Reviewerly/exHarmony huggingface dataset:
Sistem Pakar Untuk Mendeteksi Kerusakan Pada Diesel Pump Dengan Metode Forward Chaining Berbasis Web

Frequent fires can occur in or like buildings of buildings or high rise buildings. The losses incurred by the fire disaster are enormous. Care and maintenance standards for diesel engine pumps must be observed. All technicians must have expertise in the maintenance and maintenance of the diesel pump engine. Such expertise can be obtained from official training conducted by the education center, reading guidelines for dealing with diesel pump engine damage obtained from diesel pump manufacturing plants and additional knowledge gained from learning while attending education and training. In this study the researchers set "Symptoms of Damage to the diesel engine of the Fire Pump" as a variable. The expert system application detects web-based diesel pump damage with a forward chaining method that has been made, after conducting an expert analysis test then testing the user's response to this expert system application, the test is carried out by consulting using this expert system application. Expert systems are components and elements that are combined into one to achieve certain goals. The system design is done after analyzing the diesel pump damage data and designing a web-based expert system. Chaining forward method is a method of inference that makes sense from a problem to a solution. Damage to the diesel pump by using the forward chaining method provides the right solution of the problem at hand. Based on these suggestions it can be concluded that an expert system detects damage to a web-based diesel pump that has been made can help all users in analyzing the problem based on consultation
Computer aspect of interdisciplinary research in philosophy of education and computer science

A large-scale global study of events both within and between disciplines provides strong evidence that leading thinkers have begun to introduce new ways of thinking to the point that most academic disciplines have undergone major paradigm changes throughout the twentieth century, according to Guidley. Increasingly, over the past forty years, we can see significant changes, if not in all, then in the main scientific disciplines. New ways of thinking have appeared in almost all disciplines. With the help of interdisciplinary approaches, a movement has emerged to integrate knowledge, moving beyond the fragmentation of knowledge related to disciplinary specialization. Computer technologies and computer concepts have penetrated almost all areas of academic research. These events can be considered as the implementation of new ways of thinking and new models of knowledge, respectively, and may be edges of the evolution of consciousness, in which computer technologies occupy a significant place. Education and communication in the age of globalization and computerization are an obvious necessity for the continued existence of society.
A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology

South Korea has the lowest fertility rate in the world despite considerable governmental efforts to boost it. Increasing the fertility rate and achieving the desired outcomes of any implemented policies requires reliable data on the ongoing trends in fertility and preparations for the future based on these trends.The aims of this study were to (1) develop a determinants-of-fertility ontology with terminology for collecting and analyzing social media data; (2) determine the description logics, content coverage, and structural and representational layers of the ontology; and (3) use the ontology to detect future signals of fertility issues.An ontology was developed using the Ontology Development 101 methodology. The domain and scope of the ontology were defined by compiling a list of competency questions. The terms were collected from Korean government reports, Korea's Basic Plan for Low Fertility and Aging Society, a national survey about marriage and childbirth, and social media postings on fertility issues. The classes and their hierarchy were defined using a top-down approach based on an ecological model. The internal structure of classes was defined using the entity-attribute-value model. The description logics of the ontology were evaluated using Protégé (version 5.5.0), and the content coverage was evaluated by comparing concepts extracted from social media posts with the list of ontology classes. The structural and representational layers of the ontology were evaluated by experts. Social media data were collected from 183 online channels between January 1, 2011, and June 30, 2015. To detect future signals of fertility issues, 2 classes of the ontology, the socioeconomic and cultural environment, and public policy, were identified as keywords. A keyword issue map was constructed, and the defined keywords were mapped to identify future signals. R software (version 3.5.2) was used to mine for future signals.A determinants-of-fertility ontology comprised 236 classes and terminology comprised 1464 synonyms of the 236 classes. Concept classes in the ontology were found to be coherently and consistently defined. The ontology included more than 90% of the concepts that appeared in social media posts on fertility policies. Average scores for all of the criteria for structural and representations layers exceeded 4 on a 5-point scale. Violence and abuse (socioeconomic and cultural factor) and flexible working arrangement (fertility policy) were weak signals, suggesting that they could increase rapidly in the future.The determinants-of-fertility ontology developed in this study can be used as a framework for collecting and analyzing social media data on fertility issues and detecting future signals of fertility issues. The future signals identified in this study will be useful for policy makers who are developing policy responses to low fertility.
Impact of Service Quality Factors in the Perceived Welfare among Customers of Public Services in the UAE

