Reviewerly/exHarmony huggingface dataset:
A Study on the GAN Algorithm Performance Improvement Method in Motor Failure Diagnosis Using Deep Learning Algorithm

When deep learning is applied to motor fault diagnosis, the Generative Adversarial Network (GAN) algorithm is used to compensate for the insufficient number of data. However, the model’s performance includes not only classification performance, but also overfitting degree and outlier detection performance. Unlike classification performance, outlier detection performance is affected not only by the amount of data but also by the quality. However, increasing similar data is not the best way to increase the model’s overall performance. Therefore, an RMSE-based data evaluation technique is proposed to find virtual data that maximizes model performance. It was confirmed that the proposed method helps to improve the outlier data detection performance of the model.
Bias in machine learning

In my opinion, this book would not be complete without mentioning the topic of bias in machine learning.
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables

Hamiltonian Monte Carlo (HMC) has emerged as a powerful Markov Chain Monte Carlo (MCMC) method to sample from complex continuous distributions. However, a fundamental limitation of HMC is that it can not be applied to distributions with mixed discrete and continuous variables. In this paper, we propose mixed HMC (M-HMC) as a general framework to address this limitation. M-HMC is a novel family of MCMC algorithms that evolves the discrete and continuous variables in tandem, allowing more frequent updates of discrete variables while maintaining HMC's ability to suppress random-walk behavior. We establish M-HMC's theoretical properties, and present an efficient implementation with Laplace momentum that introduces minimal overhead compared to existing HMC methods. The superior performances of M-HMC over existing methods are demonstrated with numerical experiments on Gaussian mixture models (GMMs), variable selection in Bayesian logistic regression (BLR), and correlated topic models (CTMs).
MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics

We study MinMax solution methods for a general class of optimization problems related to (and including) optimal transport. Theoretically, the focus is on fitting a large class of problems into a single MinMax framework and generalizing regularization techniques known from classical optimal transport. We show that regularization techniques justify the utilization of neural networks to solve such problems by proving approximation theorems and illustrating fundamental issues if no regularization is used. We further study the relation to the literature on generative adversarial nets, and analyze which algorithmic techniques used therein are particularly suitable to the class of problems studied in this paper. Several numerical experiments showcase the generality of the setting and highlight which theoretical insights are most beneficial in practice.
Image Animation with Perturbed Masks

We present a novel approach for image-animation of a source image by a driving video, both depicting the same type of object. We do not assume the existence of pose models and our method is able to animate arbitrary objects without the knowledge of the object's structure. Furthermore, both, the driving video and the source image are only seen during test-time. Our method is based on a shared mask generator, which separates the foreground object from its background, and captures the object's general pose and shape. To control the source of the identity of the output frame, we employ perturbations to interrupt the unwanted identity information on the driver's mask. A mask-refinement module then replaces the identity of the driver with the identity of the source. Conditioned on the source image, the transformed mask is then decoded by a multi-scale generator that renders a realistic image, in which the content of the source frame is animated by the pose in the driving video. Due to the lack of fully supervised data, we train on the task of reconstructing frames from the same video the source image is taken from. Our method is shown to greatly outperform the state-of-the-art methods on multiple benchmarks. Our code and samples are available at https://github.com/itsyoavshalevlImage-Animation-with-Perturbed-Masks.
Prototyping program platform to generate modern library services

A prototype for program platform for library services is examined. It is based on the microservice architecture and can be used as an individual service for various programs and apps, based on open protocol. The platform prototype is characterized in brief, along with different strategies for generating modern library services, i. e. OPAC, Distributed E-catalog, Awareness System and SEO. Platform prototype implementation does not depend on proprietary software.
Peningkatan kompetensi guru melalui pelatihan media video di Kabupaten Sinjai

The benefits of video media in education are able to increase students interest in learning because students can listen and see pictures. Teachers are expected to design an innovative, creative, efficient and effective learning media that can enhance the active learners in the learning process. Malasalah found between 1) teachers ability is still low in the production of their own learning media mainly in making learning videos, 2) Availability of free software that can be used to produce learning video media But never utilized by teachers, 3) improved teaching and learning process is effective, efficient, and more interesting. Overcoming the problem is take steps to conduct training with a variety of lecture methods that are divided into three phases: planning, implementation and evaluation. The external result of this activity is 1) the availability of video editing applications 2) Guru has been able to use video editing application, and 3) product produced by the teacher.
Spatio-Temporal Clustering for Fire Hot Spots Occurrence Over Sulawesi Island

