Reviewerly/exHarmony huggingface dataset:
Interferometric phase image denoising method via residual learning

The denoising of interferometric phase images attracts many researchers. Natural image denoising algorithms based on neural networks are often proposed in the development of deep learning methods. A neural network, In-CNN, is derived from an advanced natural image denoising network and proposed for interferometric phase image denoising. Preactivation and residual learning methods are combined and applied to the function of the neural network nodes. Considering the particularity of the interferometric phase image, we propose a neural network based on the rational application of the preactivation mode and feedforward mapping, which is different from previous natural image denoising networks. We also construct a training set for an interferometric phase image denoising neural network. We experimentally verify that our model performs better than state-of-the-art interferometric phase image denoising methods based on sparse representation and advanced natural image denoising networks. We discuss the complexity of traditional interferometric phase image denoising algorithms to demonstrate the efficiency of the proposed method.
Fourier-Transform-Based Surface Measurement and Reconstruction of Human Face Using the Projection of Monochromatic Structured Light

This work presents a new approach of surface measurement of human face via the combination of the projection of monochromatic structured light, the optical filtering technique, the polarization technique and the Fourier-transform-based image-processing algorithm. The theoretical analyses and experimental results carried out in this study showed that the monochromatic feature of projected fringe pattern generated using our designed laser-beam-based optical system ensures the use of optical filtering technique for removing the effect of background illumination; the linearly-polarized characteristic makes it possible to employ a polarizer for eliminating the noised signal contributed by multiply-scattered photons; and the high-contrast sinusoidal fringes of the projected structured light provide the condition for accurate reconstruction using one-shot measurement based on Fourier transform profilometry. The proposed method with the portable and stable optical setup may have potential applications of indoor medical scan of human face and outdoor facial recognition without strict requirements of a dark environment and a stable object being observed.
Machine-driven earth exploration: Artificial intelligence in oil and gas

Artificial intelligence (AI), specifically machine learning (ML), has emerged as a powerful tool to address many of the challenges we face as we try to illuminate the earth and make the proper prediction of its content. From fault detection, to salt boundary mapping, to image resolution enhancements, the quest to teach our computing devices how to perform these tasks accurately, as well as quantify the accuracy, has become a feasible and sought-after objective. Recent advances in ML algorithms and availability of the modules to apply such algorithms enabled geoscientists to focus on potential applications of such tools. As a result, we held the virtual workshop, Artificially Intelligent Earth Exploration Workshop: Teaching the Machine How to Characterize the Subsurface, 23–26 November 2020.
Selecting Cover Images for Restaurant Reviews: AI vs. Wisdom of the Crowd

Restaurant review platforms, such as Yelp and Tripadvisor, routinely receive large numbers of photos in their review submissions. These photos provide significant value for users who seek to compare restaurants. In this context, the choice of cover images (i.e., representative photos of the restaurants) can greatly influence the level of user engagement on the platform. Unfortunately, selecting these images can be time consuming and often requires human intervention. At the same time, it is challenging to develop a systematic approach to assess the effectiveness of the selected images. In this paper, we collaborate with a large review platform in Asia to investigate this problem. We discuss two image selection approaches, namely crowd-based and AI-based systems. The AI-based system we use learn complex latent image features, which is further enhanced by transfer learning to overcome the scarcity of labeled data. We collaborate with the platform to deploy our AI-based system through a randomized field experiment to carefully compare both systems. We find that the AI-based system outperforms the crowd-based counterpart and boosts user engagement by 12.43%-16.05% on average. We then conduct empirical analyses on observational data to identify the underlying mechanisms that drive the superior performance of the AI-based system. Finally, we infer from our findings that the AI-based system outperforms the crowd-based system for restaurants with a (i) longer tenure on the platform, (ii) limited number of user-generated photos, (iii) lower star rating, and (iv) lower user engagement during the crowd-based system.
On Robustness of Neural Semantic Parsers

