Reviewerly/exHarmony huggingface dataset:
RaViS: Real-time accelerated View Synthesizer for immersive video 6DoF VR

MPEG-I, the upcoming standard for immersive video, has steadily explored immersive video technology for free navigation applications, where any virtual viewpoint to the scene is created using Depth Image-Based Rendering (DIBR) from any number of stationary cameras positioned around the scene. This exploration has recently evolved towards a rendering pipeline using camera feeds, as well as a standard file format, containing all information for synthesizing a virtual viewpoint to a scene. We present an acceleration of our Reference View Synthesis software (RVS) that enables the rendering in real-time of novel views in a head mounted display, hence supporting virtual reality (VR) with 6 Degrees of Freedom (6DoF) including motion parallax within a restricted viewing volume. In this paper, we explain its main engineering challenges.
Houghencoder: Neural Network Architecture for Document Image Semantic Segmentation

In this paper, we propose a HoughEncoder neural network architecture for the semantic image segmentation task. The main feature of the proposed architecture is that it contains layers calculating direct and transposed integral operators, namely Fast Hough Transform. These layers split deep fully convolutional architecture into three blocks. Therefore, the neural network inherits a possibility to make a decision in every point using integral features along different lines. It is important, that by doing this we do not increase the complexity of the neural network in terms of the number of trainable parameters. Our experiments on the publicly available datasets MIDV-500 and MIDV-2019 (both train and test) show that the suggested modification greatly increases quality. HoughEncoder outperforms UNet which shows state-of-the-art results in many semantic image segmentation tasks even while it has a one hundred times fewer parameters.
Using Bidirectional LSTM and Shortest Dependency Path for Classifying Arabic Temporal Relations

The classification of temporal relations between two entities is a crucial task for the comprehension of natural languages. Several methods have been proposed, but most of them have focused on English and several other languages. Contrarily, Arabic has received less attention for the classification of temporal relations. Recently, methods based on deep learning have obtained more relevant results than traditional methods. These latter have used several features, but they have lacked the modeling of syntactic structures. In this context, we present a first model for classifying temporal relations between pairs of events (E-E) or an event and a temporal expression (E-T) in Arabic intra-sentence. Our idea is to combine two branches of bidirectional LSTM networks to extract syntactic information by modeling the Shortest Dependency Path (SDP) between two target entities as well as the whole sentence sequence. Each word in the sentence is concatenated with its Parts Of Speech (POS). For the SDP branch, each word is concatenated with its POS as well as its generated dependency relations. To evaluate our model, and due to the lack of publicly available Arabic resources, we create an annotated Arabic corpus for temporal relations between E-E or E-T based on TimeML specifications. In addition, we propose a data augmentation method using transitivity and symmetry rules for temporality. The experimental results based on our annotated corpus give 84.6% of F1 for the classification of temporal relations between E-E, and 73.32% of F1 for the same task between E-T.
Online scheduling of heterogeneous distributed machine learning jobs

Distributed machine learning (ML) has played a key role in today's proliferation of AI services. A typical model of distributed ML is to partition training datasets over multiple worker nodes to update model parameters in parallel, adopting a parameter server architecture. ML training jobs are typically resource elastic, completed using various time lengths with different resource configurations. A fundamental problem in a distributed ML cluster is how to explore the demand elasticity of ML jobs and schedule them with different resource configurations, such that the utilization of resources is maximized and average job completion time is minimized. To address it, we propose an online scheduling algorithm to decide the execution time window, the number and the type of concurrent workers and parameter servers for each job upon its arrival, with a goal of minimizing the weighted average completion time. Our online algorithm consists of (i) an online scheduling framework that groups unprocessed ML training jobs into a batch iteratively, and (ii) a batch scheduling algorithm that configures each ML job to maximize the total weight of scheduled jobs in the current iteration. Our online algorithm guarantees a good parameterized competitive ratio with polynomial time complexity. Extensive evaluations using real-world data demonstrate that it outperforms state-of-the-art schedulers in today's AI cloud systems.
Data‐Driven Optimization of Seismicity Models Using Diverse Data Sets: Generation, Evaluation, and Ranking Using Inlabru

