Reviewerly/exHarmony huggingface dataset:
An Item Analysis of the Mock CSAT English Test

This study analyzed students’ test performance on the mock College Scholastic Ability Test (CSAT) English test to examine the reliability and validity of the test items within a framework of Classical Test Theory (CTT). The test used for the present study was the mock CSAT English test administered in June 2021 and item difficulty data was collected from 100 3rd grade high school students who took the test. For the item analysis, Test Analysis Program (TAP), a computational tool was used to investigate the extent to which the mock CSAT is a valid test based on item difficulty, item discrimination and distractors’ attractiveness. According to the item analysis, it was found that the reliability of the test was as high as .876, which is reliable and the level of difficulty was .713, indicating that it wasin the desirable range between .25 and .75. The mean discrimination power was .337, which is within the desirable range of over .3. However, an imbalance of item difficulty between the listening and reading section were detected with unreasonably weighted items and implausible distractors. Therefore, this study provides implications for improvement for the mock CSAT, which can also apply to future CSATs.
OCLAR: Logistic Regression Optimization for Arabic Sentiment Costumer Reviews

The recognition and classification of sentiments in customer feedback are crucial for improving the service experience. Sentiment analysis (SA), as one of natural language processing (NLP) applications, evaluates customers' reviews by computing polarity text sequences. This paper extends the brute grid search methodology with pipeline architecture on OCLAR dataset. The architecture facilitates the search for inverse regularisation strength (IRS) parameter of logistic regression (LR). The experiments are evaluated on simultaneously different measures as like accuracy, area under the curve (AUC), etc. The experiments showed 0.10% improvement in AUC measure using bag-of-words (BoW) with n-gram levels of unigrams, bigrams, and trigrams, whereas 0.9% enhancement has been achieved by using term frequency and inverse document frequency (TF*IDF). In particular, the BoW classifier has achieved the largest improvement of 0.21% recall and 0.27% f-measure in negative predication, whereas it has achieved 0.03% precision and 0.01% f-measure in positive predication.
1. Learning Analytics as a Metacognitive Tool

The research described in this book searches for the answers on how learners learn in todays open and networked learning environments and how learners, educators, institutions, and researchers can best support this process. There is sufficient data available on virtual learning environments, provided by learning analytics, on student and teacher behaviour and performance, but there is no common practice among teachers in higher education for using this data to improve the learning and teaching process. Learning analytics and data may inform and improve open and online learning from the point of view of teacher and learner awareness about their behaviour and their learning and teaching methods. The idea of describing learning analytics as a metacognitive tool, suggesting a development of metacognitive decision-making skills in teacher education, and focusing on learning design in higher education by using data from learning analytics served as the main focus of this research. The aim of the research was to create the model of application of learning analytics method as a metacognitive tool to enhance student success. The aim of the research was reached through theoretical and empirical objectives, namely: describing the learning analytics method as a metacognitive tool; revealing teacher metacognitive practices
Teachers' Awareness and Analysis of the Current Status of Disposable Plastic in Science Textbooks for Elementary School

This study analyzed the disposable plastic experiment equipment and materials included in science textbooks for elementary schools to understand their current status and surveyed elementary school teachers on their awareness of the need for such equipment to be included in textbooks, their educational benefits, and possible alternatives. The analysis of the inquiry activities revealed that various disposable plastic equipment and materials were included for different grades and domains. The annual consumption of such experiment equipment and materials in the scientific inquiry in each year was estimated based on the consumption amount by school years and items. The survey of the teachers indicated that most used plastic in their inquiry activities and had a positive idea on the use of plastic equipment and materials in consideration of the interest, safety, and convenience for students. They also agreed that disposable plastics need to be substituted, which should be reflected in the scheduled revision of textbooks.
Machine Learning on Graph-Structured Data

Several real-world complex systems have graph-structured data, including social networks, biological networks, and knowledge graphs. A continuous increase in the quantity and quality of these graphs demands learning models to unlock the potential of this data and execute tasks, including node classification, graph classification, and link prediction. This tutorial presents machine learning on graphs, focusing on how representation learning - from traditional approaches (e.g., matrix factorization and random walks) to deep neural architectures - fosters carrying out those tasks. We also introduce representation learning over dynamic and knowledge graphs. Lastly, we discuss open problems, such as scalability and distributed network embedding systems.
Learning Discriminative Models in General

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Methods

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Taming Educators’ Tech Beliefs