Semantic parsing maps natural language (NL) utterances into logical forms (LFs), which underpins many advanced NLP problems.Semantic parsers gain performance boosts with deep neural networks, but inherit vulnerabilities against adversarial examples.In this paper, we provide the empirical study on the robustness of semantic parsers in the presence of adversarial attacks.Formally, adversaries of semantic parsing are considered to be the perturbed utterance-LF pairs, whose utterances have exactly the same meanings as the original ones.A scalable methodology is proposed to construct robustness test sets based on existing benchmark corpora.Our results answered five research questions in measuring the sateof-the-art parsers' performance on robustness test sets, and evaluating the effect of data augmentation.
Sentiment Analysis with Deep Learning: A Bibliometric Review

Sentiment analysis is an active area of research in natural language processing field. Prior research indicates numerous techniques have been used to perform the sentiment classification tasks which include the machine learning approaches. Deep learning is a specific type of machine learning that has been successfully applied in various field such as computer vision and various NLP tasks including sentiment analysis. This paper attempts to provide a bibliometricanalysisof academic literature related to the sentiment analysis with deep learningmethods which were retrieved from Scopusuntil the third quarter of 2020. We focus on the analysis of the research productivity in this field, the distribution of subject categories, the sources and types of the publications, their geographic distributions, the most prolific and impactful authors and institutions, the most cited papers and the trends of keywords.This study can help researchers and practitionersin keeping abreast with the global research trends in thearea of sentiment analysis using deep learning approaches.
How to Design Cryptocurrency Value and How to Secure Its Sustainability in the Market

The purpose of this study is to analyze the contents of cryptocurrency value design based on adaptability to the current market. It is also intended to provide a method of issuing cryptocurrency before its creation, and an operation method afterwards. Activities before the creation of cryptocurrency must determine desirable behaviors and rewards to create value, and suggest countermeasures to prevent participants from engaging in undesirable behaviors. After the creation of a cryptocurrency, it is necessary to propose a method to induce scarcity and increase demand so that the value of the generated cryptocurrency can be sustained. To observe this, we looked at the contents of the value design of the eight types of cryptocurrencies currently in use in the market. Some cryptocurrencies, such as Bitcoin, are choosing mining as a reward, to secure scarcity for maintaining the value of cryptocurrency, limiting the amount of issuance, and burning the already issued cryptocurrency in the market. Also, increasing demand helps maintain the value of cryptocurrency. This study can contribute to supporting the growth of a healthy cryptocurrency market through cryptocurrency-related research.
Modul Front-End Sistem Informasi Geospasial Patroli Terpadu Kebakaran Hutan dan Lahan

To prevent and handle forest and land and forest fire (karhutla), the Ministry of Environment and Forestry assembled a patrol team that conducts a daily task to observe directly to the hotspot location as an indication for land fire. Currently, the patrol team reported the investigation result into a group chat. This method consumed many storage spaces and not suitable for formal reporting. This study aims to develop a front-end module for a web GIS application that visualizes the patrol team's daily report. The application has its data recapitulation method and able to create a formal report. The data used in this study are a set of the report that collected in 2016 by Sumatera and Kalimantan patrol team. The steps to build this application include communication, integrate with the API from the back-end system, developing functional needs, software testing, and the last is software release. The application was build using HTML and CSS for its interface and Javascript and API from the back-end module for its content management. The system uses Google Maps services and library to support the functionalities of the application. The unit testing method's test result shows that the module runs well and can afford all of the required functionality. In addition, the system testing result that the ratio between actual error and expected error is equal to 1. This result indicates the functions of the system are working properly according to the use cases of the system.&#x0D;
Click Consonants: an Introduction

Click consonants have proven to be a challenge to researchers over the years. This chapter describes the various ways of transcribing them and various instrumental techniques for studying them. This chapter provides a survey of approaches that have been used to study various aspects of clicks, including their phonetic description, acquisition, and diachronic development. Readers will be introduced to little-known contrasts, such as ejective vs. glottalized clicks, and uvular vs. velar clicks. New evidence is provided for three click types that are not yet recognized by the IPA, the fricated palatal laminal /⨎/, the retroflex /ǃǃ/, and the forward-released lateral /ǀǀǀ/. Trends for future avenues of research are also discussed.
Analysis of the Nosema Cells Identification for Microscopic Images

