Reviewerly/exHarmony huggingface dataset:
Advanced machine learning

Machine learning is the branch of artificial intelligence concerned with the use and development of computer systems that can learn and improve through experience. It has become the dominant data-driven approach in a great number of fields where an enormous amount of data is available, but it is difficult or unfeasible to develop conventional algorithms to perform the task. In this entry we focus on deep learning: a family of machine learning methods for extracting patterns from data using artificial neural networks, which has had tremendous success in performing complex tasks, from image recognition and medical diagnosis to language translation and content generation. A plethora of papers, pre-trained models and software frameworks have emerged to support neural network development. Neural networks have also become increasingly important in educational assessment and learning. Automatic feature extraction from student essays, predicting student behavior and future success, automated essay scoring, and content generation, are but a few current applications.
Evidence-centered design

Evidence-centered design (ECD) is a comprehensive framework that makes explicit the structure of assessment arguments, the elements and processes through which they are instantiated, and the interrelationships among them. This article provides an overview of ECD. It highlights the idea of layers in this process, structures and representations within each layer, and terms and concepts that can be used to guide the design of educational assessment. Examples from large-scale assessment and game-based assessment illustrate how ECD helps assessment designers articulate the design process.
Conversation-based assessment: current findings and future work

Conversation-based assessments (CBAs) build on advances in areas such as dialog systems, performance-based assessments, and adaptive, technology rich learning and assessment environments. CBAs have the potential to engage students in meaningful exchanges of information that can result in additional insights on the cognitive processes and strategies students use when solving problems, laying the groundwork for designing more personalized, “caring” assessment and learning tasks. This article reviews past and current work on CBAs and discusses challenges and future areas of research involving dialog with artificial characters for assessment and learning purposes.
The current trends and opportunities for machine learning in learning analytics

The goal of this article is to provide a brief review including the current trends and opportunities of the areas of research within the field of Learning Analytics (LA) and the Machine Learning (ML) to improve the educational experience of students. We begin by explaining the purpose of LA to help adapt computerized systems and inform teachers of student's knowledge, cognitive processes, and even affective states. Next, we provide a primer on ML models that are often included in LA. We then explain how LA with ML can adapt and provide recommendations based on students' needs as well as immediate feedback to students and score reports to teachers and other stakeholders. Finally, we address the current trends and opportunities within LA and how within this current time in history LA could be more useful than ever in helping all students achieve their educational goals.
Diagnostic classification modeling

This entry presents the diagnostic classification modeling (DCM) framework within the log-linear cognitive diagnosis model framework. DCMs are a family of psychometric models that measure categorical latent variables called attributes. Typically, a DCM-based assessment measures multiple attributes. Results are communicated via an estimated attribute profile which delineates an examinee's status for each attribute. In contrast with item response models using continuous latent variables, the multidimensional pattern of results from a DCM-based assessment offers more reliable information about fine-grained examinee characteristics. The intended uses of DCM-based results include better informing interventions tailored to examinee needs.
Bayesian networks

In this chapter, we provide a basic overview of the notion and properties of Bayesian networks. We then discuss belief propagation, message-passing, and the recently developed R package, BayesNetBP, that facilitates belief propagation on Bayesian networks. We provide a motivating example using BayesNetBP for probabilistic reasoning in the form of a familiar multinomial Bayesian network known as the discrete IRT model (Almond et al., 2015). Finally, we conclude with comments on important developments in the field of Bayesian networks.
Statistical methods for detection of test fraud on educational assessments

Producers and consumers of test scores are increasingly concerned about fraudulent behavior before and during the test. Wollack and Schoenig (2018) divided the types of fraudulent behavior into three main types: (a) test tampering, (b) answer-copying and collusion and (c) item preknowledge. There exist a wide variety of statistical methods for detecting fraudulent behavior on tests. Wollack and Schoenig (2018) noted the use of five types of statistical/psychometric approaches to detect various types of fraud. This paper provides a review of some of those statistical/psychometric approaches. Several real data examples are provided to demonstrate the application of some of the approaches to detect various types of fraudulent behavior on tests.
Latent class analysis

