Reviewerly/exHarmony huggingface dataset:
So You Want to Be a Tutor? Professional Development and Scenario-Based Training for Adult Tutors

Tutoring is one of the most powerful academic interventions used to increase student achievement. In response, organizations are developing tutoring programs and discovering a common problem – a shortage of qualified and experienced adult tutors. We introduce Personalized Learning Squared (PLUS), a holistic tutoring platform designed to improve tutoring efficiency and workplace training. PLUS combines human tutors and artificial intelligence (AI)-powered math software to double math learning gains for low-income middle school students. In order to provide differentiated student support, tutors need to be trained in supporting math content and student socio-motivation. Currently, there is a wide range of experience among adult tutors, especially younger, part-time tutors. Not only do these tutors have varied initial skill levels, but there is also high turnover (40% annually) among such youth workers. PLUS specializes in providing efficient and low-cost tutor training while delivering situational experiences to inexperienced tutors. Through previous research, we isolated key competencies of successful tutoring, Social-Emotional Learning, Mastering Content, Advocacy, Relationships, Technology Tools (called our SMART framework) and developed synchronous, interactive training and, PLUS-housed, asynchronous lessons. We have documented evidence of tutor learning gain with typical tutors performing ~20% better on the posttest compared to the pretest simulations and scenarios. Currently, we are optimizing asynchronous lesson design using learner sourced data to create real-life scenarios and authentically challenging multiple-choice tasks. In addition, this present work discusses (a) determining the required training components and lessons completed by tutors to demonstrate mastery and (b) designing a personalized pathway to PLUS tutor certification.
Massive Open Online Courses at Ukrainian Agrarian Universities: To Be or not to Be

Many Ukrainian universities successfully use MOOCs, YouTube channels, their own distance courses and other electronic materials together with the printed textbooks, dictionaries etc. in educational process. Before COVID-19, a new version of Moodle was installed at Lviv National Environmental University (LNEU) server and newer versions of distance courses were compiled for students of different specialties at LNEU five Faculties. Because of COVID-19 as well as the Russian-Ukrainian war, the rate of compiling the distance courses at the University became slower. That is why the teaching staff of LNEU decided to use online courses out of Ukrainian and foreign MOOC platforms as additional educational materials for distance courses, textbooks, lecture courses, etc. Our university students also analyzed different MOOC platforms and tried to search for the online courses and other electronic educational materials for their specialties in the form of research work. The results of the first-year students’ research were discussed at the Ukrainian and international student’s conferences and summed up in our report in 2022. The purposes of the present report are as follows: to describe the prospects of further search for educational material from MOOC platforms, other electronic resources for the second-year students and the materials analysis; to present the ways of spreading the results of students’ research work and participation at the scientific conferences for exchanging their experience in usage of the reviewed materials; and to represent some electronic education materials discussed by professors of different Ukrainian HEIs and implemented for their students as well as the software and other materials created by the teaching staff of LNEU for the first- and second-year students.
Enhancing the Performance of Cloud Environment by a Novel Three-Stage Task Scheduling Policy

Cloud computing is a method of granting remote access to assets that have been dispersed. In distributed decentralized systems such as clouds, one of the most important performance problems is how to ensure that activities are finished within the expected durations while fairly and efficiently utilizing available bandwidth and compensating for variations in resource performance. The cloud client and specialist co-op look at cloud administration from different perspectives. The cloud customer is concerned with reducing response time and administration expenses, while the cloud provider is concerned with making effective use of cloud assets, ensuring administration reliability, and lowering maintenance costs. Previously, numerous models such as replication heuristics, migration, and reservation were used to address these types of issues using a number of different scheduling strategies; nevertheless, still they have certain unsolved issues. The proposed work presents a dynamic three-stage job scheduling model in the cloud to improve task scheduling performance. The proposed model employs the job attributes to bind them with the resource which has the shortest possible completion time, while simultaneously using the providers’ resources efficiently and satisfying the quality of service (QoS) standards of cloud users. This method of scheduling works by calculating the expected value for every task that is proportionate to how the features of that job relate to the characteristics of all other jobs combined. As a result, their virtual machine selection should be more in line with their expectations, and hence more equitable. The testing results show a significant reduction in task completion time as well as energy usage.
Tool for Processing IoT-Sensor Measurement with a Moving Horizon Filter Approach