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.
Network Representation Learning Enhanced by Partial Community Information That Is Found Using Game Theory

Presently, data that are collected from real systems and organized as information networks are universal. Mining hidden information from these data is generally helpful to understand and benefit the corresponding systems. The challenges of analyzing such data include high computational complexity and low parallelizability because of the nature of complicated interconnected structure of their nodes. Network representation learning, also called network embedding, provides a practical and promising way to solve these issues. One of the foremost requirements of network embedding is preserving network topology properties in learned low-dimension representations. Community structure is a prominent characteristic of complex networks and thus should be well maintained. However, the difficulty lies in the fact that the properties of community structure are multivariate and complicated; therefore, it is insufficient to model community structure using a predefined model, the way that is popular in most state-of-the-art network embedding algorithms explicitly considering community structure preservation. In this paper, we introduce a multi-process parallel framework for network embedding that is enhanced by found partial community information and can preserve community properties well. We also implement the framework and propose two node embedding methods that use game theory for detecting partial community information. A series of experiments are conducted to evaluate the performance of our methods and six state-of-the-art algorithms. The results demonstrate that our methods can effectively preserve community properties of networks in their low-dimension representations. Specifically, compared to the involved baselines, our algorithms behave the best and are the runners-up on networks with high overlapping diversity and density.
Image Denoising with a Convolution Neural Network using Gaussian Filtered Residuals

Deep learning using a convolutional neural network has become a state-of-art technique in image processing. In recent scenarios, image denoising using a residual image in deep learning has been popular. However, one aspect missing in these methods is that the residual image has all the noise and very small structured details of the input image. Therefore, we have developed a Gaussian filter residual convolutional neural network architecture for color image denoising. Gaussian residual learning was used to boost the denoising performance. The architecture is designed to remove additive white Gaussian noise, which is one of the most basic types of noise that affects an image when captured. The network with Gaussian residual learning removes the clean image using the features learned from the hidden layer. The peak signal-to-noise ratio and structural similarity index measure achieved by our method reveals that the presented approach is better at denoising images with Gaussian noise than a convolutional neural network.
Introspective analysis of convolutional neural networks for improving discrimination performance and feature visualisation

Deep neural networks have been widely explored and utilised as a useful tool for feature extraction in computer vision and machine learning. It is often observed that the last fully connected (FC) layers of convolutional neural network possess higher discrimination power as compared to the convolutional and maxpooling layers whose goal is to preserve local and low-level information of the input image and down sample it to avoid overfitting. Inspired from the functionality of local binary pattern (LBP) operator, this paper proposes to induce discrimination into the mid layers of convolutional neural network by introducing a discriminatively boosted alternative to pooling (DBAP) layer that has shown to serve as a favourable replacement of early maxpooling layer in a convolutional neural network (CNN). A thorough research of the related works show that the proposed change in the neural architecture is novel and has not been proposed before to bring enhanced discrimination and feature visualisation power achieved from the mid layer features. The empirical results reveal that the introduction of DBAP layer in popular neural architectures such as AlexNet and LeNet produces competitive classification results in comparison to their baseline models as well as other ultra-deep models on several benchmark data sets. In addition, better visualisation of intermediate features can allow one to seek understanding and interpretation of black box behaviour of convolutional neural networks, used widely by the research community.
Certain Notions of Picture Fuzzy Information with Applications

In this manuscript, the theory of constant picture fuzzy graphs (CPFG) is developed. A CPFG is a generalization of constant intuitionistic fuzzy graph (CIFG) and a special case of picture fuzzy graph (PFG). Additionally, the article includes some basic definitions of CPFG such as totally constant picture fuzzy graphs (TCPFGs), constant function, bridge of CPFG, and their related results. Also, an application of CPFG in Wi-Fi network system is discussed. Finally, a comparison of CPFG is established with that of the CIFG which exhibits the superiority of the proposed idea over the existing ones is discussed.
An analysis of various Image Pre-Processing techniques in Butterfly Image