Abstract Recent developments in earthquake forecasting models have demonstrated the need for a robust method for identifying which model components are most beneficial to understanding spatial patterns of seismicity. Borrowing from ecology, we use Log‐Gaussian Cox process models to describe the spatially varying intensity of earthquake locations. These models are constructed using elements which may influence earthquake locations, including the underlying fault map and past seismicity models, and a random field to account for any excess spatial variation that cannot be explained by deterministic model components. Comparing the alternative models allows the assessment of the performance of models of varying complexity composed of different components and therefore identifies which elements are most useful for describing the distribution of earthquake locations. We demonstrate the effectiveness of this approach using synthetic data and by making use of the earthquake and fault information available for California, including an application to the 2019 Ridgecrest sequence. We show the flexibility of this modeling approach and how it might be applied in areas where we do not have the same abundance of detailed information. We find results consistent with existing literature on the performance of past seismicity models that slip rates are beneficial for describing the spatial locations of larger magnitude events and that strain rate maps can constrain the spatial limits of seismicity in California. We also demonstrate that maps of distance to the nearest fault can benefit spatial models of seismicity, even those that also include the primary fault geometry used to construct them.
Two-qubit correlation in two independent environments with indefiniteness

Decoherence of a two-qubit system is studied when each qubit interacts with a Markovian environment in an indefinite way. It is assumed that which environment of the two interacts with each qubit is determined by a controller qubit. This leads to indefiniteness of the environments. When information conveyed by the controller qubit is discarded, the quantum channel of the two qubits is given by a statistical mixture of the two Markovian channels. When appropriate projective measurement is performed on the controller qubit, it is found that the indefiniteness of the two environments significantly affects the output state of the two qubits. Once a specific measurement outcome is obtained, the output state becomes the Bell singlet state even if an initial state of the two qubit has no quantum correlation. Even when the measurement yields the other outcome, the entanglement, the Bell-inequality violation and the quantum discord can be enhanced.
Impact of the Information and Communication Technologies on Students with Disabilities. A Systematic Review 2009–2019

This article provides a bibliographic review of studies undertaken between 2009–2019 regarding the use of Information and Communication Technologies (ICTs) to support the learning of students with disabilities, included in the following databases: Web of Science (WoS,) Scopus, ERIC, SciELO, and Google Scholar. Through a descriptive and quantitative methodology, and using bibliometric maps, the most significant data are presented according to their citation and repercussion index. The results show that ICTs are decisive for student with disabilities but there is evidence of a lack of training by teachers. In this regard, findings show that both the quantity of articles published in the different databases and the citations they receive have a medium-low impact index, as well as a low application of them.
Performance Analysis of Hyperparameters on a Sentiment Analysis Model

This paper focuses on the performance analysis of hyperparameters of the Sentiment Analysis (SA) model of a course evaluation dataset. The performance was analyzed regarding hyperparameters such as activation, optimization, and regularization. In this paper, the activation functions used were adam, adagrad, nadam, adamax, and hard_sigmoid, the optimization functions were softmax, softplus, sigmoid, and relu, and the dropout values were 0.1, 0.2, 0.3, and 0.4. The results indicate that parameters adam and softmax with dropout value 2.0 are effective when compared to other combinations of the SA model. The experimental results reveal that the proposed model outperforms the state-of-the-art deep learning classifiers.
Using corpora to foster L2 construction learning: A data‐driven learning experiment

Abstract This paper adopts a construction grammar approach to test the efficiency of data‐driven learning (DDL) when applied to constructions. High‐intermediate learners of English as a foreign language took part in an experiment consisting, for each construction, in (i) a pre‐test, in which the learners had to produce as many sentences as possible illustrating the constructions, (ii) a DDL intervention, which involved various concordance‐based activities, and (iii) several post‐tests, similar to the pre‐test, performed at regular intervals. The analysis looks at the number of (valid and invalid) constructions produced as well as the native‐like nature of these constructions, as established through a comparison with native corpus data. The analysis also examines the evolution of these results over the different post‐tests. Finally, it considers learners’ attitudes towards the DDL interventions, as evaluated through a questionnaire.
SAN-M: Memory Equipped Self-Attention for End-to-End Speech Recognition

End-to-end speech recognition has become popular in recent years, since it can integrate the acoustic, pronunciation and language models into a single neural network. Among end-to-end approaches, attention-based methods have emerged as being superior. For example, Transformer, which adopts an encoder-decoder architecture. The key improvement introduced by Transformer is the utilization of self-attention instead of recurrent mechanisms, enabling both encoder and decoder to capture long-range dependencies with lower computational this http URL this work, we propose boosting the self-attention ability with a DFSMN memory block, forming the proposed memory equipped self-attention (SAN-M) mechanism. Theoretical and empirical comparisons have been made to demonstrate the relevancy and complementarity between self-attention and the DFSMN memory block. Furthermore, the proposed SAN-M provides an efficient mechanism to integrate these two modules. We have evaluated our approach on the public AISHELL-1 benchmark and an industrial-level 20,000-hour Mandarin speech recognition task. On both tasks, SAN-M systems achieved much better performance than the self-attention based Transformer baseline system. Specially, it can achieve a CER of 6.46% on the AISHELL-1 task even without using any external LM, comfortably outperforming other state-of-the-art systems.
How Can I See My Future? FvTraj: Using First-Person View for Pedestrian Trajectory Prediction

