Reviewerly/exHarmony huggingface dataset:
Communication Compliance

Organizations face a broad range of risks from insiders. These risks could be everything from workplace harassment, IP theft, violence, insider trading, accidental data spillage, other types of data handling violations, fraud, conflicts of interest, or sensitive data leaks.
Development of Hypercontent Module using Jonnuro Model Learning Desain for Candidates Master Guide

The aim of this study is to produce a hyper content teaching material for prospective master quide in the adventist church. Which can be used for independent learning. This study uses a combination Derek Rowntree’s model and borg and gall models. The product testing phase begin with testing material expert, media experts, and learning disign expert. Then the product was tested to a number of master quide candiates, namely 3 people for individual evaluation, 8 people for small group evaluation and 20 people for large group evaluation. The result of the study showed the value of material expert, linguistic expert, media expert and learning design expert was very good. Judging from the readability test conducted by researchers using fog index, the module material is included in thecategory easy. According to the material expert, the hypercontent module, in terms of content feasibility, the feasibility of the feasibility of presentation according to material experts has been very good.According to linguists, the hypercontent module, in terms of the feasibility of language feasibility it has been very good. According to the learning design, the module is hypercontent, in terms of learning design is very good. Whereas according to expert media and graphic design, all module components are complete, the module size is appropriate and the visual message design principle is very good. According to the candidate master guide this module, in terms of method, evaluation, module size, language, sentence structure, layout, typography, illustrations and colors in the module have been very good.
Blockchain and Internet of Things: A Survey

In the age of next-generation computer, the role of the cloud, the internet and smart devices will become stronger. These days we all know the word smart well. This word is often used in our daily lives. The Internet of Things (IoT) will generate a variety of information from a variety of resources. It can store big data in the cloud. Fog computing acts as a signal between cloud and IoT. Fog extensions in this framework apply to material under IoT. IoT devices are called Fog nodes, which can be accessed anywhere within the network range. A blockchain is a novel way of recording in a secure sequence. Creating a new framework in the development of Internet of Things is one of the critical problems of wireless communication where solving such a problem can lead to continued growth in the use and popularity of IoT. Proposed research creates a framework for providing a framework for middleware on the internet of smart devices network for the internet of things using blockchains technology. Our great offering connects new research that integrates blockchains into the Internet of Things and provides secure Internet connection for smart devices. Blockchain (BC) Internet of Things (IoT) is a new technology that works with low-level, distributed, public and real-time leaders to maintain transactions between IoT sites. A blockchain is a series of blocks, each block being linked to its previous blocks. All blocks have cryptographic hash code, previous block hash, and its data. Transactions in BC are the basic components used to transfer data between IoT nodes. IoT nodes are a variety of portable but smart devices with embedded sensors, actuators, systems and the ability to communicate with other IoT nodes. The role of BC in IoT is to provide a process for processing secure data records using IoT nodes. BC is a protected technology that can be used publicly and openly. IoT requires this type of technology to allow secure communication between IoT nodes in different environments. Events in BC can be tracked and monitored by anyone who is certified to communicate within IoT.
Survey on the Analysis of User Interactions and Visualization Provenance

There is fast-growing literature on provenance-related research, covering aspects such as its theoretical framework, use cases, and techniques for capturing, visualizing, and analyzing provenance data. As a result, there is an increasing need to identify and taxonomize the existing scholarship. Such an organization of the research landscape will provide a complete picture of the current state of inquiry and identify knowledge gaps or possible avenues for further investigation. In this STAR, we aim to produce a comprehensive survey of work in the data visualization and visual analytics field that focus on the analysis of user interaction and provenance data. We structure our survey around three primary questions: (1) WHY analyze provenance data, (2) WHAT provenance data to encode and how to encode it, and (3) HOW to analyze provenance data. A concluding discussion provides evidence-based guidelines and highlights concrete opportunities for future development in this emerging area.
Experimental Design and Implementation of IT2FL-controlled BLDCM Based on LabVIEW™

This article presents design and implementation of Interval Type-2 Fuzzy Logic Controller (IT2FLC) for speed control of brushless DC motor (BLDCM) based on LabVIEW package. For the purpose of comparison, the performance of IT2FLC is compared to Type-1 FL controller (T1FLC). For fair comparison, both schemes of FL controllers are set with the same number of membership functions and input-output gain values. The robustness characteristics of both FL controllers are assessed in terms of dynamic performance and load rejection capaility. The design of IT2FLC and T1FLC are made and implemented within LabVIEW environment and the effectiveness of controllers are verified experimentally. The experimental results showed that IT2FL controller gives better robustness under load variation than conventional T1FL controller.
Modeling of digital control systems

