Reviewerly/exHarmony huggingface dataset:
A Theory of the Distortion-Perception Tradeoff in Wasserstein Space

The lower the distortion of an estimator, the more the distribution of its outputs generally deviates from the distribution of the signals it attempts to estimate. This phenomenon, known as the perception-distortion tradeoff, has captured significant attention in image restoration, where it implies that fidelity to ground truth images comes at the expense of perceptual quality (deviation from statistics of natural images). However, despite the increasing popularity of performing comparisons on the perception-distortion plane, there remains an important open question: what is the minimal distortion that can be achieved under a given perception constraint? In this paper, we derive a closed form expression for this distortion-perception (DP) function for the mean squared-error (MSE) distortion and the Wasserstein-2 perception index. We prove that the DP function is always quadratic, regardless of the underlying distribution. This stems from the fact that estimators on the DP curve form a geodesic in Wasserstein space. In the Gaussian setting, we further provide a closed form expression for such estimators. For general distributions, we show how these estimators can be constructed from the estimators at the two extremes of the tradeoff: The global MSE minimizer, and a minimizer of the MSE under a perfect perceptual quality constraint. The latter can be obtained as a stochastic transformation of the former.
Online Selective Classification with Limited Feedback

Motivated by applications to resource-limited and safety-critical domains, we study selective classification in the online learning model, wherein a predictor may abstain from classifying an instance. For example, this may model an adaptive decision to invoke more resources on this instance. Two salient aspects of the setting we consider are that the data may be non-realisable, due to which abstention may be a valid long-term action, and that feedback is only received when the learner abstains, which models the fact that reliable labels are only available when the resource intensive processing is invoked. Within this framework, we explore strategies that make few mistakes, while not abstaining too many times more than the best-in-hindsight error-free classifier from a given class. That is, the one that makes no mistakes, while abstaining the fewest number of times. We construct simple versioning-based schemes for any $\mu \in (0,1],$ that make most $T^\mu$ mistakes while incurring \smash{$\tilde{O}(T^{1-\mu})$} excess abstention against adaptive adversaries. We further show that this dependence on $T$ is tight, and provide illustrative experiments on realistic datasets.
Black Box Probabilistic Numerics

Probabilistic numerics casts numerical tasks, such the numerical solution of differential equations, as inference problems to be solved. One approach is to model the unknown quantity of interest as a random variable, and to constrain this variable using data generated during the course of a traditional numerical method. However, data may be nonlinearly related to the quantity of interest, rendering the proper conditioning of random variables difficult and limiting the range of numerical tasks that can be addressed. Instead, this paper proposes to construct probabilistic numerical methods based only on the final output from a traditional method. A convergent sequence of approximations to the quantity of interest constitute a dataset, from which the limiting quantity of interest can be extrapolated, in a probabilistic analogue of Richardson's deferred approach to the limit. This black box approach (1) massively expands the range of tasks to which probabilistic numerics can be applied, (2) inherits the features and performance of state-of-the-art numerical methods, and (3) enables provably higher orders of convergence to be achieved. Applications are presented for nonlinear ordinary and partial differential equations, as well as for eigenvalue problems-a setting for which no probabilistic numerical methods have yet been developed.
How Modular should Neural Module Networks Be for Systematic Generalization

Neural Module Networks (NMNs) aim at Visual Question Answering (VQA) via composition of modules that tackle a sub-task. NMNs are a promising strategy to achieve systematic generalization, i.e. overcoming biasing factors in the training distribution. However, the aspects of NMNs that facilitate systematic generalization are not fully understood. In this paper, we demonstrate that the stage and the degree at which modularity is defined has large influence on systematic generalization. In a series of experiments on three VQA datasets (MNIST with multiple attributes, SQOOP, and CLEVR-CoGenT), our results reveal that tuning the degree of modularity in the network, especially at the image encoder stage, reaches substantially higher systematic generalization. These findings lead to new NMN architectures that outperform previous ones in terms of systematic generalization.
THEORETICAL AND METHODOLOGICAL BASES OF FORMATION OF LISTENING COMPREHENSION SKILLS IN STUDENTS

This article analyzes a number of listening comprehension problems and solutions for musicians and students studying in this field. It is widely covered as an example of contemporary and 20th century music.
LibGuides: SAGE Research Methods: Start Your Research

Below are a set of videos that will support the use of specific functions of SAGE Research Methods to help you to fully utilize the features available.
WRITTEN CORRECTIVE FEEDBACK: STUDENTS’ PERCEPTION ON ONLINE LEARNING