An interactive interface for measurement extraction and estimation is addressed. The interface aims to facilitate the measurement collection, extraction, and preprocessing with built-in tools and an adopted estimator. Such an interface allows the end-user to establish a connection with field devices (third party interfacing tool) and import the measurement into a computation platform (in this paper we consider Matlab). Moving-horizon filter is adopted with a pre-defined parameter where a set of least squares functions are minimized with a particular set approach. The user is able to visualize the original measurement, outlier detection, and localization with choice of methods, and applying polynomial regression as well as moving horizon filter. Such interactive interface provides no code/low code approach and all generated batches and dataset are exported to a workspace with saved parameters. Hence, initial field data collection, and pre-processing can be achieved with minimum efforts and with visualized results.
Visual Explainable Machine Learning for High-Stakes Decision-Making with Worst Case Estimates

A major motivation for explaining and rigorous evaluating Machine Learning (ML) models is coming from high-stake decision-making tasks like cancer diagnostics, self-driving cars, and others with possible catastrophic consequences of wrong decisions. This chapter shows that visual knowledge discovery (VKD) methods, based on the General Line Coordinates (GLC) recently developed, can significantly contribute to solving this problem. The concept of hyperblocks (n-D rectangles) as interpretable dataset units and GLC are combined to create visual self-service machine learning models. Two variants of Dynamic Scaffold Coordinates (DSC) are proposed. It allows losslessly mapping high-dimensional datasets to a single two-dimensional Cartesian plane and building interactively an ML predictive model in this 2-D visualization space. Major benefits of DSC1 and DSC2 is their highly interpretable nature. They allow domain experts to control or establish new machine learning models through visual pattern discovery. It opens a visually appealing opportunity for domain experts, who are not ML experts, to build ML models as a self-service bringing the domain expertise to the model discovery, which increases model explainability and trust for the end user. DSC were used to find, visualize, and estimate the worst-case validation splits in several benchmark datasets, which is important for high-risk application. For large datasets DSC is combined with dimensionality reduction techniques such as principal component analysis, singular value decomposition, and t-distributed stochastic neighbor embedding. A software package referred to as Dynamic Scaffold Coordinates Visualization System (DSCViz) was created to showcase the DSC1 and DSC2 systems.
Anomaly Detection with Neural Network Using a Generator

This paper concerns with the problem of detecting anomalies on X-ray images taken by Full Body Scanners (FBS). Our previous work describes the sequence of image preprocessing methods used to convert the original images, which are produced with FBS, to an images with visually distinguishable anomalies. In this paper we focus on development of the proposed methods, including the addition of preprocessing methods and the creation of generator which can produce synthetic anomalies. Examples of processed images are given. The results of using a neural network for anomaly detection are shown.
Model Building and Heuristic Evaluation of Various Machine Learning Classifiers

Machine learning is used to analyze data from divergent perspectives, summarize it into expedient information, and use that information to predict the likelihood of future events. Classification is one of the main problems in the domain of machine learning. It is used to classify the predetermined data for a specific class and to predict the class label for unseen data. The aim here is to study various classification algorithms in machine learning applied to the considered Census income dataset. The algorithms used for this analysis are Logistic Regression, KNN Classifier, Decision Tree, Random Forest, AdaBoost Classifier, Support Vector Machine, Gradient Boosting Classifier, and Xtrim Gradient Boosting classifier. The performance is analyzed using various metrics such as Precision, Recall, F1-score, Accuracy, Macro Average, Weighted average, and ROC Area. It has been observed that the performance of the AdaBoost classifier, Gradient Boosting classifier, and Xtrim Gradient Boosting classifier is better than other algorithms for the considered dataset. The experimental analysis shows that the AdaBoost classifier, Gradient Boosting classifier, and Xtrim Gradient Boosting classifier are giving the best scores in terms of diversified contemplated performance measures.
Design and Implementation of a Secure Patient Recommender and Prediction System

