Reviewerly/exHarmony huggingface dataset:
GEOINFORMATICS OF TUBERCULOSIS (TB) DISEASE IN JAKARTA CITY INDONESIA

Tuberculosis (TB) disease has still become the leading cause of morbidity and mortality in Indonesia. TB had a relationship between human and their environment, especially in an urban area. The urban area had the highest population and density, that is the way the accurate information about the urban environment of the TB area is critical. The research objective to representing characteristic spatial of TB patients lived in Jakarta year 2017. The research used a method using visual interpretation from superimposed spatial data. Spatial data collection from google earth data, household area (RW boundary), and TB patient addressed from the hospital. The total TB patients in Jakarta City year 2017 in total 114 from a hospital and distributed in 108 household areas (RW boundary). The result of geoinformatics of the urban environment, first building population, building density, vegetated covered, and distance from the main road. The TB area characteristic had two classified high and low, based on total patient TB disease in a household area (RW boundary). The research concluded of the geoinformatics of TB disease in Jakarta City year 2017 with highly classified is high building population and building density, lowest vegetated covered (high temperature), and near the main road. The research also uses the PAM algorithm to cluster the patient and resulted in four clusters with differ mostly in building density, building temperature, and the distance to the nearest river.
S <sup>3</sup> -NET

Machine reading comprehension question answering (MRC-QA) is the task of understanding the context of a given passage to find a correct answer within it. A passage is composed of several sentences; therefore, the length of the input sentence becomes longer, leading to diminished performance. In this article, we propose S 3 -NET, which adds sentence-based encoding to solve this problem. S 3 -NET, which is based on a simple recurrent unit architecture, is a deep learning model that solves the MRC-QA by applying matching network to sentence-level encoding. In addition, S 3 -NET utilizes self-matching networks to compute attention weight for its own recurrent neural network sequences. We perform MRC-QA for the SQuAD dataset of English and MindsMRC dataset of Korean. The experimental results show that for SQuAD, the S 3 -NET model proposed in this article produces 71.91% and 74.12% exact match and 81.02% and 82.34% F1 in single and ensemble models, respectively, and for MindsMRC, our model achieves 69.43% and 71.28% exact match and 81.53% and 82.77% F1 in single and ensemble models, respectively.
Bayesian x-vector: Bayesian Neural Network based x-vector System for Speaker Verification

Speaker verification systems usually suffer from the mismatch problem between training and evaluation data, such as speaker population mismatch, the channel and environment variations. In order to address this issue, it requires the system to have good generalization ability on unseen data. In this work, we incorporate Bayesian neural networks (BNNs) into the deep neural network (DNN) x-vector speaker verification system to improve the system's generalization ability. With the weight uncertainty modeling provided by BNNs, we expect the system could generalize better on the evaluation data and make verification decisions more accurately. Our experiment results indicate that the DNN x-vector system could benefit from BNNs especially when the mismatch problem is severe for evaluations using out-of-domain data. Specifically, results show that the system could benefit from BNNs by a relative EER decrease of 2.66% and 2.32% respectively for short- and long-utterance in-domain evaluations. Additionally, the fusion of DNN x-vector and Bayesian x-vector systems could achieve further improvement. Moreover, experiments conducted by out-of-domain evaluations, e.g. models trained on Voxceleb1 while evaluated on NIST SRE10 core test, suggest that BNNs could bring a larger relative EER decrease of around 4.69%.
Emotion Classification in Parkinson's Disease EEG using RQA and ELM

Most of the earlier works focused on diagnosing the Parkinson's Disease (PD) through behavioral measures. Very few researches attempted to identify the emotion impairment in PD through EEG signals. The main objective of this work is to classify the emotions perceived by the PD subjects through audio-visual stimuli using Electroencephalogram (EEG) signals. EEG database of 20 subjects on PD and 20 NC is developed using a 14 channel wireless EEG device at a sampling frequency of 128 Hz. Audiovisual stimuli of six emotions (happiness, sadness, anger, fear, surprise, and disgust) are used to induce the emotions. The acquired EEG signals are pre-processed using the IIR Butterworth filter to remove the noises, artifacts, and interferences in EEG signals and used to derive three frequency bands (alpha, beta and gamma) of EEG data. Recurrence Quantification Analysis (RQA) is used to extract the two most significant features (Maximum Line Length, Maximum Vertical Line Length) from Recurrence Plot (RP). Besides, these features are combined together called ALL features. Therefore, three types of features were tested using one-way analysis of variance (ANOV A) to test its significance in classifying emotions in PD and NC and a five-fold cross-validation method is used to split the features into training and testing set. Finally, the Extreme Learning Machine (ELM) classifier with two different types of kernel functions used to classify the emotions of PD and NC. The maximum mean accuracy of 89.17%, 84.50% is achieved on NC and PD, respectively. The maximum individual class accuracy of NC/PD is sadness - 90.90/87.50, happiness - 91.10/84.30, fear- 88/84.10, disgust - 88.5/82.70, surprise - 87.4/84.60, and anger - 89.10/83.80. Experimental results indicate that RQA features are highly useful in detecting the emotions in PD compared to other methods and ELM gives the highest mean accuracy compared to other works in the literature.
Extending a Soft-Core RISC-V Processor to Accelerate CNN Inference

