Reviewerly/exHarmony huggingface dataset:
Detection of acini in histopathology slides: towards automated prediction of breast cancer risk

Terminal duct lobular units (TDLUs) are structures in the breast which involute with the completion of childbearing and physiological ageing. Women with less TDLU involution are more likely to develop breast cancer than those with more involution. Thus, TDLU involution may be utilized as a biomarker to predict invasive cancer risk. Manual assessment of TDLU involution is a cumbersome and subjective process. This makes it amenable for automated assessment by image analysis. In this study, we developed and evaluated an acini detection method as a first step towards automated assessment of TDLU involution using a dataset of histopathological whole-slide images (WSIs) from the Nurses’ Health Study (NHS) and NHSII. The NHS/NHSII is among the world's largest investigations of epidemiological risk factors for major chronic diseases in women. We compared three different approaches to detect acini in WSIs using the U-Net convolutional neural network architecture. The approaches differ in the target that is predicted by the network: circular mask labels, soft labels and distance maps. Our results showed that soft label targets lead to a better detection performance than the other methods. F<sub>1 </sub>scores of 0.65, 0.73 and 0.66 were obtained with circular mask labels, soft labels and distance maps, respectively. Our acini detection method was furthermore validated by applying it to measure acini count per mm<sup>2</sup> of tissue area on an independent set of WSIs. This measure was found to be significantly negatively correlated with age.
The research process of generative adversarial networks

This paper reviews the research progress of the generative adversarial nets (GAN). Firstly, we introduce the principle and structure of GAN. Secondly, we explain its training method and the classification and typical types of GAN, such as fully connected GAN and conditional GAN. Finally, we give a brief introduction of GAN in the intelligent life, and show the outstanding significance in the field of image processing.
Ensuring Core Competencies for Cybersecurity Specialists

Within an organization, it is critical that all employees possess a security awareness and thus play a part in the protection of said organization's information assets. Some employees will have key roles and responsibilities and require specific skills to support them. However, organizations can face challenges in regard to recognizing the required specialized skills as well as where to obtain them. For this reason, whether an organization chooses to hire new staff, developing existing staff, or outsource the activities altogether, it is necessary to know the type and level of expertise required. To this end, this chapter discusses the need for organizations to understand and identify the essential skills related to cybersecurity in order for their employees to develop core competencies in these areas.
Use of Sentiment Analysis for Predicting Public Opinion on Referendum: A Feasibility Study

This study explored the potential of using sentiment analysis of tweets to predict referendum choices (Brexit). The feasibility of using StreamKM++ in the massive online analysis framework was examined over five categories, ranging from strongly agree to strongly disagree (to exit). A Naïve Bayes classifier was used to classify people’s opinions according to these categories. The prediction model resulted in high accuracy (97.98%), making it possible to use it in predicting opinions about public events and issues. The findings from this study may help practitioners, and policymakers understand the importance of sentiment analysis of social media in assessing public opinion and, accordingly, making certain voting predictions.
Investigation of the effect of image resolution on automatic classification of mammary gland density in mammography images using deep learning

Mammary gland density is used as one of the measures in managing the risk of breast cancer. It can be divided into four categories. In addition, mammography is used for population-based breast cancer screening in Japan. However, mass and calcification are assumed to be hidden in the shadow of the mammary gland as displayed by the mammogram when patients showing heterogeneously dense or extremely dense in the mammary gland density category are scanned with mammography. Therefore, it is necessary to recommend an examination suitable for each category of mammary gland density. In one example, a doctor recommends ultrasonography in addition to mammography for patients with dense breasts. However, mammary gland density is distinguished visually using subjective judgment. Against such a background, we have worked on an automatic classification of mammary gland densities using a deep learning technique. Moreover, we investigated the effect of image resolution on the classification results in the automatic classification of mammary gland density with deep learning. The resolution was varied from 1/100 (474 × 354) to 1/3600 (79 × 59) using 1106 cases of resolution 4740 × 3540 (pixels) obtained with Fuji Computed Radiography (FCR) by Fujifilm Co. Ltd. As a result, the accuracy of automatic classification of mammary gland density exceeded 90% up to a resolution of 1/400 (237 × 177), and was 89% even at the lowest resolution of 1/3600 (79 × 59).
Hardness on Style Transfer Deep Learning for Rococo Painting Masterpieces