Forty years of empirical research on the use of ICT (Information and Communication Technologies) in schools has repeatedly shown that ICT is by no means “wild” and “threatening”, but rather largely ineffective. Therefore, I argue that the “wildness” of ICT in schools is primarily a matter of impactful teachers’ beliefs. I analyse some elements of these beliefs and suggest that its them that need “taming”. On this basis I propose that they can be tamed by becoming closely familiar with their content and structures, by professionalising them based on research and thirdly by reframing the use of ICT in school as a question of enabling participation in a world that is changing through digital transformation.
Modules

In this chapter, you will learn: how to declare modules, what the implicit readability of a module means and how to declare it, the difference between unqualified and qualified exports, declaring the runtime optional dependency of a module, and much more about modules in Java.
Observer-Based Secure Communication of Chaotic Systems

In this paper, the problem of the secure communication using the observer-based chaotic master-slave scheme will be investigated. First, the transmitted signals will be retrieved in a specified finite time using the dual-observation scheme. Next, without exact but only the element bounds knowledge of initial conditions of chaotic systems, the state synchronization and signal retrieval errors will be bounded at the prescribed moments by the interval state estimation strategy. Finally, a modified interval state estimation strategy will be proposed to relax the cooperativity Hurwitz and Metzler assumptions. An example with simulations is given to illustrate the application of our results.
Mediator

A large proportion of the code we write has different components (classes) that communicate with one another through direct references or pointers. However, there are situations when you don’t want objects to be aware of each other’s presence. Or, perhaps you do want them to be aware of one another, but you still don’t want them to communicate through pointers or references because those can go stale and the last thing you want is to dereference a nullptr.
Effects and Mechanism of Weibo’s Negative Emotions on Covid-19 Related Retweets Based on Big Data Collection Technology

This study based on big data collection technology with Weibo contents to reveal the relationship between negative emotion and information diffusion during Covid-19 pandemic. Specifically, focusing on how negative emotion influences the number of reposts (retweets). From January 23 to February 7 2020, 176,934 Weibo posts collected with the keyword “novel coronavirus pneumonia”. Negative binomial regression method is applied to construct an empirical model between negative emotion and retweets. Regression results demonstrated that there is not a single linear relationship between the two, when the negative emotion exceed a certain level, retweets would decrease instead. Our results implicate risk communication can be manipulated by controlling the negative intensity in social media contents, even under extreme risk.
Images, Transforms and Sampling

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Reducing Organizational Anomie in Light of Entrepreneurial Behavior

The research aims to clarify the role of the main variable represented by the four dimensions of entrepreneurial behavior (creative, risk-taking, seizing opportunities, proactivity), in Reducing the dependent variable of organizational anomie with the dimensions (Organizational Normlessness, Organizational Cynicism, Organizational Valuelessness).&#x0D; The experimental, analytical method was adopted in the completion of the research, and an intentional sample of (162) individuals in the administrative levels (higher and middle) in the factory was taken. The questionnaire was also adopted as the main tool, which included (54) paragraphs, as (162) forms were distributed and 144 were retrieved from them, valid for statistical analysis from the total forms distributed to the factory.&#x0D; The most important results of the research showed the effect of the dimensions of entrepreneurial behavior and directly on organizational anomie, and that the relationship of entrepreneurial behavior and at the total level was positive with organizational anomie. They ignore the values ​​of work to achieve their goals, as well as the existence of a spirit of cooperation and one team among workers in the factory
The Learner in MALL

The main focus of this chapter is the ultimate user of MALL, the learner. The chapter begins with exploring learner agency to help us to gain a better understanding of how and why learners are or are not able to make appropriate choices applicable to their learning. Agency may be related to individual characteristics of the learner, but it may also be supported by proxy or collective agency from the teacher or larger environment. Even learners who exhibit agency experience difficulties in making appropriate choices about their learning, which is why learner training is essential. In MALL, learner training is key in ensuring that learners understand the reasons for engaging in mobile activities as well as what is expected of them from teachers. The chapter describes evidence that ongoing training in technological, strategic and pedagogical aspects can have a very powerful effect on the ways in which learners view mobile learning activities.
Mobile Creches: Innovating for Inclusive and Quality Early Childhood Development Programs

The benefits of early childhood education are well known, but access to quality programs is a challenge for children living in marginalized communities.
Virtual assistants and privacy: An anticipatory ethical analysis

