Reviewerly/exHarmony huggingface dataset:
PV-VR: A Virtual Reality Training Application Using Guided Virtual Tours of the Photovoltaic Applied Research and Testing (PART) Lab

Leaving the classroom and engaging in learning through first-hand experience has a long tradition in science and engineering education.A virtual field-trip is a way of providing users with some knowledge and virtual experience of a facility without requiring them to physically visit the location.Virtual labs can provide remote-access to various disciplines of Science, Technology, and Engineering (STE) disciplines and are a cost-efficient way for schools and universities to organize high-quality laboratory work.Due to constrictions on time and geographical distances, virtual-labs can be used to share costly equipment and resources, which are otherwise available to a limited number of users.The Photovoltaic (PV) Applied Research and Testing (PART) Lab encompasses a 1.1 MW PV power plant with three solar panel technologies, metrological and radiometer stations, and PV testing lab.PV-VR is an application that uses a scale model of the PART lab for educational interactive lab tours in virtual reality (VR).The actual PV solar power plant is virtualized and virtual representations of all the objects in the plant are inserted into the application.PV-VR takes users on a guided educational tour that allows them to interact with the various technologies to facilitate virtual hands-on learning.The virtual tour is guided by a teacher avatar that explains each technology and initiates animations and interactive educational games with constructive feedback to reinforce learning.PV-VR allows users to learn about solar power technologies and, how power is produced, converted and transmitted.This paper is a summary of the PV-VR application and initial qualitative results from a pilot study.
End-to-End Neural Systems for Automatic Children Speech Recognition: An Empirical Study

A key desiderata for inclusive and accessible speech recognition technology is ensuring its robust performance to children's speech. Notably, this includes the rapidly advancing neural network based end-to-end speech recognition systems. Children speech recognition is more challenging due to the larger intra-inter speaker variability in terms of acoustic and linguistic characteristics compared to adult speech. Furthermore, the lack of adequate and appropriate children speech resources adds to the challenge of designing robust end-to-end neural architectures. This study provides a critical assessment of automatic children speech recognition through an empirical study of contemporary state-of-the-art end-to-end speech recognition systems. Insights are provided on the aspects of training data requirements, adaptation on children data, and the effect of children age, utterance lengths, different architectures and loss functions for end-to-end systems and role of language models on the speech recognition performance.
ERA: Extracting Planning Macro-Operators from Adjacent and Non-adjacent Sequences

Intuitively, Automated Planning systems capable of learning from previous experiences should be able to achieve better performance. One way to build on past experiences is to augment domains with macro-operators (i.e. frequent operator sequences). In most existing works, macros are generated from chunks of adjacent operators extracted from a set of plans. Although they provide some interesting results this type of analysis may provide incomplete results. In this paper, we propose ERA, an automatic extraction method for macro-operators from a set of solution plans. Our algorithm is domain and planner independent and can find all macro-operator occurrences even if the operators are non-adjacent. Our method has proven to successfully find macro-operators of different lengths for six different benchmark domains. Also, our experiments highlighted the capital role of considering non-adjacent occurrences in the extraction of macro-operators.
Set-valued classification -- overview via a unified framework

Multi-class classification problem is among the most popular and well-studied statistical frameworks. Modern multi-class datasets can be extremely ambiguous and single-output predictions fail to deliver satisfactory performance. By allowing predictors to predict a set of label candidates, set-valued classification offers a natural way to deal with this ambiguity. Several formulations of set-valued classification are available in the literature and each of them leads to different prediction strategies. The present survey aims to review popular formulations using a unified statistical framework. The proposed framework encompasses previously considered and leads to new formulations as well as it allows to understand underlying trade-offs of each formulation. We provide infinite sample optimal set-valued classification strategies and review a general plug-in principle to construct data-driven algorithms. The exposition is supported by examples and pointers to both theoretical and practical contributions. Finally, we provide experiments on real-world datasets comparing these approaches in practice and providing general practical guidelines.
Chalk and Talk Versus Powerpoint: Perception among Medical Students

The paper proposes deep learning algorithm used to classify the oral images into either normal or abnormalimages. The cancer regions are segmented using morphological operations. The segmented cancer regionsare further diagnosed into ‘Mild’ or ‘Severe’ using deep learning algorithm. The main advantage of the deeplearning algorithm is that it requires minimum number of oral images for both classification and diagnosisstages of the proposed work. In this paper, the total number of cancers affected oral images used is about160 and the proposed oral cancer detection system using CNN classification approach classifies 159 canceraffected oral images correctly and achieves 99.3% of detection rate.
Morphological Change Forecasting For Prostate Glands Using Feature-Based Registration And Kernel Density Extrapolation

