Reviewerly/exHarmony huggingface dataset:
Data analytics and visualization using Tableau utilitarian for COVID-19 (Coronavirus)

In 2020 the world has generating 52 times the amount of data as in 2010, and 76 times the number of information sources.Being able to use this data provides huge opportunities and to turn these opportunities into reality, people need to use data to solve problems.Unfortunately in the midst of a global pandemic, when people throughout the world are looking for reliable, trustworthy information about .In this scenario tableau play the vital role, because Tableau is an extremely powerful tool for visualizing massive sets of data very easily.It has an easy to use drag and drop interface.You can build beautiful visualizations easily and in a short amount of time.Tableau supports a wide array of data sources.COVID-19(Coronavirus) analytics with Tableau, you will create dashboards that help you identify the story within our data, and we will better understand the impact of .In this paper comprehensive review about Tableau.The Tableau are the tools which deals with the big data analytics also it generates the output in visualization technique i.e., more understandable and presentable.Its features include data blending, realtime reporting and collaboration of data.In the end, this paper gives the clear picture of growing COVID-19(Coronavirus) data and the tools which can help more effectively, accurately and efficiently.
3D Gaze Estimation for Head-Mounted Eye Tracking System With Auto-Calibration Method

The general challenges of 3D gaze estimation for head-mounted eye tracking systems are inflexible marker-based calibration procedure and significant errors of depth estimation. In this paper, we propose a 3D gaze estimation with an auto-calibration method. To acquire the accurate 3D structure of the environment, an RGBD camera is applied as the scene camera of our system. By adopting the saliency detection method, saliency maps can be acquired through scene images, and 3D salient pixels in the scene are considered potential 3D calibration targets. The 3D eye model is built on the basis of eye images to determine gaze vectors. By combining 3D salient pixels and gaze vectors, the auto-calibration can be achieved with our calibration method. Finally, the 3D gaze point is obtained through the calibrated gaze vectors, and the point cloud is generated from the RGBD camera. The experimental result shows that the proposed system can achieve an average accuracy of 3.7° in the range of 1 m to 4 m indoors and 4.0° outdoors. The proposed system also presents a great improvement in depth measurement, which is sufficient for tracking users' visual attention in real scenes.
A machine learning approach to Bayesian parameter estimation

Abstract Bayesian estimation is a powerful theoretical paradigm for the operation of the approach to parameter estimation. However, the Bayesian method for statistical inference generally suffers from demanding calibration requirements that have so far restricted its use to systems that can be explicitly modeled. In this theoretical study, we formulate parameter estimation as a classification task and use artificial neural networks to efficiently perform Bayesian estimation. We show that the network’s posterior distribution is centered at the true (unknown) value of the parameter within an uncertainty given by the inverse Fisher information, representing the ultimate sensitivity limit for the given apparatus. When only a limited number of calibration measurements are available, our machine-learning-based procedure outperforms standard calibration methods. Our machine-learning-based procedure is model independent, and is thus well suited to “black-box sensors”, which lack simple explicit fitting models. Thus, our work paves the way for Bayesian quantum sensors that can take advantage of complex nonclassical quantum states and/or adaptive protocols. These capabilities can significantly enhance the sensitivity of future devices.
FACTORS INFLUENCING URBAN INVESTMENT ATTRACTIVENESS: AN FCM-SD APPROACH

The increasing concentration of populations in urban areas in recent decades has strengthened the interest in – and the importance given to – these zones. Cities have become quite attractive from investors’ point of view because of the wide array of opportunities and growing need for investment in urban areas. Thus, city strategic planning quite often requires an understanding of the determinants that attract investment to urban zones. This study sought to identify the factors that strengthen urban investment based on the knowledge of a panel of experts. Fuzzy cognitive mapping techniques were applied to understand the concepts and decision criteria included in the decision-support model and their cause-and-effect relationships. The results provide insights into which determinants most strongly influence urban investment, namely, infrastructure, supporting services, and political-administrative factors. Diverse scenarios at the intra- and inter-cluster levels were created to clarify the impacts of variable changes on the model developed. The findings were validated by both the expert panel members and the vice-president of the Portuguese Association of Real Estate Developers and Investors. Advantages and limitations of the proposed framework are presented, as well as recommendations for future research.
PGAN: Part-Based Nondirect Coupling Embedded GAN for Person Reidentification