Convolutional Neural Networks (CNNs) are the gold-standard for computer vision. Using CNN on embedded hardware that has limited computational capability is an area of active investigation and optimization. In this paper, we investigate the potential of extending the RISC-V Instruction Set Architecture for accelerating the inference of a CNN using in-pipeline hardware blocks and custom instructions. Our preliminary designs have a small footprint and minimal impact on maximum core frequency. The new designed instructions were used to extend an existing soft-core processor. This processor was synthesized to an FPGA for cycle-accurate testing and performance evaluation.
Pemanfaatan Portal Rumah Belajar Kemendikbud sebagai Model Pembelajaran Daring di Sekolah Dasar

This research is a qualitative research with a type of phenomenology. This study aims to describe the use of the Kemendikbud Learning House Portal in the learning process in elementary schools. The sampling technique used in the study is purposive sampling with the subject of research being the teacher who has used the Kemendikbud Learning House Portal in the learning process in the classroom. Data collection techniques are carried out through observation, structured interviews and document studies. The data obtained are qualitative data which contains a description of the results of learning observations and teacher interviews regarding the use of the portal Home Learning, learning activities and steps taken by teachers in utilizing the Kemendikbud Learning House portal as a model of online learning in elementary schools.  The study was carried out at  Public Elementary School  112 /I,  Perumnas and  Public  Elementary School 13 /I,  Muara Bulian. The results showed that the learning steps taken by the teacher in utilizing the Kemendikbud Learning House portal were started from the preparation and implementation of learning which included the opening, core and closing learning activities. Some features that have been used by teachers in learning are Learning Resources, Electronic School Books (BSE), and Question Banks. While the methods used by teachers in utilizing the home learning portal as an online learning model during the learning process can be grouped in (1) Classical / Demonstration Presentations, (2) Small Group Discussions and (3) Individual Learning .
Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image

Convolutional Neural Network (CNN) has achieved particularly good results on depth estimation from a single image. However, certain disadvantages exist including: (1) Traditional CNNs adopt pooling layers to increase the receptive field, but it will lower the resolution and cause the information loss. (2) Almost all frameworks of CNN proposed for depth estimation apply the fully connected layers to obtain global information and they will introduce too many parameters which often result in out-of-memory problems. In this paper, we present a new module named dilated fully convolutional neural network to tackle these disadvantages. On one hand, the developed method takes advantages of dilated convolutions that can support the exponential expansion of the receptive field without loss of resolution; On the other hand, our module replaces the fully connected layers with the fully convolutional layers, which can significantly reduce the number of parameters to make our module more universal. By experiments, we show that the presented module achieves state-of-the-art results on NYU Depth V2 datasets.
DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation.

As a pixel-level prediction task, semantic segmentation needs large computational cost with enormous parameters to obtain high performance. Recently, due to the increasing demand for autonomous systems and robots, it is significant to make a tradeoff between accuracy and inference speed. In this paper, we propose a novel Depthwise Asymmetric Bottleneck (DAB) module to address this dilemma, which efficiently adopts depth-wise asymmetric convolution and dilated convolution to build a bottleneck structure. Based on the DAB module, we design a Depth-wise Asymmetric Bottleneck Network (DABNet) especially for real-time semantic segmentation, which creates sufficient receptive field and densely utilizes the contextual information. Experiments on Cityscapes and CamVid datasets demonstrate that the proposed DABNet achieves a balance between speed and precision. Specifically, without any pretrained model and postprocessing, it achieves 70.1% Mean IoU on the Cityscapes test dataset with only 0.76 million parameters and a speed of 104 FPS on a single GTX 1080Ti card.
Research on path planning based on new fusion algorithm for autonomous vehicle

Ant colony algorithm or artificial potential field is commonly used for path planning of autonomous vehicle. However, vehicle dynamics and road adhesion coefficient are not taken into consideration. In addition, ant colony algorithm has blindness/randomness due to low pheromone concentration at initial stage of obstacle avoidance path searching progress. In this article, a new fusion algorithm combining ant colony algorithm and improved potential field is introduced making autonomous vehicle avoid obstacle and drive more safely. Controller of path planning is modeled and analyzed based on simulation of CarSim and Simulink. Simulation results show that fusion algorithm reduces blindness at initial stage of obstacle avoidance path searching progress and verifies validity and efficiency of path planning. Moreover, all parameters of vehicle are changed within a reasonable range to meet requirements of steering stability and driving safely during path planning progress.
Deep temporal motion descriptor (DTMD) for human action recognition

