Reviewerly/exHarmony huggingface dataset:
AlternationSpecial Edition 27(2019) 37-6437Print ISSN 1023-1757; Electronic ISSN: 2519-5476; DOIhttps://doi.org/10.29086/2519-5476/2019/sp27a2Curriculum Development: An Enriched Approach for Twenty-First Century Open Distance Learning

The aim of this paper is to analyse and explore the progress of curriculum development, which remains a contentious issue, particularly in the context of higher education expansion, massification, commodification and the so-called 'Fourth Industrial Revolution'.This paper examines how the curriculum is developed within the complexities of a shifting job market and changing student aspirations.Existing literature in curriculum development focuses on the instrumental value of education and graduate attributes for economic development, with a lesser focus on the intrinsic value of education.There is a paucity of evidence, particularly regarding Open Distance Learning (ODL), that researchers have explored the issue of curriculum development and capabilities formation.These two aspects are important in that they potentially offer students an effective way to change their lives, improve their communities and direct their own destinies.The research was undertaken by means of a desktop literature review; to examine curriculum development in South Africa after 1994 and includes the function of education while exploring the theoretical frameworks underpinning curriculum development.In so doing, the paper locates curricula in a broader capabilities framework, challenging existing practices.It then examines the challenges of curriculum development in ODL and how we might start rethinking curriculum development and capabilities formation and initiate a dialogue of capabilities formation in ODL in South Africa.It further suggests numerous innovative ways of curriculum conceptualisation, interpretation and implementation.
Update: Mail Ballot Elections

Traditionally, the NLRB has not supported mail ballot elections for a number of reasons. As noted in Aspirus Keweenaw , 370 NLRB No. 45 (2020), manual elections provide for an added layer of security as an NLRB agent is physically present to monitor and enforce rules to ensure a fair election. They also encourage greater voter participation, with the NLRB recently finding that voter participation increases 20–30% in manual elections as compared to mail ballot elections.
Azure Storage

In the previous chapters, we discussed and explained the most used cloud services in Azure, such as virtual machines, scale sets, and App Service, and learned how to use them. One Azure service that we mentioned but still did not go into details about is Storage Account, which will be the subject of this chapter.
PDBe-KB: a community-driven resource for structural and functional annotations

The Protein Data Bank in Europe -Knowledge Base (PDBe-KB, https://pdbe-kb.org) is a community-driven, collaborative data resource that provides literature-derived, curated and predicted structural and functional annotations of molecular structure data [1].Consortium members provide annotations including catalytic sites, ligand binding sites, protein flexibility, post-translational modification sites, and the effect of genetic variability or mutations (Fig 1 .).PDBe-KB aims to increase the visibility and reduce the fragmentation of these annotations and place macromolecular structure data in their biological context, thus facilitating their use by the broader scientific community in fundamental and applied research.PDBe-KB currently collaborates with 31 resources from 11 countries, and we integrate their annotations with core PDB structural data in a novel and distributable PDBe graph database.Researchers can access all the annotations either by using the graph database or programmatically via API endpoints [2].We have also created web pages called "Aggregated Views" that provide an overview of all the structure data related to a full-length protein (i.e.UniProtKB accession).These views are better at displaying the biological context of proteins instead of the conventional PDBe entry-page focus on a single PDB entry.We have been continuously improving and expanding PDBe-KB since its inception in 2018.Last year we rolled out a significant update to support the global effort to tackle the COVID-19 pandemic by creating dedicated pages gathering all the structural information for the new coronavirus SARS-CoV-2 (e.g.https://www.ebi.ac.uk/pdbe/pdbe-kb/protein/P0DTC2).We have also added new functionalities, such as viewing the superposition of every PDB chain that corresponds with a protein of interest.These views are instrumental in identifying ligand-binding hotspots and conformationally flexible regions.Most recently, we are linking to and display data from predicted model providers such as AlphaFold DB and SWISS-MODEL.Figure 1 -Schematic overview of data integration in PDBe-KB
Implementing Information Security Mechanism Over Cloud Network

The security parts of distributed computing, particularly the security of information, turn out to be increasingly essential. It is important to build up another component to verify the information in the cloud. Cryptographic based symmetric key cryptosystems helps to provide an efficient way to protect information. Enhanced symmetric key cryptosystem AES algorithm has been proposed for securing the data and safely storing to the cloud. This paper is to provide an overall security to the files which are in the cloud so the encrypted file can be retrieved at any time from the cloud. The paper also aims to introduce the mechanism use to secure cloud computing applications as well as to compare some existing algorithm like RSA and AES with an enhanced AES algorithm and to prove that the proposed algorithm is more effective than the other two algorithms. The encryption speed of the algorithms is graphically represented in the paper.
Chapter 12: Grid-Based Clustering Algorithms

