MOZAMBIQUE: Dissidents Linked to Attacks

Africa Research Bulletin: Political, Social and Cultural SeriesVolume 56, Issue 9 p. 22448C-22449C National Security MOZAMBIQUE: Dissidents Linked to Attacks First published: 15 October 2019 https://doi.org/10.1111/j.1467-825X.2019.09084.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume56, Issue9October 2019Pages 22448C-22449C RelatedInformation
Sequential Recommendation with Recurrent Convolutional Model

Personalized sequential recommendation refers to making recommendation based on users' historical consumption behaviors.Most works based on RNN only model long-term patterns, which fail to capture skip behaviors.Contrarily, the CNN-based model whose target is to handle this problem can only leverage part of sequential behaviors and ignores global patterns, which limits its performance.In this paper, we propose a Recurrent Convolutional Recommendation Model (RCRM) to simultaneously catch global and local patterns.Specifically, we employ a recurrent layer to capture global patterns and a convolutional layer to extract local patterns.An attention mechanism is then introduced to generate the final attentive local pattern, which can further concatenate with global patterns to predict next item.We conduct extensive experiments on two benchmark datasets and the results demonstrate that RCRM outperforms state-of-the-art baselines by a large margin over a variety of common evaluation metrics.
Automatic Visual Reading of Meters Using Deep Learning

In this paper, we present a novel approach to the problem of reading residential meters using deep learning algorithms.As a starting point we use Faster R-CNN method and, to acquire more precise readings, we modify its functionality.As there were no databases for this kind of task, one had to be collected and properly annotated.This paper also provides a brief introduction to methods for image augmentation and a technique to augment annotated image dataset.For each part of the presented method as well as the whole method as one unit experiments were conducted to show the overall successfulness.
Epsilon Numbers

In this chapter let us begin with the celebrated result by G. Gentzen stating that the proof-theoretic ordinal of the first-order arithmetic PA is equal to the first epsilon number $$|\mathsf{PA}|=\varepsilon _{0}=\min \{\alpha >\omega : 2^{\alpha }=\alpha \}$$ . Our proof of the fact $$|\mathsf{PA}|\le \varepsilon _{0}$$ is based on an elimination of first-order cut formulas. When we lower the cut degree by one, the tree-height of the resulting proof increases by one exponential. Thus the ordinal $$\alpha =|\mathsf{PA}|$$ needs to be a fixed point $$2^{\alpha }=\alpha $$ of an exponential function. Moreover $$|\mathsf{PA}|>\omega $$ due to the presence of complete induction schema. The main result in this chapter is a slight generalization of Gentzen’s.
On Vision-Based Human-Centric Virtual Character Design

Enabling cognition in a Virtual Character (VC) may be an exciting endeavor for its designer and for the character. A typical VC interacts primarily with its virtual world, but given some sensory capabilities (vision or hearing), it would be expected to explore some of the real world and interact with the intelligent beings there. Thus a virtual character should be equipped with some algorithms to localize and track humans (e.g. via 2D or 3D models), recognize them (e.g. by their faces) and communicate with them. Such perceptual capabilities prompt a sophisticated Cognitive Architecture (CA) to be integrated into the design of a virtual character, which should enable a VC to learn from intelligent beings and reason like one. To seem natural, this CA needs to be fairly seamless, reliable and adaptive. Hence a vision-based human-centric approach to the VC design is explored here.
Supplementary material to &amp;quot;Gains and losses in surface solar radiation with dynamic aerosols in regional climate simulations for Europe&amp;quot;

Terrain Height m Supplementary Figure 1.Spatial domains used for the WRF simulations.The outer domain (gray shaded) has an horizontal resolution of 1.32 o in latitude and longitude; the inner target domain (colored) of 0.44 o (Euro-Cordex compliant).The nesting was one-way (outer-to-inner domain).
Human activity recognition based on mobile phone sensor data using stacking machine learning classifiers

