Reviewerly/exHarmony huggingface dataset:
Mmino wa bana (Children’s Songs) as a Determinant of Reading Recorded Knowledge among Rural Teenagers in Ga Molepo, South Africa

&lt;p&gt;The National Library of South Africa (NLSA) in conjunction with the Department of Sport, Arts and Culture (DSAC) and UNISA’s Information Services Department hosted the National Reading Summit from 24 to 26 March 2021.&lt;/p&gt;&lt;p&gt;The National Reading Summit responded to national reading initiatives as well as the National Reading Survey that was commissioned by the Department of Sport, Arts and Culture through the South African Book Development Council (SABDC) published in 2017. The survey found that a lot more needs to be done if we are to achieve significant literacy levels.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;This pre-recorded virtual presentation was featured on the last day of the National Reading Summit. More details on the research is available in a peer reviewed article that was published on 27 May 2021 in Vol. 38 No.1 of &lt;i&gt;Mousaion: &lt;/i&gt;The South African Journal of Information Studies.&lt;/p&gt;
Sound and movie examples – issue 24(1)

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Response to the interactive comment on “Remote monitoring of seismic swarms and the August 2016 seismic crisis of Brava, Cape Verde, using array methods” by Carola Leva et al.

Response to the interactive comment on "Remote monitoring of seismic swarms and the August 2016 seismic crisis of Brava, Cape Verde, using array methods" by Carola Leva et al.
No. 52113 International Bank for Reconstruction and Development and Egypt

Loan Agreement (Helwan South Power Project) between the Arab Republic of Egypt and the International Bank for Reconstruction and Development (with schedules, appendix and International Bank for Reconstruction and Development General Conditions for Loans, dated 12 March 2012). Washington, 5 November 2013
A Good Girl’s Guide to Murder by Holly Jackson

Reviewed by: A Good Girl’s Guide to Murder by Holly Jackson Kate Quealy-Gainer Jackson, Holly DA Good Girl’s Guide to Murder. Delacorte, 2020 [400p] Library ed. ISBN 978-1-9848-9637-7 $20.99 Trade ed. ISBN 978-1-9848-9636-0 $17.99 E-book ed. ISBN 978-1-9848-9638-4 $10.99 Reviewed from galleys Ad Gr. 7-10 For her senior capstone project, aspiring investigative journalist Pippa Fitz-Amobi examines the event that has haunted her town for the last five years: the murder of golden girl Andie Belle by her boyfriend, Sal Singh, and his subsequent suicide. Pippa doesn’t believe Sal was guilty, and one of the first steps in clearing his name is to contact his younger brother, Ravi, who is not only willing to talk to her but wants to join her investigation. Together, the pair go down a rabbit hole of clues, tracking down Andie and Sal’s old friends, digging up old or unknown evidence, and eventually coming up with theories that put some of the best people they know at the center of the crime. There’s little character development in this British import, with Pippa and Ravi decidedly the good guys, and everyone else clearly demarcated by the singular trait that puts them on the suspect list; the racism that may have motivated the town’s quick assumption of Sal’s guilt is only glancingly dealt with. This isn’t really about nuance as much it is about the fun of following Pippa through interviews, transcripts, crime reports, and aha moments until she finally gets her man (and woman) in a resolution that it not quite believable but entertainingly titillating. The legions of fans of true crime podcasts may therefore flock to this one, and they’re unlikely to be disappointed. Copyright © 2020 The Board of Trustees of the University of Illinois
Review of the covariance filtering aspects

In this article, the authors use covariance filtering methods recently developed in the NWP data assimilation context and adapt them in the atmospheric transport framework.Their goal is to get a better estimation of the forecast error spatial structures for in situ carbon dioxide mole fractions and total column dry air mole fractions.I think that it is a very good idea to import methods from one domain to another, and I congratulate the authors for this effort.
Integrated Security, Authentication and Decentralized Access Control (ISADA) Framework Based on Novel Key Exchange Mechanism for a Public Cloud Environment

In modern days cloud computing has found its application to a great extent.People are sure that it will be the future technology that occupies digital world, but the security related issues need to be overcome.By using cloud computing, users can access their data from remote servers through internet.Cloud computing provides cheaper and faster services to users.At the same time there is security issues associated with cloud such as data loss, abuse of data, cyber security attacks and so on.While off-shoring sensitive data through third party cloud servers, access control ensures that an unauthenticated person cannot access data without user's knowledge.This research paper proposes a Data security and User centric access control framework which provides two levels of security and multilayer access control mechanism by using key exchange.This mechanism allows user to store shuffled and encrypted data in a cloud server which is only accessed by the authenticated users.A novel key management mechanism is used to achieve multilayer access control.User centric access control makes this mechanism more vigorous because there is no need for third party auditors and key service providers.All the communications and key transactions are only between the owner of the data, consumer of the data and the cloud host.This proposed framework (ISADA) gives a better solution for broken access control under horizontal privilege escalation.
Aspects of writing and its correlation with students’ reading habit of the eleventh grade in SMAN 1 Natar

