Reviewerly/exHarmony huggingface dataset:
Volumetric Capture of Humans With a Single RGBD Camera via Semi-Parametric Learning

Volumetric (4D) performance capture is fundamental for AR/VR content generation. Whereas previous work in 4D performance capture has shown impressive results in studio settings, the technology is still far from being accessible to a typical consumer who, at best, might own a single RGBD sensor. Thus, in this work, we propose a method to synthesize free viewpoint renderings using a single RGBD camera. The key insight is to leverage previously seen “calibration” images of a given user to extrapolate what should be rendered in a novel viewpoint from the data available in the sensor. Given these past observations from multiple viewpoints, and the current RGBD image from a fixed view, we propose an end-to-end framework that fuses both these data sources to generate novel renderings of the performer. We demonstrate that the method can produce high fidelity images, and handle extreme changes in subject pose and camera viewpoints. We also show that the system generalizes to performers not seen in the training data. We run exhaustive experiments demonstrating the effectiveness of the proposed semi-parametric model (i.e. calibration images available to the neural network) compared to other state of the art machine learned solutions. Further, we compare the method with more traditional pipelines that employ multi-view capture. We show that our framework is able to achieve compelling results, with substantially less infrastructure than previously required.
Use of virtual reality in underground roof fall hazard assessment and risk mitigation

Virtual reality (VR) simulations are some of the alternative practice environments for safety education. Trainees gain experience under the most realistic conditions without being exposed to any real hazard. Virtual reality also serves as a tool to expedite the decision-making process where quick decisions and actions are necessities for a safe operation. One example of such a situation in underground mines is the failure risks in a tunnel face after blasting. Ground support systems must be installed based on the stand-up time to ensure the safe access of personnel and equipment to the working area. Generally, a competent person inspects the area to identify risks and determines the required support systems. However, training of a competent person requires large amount of time and effort. This study presents virtual reality simulations developed for improving the assessment of the roof fall hazards and taking necessary measures for mitigating the risk related to roof fall. The virtual reality simulation involves decision-making tasks for a particular scenario in which participants can experience creating a safe working environment in a timely manner. First, they are asked to identify the potential hazard in a tunnel after blasting, namely, rock fall, wedge failure or groundwater inflow. If the hazard identification is successful, participants are requested to install rock bolts as a safety measure by selecting the bolt pattern (e.g. bolt length and spacing). Finally, participants evaluate the roof fall hazard by viewing displacements around the opening, before and after the support measures. This study illustrates the benefits of VR in safety training as well as enhancing the decision-making process to enhance safety.
Multi-objective item evaluation for diverse as well as novel item recommendations

Most of the traditional Recommendation Systems (RSs) focus on recommending only the popular items as they deal with a single objective precision/popularity. However, focusing on the diversity of the items in the recommendation list is also equally important to improve its relevance to the user, i.e., it is required to view RSs as a multi-objective optimization problem. Nevertheless, owing to popularity and diversity to be conflicting with each other, it degrades the accuracy of the recommendation list. Therefore, in this work, we use a multi-objective optimization method to maintain a trade-off between the popularity and the diversity and obtain multiple trade-off solutions in a single run. We first incorporate Bhattacharyya Coefficient in an existing nonlinear similarity computation model to create a new similarity model named as Bhat_sim to increase the prediction accuracy of the exiting rating evaluation methods. Further, we formulate a multi-parent crossover mechanism NewCross in the proposed multi-objective recommendation filtering NewCrossPMOEA which preserves the order and the frequency in the parents genes to bring good objectivity in the trade-off of recommending popular and diverse items in the recommendation list. The obtained results on the Movielens dataset demonstrate that the NewCrossPMOEA performs superior in terms of average precision, diversity, and novelty to its competing methods. Moreover, the Pareto-dominance concept of NewCrossPMOEA suggests multiple recommendation solutions of diverse and novel items to the target users in a single run.
Multimodal Multiobjective Optimization in Feature Selection