Forest fire is a condition where an area where there are many trees and plants undergo a change in shape caused by massive burning. Forest fires occur in Indonesia especially on the island of Sulawesi. Sulawesi Island itself is an island that still has quite extensive forests. The cause of forest fires is caused by two factors, namely natural and human. Indicators of forest fires can be known from the appearance of hotspots or hot spots. A hotspot is an indicator of an area that has a relatively higher surface temperature than the surrounding surface temperature that has been detected by Terra / Aqua satellites with a MODIS sensor. Furthermore, the hotspots are not only randomly distributed but also clustered. The research related to the identification process of hotspot distribution patterns in an area in Sulawesi is still limited and has its novelty. Here we showed the implementation of Spatio-temporal clustering method with ST-DBSCAN algorithm, aided with Waterfall system development. The result showed that there are several types of distribution patterns generated by the system (Stationary, Regular Reaping, Irregular, Occasional, and Track) from the data used from 2016 to 2018 over Sulawesi island. In this research, we also found that between 2016 and 2018, the emergence of high tendency hotspots occurred in July, August, September, and October.
An Optimal Elimination Algorithm for Learning a Best Arm

We consider the classic problem of $(\epsilon,\delta)$-PAC learning a best arm where the goal is to identify with confidence $1-\delta$ an arm whose mean is an $\epsilon$-approximation to that of the highest mean arm in a multi-armed bandit setting. This problem is one of the most fundamental problems in statistics and learning theory, yet somewhat surprisingly its worst-case sample complexity is not well understood. In this paper, we propose a new approach for $(\epsilon,\delta)$-PAC learning a best arm. This approach leads to an algorithm whose sample complexity converges to \emph{exactly} the optimal sample complexity of $(\epsilon,\delta)$-learning the mean of $n$ arms separately and we complement this result with a conditional matching lower bound. More specifically:
Pelatihan Pelaksanaan K3 Terhadap Risiko Kebakaran Di Madrasah Ibtidaiyah Al Huda

Indonesia is the most vulnerable country to disasters in the world based on data released by the United Nations Agency for the International Strategy for Disaster Risk Reduction (UN-ISDR) [1]. This will certainly have an impact on the number of fatalities if there is no understanding and good handling of the parties involved. Alert and alert attitudes are still very minimal and are often not taught in formal teaching and learning activities. In addition to human resources, disaster risk reduction can also be pursued by providing adequate K3 facilities in each building. Madrasah Ibtidaiyah Al Huda which is on Jalan Al Huda Rawasapi RT 02/09 Kelurahan Jatimulya - Tambun Selatan Bekasi District has a moderate earthquake risk. In addition, the location of schools that are not on the main road and access roads to locations that are relatively difficult to pass by fire trucks make it vulnerable in the event of a fire disaster. The solution offered is education of OHS implementation of disasters and the provision of facilities and infrastructure to deal with these disasters. The expected output targets are one PKM article published through an ISSN journal, one ISBN proceeding article from a national seminar organized by STT-PLN, and publication in print / electronic media.
Optimal partitioning for the proportional hazards model

This paper discusses methods for clustering a continuous covariate in a survival analysis model. The advantages of using a categorical covariate defined from discretizing a continuous covariate (via clustering) is (i) enhanced interpretability of the covariate's impact on survival and (ii) relaxing model assumptions that are usually required for survival models, such as the proportional hazards model. Simulations and an example are provided to illustrate the methods.
Model-based Adversarial Meta-Reinforcement Learning

Meta-reinforcement learning (meta-RL) aims to learn from multiple training tasks the ability to adapt efficiently to unseen test tasks. Despite the success, existing meta-RL algorithms are known to be sensitive to the task distribution shift. When the test task distribution is different from the training task distribution, the performance may degrade significantly. To address this issue, this paper proposes Model-based Adversarial Meta-Reinforcement Learning (AdMRL), where we aim to minimize the worst-case sub-optimality gap -- the difference between the optimal return and the return that the algorithm achieves after adaptation -- across all tasks in a family of tasks, with a model-based approach. We propose a minimax objective and optimize it by alternating between learning the dynamics model on a fixed task and finding the adversarial task for the current model -- the task for which the policy induced by the model is maximally suboptimal. Assuming the family of tasks is parameterized, we derive a formula for the gradient of the suboptimality with respect to the task parameters via the implicit function theorem, and show how the gradient estimator can be efficiently implemented by the conjugate gradient method and a novel use of the REINFORCE estimator. We evaluate our approach on several continuous control benchmarks and demonstrate its efficacy in the worst-case performance over all tasks, the generalization power to out-of-distribution tasks, and in training and test time sample efficiency, over existing state-of-the-art meta-RL algorithms.
CircleGAN: Generative Adversarial Learning across Spherical Circles

