Reviewerly/exHarmony huggingface dataset:
Mixtures of Variational Autoencoders

In this paper, we develop a new deep mixture learning framework, aiming to learn underlying complex data structures. Each component in the mixture model is implemented using a Variational Autoencoder (VAE). VAE is a well known deep learning model which models a latent space data representation on a variational manifold. The mixing parameters are estimated from a Dirichlet distribution modelled by each encoder. In order to train this mixture model, named M-VAE, we derive a mixture evidence lower bound on the sample log-likelihood, which is optimized in order to jointly estimate all mixture components. We further propose to use the d-variables Hilbert-Schmidt Independence Criterion (dHSIC) as a regularization criterion in order to enforce the independence among the encoders' distributions. This criterion encourages the proposed mixture components to learn different data distributions and represent them in the latent space. During the experiments with the proposed M-VAE model we observe that it can be used for discovering disentangled data representations which can not be achieved with a single VAE.
Unmanned aerial vehicle oblique photography‐based superposed fold analysis of outcrops in the Xuhuai region, North China

Unmanned aerial vehicle (UAV) oblique photography technology is widely utilized in various fields owing to such advantages as true three‐dimensional (3D), high precision, and low expenses. We explored the application of UAV oblique photography in the superposed fold analysis conducted in the Xuhuai region. Three feasible and time‐efficient steps were established to fulfil the task. The tile images from UAV oblique photography are first synthesized on the Altizure website into a 3D outcrop model, which is then imported into ArcScene to extract bedding and lineation attitudes by GeoAnalysis Tools. The fold geometry and superposed style were characterized and analysed based on the 3D model, followed by extending the 3D structure morphology across the entire outcrop scale and illustrating the superposition style and sequence in the GOCAD platform. The geological conclusions were drawn by applying the method for analysing two outcrops in the Xuhuai region. Two phases of folds were identified, with the first generation nearly E‐W‐trending oblique‐recumbent folds overprinted by the second‐generation NNE‐NE‐trending wide and upright folds. The nearly E‐W‐striking fold is inferred to be related to the compressive orogeny in the Dabie Orogenic Belt in the Triassic. The NNE‐NE‐striking fold is related to the northwest subduction of the Pacific Plate or the northward thrust of the Sulu Orogenic Belt in the Jurassic. This example demonstrates that UAV oblique photography is a powerful and reliable method for structural analysis at the outcrop scale, especially for the inaccessible outcrops with weak vegetation cover.
Reward Learning From Very Few Demonstrations

This article introduces a novel skill learning framework that learns rewards from very few demonstrations and uses them in policy search (PS) to improve the skill. The demonstrations are used to learn a parameterized policy to execute the skill and a goal model, as a hidden Markov model (HMM), to monitor executions. The rewards are learned from the HMM structure and its monitoring capability. The HMM is converted to a finite-horizon Markov reward process (MRP). A Monte Carlo approach is used to calculate its values. Then, the HMM and the values are merged into a partially observable MRP to obtain execution returns to be used with PS for improving the policy. In addition to reward learning, a black box PS method with an adaptive exploration strategy is adopted. The resulting framework is evaluated with five PS approaches and two skills in simulation. The results show that the learned dense rewards lead to better performance compared to sparse monitoring signals, and using an adaptive exploration lead to faster convergence with higher success rates and lower variance. The efficacy of the framework is validated in a real-robot settings by improving three skills to complete success from complete failure using learned rewards where sparse rewards failed completely.
A novel approach for the next software release using a binary artificial algae algorithm

In software incremental development methodology, the product develops in several releases. In each release, one set of the requirements is suggested for development. The development team must select a subset of the proposed requirements for development in the next release such that by consideration the limitation of the problem provides the highest satisfaction to the customers and the lowest cost to the company. This problem is known as the next release problem. In complex projects where the number of requirements is high, development teams cannot choose an optimized subset of the requirements by traditional methods, so an intelligent algorithm is required to help in the decision-making process. The main contributions of this study are fivefold: (1) The customer satisfaction and the cost of every requirement are determined by use of fuzzy numbers because of the possible changing of the customers’ priorities during the product development period; (2) An improved approximate approach is suggested for summing fuzzy numbers of different kinds, (3) A new metaheuristic algorithm namely the Binary Artificial Algae Algorithm is used for choosing an optimized subset of requirements, (4) Experiments performed on two fuzzy datasets confirm that the resulted subsets from the suggested algorithm are free of human mistake and can be a great guidance to development teams in making decisions.
Correlation between Geochemical and Multispectral Patterns in an Area Severely Contaminated by Former Hg-As Mining

In the context of soil pollution, plants suffer stress when exposed to extreme concentrations of potentially toxic elements (PTEs). The alterations to the plants caused by such stressors can be monitored by multispectral imagery in the form of vegetation indices, which can inform pollution management strategies. Here we combined geochemistry and remote sensing techniques to offer a preliminary soil pollution assessment of a vast abandoned spoil heap in the surroundings of La Soterraña mining site (Asturias, Spain). To study the soil distribution of the PTEs over time, twenty-seven soil samples were randomly collected downstream of and around the main spoil heap. Furthermore, the area was covered by an unmanned aerial vehicle (UAV) carrying a high-resolution multispectral camera with four bands (red, green, red-edge and near infrared). Multielement analysis revealed mercury and arsenic as principal pollutants. Two indices (from a database containing up to 55 indices) offered a proper correlation with the concentration of PTEs. These were: CARI2, presenting a Pearson Coefficient (PC) of 0.89 for concentrations &gt;200 mg/kg of As; and NDVIg, PC of −0.67 for &gt;40 mg/kg of Hg. The combined approach helps prediction of those areas susceptible to greatest pollution, thus reducing the costs of geochemical campaigns.
Coverage Path Planning Techniques for Inspection of Disjoint Regions With Precedence Provision

Recent times are witnessing an emergence of sites that are hazardous for human access.This has created a global demand to equip agents with the ability to autonomously inspect such environments by computing a coverage path effectively and efficiently.However, inspection of such sites requires agents to consider the correlation of work, providing precedence provision in visiting regions.The current approaches to compute coverage path in the hazardous sites, however, do not consider precedence provision.To this end, coverage path planning strategies are proposed, which provide precedence provision.To meet the challenges, the problem is divided into two phases: inter-region and intra-region path planning.In the 'inter-region' path planning of the approach, the site comprising of multiple disjoint regions is modelled as connectivity graph.Two novel approaches, Mixed Integer Linear Programming (MILP) solution and heuristic based techniques, are proposed to generate the ordered sequence of regions to be traversed.In the 'intra-region' path planning of the approach, each region is decomposed into a grid and Boustrophedon Motion is planned over each region.The ability of combined approach to provide complete coverage is proved under minor assumption.An investigative study has been conducted to elucidate the efficiency of the proposed approach in different scenarios using simulation experiments.The proposed approach is evaluated against baseline approaches.The results manifest a significant reduction in cost and execution time, which caters to inspection of target sites comprising of multiple disjoint regions with precedence provision.
Sequential Depth Completion With Confidence Estimation for 3D Model Reconstruction

This letter addresses a depth-completion problem for sequential data to reconstruct 3D models of outdoor scenes. While many deep-learning-based approaches have recently achieved promising results, their results are not directly applicable to 3D modeling because of several reasons. First, most results contain a lot of outliers because of irregularly distributed sparse measurements. Second, they ignore temporal coherence in sequential frames and produce temporally inconsistent depths. Therefore, we propose a new method that predicts temporally consistent depths with corresponding confidences from sequential frames. The suggested method can efficiently remove the outliers based on confidence estimates, which accurately represent the true prediction errors. The method also produces temporally consistent depths by integrating the depth information of consecutive frames. In addition, we present a 3D-modeling system that reconstructs a globally consistent 3D model in real-time using the results from the proposed depth completion method. Extensive experiments on synthetic and real-world datasets show that our method outperforms the other state-of-the-art methods in terms of both depth-completion and 3D-modeling accuracies.
A Bayesian method for calibration and aggregation of expert judgement

This paper outlines a Bayesian framework for structured expert judgement (sej) that can be utilised as an alternative to the traditional non-Bayesian methods (including the commonly used Cooke's Classical model [13]). We provide an overview of the structure of an expert judgement study and outline opinion pooling techniques noting the benefits and limitations of these approaches. Some new tractable Bayesian models are highlighted, before presenting our own model which aims to combine and enhance the best of these existing Bayesian frameworks. In particular: clustering, calibrating and then aggregating the experts' judgements utilising a Supra-Bayesian parameter updating approach combined with either an agglomerative hierarchical clustering or an embedded Dirichlet process mixture model. We illustrate the benefit of our approach by analysing data from a number of existing studies in the healthcare domain, specifically in the two contexts of health insurance and transmission risks for chronic wasting disease.
Understanding Global Reaction to the Recent Outbreaks of COVID-19: Insights from Instagram Data Analysis