A statistical model can be called a latent class (LC) or mixture model if it assumes that some of its parameters differ across unobserved subgroups, latent classes, or mixture components. This rather general idea has several seemingly unrelated applications, the most important of which are clustering, scaling, density estimation, and random-effects modeling. This article describes simple LC models for clustering, restricted LC models for scaling, and mixture regression models for nonparametric random-effects modeling, as well as gives an overview of recent developments in the field of LC analysis. Moreover, attention is paid to topics such as maximum likelihood estimation, identification issues, model selection, and software.
A pedagogical framework for mobile learning in science education

As mobile technology is increasingly used in science teaching, it is crucial to understand how mobile devices can most effectively improve students' learning. We present a three-dimensional analytical framework: integration with learning activities; roles that students and teachers play; and learners' perception of the technology. Based on the framework, four implications are drawn regarding how to better implement mobile technology in science learning: (a) develop learning materials and activities that deeply integrate mobile technology; (b) coordinate students' and teachers' roles; (c) consider affective factors contributing to student achievement; and (d) design mobile learning in light of student gender differences.
Intelligent tutoring systems and conversational agents

This article reviews advances in computerized intelligent tutoring systems, particularly those with conversational agents that simulate human tutors, peers, and mentors. Empirical research on learning gains, engagement, and learner impressions have confirmed the promise of these digital technologies. The article identifies major components of these systems and presents example systems that cover different subject matters, skills, learner populations, and pedagogical functions. We also point out major challenges that need to be addressed for scaling up these systems to millions of learners throughout the globe.
Displaying information: from woolly mammoths to the Great Migration

Successfully negotiating the modern world often requires full and facile comprehension of quantitative phenomena. Experience gathered over more than three centuries has taught us this is best done by transforming the evidence into various kinds of pictures, so that we can enlist the remarkable power of the human visual system to rapidly decode and store complex information. This article traces the evolution of data visualization and in the process reveals many mysteries and their solutions.
Problem solving

A problem occurs when a situation is in one state, the problem solver wants it to be in another state, and there are obstacles preventing a smooth transition from the given state to the goal state. Problem solving refers to cognitive processing aimed at overcoming a problem. This entry examines the definitions of key terms, types of problems, phases in problem solving, and types of knowledge involved in problem solving. This entry also explores findings about problem solving that are most relevant to education including research on rigidity in thinking, problem solving transfer, productive thinking, insight, computer simulation of problem solving, problem solving in realistic situations, and teaching of thinking.
Networks and learning

This chapter introduces the field of research and educational innovation known as networked learning. It starts by arguing that taking a network perspective is a methodological choice, for researchers and for educators. Choosing to look at educational phenomena in network terms brings certain things into sharper focus, while shifting others to the margins. In particular, the network view emphasizes connections. It helps researchers to see relationships and educators to build them. The article summarizes a number of core ideas that are helpful in coming to understand learning networks and how they function. It provides a short history of developments in the field and some practical examples of networked learning, drawn from the area of teacher professional development.
Machine learning

Over the past several decades, machine learning has been used in many areas—including education. In this article, we generally describe artificial intelligence (AI) and machine learning (ML) and discuss how they relate to one another using everyday examples. Next, we zoom in on ML in terms of its definition, some well-known approaches, its historical roots, and uses in education. We then illustrate how ML can aid instructors and students relative to teaching and learning and present some ideas regarding the future of ML in education. We conclude with some important concerns related to ML applications in education moving forward.
Virtual reality and science, technology, engineering, and mathematics education

Virtual Reality (VR) is a three-dimensional digital environment which allows multiple degrees of freedom for the user to interact with the environment and engage in immersive interactions. Current foci in STEM Education disciplines require student to understand, manipulate, and incorporate complex and abstract ideas and observations. Some of the environments related to these ideas are impossible to replicate in educational settings. VR has the potential to create individual custom learning environments with appropriate levels of fluidity and immersion which promotes exploration and discovery helping to motivate, encourage, and excite students about STEM discipline content. Using VR, it becomes possible to replicate environments not otherwise available for students to explore. When considering the user experience, it is necessary to ensure there is sufficient fluidity and immersion within the VR environment for learning to occur. The lack of fluidity and immersion in a VR environment significantly negatively impacts learning outcomes and the user experience.
Your3dEmoji: Creating Personalized Emojis via One-shot 3D-aware Cartoon Avatar Synthesis