This research was intended to investigate what aspect of writing has the highest correlation with students’ reading habit. This research is quantitative using ex-post facto design. The study was conducted on 28 students of the eleventh grade in SMAN 1 Natar. Research instruments included reading habit questionnaire and writing test. This research used scoring rubric for writing test which covered five aspects of writing; content, organization, vocabulary, language use and mechanic. The data were in form of scores taken from questionnaire and writing test. Moreover, the score of each aspect of writing was analyzed by using Pearson Product Moment Correlation analysis in SPSS 20.0 along with the score of reading habit to get the correlation between two variables. The result of analysis showed that the coefficient correlation between content aspect and reading habit was determined to be 0.514. Thus, it was implied that content aspect has the highest correlation with reading habit among the five aspects of writing.
A Sentimental Analysis on Facial Expression Recognition

In the course of recent years’ numerous procedures have been proposed for face acknowledgment. Numerous methods proposed at first can't be viewed as fruitful yet practically all the ongoing ways to deal with the face recoginisation improves the results. Face acknowledgment is the errand of distinguishing a picture which is as of now recognized. We need huge information base of pictures that choose the given picture is known or obscure. Calculation is utilized that concentrates facial highlights and contrast with database with locate the best match. For recoginisation reason profound neural system is utilized, for example, Convolution Neural Network (CNN) and streamlining calculation, for example, Artificial Bee Colony (ABC) calculation. In this paper we give the short acquaintance about huge information with sort out our datasets for research work. For this specific research work we need information kind of pictures records put away by the enormous information. Past methodologies experience the ill effects of different weaknesses like interpretation in facial picture this may diminish the acknowledgment execution. To illuminate these issues, we utilized ongoing ways to deal with improve the results.
Sarcastic Detection of Twitter Comments using Python

A sarcasm / joke is a language of expressing the feeling in an opposite manner. In case of aural or cinematographic information the sarcasm can be identified easily through the tonal stress, gestures and facial expressions. But the main challenge of sarcasm is sarcastic detection in textual information where there is an absence of expressions or tonal stress. Presently a days, the utilization of informal organizations are enormously expanded. Most of the people express their feelings in their posts and comments in sarcastic manner. Sarcasm creates inquisitiveness and courtesy towards it in sentimental analysis. Wistful Analysis is the procedure or investigation of examining the sentiments. In this project, I have chosen twitter comments for this sarcastic sentimental analysis which is commonly an opinion mining. The importance of the project is to increase the accuracy rate by feeding huge data set for training. The purpose of finding the sarcasm in social networks is to block the user who points particularly or attack any victim which is not considered as sarcasm.
Classification of Digital Mammograms Into Masses and Non-Masses using Texture Combination Features and SVM

This paper presents a framework for grouping of mammogram images into carcinogenic and non- carcinogenic images. In this paper, image is enhanced using nonlinear contrast enhancement algorithm and then segmented using internal and external mask segmentation. The features used in the proposed work are based on the texture feature which means that the segmentation results will have minimal impact on the classification results of mammograms into masses and non – masses. The features used are Contrast, Energy, Homogeneity, Entropy, Mean Intensity, Standard Deviation, Taxonomic distinctness, Taxonomic distance and learning takes place by Support Vector Machine. The algorithm was applied on Mammographic Image Analysis Society database and when compared with contemporary techniques , the results were improved.
Blockchain Technology

Contracts and transactions are at the heart of our modern society. Their paper-based and digital records define the most important structures of our administrative economic, legal, political, and social systems by setting associated regulatory boundaries. They govern interactions among nations, public and private organizations, communities, and individuals worldwide [1]. This is why they are frequently subject to digital abuse and fraud, which is why cybersecurity is more important than ever in the digital age. Blockchain technology promises to play a crucial role in this context since it might be as disruptive for data integrity and recording as the Internet has been for data access and exchange.
Machine learning algorithm for information extraction from gynaecological domain in Tamil