The coronavirus disease, also known as the COVID-19, is an ongoing pandemic of a severe acute respiratory syndrome.The pandemic has led to the cancellation of many religious, political, and cultural events around the world.A huge number of people have been stuck within their homes because of unprecedented lockdown measures taken globally.This paper examines the reaction of individuals to the virus outbreak-through the analytical lens of specific hashtags on the Instagram platform.The Instagram posts are analyzed in an attempt to surface commonalities in the way that individuals use visual social media when reacting to this crisis.After collecting the data, the posts containing the location data are selected.A portion of these data are chosen randomly and are categorized into five different categories.We perform several manual analyses to get insights into our collected dataset.Afterward, we use the ResNet-50 convolutional neural network for classifying the images associated with the posts, and attention-based LSTM networks for performing the caption classification.This paper discovers a range of emerging norms on social media in global crisis moments.The obtained results indicate that our proposed methodology can be used to automate the sentiment analysis of mass people using Instagram data.
Estimating gray intensities for saturated speckle to improve the measurement accuracy of digital image correlation

Accuracy of digital image correlation (DIC) is deeply affected by illumination variation, especially the local pixel saturation exists. A simple and efficient gray intensity adjustment strategy based on feature matching and standard DIC method is presented for estimating the intensities of saturated pixels. Principle of the strategy based on surrounding pixels around the saturated pixel and the principle of the new proposed strategy are first detailed. Simulations and experiments are performed to verify the efficiency of the proposed strategy. The results indicate that the DIC measurements based on the proposed strategy are able to provide more reliable and accurate results as compared to the existing strategy, even though the large local speckles are saturated.
Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks

We present an automated method for extrapolating missing regions in label data of the skull in an anatomically plausible manner. The ultimate goal is to design patient-specific cranial implants for correcting large, arbitrarily shaped defects of the skull that can, for example, result from trauma of the head. Our approach utilizes a 3D statistical shape model (SSM) of the skull and a 2D generative adversarial network (GAN) that is trained in an unsupervised fashion from samples of healthy patients alone. By fitting the SSM to given input labels containing the skull defect, a first approximation of the healthy state of the patient is obtained. The GAN is then applied to further correct and smooth the output of the SSM in an anatomically plausible manner. Finally, the defect region is extracted using morphological operations and subtraction between the extrapolated healthy state of the patient and the defective input labels. The method is trained and evaluated based on data from the MICCAI 2020 AutoImplant challenge. It produces state-of-the art results on regularly shaped cut-outs that were present in the training and testing data of the challenge. Furthermore, due to unsupervised nature of the approach, the method generalizes well to previously unseen defects of varying shapes that were only present in the hidden test dataset.
UAV swarm autonomous control based on Internet of Things and artificial intelligence algorithms

At present, the UAV swarm positioning solution has the problems of poor positioning accuracy and instability. Therefore, it is necessary to design a sliding mode formation controller to realize formation. This study analyzes the self-service control strategy of the UAV swarm and establishes the behavior-based formation control strategy as the main research point of this article. This paper combines the Internet of Things and artificial intelligence algorithms to build an autonomous control model for the UAV swarm and designs the UAV formation control law from the disturbed and undisturbed conditions respectively. With reference to the basic architecture of the Internet of Things, this study imitates ZigBee’s self-organizing network to propose an adaptive networking scheme based on the Internet of Things by using the AP+STA working mode of the Internet of Things module in the node device. The results of the experiment show that the positioning accuracy of the UAV is high, which can meet the needs of cluster flight. Based on the Z-axis coordinates of the UAV, the accuracy of the laser distance measurement and the barometer value is significantly improved, the root mean square error is reduced, and the positioning result is significantly better than the data of direct traditional positioning.
Student Feedback on Online Learning by Using Sentiment Analysis: A Literature Review

The purpose of this study was to explore the implementation of sentiment analysis techniques on student feedback in an online learning environment. This research used the systematic literature review Methods on A QualitativeQuantitative Continuum. The results of this study present several articles in the last six years period between 2014- 2019. The number of articles obtained was 12 articles relating to student feedback analysis using sentiment analysis.
Concurrent weight encoding-based detection for bit-flip attack on neural network accelerators

The recent revealed Bit-Flip Attack (BFA) against deep neural networks (DNNs) is highly concerning, as it can completely mislead the inference of quantized DNNs by only flipping a few weight bits in hardware memories through manners like DRAM rowhammer. A key question before applying any BFA mitigation solutions, such as retraining or model reloading, is how to quickly and accurately detect such an attack without impacting the normal inference. In this paper, we propose a weight encoding-based framework to concurrently detect BFA by leveraging the spatial locality of bit flipping in BFA and a fast encoding of sensitive weights only. Extensive experimental results show that our method can accurately differentiate the malicious fault models under BFA and the random bit flipping that could also occur in weight memories but does not impact accuracy as that of BFA, with very low overhead across various DNNs on both CIFAR-10 and ImageNet datasets. To the best of our knowledge, this is the first real-time detection framework for BFA attack against quantized DNNs that are widely deployed in hardware accelerators.
Vector Borne Disease Outbreak Prediction by Machine Learning