This paper considers the general adaptation of the application of raw painting images via style transfer. Experimental results show that both the previous studies style transfer using pre-trained CNN and style transfer using GAN has only different algorithms or structure but same problem. That is the un-general application in various painting styles. A striking difference between experiment results in Rococo painting style and experiment results in Impressionism painting style speak for the above problem. In particular, the derivation of awkward results for the application of style transfer method in Rococo painting style represents this kind of problem.
Quantum-memory-assisted entropic uncertainty in fluctuating electromagnetic field with a boundary

In this work, we investigate the dynamics of quantum-memory-assisted entropic uncertainty relation for a two-level atom coupled with fluctuating electromagnetic field in the presence of a perfectly reflecting plane boundary. The solution of the master equation that governs the system evolution is derived. We find that entropic uncertainty and mixedness increase to a stable value with evolution time, but quantum correlation reduces to zero with evolution time. That is, the mixedness is positively associated with entropic uncertainty, however, increasing quantum correlation can cause the decrease of the uncertainty. The tightness of entropic uncertainty grows at first and then declines to zero with evolution time. In addition, entropic uncertainty fluctuates to relatively stable values with increasing the atom’s distance from the boundary, especially for short evolution time, which suggests a possible way of testing the vacuum fluctuating and boundary effect. Finally, we propose an effective method to control the uncertainty via quantum weak measurement reversal.
Studies on the Rabi Model

We present our recent studies on the quantum Rabi model (QRM). Firstly, by using a variational wave function, which facilitates to extract physics in entire parameter regime with high accuracy, we unveil a ground-state phase diagram of the QRM and argue that the main constituents are polaron and anti-polaron. Secondly, introducing an anisotropy into the QRM, in which the rotating- and counter-rotating terms are allowed to have different coupling strength, we explore that how the criticality of the model interpolates between two known limits with distinct universal properties. Through a combination of analytic and numerical approaches we compute phase diagram, scaling functions and critical exponents, and establish that the universality class at the finite anisotropy is the same as that of the isotropic limit.
BigFeel—A Distributed Processing Environment for the Integration of Sentiment Analysis Methods

Abstract Sentiment analysis has been the main focus of plenty of research efforts, particularly justified by its commercial significance, both for consumers and businesses. Thus, many methods have been proposed so far, and the most prominent have been compared in terms of effectiveness. Nonetheless, the literature is deficient when it comes to assessing the efficiency of these methods for processing large volumes of data. In this study, we performed an experimental assessment of the efficiency of 22 methods in total, whose implementations were available. We also proposed and assessed an environment for distributed processing methods for sentiment analysis, using the Apache Spark platform, named BigFeel. In this environment, the existing methods, outlined to run in a non-distributed way, can be adapted, without altering their source code, to run in a distributed manner. The experimental results reveal that (i) few methods are efficient in their native form, (ii) the methods improve their efficiency after having been integrated into BigFeel, (iii) some of them, which were unfeasible to process a large dataset, became viable when deployed in a computer cluster and (iv) some methods can only handle small datasets, even in a distributed manner.
A Cybersecurity Skills Framework

This chapter traces the evolution of cybersecurity skills requirements and development over the past 40 years, from the early days of computer security (Compusec) to the present day. The development of cybersecurity skills is traced from an initial focus upon national security and confidentiality through to the current recognition as business driver. The main part of the chapter concentrates on the development of a specific skills framework from the Institute of Information Security Professionals. Originally conceived in 2006 and initially used for purposes of membership accreditation, the IISP Skills Framework has since been used extensively by commerce, industry, government and academia in the UK and more widely. Version 2 of the framework was published in 2016, and the chapter discussion outlines both the original structure and the notable changes in the later release. These developments collectively illustrate the ongoing recognition of cybersecurity skills, as well as the evolution of the skills themselves.
Design and Implementation of English Aided Instruction Platform Based on Intelligent Expert System

Based on existed English aided teaching expert system, and educational information background, this paper analyzes rule-based uncertainty expert system and proposes the problems of insufficient teachers and lack of attention to the basic knowledge of students, in the reform of college English teaching. The overall realization scheme of college English aided teaching system based on artificial intelligence. The system uses the knowledge expression method combining production and framework, adopts uncertainty reasoning technology, and integrates the current mainstream programming technology to develop a high coupling, multi-collaboration expert system, which can help students to improve learning efficiency and learning content. The practical research shows that the system has certain application value, and it provides a model for similar methods, which has certain reference significance.
Patterns of Ambiguity in Textual Requirements Specification