Information Extraction is a significant task in Natural Language Processing. It is the process of extracting useful information from unstructured text. Information extraction helps in most of the recent NLP applications like scientific research, financial investigation, business intelligence, media monitoring, healthcare records management, agriculture, and pharmacy research. There are several information extraction research approaches using many techniques from English dataset. As a multi lingual country India, it is actually challenging task to extract information from text in Indian language. Such research work has been done in the following domain data travel, food, agriculture, weather casting, social media, marketing and bio- medical. In this research work the relevant information extracted from Gynaecology related text data in Tamil language. The combination of machine learning based classification model and ontology representation is used extract the useful information. Auto filling IE framework is designed to extract the appropriate information in a structured format. The user query will be pre-processed and converted into entities to check from the classified data using ontological representation by using machine learning based classification model naïve bayes classification. From the ontological representation entity based relation extraction will be performed to fill the IE framework. The proposed IE framework given good results in extracting relevant information based on user query. It was analyzed for more than 57 user queries regarding gynaecological issues. The 75% of accuracy obtained for the correctness in user queries.
Graph Algorithms

It is obvious that the main purpose of many algorithms relates with operations on data, and the data structure plays an important role in choosing an algorithm. In this chapter, we will discuss nonlinear data structures such as graphs.
Non-reference assessment of video quality using statistical methods

The article considers the problem of finding a way to assess the quality of video in the absence of a standard for comparison. In the literature, such methods of assessing image quality are called no-reference (NR) or NR-methods. First of all, the article examines the artifacts of image compression. The relevance of this approach is that the data is compressed when material transmitting over the Internet to save information. This method is based on criteria that characterize the degree of change in the brightness of video frames. By themselves, the criteria allow to conduct a comparative analysis of image quality not in all cases. In this article, to assess the quality it is proposed to use criteria which are based on statistical methods, which reflects the degree of change in brightness in the aggregate. These criteria are completely new in the field of research the quality of both video streaming and images in general. The proposed method takes into account all possible changes in the characteristics of the image with deteriorating quality. During the experiment, the feasibility of using these methods in the problem of ranking the material by the level of compression artifacts was demonstrated. It has been experimentally shown that none of the studied non-reference methods of image quality assessment is universal, and the calculated assessment cannot be converted into a quality scale without taking into account the factors influencing the distortion of image quality. Also, this method forms the final estimate as the arithmetic mean of the estimates of rows and columns of the image. In the case of local distortions, the proposed methods may not give completely true results. To conduct the experiment, the program code was implemented in the MATLAB environment, using the library for computer image processing Image Processing Toolbox.
Hybrid LRU Algorithm for Enterprise Data Hub

In the computing theory, cache is key concept to process the in memory data from slow storage layer into faster. Key objective is to speed up the end user requests from cache storage. As cache is limited in size, it is essential to build the efficient algorithm to replace the existing unused content from faster memory. Enterprise Data Hub makes a single golden storage to produce any kind of reports at any point of time from the various sources of any system. Data integrity and governance parameters add the credibility to this centralized data. In general, Big Data processing uses the disk based technique to handles the business logic processing. This research paper is to leverage the in memory processing for the business use case of Enterprise Data Hub. This paper provides an algorithm to handle Enterprise Data Hub in efficient design using prioritized Least Recently Used algorithm. This paper depicts about the experimental advantage of execution time optimization and efficient page/cache hit ratio, using hybrid Least Recently Used algorithm with priority mechanism. It helps the industry Enterprise Data Hub for the faster execution model.
Retraction Note to: A Cooperative Placement Method for Machine Learning Workflows and Meteorological Big Data Security Protection in Cloud Computing

The authors have retracted this chapter [1] because after publication they realized that the data set simulated in this paper was incorrectly selected in the experiment in Section 5. This resulted in serious errors in the meteorological workflows experimental results. Attempts at repeating the experiment with the appropriate data set failed due to other unknown errors.
High Dimensionality Dataset Reduction Methodologies in Applied Machine Learning

A common problem faced while handling multi-featured datasets is the high amount of dimensionality that they often consist of, leading to barriers in generalized hands-on machine learning. These datasets also give a drastic impact on the performance of machine learning algorithms, being memory inefficient and frequently leading to model overfitting. It often becomes difficult to visualize or gain insightful knowledge on the data features such as presence of outliers. This chapter will help data analysts reduce data dimensionality using various methodologies such as: Feature Selection using Covariance Matrix t-distributed Stochastic Neighbour Embedding (t-SNE) Principal Component Analysis (PCA) Under applications of Dimensionality Reduction Algorithms with Visualizations, firstly, we introduce the Boston Housing Dataset and use the Correlation Matrix to apply Feature Selection on the strongly correlated data and perform Simple Linear Regression over the new features. Then we apply t-SNE to MNIST Handwritten Digits Dataset and use k-Nearest Neighbors (kNNs) clustering for classification. Lastly, we use the UCI Breast Cancer Dataset to perform PCA Analysis with Support Vector Machine (SVM) Classification. Finally, we explore the benefits of using Dimensionality Reduction Methods and provide a comprehensive overview of reduction in storage space, efficient models, feature selection guidelines, redundant data removal, and outlier analysis.
"An Overview on the Study of Data Encryption and Decryption in Cloud Computing"

