Reviewerly/exHarmony huggingface dataset:
Study on numerical model of classical guitar

Classical guitar has an aging effect that can be expected to improve sound quality by playing. However, the mechanism of its effect has not been clarified. Therefore, we are conducting research to clarify the structure of classical guitars and the characteristics of their components, build numerical models based on them, and elucidate the mechanism of the aging effect by numerical analysis. Here, the vibration characteristics of the constituent wood of the guitar are experimentally identified in consideration of anisotropy. Furthermore, the effect of the moisture content of wood on the vibration characteristics will be investigated.
Efficient Aspect Finding in Sentiment Analysis using Optimal Binary Search Tree

Sentiment Analysis is an interesting research area. It useful for analyze the user comments with the help of natural language processing. While consider a huge data and improper data, the sentiment classification with better accuracy is a very big challenge in sentiment analysis. The social media records relevant to Sentiment analysis is a difficult one to identify with a people’s situation, thoughts, and view in the direction of a certain event, which has a number of applications such as election guess and product assessment. This paper focuses on the basic idea of optimal binary search Tree Technique to find the optimal solution (aspect) from a sentence among the several aspects; specifically, we perform the classification process appropriate to various features, which is used to identify the best one aspect among the several aspect terms in a sentence. In the proposed system, We have introduced optimal binary search Tree aspect level sentiment analysis technique. Through this we are getting best and efficient aspect level solution compare to General Tree. The main novelty of the paper is analyzing how aspect sentiment extracted from each word in a sentence through Dictionary based approach with help of the syntactic Patterns. It provides a well understanding of the appropriate role of every words to obtain optimal Aspect in an efficiency way.
Names, Namespaces, and Templates

The basics of using namespaces and templates are straightforward and easy to learn. Taking advantage of argument-dependent lookup (ADL) is also simple: declare free functions and operators in the same namespace as your classes. But sometimes life isn’t so simple. Especially when using templates, you can get stuck in strange corners, and the compiler issues bizarre and useless messages, and you realize you should have spent more time studying names, namespaces, and templates beyond the basics.
More Challenges

This chapter answers further challenges to the truth-functional interpretation of ‘if’. Some challenges depend on data which can be understood in terms of the interplay between supposition-based and testimony-based heuristics. Other challenges involve conditional commands and conditional questions but depend on independently objectionable principles about commands and questions. Yet another kind of challenge depends on alleged connections between indicative conditionals and the epistemic modal ‘must’, but the connections are shown not to hold with full generality: some fail when conditionals are used to voice speculations, and some can be explained as products of independently motivated context-sensitivity in ‘must’.
Differentiating Features of Mammographic Views Repeated for Image Blur

Thank you very much for joining EUSOBI 2021 ONLINE and celebrating the Breast Cancer Awareness Month with us! This year we had a terrific programme and we are indebted to Dr. Ritse Mann and his Programme Committee for all their hard work in putting this together. We had a wonderful selection of international speakers who gave the latest updates in all your favourite topics and ensured that you are kept up to date with the latest developments in breast imaging. A special focus was given to the interactivity of this year’s programme, by adding expert discussions and concluding with the first virtual edition of our famous Breast Interpretation Quiz. Thanks to all of you who submitted abstracts and therefore having been an important part of this scientific event. We are also very grateful for the support of our industry partners who are listed here: https://www.eusobi.org/congress-sponsors-2/ Most of them have contributed to the extension of our scientific programme by offering interesting sessions and workshops. Have a look by scrolling through all sessions. All sessions have been recorded and are available on-demand.
A Qualitative Interpretation of Code Clone Detection Techniques

Clone is the software code snippets that are similar to each other with little modifications. There is a 10-20 percent clone mostly present in the software. Many techniques are developed for detection. With the code clone detection, the software developer gets an idea of removing, refactoring the clone. Code clone has both advantages and disadvantages in the particular software. In this paper, we explore the types of code clones, advantages, and disadvantages, the reason for cloning. Typically, this paper describes various techniques by using several parameters. Lastly, we discuss gaps in the research.
Clinical Applications for Artificial Intelligence on a Multidimensional Cardiology Dataset

