Reviewerly/exHarmony huggingface dataset:
How I Learned to Stop Worrying and Give Up Journal Check-In

This workshop addressed the experiences at the University of Nevada, Reno, as they discontinued check-in for print journals. The presenters reviewed the circumstances that led to this decision, such as the reduced number of print subscriptions and the increasing number of electronic journals in the collection. The Libraries also stopped binding the majority of their journals, and claiming is based more on shelf reviews than check-in records. Nearly a year after implementing this decision, the presenters deem it a success. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com&gt; Website: &lt;http://www.HaworthPress.com&gt;]
Multi-objective genetic programming for manifold learning: balancing quality and dimensionality

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Manifold learning techniques have become increasingly valuable as data continues to grow in size. By discovering a lower-dimensional representation (embedding) of the structure of a dataset, manifold learning algorithms can substantially reduce the dimensionality of a dataset while preserving as much information as possible. However, state-of-the-art manifold learning algorithms are opaque in how they perform this transformation. Understanding the way in which the embedding relates to the original high-dimensional space is critical in exploratory data analysis. We previously proposed a Genetic Programming method that performed manifold learning by evolving mappings that are transparent and interpretable. This method required the dimensionality of the embedding to be known a priori, which makes it hard to use when little is known about a dataset. In this paper, we substantially extend our previous work, by introducing a multi-objective approach that automatically balances the competing objectives of manifold quality and dimensionality. Our proposed approach is competitive with a range of baseline and state-of-the-art manifold learning methods, while also providing a range (front) of solutions that give different trade-offs between quality and dimensionality. Furthermore, the learned models are shown to often be simple and efficient, utilising only a small number of features in an interpretable manner.
Formal Models

This chapter introduces formal models of cognition and explains how they are similar to verbal theories but use computer programs and mathematics to avoid the many limitations of human reasoning, thereby adding precision and rigor to their explanations. The chapter discusses Marr’s (1982) levels of analyses and how information-processing systems can be understood and described in terms of the task being performed, the representations and algorithms used to perform the task, and how the latter are implemented by physical systems. This then motivates discussion of three common approaches to modeling human cognition and behavior: process models, production-system models, and connectionist models. Each of these approaches is critiqued, with discussion of its merits and limitations. The three modeling approaches are then further illustrated by showing how each might be used to explain the finding that words can be identified more efficiently if they occur in predictable sentence contexts. The chapter closes with a discussion of how cognitive models are evaluated using their simplicity, theoretical scope, compatibility (e.g., with biology), and their capacity to generate novel predictions for guiding research.
Special issue on “Mind and Cognition in the Future of Decision Support Systems”

International Transactions in Operational ResearchVolume 27, Issue 6 p. 3180-3181 Call for Papers Special issue on “Mind and Cognition in the Future of Decision Support Systems” First published: 14 June 2020 https://doi.org/10.1111/itor.12838Read 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 No abstract is available for this article. Volume27, Issue6November 2020Pages 3180-3181 RelatedInformation
P086. Novel pulse biopsy platform incorporating adaptive open-tip sampling needle increases sampling yield and needle control

Introduction: Certain lesions are challenging to reach and target, e.g. axillary lymph nodes. A novel biopsy platform, NeoNavia (NeoDynamics, Sweden), incorporates a pneumatic needle insertion mechanism intended to provide better control of needle progression. It offers a 14G automated core needle (CorePulse), 10G vacuum biopsy needle (VacuPulse) and newly developed adaptive 14G open-tip sampling needle (FlexiPulse). Sampling yield of the 14G open-tip needle was benchmarked against a currently used device in a turkey tissue model and needle velocity was measured.
Order Selection in Unsupervised Learning and Clustering for Arbitrary and Non-Arbitrary Shaped Data

&lt;p&gt;This thesis focuses on clustering for the purpose of unsupervised learning. One topic of our interest is on estimating the correct number of clusters (CNC). In conventional clustering approaches, such as X-means, G-means, PG-means and Dip-means, estimating the CNC is a preprocessing step prior to finding the centers and clusters. In another word, the first step estimates the CNC and the second step finds the clusters. Each step having different objective function to minimize. Here, we propose minimum averaged central error (MACE)-means clustering and use one objective function to simultaneously estimate the CNC and provide the cluster centers. We have shown superiority of MACEmeans over the conventional methods in term of estimating the CNC with comparable complexity. In addition, on average MACE-means results in better values for adjusted rand index (ARI) and variation of information (VI). Next topic of our interest is order selection step of the conventional methods which is usually a statistical testing method such as Kolmogrov-Smrinov test, Anderson-Darling test, and Hartigan's Dip test. We propose a new statistical test denoted by Sigtest (signature testing). The conventional statistical testing approaches rely on a particular assumption on the probability distribution of each cluster. Sigtest on the other hand can be used with any prior distribution assumption on the clusters. By replacing the statistical testing of the mentioned conventional approaches with Sigtest, we have shown that the clustering methods are improved in terms of having more accurate CNC as well as ARI and VI. Conventional clustering approaches fail in arbitrary shaped clustering. Our last contribution of the thesis is in arbitrary shaped clustering. The proposed method denoted by minimum Pathways is Arbitrary Shaped (minPAS) clustering is proposed based on a unique minimum spanning tree structure of the data. Our simulation results show advantage of minPAS over the state-of-the-art arbitrary shaped clustering methods such as DBSCAN and Affinity Propagation in terms of accuracy, ARI and VI indexes.&lt;/p&gt;
Configuration Management