This work presents a novel First-person View based Trajectory predicting model (FvTraj) to estimate the future trajectories of pedestrians in a scene given their observed trajectories and the corresponding first-person view images. First, we render first-person view images using our in-house built First-person View Simulator (FvSim), given the ground-level 2D trajectories. Then, based on multi-head attention mechanisms, we design a social-aware attention module to model social interactions between pedestrians, and a view-aware attention module to capture the relations between historical motion states and visual features from the first-person view images. Our results show the dynamic scene contexts with ego-motions captured by first-person view images via FvSim are valuable and effective for trajectory prediction. Using this simulated first-person view images, our well structured FvTraj model achieves state-of-the-art performance.
On Aligning OpenIE Extractions with Knowledge Bases: A Case Study

Open information extraction (OIE) is the task of extracting relations and their corresponding arguments from natural language text in unsupervised manner.Outputs of such systems are used for downstream tasks such as question answering and automatic knowledge base (KB) construction.Many of these downstream tasks rely on aligning OIE triples with reference KBs.Such alignments are usually evaluated w.r.t. a specific downstream task and, to date, no direct manual evaluation of such alignments has been performed.In this paper, we directly evaluate how OIE triples from the OPIEC corpus are related to the DBpedia KB w.r.t.information content.First, we investigate OPIEC triples and DBpedia facts having the same arguments by comparing the information on the OIE surface relation with the KB relation.Second, we evaluate the expressibility of general OPIEC triples in DBpedia.We investigate whether-and, if so, how-a given OIE triple can be mapped to a single KB fact.We found that such mappings are not always possible because the information in the OIE triples tends to be more specific.Our evaluation suggests, however, that significant part of OIE triples can be expressed by means of KB formulas instead of individual facts.
Augmented Reality Technology as a Teaching Strategy for Learning Pediatric Asthma Management: Mixed Methods Study

Background Asthma is a major chronic disease affecting 8.6% of children in the United States. Objective The purpose of this research was to assess the use of clinical simulation scenarios using augmented reality technology to evaluate learning outcomes for nurse practitioner students studying pediatric asthma management. Methods A mixed-methods pilot study was conducted with 2 cohorts of graduate pediatric nurse practitioner students (N=21), with each cohort participating for 2 semesters. Results Significant improvements in pediatric asthma test scores (P&lt;.001) of student learning were found in both cohorts at posttest in both semesters. Student satisfaction with the augmented reality technology was found to be high. The focus group discussions revealed that the simulation was realistic and helpful for a flipped classroom approach. Conclusions The study results suggest augmented reality simulation to be valuable in teaching pediatric asthma management content in graduate nursing education.
Unsupervised learning of Lagrangian dynamics from images for prediction and control

Recent approaches for modelling dynamics of physical systems with neural networks enforce Lagrangian or Hamiltonian structure to improve prediction and generalization. However, when coordinates are embedded in high-dimensional data such as images, these approaches either lose interpretability or can only be applied to one particular example. We introduce a new unsupervised neural network model that learns Lagrangian dynamics from images, with interpretability that benefits prediction and control. The model infers Lagrangian dynamics on generalized coordinates that are simultaneously learned with a coordinate-aware variational autoencoder (VAE). The VAE is designed to account for the geometry of physical systems composed of multiple rigid bodies in the plane. By inferring interpretable Lagrangian dynamics, the model learns physical system properties, such as kinetic and potential energy, which enables long-term prediction of dynamics in the image space and synthesis of energy-based controllers.
Adversarial attacks on deep learning models for fatty liver disease classification by modification of ultrasound image reconstruction method

Convolutional neural networks (CNNs) have achieved remarkable success in medical image analysis tasks. In ultrasound (US) imaging, CNNs have been applied to object classification, image reconstruction and tissue characterization. However, CNNs can be vulnerable to adversarial attacks, even small perturbations applied to input data may significantly affect model performance and result in wrong output. In this work, we devise a novel adversarial attack, specific to ultrasound (US) imaging. US images are reconstructed based on radio-frequency signals. Since the appearance of US images depends on the applied image reconstruction method, we explore the possibility of fooling deep learning model by perturbing US B-mode image reconstruction method. We apply zeroth order optimization to find small perturbations of image reconstruction parameters, related to attenuation compensation and amplitude compression, which can result in wrong output. We illustrate our approach using a deep learning model developed for fatty liver disease diagnosis, where the proposed adversarial attack achieved success rate of 48%.
NLP North at WNUT-2020 Task 2: Pre-training versus Ensembling for Detection of Informative COVID-19 English Tweets