We present a novel discriminator for GANs that improves realness and diversity of generated samples by learning a structured hypersphere embedding space using spherical circles. The proposed discriminator learns to populate realistic samples around the longest spherical circle, i.e., a great circle, while pushing unrealistic samples toward the poles perpendicular to the great circle. Since longer circles occupy larger area on the hypersphere, they encourage more diversity in representation learning, and vice versa. Discriminating samples based on their corresponding spherical circles can thus naturally induce diversity to generated samples. We also extend the proposed method for conditional settings with class labels by creating a hypersphere for each category and performing class-wise discrimination and update. In experiments, we validate the effectiveness for both unconditional and conditional generation on standard benchmarks, achieving the state of the art.
Variational Bayesian Monte Carlo with Noisy Likelihoods

Variational Bayesian Monte Carlo (VBMC) is a recently introduced framework that uses Gaussian process surrogates to perform approximate Bayesian inference in models with black-box, non-cheap likelihoods. In this work, we extend VBMC to deal with noisy log-likelihood evaluations, such as those arising from simulation-based models. We introduce new `global' acquisition functions, such as expected information gain (EIG) and variational interquantile range (VIQR), which are robust to noise and can be efficiently evaluated within the VBMC setting. In a novel, challenging, noisy-inference benchmark comprising of a variety of models with real datasets from computational and cognitive neuroscience, VBMC+VIQR achieves state-of-the-art performance in recovering the ground-truth posteriors and model evidence. In particular, our method vastly outperforms `local' acquisition functions and other surrogate-based inference methods while keeping a small algorithmic cost. Our benchmark corroborates VBMC as a general-purpose technique for sample-efficient black-box Bayesian inference also with noisy models.
Real Time Big Data Analysis by Using Apache Kudu and NoSQL Redis in Web Applications

In the recent years data processing in Big Data applications are moving from a batch processing model to a live streaming processing model. The live streaming processing model allows to process data in a real time or near real time manner. In many cases, there are need of combining data from various sources - both new generated data and data previously gathered and archived. In each case a different storage mechanism is appropriate to achieve best flexibility and to avoid the so-called bottleneck when processing data. In this article we review some of the possibilities of using Apache Kudu and NoSQL Redis systems which made them suitable to be used together for fast processing of streaming data.
Optimal Learning from Verified Training Data

Standard machine learning algorithms typically assume that data is sampled independently from the distribution of interest. In attempts to relax this assumption, fields such as adversarial learning typically assume that data is provided by an adversary, whose sole objective is to fool a learning algorithm. However, in reality, it is often the case that data comes from self-interested agents, with less malicious goals and intentions which lie somewhere between the two settings described above. To tackle this problem, we present a Stackelberg competition model for least squares regression, in which data is provided by agents who wish to achieve specific predictions for their data. Although the resulting optimisation problem is nonconvex, we derive an algorithm which converges globally, outperforming current approaches which only guarantee convergence to local optima. We also provide empirical results on two real-world datasets, the medical personal costs dataset and the red wine dataset, showcasing the performance of our algorithm relative to algorithms which are optimal under adversarial assumptions, outperforming the state of the art.
SU-NLP at WNUT-2020 Task 2: The Ensemble Models

In this paper, we address the problem of identifying informative tweets related to COVID-19 in the form of a binary classification task as part of our submission for W-NUT 2020 Task 2. Specifically, we focus on ensembling methods to boost the classification performance of classification models such as BERT and CNN. We show that ensembling can reduce the variance in performance, specifically for BERT base models.
Self-compensation method for out-of-plane motion-induced strain error in 2D digital image correlation

Two-dimensional digital image correlation (2D-DIC) has the advantages of high computation efficiency and simple experimental setup compared to three-dimensional DIC (3D-DIC). However, 2D-DIC is sensitive to out-of-plane motion, which leads to rather poor strain results. Recently, optical extensometers realized by dual-reflector imaging have been proposed, which can self-compensate the strain errors introduced by out-of-plane motion of the specimen. We will extend strain measurement from an extensometer to full-field deformation measurement. To this end, first, two corresponding areas of interest (AOIs) are created based on the symmetrical axis of the reference image. Second, the displacement and strain fields of these AOIs are computed with a common 2D-DIC algorithm, respectively. Subsequently, high-accuracy deformation results are obtained by taking the average of the deformation of the corresponding calculation points in two AOIs. Two types of uniaxial tensile tests that correspond to uniform and nonuniform strain fields were conducted to validate the feasibility of the proposed self-compensation method. The first experiment indicates that the strain results obtained using the proposed method are in good agreement with those with strain gauges and that the proposed method can achieve higher strain accuracy than 3D-DIC with a small stereo-angle. The results of the second experiment are basically in agreement with those of the ANSYS numerical simulation, which demonstrates the feasibility of the proposed method for the measurement of nonuniform strain fields. Both of the experiments demonstrate that rigid out-of-plane motion will lead to a global strain field error for common 2D-DIC. With no external compensation device, the proposed self-compensation method has the potential in 2D-DIC for accurate strain measurement due to its high level of accuracy.
The Bayes Security Measure