With the use of the simple phrase, “OK, Google,” or “Hey Alexa…” a user has the world at their fingertips. Virtual assistants are what makes up a whole new artificial intelligence market where these new voice-activated systems are now assisting humans not only with daily tasks but also with making decisions. The virtual assistant market is focused on having the virtual assistant perform a wide range of activities where they want humans do less, and technology does more. Virtual assistants provide help with simple tasks but they are now a major source for research, finance management and other services that use machine learning as the basis of analysis. Virtual assistants function through the use cloud-based program where they use previous data to enhance the current device’s performance. While virtual assistants make day-to-day life much easier for the individuals who use it, there is still a question of privacy and who has access to the information gathered by the personal assistant. The technical issues with virtual assistants include data being skewed and a breach of privacy by storing information on the cloud server rather than on the computer’s hard drive, queries being sent to various companies where the data may be stored for up to two years, and saved queries being connected to their users data in order to associate the collected information with a profile created with the user’s data. By having these flaws related virtual assistant devices, consumers are less likely to trust this form of technology that the market is trying to promote and enlarge. These technical issues lead to ethical concerns that will analyzed in this discussion. The ethical issues that arise from the technological concerns with virtual assistants are the lack of notification that is given to the user when this information gets stored. The user has little, or no, knowledge regarding to where their personal data is going and where it is being stored. Companies are known to have used the information collected to create a more micro-targeted form of marketing, leading them to deeper profits. The social consequence of virtual assistants is that the consumer is left in the dark when it comes to their own, private, personal information. Companies claim that once a user agrees to their terms and conditions that they now own the consumers data but that information still belongs to an individual, creating a confusing dynamic of who owns what data. Despite the technical and ethical issues that are related to virtual assistants, there are several benefits to this new technology that has helped shaped the climate of the technological world. This analysis will examine examples of different virtual assistants, the ethical problems that are associated with them, and carry out an anticipatory ethical analysis of problems that may arise in the future. From this analysis policy can be developed about personal assistants.
Datatypes

There are a lot of datatypes to choose from. This chapter explores each of the 22 built-in datatypes, explaining how they are implemented, and how and when to use each one. First up is a brief overview of National Language Support (NLS), a basic knowledge of which is necessary to fully understand the simple string types in Oracle. We then move on to the ubiquitous NUMBER type. Next the LONG and LONG RAW types are covered, mostly from a historical perspective. The main objective here is to show how to deal with legacy LONG columns in applications and migrate them to the LOB type. Next, we delve into the various datatypes for storing dates and time, and investigating how to manipulate the various datatypes to get what we need from them. The ins and outs of time zone support are also covered. Next up are the LOB datatypes. We’ll cover how they are stored and what each of the many settings such as IN ROW, CHUNK, RETENTION, CACHE, and so on mean to us. When dealing with LOBs, it is important to understand how they are implemented and how they are stored by default—especially when it comes to tuning their retrieval and storage. We close the chapter by looking at the ROWID and UROWID types. These are special types, proprietary to Oracle, that represent the address of a row. We’ll cover when to use them as a column datatype in a table (which is almost never). Lastly, we cover the new JSON datatype and explore its uses.
An analysis of the research trend in Korean education for learners in the Arab region.

The purpose of this study is to analyze the research trends in Korean education for learners in the Arab region. This paper also suggests directions for the future research on the field of Korean education for these learners. To conduct this study, a process of collection and sorting of research papers was held as the first step. Consequently, 64 papers were chosen. Among 64 papers, 31 academic journals and 33 theses were included. The study then reviewed the research trend by years, publishing types, and topics. Although there were very few studies in the 2000s, the number of studies has steadily increased after 2010. This was related to the Korean Wave in Arab countries and the increase in Arab students who study in Korean from 2013. In the topic analysis by the topics, ‘Teaching and Learning’ accounted for the largest proportion. The second largest proportion was ‘Korean Education Content’. Next was ‘Korean Education in general’, followed by ‘Contrastive Analysis’. Overall, there have been many studies based on contrast or error analysis between Arabic and Korean languages. In terms of contents, there were many studies on phonetics and culture. However, it was found that there are many studies that need to be addressed in each sub-topic. Lastly, based on the analysis in this study, the characteristics and limitations of research trends were analyzed, and future research directions were suggested.
A Comprehensive Survey on Data Mining Techniques for Logical Analysis of Data in Content Based Image Retrieval System