While big data technologies in cloud computing are rapidly growing in popularity, privacy issues have increased dramatically. Encrypting data in real-time is one of the greatest important issues throughout data acquisition and transfer. In order to reach an acceptable performance level, many modern applications forego data encryptions, which is unsuitable with privacy issues. In light of cloud computing concepts and features, this article examines various cloud computing systems and analyzes the cloud computing security problem and solution. Data privacy but instead service availability are major security problems in cloud computing. A single security solution will not address the cloud computing security challenge; to defend the whole cloud computing system, a mix of old and new technologies and procedures must be employed in harmony. The author demonstrates the use of encryption and decryption algorithms in terms of data confidentiality, great computational efficiency, and cloud-based system effectiveness. On top of this architecture, they may enable dynamic block-level actions on data encryption blocks for insertion, removal, including update, which we believe will be future work for enhancement.
Improving the Quality of KB Harvest by Leveraging Multimodal signals based on Event and Place

New elements are being made every day. Even though the oddity of these substances normally pulls in notices, because of absence of earlier learning, it is additionally testing to gather information about such elements than previous elements, whose KBs are extensively commented on through LBSNs and EBSNs. In this we center around learning gathering for developing spatial elements ESEs, for example, new organizations and settings, expecting we have just a rundown of ESE names. Existing systems for learning base (KB) reaping are fundamentally connected with data extraction from literary corpora. Conversely, we propose a multimodal technique for occasion discovery dependent on the reciprocal connection of picture, content, and client data between multi-source stages, specifically Flickr and Twitter. We exactly approve our collecting approaches enhance the nature of KB with advanced place and occasion learning.
Penetration Testing

In this chapter, you will learn about security assessments, penetration testing, risk management, and various testing tools.
Writer Identification with Hybrid Model using Hybrid HMM and ANN

In this paper, writer identification is performed with three models, namely, HMMBW, HMMMLP and HMMCNN. The features are extracted from the HMM and are classified using Baum Welch algorithm (BW), Multi layer perceptron (MLP) model and Convolutional neural network (CNN) model. A dataset, namely, VTU-WRITER dataset is created for the experiential purpose and the performance of the models were tested. The test train ratio was varied to derive its relation to accuracy. Also the number of states was varied to determine the optimum number of states to be considered in the HMM model. Finally the performance of all the three models is compared.
Voting Classification Method for Network Traffic Prediction

The prediction analysis is the approach of data mining which is applied to predict future possibilities based on the current information. The network traffic classification is the major issue of the prediction analysis due to complex dataset. The network traffic techniques have three steps, which are pre-processing, feature extraction and classification. In the phase of pre-processing data set is collected which is processed to removed missing and redundant values. In the second phase, the relationship between attribute and target set is established. In the last phase, the technique of classification is applied for the classification. This research study has been influenced by the different intrusion threats on internet and the ways to detect them. In this research, we have studied and analyzed the famous network traffic data -NSL KDD dataset and its various features. The proposed model is a hybrid of Logistic Regression and K-nearest neighbor classifier combined using voting classifier, which aims at classifying the data into malicious and non-malicious with more accuracy than existing methods.
The Search for Solutions to Mysterious Anomalies in the Geologic Column

There is convincing evidence that soft tissue and other biomolecules have survived from the Mesozoic to the present, possibly because of their interaction with blood iron and/or carbonate adsorption.Here we present the results of investigations showing that ancient biomolecules and their decay products contain significantly higher percent Modern carbon (pMC = 14 C/ 12 C) values than diamond and coal.South African diamonds yielded pMC values of from 0.16 to 0.11 pMC, or ages of 52,000 to 55,000 Carbon-14 years before present ( 14 C years BP) [Baumgardner et al., 2003].Ten coal specimens from the United States from Eocene and Pennsylvanian strata were 0.33 to 0.16 pMC, or 46,000 to 52,000 14 C years BP.By comparison, our field and lab study of ten dinosaurs from Texas to Alaska plus China yielded much higher pMC's of 6.50 to 0.61, or 22,000 to 41,000 14 C years BP after pretreatment to remove old and modern soil contaminants.The evidence for endogenous pMC was further enhanced by the δ 13 C range of -20.1 to -23.8 for collagen, -16.6 to -28.4 for bulk organic, and -3.1 to -9.1 for CO3 fractions.This data clarifies why such biomolecules have persisted.These unexpected results call for replication to determine whether they are anomalous.If not, the implication is that a portion of the geological time scale should be condensed, indicating a higher risk to Earth of meteorite impact due to greater frequency.We recommend systematic 14 C dating of similar samples taken from different parts of the entire geologic column.