Density-based and/or grid-based approaches are popular for mining clusters in a large multidimensional space, wherein clusters are regarded as denser regions than their surroundings. In this chapter, we present some grid-based clustering algorithms.
SysMon GUI

The goal of this project is to make SysMonApp a graphical user interface. SysMonApp is an Android program that interfaces with Q6s on (Application DSP)ADSP/(Computational DSP)CDSP/(Sensor DSP)SDSP subsystems over FastRPC and supports different user functions such as profiling Q6 workload, getting clock information, setting/removing clocks, and getting software thread information, among other things. SysMonApp offers a variety of features, each with its own set of parameters. Users have a hard time remembering many arguments. The user interface will make it easy for users to access all of the functions in one location.
Introduction

This chapter introduces the theoretical context for the compositional semantic framework to be developed in the book. A key innovation is to posit explicit representations of context – formally, variables for assignment functions – in the syntax and semantics of natural language. A primary focus is on a spectrum of linguistic shifting phenomena, in which the context relevant for interpretation depends on features of the linguistic environment. The proposed theory affords a standardization of quantification across domains, and an improved framework for theorizing about linguistic meaning and the role of context in interpretation. Comparisons with alternative operator-based theories are briefly considered. An outline of the subsequent chapters is presented.
Shrinking gravity

The first law of thermodynamics is the conservation of energy. The first law of geology is that rocks fall downhill. Falling rocks gain energy. The energy must come from gravity. So why doesn't gravity get less every day? Anthony Woodward, Portland, Oregon, US
Theories and models of creative processes

Strategic search approaches (Simon's information processing model; Geneplore and Weisberg’s analytic-thinking theory) that emphasize conscious strategies for finding useful new combinations of ideas and associative approaches (Mednick; Campbell and Simonton's: blind variation and selective retention model) which assume unconscious processes are presented and discussed. We discuss formal network models which assume that ideas can be represented as nodes in semantic networks that activation flows from nodes to associated nodes, and that connection patterns can be changed by over time. Semantic networks tend to show small-world properties, i.e., concepts tend to cluster in tightly related groups but also have short cut links between clusters so that most nodes are within a small number of steps (links) from any other node. Individual measures of “forward flow” indicate how unusual an individual's flow of associations were found predictive of performance on divergent thinking tasks.
Algebraic Structures of m-polar Fuzzy Matrices

Problems from real life situations related to multiple agents n  5 and Big data are efficiently solved by Computational Mathematics using N-Dimensional Polar information. This information cannot be well-represented by means of fuzzy matrices or bipolar fuzzy matrices. Therefore, mpolar fuzzy matrix theory is applied to graphs to describe the relationships among several individuals. In this paper, some operations are defined to formulate these matrices. we proved the properties of m-polar fuzzy matrices by exploiting the binary operations ring sum (  ) and ring subtraction ( ). In addition to this we also extended various operations such as reflexive, irreflexive, maximum and minimum for the idea of m-polar fuzzy matrices.
A PRACTICAL STUDY ON PHISHING ATTACKS

Phishing attacks are one of the most common security threats today. Despite the significant attention it has received over the years, the phishing problem is broad and no single solution exists to mitigate all the vulnerabilities effectively. For this reason, it is crucial that students and professionals of computer science, especially those focused on information security and computer systems security, are prepared to react to those attacks. In this context, based on a reference scenario, a practical study on phishing attacks is discussed in this work. For this, issues related to social engineering and attacks on Internet end-systems are addressed throughout the text. This discussion is important, for example, for the development of specific security mechanisms and for teaching and/or research activities related to the topic.
Universal Translatability: An Optimality- Based Justification of (Classical) Logic

In order to prove the validity of logical rules, one has to assume these rules in one’s metalogic. But how is a non-circular justification of a logical system possible? The question becomes especially pressing insofar in present time a variety of non-classical alternatives to classical logics have been developed. Is the threatening situation of an epistemic circle or infinite regress unavoidable? The situation seems hopeless. Yet, in this paper I suggest a positive solution to the problem based on the fact that logical systems are translatable into each other. I propose a translation method based on introducing additional concepts into the language of classical logic. Based on this method I demonstrate that all finite multi-valued logics - and I conjecture all non-classical logics - can be translated into classical logic. If this argument is correct, it would show that classical logic is optimal in the following sense: by using it we cannot lose, because if another logic turns out to have advantages for certain purposes, we can translate and thus embed it into classical logic. This optimality argument does not exclude that there can be other, non-classical logics that are likewise optimal in the explained sense.
PERENCANAAN ARSITEKTUR ENTERPRISE BERBASIS CLOUD COMPUTING MENGGUNAKAN TOGAF (Studi Kasus: PT. XYZ)