As in the case of analog control, mathematical models are needed for the analysis and design of digital control systems. A common configuration includes a digital-to-analog converter (DAC), an analog subsystem, and an analog-to-digital converter (ADC). The DAC converts numbers calculated by a microprocessor or computer into analog electrical signals that can be amplified and used to control an analog plant. The analog subsystem includes the plant and the amplifiers and actuators necessary to drive it. The output of the plant is periodically measured and converted to a number that can be fed back to the computer using an ADC. In this chapter, we develop models for the various components of this digital control configuration. Many other configurations that include the same components can be similarly analyzed. We begin by developing models for the ADC and DAC, then for the combination of DAC, analog subsystem, and ADC. We also show how to calculate the steady-state tracking error for standard test inputs and the error due to a disturbance. Next, we show how to simulate a digital control system using SIMULINK. Finally, we discuss the sensitivity function and how it can be used to assess the effect of modeling errors.
Algebraic Structures of Mathematical Foundations

In this paper, we continue to examine the role of algebra underlying geometry.The mathematics that we examine are abstract and are quite far removed from applications.Those who may wish to examine the possible uses of abstract geometry in physics might well begin by reading (Schlichenmaier, 2007;Bub, 1997).The method of research that we apply is to use the logic of H. S. Leonard to extract the underlying algebra of mathematical proofs and then use the results for the purpose of finding the algebra that underlies mathematical results.
Quick Response Code as a Communication Medium

Quick Response (QR) Codes are tiny compatible printed pattern on solid surface to transfer a data from a printed medium to a digital medium. The coding information cannot be viewed by the naked eye thus the manipulation of printed pattern is very difficult. In general, QR code is printed in the two-dimension using white and block named QR code modules or data pixels. Static and Dynamic QR codes are available now a days. More than 3000 characters can be encoded over a very tiny space using QR graphics. The data are encoded by QR generator using ISO/IEC 18004:2006. Various types of data can be restored using QR codes and every QR Code contains segment devoted to informing the reader what sort of data it holds and used for coding and decoding the data easily. The most common data types of QR codes are SMS, Email address, phone number, plain text, geo location etc. Nowadays the QR codes are used in various fields. For example, it is used in the marketing and industries to provide the information about the products. In education it is used to register the information about the students in their certificates and to access the information in a website.
Assessment of the Impact of Blockchain Technology on National Economy: Methodological Approaches and Approbation

Although international and national financial institutions, as well as real economy enterprises, take rather strong interest in distributed data storage technologies and research on assessment of the use of potential of blockchain platforms in social and economic environment, there is little theoretical understanding of the issue. In this regard, the paper attempts to fill the vacuum concerning the matters revealing other aspects of the research subject, for example economic and social effects of the introduction of blockchain technologies into activities of the economic entities. Formal assessment and scenario modeling of GDP increment dynamics in new institutional conditions of economic management are carried out on the basis of the assessment of the emerging effects caused by the integration of distributed data storage technologies into the system of economic operations
Dynamically Interchangeable Framework for Component Behavior of Embedded Component Systems

The development of information technology and the improved performance of embedded devices have led to embedded software of increasing scale and complexity. Component-Based Development (CBD), which divides software into components and subsystems, is effective for efficiently developing large-scale embedded software. However, components created in CBD are not interchangeable dynamically while the application is running, and components written in C must be compiled to change and run. To address this issue, this paper proposes the component framework using mruby to change the component behavior dynamically. The proposed framework works on TOPPERS Embedded Component System written in C. Experimental results show that the time it takes to change the behavior of the component is shorter using the proposed framework than using a component written in C. The proposed framework can realize CBD more efficiently.
Understanding

Chapter 4 moves on to inquiry into general phenomena and develops a non-reductive systematic knowledge account of understanding. According to this view, roughly, maximal understanding of a phenomenon is maximally comprehensive and well-connected knowledge of it, degrees of understanding are a function of distance from maximal understanding, and understanding a phenomenon can be truly attributed when one surpasses a contextually determined threshold on degrees of understanding. Chapter 4 discusses a number of objections to the account and compares it with the most prominent rival views in the literature, according to which understanding is, in essence, knowledge of explanations. Again, it is argued that the systematic knowledge account comes out on top.
Entropy-based model for cognitive systems