Background: The New Zealand health system has a wealth of electronic-based data that are readily available for research. This data can be used for a variety of clinical applications such as detecting new disease phenotypes, performing embedded cohort studies, and developing prediction models using artificial intelligence and machine learning. Method: A total of 75,000 patients who had undergone an echocardiogram between 2009 and 2018 at Waitemata District Health Board (WDHB) were identified from an Excelera database. Using SQL and Python, these data were linked with other electronic databases containing ICD10 codes, laboratory data, electrocardiogram meta-data, e-referrals, clinic letters, electronic discharge summaries, and Pyxis inpatient prescribing data. Deep learning-based neural networks were used to remove patient identifiers from free-text data, with up to 99.7% accuracy. Machine-learning algorithms were applied to the dataset and predictions were made for various outcomes such as mortality, echocardiogram results, direct current (DC) cardioversion efficacy, and genetic disorders such as familial hyperlipidaemia. Results: Mortality and age of mortality can be predicted with a high level of accuracy (AUC 0.92, F-measure 0.93, precision 97%, recall 90%, Phi 0.53, R2 0.77). Pathology found on echocardiography could be predicted (AUC 0.79, F-measure 0.94, precision 89%, recall 99%, Phi 0.22). Important features for prediction of DC cardioversion for atrial fibrillation (AF) were identified as duration since AF onset and left atrial dimensions; both features were subsequently added to e-referral forms at WDHB. Conclusion: Artificial intelligence has great potential for clinical application within the New Zealand health system, particularly due to the availability of high-quality electronic health records.
Related Work

This chapter presents a body of foundational, related work. From the Cognitive Psychology field, a theory of multiple modes of learning is described, with emphasis on nine of such modes: advice, declarative knowledge, goal analysis, worked-out examples, analogy, positive feedback, negative feedback, short-cuts and quantitative regularities. From the field of Linguistics, elements of pragmatics and dialogue processing are presented. From the field of Education, the importance and challenges of Computer Science education at multiple school levels are discussed. Finally, an overview of related research in the field of Intelligent Tutoring Systems is presented.
Sources

“Why blockchain?” is one of the most frequently asked questions. These and many other questions are answered in a simple and understandable way in this book about blockchain.
Building federated data warehouse schema using web service approach

The data warehouse is considered as a repository that stores data from disparate sources. It is used mainly in the process of decision making. Because of the diversity of the applications, their heterogeneity and their physical spacing, building a data warehouse, to make decisions in a federated architecture, is a very complicated task. To overcome this problem, we propose a new approach for the design of a federated data warehouse schema based on the web services. The latter are applied because of their capacity to solve many technical problems related to the integration of data source in a federated environment. The proposed solution is based on the bottom-up approach where the data warehouse schema is generated from the set schemas of the data mart. It is composed by two steps. In the first one, a web service is used to generate the schemas of the data mart one for each data source. In the second step, a second web service is used to merge the generated schemas to build the final schema of the data warehouse.
Machine Learning and Security Applications in Digital Library

This paper presents the applications of machine learning machine learning applications in the digital library. Using machine learning it is possible to search and retrieve non-textual information. The paper also discusses the machine learning applications in security aspects. A systematic review of literature is also done and with the help of citation mapping in Web of Science citation network analysis is presented
Bright future

Chemistry & IndustryVolume 84, Issue 6 p. 34-35 Energy Bright future First published: 15 June 2020 https://doi.org/10.1002/cind.846_8.xAboutPDF 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. Volume84, Issue6June 2020Pages 34-35 RelatedInformation
Machine-Learning Based Model for Improving Effort Estimation using Risk

In the real world, many software projects have effort, schedule, and cost overrun. Estimation accuracy is vital for a successful software project. This is a challenge since mostly there is a tendency to over-estimate or under-estimate the size and effort needed in a software project. The prediction of required effort is a critical activity, and a greater focus is required by considering additional factors such as project risk. The importance of project risks and their management has been indicated in literature and its consideration in effort estimation is the focus of this research. This research paper has a focus on proposing a model based on machine learning techniques for improving the effort estimation through inclusion of risk score. The proposed solution considered aggregating the capability of various machine learning models for prediction. The methodology involved usage of extreme gradient boosting algorithm. Data for the research included projects from industry standard dataset and organizational projects. The analysis revealed a reduction in the root mean square error values over multiple iterations suggesting an improvement in model performance. This reveals a better estimation due to minimization of the gap between estimated and actual efforts in a project. This, in turn, would enhance the potential for success of the project through an improved estimation process integrating risk score along with other parameters for estimation of software development effort. The feature importance chart also revealed that project risk score is an important attribute to be considered for effort estimation.
Development of Novel Classifying System to Identify the Right Sense of Audio Conversation in Social Networks using Deep Convolution Neural Network

