Reviewerly/exHarmony huggingface dataset:
A data-centric reinforcement learning approach for self-updating machine learning models

Continuously Updating Reinforcement Learning (CURL) demonstrates the ability to rapidly maintain deployed ML models when there is a change in use case such as a denied target with minimal performance effects. Traditional Machine Learning (ML) lifecycle requires ML models to be retrained and redeployed in order to maintain performance of deployed models experiencing changes in underlying data such as data drift. Data drift can include a wide variety of changes in data such as the addition of a new class, operating in an entirely new environment, mislabeled data, or subtle changes in targets over time. (CURL) deviates from this traditional lifecycle with dynamic updates using Reinforcement Learning (RL) to identify and capture data changes, and then automatically retrain the model with data changes. CURL learns to identify changes in data through its RL policy that is designed to maximize the reward for identifying changes in data. Specifically, CURL’s RL approach includes an environment with both the model’s performance and current prediction confidence as the observation space for the agent to act on the discrete action space, and reward function of the model’s accuracy subtracted by the labeling cost to learn data changes. Our controlled experiment demonstrated that the same distribution of denied target data (3%) was found by our RL policy, and our retrained model exceeded the initial classifier performance. CURL can be considered a general purpose technology that could be applied to a wide spectrum of fielded ML systems.
Understanding, Evaluating, and Improving Performance

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.
Designing Electrolysis Ion Display on Everyday Open Wet Surfaces

This pictorial presents a novel method to render color patterns using electrolysis applied onto open wet surfaces. By implementing electrodes within a wet object and electrifying them, electrolysis can occur and generate ions. We designed a color-forming display using color indicators reacting with such ions. This method can display patterns according to the shape of the electrode. By reversing the polarity of the electrodes and generating reversed-polarity ions, it is possible to fade the existing pattern and display a contrasting color. The proposed method does not require moving parts; thus, it is low cost and easy to compact. Also, some foods containing pH-reactive substances can be used as a display medium. Therefore, the proposed method can realize an information display using various objects common in everyday life, such as decor or food.
Accuracies of Training Labels and Machine Learning Models: Experiments on Delirium and Simulated Data

Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate. In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data.
Formation of Students' Logical Universal Educational Actions in the Process of Teaching Chemistry As a Planned Result of the Federal State Educational Standard of General Education

Within the framework of the article, methodological features of the formation of logical universal educational actions based on the logic of selection and structuring of the content of the section "Organic Chemistry" are considered. Particular attention is paid to the procedure for entering topics. The dynamics of the level of formation of logical universal educational actions among students is demonstrated. Conclusions are drawn about the peculiarities of the formation of logical universal educational actions, taking into account the age of students and the type of educational institution.
Performance analysis through predictive modeling: application to an anomaly detection algorithm in operational scenarios

Understanding a system’s performance while operating under different scenarios is difficult because of the vast number of varying parameters that need to be accounted for. To mitigate some of the difficulty a model can be developed that provides some predictability in a system’s performance thereby reducing material usage and laboratory time. It is therefore prudent to understand these parameters and capture that information in order to increase the predictability of a system, especially prior fielding. Through modeling, we connect laboratory scale data with potential scenarios in the field to accomplish this. In this paper, we show that through the modeling of a combination of spectra and instrument operating characteristics we can provide a predictive capability of a system’s performance. Our anomaly detection algorithm can predict a limit of anomaly detection (LOAD) for potential scenarios and then compare them to actual data for validation of our predictive capability. We show similar LOADs in both simulation and actual data collected. We further develop our model to account for realistic field scenarios and evaluate changes in performance.
Motion tracking for augmented and mixed reality: how good is good enough?

The proliferation of AR/MR/XR technology continues to grow. However, with few exceptions, the advances in motion tracking to support placement of symbols and images on the real world scene have not kept up. In order for AR/MR/XR to move from narrow niche applications to more general use, motion tracking providing accurate pose will also need to work in diverse environments and conditions. Many additional factors comprise tracking performance, but they are more easily addressed. This paper uses several real-world, dismounted and mounted scenarios to determine the minimum tracking accuracy needed to achieve success. It then proposes a minimum performance specification for motion tracking to support general AR/MR/XR displays.
STEM Approach in Teaching Mathematics in Second Grade

The report presents a series of STEM exercises realized with the students from the second grades (“A”, “B” and “C”) of the primary school “Alexander Georgiev -Kodzhakafaliyata” – city of Burgas. The tasks include mathematics, engineering, creativity and are performed in the classes for activities of interest within the all-day organization of educational activities, as well as in the classes for activities of interest to a group of children formed under the Ordinance on Inclusive Education. STEM tasks realized with second graders can be divided into: Making models of objects related to a particular plot; Making useful objects for our everyday life. To make the models, the students are divided into teams of two. The task of each team is the same: to make a model. Students take on the role of engineers, who have to design and build a model, working within certain limits. To make it easier for the little “engineers”, they are given an algorithm at the beginning of the class.
Generation and estimation of randomly maneuvering target trajectories towards metacognitive tracking radar applications

There is growing research interest to merge the idea of a metacognitive radar with that of a tracking radar. The concept of metacognition can be broadly summarized as the process of learning about learning. In a metacognitive tracking radar, the system uses cognitive processes to detect and track a target in a dynamic environment. The radar then applies metacognitive techniques to select the cognitive process that yields the most accurate target track. In the context of target tracking, cognitive processes are various tracking algorithms. Currently, metacognitive tracking radar systems have only been demonstrated on targets of known trajectories. Their performance in the case of a randomly maneuvering target has not been explored. This paper presents an initial approach to this problem. First, an algorithm to generate random target trajectories is presented. Then, these trajectories are estimated using two estimation algorithms: the Extended Kalman Filter (EKF) and the Interacting Multiple Model (IMM) estimator. Finally, the performances of these two algorithms are compared.
An (entrepreneurial) behavior style test using prototype theory and fuzzy logic theory

Testing a behavior style is stereotyping human behavior in categories: fuzzy sets. As human beings, we categorize things automatically in order to make sense of the world around us. In the behavior style test we use "fuzzy categories", based on fuzzy sets of behavior characteristics, in contrast to so called "classical categories" with clear boundaries. In behavior the boundaries of the categories are "vague". For example no one is 100% extrovert and 0% introvert or 100% introvert and 0% extrovert.
Voronoi diagram based handwritten word segmentation

Handwritten word segmentation is one of the important components in the offline handwritten word recognition systems and has attracted many researchers. In this paper, we propose a Voronoi diagram approach to segment a handwritten word into stroke elements for handwritten word recognitions. Different from the conventional thinning techniques, the Voronoi diagram approach is proposed to create the segmentation path. Then the Chebyshev distance transform is suggested for implementing the Voronoi diagram. Test results and theoretical analysis show that the proposed method is capable to create similar quality handwritten word segmentation as the conventional thinning approach with much lower computational cost.
Ethics in Machine Learning

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.
Memory-efficient single-image super-resolution

Modern displays are steadily increasing in resolution, though sensors can be prohibitively expensive to capture images and video at such high resolutions. Image super-resolution, or upsampling, has recently been applied to alleviate these shortcomings. There exist many deep learning image super-resolution models that reconstruct very high quality high-resolution images from a low-resolution base. However, most of these models use a tremendous amount of parameters, requiring a large amount of free memory and computational power to super resolve a single image. As a result, many modern super-resolution models are not entirely practical due to the computational or memory usage requirements. We propose a highly efficient, small super-resolution model utilizing the sub-pixel convolution block for single image super-resolution.
Design and practice of real-time online management system of power grid stability control parameters based on blockchain

At present, the management of security and stability control system is faced with key parameter maintenance, action command tracing, node identity authentication, data security and other management and control problems. Make full use of the distributed, scalable, autonomous and invariable characteristics of blockchain, study the construction of a safe and efficient security control key parameter management and control system with marks left in the whole process, easy to prevent and difficult to attack, and comprehensively improve the management and control level of security control system based on blockchain Technology. Firstly, this paper analyzes the shortcomings of the current stability control management system, puts forward a complete solution of the stability control parameter management system based on blockchain, and designs the technical architecture, hardware structure and software functions of the stability control management system, so as to realize the whole process management and control functions of the stability control system, such as reliable identity authentication, parameter management, accident tracing and risk assessment. Finally, a test environment is built based on the actual stability control system. The experimental results prove the security and reliability of the system proposed in this paper.
The Education Scene of the Schools in Latin America and the Return to the Classroom in the Context of COVID-19

Although the education systems have reflected inequalities in terms of time of teaching, quality management, digital skills, as well as the state of the infrastructure of the educational facilities; the pandemic has revealed new challenges in the inequality of distance education in times of COVID-19. Consequently, when choosing a teaching strategy where virtuality became the primary means of connection in the relationship between teacher-student-family. State and local governments to take on the challenge of Latin America and the increase of the education gap, with strategies and recommendations on the practice of teaching and thus to decide how and when to reopen schools. To change this reality, the investment in education to improve the spaces and technology platforms should be increased, depending on the criterion of equity and collaboration in order to advance in the field of education at the regional level. Then, the digital skills of teachers to face the new scenario and the actions of training to be undertaken; will be part of curricular adaptations, program design and educational resources, emerging for the return to the classroom.
User Aspects of Machine Learning

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.
Quantum Machine Learning with SQUID

In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. The classical infrastructure is based on PyTorch and we provide a standardized design to implement a variety of quantum models with the capability of back-propagation for efficient training. We present the structure of our framework and provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset. In particular, we highlight the implications for scalability for gradient-based optimization of quantum models on the choice of output for variational quantum models.
Learning Parametric Models

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.
DTTNet: Depth transverse transformer network for monocular depth estimation

Depth estimation is an essential component in understanding the 3D geometry of a scene. In comparison to traditional depth estimation methods such as structure from motion and stereo vision matching, determining depth relation using a single camera is challenging. The recent advancements in convolutional neural networks have accelerated the research in monocular depth estimation. However, most technologies infer depth maps using lower resolution images due to network capacity and complexity issues. Another challenge in depth estimation is ambiguous and sparse depth maps. These issues are caused due to labeling errors, hardware faults, or occlusions. This paper presents a novel end-to-end trainable convolutional neural network architecture – depth transverse transformer network (DTTNet). The proposed network is designed and optimized to perform monocular depth estimation. This network aims at exploring the multi-resolution representation to perform pixel-wise depth estimation more accurately. In order to improve the accuracy of depth estimation, different kinds of ad hoc networks are proposed subsequently. Extensive computer simulations on NYU Depth V2 and SUN RGB-D dataset demonstrate the effectiveness of the proposed DTTNet against state-of-the-art methods. DTTNet can potentially optimize depth perception in intelligent systems such as automated driving and video surveillance applications, computational photography, and augmented reality. The source code is available at https://github.com/shreyaskamathkm/DTTNet
Generative Models and Learning from Unlabelled Data

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.
Whose Justice?—Media Literacy for Handling Internet Public Trials

This chapter explores the relationships between Internet public trials, social justice, and media literacy in civic and political contexts. It challenges some of the claims of social justice embedded in a public trial. It introduces the problems of public trials and discusses how media literacy can be a preventive strategy. A media literacy intervention model, which includes components of accessing, evaluating, and using information, is put forward to handle the issue. Media literacy helps members of society responsibly use information and support social justice, particularly during online social participation.
Radar technical language modeling with named entity recognition and text classification

This paper introduces the radar text data set (RadarTD) for technical language modeling. This data set is comprised of sentences containing radar parameters, values, and units determined from real-world values. This data set is created based on values determined from published academic research. Additionally, each statement is assigned a sentiment label and goal priority label. Preliminary investigations into the applicability of this data set are explored using the BERT model and several bi-directional LSTM models. These models are evaluated on text classification and named entity recognition tasks. This study evaluates the applicability of technical language modeling using neural networks to analyze input statements for cognitive radar applications. These findings suggest that this data set can be used to achieve reasonable performance for both text classification and named entity recognition for autonomous radar applications.