The block-based representation learning method has been proven to be a very effective method for person reidentification (Re-ID), but the features extracted by the existing block-based approach tend to have a high correlation among different blocks. Also, these methods perform less well for persons with large posture changes. Thus, part-based nondirect coupling representation learning method is proposed by introducing a similarity measure loss to constrain features of different blocks. Moreover, part-based nondirect coupling embedded GAN method is proposed, which aims to extract more common features of different postures of a same person. In this way, the extracted features of the network are robust for posture changes of a person, and there are no auxiliary pose information and additional computational cost required in the test stage. Experimental results on public datasets show that our proposed method achieves good performances, especially, it outperforms the state-of-the-art GAN-based methods for person Re-ID.
Reference architecture design for farm management information systems: a multi-case study approach

Abstract One of the key elements of precision agriculture is the farm management information system (FMIS) that is responsible for data management, analytics and subsequent decision support. Various FMISs have been developed to support the management of farm businesses. A key artefact in the development of FMISs is the software architecture that defines the gross level structure of the system. The software architecture is important for understanding the system, analysing the design decisions and guiding the further development of the system based on the architecture. To assist in the design of the FMIS architecture, several reference architectures have been provided in the literature. Unfortunately, in practice, it is less trivial to derive the application architecture from these reference architectures. Two underlying reasons for this were identified. First of all, it appears that the proposed reference architectures do not specifically focus on FMIS but have a rather broad scope of the agricultural domain in general. Secondly, the proposed reference architectures do not seem to have followed the proper architecture documentation guidelines as defined in the software architecture community, lack precision, and thus impeding the design of the required application architectures. Presented in this article is a novel reference architecture that is dedicated to the specific FMIS domain, and which is documented using the software architecture documentation guidelines. In addition, the systematic approach for deriving application architectures from the proposed reference architecture is provided. To illustrate the approach, the results of multi-case study research are shown in which the presented reference architecture is used for deriving different FMIS application architectures.
CDS: A Cross–Version Software Defect Prediction Model With Data Selection

Over the past decade, a large number of software defect prediction approaches have been proposed to identify the defect-prone modules by mining software repositories. Recently, a novel scenario called Cross-Version Defect Prediction (CVDP) begins to draw increasing research interests, as it is more reasonable and applicable in practice to adopt the labeled defect data of previous versions to predict defects in the current version of the same project. As a software project often has multiple previous versions, CVDP on this kind of projects will face the following two critical but seldom reported issues, namely, data distribution difference and class overlapping. In this paper, we address these two issues by solving a version selection problem via a Cross-version model with Data Selection (CDS). The proposed CDS is a novel framework which treats the defect prediction of existing and new files in different ways. For the existing files, we propose a novel Clustering-based Multi-Version Classifier (CMVC), which can automatically select the training data from the most relevant and noise-free versions by assigning them higher weights than the others. We proposed a Weighted Sampling Model (WSM) for the new files which have never appeared in previous version by incorporating the outputs of CMVC. We evaluate the proposed CDS model on 28 versions across 8 software projects, and the experimental results demonstrate that CDS outperforms three baseline methods and a state-of-the-art approach in terms of three prevalent performance indicators.
Cascade Detector With Feature Fusion For Arbitrary-Oriented Objects In Remote Sensing Images

Detection of multi-class rotated objects is a challenging task in optical remote sensing images because of large-scale variations, arbitrary orientations and complex backgrounds, etc. Most of the state-of-the-art object detectors for natural images, that use horizontal bounding boxes, are not suitable for oriented objects in remote sensing images. In this paper, we propose an end-to-end cascade detector that can effectively detect rotated objects in complex remote sensing images. Specifically, a feature fusion block is designed to capture features with more details. Meanwhile, a supervised spatial attention mechanism is adopted to improve performance in detecting objects with complex backgrounds by weakening noise and enhancing object regions. Finally, to obtain more accurate object position, a cascade of multi-step detection subnet is implemented to refine anchors. Experiments using a publicly available remote sensing dataset DOTA show that our object detector achieves superior performance over other state-of-the-art approaches.
CluHTM - Semantic Hierarchical Topic Modeling based on CluWords

Hierarchical Topic modeling (HTM) exploits latent topics and relationships among them as a powerful tool for data analysis and exploration.Despite advantages over traditional topic modeling, HTM poses its own challenges, such as (1) topic incoherence, (2) unreasonable (hierarchical) structure, and (3) issues related to the definition of the "ideal" number of topics and depth of the hierarchy.In this paper, we advance the stateof-the-art on HTM by means of the design and evaluation of CluHTM, a novel nonprobabilistic hierarchical matrix factorization aimed at solving the specific issues of HTM.CluHTM's novel contributions include: (i) the exploration of richer text representation that encapsulates both, global (dataset level) and local semantic information -when combined, these pieces of information help to solve the topic incoherence problem as well as issues related to the unreasonable structure; (ii) the exploitation of a stability analysis metric for defining the number of topics and the "shape" the hierarchical structure.In our evaluation, considering twelve datasets and seven stateof-the-art baselines, CluHTM outperformed the baselines in the vast majority of the cases, with gains of around 500% over the strongest state-of-the-art baselines.We also provide qualitative and quantitative statistical analyses of why our solution works so well.
Models Versus Satisfaction