In this contribution, we investigate the ambiguity problem in textual requirements specifications. We focused on the structural ambiguity and extracted some patterns to indicate this kind of ambiguity. We show that the standard methods of linguistics are not enough in some cases, and we describe a class of ambiguity caused by coreference that needs an underlying domain model or a knowledge base to be solved. Part of our implemented solution is a cooperation of our tool TEMOS with the Prolog inference machine working with facts and rules acquired from OCL conditions of the domain model.
A K-Means Clustering Algorithm: Using the Chi-Square as a Distance

The recurrent use of databases with variables of the categorical type in different fields of science. Demands new approaches when using cluster analysis techniques on this type of database. For this reason, in this article we compare the function kmeans() of Matlab with a function K-Means implemented by us, with the addition that it has integrated a measure of similarity that the function of Matlab does not have, the distance chi-square, both algorithms were tested in databases with quantitative and categorical variables. The experimental results showed a higher level of classification success in favor of the function implemented by us, explaining the correct functioning of the implemented algorithm and demonstrating that the chi-square distance is the measure of appropriate similarity for categorical type databases.
Object Detection Method Based on Three-dimension Information Extraction of Laser Point Cloud

Complexity of object movement and lack of object information due to occlusion have been the bottleneck which restricts the progress of object detection research. Whereas laser point cloud makes up the above deficiencies due to its 3D information. This paper analyzes the characteristics of laser data which is interpreted according to the data format and mathematical model of scanning equipment. Through extracting 3D object information and excavating object features of laser point cloud, various feature information of object is defined. Based on integrated learning method, train the object detector of laser point cloud and then achieve object detection in complex scenes. Experimental results show that object detection method based on 3D information extraction of laser point cloud improves the detection performance. Research results provide new research ideas and theoretical basis for the improvement of object detection performance.
Scalable and distributed architecture based on Apache Spark Streaming and PROM6 for processing RoRo terminals logs

With the increasing number of companies encompassing Big data, it is evident that the utmost challenge is to associate enormous quantity of event data to operational business processes that are extremely dynamic. To unbind the value of event data, events need to be firmly affiliated to the monitoring and control of operational processes. Nevertheless, Big data technologies converge essentially on storage, processing and rarely center on improving processes. In view of this, we advocate the integration of technologies of big data and process analysis namely Apache Kafka, Spark Streaming and PROM6. In this work, we design a scalable and distributed architecture for real time monitoring of operational business processes of a RoRo port terminal. The proposed solution permits the exploitation of process mining techniques to process large amount of events logs of several hundreds of gigabytes for process mining analysis.
Social and psychological features of career strategies mobility of the college students

The modern world sets high standards for competitiveness, productivity, leadership qualities and commitment of young people who have just received secondary professional education. In this regard, the article deals with the problem of building a career and forming effective career strategies. The authors study different approaches to defining "career" and "career strategies", analyze how students understand what a career means, their attitude to building a career, the appropriate traits for a successful career and the role, which a career plays in people's lives. To conduct the research and gather the data the authors used Solomin's method of colour metaphors, Schein's "Career Anchors" method and a questionnaire survey. On the basis of the survey results the authors proposed a program of development of effective career strategies "My Success" that is aimed at optimizing the process of training qualified specialists for main industrial companies of the region.
Towards New Requirements Engineering Competencies

Many of the requirements engineering (RE) difficulties have been argued to be due to the evolving nature of design problems in dynamic environments, characterized by high levels of uncertainty, ambiguity and emergence. It has also been argued that these challenges cannot be solved by focusing primarily on notations, tools, and methods. The purpose of this vision paper is to understand better what kinds of new competencies are needed when expanding RE practices to cope with complex systems in dynamic environments. We intend to achieve our goal by discussing: 1) how increased complexity affects RE practices, and 2) what viewpoints have been found most salient when aligning RE practices with the design problem at hand. Based on our findings, we argue for the importance of contextual intelligence, the ability to recognize and diagnose contextual factors and then intentionally and intuitively adjust behavior. We also outline some of the important competencies that need to be developed for future RE practitioners to deal with complex problems.
A New Type of Eye Movement Model Based on Recurrent Neural Networks for Simulating the Gaze Behavior of Human Reading