Creating cartoon-style avatars has drawn growing attention recently, however previous methods only learn face cartoonization in the 2D image level. In this paper, we propose a novel 3D generative model to translate a real-world face image into its corresponding 3D avatar with only a single style example provided. To bridge the gap between 2D real faces and 3D cartoon avatars, we leverage the state-of-the-art StyleGAN and its style-mixing property to produce a 2D paired cartoonized face dataset. We then finetune a pretrained 3D GAN with the pair data in a dual-learning mechanism to get the final synthesized 3D avatar. Furthermore, we analyze the latent space of our model, enabling manual control in what degree a style is applied. Our model is 3D-aware in the sense and also able to do attribute editing, such as smile, age, etc directly in the 3D domain. Experimental results demonstrate that our method can produce high-fidelity cartoonized avatars with true-to-life 3D geometry.
One port impedance quantization for a class of annihilation operator linear quantum systems

Theoretical developments in the field of superconducting microwave circuits involving continuous measurement feedback control as well as coherent feedback control are essential for advances in the domain of quantum technology. The smallest structures that can be fabricated by lithographic technologies request the inevitable development of the quantum control theory in order for quantum systems to function reliably in an engineering sense. Within these perspectives, this paper develops the positive real properties for a class of quantum systems involving annihilation operators only and then relates them to the corresponding bounded real properties and consequently to physical realizability conditions developed earlier by the authors. Based on these positive real properties of the annihilation operators quantum systems, it is shown that it is possible to use the Brune algorithm in order to find electrical circuits that can physically implement the type of quantum systems involving annihilation operators only. This would involve Josephson junctions, microwave cavities, and voltage sources. This theory, illustrated for one-port circuits, is essential for the implementation of microwave circuits especially those related to quantum filters found in the field of quantum computing.
The Register Variation in Translated Epidemic Prevention Manuals as Emergency Language Services

Using Biber's MD model, this paper investigates the register variation in crisis translation and discovers that learned exposition is the text type that comes closest to crisis translation. Crisis translation has “explicit” and “informational” features, according to statistics. When compared to the non-translational “learned and scientific” corpus, the translated corpus includes feature of “nominalization.” Furthermore, the researchers discovered that in Biber's MD model, the variables AWL and PHC had a strong liner connection with NOMZ.
Methodology of Designing Digital Services for the Educational Market

The article discusses methodological aspects and practical experience in designing digital services for the educational market. There are defined basic requirements for the creation of digital services, their properties and characteristics. Based on the theoretical analysis, it is proved that the most promising way of designing digital services, which determines their configuration and user capabilities, is the platform approach. The analysis of the available educational platforms is carried out, their advantages and disadvantages are shown. The authors present a digital services designing methodology of their own, worked out with the help of the progressive Agile and Waterfall methodologies. The paper gives an example of the developed methodology approbation within the project of the Citadel Education online learning platform, focused on the implementation of adaptive educational programs with the possibility of choosing individual trajectories. This research is brand new, as it systematizes theoretical ideas about the creation of digital services with specific product characteristics and develops a methodological base for the creation of customized and technological educational services that meet today’s user needs. The results of the study are of particular interest in the context of foreign educational platforms withdrawal from the Russian market and may be useful for university leaders, who solve the problems of improving educational programs competitiveness, implementing hybrid learning models, and managing the portfolio of training courses.
Not Exactly Lying: Fake News and Fake Journalism in American History, Andie Tucher (2022)

Review of: Not Exactly Lying: Fake News and Fake Journalism in American History , Andie Tucher (2022) New York: Columbia University Press, 384 pp., ISBN 978-0-23118-634-6, h/bk, USD 115.00/GBP 90.00, ISBN 978-0-23118-635-3, p/bk, USD 28.00/GBP 22.00, ISBN 978-0-23154-659-1, e-book, USD 27.99/GBP 22.00
Method for measuring the center of mass and moment of inertia of a model using 3D point clouds

We propose a method for measuring the center of mass and moment of inertia of a model using 3D point clouds. First, we use point cloud registration and segmentation to obtain point clouds of model parts with different material densities. Then, we correct the point cloud coordinates by principal component analysis, and we perform 2.5D volume calculations in three directions. Finally, we input the material density for each model part, perform point cloud reconstruction, and calculate the center of mass and moment of inertia. In experiments, we measured the center of mass and moment of inertia simultaneously by using multiple time-of-flight cameras. The results demonstrated that the proposed method accurately measured the center of mass and moment of inertia with errors of <1mm and <3%, respectively. This is a promising approach for automatically measuring models of aircraft, cars, etc.
On the Problem of a Student’s Obtaining Several Qualifications at the Same Time