Evaluation metrics play an important role in the batch evaluation of IR systems. Based on a user model that describes how users interact with the rank list, an evaluation metric is defined to link the relevance scores of a list of documents to an estimation of system effectiveness and user satisfaction. Therefore, the validity of an evaluation metric has two facets: whether the underlying user model can accurately predict user behavior and whether the evaluation metric correlates well with user satisfaction. While a tremendous amount of work has been undertaken to design, evaluate, and compare different evaluation metrics, few studies have explored the consistency between these two facets of evaluation metrics. Specifically, we want to investigate whether the metrics that are well calibrated with user behavior data can perform as well in estimating user satisfaction. To shed light on this research question, we compare the performance of various metrics with the C/W/L Framework in estimating user satisfaction when they are optimized to fit observed user behavior. Experimental results on both self-collected and public available user search behavior datasets show that the metrics optimized to fit users' click behavior can perform as well as those calibrated with user satisfaction feedback. We also investigate the reliability in the calibration process of evaluation metrics to find out how much data is required for parameter tuning. Our findings provide empirical support for the consistency between user behavior modeling and satisfaction measurement, as well as guidance for tuning the parameters in evaluation metrics.
An Investigation Into the Stochasticity of Batch Whitening

Batch Normalization (BN) is extensively employed in various network architectures by performing standardization within mini-batches. A full understanding of the process has been a central target in the deep learning communities. Unlike existing works, which usually only analyze the standardization operation, this paper investigates the more general Batch Whitening (BW). Our work originates from the observation that while various whitening transformations equivalently improve the conditioning, they show significantly different behaviors in discriminative scenarios and training Generative Adversarial Networks (GANs). We attribute this phenomenon to the stochasticity that BW introduces. We quantitatively investigate the stochasticity of different whitening transformations and show that it correlates well with the optimization behaviors during training. We also investigate how stochasticity relates to the estimation of population statistics during inference. Based on our analysis, we provide a framework for designing and comparing BW algorithms in different scenarios. Our proposed BW algorithm improves the residual networks by a significant margin on ImageNet classification. Besides, we show that the stochasticity of BW can improve the GAN's performance with, however, the sacrifice of the training stability.
Self-Supervised Tuning for Few-Shot Segmentation

Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples. It is a challenging task since the dense prediction can only be achieved under the guidance of latent features defined by sparse annotations. Existing meta-learning based method tends to fail in generating category-specifically discriminative descriptor when the visual features extracted from support images are marginalized in embedding space. To address this issue, this paper presents an adaptive tuning framework, in which the distribution of latent features across different episodes is dynamically adjusted based on a self-segmentation scheme, augmenting category-specific descriptors for label prediction. Specifically, a novel self-supervised inner-loop is firstly devised as the base learner to extract the underlying semantic features from the support image. Then, gradient maps are calculated by back-propagating self-supervised loss through the obtained features, and leveraged as guidance for augmenting the corresponding elements in the embedding space. Finally, with the ability to continuously learn from different episodes, an optimization-based meta-learner is adopted as outer loop of our proposed framework to gradually refine the segmentation results. Extensive experiments on benchmark PASCAL-5i and COCO-20i datasets demonstrate the superiority of our proposed method over state-of-the-art.
Non-Autoregressive Neural Text-to-Speech

In this work, we propose ParaNet, a non-autoregressive seq2seq model that converts text to spectrogram. It is fully convolutional and brings 46.7 times speed-up over the lightweight Deep Voice 3 at synthesis, while obtaining reasonably good speech quality. ParaNet also produces stable alignment between text and speech on the challenging test sentences by iteratively improving the attention in a layer-by-layer manner. Furthermore, we build the parallel text-to-speech system and test various parallel neural vocoders, which can synthesize speech from text through a single feed-forward pass. We also explore a novel VAE-based approach to train the inverse autoregressive flow (IAF) based parallel vocoder from scratch, which avoids the need for distillation from a separately trained WaveNet as previous work.
Label-Attended Hashing for Multi-Label Image Retrieval