Organ morphology is a key indicator for prostate disease diagnosis and prognosis. For instance, In longitudinal study of prostate cancer patients under active surveillance, the volume, boundary smoothness and their changes are closely monitored on time-series MR image data. In this paper, we describe a new framework for forecasting prostate morphological changes, as the ability to detect such changes earlier than what is currently possible may enable timely treatment or avoiding unnecessary confirmatory biopsies. In this work, an efficient feature-based MR image registration is first developed to align delineated prostate gland capsules to quantify the morphological changes using the inferred dense displacement fields (DDFs). We then propose to use kernel density estimation (KDE) of the probability density of the DDF-represented future morphology changes, between current and future time points, before the future data become available. The KDE utilises a novel distance function that takes into account morphology, stage-of-progression and duration-of-change, which are considered factors in such subject-specific forecasting. We validate the proposed approach on image masks unseen to registration network training, without using any data acquired at the future target time points. The experiment results are presented on a longitudinal data set with 331 images from 73 patients, yielding an average Dice score of 0.865 on a holdout set, between the ground-truth and the image masks warped by the KDE-predicted-DDFs.
The Effects of Learning Motivation towards Student’s Learning Achievement in English

English is still regarded as a difficult lesson by most of the students, so student’s learning achievement in English is low. The Purpose of this research determines The effects of Learning Motivation Towards Student’s Learning Achievement in English at Kota Tangerang Selatan. The research method used is survey method. Sample size as many as 90 students from SMP Negeri 4 represents International Designed Rating School, SMP Negeri 11 represents National Rating School and SMP Negeri 6 Kota Tangerang Selatan. Data Collection carried out by interview, giving questionnaires and carrying out learning achievement in English test. The research is conducted in May 2012.Research results show learning motivation variable has significant effects towards students’ learning achievement in English. Research results state there is the effects of learning motivation towards student’s learning achievement in English of class VIII SMP Negeri Kota Tangerang Selatan From the research results, it can be concluded that each increase in learning motivation by one unit will be followed by changing in one unit of student achievement in English as well. Suggestions that can be conveyed are to increase learning motivation, many things can be done, including by giving awards to outstanding students.
Generating Structured Adversarial Attacks Using Frank-Wolfe Method

White box adversarial perturbations are generated via iterative optimization algorithms most often by minimizing an adversarial loss on a $\ell_p$ neighborhood of the original image, the so-called distortion set. Constraining the adversarial search with different norms results in disparately structured adversarial examples. Here we explore several distortion sets with structure-enhancing algorithms. These new structures for adversarial examples might provide challenges for provable and empirical robust mechanisms. Because adversarial robustness is still an empirical field, defense mechanisms should also reasonably be evaluated against differently structured attacks. Besides, these structured adversarial perturbations may allow for larger distortions size than their $\ell_p$ counter-part while remaining imperceptible or perceptible as natural distortions of the image. We will demonstrate in this work that the proposed structured adversarial examples can significantly bring down the classification accuracy of adversarialy trained classifiers while showing low $\ell_2$ distortion rate. For instance, on ImagNet dataset the structured attacks drop the accuracy of adversarial model to near zero with only 50\% of $\ell_2$ distortion generated using white-box attacks like PGD. As a byproduct, our finding on structured adversarial examples can be used for adversarial regularization of models to make models more robust or improve their generalization performance on datasets which are structurally different.
Specifics of introducing PDM-systems in the space industry enterprises and new possibilities for the personnel evaluation

The relevance of the study arises from the need to increase the efficiency of enterprises of the aerospace industry in the Russian Federation, along with other industries determining the level of development and influence of Russia on the world stage. This paper provides a review on modern PDM systems and describes the specifics of their implementation at industrial enterprises in the space industry. The results of the review showed that introducing such systems in the enterprise management system can not only improve quality, but also reduce the time for product design. In addition, the statistical information obtained during the functioning of the system opens up new possibilities for qualitative evaluation of the personnel potential of an enterprise.
KEMAMPUAN MENENTUKAN FUNGSI SINTAKSIS DALAM KALIMAT TUNGGAL OLEH SISWA KELAS VI SD