This paper studies the issue of simultaneous gaining several qualifications by a student. There are considered two models (schemes) of such a possibility: 1) implementation of one bachelor or master program within several areas of training; 2) implementation of an educational program in the area of training with additional professional competencies assigned to another area of training. The author names the debating points and the limitations of the models considered.
High-throughput and high-accuracy diagnosis of multiple myeloma with multi-object detection

Multiple myeloma (MM) is a type of blood cancer where plasma cells abnormally multiply and crowd out regular blood cells in the bones. Automated analysis of bone marrow smear examination is considered promising to improve the performance and reduce the labor cost in MM diagnosis. To address the drawbacks in established methods, which mainly aim at identifying monoclonal plasma cells (monoclonal PCs) via binary classification, in this work, considering that monoclonal PCs is not the only basis in MM diagnosis, for the first we construct a multi-object detection model for MM diagnosis. The experimental results show that our model can handle the images at a throughput of 80 slides/s and identify six lineages of bone marrow cells with an average accuracy of 90.8%. This work makes a step further toward full-automatic and high-efficiency MM diagnosis.
Experiences from 30 years of low-density geochemical mapping at the subcontinental to continental scale in Europe

Experiences from 30 years of geochemical mapping at the (sub)continental scale in Europe using a wide range of different sample media are reviewed and discussed with a focus on the most recent GEMAS (GEochemical Mapping of Agricultural Soils) project. Comparing results from the different surveys it is possible to come to conclusions as to how geochemical surveys at the continental scale could best be designed. High analytical quality and lowest possible detection limits are key requirements. In Europe good outcomes were achieved with the &lt;2 mm fraction of soil samples and aqua regia extraction. Focus should be on high quality of sampling and analyses and more determined parameters rather than on more samples. The sample density of 1 site/2500 km 2 provides a good overview of the processes governing geochemistry at the continental scale. Results should be extensively published by the project team to allow the dataset to be known and utilized by the wider scientific community. Thematic collection: This article is part of the Continental-scale geochemical mapping collection available at: https://www.lyellcollection.org/topic/collections/continental-scale-geochemical-mapping
A Hybrid Multiple Models Transfer Approach for Cross-Project Software Defect Prediction

For a new project, it is impossible to get a reliable prediction model because of the lack of sufficient training data. To solve the problem, researchers proposed cross-project defect prediction (CPDP). For CPDP, most researchers focus on how to reduce the distribution difference between training data and test data, and ignore the impact of class imbalance on prediction performance. This paper proposes a hybrid multiple models transfer approach (HMMTA) for cross-project software defect prediction. First, several instances that are most similar to each target project instance are selected from all source projects to form the training data. Second, the same number of instances as that of the defected class are randomly selected from all the non-defect class in each iteration. Next, instances selected from the non-defect classes and all defected class instances are combined to form the training data. Third, the transfer learning method called ETrAdaBoost is used to iteratively construct multiple prediction models. Finally, the prediction models obtained from multiple iterations are integrated by the ensemble learning method to obtain the final prediction model. We evaluate our approach on 53 projects from AEEEM, PROMISE, SOFTLAB and ReLink four defect repositories, and compare it with 10 baseline CPDP approaches. The experimental results show that the prediction performance of our approach significantly outperforms the state-of-the-art CPDP methods. Besides, we also find that our approach has the comparable prediction performance as within-project defect prediction (WPDP) approaches. These experimental results demonstrate the effectiveness of HMMTA approach for CPDP.
Research on construction method of modular missile family general components based on self-organizing mapping

Abstract -As far as the missile family is concerned, the general components are an important part of the missile types in the missile family. At the same time, they are also a major advantage and feature of collaborative design of multiple missile types to form the missile family. In view of the poor universality of modules in traditional missile design, this paper proposes a general components construction method based on self-organizing mapping, which aggregates the components with similar structure and little difference in performance parameters as general components, so as to maximize the proportion of general components in the component library under the condition of meeting the performance index, and then improve the efficiency of missile equipment system while reducing the design cost.
A vector autoregression (VAR) analysis of corruption, economic growth, and foreign direct investment in Ghana

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