WCF from the lecturer is useful information for student writing improvement. It aids students in identifying their English learning problems and skills. Students, on the other hand, may interpret lecturer WCF differently than the lecturer intended. This study is aimed at describing the type of lecturer’s WCF and the students’ perception of lecturer’s WCF in a paragraph writing class. It is descriptive qualitative research, employing documentation and interview as research instruments, which was conducted with 18 students of the third semester English department students of Muhammadiyah University of Ponorogo. Furthermore, there were five out of 18 students randomly selected to be interviewed to discover students’ perspective of the given feedback. The findings revealed that, firstly, the lecturer use praise, criticism, and suggestion based on students task in paragraph writing class. Secondly, students’ positive responses of having feedback and the benefit of written corrective feedback that students get. Finally, most students responded that they appreciated the lecturer’s WCF. In addition, the students believed that lecturer’s WCF could improve student’s writing especially on grammar accuracy and organization, and it could motivate the student in developing the ideas. Keywords :  Student’s Perspective, Written Corrective Feedback, Writing
Neural Scene Flow Prior

Before the deep learning revolution, many perception algorithms were based on runtime optimization in conjunction with a strong prior/regularization penalty. A prime example of this in computer vision is optical and scene flow. Supervised learning has largely displaced the need for explicit regularization. Instead, they rely on large amounts of labeled data to capture prior statistics, which are not always readily available for many problems. Although optimization is employed to learn the neural network, the weights of this network are frozen at runtime. As a result, these learning solutions are domain-specific and do not generalize well to other statistically different scenarios. This paper revisits the scene flow problem that relies predominantly on runtime optimization and strong regularization. A central innovation here is the inclusion of a neural scene flow prior, which uses the architecture of neural networks as a new type of implicit regularizer. Unlike learning-based scene flow methods, optimization occurs at runtime, and our approach needs no offline datasets -- making it ideal for deployment in new environments such as autonomous driving. We show that an architecture based exclusively on multilayer perceptrons (MLPs) can be used as a scene flow prior. Our method attains competitive -- if not better -- results on scene flow benchmarks. Also, our neural prior's implicit and continuous scene flow representation allows us to estimate dense long-term correspondences across a sequence of point clouds. The dense motion information is represented by scene flow fields where points can be propagated through time by integrating motion vectors. We demonstrate such a capability by accumulating a sequence of lidar point clouds.
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification

The interdependence between nodes in graphs is key to improve class predictions on nodes and utilized in approaches like Label Propagation (LP) or in Graph Neural Networks (GNN). Nonetheless, uncertainty estimation for non-independent node-level predictions is under-explored. In this work, we explore uncertainty quantification for node classification in three ways: (1) We derive three axioms explicitly characterizing the expected predictive uncertainty behavior in homophilic attributed graphs. (2) We propose a new model Graph Posterior Network (GPN) which explicitly performs Bayesian posterior updates for predictions on interdependent nodes. GPN provably obeys the proposed axioms. (3) We extensively evaluate GPN and a strong set of baselines on semi-supervised node classification including detection of anomalous features, and detection of left-out classes. GPN outperforms existing approaches for uncertainty estimation in the experiments.
Fine-grained Typing of Emerging Entities in Microblogs

Analyzing microblogs where we post what we experience enables us to perform various applications such as social-trend analysis and entity recommendation. To track emerging trends in a variety of areas, we want to categorize information on emerging entities (e.g., Avatar 2) in microblog posts according to their types (e.g., Film). We thus introduce a new entity typing task that assigns a fine-grained type to each emerging entity when a burst of posts containing that entity is first observed in a microblog. The challenge is to perform typing from noisy microblog posts without relying on prior knowledge of the target entity. To tackle this task, we build large-scale Twitter datasets for English and Japanese using time-sensitive distant supervision. We then propose a modular neural typing model that encodes not only the entity and its contexts but also meta information in multiple posts. To type ‘homographic’ emerging entities (e.g., ‘Go’ means an emerging programming language and a classic board game), which contexts are noisy, we devise a context selector that finds related contexts of the target entity. Experiments on the Twitter datasets confirm the effectiveness of our typing model and the context selector.
An Information-theoretic Approach to Distribution Shifts

Safely deploying machine learning models to the real world is often a challenging process. Models trained with data obtained from a specific geographic location tend to fail when queried with data obtained elsewhere, agents trained in a simulation can struggle to adapt when deployed in the real world or novel environments, and neural networks that are fit to a subset of the population might carry some selection bias into their decision process. In this work, we describe the problem of data shift from a novel information-theoretic perspective by (i) identifying and describing the different sources of error, (ii) comparing some of the most promising objectives explored in the recent domain generalization, and fair classification literature. From our theoretical analysis and empirical evaluation, we conclude that the model selection procedure needs to be guided by careful considerations regarding the observed data, the factors used for correction, and the structure of the data-generating process.
FPGA Implementation of Neural Network Digital Systems