Configuration management is concerned with identifying those deliverables that must be subject to change control, and controlling changes to them. It provides full traceability of the changes made during the project including a record of what has been changed, as well as who changed it. It involves identifying the configuration items of the system; controlling changes to them; and maintaining integrity and traceability.
On Reducing Misclassifications and Error on CART Models using Rtosynr Dataset for Improved Debit Card Fraud Detection

This work describes a way of reducing the number of misclassifications in Classification and Regression Tree (CART) classifiers for debit card fraud detection using the Real-to-Synthetic-Real (RtoSynR) model; thereby increasing the classification accuracy of the classifier.RtoSynR model involves the generating of synthetic transaction data from a sample of real data to augment the available real data during the training of the model.The joining of the generated synthetic data with the available real data produces the RtoSynR dataset which is used to train and improve the classification accuracy of the classifier.The RtoSynR dataset is updated first by the customer feedback and then by the modeling and generating of newly observed transaction patterns (fraudulent and non-fraudulent patterns) to solve the problem of class imbalance in the training set and concept drift in machine learning which results in underfitting and overfitting classifiers.The detailed algorithm for RtoSynR implementation was described and implemented.Result of the tests shows an improvement on the classification accuracy of the classifiers developed with RtoSynR dataset over those developed with real dataset.
Unsupervised learning for biomedical applications

With the growth of application of computers in the generation and analysis of biomedical data, a variety of computerized methods and algorithms have been proposed to optimize the process of acquisition and analysis of the data. Although advanced computerized techniques have provided the means for more precise diagnosis, the interpretation of the recorded data in some cases is an issue due to the large amount of the data or complexity of it. While most of the existing work in the literature consider supervised techniques for analysis of the collected data, the use of unsupervised techniques in the area of analysis and classification of biomedical signals is relatively unexplored compared to supervised approaches. In general, the investigation of application of unsupervised techniques for analysis of biomedical signals can be worthwhile from different view points. In some cases, biomedical databases tend to contain a large amount of data. Genomic databases or pathological speech databases are examples of this kind. The development of any supervised method for analysis of such databases requires precise manual labeling of the data, which can be extremely costly. However, the use of an unsupervised classifier can be beneficial to accelerate the process and to acquire information about the structure of the dataset. In addition, the characteristics of the collected biomedical data can be affected by the recording process. In this work application of unsupervised learning in two biomedical signal processing problems is investigated. In the first problem, fuzzy C-means clustering has been used in design of a computer aided diagnosis method for detection of abnormalities in small bowel capsule endoscope images. The performance of the system shows an accuracy of 76which is an acceptable rate for an unsupervised method. In the second case, self organizing tree maps (SOTM) has been applied to audio signal classification for hearing aids. An accuracy of 96% was achieved for discrimination of human voice from the environmental noise, which is one the major classification scenarios for hearing aid applications.
Predictability of Yield Curve Inversion and Moving Average Crossover

We take the yield curve inversion and add it to a moving average crossover strategy. This combines past prices and technical timing with macro fundamentals to see whether we can forecast historic recession periods from 1967-2019. The main technical signal is the 21 day Moving Average (MA 21) below the 200 day moving average (MA 200) following yield curve inversion periods. The inversion periods are 1 month 60 month (1,60) 12 month 60 month (12,60) 24 month 60 month (24,60) and 1month 120 month (1,120) 12 month 120 month (12,120) and 24 month 120 month (24,120). Data is obtained from the Center for Research in Security Prices (CRSP). We find that applying the moving average timing (MA TIMING) strategy following yield curve (YC) inversion has promising results for exiting the market before recession periods. We miss several of the last major recessions including the 2008-2009 recession, thus generating positive excess returns. We validate our results using CAPM, Fama and French 3 and 5 Factors, and Kolmgorov Simonoff tests.
Reviews