The study aims to examine the impact of the five different service quality dimensions on customer welfare in the UAE. The five service quality dimensions are tangible, reliability, responsiveness, assurance, and empathy. The proposed model has five independent variables and one dependent variable. The study has five direct relations. The following is a discussion of the hypothesis development. The study is a scientific study and designed as a quantitative mono-method based on original data collected by the use of a questionnaire and statistical analysis by using PLS-SEM Techniques. The population includes 8 million cases, and the sample size based on the Morgan table is 384. Three cities are chosen based on the population size, as it is the main and most populated city. However, in every city, data collection will take place in main commercial locations, public areas such as parks, malls, etc., or could be collected online. The results show that tangibility and reliability have no significant impact, but the other three dimensions have.
FPGA Implementation of Low Latency and Highly Accurate Median Filter Architecture for Image Processing Applications

The proposed work represents the hardware design and implementation of a median filter that uses architecture, which produces median results hierarchically once data in the sliding window is completely available. The median filter was designed based on the sorting methods; here, it makes use of a non-conventional algorithm where entire sorting of data is not required. The proposed three-value sorter where the parallel comparison of data is done is the fastest method as compared to other methods. The work is implemented in Xilinx Vivado 2016.4 software, and the performance analysis is carried out using Cadence 90 nm technology and with the supply voltage of 0.9 V. Area, power, and delay parameters of the implemented architecture for different filter sizes are computed. The hardware implementation of the median filter architecture is done by using Nexys4 DDR FPGA kit making use of block ROM, block RAM, and clocking wizard. Here, the entire operation is carried out at 25.132 MHz frequency.
Pretrain-Finetune Based Training of Task-Oriented Dialogue Systems in a Real-World Setting

One main challenge in building task-oriented dialogue systems is the limited amount of supervised training data available.In this work, we present a method for training retrievalbased dialogue systems using a small amount of high-quality, annotated data and a larger, unlabeled dataset.We show that pretraining using unlabeled data can bring better model performance with a 31% boost in Recall@1 compared with no pretraining.The proposed finetuning technique based on a small amount of high-quality, annotated data resulted in 26% offline and 33% online performance improvement in Recall@1 over the pretrained model.The model is deployed in an agentsupport application and evaluated on live customer service contacts, providing additional insights into the real-world implications compared with most other publications in the domain often using asynchronous transcripts (e.g.Reddit data).The high performance of 74% Recall@1 shown in the customer service example demonstrates the effectiveness of this pretrain-finetune approach in dealing with the limited supervised data challenge.
Working Collaboratively with Vendors to Create the Products You Want: Smooth Sailing Ahead

SUMMARY. Yvette Diven and Cathy Jones talked about how collaboration between librarian and vendor teams, or even between vendors, can be mutually productive and help create products and services that meet real needs of libraries. This session was intended to give librarians a better idea of how and with whom they may consider collaborating. It gave some definitions and descriptions of the software development process. It also provided some common sense guidelines for the process for both librarians and vendors. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com&gt; Website: &lt;https://www.HaworthPress.com&gt;]
Progress towards Czech National Cybersecurity Qualifications Framework

Cybersecurity is a crucial aspect of the progress towards cybergovernance. The coordinated and joined approach to ensure high level of cybersecurity throughout the country´s ICT infrastructures involves apart from the centralized cybersecurity activities and collaboration among CSIRTs also unified understanding of cybersecurity requirements, roles and activities among commercial entities, governmental agencies as well as individual cybersecurity experts. We present our current progress towards establishing such qualifications framework in the Czech Republic.
Modern Pedagogical Technologies in Teaching Socio-Humanitarian and Natural-science Disciplines in Higher Educational Institutions

Technological development of the educational process means the organization of an effective didactic process in accordance with the established educational objectives. The development of professional and pedagogical skills of teachers of socio-humanitarian and natural-science disciplines requires the organization of their educational and cognitive activities on the basis of a technological approach. For this purpose, it is used in the design of pedagogical technologies related to foreign didactic. These projects are tested by pedagogical experience — the dynamics of the development of pedagogical abilities of teachers of socio-humanitarian and natural-science disciplines are studied and conclusions are made. The scientific solution of this social order requires the transformation of the professional and pedagogical activity of higher educational institutions teachers and the development of personal qualities into a purposeful, consistent and continuous process. Therefore, the essence of this research is to improve the quality of lessons using the advanced pedagogical technologies and their effective use in the education of the younger generation on the basis of an integrated approach.
Taking Care of The Discretization Problem:A Black-Box Adversarial Image Attack in Discrete Integer Domain