Social media has paved a new way for communication and interacting with others. The use of social media differs according to the socio-cultural, demographic and psychological aspects of individuals. People chat, share ideas and visual material, and feel that they satisfy their needs of belonging along with the groups they have joined. Social networks is not only a area of freedom where persons express themselves openly or furtively, but also an area where several ways of violence emerge or even a means used for some aspects of violence.. The present research throws light on a few of the regular and trendy methods of abuse and risks faced by the users of social media. Develop a system to identify abusing audio file by an individual on a people/ group based on common language, race, sexual preferences, religion, or nationality. We examine a new model from machine learning, namely deep machine learning by probing design configurations of deep Convolutional Neural Networks (CNN) and the impact of different hyper-parameter settings in identifying the negative aspects in social media. Deep CNN automatically generate powerful features by hierarchical learning strategies from massive amounts of training data with a minimum of human interaction or expert process knowledge. An application of the proposed method demonstrates excellent results with low false alarm rates for Twitter data
Blockchain and Cryptoassets—The Role Accountants Can Play

Accounting PerspectivesVolume 19, Issue 4 p. 279-282 Miscellaneous Blockchain and Cryptoassets—The Role Accountants Can Play First published: 13 December 2020 https://doi.org/10.1111/1911-3838.12244Read 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 Volume19, Issue4December 2020Pages 279-282 RelatedInformation
The Myth of the World Wide Web

Our logo, an open laptop and an open book forming a W, was intended as a succinct comment and a visual representation of our mission.For UWP the most signficant development in the last year has been the addition of three new journal titles: the first, Anthropocenes -Human, Inhuman, Posthuman (p.34) an interdisciplinary title of great range tackling some of the big questions of our age including climate change, species extinction and latterly Covid-19.Likewise we are delighted to welcome the Journal of Deliberative Democracy (p.32).As populism surges across the world, the need for democratic legitimacy and real engagement continues to grow.JDD's August 2020 relaunch with UWP highlights key debates in participative democracy and public deliberation and considers how new insights might assist politics grapple with mounting challenges.We also look forward, later in the year to the first issue of Active Travel Studies (p.31).Healthier and more environmentally conscious transport is the focus of the journal's parent research body, the Active Travel Academy at the University of Westminster.Also during this period two of our existing journals Silk Road (p.36) and Westminster Papers in Communication and Culture (p.38) are now presented in a new research environment that of ScienceOpen.We welcome ScienceOpen and other new channels assisting readers in discovering our publications.UWP book titles remain available via
Symmetric Hash Encryption Image as Key (SHEIK)

Cloud storage model have become a widely accepted solution for the growing demand of online storage and remote accessing. Cloud service provider (CSP) provides the online storage facility for the cloud customer (CC) as per pay-by-use model. Data security still remains a threat for the CC to have a complete trust on the technology. The existing symmetric key algorithms to some extent have been successful in providing the confidentiality of the data. The main issue in implementing the algorithm is sharing of the key, if the key is known to others the security of the data is exploited. This paper tries to sort out the problem by making image as a key for the algorithm. The CC can perform the encryption before uploading the data to have the control of the process. The selection of the algorithm decides the key length to be extracted from the image making the method more secure and reliable for the CC. The experimental results have shown that the proposed Symmetric Hash Encryption Image as Key(SHEIK) method works satisfactorily for encrypting and decrypting the data efficiently.
Faculty Development

Higher Education AbstractsVolume 55, Issue 4 p. 364-364 FACULTY DEVELOPMENT Faculty Development First published: 22 November 2020 https://doi.org/10.1111/hea.17_12158Read 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. Volume55, Issue4October 2020Pages 364-364 RelatedInformation
Analysing the influence of demographics on depositor behaviour