In feature selection, the number of selected features and the classification accuracy are two common objectives to be optimized. However, few studies pay attention to which features are selected. In many feature selection problems, different feature subsets with the same number of selected features can achieve similar classification accuracy. These are multimodal multiobjective optimization (MMO) problems in feature selection. In this paper, the MMO problems in feature selection are described in detail. Then, the great significance and importance to find these different feature subsets are discussed. Two modified MMO algorithms are used to solve the MMO feature selection problems. Simulation results show that these MMO algorithms can find more feature subsets than unimodal optimization algorithms.
Semi-Heterogeneous Three-Way Joint Embedding Network for Sketch-Based Image Retrieval

Sketch-based image retrieval (SBIR) is a challenging task due to the large cross-domain gap between sketches and natural images. How to align abstract sketches and natural images into a common high-level semantic space remains a key problem in SBIR. In this paper, we propose a novel semi-heterogeneous three-way joint embedding network (Semi3-Net), which integrates three branches (a sketch branch, a natural image branch, and an edgemap branch) to learn more discriminative cross-domain feature representations for the SBIR task. The key insight lies with how we cultivate the mutual and subtle relationships amongst the sketches, natural images, and edgemaps. A semi-heterogeneous feature mapping is designed to extract bottom features from each domain, where the sketch and edgemap branches are shared while the natural image branch is heterogeneous to the other branches. In addition, a joint semantic embedding is introduced to embed the features from different domains into a common high-level semantic space, where all of the three branches are shared. To further capture informative features common to both natural images and the corresponding edgemaps, a co-attention model is introduced to conduct common channel-wise feature recalibration between different domains. A hybrid-loss mechanism is designed to align the three branches, where an alignment loss and a sketch-edgemap contrastive loss are presented to encourage the network to learn invariant cross-domain representations. Experimental results on two widely used category-level datasets (Sketchy and TU-Berlin Extension) demonstrate that the proposed method outperforms state-of-the-art methods.
The MuCoW Test Suite at WMT 2019: Automatically Harvested Multilingual Contrastive Word Sense Disambiguation Test Sets for Machine Translation

Supervised Neural Machine Translation (NMT) systems currently achieve impressive translation quality for many language pairs.One of the key features of a correct translation is the ability to perform word sense disambiguation (WSD), i.e., to translate an ambiguous word with its correct sense.Existing evaluation benchmarks on WSD capabilities of translation systems rely heavily on manual work and cover only few language pairs and few word types.We present MU-COW, a multilingual contrastive test suite that covers 16 language pairs with more than 200 000 contrastive sentence pairs, automatically built from word-aligned parallel corpora and the wide-coverage multilingual sense inventory of BabelNet.We evaluate the quality of the ambiguity lexicons and of the resulting test suite on all submissions from 9 language pairs presented in the WMT19 news shared translation task, plus on other 5 language pairs using pretrained NMT models.The MUCOW test suite is available at http://github.com/Helsinki-NLP/MuCoW.
Automating orthogonal defect classification using machine learning algorithms

Software systems are increasingly being used in business or mission critical scenarios, where the presence of certain types of software defects, i.e., bugs, may result in catastrophic consequences (e.g., financial losses or even the loss of human lives). To deploy systems in which we can rely on, it is vital to understand the types of defects that tend to affect such systems. This allows developers to take proper action, such as adapting the development process or redirecting testing efforts (e.g., using a certain set of testing techniques, or focusing on certain parts of the system). Orthogonal Defect Classification (ODC) has emerged as a popular method for classifying software defects, but it requires one or more experts to categorize each defect in a quite complex and time-consuming process. In this paper, we evaluate the use of machine learning algorithms (k-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Nearest Centroid, Random Forest and Recurrent Neural Networks) for automatic classification of software defects using ODC, based on unstructured textual bug reports. Experimental results reveal the difficulties in automatically classifying certain ODC attributes solely using reports, but also suggest that the overall classification accuracy may be improved in most of the cases, if larger datasets are used.
Matplotlib and Seaborn

It is critical to be able to plot the observations and variables of a dataset before subjecting the dataset to some machine learning algorithm or another. Data visualization is essential to understand your data and to glean insights into the underlying structure of the dataset. These insights help the scientist in deciding with statistical analysis or which learning algorithm is more appropriate for the given dataset. Also, the scientist can get ideas on suitable transformations to apply to the dataset.
The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas

Ontologies of research areas are important tools for characterizing, exploring, and analyzing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 14K topics and 162K semantic relationships. It was created by applying the Klink-2 algorithm on a very large data set of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO, we have also released the CSO Classifier, a tool for automatically classifying research papers, and the CSO Portal, a Web application that enables users to download, explore, and provide granular feedback on CSO. Users can use the portal to navigate and visualize sections of the ontology, rate topics and relationships, and suggest missing ones. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various research communities engaged with scholarly data.
Investigation on human rights ethics in artificial intelligence researches with library literature analysis method

Purpose The purpose of this paper was to identify whether artificial intelligence (AI) products can possess human rights, how to define their rights and obligations and what ethical standards they should follow. In this study, the human rights ethical dilemma encountered in the application and development of AI technology has been focused on and analyzed in detail in the light of the existing research status of AI ethics. Design/methodology/approach In this study, first of all, the development and application of AI technology, as well as the concept and characteristics of human rights ethics, are introduced. Second, the human rights ethics of AI technology are introduced in detail, including the human rights endowment of AI machines, the fault liability of AI machines and the moral orientation of AI machines. Finally, the approaches to human rights ethics are proposed to ensure that AI technology serves human beings. Every link of its research, production and application should be strictly managed and supervised. Findings The results show that the research in this study can provide help for the related problems encountered in AI practice. Intelligent library integrates human rights protection organically so that readers or users can experience more intimate service in this system. It is a kind of library operation mode with more efficient and convenient characteristics, which is based on digital, networked and intelligent information science. It aims at using the greenest way and digital means to realize the reading and research of human rights protection literature in the literature analysis method. Originality/value Intelligent library is the future development mode of new libraries, which can realize broad interconnection and sharing. It is people-oriented and can make intelligent management and service and establish the importance of the principle of human rights protection and the specific idea of the principle. The development of science and technology brings not only convenience to people's social life but also questions to be thought. People should reduce its potential harm, so as to make AI technology continue to benefit humankind.
A Machine Learning Based Smart Irrigation System with LoRa P2P Networks

In agriculture, the experiences of farmers are very valuable but difficult to replace and passing on. The lack of working power is also a serious problem for many agriculture countries. For planting organic crops, irrigation is one of the most critical steps but also a very labor intensive work. This paper provides a machine learning-based precise and smart irrigation system with LoRa P2P networks to automatically and seamlessly learn the irrigation experiences from expert farmers for greenhouse organic crops. The proposed system will firstly calculate the amount of water for each irrigation based on the trained irrigation model combined with the environment data, such as air temperature/humidity, soil temperate/humidity, light intensity, etc., and then irrigate the crops automatically via the long-distance and low-power wireless LoRa P2P network. The MAC protocol of standard LoRaWAN is Aloha based (random access) and may not be suitable for real-time automatically control. We implement the automatic irrigation system with LoRa P2P network which is a master-slave and TDM-based MAC protocol. Experimental results show that the proposed smart and precise irrigation system is very suitable for modern green house-based agriculture.
Stereoscopic Omnidirectional Image Quality Assessment Based on Predictive Coding Theory

Objective quality assessment of stereoscopic omnidirectional images is a challenging problem since it is influenced by multiple aspects such as projection deformation, field of view (FoV) range, binocular vision, visual comfort, etc. Existing studies show that classic 2D or 3D image quality assessment (IQA) metrics are not able to perform well for stereoscopic omnidirectional images. However, very few research works have focused on evaluating the perceptual visual quality of omnidirectional images, especially for stereoscopic omnidirectional images. In this article, based on the predictive coding theory of the human vision system (HVS), we propose a stereoscopic omnidirectional image quality evaluator (SOIQE) to cope with the characteristics of 3D 360-degree images. Two modules are involved in SOIQE: predictive coding theory based binocular rivalry module and multi-view fusion module. In the binocular rivalry module, we introduce predictive coding theory to simulate the competition between high-level patterns and calculate the similarity and rivalry dominance to obtain the quality scores of viewport images. Moreover, we develop the multi-view fusion module to aggregate the quality scores of viewport images with the help of both content weight and location weight. The proposed SOIQE is a parametric model without necessary of regression learning, which ensures its interpretability and generalization performance. Experimental results on our published stereoscopic omnidirectional image quality assessment database (SOLID) demonstrate that our proposed SOIQE method outperforms state-of-the-art metrics. Furthermore, we also verify the effectiveness of each proposed module on both public stereoscopic image datasets and panoramic image datasets.
Relationship-Embedded Representation Learning for Grounding Referring Expressions