• Explorations in Time-Frequency Analysis, by Patrick Flandrin, ISBN 978-1-108-42102-7, 2018, Cambridge University Press, 226 p.
Application of E-Learning Based on Enriched Virtual Model in the Subject Database

The study aims to discover the quality of e-learning based on Enriched Virtual model in database subject in the Computer and Informatics Engineering Study Program uses the ISO/IEC 25010 software quality metric method on the aspects of external quality and quality in use. The development model used in this study was adapted from Borg and Gall development model, consisting of 8 stages, namely, (1) Research and information collecting (initial research and information gathering), (2) Planning, (3) Develop preliminary form of product, (4) Preliminary field testing, (5) Main product revision, (6) Main field testing, (7) Final product revision, and (8) Final product. The subjects in this study were 74 students of Computer and Informatics Engineering Education Study Program. Data were collected through interview, observation, and questionnaire techniques. Data were analyzed using descriptive statistical analysis techniques and software quality metric methods ISO/IEC 25010. Based on the results of the study, e-learning based on Enriched Virtual model is produced in the database subject. The product developed has fulfilled all aspects of development. Media and material validation are in very good category, while the student’s responses are in good category. The results of software quality testing using the quality metric method concludes that the final value of external quality is 0.45 and which is in medium category.
Thresholding based on Grey Levels, Gradient Magnitude and Spatial Correlation

Image segmentation gained significant importance in recent years. The goal of segmentation is partitioning an image into distinct regions containing each pixel with similar attributes. Several Image segmentation techniques exist based on thresholding and clustering. Image segmentation based on thresholding is typically doesn’t find any objects and bounds (lines, curves, etc.) in image. To boost the segmentation performance based on thresholding strategies, a unique strategy that integrates the spacial information between pixel’s is designed. The proposed strategy utilizes pixel’s grey level Gradient magnitude and gray level spacial correlation at intervals a part to construct a unique two dimensional bar graph, known as GLGM &amp; GLSC. This technique is valid through segmenting many real world pictures. Experimental results proved this method outperforms several existing Thresholding strategies.
FRILUX-ing

New practices and innovation are changing the culture at the University of Oslo Library. This has resulted in consistently using user experience/UX methods to put the user needs at the very center when new services are developed.&#x0D; The paper will outline why there is a need of a UX strategy at a leadership level in service and user centric organizations, like academic libraries, and show the value of UX as a competitive advantage. Our case study is from a Norwegian University Library, which has evolved over a period of six year to now be at the forefront in Scandinavia when it comes to using UX into the academic library. Thanks to the support of he Norwegian National Library and the continued efforts of many “UX-librarians” at our library, we are now using design tools in different contexts to create user-friendly services. UX has evolved over time and staff now uses it as an active part of different practices in the organization. In addition, the leadership has included the use of UX-methods in projects in their strategic plan for the library to ensure that the user perspective is taken in account in the services delivered by the library. In this paper we will also explain why we do not have a UX group, and why we instead use a hub approach to gather different, but relevant, staff for each project. Findings, like relevant activity in the organization or new services, will be presented. Furthermore, the paper will give a long term perspective on the use of UX, emphasizing the need for constant evolvement (Gasparini &amp; Culén, 2017), user research and ethnography (Gasparini, 2015a) in order to enhance the user experience of the library (Gasparini, 2015b). Finally we wish to introduce Frilux (www.frilux.no). Frilux is a platform for sharing UX experience. We will outline how we have arrived at this new platform, why we wish to share it with other libraries, and why Frilux is a useful tool for libraries who want to: &#x0D; • Apply UX and Design Thinking (Brown, 2008)for library development• Use UX methods when working together in the organization across subjects and system• Share their experiences with others• Meet like-minded&#x0D; ReferencesBrown, T. (2008). Design Thinking. Harvard Business Review, 86(6), 84–92.Gasparini, A. (2015a). A Holistic Approach to User Experience in the Context of an Academic Library Interactive System. In A. Marcus (Ed.), Design, User Experience, and Usability: Interactive Experience Design (pp. 173–184). Springer International Publishing. Retrieved from http://link.springer.com/chapter/10.1007/978-3-319-20889-3_17&#x0D; Gasparini, A. (2015b). Perspective and Use of Empathy in Design Thinking (pp. 49–54). Presented at the ACHI 2015, The Eighth International Conference on Advances in Computer-Human Interactions. Retrieved from http://www.thinkmind.org/index.php?view=article&amp;articleid=achi_2015_3_10_20121&#x0D; Gasparini, A., &amp; Culén, A. (2017). Temporality and Innovation in Digital Humanities: The Case of Papyri from Tebtunis. Interaction Design &amp; Architectures(s) IxD&amp;A, (34), 161–184
Novel methods for automatic segmentation of abnormal lung parenchyma utilizing watershed and wavelet transforms