This study aims to describe the level of ability of the sixth grade students of Public Elementary School NO. 173171 Sirmahompa in Academic Year 2012/2013. Determining the Syntactic Function in a Single Sentence. The population of this study is all of the sixth grade students of SD Negeri No. 173171 Simarhompa District of Sipahutar in Academic Year 2012/2013 with the total number is 22 students. And all them were taken as the sample. This was done because the number of population was less than one hundred people. The method in this research was descriptive method. Data collection tool used is a multiple choice test of 20 items of questions. From the results of data analysis, the ability to determine syntactic functions in single sentences by 22 students, 3 students or 13.64% got the category Very Good (A), 4 students or 18.18 get the Good category (B), 7 students or 31.82% get enough categories (C), 4 students get the less category (D), 4 students get the less category (E). The percentage of ability to determine syntactic functions in single sentences by sixth grade students of SD Negeri No. 173171 Simarhompa Sipahutar District in Academic Year 2012/2013. The average value of 61.36, still in the sufficient category (C) Students' ability to determine syntactic functions in a single sentence still needs to be improved. From the results of the analysis of the items known from all syntactic functions no student is able to answer the question correctly.\Keywords: students’ ability, syntax function, single sentence
A patch-based-approach for aortic landmarking

Accurate landmark localization in 3D medical volumes is nowadays needed for many medical and technical applications such as segmentation, detection of anatomical structures, image registration, and more. Due to the time-consuming process of manual landmarking and the variety of landmark positioning by individual observers, a more uniform and automatic solution is needed. This paper examines a state-of-the-art approach based on a patch-based Convolutional Neural Network (CNN), that is able to predict multiple landmarks simultaneously, and its performance on ill-posed landmarks. It combines regression and classification in one framework to improve the localization accuracy of individual landmarks by taking into consideration the relationship between an image patch and an anatomical landmark position. During inference, image patches are passed to the CNN as input, which are then moved by an amount, specified by the regression output, in the direction of highest probability, which is specified by the classification output. One of the main challenges here was that there are no salient landmarks in the aorta, they are all just generic points in a tube. This adds variability in the manual positioning of the landmarks and even the predictions of the network for each volume. The network’s prediction accuracy was quantitatively evaluated and the best setup achieved an average landmark localization error of 16.04mm, with 99.0% of all predictions having a mean error smaller than 30 mm, while the best prediction during evaluation had a mean error of 3.53 mm over all landmarks.
Quantum transport in a combined kicked rotor and quantum walk system

We present a theoretical and numerical study of the competition between two opposite interference effects, namely interference-induced ballistic transport on one hand, and strong (Anderson) localization on the other. While the former effect allows for resistance free transport, the latter brings the transport to a complete halt. As a model system, we consider the quantum kicked rotor, where strong localization is observed in the discrete momentum coordinate. In this model, we introduce the ballistic transport in the form of a Hadamard quantum walk in that momentum coordinate. The two transport mechanisms are combined by alternating the corresponding Floquet operators. Extending the corresponding calculation for the kicked rotor, we estimate the classical diffusion coefficient for the combined dynamics. Another argument, based on the introduction of an effective Heisenberg time should then allow to estimate the localization time and the localization length. While this is known to work reasonably well in the kicked rotor case, we find that it fails in our case. While the combined dynamics still shows localization, it takes place at much larger times and shows much larger localization lengths than predicted. Finally, we combine the kicked rotor with other types of quantum walks, namely diffusive and localizing quantum walks. In the diffusive case, the localizing dynamics of the kicked rotor is completely canceled and we get pure diffusion. In the case of the localizing quantum walk, the combined system remains localized, but with a larger localization length.
Evaluating Multi-label Classifiers with Noisy Labels

Multi-label classification (MLC) is a generalization of standard classification where multiple labels may be assigned to a given sample. In the real world, it is more common to deal with noisy datasets than clean datasets, given how modern datasets are labeled by a large group of annotators on crowdsourcing platforms, but little attention has been given to evaluating multi-label classifiers with noisy labels. Exploiting label correlations now becomes a standard component of a multi-label classifier to achieve competitive performance. However, this component makes the classifier more prone to poor generalization - it overfits labels as well as label dependencies. We identify three common real-world label noise scenarios and show how previous approaches per-form poorly with noisy labels. To address this issue, we present a Context-Based Multi-LabelClassifier (CbMLC) that effectively handles noisy labels when learning label dependencies, without requiring additional supervision. We compare CbMLC against other domain-specific state-of-the-art models on a variety of datasets, under both the clean and the noisy settings. We show CbMLC yields substantial improvements over the previous methods in most cases.
Decomposition of Clifford Gates.