Grounding referring expressions in images aims to locate the object instance in an image described by a referring expression. It involves a joint understanding of natural language and image content, and is essential for a range of visual tasks related to human-computer interaction. As a language-to-vision matching task, the core of this problem is to not only extract all the necessary information (i.e., objects and the relationships among them) in both the image and referring expression, but also make full use of context information to align cross-modal semantic concepts in the extracted information. Unfortunately, existing work on grounding referring expressions fails to accurately extract multi-order relationships from the referring expression and associate them with the objects and their related contexts in the image. In this paper, we propose a cross-modal relationship extractor (CMRE) to adaptively highlight objects and relationships (spatial and semantic relations) related to the given expression with a cross-modal attention mechanism, and represent the extracted information as a language-guided visual relation graph. In addition, we propose a Gated Graph Convolutional Network (GGCN) to compute multimodal semantic contexts by fusing information from different modes and propagating multimodal information in the structured relation graph. Experimental results on three common benchmark datasets show that our Cross-Modal Relationship Inference Network, which consists of CMRE and GGCN, significantly surpasses all existing state-of-the-art methods.
On the Upload versus Download Cost for Secure and Private Matrix Multiplication

In this paper, we study the problem of secure and private distributed matrix multiplication. Specifically, we focus on a scenario where a user wants to compute the product of a confidential matrix A, with a matrix B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sub> , where θ ∈ {1,..., M}. The set of candidate matrices {B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,..., B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> } are public, and available at all the N servers. The goal of the user is to distributedly compute AB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sub> , such that (a) no information is leaked about the matrix A to any server; and (b) the index θ is kept private from each server. Our goal is to understand the fundamental tradeoff between the upload vs download cost for this problem. Our main contribution is to show that the lower convex hull of following (upload, download) pairs: (U,D) = (N/(K - 1), (K/(K - 1)) (1 + (K/N) + ··· + (K/N) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M-1</sup> )) for K = 2, ..., N is achievable. The scheme improves upon state-of-the-art existing schemes for this problem, and leverages ideas from secret sharing and coded private information retrieval.
Validation of finite‐element models using full‐field experimental data: Levelling finite‐element analysis data through a digital image correlation engine

Abstract Full‐field data from digital image correlation (DIC) provide rich information for finite‐element analysis (FEA) validation. However, there are several inherent inconsistencies between FEA and DIC data that must be rectified before meaningful, quantitative comparisons can be made, including strain formulations, coordinate systems, data locations, strain calculation algorithms, spatial resolutions and data filtering. In this paper, we investigate two full‐field validation approaches: (1) the direct interpolation approach , which addresses the first three inconsistencies by interpolating the quantity of interest from one mesh to the other, and (2) the proposed DIC‐levelling approach , which addresses all six inconsistencies simultaneously by processing the FEA data through a stereo‐DIC simulator to ‘level' the FEA data to the DIC data in a regularisation sense. Synthetic ‘experimental' DIC data were generated based on a reference FEA of an exemplar test specimen. The direct interpolation approach was applied, and significant strain errors were computed, even though there was no model form error, because the filtering effect of the DIC engine was neglected. In contrast, the levelling approach provided accurate validation results, with no strain error when no model form error was present. Next, model form error was purposefully introduced via a mismatch of boundary conditions. With the direct interpolation approach, the mismatch in boundary conditions was completely obfuscated, while with the levelling approach, it was clearly observed. Finally, the ‘experimental' DIC data were purposefully misaligned slightly from the FEA data. Both validation techniques suffered from the misalignment, thus motivating continued efforts to develop a robust alignment process. In summary, direct interpolation is insufficient, and the proposed levelling approach is required to ensure that the FEA and the DIC data have the same spatial resolution and data filtering. Only after the FEA data have been ‘levelled' to the DIC data can meaningful, quantitative error maps be computed.