CT scan of the thorax is widely used to diagnose and evaluate numerous lung diseases. These scans yield a large amount of image data. The expanding volume of thoracic CT studies along with the increase of image data, elucidates the need of computer-aided diagnosis (CAD) schemes to assist the radiologists. Since several lung diseases are diagnosed based on the patterns of lung tissue in medical images, texture segmentation is an essential part of the [sic] most CAD systems. The processing step of most CAD systems is lung segmentation. In the first part of this thesis a novel approach for lung segmentation is proposed. The proposed method is based on watershed transform, which is fast and accurate. Lung region is precisely marked with internal and external markers. The markers are combined with the gradient image of the original data, then watershed transform is applied on the combined data to find the lung borders. A "Rolling ball" filter is used to fill the cavities and make the contour smooth while preserving the original borders. In the second part of this research work a novel composite method is proposed to segment the abnormality in lung tissue. The proposed approach is based on wavelet transform and intensity similarities. Our focus is on the honeycomb texture in lung tissue, which occurs with several interstitial lung diseases. After segmenting lung regions, Wavelet Transform is applied to decompose the image. The transformed lung region is thresholded to extract high resolution areas. Then the regions with low pixel intensities are kept and grown to segment the honeycomb regions. The proposed method has been tested on 91 pediatric chest CT images containing healthy and unhealthy lung images. Statistical analysis has been done and the results show the sensitivity of 100% along with the average Specificity of 95.14 %. A comparison with AMFM (82.5 % sensitivity and 99.9 % specificity) and ANN methods (100 % sensitivity and 88.1 % specificity) reveals the superiority of the proposed approach. The test results of both the lung segmentation and abnormal lung tissue segmentation techniques validate the robustness of the proposed methods.
Next Steps

Chapter 13 investigates cryptocurrency and quantum computing. Bitcoin – the world’s first cryptocurrency - was released by Satoshi Nakamoto in 2009. Unlike conventional money, Bitcoin transactions are anonymous - maintained by a worldwide collection of computers operated by volunteers. Bitcoin took off on the black market but then migrated to legitimate business. The current value of all bitcoins is a staggering $41 billion. Strangely, no one knows who Nakamoto is – he, she, or they have yet to reveal themselves. Quantum computing was first proposed by physicist Richard Feynman in 1981. His idea was that the weird behaviour of sub-atomic particles could be exploited to perform computations. In theory, quantum computing allows huge numbers of calculations to be performed simultaneously. Google, IBM and others are now in race to build a practical quantum computer. Such a machine might well crack the encryption algorithms that currently underpin both the Internet and Bitcoin.
Interpretability and Accuracy Analysis of Fuzzy Rule Based System Designed for Abalone

Fuzzy Rule Based Systems are playing vital role in the implementation of human decision making. The development of interpretable Fuzzy Rule Based Systems with improved accuracy is a crucial research aspect in fuzzy based systems. Mamdani type fuzzy rule based systems are used to implement the proposed model. In this manuscript a FRBS is implemented with Guaje Open-Access Java based software. The interpretability and accuracy assessments are recorded on the different experiments with various rule generation methods, like Fuzzy decision tree and Wang Mendel method. The results are found satisfactory and a trade-off is handled between interpretability and accuracy. The major concern of the experimentation is number and type of fuzzy partitions. K-means and Hierarchical Fuzzy Partitions are used in the experiments with three and five number of fuzzy partitions.
THE LEADING EDGE FIRE FIGHTING ROBOT - A STUDY

In today's world nothing is exactly predictable about any accidents or mishapenings and for that we are just supposed to be highly active and stand strong for any such situations in our daily life.We as an engineer's join hands for making a smart and a better world.Security is all that our life demands and for securing our lives we must need to secure the buildings where we live , the offices, the factories where we work, the laboratories, and what not.So what's going to happen if a building catches fire?Obviously we are going to call the fire extinguishers and they are going to risk their lives for us or sometimes even for small fires user may take a long time in finding water source to extinguish fire.But what if we can control the fire by not getting much close to it and in much lesser time as compared to the manual extinguishing, isn't it interesting?So here we have a leading edge firefighting robot which not only extinguishes fire but also notifies you about the emergency via sms.I.
Typography

Bootstrap defines styles for some HTML elements directly, such as the basic type of appearance, typography, and color. In contrast to the classic concept of typography, font, writing, design, and printing material, Bootstrap is quite modest in its ease of use. The following are specifically regulated: