Reviewerly/exHarmony huggingface dataset:
Playing Pool with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo stretchy="false">|</mml:mo></mml:mrow><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>ψ</mml:mo></mml:mrow><mml:mo fence="false" stretchy="false">⟩</mml:mo></mml:math>: from Bouncing Billiards to Quantum Search

In ``Playing Pool with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>π</mml:mi></mml:math>'' \cite{Galperin}, Galperin invented an extraordinary method to learn the digits of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>π</mml:mi></mml:math> by counting the collisions of billiard balls. Here I demonstrate an exact isomorphism between Galperin's bouncing billiards and Grover's algorithm for quantum search. This provides an illuminating way to visualize Grover's algorithm.
INNOVATIVE TECHNOLOGIES IN THE LITERARY EDUCATION OF FUTURE PRIMARY SCHOOL TEACHERS

The article substantiates the need for the use of innovative technologies in teaching children’s literature to students majoring in “Primary Education”. The introduction of an integrated course “Native education: children’s literature with studying methods” in the training of future primary school teachers gives them the opportunity to navigate in current trends in children’s literature and model various algorithms of perception and interpretation of the texts. The study analyzes the introduction of interactive technology in literary education, which changes the vector of lectures to dialogic interaction with the student audience, provides a formal update of practical classes using quests and workshops. In accordance with the tasks of the New Ukrainian School and higher education of future primary school teachers, the authors analyze the possibilities of using critical thinking technology, ICT, augmented reality, projects in the system of literary education of the Pedagogical Institute.The importance of acquiring knowledge, skills and abilities to apply AR-applications to interactive editions of fiction by future primary school teachers during their organization of literary reading is substantiated. Tasks with the use of ICT tools have been developed for students. This mainly applies to creation of book trailers and comics based on works of art for children; QR-quests for junior pupils on the texts of fictionsof modern writers; mental maps (Mindmeister resource) based on prose plots; video presentations about writers and their works (resources “PoowToon”, “Prezi”); visualized plan of fictions made with the help of infographics; intellectual game tasks for younger students (LearningApps resource), etc. It was also recognized that the effective type of intellectual activity of students during the study of literature is a project, the result of which is the creation of students’ own literary product (fairy tales, poetry, essays, game content) and its publication in electronic edition.
Reduction monads and their signatures

In this work, we study reduction monads , which are essentially the same as monads relative to the free functor from sets into multigraphs. Reduction monads account for two aspects of the lambda calculus: on the one hand, in the monadic viewpoint, the lambda calculus is an object equipped with a well-behaved substitution; on the other hand, in the graphical viewpoint, it is an oriented multigraph whose vertices are terms and whose edges witness the reductions between two terms. We study presentations of reduction monads. To this end, we propose a notion of reduction signature . As usual, such a signature plays the role of a virtual presentation, and specifies arities for generating operations—possibly subject to equations—together with arities for generating reduction rules. For each such signature, we define a category of models; any model is, in particular, a reduction monad. If the initial object of this category of models exists, we call it the reduction monad presented (or specified) by the given reduction signature . Our main result identifies a class of reduction signatures which specify a reduction monad in the above sense. We show in the examples that our approach covers several standard variants of the lambda calculus.
Approbation of the data classification method based on the SVM algorithm and the k nearest neighbors algorithm

Abstract The aim of the work is to test the classification method based on the combined use of SVM and k NN classifiers. At the first stage of the classification method, on the basis of the initial training dataset U, SVM classifier is developed and the width of the Ω area is determined. This Ω area contains all objects incorrectly classified by SVM classifier, which form together with correctly classified objects that have fallen into the Ω area and the corresponding object class labels from the Ω area a new dataset G . At the second stage of the classification method, the k NN classifier developed on the basis of information about the objects of the set U\G is applied to all objects of the dataset G from the Ω area. The values of the parameters of the k NN classifier are determined experimentally in such a way as to ensure the highest possible accuracy of object classification. Since the correctly classified objects can also enter the Ω area formed in the above way, the condition for the applicability of the proposed method is a general improvement in the classification quality. The presented results of experimental studies confirm the effectiveness of the proposed method in the problem of classifying complex multidimensional data.
Weighted Bagging in Decision Trees: Data Mining

The main focus of this paper is on the use of resampling techniques to construct predictive models from data and the goal is to identify the best possible model which can produce better predications. Bagging or Bootstrap aggregating is a general method for improving the performance of given learning algorithm by using a majority vote to combine multiple classifier outputs derived from a single classifier on a bootstrap resample version of a training set. A bootstrap sample is generated by a random sample with replacement from the original training set. Inspired by the idea of bagging, we present an improved method based on a distance function in decision trees, called modified bagging (or weighted Bagging) in this study. The experimental results show that modified bagging is superior to the usual majority vote. These results are confirmed by both real data and artificial data sets with random noise. The Modified bagged classifier performs significantly better than usual bagging on various tree levels for all sample sizes. An interesting observation is that the weighted bagging performs somewhat better than usual bagging with sumps.
An Introduction to AWS—EC2 (Elastic Compute Cloud)

Most recent two decades IT is following conventional methodologies for dealing with its framework since the beginning of Cloud administrations, for example, Amazon Web Services, Google Cloud Platform, IBM Cloud and a lot all the more giving on the web foundation administrations.Associations have acknowledged an adjustment in their working model, if an association searches for an extension to expand its registering power, they just get it online by starting a virtual machine on the cloud.Virtual machines can be immediately propelled and shutdown through programming interfaces, offering adaptability to the client rather than customary methodologies.There were times when one was restricted by the boundaries of a machine e.g.; a data scientist has a large scale of data and would like to perform some analysis, however, encounters an error as below while uploading this data. > data <-read.csv (file='no_of_bug.csv') Error: cannot allocate vector of size 500.0 MbThe error is the result of the unavailability of the RAM since the operating system does not have enough RAM available as there are many solutions available of such problems following the traditional approach one should upgrade the RAM of the machine with the restriction to the RAM up-gradation limit to the machine's compatibility.However, there is another way by introducing the concept of virtualization in the could with more RAM and CPU.This paper explores the concept of Virtual machines on AWS EC2 as they are called instances with many advantages few of them includes being highly scalable (one can choose instances with more CPU and RAM etc.) most reliable and scalable easy to use for users as pay for what being used also allows a selection of different platform (OS) as an outcome of this paper will create an understating of the working fundamental of AWS EC2.
Identifying nonclassicality from experimental data using artificial neural networks

The fast and accessible verification of nonclassical resources is an indispensable step towards a broad utilization of continuous-variable quantum technologies. Here, we use machine learning methods for the identification of nonclassicality of quantum states of light by processing experimental data obtained via homodyne detection. For this purpose, we train an artificial neural network to classify classical and nonclassical states from their quadrature-measurement distributions. We demonstrate that the network is able to correctly identify classical and nonclassical features from real experimental quadrature data for different states of light. Furthermore, we show that nonclassicality of some states that were not used in the training phase is also recognized. Circumventing the requirement of the large sample sizes needed to perform homodyne tomography, our approach presents a promising alternative for the identification of nonclassicality for small sample sizes, indicating applicability for fast sorting or direct monitoring of experimental data.
Neural Pruning via Growing Regularization

Regularization has long been utilized to learn sparsity in deep neural network pruning. However, its role is mainly explored in the small penalty strength regime. In this work, we extend its application to a new scenario where the regularization grows large gradually to tackle two central problems of pruning: pruning schedule and weight importance scoring. (1) The former topic is newly brought up in this work, which we find critical to the pruning performance while receives little research attention. Specifically, we propose an L2 regularization variant with rising penalty factors and show it can bring significant accuracy gains compared with its one-shot counterpart, even when the same weights are removed. (2) The growing penalty scheme also brings us an approach to exploit the Hessian information for more accurate pruning without knowing their specific values, thus not bothered by the common Hessian approximation problems. Empirically, the proposed algorithms are easy to implement and scalable to large datasets and networks in both structured and unstructured pruning. Their effectiveness is demonstrated with modern deep neural networks on the CIFAR and ImageNet datasets, achieving competitive results compared to many state-of-the-art algorithms. Our code and trained models are publicly available at https://github.com/mingsuntse/regularization-pruning.
Random-Sampling Monte-Carlo Tree Search Methods for Cost Approximation in Long-Horizon Optimal Control

We develop Monte-Carlo based heuristic approaches to approximate the objective function in long horizon optimal control problems. In these approaches, to approximate the expectation operator in the objective function, we evolve the system state over multiple trajectories into the future while sampling the noise disturbances at each time-step, and find the average (or weighted average) of the costs along all the trajectories. We call these methods random sampling - multipath hypothesis propagation or RS-MHP. These methods (or variants) exist in the literature; however, the literature lacks results on how well these approximation strategies converge. This letter fills this knowledge gap to a certain extent. We derive stochastic convergence results for the cost approximation error from the RS-MHP methods and discuss their convergence (in probability) as the sample size increases. We consider two case studies to demonstrate the effectiveness of our methods - a) linear quadratic control problem; b) unmanned aerial vehicle path optimization problem.
Numerical Estimation of Wiebe Function Parameters Using Artificial Neural Networks in SI Engine

In modeling an Internal Combustion Engine, the combustion sub-model plays a critical role in the overall simulation of the engine as it provides the Mass Fraction Burned (MFB).Analytically, the Heat Release Rate (HRR) can be obtained using the Wiebe function, which is nothing more than a mathematical formulation of the MFB.The aforementioned function depends on the following four parameters: efficiency parameter, shape factor, crankshaft angle, and duration of the combustion.In this way, the Wiebe function can be adjusted to experimentally measured values of the mass fraction burned at various operating points using a least-squares regression, and thus obtaining specific values for the unknown parameters.Nevertheless, the main drawback of this approach is the requirement of testing the engine at a given engine load/speed condition.Furthermore, the main objective of this study is to propose a predictive model of the Wiebe parameters for any operating point of the tested SI engine.For this purpose, an Artificial Neural Network (ANN) is developed from the experimental data.A criterion was defined to choose the best-trained network.Finally, the Wiebe parameters are estimated with the neural networks for different operating conditions.Moreover, the mass fractions burned generated from the Wiebe functions are compared with the respective experimental values from several operating points measured in the engine test bench.Small differences were found between the estimated and experimental mass fractions burned.Therefore, the effectiveness of the developed ANN model as a prediction tool for the engine MFB is verified.
The Costs of and Net Returns to College Major

This paper uses administrative student and expenditure data from Florida public universities to describe a) how the cost of producing graduates varies by major, b) how the inclusion of major-specific instructional costs alters the estimated net returns to different fields of study, and c) how major-specific instructional expenditures changed between 1999 and 2013. We find that the cost of producing graduates in the highest cost major (engineering) is roughly double that of producing graduates in low-cost majors, such as business. Cross-major comparisons of per graduate earnings returns net of costs differ from comparisons based on earnings outcomes alone in economically significant ways for a number of fields. Differences between net returns and earnings returns per dollar of instructional spending are even more pronounced. Our analysis of trends in instructional expenditures shows that per credit expenditures for undergraduate classes dropped by 16% in Florida universities between 1999 and 2013. The largest drops occurred in engineering and health, where per credit spending fell by more than 40%. Observed spending changes have little relationship with per credit costs or earnings outcomes.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Design of Intelligent E-Learning Assessment Framework Using Bayesian Belief Network

An Intelligent Tutoring Systems is a type of knowledge-based system whose main agenda is to efficiently supplement a human tutor with a machine. Dissimilar to conventional classroom teaching, Intelligent Tutoring Systems (ITSs) have the ability to fit according to the necessity of an individual learner. More emphasis has been laid on various types of e-learning systems. In this work, a probability-based ITSs system is proposed consisting of four models specifically the learner�s behaviour model, pedagogical model, knowledge base model and learner assessment model. The importance has been given to the learner assessment model where an element of uncertainty has been introduced and handled by the Bayesian Belief Network (BBN). The purpose of the learner assessment model is to rightly detect the knowledge level of each learner based on their reply to the level of questions, where the level of questions is random to the process of assessment to the learner. The uncertainty factor has been defined in terms of success and failure parameters. Success is the probability that a learner of low cleverness level gives a right reply to a level of questions and is increased by a small probability of 0.07, whereas Failure is the probability that a learner of high cleverness level gives a wrong reply to a level of questions and is reduced by a small probability of 0.04. In this work during an assessment of the knowledge level of a learner, the system has incorporated the uncertainty factors of Success and Failure with the help of Bayes� rule and has found promising results that take into account the possibility of Success or Failure.
Supporting interaction in augmented reality assisted industrial processes using a CNN-based semantic layer

Even though Augmented Reality (AR) is far from its maturity, we already have solutions and devices that give us an efficient technological frame in different industrial environments. Widely used mobile devices, such as tablets, or more specific ones, such as the current AR glasses available, are enough to offer solutions that improve many industrial processes; repairing, maintenance, plant control, product line reconfiguration are some examples of these tasks. Many industrial applications already utilise AR-based applications to solve those problems. In this work, we aim to go a little bit further beyond current possibilities that only focus on providing visual guidance. Our main goal is to add a semantic layer for existing AR-based applications, that visually validate worker's actions based on visual interpretation of switches, potentiometers, analog needles or buttons, among others. This semantic layer allows a new level of interaction by adding automatic interpretation of the context that affects the actions of the operator and the display of information of interest in the AR system. We propose and explain the architecture and training of the Convolutional Neural Networks (CNN) used for the semantic layer and its integration in the AR technology.
A fast level set image segmentation driven by a new region descriptor

In order to deal with the intensities inhomogeneities and to overcome the effect of different types of noise in the image segmentation process, we have formulated a new level set function to implement a fast and robust active contour model. The proposed model was formulated by combining the SBGFRLS model and Legendre polynomials. With the aim of ensuring the segmentation accuracy and for dealing in the best way with the presence of noises and inhomogenous distribution of intensity, we define a local region descriptor for image intensities based on the Legendre polynomial. Instead of using the average intensity of the region, we regularised our level set function using a Gaussian filtering process. Experimental results on challenging images demonstrate the efficiency, robustness and the low cost and computational time of our model against the well-known active contour models.
A Comprehensive Survey on Hardware-Aware Neural Architecture Search

Neural Architecture Search (NAS) methods have been growing in popularity. These techniques have been fundamental to automate and speed up the time consuming and error-prone process of synthesizing novel Deep Learning (DL) architectures. NAS has been extensively studied in the past few years. Arguably their most significant impact has been in image classification and object detection tasks where the state of the art results have been obtained. Despite the significant success achieved to date, applying NAS to real-world problems still poses significant challenges and is not widely practical. In general, the synthesized Convolution Neural Network (CNN) architectures are too complex to be deployed in resource-limited platforms, such as IoT, mobile, and embedded systems. One solution growing in popularity is to use multi-objective optimization algorithms in the NAS search strategy by taking into account execution latency, energy consumption, memory footprint, etc. This kind of NAS, called hardware-aware NAS (HW-NAS), makes searching the most efficient architecture more complicated and opens several questions. In this survey, we provide a detailed review of existing HW-NAS research and categorize them according to four key dimensions: the search space, the search strategy, the acceleration technique, and the hardware cost estimation strategies. We further discuss the challenges and limitations of existing approaches and potential future directions. This is the first survey paper focusing on hardware-aware NAS. We hope it serves as a valuable reference for the various techniques and algorithms discussed and paves the road for future research towards hardware-aware NAS.
SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning

This paper introduces SelfMatch, a semi-supervised learning method that combines the power of contrastive self-supervised learning and consistency regularization. SelfMatch consists of two stages: (1) self-supervised pre-training based on contrastive learning and (2) semi-supervised fine-tuning based on augmentation consistency regularization. We empirically demonstrate that SelfMatch achieves the state-of-the-art results on standard benchmark datasets such as CIFAR-10 and SVHN. For example, for CIFAR-10 with 40 labeled examples, SelfMatch achieves 93.19% accuracy that outperforms the strong previous methods such as MixMatch (52.46%), UDA (70.95%), ReMixMatch (80.9%), and FixMatch (86.19%). We note that SelfMatch can close the gap between supervised learning (95.87%) and semi-supervised learning (93.19%) by using only a few labels for each class.
De Morgan Dual Nominal Quantifiers Modelling Private Names in Non-Commutative Logic

This article explores the proof theory necessary for recommending an expressive but decidable first-order system, named MAV1, featuring a De Morgan dual pair of nominal quantifiers. These nominal quantifiers called “new” and “wen” are distinct from the self-dual Gabbay-Pitts and Miller-Tiu nominal quantifiers. The novelty of these nominal quantifiers is they are polarised in the sense that “new” distributes over positive operators while “wen” distributes over negative operators. This greater control of bookkeeping enables private names to be modelled in processes embedded as formulae in MAV1. The technical challenge is to establish a cut elimination result from which essential properties including the transitivity of implication follow. Since the system is defined using the calculus of structures, a generalisation of the sequent calculus, novel techniques are employed. The proof relies on an intricately designed multiset-based measure of the size of a proof, which is used to guide a normalisation technique called splitting . The presence of equivariance, which swaps successive quantifiers, induces complex inter-dependencies between nominal quantifiers, additive conjunction, and multiplicative operators in the proof of splitting. Every rule is justified by an example demonstrating why the rule is necessary for soundly embedding processes and ensuring that cut elimination holds.