Early disease prediction can help sick persons determine the severity of the disease and take quick action, thus, a healthcare recommended system is viewed as additional tools to help patients control and manage their ill-health. Medical recommended system which provides users with quick and optimal disease predictions has been in existence for a while; however, it is faced with several data security issues. Sometimes, patients confidential data which are stored of the archive after each recommendation may be accessed by unauthorized persons, and this can warrant a serve data breach and disclosure of private medical information. Thus, the focus of our project is to design a privacy-aware recommended system that not just makes facilitates quick and easy recommendation for sick persons but also securely protects stored medical information from unauthorized access. This system will be designed to support quick search, recommendation septimal confidentiality and integrity.
Project DOCE as a Pedagogical Experience for Innovative Teaching

School of Design (ESD) of the Polytechnic Institute of Cávado and Ave (IPCA) created, in 2017, a project in the field of drawing with the main objective of developing innovative pedagogical practices. The project is called DOCE, or “Drawing, Order, Chaos and Teaching,” and, so far, four editions have taken place. The project began with an invitation to several authors and designers who presented and discussed with the students’ various themes in the field of drawing. This allowed to dynamize the pedagogies developed in the curricular units and provide collaborative and holistic learning with group dynamics and with a successful involvement among all. It can be said that two stages took place: a first working session on methodologies and techniques applied in professional and artistic practice, promoting a wide debate with the students; and a second stage, with the definition of drawing exercises, carried out within the scope of the curricular units, based on the themes discussed in the working sessions, with the invited authors. The COVID-19 pandemic brought changes that influenced the methodologies of working and demanded a re-adaptation with consequences for the dynamic and functionality of the curricular units and some adaptations to this project. For example, last edition (2020) was entirely held online, a way to maintain the project but also as a way to get closer, even in a physical distance, to enhance the students’ connection with their interest in drawing. At the moment, the next editions are currently being analysed, and the intention is to enrich the event with two new approaches: strategies for its dissemination at various levels and for publicizing the pedagogical exercises carried out and their results.
Learning and Performance Technology for the Performing Arts

Throughout the SARS-CoV-2 pandemic, performing arts organizations worldwide became creative with technology-based learning and performance support tools to maintain continuity for their respective crafts. While shared cloud repositories and collaboration tools were in use prior to the pandemic, circumstances catapulted many advances. Virtual rehearsals were initially problematic because of bandwidth variations from performer to performer. Early adopter tools, like Jamulus, emerged to synchronize real-time rehearsal for choirs, ensembles, and orchestras (Jamulus: Play music online with friends. https://jamulus.io/ . Accessed 10 March 2023.). Applications also emerged to support artist learning, performance, composition, production design, and adjudication. At the same time, technologies have matured that significantly enhance the audience experience. What we now refer to as workplace learning and performance (L&P) was once simply “training.” The transition began in the late 1980s when focus shifted from the teacher (trainer, instructor, lecturer) to the learner, with primary focus on doing — the performance outcome. Arguably, the workplace performance metaphor was borrowed from the performing arts. This paper reexamines this metaphor, surveying tools, techniques, and protocol that have emerged, through interviews with performing arts professionals, and discusses how workplace learning might further benefit from such tools and protocol. Included are innovations that foster shorter time to performance competency, lower costs, and improved quality, while also uncovering unintended consequences of technology-mediated performance.
Augmented Reality in STEM Education: Mapping Out the Future

While several review papers have proven the applicability of augmented reality (AR) in various domains of education, we are still short of an in-depth quantitative and qualitative assessment of the usefulness of AR in STEM education. To address this problem, this paper presents the results of a meta-analysis of 42 peer-reviewed papers in which AR has been implemented in STEM education. Cohen's d measure of effect was calculated from the reported statistics, and the content of these papers was analyzed. It was thus shown that: a) AR is useful in STEM education regardless of the number or age of participants (although its effectiveness tends to diminish with age in mathematics education; b) it is considerably more effective in the science and mathematics components of STEM education rather than technology and engineering. It was also found that, although some negative aspects have been expressed, student's attitudes were quite positive about it, as were the reported advantages it was shown to have in the entire range of STEM education.
Thinking and Chatting Deontically—Novel Support of Communication for Learning and Training with Time Travel Prevention Games