In fault-tolerant quantum computation and quantum error-correction one is interested on Pauli matrices that commute with a circuit/unitary. We provide a fast algorithm that decomposes any Clifford gate as a $\textit{minimal}$ product of Clifford transvections. The algorithm can be directly used for finding all Pauli matrices that commute with any given Clifford gate. To achieve this goal, we exploit the structure of the symplectic group with a novel graphical approach.
Quality Evaluation Model of Intelligent Building Project Cost Audit based on Integrated Learning

A high-quality audit can ensure that the construction project cost is more reasonable and accurate, and make the use of funds for the entire construction project more orderly. Therefore, this paper constructs a quality evaluation model of intelligent building engineering cost audit based on integrated learning. The construction of evaluation system includes the design principle of evaluation index system, the primary selection of evaluation index, the selection of evaluation index and the construction of evaluation index system; the weight of index is calculated by AHP; the evaluation model is constructed by using fuzzy comprehensive evaluation method according to the weight value to realize the quality evaluation of project cost audit. The results show that: under the application of the model, the overall evaluation result of the project cost audit quality is “general”, which proves the effectiveness of the model.
Scaling up learning with GAIT-prop.

Backpropagation of error (BP) is a widely used and highly successful learning algorithm. However, its reliance on non-local information in propagating error gradients makes it seem an unlikely candidate for learning in the brain. In the last decade, a number of investigations have been carried out focused upon determining whether alternative more biologically plausible computations can be used to approximate BP. This work builds on such a local learning algorithm - Gradient Adjusted Incremental Target Propagation (GAIT-prop) - which has recently been shown to approximate BP in a manner which appears biologically plausible. This method constructs local, layer-wise weight update targets in order to enable plausible credit assignment. However, in deep networks, the local weight updates computed by GAIT-prop can deviate from BP for a number of reasons. Here, we provide and test methods to overcome such sources of error. In particular, we adaptively rescale the locally-computed errors and show that this significantly increases the performance and stability of the GAIT-prop algorithm when applied to the CIFAR-10 dataset.
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine

Detecting out-of-distribution (OOD) samples is an essential requirement for the deployment of machine learning systems in the real world. Until now, research on energy-based OOD detectors has focused on the softmax confidence score from a pre-trained neural network classifier with access to class labels. In contrast, we propose an unsupervised energy-based OOD detector leveraging the Stiefel-Restricted Kernel Machine (St-RKM). Training requires minimizing an objective function with an autoencoder loss term and the RKM energy where the interconnection matrix lies on the Stiefel manifold. Further, we outline multiple energy function definitions based on the RKM framework and discuss their utility. In the experiments on standard datasets, the proposed method improves over the existing energy-based OOD detectors and deep generative models. Through several ablation studies, we further illustrate the merit of each proposed energy function on the OOD detection performance.
Modular Object-Oriented Games: A Task Framework for Reinforcement Learning, Psychology, and Neuroscience

In recent years, trends towards studying simulated games have gained momentum in the fields of artificial intelligence, cognitive science, psychology, and neuroscience. The intersections of these fields have also grown recently, as researchers increasing study such games using both artificial agents and human or animal subjects. However, implementing games can be a time-consuming endeavor and may require a researcher to grapple with complex codebases that are not easily customized. Furthermore, interdisciplinary researchers studying some combination of artificial intelligence, human psychology, and animal neurophysiology face additional challenges, because existing platforms are designed for only one of these domains. Here we introduce Modular Object-Oriented Games, a Python task framework that is lightweight, flexible, customizable, and designed for use by machine learning, psychology, and neurophysiology researchers.
An Evolutionary Game Assisted Spectrum Sharing Blockchain Framework for Internet of Vehicles

With the significant advance of Internet of Vehicles (IoV), a massive number of vehicular users will connect with the Internet via 5G and the beyond 5G (B5G) networks, which will further worse the spectrum scarcity problem in 5G/B5G networks. Therefore, how to enable dynamical and efficient spectrum resource sharing for IoV users is imperative. To this end, we investigate an evolutionary game enabled spectrum sharing blockchain framework for IoV. Specifically, We use alliance nodes and blockchain framework to assist in the completion of multi-WSP spectrum resource allocation, sharing, and the allocation results storage. We first propose an evolutionary game scheme to allocate the spectrum resource among different WSPs. To reflect the vehicle users' communication demand more appropriately, the vehicle mobility is taken into consideration when defining the payoff of the vehicle users. After completing the spectrum allocation, we illustrate the process of allocation results recording and block generation in our established blockchain verification system, where the transactions ledger of spectrum allocation will be stored in each alliance node in a distributed way. Numerical results exhibit the fast convergence speed of the spectrum allocation approach. Moreover, simulations results from our established hyperledger platform validate the effectiveness and implementability of the investigated spectrum sharing blockchain framework.