Human activity recognition aims to determine which activity is performed by individuals. It has plenty of real-world applications such as health monitoring and abnormal behaviour detection. Therefore, this study focuses on distinguishing and classifying human activities by applying statistical features and using stacking learning methods with the aim of improving the accuracy and precision of the classification. At first, features are extracted from raw sensor data and 26 subsets of the complete feature set are determined and tested to see which subset results in a higher precision. Then a feature selection technique based on the genetic algorithm is applied to the extracted features to observe if it can improve the results. Comparative results between classifiers showed that stacking models have advantages in increasing classification accuracy, especially in the case of climbing stairs and walking that are difficult to distinguish by single classifiers.
A Pilot Research on Android Based Voice Recognition Application

In recent trend, Speech recognition has become extensively used in customer service based organization. It has acquired great deal of research in pattern matching employed machine learning (learning speech by experience) and neural networks based speech endorsement domains. Speech recognition is the technology of capturing and perceiving human voice, interpreting it, producing text from it, managing digital devices and assisting visually impaired and older adults using unequivocal digital signal processing. In this paper we have presented a comprehensive study of different methodologies in android enabled speech recognition system that focused at analysis of the operability and reliability of voice note app. Subsequently we have suggested and experimented an android based speech recognizer app viz. Annotate which predominately focus on voice dictation in five different languages (English, Hindi, Tamil, Malayalam and Telugu) and extracting text from image using Automatic Speech Recognition (ASR) and Optical Character Recognition (OCR) algorithm. Finally, we identified opportunities for future enhancements in this realm.
LIBERIA: Banknote Printing Scandal Charges

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 56, Issue 2 p. 22466A-22467B Policy and Practice LIBERIA: Banknote Printing Scandal Charges First published: 01 April 2019 https://doi.org/10.1111/j.1467-6346.2019.08796.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume56, Issue2April 2019Pages 22466A-22467B RelatedInformation
An

The purpose of this study is to optimize the the lecturing schedule using the Ant Colony Optimization (ACO) algorithm. The method developed will find an optimal solution of lecturing schedule where there are some limitations that must be considered. These limitations include being a lecturer or a class that can only be scheduled to lecture a maximum of two times in a row. Another limitation is that two adjacent level may not be scheduled at the same time, because there is a possibility that students will repeat a lecture. The third limitation is that there should be no lecturers or classes that conduct lectures with too high a frequency one day. And the fourth limitation is the alternative time where lecturers can teach will be limited due to other activities that must be carried out by the lecturer. So that the course scheduling case can be solved using the ACO algorithm, a graph is made where each node is the name of the course that must be scheduled. The path created by the ants from the initial node to the end node will contain the order of courses that must be carried out one week. Based on the test results, the ACO algorithm has succeeded in scheduling courses involving 38 subjects, 4 class forces, 6 recovery locations and 12 lecturers supporting the courses. The scheduling solution obtained has a fitness value of 0.0092. Where there are no lecturers who have a high teaching frequency one day, but there are 12 class schedules that cause a class to follow a high frequency of lectures. And there are 4 courses scheduled to be close together. This final performance is considered quite good and shows that ACO has been successfully used to optimize course scheduling.
An Analysis of the Effect of Education by Using the PBL (Problem- Based Learning) Method and the Infusion-type Teaching Method

The purpose of this study is to find out the learner's teaching style preference and reasons for preference for PBL classes and infusion classes, and to look at how learners' teaching style preferences appear according to learners' personal tastes. To this end, a study was conducted on learners who took classes in manufacturing natural cosmetics during two semesters of 2018-2019 at D University in Chungbuk. The learners were asked to experience both infusion lectures and PBL classes within the same subject, and the survey revealed the learners' preference and reasons for their preference. Studies have shown that learners prefer infusion classes. For the reasons for preferring teaching methods, the cumbersome and difficult PBL activities showed a high response rate, but the learners who prefer infusion lectures perceived PBL activities negatively, and the learners who preferred PBL classes perceived it positively. Regarding the individual taste of learners, there was a significant difference in learner's preference for teaching methods according to subject grades. It was found that students with lower grades tended to prefer the infusion lecture method to the PBL teaching method. Accordingly, a new convergence teaching method was developed, in which the two are fused.