The utilization of the FPGA (Field Programmable Gate Array) for neural network execution gives adaptability and flexibility in programmable frameworks. For Artificial Neural Networks (ANNs) in a real-time application, regular chip configuration suffers the limitations of schedule and cost. For neutral network digital systems that are comparably low precision, FPGAs have higher speed and a more modest size as compared to microcontrollers. In expansion, ANN digital systems based on FPGAs have reasonably been accomplished with classification applications. The programmability of reconfigurable FPGAs yields the accessibility of quick specific reason equipment for wide applications. Its programmability could set the conditions to investigate new neural network calculations and issues of a scale that would not be doable with a customary processor. The objective of this work is to understand the implementation of neural network digital systems utilizing FPGAs.
TINGKAT PARTISIPASI MASYARAKAT DALAM PENGGUNAAN APLIKASI WEB DESA AKA-AKAE KECAMATAN WATANG SIDENRENG KEBUPATEN SIDENRENG RAPPANG

Research objectives To determine the level of community participation in the use of web applications in Aka - Akae Village, to find out the factors that influence the level of community participation in the use of web applications in Aka - Akae Village. The population of this study was 499KK, while the research sample was 83 people. The data collection techniques used in this research are (1) observation, (2) decision study, (3) questionnaire, while using scalalicert data analysis technique. The collected data were then analyzed using a frequency table and using the SPSS 2.1 application. The results of the research on the level of community participation on the use of web applications in the Aka-Akae village, Watang Sidenreng district, were included in the average category, namely 66.428%, in the good category, while the communication factor in increasing participation regarding the use of web applications in Aka-Akae Village 28 , 7%, the factor of community understanding in using web applications in Aka-Akae Village 18.9%, and the community's ability factor in using web applications in Aka-Akae Village 39.6%. This can be seen from the t test, where t count is 1.325% smaller than t table 1.993 when Ho is accepted, meaning it is not significant, so the level of community Participation has no significant effect on the use of Web applications.
수능 영어 독해력 향상을 위한 다독 프로그램에 대한 교사와 학생의 요구 조사 및 분석

The purpose of this study was to analyze the needs of teachers and high school students regarding an extensive reading-based teaching model for improvement of English reading comprehension ability in the CSAT. In order to investigate this study's purpose, thirty two high school teachers and three hundred and nine high school students in Seoul, Gyeonggi Province, Gangwon Province, and Chungcheong Province participated in a survey questionnaire and the collected data were analyzed quantitatively. The results revealed that teachers see a lack of reading materials and framework for teaching reading as the main issues at hand. The results of the students' questionnaire analysis showed that most students suffered from dealing with reading materials that are too challenging for them to understand. It was verified that they need additional reading materials rather than difficult questions beyond their ability. In conclusion, most teachers and students need effective teaching methods using extensive reading as a tool to help high school students enhance their reading skills.
ACHIEVING STUDENTS 'DETAILED, THEORETICAL AND APPROACHED LEARNING IN THE TEACHING OF THE FIELD OF FINE ARTS AND IN THIS PROCESS THE TEACHER'S RESEARCH

This article aims to provide students with a theoretical, practical and visual knowledge in the teaching of fine arts, and in this process the problem of the teacher's quest and the goals and objectives of the subject Methods of Fine Arts, the stage of research and methods, the content and methodology of training, the content of extracurricular activities in educational institutions.
Tree in Tree: from Decision Trees to Decision Graphs

Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree in Tree decision graph (TnT), a framework that extends the conventional decision tree to a more generic and powerful directed acyclic graph. TnT constructs decision graphs by recursively growing decision trees inside the internal or leaf nodes instead of greedy training. The time complexity of TnT is linear to the number of nodes in the graph, and it can construct decision graphs on large datasets. Compared to decision trees, we show that TnT achieves better classification performance with reduced model size, both as a stand-alone classifier and as a base estimator in bagging/AdaBoost ensembles. Our proposed model is a novel, more efficient, and accurate alternative to the widely-used decision trees.
KID PICKUP NOTIFICATION SYSTEM USING MOBILE APPLICATION AND ARDUINO

The final year project is to develop a Kid Pickup Notification System using Mobile Application and Arduino. The problem of the current scenario is that when the school is over, the kid needs to wait at the school gate and constantly look for the parent’s vehicle and sometimes overlook it. A system is developed that the kid carries a notification device that will alert and notify the kid when the parent almost reaches the school. So, the kid can wait inside the school first. The parent needs to use the app to set the geofence around the school.
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models