ABSTRACT&#x0D; PT. XYZ is a manufacturing company engaged in the production of tires. Good business strategy isnecessary for the company to compete with local competitors as well as with other companies in thegroup XYZ. Business strategy is also needed in order to achieve its goal in providing the best servicesfor customers. Support the implementation of information systems and information technology isneeded to support the company's business strategy. Planning of the Enterprise Architecture isnecessary for the company to run its business processes. Enterprise architecture is composed of theBusiness Architecture, Data Architecture, Application Architecture and Technology Architecture.Business processes and organizational activities executed using the data collected, managed,secured and distributed using applications, both custom applications and common applications thatrun over the technology infrastructure and computer networks. The research report in this thesisdiscusses about planning the Enterprise Architecture of Manufacturing Information SystemArchitecture Based Cloud Computing using TOGAF, a case study in PT.XYZ. In planning thisarchitecture using the TOGAF-ADM to pass the following stages: Defining planning the ArchitectureVision, Planning the Business Architecture, Planning the Information System Architecture, Planningthe Technology Architecture, Solutions and Opportunities, Migration Planning, ImplementationManagement, and Change Management Architecture. The research process is done by looking at thebusiness strategy on the manufacturing process and products distribution from PT. XYZ. The resultsof this study are planning the enterprise architecture of manufacturing information system at PT.XYZ to support the implementation of enterprise information systems as a whole, to solve theintegration problems of information systems, and improve effectiveness and efficiency in theimplementation of information technology infrastructure.
Implementation of Modified Particle Swarm Optimization Algorithm for Image Contrast Enhancement

Image contrast enhancement is mostly preferred approach for Medical Image Processing field. In this work, a new Histogram Specification (HS) based image contrast enhancement scheme is described. The optimal values of the weighing constraints are progressed through the standard of Modified Particle Swarm Optimization (MPSO) with respect to the histogram of the input processed images. The proposed scheme improves the contrast of the input processed image superior than its existing histogram equalization methods. Henceforth, this method can efficiently be utilized in the fields including image processing, electronics etc. The overall evaluation of the HS scheme can be computed by means of Discrete Entropy (DE) and Contrast Improvement Index (CII).
Deep Learning for Fully Automated Prediction of Overall Survival in Patients Undergoing Resection for Pancreatic Cancer

Background: Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer. To this end, we develop an objective and robust imaging biomarker for fully automated prediction of overall survival (OS) of pancreatic cancer by directly analyzing multiphase contrast-enhanced CT (CECT) using deep learning.Methods: This retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from five centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from two centers, was used to construct a fully-automated prognostic biomarker – DeepCT-PDAC – by training a holistic convolutional neural network for volumetric segmentation of PDAC and pancreatic anatomies and four subsequent networks for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179) to evaluate its performance, robustness, and clinical usefulness.Findings: Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts (hazard ratio [HR] 2·03, 95% CI 1·50–2·75, p<0·0001; HR 2·47, 1·35–4·53, p=0·0034) in a multivariable analysis including age, CT tumor size, tumor location, and CA 19-9. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR 2·49, 95% CI 1·89–3·28, p<0·0001; HR 2·15, 1·14–4·05, p=0·018) after adjustment for resection margin, pT stage, pN stage, tumor differentiation, perineural invasion, pathological tumor size, and treatment. For margin-negative patients, adjuvant radiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR 0·35, 95% CI 0·19–0·64, p=0·00036), but did not affect OS in the subgroup with high risk.Interpretation: Deep learning-derived CT imaging biomarker enabled objective and unbiased prediction of OS for resectable PDAC both pre- and postoperatively. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatment at the individual level.Funding: This research was supported by the National Natural Science Foundation of China (grant numbers 82071885 and 81771802 and 81771893) and the National Youth Talent Support Program of China.Declaration of Interest: We declare no competing interests.Ethical Approval: IRB approval for the retrospective review of imaging and clinical data was obtained from the local ethics committees for all cohorts. The need for informed consent was waived.
Impact of Customer Feedback System using Machine Learning Algorithms for Sentiment Mining