The work presented here looks at information as an independent entity. Without any involvement of the mind, information forms patterns and regularities which manifest themselves during cognitive experiments. Information can arrange itself within a multidimensional space and depending on the number of dimensions exhibit different characteristics. It is shown here that information analysis allows for the reproduction of some elementary cognitive processes like short- and long-term memory, chunking, long term memory categories, the formation of convictions, decision-making processes and mechanisms responsible for the formation of understanding. It is therefore argued here that some cognitive experiments do not relate to the workings of a subject’s mind but instead relate to the properties of information itself.
Blockchain for Increased Trust in Virtual Health Care: Proof-of-Concept Study (Preprint)

<sec> <title>BACKGROUND</title> Health care systems are currently undergoing a digital transformation that has been primarily triggered by emerging technologies, such as artificial intelligence, the Internet of Things, 5G, blockchain, and the digital representation of patients using (mobile) sensor devices. One of the results of this transformation is the gradual virtualization of care. Irrespective of the care environment, trust between caregivers and patients is essential for achieving favorable health outcomes. Given the many breaches of information security and patient safety, today’s health information system portfolios do not suffice as infrastructure for establishing and maintaining trust in virtual care environments. </sec> <sec> <title>OBJECTIVE</title> This study aims to establish a theoretical foundation for a complex health care system intervention that aims to exploit a cryptographically secured infrastructure for establishing and maintaining trust in virtualized care environments and, based on this theoretical foundation, present a proof of concept that fulfills the necessary requirements. </sec> <sec> <title>METHODS</title> This work applies the following framework for the design and evaluation of complex intervention research within health care: a review of the literature and expert consultation for technology forecasting. A proof of concept was developed by following the principles of design science and requirements engineering. </sec> <sec> <title>RESULTS</title> This study determined and defined the crucial functional and nonfunctional requirements and principles for enhancing trust between caregivers and patients within a virtualized health care environment. The cornerstone of our architecture is an approach that uses blockchain technology. The proposed decentralized system offers an innovative governance structure for a novel trust model. The presented theoretical design principles are supported by a concrete implementation of an Ethereum-based platform called VerifyMed. </sec> <sec> <title>CONCLUSIONS</title> A service for enhancing trust in a virtualized health care environment that is built on a public blockchain has a high fit for purpose in Healthcare 4.0. </sec>
Conversational Explainability

In this Chapter, we focus on an interface that makes explainability of a machine learning (ML) system convincing and effective. We propose a conversational interface to the decision log of an abstract ML system that enumerates the decision steps and features employed to arrive at a given decision. Conversational content augments the decision log with background knowledge to better handle questions and provide complete answers. As a result, the users who can chat with the decision-making system (such as a loan application approval) develop a substantially higher trust in it than in a black-box ML or conventional report-based explanation. Conversational explainability (CE) allows the users to get into as much details as they wish concerning the decision process. The proposed CE system delivers meaningful explanation in 65% cases, whereas a conventional, report-based explanations in 49% of cases (for the same decision sessions).
Retrieval of Land Surface Temperature From Landsat 8 Data of the Dandong-liaoyang Geothermal Area

Abstract In recent years, with the intensification of problems like the depletion of traditional fossil fuels and environmental degradation, the development of new energy sources has become a key long-term strategy in China. Geothermal energy has attracted much attention due to its advantages of abundance and low environmental impact. Based on infrared data sensed remotely by the Landsat 8 satellite, this paper reports a verification of the atmospheric-correction method for extracting the surface temperature of the Dandong-Liaoyang geothermal region in all months of 2014. The method combines the abnormal points in the inversion results with the local sites of hot springs, structures, local historical air temperatures, and land surface temperature/emissivity data (MOD11_L2). Results showed that these data sources were spatially distributed in similar ways, which indicates that these results can be used to identify promising geothermal resources from publically available thermal-infrared remote-sensing data.
Using Reduced Set of Features to Detect Spam in Twitter Data with Decision Tree and KNN Classifier Algorithms

In social media, the users share their ideas, opinions to their neighbours and friends. Spammers send spam information to the genuine users to mislead them. This spam data is a very serious problem in social media sites. To detect spam messages in social media various spam detection methodologies are developed by researchers. The researchers used more number of features to construct the models. Generally the original dataset contains many irrelevant and redundant features. Such large amount of features reduces the spam detection accuracy. To improve the spam detection accuracy in social media networks, we have to reduce the meaningless attributes from high dimensional social media dataset. In order to reduce dimensionality of dataset, we have used one of the dimensionality reduction approach, called principal component analysis (PCA). After reducing the dimensionality of the dataset, the dataset samples are classified using Decision Tree Induction classifier algorithm and K Nearest Neighbour algorithm. In our proposed work these algorithms are used to check data samples are spam samples or ham samples. In this methodology, we have used Twitter dataset for testing proposed approach. Experimental results shows that KNN classifier outperforms compared to Decision tree classifier.
New Semi-Free-Start Collision Attack Framework for Reduced RIPEMD-160