This paper provides a comprehensive survey of the recent technical achievements in high level semantic based image retrieval. It identifies five major categories of the state of the art techniques in narrowing down the semantic gap. Those are (a) Data Mining techniques for the data analysis, data accessing and knowledge discovery processor to show experimentally and practically that how consistent, able and fast are these techniques for the study in the particular field, A solid mathematical threshold (0 to 1) is set to analyze the data. (b) Low level image features like Color, Texture, Shape and Spatial location [1]. (c) Similarity measurement and reducing the semantic gap. (d) A constructed decision tree presents effective models of decision-making, which can be learned to support image classification by the expert. A tool for data mining and image processing is presented and its application to image mining is shown on the task of Hep-2 cell-image classification. (e) Most reports of CBIR systems provide only qualitative measures of performance based on how similar retrieved images are to a target. Experiment 2 puts Picture Hunter into this context with a more rigorous test. We first establish a baseline for our database by measuring the time required to find an image that is similar to a target when the images are presented in random order. To implement a full-fledged image retrieval system with high-level semantics requires, the integration of salient low-level feature extraction, effective learning of high-level semantics, friendly user interface, and efficient indexing tool.
Testing and Debugging Your Quantum Programs

Writing your quantum programs should not be a whole lot different from writing your classical programs. To be able to check that your quantum programs are running as intended, you are able to watch their behavior and check their inner workings.
What Geoscientists Want: Short and Sweet Commands with Eco-friendly Data

Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing the latest version by default [v1]What Geoscientists Want: Short and Sweet Commands with Eco-friendly DataAuthorsCharlesZenderiDSee all authors Charles ZenderiDCorresponding Author• Submitting AuthorUniv California IrvineiDhttps://orcid.org/0000-0003-0129-8024view email addressThe email was not providedcopy email address
Research on decentralized crowdsourcing mechanism based on blockchain

In recent years, crowdsourcing has been widely used in many actual businesses. Foreign websites such as Uber and Upwork, and domestic websites such as Zhubajie have all adopted the crowdsourcing model to achieve success. However, traditional crowdsourcing uses a three-party centralized model, including task publishers, workers, and crowdsourcing platforms. This scheme has problems such as a single point of failure, lack of trust in all parties, high user risks and costs, and privacy protection. This article proposes a crowdsourcing scheme based on blockchain technology to build a decentralized crowdsourcing model for task publishers and workers. Blockchain technology is used to establish transactions between task publishers and workers. And combined with smart contract technology to support crowdsourcing mechanisms and task management. At the same time, this paper will also propose a decentralized crowdsourcing operation mechanism based on blockchain to ensure that the blockchain crowdsourcing platform does not require the participation of third parties. And can complete the reasonable transaction between the task publisher and the workers through the credit mechanism, transaction mechanism, privacy protection mechanism. Finally, the feasibility of the scheme is verified through smart contract experiments on Ethereum.
Semantic Storytelling: From Experiments and Prototypes to a Technical Solution

In the past we experimented with variations of an approach we call semantic storytelling, in which we use multiple text analytics components including named entity recognition and event detection. This chapter summarizes some of our previous work with an emphasis on the detection of movement action events, and describes the long-term semantic storytelling vision as well as the setup and approach of our future work towards a robust technical solution, which is primarily driven by three industry use cases. Ultimately, we plan to contribute an implemented approach for semantic storytelling that makes use of various analytics services and that can be deployed in a flexible way in various industrial production environments.
NP vuorovaikutuksen yksikkönä

Arvioitu teos: Tsuyoshi Ono &amp; Sandra A. Thompson (toim.): The ‘noun phrase’ across languages. An emergent unit in interaction. Typological Studies in Language 128. Amsterdam/Philadelphia: John Benjamins 2020. 366 s. isbn 978-90-272-0499-8.
BIG DATA LEARNING THROUGH TEXT ANALYTICS LABELED COMPOUNDS OF THE IoT BIO ENVIRONMENT

Data studies frequently have much more time to focus and attempt to fix frequent issues from over Internet of things predictive analytics.The overall purpose of this analysis is on information visualization, associated problems, associated research methodology Development, and the possibility to develop universal inventory network bio IoT environment visualizations.This publication comprises research articles.To continue with the analysis procedure.But that's all.The publication offers a summary of whatever doesn't disclose results.Thus far in the IoT environment of data visualization and has been done.Throughout this line of science, implementation of information accumulation.This article highlights the problems of IoT