Reviewerly/exHarmony huggingface dataset:
Optimized Hybridization of Ant Colony Optimization and Genetic Algorithm (HACOGA) Based Interpretable Intuitive and Correlated-Contours Fuzzy Neural Network Classifier for Abalone

Classification generally assigns objects to enormous predefined categories and it is pervasive crisis that covers various application. Preparing the data for Classification and Prediction is the major problem in classification. In order to rectify this issue, an approximate function is proposed using Interpretable intuitive and Correlated-contours Fuzzy Neural Network (IC-FNN). For acquiring cor- related fuzzy rules and non-separable rules that comes under proper optimization problem. The extracted fuzzy rule’s parameter was fine-tuned sourced on hierarchical Levenberg Marquardt (LM) learning method for enhancing performance. But here parameters of fuzzy rules aren’t tuned per- fectly. Hybridization of Ant Colony Optimization Genetic Algorithm (HACOGA) is proposed here to rectify these issues. It tunes the parameters of the extracted fuzzy rules. Hybridization is enforced to certain factors and ACO and GA variables that share same characteristics in the computation. Experimental results shows that proposed HACOGA assist in enhancing the performance of FNN with recall, precision, accuracy and F -measure for the Abalone age prediction dataset.
Effective Usage of Various Answer Types of Mathematics e-Learning System

There are several mathematics e-Learning systems that have various answer types, such as multiple-choice, fill-in mathematical expressions including matrices, fill-in mathematical expressions with units, and interactive fill-in. However, there have been few discussions on what an effective usage of those answer types, according to educational purposes (especially STEM education), is. In this paper, we seek an effective usage of each answer type in mathematics e-Learning systems; we are planning to develop model questions suitable for the various purposes of online tests. To realize this aim, we summarize some question types for mathematics e-Learning systems and organize them to make some suggestions on the effective usage of mathematics e-Learning systems.
Marine Motion Control Using Single Neuron Fuzzy Logic Controller

The single neuron part is present in the PID controller with changeable links, so that it may provide better system turning capability. Fuzzy Logic controller is used to modify the neuron output and increases the speed of the control process. The benefits are few changeable parameters, simple variation, firm output control and desirable toughness to fit for huge time-delay or undefined systems. The MATLAB/SIMULINK is used to design the fuzzy controller and the system performance is tested.
Distribution networks ground fault protection method based on fuzzy measure fusion criterion

This paper proposes a new method for ground fault protection of distribution networks based on fuzzy measure fusion criterion. The fuzzy c-means clustering algorithm is used to characterize the historical characteristics of different operating states of protected feeders under various operating conditions. The sample set is divided into fault class and non-fault class. The sample similarity measure criterion is used to quantitatively analyze the similarity degree between the sample to be tested and different types of historical samples, forming a fuzzy measure fusion criterion matrix, and fuzzy by multi-level evaluation index system. The measurement fusion criterion matrix is used to judge, and finally a comprehensive criterion matrix is output as the protection criterion. The simulation results of PSCAD/EMTDC show that under the influence of non-linear load and other disturbance factors, the method can still accurately identify fault samples and is not affected by changes in system operation mode, and has strong robustness.
<b>Time Series: A First Course With Bootstrap Starter</b> Tucker S. McElroy and Dimitris N. Politis CRC Press, 2020, xix + 566 pages, £ 74.99/$99.95, hardcover ISBN 978‐1‐4398‐7561‐0

International Statistical ReviewVolume 88, Issue 3 p. 808-809 Book Review Time Series: A First Course With Bootstrap Starter Tucker S. McElroy and Dimitris N. Politis CRC Press, 2020, xix + 566 pages, £ 74.99/$99.95, hardcover ISBN 978-1-4398-7561-0 Yao Qiwei, Corresponding Author Yao Qiwei q.yao@lse.ac.uk Department of Statistics, London School of Economics and Political Science, London, UKSearch for more papers by this author Yao Qiwei, Corresponding Author Yao Qiwei q.yao@lse.ac.uk Department of Statistics, London School of Economics and Political Science, London, UKSearch for more papers by this author First published: 20 October 2020 https://doi.org/10.1111/insr.12422Read 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 Volume88, Issue3December 2020Pages 808-809 RelatedInformation
Tuklas: Design, Development and Testing of an Augmented Reality Experience for a Children’s Museum

We describe the design, development, and testing of Tuklas, an augmented reality experience for children visiting Museo Pambata (Children's Museum) in Manila, Philippines. Because the target audience was 7 years old and below, the game minimized the use of text. Instead, it adopted an aim-and-click interaction style coupled with a collection mechanic to increase the children's engagement with the museum exhibits. During the post-game interviews with the children's parents or guardians, feedback was highly positive. The interface was easy to use, and the game mechanic was easy to understand. The children enjoyed watching the animations after completing each scene.
An evaluation of Tshivenḓa orthography of adoptives with special reference to Pan South African Language Board spelling rule booklets