RIPEMD-160 is a hash function published in 1996, which shares similarities with other hash functions designed in this time-period like MD4, MD5 and SHA-1. However, for RIPEMD-160, no (semi-free-start) collision attacks on the full number of steps are known. Hence, it is still used, e.g., to generate Bitcoin addresses together with SHA-256, and is an ISO/IEC standard. Due to its dual-stream structure, even semifree- start collision attacks starting from the first step only reach 36 steps, which were firstly shown by Mendel et al. at Asiacrypt 2013 and later improved by Liu, Mendel and Wang at Asiacrypt 2017. Both of the attacks are based on a similar freedom degree utilization technique as proposed by Landelle and Peyrin at Eurocrypt 2013. However, the best known semi-free-start collision attack on 36 steps of RIPEMD-160 presented at Asiacrypt 2017 still requires 255.1 time and 232 memory. Consequently, a practical semi-free-start collision attack for the first 36 steps of RIPEMD-160 still requires a significant amount of resources. Considering the structure of these previous semi-free-start collision attacks for 36 steps of RIPEMD-160, it seems hard to extend it to more steps. Thus, we develop a different semi-free-start collision attack framework for reduced RIPEMD-160 by carefully investigating the message expansion of RIPEMD-160. Our new framework has several advantages. First of all, it allows to extend the attacks to more steps. Second, the memory complexity of the attacks is negligible. Hence, we were able to mount semi-free-start collision attacks on 36 and 37 steps of RIPEMD-160 with practical time complexity 241 and 249 respectively. Additionally, we describe semi-free-start collision attacks on 38 and 40 (out of 80) steps of RIPEMD-160 with time complexity 252 and 274.6, respectively. To the best of our knowledge, these are the best semi-free-start collision attacks for RIPEMD-160 starting from the first step with respect to the number of steps, including the first practical colliding message pairs for 36 and 37 steps of RIPEMD-160.
Application of quality in use model to assess the user experience of open source digital forensics tools

Open source digital forensics tools are playing an important role for forensics investigations. So, there is a need to assess these tools to ensure they meet users' needs. The existing literature does not satisfy the requirements of assessing their quality-in-use. This paper assesses three most used digital forensics tools, namely autopsy, DFF, and DART using five quality-in use characteristics, namely, effectiveness, productivity, efficiency, error safety, and cognitive load. The results demonstrated that Autopsy, DFF, and DART performances are similar in terms of efficiency and productivity. DDF outperformed the other two in effectiveness. Autopsy was the best in terms of error safety, and DART had the highest cognitive load. The relevant community may consider these findings in selecting solutions to perform its duties. The future researches can involve more studies to encompass additional aspects of software quality, to compare open and closed source digital forensics programs and to enhance testing efforts
Model Dynamic Ensemble Time Series untuk Prediksi Indeks Harga Saham Utama di Indonesia Pasca Pandemi

Forecasting or predicting stock prices in the form of time series data is still a hot topic consistently discussed in economic forums and financial markets. This article had been analyzed prediction of stock prices in Indonesia after experiencing a pandemic and one year after the Corona virus. This study had been applied a dynamic ensemble method that combines various prediction models to improve forecasting accuracy. The results showed that the model has a high level of accuracy with MAPE (Mean Absolute Percentage Error) values of 0.003714125, and RMSE (Root Mean Square Error) of 0.03958605. Furthermore, these results could be used as a basis for government policy making and stock investment decisions for investors.
Learning Domain-specific Grammars from a Small Number of Examples

In this paper we investigate the problem of grammar inference from a different perspective.The common approach is to try to infer a grammar directly from example sentences, which either requires a large training set or suffers from bad accuracy.We instead view it as a problem of grammar restriction or sub-grammar extraction.We start from a large-scale resource grammar and a small number of examples, and find a subgrammar that still covers all the examples.To do this we formulate the problem as a constraint satisfaction problem, and use an existing constraint solver to find the optimal grammar.We have made experiments with English, Finnish, German, Swedish and Spanish, which show that 10-20 examples are often sufficient to learn an interesting domain grammar.Possible applications include computer-assisted language learning, domainspecific dialogue systems, computer games, Q/A-systems, and others.