The process of discovering and analyzing the customer feedback using Natural Language Processing (NLP) is said to be sentiment analysis. Based on the surge over the concept of rating level in sentiment analysis, sentiment is utilized as an attribute for certain aspects or features that get expressed and more attention are provided to the problem of detecting the customer reviews. Despite the wide use and popularity of some methods, a better technique for identifying the polarity of a text data is hard to find. Machine learning has recently attracted attention as an approach for sentiment analysis. This work extends the idea of evaluating the performance of various Machine Learning (ML) classifiers namely logistic regression, Naive Bayes, Support Vector Machine (SVM) and Neural Network (NN).To show their effectiveness in sentiment mining of customer product reviews, the customer feedback has been collected from Grocery and Gourmet Food. Nearly 90 thousands customers feedback reviews of various product related categories namely Product ID, rating, review test, review time reviewer ID and reviewer name are used in this analysis. The performance of the classifiers is measured in terms of accuracy, specificity and sensitivity. From the experimental results, the better machine learning classification algorithm is proposed for sentiment mining using online shopping customer review data.
Social Computing: Answer vs. Vote

In many social computing systems, users decide sequentially whether to participate or not and, if they participate, whether to create a piece of content directly (i.e. answering) or to rate existing content contributed by previous users (i.e. voting). We present in this chapter a game-theoretic model that formulates the sequential decision-making of strategic users under the presence of this answering–voting externality. We prove theoretically the existence and uniqueness of a pure strategy equilibrium. We show that there exist advantages for users with higher abilities and for answering earlier. Therefore, the equilibrium exhibits a threshold structure and the threshold for answering gradually increases as answers accumulate. To show the validness of the game-theoretic model, we analyze user behavior data collected from a popular question-and-answer site Stack Overflow and show that the main qualitative predictions of the game-theoretic model match up with observations made from the data. Finally, we formulate the system designer’s problem and abstract several design principles that could potentially guide the design of incentive mechanisms for social computing systems in practice.
Afterword

One year into the COVID-19 pandemic, we reflect on how Koike’s ideas about pragmatics, co-construction, and frames will contribute to emerging and future research about digitally mediated communication in a time during which the ways in which people engage with technology are rapidly changing. This chapter addresses challenges and opportunities associated with the new ways of interacting through technology. It is suggested that understanding communication as a process of co-construction between individuals with unique histories along with a posthumanist awareness of the interconnectedness between humans and objects will allow for a more nuanced perspective on the processes of intersubjectivity that occur in virtual environments.
Weighting Comparative Analysis Using Fuzzy Logic and Rank Order Centroid (ROC) in the Simple Additive Weighting (SAW) Method

The Covid-19 outbreak has changed the learning system in Indonesia into distance learning, better known as online learning. In determining student learning outcomes on student learning satisfaction with distance learning during the Covid-19 pandemic, a study was carried out using the Simple Additive Weighting (SAW) method. This study aims to determine student learning outcomes during the Covid-19 pandemic. The type of research used in this research is applied research, in which this research is directed to obtain information that can be used to solve problems. The method used is Simple Additive Weighting (SAW) by comparing the results of the decision of the SAW method between the weighting based on the Fuzzy Logic method and the weighting based on the ROC method. The subjects involved in this study were 36 students of Vocational High School (SMK). Data collection in the study was carried out using direct observation, interviews, and questionnaires. The criteria contained in the questionnaire are factors that affect the process of student learning outcomes on learning satisfaction during the Covid-19 pandemic. The criteria used are device ownership, accessibility, ease of obtaining materials, method accuracy, monitoring ability, interactivity, and independent learning. From the seven criteria, the priority scale is determined. The results showed that the analysis of the search for weight scores using the Simple Additive Weighting (SAW) method using Fuzzy Logic and Simple Additive Weighting (SAW) using the Rank Order Centroid (ROC) method resulted in different sub-criteria weighting scores, so it can be said that the combination of SAW- ROC provides a more accurate and more selective selection of the number of students.
Construction of Industrial Robot Manipulator Sorting System based on Convolution Neural Network

With the development of intelligent manufacturing, the application of intelligent algorithms in manufacturing industry is gradually enriched. This research combines convolutional neural network with industrial manipulator, and uses the image feature extraction advantages of convolutional neural network to build a manipulator sorting system. The results show that the average accuracy of the system for sorting parts is more than 70%, and when compared with other systems, its accuracy value is also greater than that of other systems. It can be seen that the identification and positioning accuracy of this system is stronger and the performance is better.