With the COVID-19 pandemic raging worldwide since the beginning of the 2020 decade, the need for monitoring systems to track relevant information on social media is vitally important.This paper describes our submission to the WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets.We investigate the effectiveness for a variety of classification models, and found that domainspecific pre-trained BERT models lead to the best performance.On top of this, we attempt a variety of ensembling strategies, but these attempts did not lead to further improvements.Our final best model, the standalone CT-BERT model, proved to be highly competitive, leading to a shared first place in the shared task.Our results emphasize the importance of domain and task-related pre-training. 1
Higher Education Online Courses Personalized Recommendation Algorithm Based on Score and Attributes

Abstract Education through the online platform has gained popularity. While the online course keeps increasing, its benefit does not increase in a proportional way. Personalized recommendation systems can well solve this problem by mining users’ interests and preferences. Aiming at the cold start problem, this paper improves the collaborative filtering algorithm by combining the user score and project attribute characteristics of the online course platform. The network communication technology is used to obtain the user ratings and project attribute data to verify the feasibility of the recommendation algorithm, adjust the parameters in the model, and compare the accuracy of the algorithm. The designed algorithm can provide accurate and rapid personalized recommendation services, which is convenient for users and conducive to the development of the platform. The improved algorithm solves the cold start problem compared with the traditional algorithm with a significantly improved prediction accuracy. The scheme can also be modified according to the changes in user preferences and can achieve good real-time recommendation effect.
Cheaper Pre-training Lunch: An Efficient Paradigm for Object Detection

In this paper, we propose a general and efficient pre-training paradigm, Montage pre-training, for object detection. Montage pre-training needs only the target detection dataset while taking only 1/4 computational resources compared to the widely adopted ImageNet pre-training. To build such an efficient paradigm, we reduce the potential redundancy by carefully extracting useful samples from the original images, assembling samples in a Montage manner as input, and using an ERF-adaptive dense classification strategy for model pre-training. These designs include not only a new input pattern to improve the spatial utilization but also a novel learning objective to expand the effective receptive field of the pre-trained model. The efficiency and effectiveness of Montage pre-training are validated by extensive experiments on the MS-COCO dataset, where the results indicate that the models using Montage pre-training are able to achieve on-par or even better detection performances compared with the ImageNet pre-training.
Invariant Representation Learning for Infant Pose Estimation with Small Data

Infant motion analysis is a topic with critical importance in early childhood development studies. However, while the applications of human pose estimation have become more and more broad, models trained on large-scale adult pose datasets are barely successful in estimating infant poses due to the significant differences in their body ratio and the versatility of their poses. Moreover, the privacy and security considerations hinder the availability of adequate infant pose data required for training of a robust model from scratch. To address this problem, this paper presents (1) building and publicly releasing a hybrid synthetic and real infant pose (SyRIP) dataset with small yet diverse real infant images as well as generated synthetic infant poses and (2) a multi-stage invariant representation learning strategy that could transfer the knowledge from the adjacent domains of adult poses and synthetic infant images into our fine-tuned domain-adapted infant pose (FiDIP) estimation model. In our ablation study, with identical network structure, models trained on SyRIP dataset show noticeable improvement over the ones trained on the only other public infant pose datasets. Integrated with pose estimation backbone networks with varying complexity, FiDIP performs consistently better than the fine-tuned versions of those models. One of our best infant pose estimation performers on the state-of-the-art DarkPose model shows mean average precision (mAP) of 93.6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The code is available at: github.com/ostadabbas/Infant-PoseEstimation. The SyRIP dataset can be downloaded at: Synthetic and Real Infant Pose (SyRIP)..
Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis

Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids. However, the cross-domain inner-class compactness and the underlying fine-grained subtype structure remained largely underexplored. In this work, we propose to adaptively carry out the fine-grained subtype-aware alignment by explicitly enforcing the class-wise separation and subtype-wise compactness with intermediate pseudo labels. Our key insight is that the unlabeled subtypes of a class can be divergent to one another with different conditional and label shifts, while inheriting the local proximity within a subtype. The cases with or without the prior information on subtype numbers are investigated to discover the underlying subtype structure in an online fashion. The proposed subtype-aware dynamic UDA achieves promising results on a medical diagnosis task.