In Tshivenḓa, words which have been adopted from foreign languages have acquired entirely new characteristics. Such words have made a considerable contribution to the development of Tshivenḓa vocabulary. The aim of the article is to evaluate the role played by adoptives in Tshivenḓa orthography (spelling rules) and the challenges that arise as a consequence of the adoption of words. The methodology used is textual analysis, which entails a qualitative research methodology. Principles of the writing of adoptives have been extracted from the three Orthography and Spelling Rules booklets of Tshivenḓa approved by PanSALB, published respectively in 2005, 2008 and 2018. One of the rules states that Tshivenḓa words must follow the consonant-vowel pattern as indicated in the Orthography and Spelling Rules of Tshivenḓa published in 2005 and 2008. However, some of the adoptives in the 2018 booklet are written as pronounced in the original language and do not follow the open syllable system, for example Afrika, ḓresi and grisi. As a result, one finds that two different spellings exist for words, that is, according to the consonant-vowel pattern, or as pronounced in the original language, for example ḓiresi or ḓresi for English ‘address’ and ḓiraṱa or ḓraṱa for Afrikaans draad (‘wire’). The current study notes the challenges brought about by adoptives and makes recommendations to address them.
Development of Biological Sciences at the AABC

The submission rate of manuscripts to the Annals of the Brazilian Academy of Sciences (AABC) has increased continuously over the last decade, albeit more in some areas than in others (Kellner 2017).The Impact Factor (IF) of the journal has fluctuated, reaching the highest value ever (1.280) in the 2019 Journal Citation Reports (JCR).In order to get a general sense on how papers of different scientific fields published by the AABC -the only broad multidisciplinary periodical edited in Brazil -are contributing to the bibliometric indexes of the journal, I have decided to perform some brief analyses looking mainly at citations.In a pilot project, I started with Agrarian Sciences, the area showing the fastest submission growth rate, using one volume published in 2018 as a proxy (Kellner 2020).Here I am expanding my survey, addressing primarily all articles published by the AABC during 2018 in the field of Biological Sciences.In order to allow more uniform comparisons between scientific fields, I have complemented the present analysis with the citations of all papers in Agrarian Sciences published by the AABC during 2018.In my previous survey (Kellner 2020), I was surprised to find out that 15 articles of the issue AABC-90.4,dedicated to Agrarian Sciences were missing from JCR and WoS, and therefore not considered in the AABC 2019-IF.This led me to introduce the Missing Article Index (MAI).Originally defined as the number of articles published in one issue (or year) not found in WoS, divided by the total number of articles published in that issue (or year), MAI is one way to get a sense on how the lack of registration of published papers might influence the IF of a particular journal in a determinate timeframe (Kellner 2020).On some occasions, however, articles might reaper on WoS.As a consequence, the deleterious effects for authors whose articles were missing regarding their metrics of productivity and impact are mitigated.It remains to be established if these metrics (e.g., citations) start to be calculated only from the time when the paper is incorporated in WoS or if the numbers are corrected retroactively.Unlike WoS, once missing, papers tend not to be incorporated in the JCR platform at a later time, permanently affecting the performance of the journal where these articles were published (e.g., Kellner & Azevedo 2013).In fact, all other journals are also negatively affected by this omission, since the articles they have published and that were cited by papers not included in the JCR are not counted in their bibliometric indexes as well.Everyone looses.Perhaps one reason for not promoting the inclusion of missed articles in the JCR at a latter stage is the problematic involved in recalculating the IF of that particular journal.But even more complicated is the fact that the IF of all other journals whose papers were cited by the "new" included
On the Similarity Between Neural Network and Evolutionary Algorithm

Artificial neural networks, evolutionary algorithms (and swarm intelligence) are common algorithms belonging to well known soft-computing algorithms. Both classes of algorithms have their history, principles and represent two different biological areas, converted to computer technology. Despite fact that scientists already exhibited that both systems exhibit almost the same behavior dynamics (chaotic regimes etc.), researchers still take both classes of algorithms as two different classes. We show in this paper, that there are some similarities, that can help to understand evolutionary algorithms as neural networks and vice versa.
Towards a Data on the Web Lifecycle Model