Numerous methods for crafting adversarial examples were proposed recentlywith high success rate. Since most existing machine learning based classifiersnormalize images into some continuous, real vector, domain firstly, attacksoften craft adversarial examples in such domain. However, adversarialexamples may become benign after denormalizing them back into the discreteinteger domain, known as the discretization problem. This problem was mentionedin some work, but has received relatively little attention. In this work, we first conduct a comprehensive study of existing methods andtools for crafting. We theoretically analyze 34 representative methods andempirically study 20 representative open source tools for crafting adversarialimages. Our study reveals that the discretization problem is far more seriousthan originally thought. This suggests that the discretization problem shouldbe taken into account seriously when crafting adversarial examples andmeasuring attack success rate. As a first step towards addressing this problemin black-box scenario, we propose a black-box method which reduces theadversarial example searching problem to a derivative-free optimizationproblem. Our method is able to craft adversarial images by derivative-freesearch in the discrete integer domain. Experimental results show that ourmethod is comparable to recent white-box methods (e.g., FGSM, BIM and C\&W) andachieves significantly higher success rate in terms of adversarial examples inthe discrete integer domain than recent black-box methods (e.g., ZOO, NES-PGDand Bandits). Moreover, our method is able to handle models that isnon-differentiable and successfully break the winner of NIPS 2017 competitionon defense with 95\% success rate. Our results suggest that discreteoptimization algorithms open up a promising area of research into effectiveblack-box attacks.
Blockchain Technology: A Way Ahead in Insurance Sector

Blockchain is considered as one of the most important technology of the future. To do transaction in virtual money or digital currencies like bitcoin, gram, libra which is to be considered as the future money, would become possible as a result of the technologies like blockchain. Blockchain technology can be considered as one of the most important technology that can be implemented in insurance sector in near future. Everything that looks shining stars in the beginning that always have risk associated with it. Risk like huge expenditure on infrastructure, risk of adoption of technology by the investors/customers, risk of safety or security, risk that competitor will get first mover advantage. These are just a few examples, but lot more risk can be associated with it. Blockchain technology can either create the revolution in insurance sector or it can also be replaced just like the previous many technology. Even due to the pandemic situation dependency on technology can easily vouched. When transactions become digital then to record the same transaction is much more important. Blockchain technology is one of the most important technology that record the transaction through creating a block which encrypt the data so that it can only accessed by the authorized personal. Insurance sector in which paying the premium, putting a claim for the losses, settlement of claim etc. which required fast process, blockchain can play a vital role in this. It reduces time along with maintaining the secrecy. But as every coin have two sides, blockchain requires knowledge of the technology, investment in huge infrastructure as well as providing training to the associated parties. One should be very vigilant before applying the blockchain technology.This research paper is an attempt to provide the future scenario of blockchain technology in insurance sector.
Reasoning Over Virtual Knowledge Bases With Open Predicate Relations

We present the Open Predicate Query Language (OPQL); a method for constructing a virtual KB (VKB) trained entirely from text. Large Knowledge Bases (KBs) are indispensable for a wide-range of industry applications such as question answering and recommendation. Typically, KBs encode world knowledge in a structured, readily accessible form derived from laborious human annotation efforts. Unfortunately, while they are extremely high precision, KBs are inevitably highly incomplete and automated methods for enriching them are far too inaccurate. Instead, OPQL constructs a VKB by encoding and indexing a set of relation mentions in a way that naturally enables reasoning and can be trained without any structured supervision. We demonstrate that OPQL outperforms prior VKB methods on two different KB reasoning tasks and, additionally, can be used as an external memory integrated into a language model (OPQL-LM) leading to improvements on two open-domain question answering tasks.
Leveraging attention‐based visual clue extraction for image classification

Deep learning-based approaches have made considerable progress in image classification tasks, but most of the approaches lack interpretability, especially in revealing the decisive information causing the categorization of images. This paper seeks to answer the question of what clues encode the discriminative visual information between image categories and can help improve the classification performance. To this end, an attention-based clue extraction network (ACENet) is introduced to mine the decisive local visual information for image classification. ACENet constructs a clue-attention mechanism, that is global-local attention, between the image and visual clue proposals extracted from it and then introduces a contrastive loss defined over the achieved discrete attention distribution to increase the discriminability of clue proposals. The loss encourages considerable attention to be devoted to discriminative clue proposals, that is those similar within the same category and dissimilar across categories. The experimental results for the Negative Web Image (NWI) dataset and the public ImageNet2012 dataset demonstrate that ACENet can extract true clues to improve the image classification performance and outperforms the baselines and the state-of-the-art methods.
On the theory of innovative education