For the multi-label image retrieval, the existing hashing algorithms neglect the dependency between objects and thus fail to capture the attention information in the feature extraction, which affects the precision of hash codes. To address this problem, we explore the inter-dependency between objects through their co-occurrence correlation from the label set and adopt Multi-modal Factorized Bilinear (MFB) pooling component so that the image representation learning can capture this attention information. We propose a Label-Attended Hashing (LAH) algorithm which enables an end-to-end hash model with inter-dependency feature extraction. LAH first combines Convolutional Neural Network (CNN) and Graph Convolution Network (GCN) to separately generate the image representation and label co-occurrence embeddings, then adopts MFB to fuse these two modal vectors, finally learns the hash function with a Cauchy distribution based loss function via back propagation. Extensive experiments on public multi-label datasets demonstrate that (1) LAH can achieve the state-of-the-art retrieval results and (2) the usage of co-occurrence relationship and MFB not only promotes the precision of hash codes but also accelerates the hash learning. GitHub address: https://github.com/IDSM-AI/LAH.
Exploiting the Syntax-Model Consistency for Neural Relation Extraction

This paper studies the task of Relation Extraction (RE) that aims to identify the semantic relations between two entity mentions in text.In the deep learning models for RE, it has been beneficial to incorporate the syntactic structures from the dependency trees of the input sentences.In such models, the dependency trees are often used to directly structure the network architectures or to obtain the dependency relations between the word pairs to inject the syntactic information into the models via multi-task learning.The major problems with these approaches are the lack of generalization beyond the syntactic structures in the training data or the failure to capture the syntactic importance of the words for RE.In order to overcome these issues, we propose a novel deep learning model for RE that uses the dependency trees to extract the syntax-based importance scores for the words, serving as a tree representation to introduce syntactic information into the models with greater generalization.In particular, we leverage Ordered-Neuron Long-Short Term Memory Networks (ON-LSTM) to infer the model-based importance scores for RE for every word in the sentences that are then regulated to be consistent with the syntax-based scores to enable syntactic information injection.We perform extensive experiments to demonstrate the effectiveness of the proposed method, leading to the state-of-the-art performance on three RE benchmark datasets.
Stolen Probability: A Structural Weakness of Neural Language Models

Neural Network Language Models (NNLMs) generate probability distributions by applying a softmax function to a distance metric formed by taking the dot product of a prediction vector with all word vectors in a high-dimensional embedding space.The dot-product distance metric forms part of the inductive bias of NNLMs.Although NNLMs optimize well with this inductive bias, we show that this results in a sub-optimal ordering of the embedding space that structurally impoverishes some words at the expense of others when assigning probability.We present numerical, theoretical and empirical analyses showing that words on the interior of the convex hull in the embedding space have their probability bounded by the probabilities of the words on the hull.
Deep Autoencoding Topic Model With Scalable Hybrid Bayesian Inference

To build a flexible and interpretable model for document analysis, we develop deep autoencoding topic model (DATM) that uses a hierarchy of gamma distributions to construct its multi-stochastic-layer generative network. In order to provide scalable posterior inference for the parameters of the generative network, we develop topic-layer-adaptive stochastic gradient Riemannian MCMC that jointly learns simplex-constrained global parameters across all layers and topics, with topic and layer specific learning rates. Given a posterior sample of the global parameters, in order to efficiently infer the local latent representations of a document under DATM across all stochastic layers, we propose a Weibull upward-downward variational encoder that deterministically propagates information upward via a deep neural network, followed by a Weibull distribution based stochastic downward generative model. To jointly model documents and their associated labels, we further propose supervised DATM that enhances the discriminative power of its latent representations. The efficacy and scalability of our models are demonstrated on both unsupervised and supervised learning tasks on big corpora.
Joint Character-Level Word Embedding and Adversarial Stability Training to Defend Adversarial Text

Text classification is a basic task in natural language processing, but the small character perturbations in words can greatly decrease the effectiveness of text classification models, which is called character-level adversarial example attack. There are two main challenges in character-level adversarial examples defense, which are out-of-vocabulary words in word embedding model and the distribution difference between training and inference. Both of these two challenges make the character-level adversarial examples difficult to defend. In this paper, we propose a framework which jointly uses the character embedding and the adversarial stability training to overcome these two challenges. Our experimental results on five text classification data sets show that the models based on our framework can effectively defend character-level adversarial examples, and our models can defend 93.19% gradient-based adversarial examples and 94.83% natural adversarial examples, which outperforms the state-of-the-art defense models.