Security system designers favor worst-case security measures, such as those derived from differential privacy, due to the strong guarantees they provide. These guarantees, on the downside, result on high penalties on the system's performance. In this paper, we study the Bayes security measure. This measure quantifies the expected advantage over random guessing of an adversary that observes the output of a mechanism. We show that the minimizer of this measure, which indicates its security lower bound, i) is independent from the prior on the secrets, ii) can be estimated efficiently in black-box scenarios, and iii) it enables system designers to find low-risk security parameters without hurting utility. We provide a thorough comparison with respect to well-known measures, identifying the scenarios where our measure is advantageous for designers, which we illustrate empirically on relevant security and privacy problems.
Collegial Ensembles

Modern neural network performance typically improves as model size increases. A recent line of research on the Neural Tangent Kernel (NTK) of over-parameterized networks indicates that the improvement with size increase is a product of a better conditioned loss landscape. In this work, we investigate a form of over-parameterization achieved through ensembling, where we define collegial ensembles (CE) as the aggregation of multiple independent models with identical architectures, trained as a single model. We show that the optimization dynamics of CE simplify dramatically when the number of models in the ensemble is large, resembling the dynamics of wide models, yet scale much more favorably. We use recent theoretical results on the finite width corrections of the NTK to perform efficient architecture search in a space of finite width CE that aims to either minimize capacity, or maximize trainability under a set of constraints. The resulting ensembles can be efficiently implemented in practical architectures using group convolutions and block diagonal layers. Finally, we show how our framework can be used to analytically derive optimal group convolution modules originally found using expensive grid searches, without having to train a single model.
PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals

Learning with sparse rewards remains a significant challenge in reinforcement learning (RL), especially when the aim is to train a policy capable of achieving multiple different goals. To date, the most successful approaches for dealing with multi-goal, sparse reward environments have been model-free RL algorithms. In this work we propose PlanGAN, a model-based algorithm specifically designed for solving multi-goal tasks in environments with sparse rewards. Our method builds on the fact that any trajectory of experience collected by an agent contains useful information about how to achieve the goals observed during that trajectory. We use this to train an ensemble of conditional generative models (GANs) to generate plausible trajectories that lead the agent from its current state towards a specified goal. We then combine these imagined trajectories into a novel planning algorithm in order to achieve the desired goal as efficiently as possible. The performance of PlanGAN has been tested on a number of robotic navigation/manipulation tasks in comparison with a range of model-free reinforcement learning baselines, including Hindsight Experience Replay. Our studies indicate that PlanGAN can achieve comparable performance whilst being around 4-8 times more sample efficient.
Diverse Image Captioning with Context-Object Split Latent Spaces

Diverse image captioning models aim to learn one-to-many mappings that are innate to cross-domain datasets, such as of images and texts. Current methods for this task are based on generative latent variable models, e.g. VAEs with structured latent spaces. Yet, the amount of multimodality captured by prior work is limited to that of the paired training data -- the true diversity of the underlying generative process is not fully captured. To address this limitation, we leverage the contextual descriptions in the dataset that explain similar contexts in different visual scenes. To this end, we introduce a novel factorization of the latent space, termed context-object split, to model diversity in contextual descriptions across images and texts within the dataset. Our framework not only enables diverse captioning through context-based pseudo supervision, but extends this to images with novel objects and without paired captions in the training data. We evaluate our COS-CVAE approach on the standard COCO dataset and on the held-out COCO dataset consisting of images with novel objects, showing significant gains in accuracy and diversity.
Designing Educational Tabletop Games for the Inorganic Chemistry Classroom

Tabletop gaming is a fun and social experience for families and friends. By incorporating educational tabletop games into the academic classroom, students are afforded more opportunities to engage and learn from one another. This chapter details my experience crafting inorganic chemistry tabletop games and touches on the implementation of these games in the classroom. I detail five of my inorganic chemistry tabletop games and discuss their advantages in the classroom. All games are available for download as a free print and play version or can be purchased at the author’s website. Additionally, the 18 Electron Rule game is available for free online play.