Image processing technique is a method of improving the quality of an image. Many image processing operations can be grouped into some method. These are image representation, image preprocessing, image enhancement, image restoration, image analysis, image reconstruction and image data compression. Many images contain a lot of irrelevant and unwanted parts. The process of enhancing pixel intensity and image quality are also carried out after preprocessing. Image preprocessing is enhancement the quality of the image because it is eliminating noise and reducing the distortions of the input images. Thus, preprocessing techniques require for images because it is contained annoying parts in an image. Image filtering is a standard process used in almost every image processing system. In this paper, we describe the various images filtering techniques and filtering algorithms used for image filtering or smoothing. Image smoothing is one of the most important in image processing and it is widely used in image application and which algorithm is the best for smoothing and filtering the images. Image smoothing is to reduce noise and improve the visual quality of the image. The proposed system describes the median filtering, Gaussian high filtering and Gaussian low filtering in preprocessing techniques. The median filter used to remove noise and it is a nonlinear digital filtering technique. Gaussian High pass filters are usually used for sharpening. The filtering techniques have been simulated using Matlab 2018a. In performance of this filtering technique, low pass filtering technique is better and its takes less processing time than other filtering methods. After the preprocessing, the image quality is increase.
A Formal Framework of Software Product Line Analyses

A number of product-line analysis approaches lift analyses such as type checking, model checking, and theorem proving from the level of single programs to the level of product lines. These approaches share concepts and mechanisms that suggest an unexplored potential for reuse of key analysis steps and properties, implementation, and verification efforts. Despite the availability of taxonomies synthesizing such approaches, there still remains the underlying problem of not being able to describe product-line analyses and their properties precisely and uniformly. We propose a formal framework that models product-line analyses in a compositional manner, providing an overall understanding of the space of family-based, feature-based, and product-based analysis strategies. It defines precisely how the different types of product-line analyses compose and inter-relate. To ensure soundness, we formalize the framework, providing mechanized specification and proofs of key concepts and properties of the individual analyses. The formalization provides unambiguous definitions of domain terminology and assumptions as well as solid evidence of key properties based on rigorous formal proofs. To qualitatively assess the generality of the framework, we discuss to what extent it describes five representative product-line analyses targeting the following properties: safety, performance, dataflow facts, security, and functional program properties.
Homogeneous vector bundles and G-equivariant convolutional neural networks

Abstract G -equivariant convolutional neural networks (GCNNs) is a geometric deep learning model for data defined on a homogeneous G -space $$\mathcal {M}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>M</mml:mi> </mml:math> . GCNNs are designed to respect the global symmetry in $$\mathcal {M}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>M</mml:mi> </mml:math> , thereby facilitating learning. In this paper, we analyze GCNNs on homogeneous spaces $$\mathcal {M} = G/K$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>M</mml:mi> <mml:mo>=</mml:mo> <mml:mi>G</mml:mi> <mml:mo>/</mml:mo> <mml:mi>K</mml:mi> </mml:mrow> </mml:math> in the case of unimodular Lie groups G and compact subgroups $$K \le G$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>K</mml:mi> <mml:mo>≤</mml:mo> <mml:mi>G</mml:mi> </mml:mrow> </mml:math> . We demonstrate that homogeneous vector bundles are the natural setting for GCNNs. We also use reproducing kernel Hilbert spaces (RKHS) to obtain a sufficient criterion for expressing G -equivariant layers as convolutional layers. Finally, stronger results are obtained for some groups via a connection between RKHS and bandwidth.
Temporal Gaussian Process Regression in Logarithmic Time

The aim of this article is to present a novel parallelization method for temporal Gaussian process (GP) regression problems. The method allows for solving GP regression problems in logarithmic <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(\log N)$</tex> time, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> stands for the number of observations and test points. Our approach uses the state-space representation of GPs which, in its original form, allows for linear <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(N)$</tex> time GP regression by leveraging Kalman filtering and smoothing methods. By using a recently proposed parallelization method for Bayesian filters and smoothers, we are able to reduce the linear computational complexity of the temporal GP regression problems into logarithmic span complexity. This ensures logarithmic time complexity when parallel hardware such as a graphics processing unit (GPU) are employed. We experimentally show the computational benefits of our approach on simulated and real datasets via our open-source implementation leveraging the GPflow framework.