The authors’ key area of application is training for the prevention of accidents in the process technology industries. They run a professional training center with own 3D virtual environments. At TLIC 2021, four of the present authors delivered a contribution advocating planning of human training experiences as dynamically as managing some severely disturbed technical system back into a normal operation—such as an out of control chemical reactor—and enabling human trainees who failed to complete a risky task—thereby possibly ruining a (fortunately only virtual) technical installation—to virtually travel back in time to make good the damage. At TLIC 2022, they introduced cascades of gradually more intricate categories of time travel games. With every step from one category to the next, the deployed AI gets more powerful and effective in providing adaptive guidance of human trainees. The most advanced time travel games are those that allow for the dynamic modification of events experienced in the virtual past. In this way, the game system evolves over time and adapts to the needs of human trainees with emphasis on guidance for trainees who fail repeatedly. The extended team of authors presents a novel perspective at time travel prevention games that leads to a more human-centered adaptive guidance. Training is seen through the lens of deontic modal logic. The focus is on undesired events such as explosions, fire, health hazards due to toxic vapors, and the like. The game system’s AI is reasoning about necessity and possibility of such events. It offers to human trainees/players helpful chats about modalities of decisive events of training.
Gesture Recognition Based Virtual Mouse and Keyboard

Today, computer vision has advanced to an extent where a machine can recognize its owner by running a straightforward picture processing application. In the modern day, individuals use this vision for various activities in their daily life, including automatic cars, face recognition, and color detection. Hand gesture recognition is important and common since it also plays a role in the understanding of sign language. In the proposed framework, Human Computer Interaction (HCI) approach has been used. An optical mouse and keyboard that employ hand motions are created using computer vision. The computer’s camera will recognize various hand gestures made by a user, and the computer’s mouse or pointer moves according to the movements of the motions. Users may even conduct right and left clicks using various gestures. Similar to this, several gestures might be used to operate the keyboard, such as the one-fingered gesture for selecting an alphabet and the two-fingered motion for swiping left and right. It won’t require a cable or other external accessories to function as a virtual mouse and keyboard.
Purifying Data by Machine Learning with Certainty Levels

For autonomic computing, self-managing systems, and decision-making under uncertainty and faults, in many cases we are using machine learning models and combine them to solve any problem. This models uses a data-set, or a set of data-items, and data-item is a vector of feature values and a classification. in many cases these data sets includes outlier and/or misleading data items that were created by input device malfunctions, or were maliciously inserted to lead the machine learning to wrong conclusions. A reliable machine learning model must be able to handle a corrupted data-set, otherwise, a malfunctioning input device that corrupts a portion of the data-set, or malicious adversary may lead to inaccurate classifications. Therefore, the challenge is to find an effective methods to evaluate and increase the certainty level of the learning process as much as possible. This work introduces the use of a certainty level measure to obtain better classification capability in the presence of corrupted or malicious data items. Assuming we know the data distribution, e.g., is a normal distribution (which is a reasonable assumption in a large amount of data items) and/or a known upper bound on the given number of corrupted data items, our techniques define a certainty level for classifications. Another approach that will be presented in this work suggests enhancing the random forest techniques (the original model was developed by Leo Breiman) to cope with corrupted data items by augmenting the certainty level for the classification obtained in each leaf in the forest. This method is of independent interest, that of significantly improving the classification of the random forest machine learning technique in less severe settings.
Postdigital and The Incomputable

This entry begins with an abbreviated history concerning the axiomatic limits of mathematics to illustrate how mathematical systems contain paradox, randomness, and incompleteness. This history is then directed to describe ideas about the postdigital, and specifically, to explain how our everyday lives are intertwined with digital computing, digital computation, and digital algorithms. Incomputability describes the ubiquitous digitality of our everyday lives as constituted by the axiomatic limits of mathematics. Our postdigital lives are constituted in mathematical incompleteness, algorithmic randomness, and incomputable probabilities.
Postdigital Activism

Postdigital activism is the idea that organizing, mobilizing, petitioning, protesting, and other forms of social and political activism can be effectuated through technological means that bridge between analog and digital media. Postdigital activism may involve artistic performance, computer hacking, slacktivism, publishing at the margins, and so on. It can be conducted through memes, algorithms, politics of identity, transdisciplinarity, (Marxist) critiques of capitalism, and so on. The objective of postdigital activism is social and political change.