Due to its exponential growth, the Web has become an important platform for sharing and exchanging data. Several methodologies, guidelines and good practices have been proposed to ease and guide users to consume and publish data on the Web. However, there is still a shortage of initiatives to clearly define the phases the data travels through the Web. Lifecycle models could be proposed to fill this gap. In this context, this paper proposes a Data on the Web Lifecycle Model, which aims to clearly specify the roles, phases and activities regarding Data on the Web publishing and consuming.
Criticality analysis of ineffectual lathe machine using interval - valued pentadecagonal fuzzy number using defuzzification strategy

The process of computing based on "degrees of truth", the generalization of a real number which is a connected set of possible values rather than a exact value. Thus the interval valued pentadecagonal fuzzy number is illustrated and the fault tree analysis is used to determine the malfunctioning of lathe machine. Defuzzication process is used here to convert the numbers in crisp value. Here we use this technique to find the failure mode effect and criticality analysis of ineffectual lathe machine.
Simulation of BB84 Quantum Key Exchange Protocol and Attack Analysis

The main purpose of quantum key distribution protocols is to provide a practical and secure transmission of the single-use key used in encryption at the sender and receiver side. In the classical method, both parties cannot be sure that the key has not been taken over by an attacker. Since the quantum key exchange method basically relies on quantum physics, it provides higher security than conventional key distribution methods. In this study, the first and most used BB84 protocol, which is one of the quantum key distribution (QKD) protocols, and the distribution of the key between the receiver and the sender in the simulation environment and the attempt of the attacker to achieve the key was performed in the simulation environment. The results obtained in the last section are analyzed and suggestions are made for future studies.
Feature Acquisition for Facial Expression Recognition Using Deep Convolutional Neural Network

We present a convolutional neural network for facial expression recognition based on feature acquisition. The proposed method adopts the structure of dual-channel convolution neural network, the network structure of each channel is designed according to the input sets, the extracted face and the extracted mouth are used as input to two channels simultaneously. Experiments are carried out on two different data sets include JEFFA and FER-2013 to determine the recognition accuracy, and we build a set to test our model, and we compare the generalization performance by using the confusion matrix, then we compared and analyzed the experiment results of recognition accuracy under different facial expressions. Finally, our facial expression recognition system got an accuracy of 82% and 78% respectively, and learning meta face recognition in unseen domains should be researched in the future.
PRS Generic Data Store Service

Philips Health Care systems currently use Relational Database Management Systems (RDBMS) which perform complex data analytics on data. The programming language used earlier was java which increases the processing time when used to manage the data base in PRS (Philips Remote Services). As PRS came into existence for proactive monitoring of diagnostic devices and provide services immediately. A simple NoSQL dynamo API (Application Program Interface) created to offload the administrative burden of operating and scaling. Hence Generic Data Store service is implemented as a microservice and exposed to consumers via POSTMAN as an end point of Representational State Transfer (REST). The PRS Generic Data Store Service is a RESTful web service that is exposed to consumers who can use it as their personalized data store to store / recover / modify / remove usage cases that may require lightweight data persistence in remote applications. The Health Care Services, Hospitals etc. may use this microservice to conduct CRUD operations on patient details and diagnostic instruments such as Magnetic Resonance Imaging (MRI), X-ray and Computerized Tomography (CT scan). Storing such data in. json or other file extensions and share such data to Hospitals whenever required and also the log files can be transferred to enterprise using various Transport protocols.
A software platform for monitoring radiation detection systems

In order to permanently monitor the environmental radiation which could result in radiological hazards but also to improve accuracy of scientific results regarding cosmic particles, new systems are being developed. However, from various causes these systems are prone to errors and false alarms can be triggered thus any change in the operating parameters of the radiation detection systems has to be detected immediately. The main objective of this paper is to provide a universal solution for automatic monitoring of radiation detection systems. The proposed software solution fulfills the high-level objectives of this paper – the implementation of a software solution which automatically monitors a radiation detection system. The solution was developed and works for a specific system, but it can be used for other detection systems as well with minor changes such as definition of the class attributes.
A Text Mining Approach to Extract and Rank Innovation Insights from Research Projects

Open innovation is a new paradigm embraced by companies to introduce transformations. It assumes that firms can and should use external and internal ideas to innovate. Recently, commercial and research projects have undergone an exponential growth, leading the open challenge of identifying possible insights on interesting aspects to work on. The existing literature has focused on the identification of goals, topics, and keywords in a single piece of text. However, insights do not have a clear structure and cannot be validated by comparing them with a straightforward ground truth, thus making their identification particularly challenging. Besides the extraction of insights from previously existing initiatives, the issue of how to present them to a company in a ranking also emerges. To overcome these two issues, we present an approach that extracts insights from a large number of projects belonging to distinct domains, by analyzing their abstract. Then, our method is able to rank these results, to support project preparation, by presenting first the most relevant and timely/recent insights. Our evaluation on real data coming from all the Horizon 2020 European projects, shows the effectiveness of our approach in a concrete case study.
EFUMS:Efficient File Upload and Mutli-Keyword Search over Encrypted Cloud Data