We analyze a class of stochastic gradient algorithms with momentum on a high-dimensional random least squares problem. Our framework, inspired by random matrix theory, provides an exact (deterministic) characterization for the sequence of loss values produced by these algorithms which is expressed only in terms of the eigenvalues of the Hessian. This leads to simple expressions for nearly-optimal hyperparameters, a description of the limiting neighborhood, and average-case complexity. As a consequence, we show that (small-batch) stochastic heavy-ball momentum with a fixed momentum parameter provides no actual performance improvement over SGD when step sizes are adjusted correctly. For contrast, in the non-strongly convex setting, it is possible to get a large improvement over SGD using momentum. By introducing hyperparameters that depend on the number of samples, we propose a new algorithm sDANA (stochastic dimension adjusted Nesterov acceleration) which obtains an asymptotically optimal average-case complexity while remaining linearly convergent in the strongly convex setting without adjusting parameters.
Well-tuned Simple Nets Excel on Tabular Datasets

Tabular datasets are the last unconquered castle for deep learning, with traditional ML methods like Gradient-Boosted Decision Trees still performing strongly even against recent specialized neural architectures. In this paper, we hypothesize that the key to boosting the performance of neural networks lies in rethinking the joint and simultaneous application of a large set of modern regularization techniques. As a result, we propose regularizing plain Multilayer Perceptron (MLP) networks by searching for the optimal combination/cocktail of 13 regularization techniques for each dataset using a joint optimization over the decision on which regularizers to apply and their subsidiary hyperparameters. We empirically assess the impact of these regularization cocktails for MLPs in a large-scale empirical study comprising 40 tabular datasets and demonstrate that (i) well-regularized plain MLPs significantly outperform recent state-of-the-art specialized neural network architectures, and (ii) they even outperform strong traditional ML methods, such as XGBoost.
Continuous Doubly Constrained Batch Reinforcement Learning

Reliant on too many experiments to learn good actions, current Reinforcement Learning (RL) algorithms have limited applicability in real-world settings, which can be too expensive to allow exploration. We propose an algorithm for batch RL, where effective policies are learned using only a fixed offline dataset instead of online interactions with the environment. The limited data in batch RL produces inherent uncertainty in value estimates of states/actions that were insufficiently represented in the training data. This leads to particularly severe extrapolation when our candidate policies diverge from one that generated the data. We propose to mitigate this issue via two straightforward penalties: a policy-constraint to reduce this divergence and a value-constraint that discourages overly optimistic estimates. Over a comprehensive set of 32 continuous-action batch RL benchmarks, our approach compares favorably to state-of-the-art methods, regardless of how the offline data were collected.
Adversarial Feature Desensitization

Deep neural networks can now perform many tasks that were once thought to be only feasible for humans. While reaching impressive performance under standard settings, such networks are known to be susceptible to adversarial attacks -- slight but carefully constructed perturbations of the inputs which drastically decrease the network performance. Here we propose a new way to improve the network robustness against adversarial attacks by focusing on robust representation learning based on adversarial training procedure, called here Adversarial Feature Desensitization (AFD). AFD desensitizes the representation via an adversarial game between the embedding network and an adversarial discriminator introduced on top of the standard predictive model, which is trained to distinguish between the clean and perturbed inputs from their high-level representations. Our method substantially improves the state-of-the-art in robust classification on MNIST, CIFAR10, and CIFAR100 datasets. More importantly, we demonstrate that AFD has better generalization ability than previous methods, as the learned features maintain their robustness across a wide range of perturbations, including perturbations not seen during training. These results indicate that reducing feature sensitivity is a promising approach for ameliorating the problem of adversarial attacks in deep neural networks.
Scalable Font Reconstruction with Dual Latent Manifolds

We propose a deep generative model that performs typography analysis and font reconstruction by learning disentangled manifolds of both font style and character shape. Our approach enables us to massively scale up the number of character types we can effectively model compared to previous methods. Specifically, we infer separate latent variables representing character and font via a pair of inference networks which take as input sets of glyphs that either all share a character type, or belong to the same font. This design allows our model to generalize to characters that were not observed during training time, an important task in light of the relative sparsity of most fonts. We also put forward a new loss, adapted from prior work that measures likelihood using an adaptive distribution in a projected space, resulting in more natural images without requiring a discriminator. We evaluate on the task of font reconstruction over various datasets representing character types of many languages, and compare favorably to modern style transfer systems according to both automatic and manually-evaluated metrics.