Innovative education is a purposeful process of education and innovative training of a person, contributes to the development of his creative abilities, self-learning skills, self-improvement. The main objective of innovative education, formulated from the perspective of the mechanisms for the development of post-industrial civilization, is to ensure that the quality of human development is ahead of the way. As an important area of social activity, education outpaces other forms of social activity in its development. Innovative activity in education is a multi-component social activity. Innovations in professional education express the integrative content of technical and technological, pedagogical, organizational and managerial, socio-economic innovations. These innovations ensure the innovative development of not only professional education, but also science, production, economy, management and the social sphere. Innovative processes in vocational education are put into practice in innovative education. Pedagogical innovations involve a personal and creative process of organizing the educational process, they cover the entire field of education. The contradiction between the demand of society for the quality of vocational training of university graduates, able to purposefully and effectively carry out self-education and self-preparation for productive activities in the constantly changing external environment and the real practice of educational process in universities, actualizes the problem of self-education and self-training of students for innovative professional activities. Innovative process and innovation in connection with the implementation of new state educational standards in Russian secondary and higher education are gaining special status.
Development of the Evaluation Instrument of the Child-Friendly School Policy in Elementary Schools

Policy evaluation is essential to determine the success of the policy implementation in achieving its objectives.For evaluation purposes, a set of valid and reliable instruments is needed in order for the data could describe the object being measured.It is necessary to use a child-friendly school assessment instruments (CFS-AI) when the child-friendly policy is implemented.This research aimed to develop a measurement instrument of child-friendly school policy and program in elementary school with the Context, Input, Process, and Product (CIPP) evaluation model.The research used a quantitative-qualitative approach as suggested by Onwuegbuzie (2010), which an assessment instrument developed with mixed-methods analysis.The sample of this research consist of teachers, principals, parents, and community members around the school.The data were analyzed by Exploratory Factor Analysis (EFA) using the SPSS program to determine the validity and reliability of an instrument.The results showed the child-friendly school assessment instrument (CFS-AI) developed in four assessment aspects has fulfilled the requirements of validity and reliability.It implies that CFS-AI has fullfilled the standards to measure the success of the implementation of child-friendly schools in elementary schools.This instrument is comprehensive, precise, and consistent to measure the implementation of childfriendly school policy.
Improving Label Noise Robustness with Data Augmentation and Semi-Supervised Learning (Student Abstract)

Modern machine learning algorithms typically require large amounts of labeled training data to fit a reliable model. To minimize the cost of data collection, researchers often employ techniques such as crowdsourcing and web scraping. However, web data and human annotations are known to exhibit high margins of error, resulting in sizable amounts of incorrect labels. Poorly labeled training data can cause models to overfit to the noise distribution, crippling performance in real-world applications. In this work, we investigate the viability of using data augmentation in conjunction with semi-supervised learning to improve the label noise robustness of image classification models. We conduct several experiments using noisy variants of the CIFAR-10 image classification dataset to benchmark our method against existing algorithms. Experimental results show that our augmentative SSL approach improves upon the state-of-the-art.
Adaptive color space model based on dominant colors for image and video compression performance improvement

This paper describes the use of some color spaces in JPEG image compression algorithm and their impact in terms of image quality and compression ratio, and then proposes adaptive color space models (ACSM) to improve the performance of lossy image compression algorithm. The proposed ACSM consists of, dominant color analysis algorithm and YCoCg color space family. The YCoCg color space family is composed of three color spaces, which are YCcCr, YCpCg and YCyCb. The dominant colors analysis algorithm is developed which enables to automatically select one of the three color space models based on the suitability of the dominant colors contained in an image. The experimental results using sixty test images, which have varying colors, shapes and textures, show that the proposed adaptive color space model provides improved performance of 3 % to 10 % better than YCbCr, YDbDr, YCoCg and YCgCo-R color spaces family. In addition, the YCoCg color space family is a discrete transformation so its digital electronic implementation requires only two adders and two subtractors, both for forward and inverse conversions.