In the present era, cloud computing is built to provide many computation techniques and storage resources to the data user for later access. Data encryption is very important to ensure privacy before outsourcing it to the cloud server. Querying the cloud for encrypted data retrieval is a time-consuming process because of processing overhead and huge amount of data stored in cloud. In the existing system, the VPSearch scheme offers only verifiability of search results and privacy protection. It does not offer an efficient file uploading and index generation which consumes more time thereby slowing the searching process. It would be a challenging task to minimize the time to efficiently search on the cloud for a particular document. In order to overcome these challenges, we have proposed an efficient index generation scheme using tree based index technique with Greedy Depth-first search algorithm, that minimizes the file uploading and search time. The proposed EFUMS-Efficient File Upload and Mutli-Keyword Search over Encrypted Cloud Data scheme reduces the time taken to compute an index tree for all the files that are to be uploaded in a document and also helps to store the files in a structured tree format. This resulted in minimizing the document upload time and a faster and efficient data access using multi-keyword search.
Optimization of the Values of Classifiers Parameters – Is it Still Worthwhile to Deal with it?

This paper presents a comparison of the use of several selected optimization algorithms. They were applied to determine the parameters of classifiers. The value of these parameters should have a significant impact on the quality of the classification, and their determination is not a trivial process. This article checks how selected optimization algorithms deal with the task of determining the values of classifier parameters. Four algorithms were selected: particle swarm optimization, simulated annealing, cuckoo optimization algorithm, and lion optimization algorithm. The process of parameter determination was carried out using the five selected classifiers on several selected sets of data.
Implementation Of Automatic Clustering In Parallel Computation With Multi-Threading And Socket Programming

Often in conducting clustering we cannot determine the optimal number of clusters for clustering. Therefore Automatic Clustering algorithm is used to determine the optimal number of clusters in a dataset. One method of Automatic Clustering is Valley Tracing. But now the complexity that is generally owned by the clustering algorithm is O (n3). So the amount of data compared to computation time is cubic. This makes the clustering process very long when we add large amounts of data. In this study we propose a new approach by utilizing Parallel Computing with the Socket Programming and Multi-Threading methods. With parallel computing, the computing process can be done faster because the computing process is carried out on several computers simultaneously. This approach has 4 main features for solving problems. The first feature is Addressing to connect the Socket contained on the server with the client. The second feature is Active Status Detection for which clients are active and also busy when assigning tasks. The third feature of Task Management aims to manage existing Tasks on the server computer. The fourth feature is the Task Pool for combining the results of calculations from the client to the server computer for further calculations. The results of experiments in this study, using 3 computers connected to each other showed an increase in computing performance by 62%.
CEREC: Causality Extraction from Requirements Artifacts

The cause-effect recognition (CEREC) system provides an API for causality extraction tailored to the requirements engineering context. The library is written in Java and is released under the MIT open source license. In this paper, the underlying algorithm is described, and a demonstration of the active learning component for causality extraction is outlined. The results are promising and strengthen the confidence in exploring automation approaches for model-based testing.
A Population-Based Method with Selection of a Search Operator

This paper presents a method based on a population in which the parameters of individuals can be processed by operators from various population-based algorithms. The mechanism of selecting operators is based on the introduction of an additional binary parameters vector located in each individual, on the basis of which it is decided which operators are to be used to modify individuals’ parameters. Thus, in the proposed approach, many operators can be used simultaneously for this purpose. As part of the paper various methods of initializing binary parameters, various population sizes, and their impact on the operation of the algorithm were tested. The simulation was carried out on a well-known set of benchmark functions.
Two Test Collections for Retrieval Using Named Entity Markup

Studying the effects of semantic analysis on retrieval effectiveness can be difficult using standard test collections because both queries and documents typically lack semantic markup. This paper describes extensions to two test collections, CLEF 2003/2004 Russian and TDT-3 Chinese, to support study of the utility of named entity annotation. A new set of topic aspects that were expected to benefit from named entity markup were defined for topics in those test collections, with two queries for each aspect. One of these queries uses named entities as bag-of-words query terms or as semantic constraints on a free-text query term; the other is a bag-of-words baseline query without named entity markup. Exhaustive judgment of the documents annotated by CLEF or TDT as relevant to each corresponding topic was performed, resulting in relevance judgments for 133 Russian and 33 Chinese topic aspects that each have at least one relevant document. Named entity tags were automatically generated for the documents in both collections. Use of the test collections is illustrated with some preliminary experiments.