Traditional eye movement models are based on psychological assumptions and empirical data that are not able to simulate eye movement on previously unseen text data. To address this problem, a new type of eye movement model is presented and tested in this paper. In contrast to conventional psychology-based eye movement models, ours is based on a recurrent neural network (RNN) to generate a gaze point prediction sequence, by using the combination of convolutional neural networks (CNN), bidirectional long short-term memory networks (LSTM), and conditional random fields (CRF). The model uses the eye movement data of a reader reading some texts as training data to predict the eye movements of the same reader reading a previously unseen text. A theoretical analysis of the model is presented to show its excellent convergence performance. Experimental results are then presented to demonstrate that the proposed model can achieve similar prediction accuracy while requiring fewer features than current machine learning models.
Commercial Video Evaluation via Low Level Feature Selection

To discover the influence from the commercial videos' low-level features to the popularity of the videos, the feature selection method should be used to get the video features influenced the videos' evaluation mostly after analyzing the source data and the audiences' evaluations of the videos. After extracting the low-level features of the his paper improved the Correlation-Based Feature Selection (CFS) which is widely used and proposed an algorithm named CFS-Spearmen which combined the Spearmen correlation coefficient and the classical CFS to select features. The SVM was used to test the method in this paper. Finally, the proposed method was employed in commercial videos' feature selection and the most influential feature set was chosen.
Compositional data analysis in practice. Michael J.Greenacre. (2018). London: CRC Press. 136 pages, ISBN: 978‐1‐138‐31661‐4

Biometrical JournalVolume 61, Issue 4 p. 1088-1089 BOOK REVIEW Compositional data analysis in practice. Michael J. Greenacre. (2018). London: CRC Press. 136 pages, ISBN: 978-1-138-31661-4 Jan Graffelman, Jan Graffelman jan.graffelman@upc.edu Department of Statistics and Operations Research, Technical University of Catalonia, Barcelona, SpainSearch for more papers by this author Jan Graffelman, Jan Graffelman jan.graffelman@upc.edu Department of Statistics and Operations Research, Technical University of Catalonia, Barcelona, SpainSearch for more papers by this author First published: 26 March 2019 https://doi.org/10.1002/bimj.201900080Citations: 2Read 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.Citing Literature Volume61, Issue4July 2019Pages 1088-1089 RelatedInformation
Interdisciplinary palimpsest: visual representations of coastal change combining digital craft and geomorphology

From rocks to clay, a shared interest in natural materials and their physical transformation provided the initial common ground for an interdisciplinary art-geoscience collaborative project that also opened up a novel and engaging public communication channel. Scientific data collected for a location-based geomorphology mapping project was collaboratively re-interpreted and re-presented as a craft installation by using digital technologies and hand-crafted processes. The project explored how creative practice can uncover and broaden narratives, layering interpretations whilst respecting and embracing the need for accurate visual representation of scientific data. As the practice-based element of a broader digital craft PhD research programme, the project effectively demonstrated an enlarged field of practice for digital craft. The collaboration resulted in a large-scale, porcelain panelled, wall-mounted installation for public exhibition and has led to subsequent significant unforeseen developments in the scope and outlook of research work undertaken by the collaborators. This paper reflects on the synergies between disciplines that were uncovered and how project challenges were met. We conclude that the project work acted as a ‘boundary object’ for the two collaborating parties, able to represent different values and fulfil different objectives for each party at the same time, while also moving forward practice for both.
Incorporating Description Embeddings into Medical Knowledge Graphs Representation Learning

Representation learning of medical knowledge graphs aims to embedding entities and relations in low-dimensional vector spaces, which is beneficial to the application of medical knowledge graphs in intelligent medical systems such as intelligent guidance, disease risk prediction and question answering system of medical field. Recently, some translation-based methods including TransE, TransH and TransR built entity and relation embeddings by regarding a relation as translation from head entity to tail entity. These methods solely use the information of triplets and don't take text information into consideration. In this paper, we process a novel representation learning method by incorporating the embeddings of entity descriptions with classical translation-based methods. The embeddings of entity descriptions are built by Doc2Vec. It is easily applied for a large-scale domain-specific knowledge graphs because of its simplicity. Besides, we compare our method with classical translation-based methods to demonstrate the effectiveness of our method in medical knowledge graphs representation learning.