Banks are exposed to operational risk events on a daily basis and constitute a large portion of a bank's risk exposure. The most important role of a bank is to establish who their key stakeholders are and to prioritise responsibilities according to these stakeholder characteristics, needs, perceptions and behaviour. This paper made use of hypothetical operational risk events within a bank in order to test how likely depositors will be to withdraw funds from their bank accounts. A non-parametric Spearman correlation and analysis of variation was used to test the relationship between demographical factors and depositors' likelihood to withdraw funds. Concerning the level of education, a negative relationship exists between the level of education and depositors' likelihood to withdraw funds. A negative relationship also exists between the income level and depositors' likelihood to withdraw funds.
Accurate Breast Cancer Prediction using Machine Learning Techniques

Applications of machine learning (ML) have been increasing widely in various fields like recommendation, fault identification and disease prediction. In ML, different algorithms were available and utilized in disease prediction such as heart disease prediction, cancer prediction and other forms disease prediction. In our proposed work, breast cancer prediction using ML has been implemented. Initially breast cancer images have been taken as input, preprocessing steps will be done to remove noisy and irrelevant data from image. Then 2D median filter is a nonlinear operation often used in image processing to reduce "salt and pepper" noise. To increase contrast of image contrast-limited adaptive histogram equalization is used. Segmentation has been implemented and GLCM feature extraction is deployed based on this information classification is implemented. For accurate classification Artificial neural network (ANN) is used to predict whether the patient is affected by breast cancer or not. Compared to other existing method our method predicts results in accurate way.
A technique for securing big data using k-anonymization with a hybrid optimization algorithm

The recent techniques built on cloud computing for data processing is scalable and secure, which increasingly attracts the infrastructure to support big data applications. This paper proposes an effective anonymization based privacy preservation model using k-anonymization criteria and Grey wolf-Cat Swarm Optimization (GWCSO) for attaining privacy preservation in big data. The anonymization technique is processed by adapting k- anonymization criteria for duplicating k records from the original database. The proposed GWCSO is developed by integrating Grey Wolf Optimizer (GWO) and Cat Swarm Optimization (CSO) for constructing the k-anonymized database, which reveals only the essential details to the end users by hiding the confidential information. The experimental results of the proposed technique are compared with various existing techniques based on the performance metrics, such as Classification accuracy (CA) and Information loss (IL). The experimental results show that the proposed technique attains an improved CA value of 0.005 and IL value of 0.798, respectively.
High-level feature extraction: deformable shape analysis

The previous chapter covered finding shapes by matching. This implies knowledge of a model (mathematical or template) of the target shape (feature). The shape is fixed in that it is flexible only in terms of the parameters that define the shape, or the parameters that define a template's appearance. Sometimes, however, it is not possible to model a shape with sufficient accuracy, or to provide a template of the target. It might be that the exact shape is unknown or it might be that the perturbation of that shape is impossible to parameterise. In this case, we seek techniques that can evolve to the target solution, or adapt their result to the data. This implies the use of flexible shape formulations. This chapter presents four techniques that can be used to find flexible shapes in images. If the shape is flexible or deformable, so as to match the image data, we have a deformable template. This is where we shall start. Later, we shall move to techniques that are called snakes because of their movement. We shall explain two different implementations of the snake model. The first one is based on discrete minimisation, and the second one on finite element analysis. We shall also look at determining a shape's skeleton, by distance analysis and by the symmetry of their appearance. This technique finds any symmetric shape by gathering evidence by considering features between pairs of points. Finally, we shall consider approaches that use the statistics of a shape's possible appearance to control selection of the final shape, called active shape models.
Privacy Preserving with Association Rule Mining using Evolutionary Algorithm

Privacy-Preserving-Data-Mining (PPDM) is a novel study which goals to protect the secretive evidence also circumvent the revelation of the evidence through the records reproducing progression. This paper focused on the privacy preserving on vertical separated databases. The designed methodology for the subcontracted databases allows multiple data viewers besides vendors proficiently to their records securely without conceding the secrecy of the data. Privacy Preserving Association Rule-Mining (PPARM) is one method, which objects to pelt sensitivity of the association imperative. A new efficient approach lives the benefit since the strange optimizations algorithms for the delicate association rule hiding. It is required to get leak less information of the raw data. The evaluation of the efficient of the proposed method can be conducting on some experiments on different databases. Based on the above optimization algorithm, the modified algorithm is to optimize the association rules on vertically and horizontally separated database and studied their performance.