Vector Borne Disease is a form of illness which is caused by parasites, viruses and bacteria. The infection is transferred through blood-feeding arthropods such as mosquitoes, fleas ticks etc. Every year from diseases such as yellow fever, Malaria more than 700,000 deaths occur. These diseases are most common in tropical and subtropical areas and affect the underprivileged populations. Deep learning an essential part of Artificial Intelligence provides an uncanny power to systems to construct a complex network using layers of perceptrons which mimic the human neurons. This network Combined with algorithms of Machine Learning may serve as one of the most powerful tool in healthcare to classify and analyze huge amount of medical data and predict future trends through Supervised Learning. The paper we focused on effective prediction of the vector borne disease outbreak (Multiclass Classification) of three diseases (Chikungunya, Malaria, Dengue) across the Indian-subcontinent. We have examined and refined our model over data collected across India in 2013-2017. We have put forward a Convolutional Neural Network outbreak risk prediction algorithm using contrasting data. To our finest understanding, none of the previous works have centered on contrasting data in area of analysis of medical data. The prediction accuracy of our suggested CNN algorithm is 88%.
Whole and Part Adaptive Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition

Spatiotemporal graph convolution has made significant progress in skeleton-based action recognition in recent years. Most of the existing graph convolution methods take all the joints of the human skeleton as the overall modeling graph, ignoring the differences in the movement patterns of various parts of the human, and cannot well connect the relationship between the different parts of the human skeleton. To capture the unique features of different parts of human skeleton data and the correlation of different parts, we propose two new graph convolution methods: the whole graph convolution network (WGCN) and the part graph convolution network (PGCN). WGCN learns the whole scale skeleton spatiotemporal features according to the movement patterns and physical structure of the human skeleton. PGCN divides the human skeleton graph into several subgraphs to learn the part scale spatiotemporal features. Moreover, we propose an adaptive fusion module that combines the two features for multiple complementary adaptive fusion to obtain more effective skeleton features. By coupling these proposals, we build a whole and part adaptive fusion graph convolution neural network (WPGCN) that outperforms previous state-of-the-art methods on three large-scale datasets: NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400.
Software Requirements Quality: Using Analytics to Challenge Assumptions at Intel

Creating high-quality software requirements is critically important in the complex, dynamic, and pressurized context of contemporary software development. Our findings challenge assumptions by management and project teams regarding the relationship among requirements quality, and requirements priority, compliance, and defect rates.
Cloud-based Encrypted EHR System with Semantically Rich Access Control and Searchable Encryption

Cloud-based electronic health records (EHR) systems provide important security controls by encrypting patient data.However, these records cannot be queried without decrypting the entire record.This incurs a huge amount of burden in network bandwidth and the client-side computation.As the volume of cloud-based EHRs reaches Big Data levels, it is essential to search over these encrypted patient records without decrypting them to ensure that the medical caregivers can efficiently access the EHRs.This is especially critical if the caregivers have access to only certain sections of the patient EHR and should not decrypt the whole record.In this paper, we present our novel approach that facilitates searchable encryption of large EHR systems using Attribute-based Encryption (ABE) and multi-keyword search techniques.Our framework outsources key search features to the cloud side.This way, our system can perform keyword searches on encrypted data with significantly reduced costs of network bandwidth and client-side computation.
The Cognitive Load and Usability of Three Walking Metaphors for Consumer Virtual Reality

Walking metaphors have been extensively researched for travel in virtual reality (VR) applications. However, only a few walking metaphors are feasible for most consumer VR systems. In this paper, we present a study that compares three of these suitable metaphors: Scaled Walking, Human Joystick, and Walking-In-Place. Our study empirically assesses the cognitive loads and travel performances of these three walking metaphors by employing a novel dual-task methodology. We also evaluated their effects on simulator sickness, presence, and perceived usability. The results of our study indicate that Scaled Walking afforded significantly better travel performances and perceived usability than Human Joystick and Walking-In-Place. Our results also indicate that Walking-In-Place required the worst cognitive loads and that Human Joystick induced the worst simulator sickness. Given these results, we discuss the implications of using a high-fidelity, full-gait walking metaphor.