Spatiotemporal features have significant importance in human action recognition, as they provide the actor's shape and motion characteristics specific to each action class. This paper presents a new deep spatiotemporal human action representation, the deep temporal motion descriptor (DTMD), which shares the attributes of holistic and deep learned features. To generate the DTMD descriptor, the actor?s silhouettes are gathered into single motion templates by applying motion history images. These motion templates capture the spatiotemporal movements of the actor and compactly represent the human actions using a single 2D template. Then deep convolutional neural networks are used to compute discriminative deep features from motion history templates to produce the DTMD. Later, DTMD is used for learning a model to recognize human actions using a softmax classifier. The advantage of DTMD are that DTMD is automatically learned from videos and contains higher-dimensional discriminative spatiotemporal representations as compared to handcrafted features; DTMD reduces the computational complexity of human activity recognition as all the video frames are compactly represented as a single motion template; and DTMD works effectively for single and multiview action recognition. We conducted experiments on three challenging datasets: MuHAVI-Uncut, iXMAS, and IAVID-1. The experimental findings reveal that DTMD outperforms previous methods and achieves the highest action prediction rate on the MuHAVI-Uncut dataset.
Twisted particle collisions: A new tool for spin physics

Collisions of twisted particles---that is, non-plane-wave states of photons, electrons, or any other particle, equipped with a nonzero orbital angular momentum (OAM) with respect to its propagation direction---offer novel ways to probe particle structure and interactions. Recently, we argued that resonance production in twisted photon collisions or twisted ${e}^{+}{e}^{\ensuremath{-}}$ annihilation gives access to parity- and spin-sensitive observables in inclusive cross sections, even when the initial particles are unpolarized. Here, we explore these features in detail, providing a qualitative picture and illustrating it with numerical examples. We show how one can detect parity-violating effects in collisions of unpolarized twisted photons and how one can produce almost 100% polarized vector mesons in unpolarized twisted ${e}^{+}{e}^{\ensuremath{-}}$ annihilation. These examples highlight the unprecedented level of control over polarization offered by twisted particles, impossible in the usual plane wave collisions.
Neural Edit Completion.

We address the problem of predicting edit completions based on a learned model that was trained on past edits. Given a code snippet that is partially edited, our goal is to predict a completion of the edit for the rest of the snippet. We refer to this task as the EditCompletion task and present a novel approach for tackling it. The main idea is to directly represent structural edits. This allows us to model the likelihood of the edit itself, rather than learning the likelihood of the edited code. We represent an edit operation as a path in the program's Abstract Syntax Tree (AST), originating from the source of the edit to the target of the edit. Using this representation, we present a powerful and lightweight neural model for the EditCompletion task. We conduct a thorough evaluation, comparing our approach to a variety of representation and modeling approaches that are driven by multiple strong models such as LSTMs, Transformers, and neural CRFs. Our experiments show that our model achieves 28% relative gain over state-of-the-art sequential models and 2$\times$ higher accuracy than syntactic models that learn to generate the edited code instead of modeling the edits directly. We make our code, dataset, and trained models publicly available.
Spatiotemporal Interaction Residual Networks with Pseudo3D for Video Action Recognition

Action recognition is a significant and challenging topic in the field of sensor and computer vision. Two-stream convolutional neural networks (CNNs) and 3D CNNs are two mainstream deep learning architectures for video action recognition. To combine them into one framework to further improve performance, we proposed a novel deep network, named the spatiotemporal interaction residual network with pseudo3D (STINP). The STINP possesses three advantages. First, the STINP consists of two branches constructed based on residual networks (ResNets) to simultaneously learn the spatial and temporal information of the video. Second, the STINP integrates the pseudo3D block into residual units for building the spatial branch, which ensures that the spatial branch can not only learn the appearance feature of the objects and scene in the video, but also capture the potential interaction information among the consecutive frames. Finally, the STINP adopts a simple but effective multiplication operation to fuse the spatial branch and temporal branch, which guarantees that the learned spatial and temporal representation can interact with each other during the entire process of training the STINP. Experiments were implemented on two classic action recognition datasets, UCF101 and HMDB51. The experimental results show that our proposed STINP can provide better performance for video recognition than other state-of-the-art algorithms.
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning

Value decomposition is a popular and promising approach to scaling up multi-agent reinforcement learning in cooperative settings. However, the theoretical understanding of such methods is limited. In this paper, we introduce a variant of the fitted Q-iteration framework for analyzing multi-agent Q-learning with value decomposition. Based on this framework, we derive a closed-form solution to the empirical Bellman error minimization with linear value decomposition. With this novel solution, we further reveal two interesting insights: 1) linear value decomposition implicitly implements a classical multi-agent credit assignment called counterfactual difference rewards; and 2) On-policy data distribution or richer Q function classes can improve the training stability of multi-agent Q-learning. In the empirical study, our experiments demonstrate the realizability of our theoretical closed-form formulation and implications in the didactic examples and a broad set of StarCraft II unit micromanagement tasks, respectively.
"Vera"  Crossing the Fourth Wall

"Vera" is an adaptation of a short story by New Zealand's early XXth-century feminist writer, Katherine Mansfield. It is a unique artificial reality experience that allows viewers to cross the so-called fourth wall between the film reality and the audience's reality. The experience allows the viewer to "enter" a film frame via a mixed reality tabletop experience. To our knowledge, "Vera" is the first immersive experience that utilizes both volumetric capture (instead of avatar animation) and photogrammetric reconstruction of the set decoration and props. It is also the first experiment that literally rather than metaphorically crosses the fourth wall.
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks

Adversarial or test time robustness measures the susceptibility of a classifier to perturbations to the test input. While there has been a flurry of recent work on designing defenses against such perturbations, the theory of adversarial robustness is not well understood. In order to make progress on this, we focus on the problem of understanding generalization in adversarial settings, via the lens of Rademacher complexity. We give upper and lower bounds for the adversarial empirical Rademacher complexity of linear hypotheses with adversarial perturbations measured in $l_r$-norm for an arbitrary $r \geq 1$. This generalizes the recent result of [Yin et al.'19] that studies the case of $r = \infty$, and provides a finer analysis of the dependence on the input dimensionality as compared to the recent work of [Khim and Loh'19] on linear hypothesis classes. We then extend our analysis to provide Rademacher complexity lower and upper bounds for a single ReLU unit. Finally, we give adversarial Rademacher complexity bounds for feed-forward neural networks with one hidden layer. Unlike previous works we directly provide bounds on the adversarial Rademacher complexity of the given network, as opposed to a bound on a surrogate. A by-product of our analysis also leads to tighter bounds for the Rademacher complexity of linear hypotheses, for which we give a detailed analysis and present a comparison with existing bounds.
Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks

Reinforcement learning systems require good representations to work well. For decades practical success in reinforcement learning was limited to small domains. Deep reinforcement learning systems, on the other hand, are scalable, not dependent on domain specific prior knowledge and have been successfully used to play Atari, in 3D navigation from pixels, and to control high degree of freedom robots. Unfortunately, the performance of deep reinforcement learning systems is sensitive to hyper-parameter settings and architecture choices. Even well tuned systems exhibit significant instability both within a trial and across experiment replications. In practice, significant expertise and trial and error are usually required to achieve good performance. One potential source of the problem is known as catastrophic interference: when later training decreases performance by overriding previous learning. Interestingly, the powerful generalization that makes Neural Networks (NN) so effective in batch supervised learning might explain the challenges when applying them in reinforcement learning tasks. In this paper, we explore how online NN training and interference interact in reinforcement learning. We find that simply re-mapping the input observations to a high-dimensional space improves learning speed and parameter sensitivity. We also show this preprocessing reduces interference in prediction tasks. More practically, we provide a simple approach to NN training that is easy to implement, and requires little additional computation. We demonstrate that our approach improves performance in both prediction and control with an extensive batch of experiments in classic control domains.
Facial recognition using two-dimensional principal component analysis and k-nearest neighbor: a case analysis of facial images

Abstract Science and Technology Innovation in Computer Science, especially in the facial image is increasingly needed in the Industrial Era of 4.0. The problem: How to use the working pattern of the Two-Dimensional Principal Component Analysis (2DPCA) method that integrated with K-Nearest Neighbors (KNN) in the Facial Image Recognition for various purposes? The purpose of this study, to analyze the Facial Image Recognition based on the method of Two-Dimensional Principal Component Analysis (2DPCA) which is integrated with KNN. This research uses the method of Two-Dimensional Principal Component Analysis (2DPCA) for the feature extraction process and the KNN classification method is applied to perform the data classification process so that the desired accuracy value is obtained. Research subjects use the image database from UCI repository, consists of 190 black and white face images of people taken with varying pose (straight, left, right, up), expression (neutral, happy, sad, angry), and size. The result of this study is the performance analysis of Facial Image Recognition based on the method of Two-Dimensional Principal Component Analysis (2DPCA) that is integrated with k-Nearest Neighbors (KNN).