Reviewerly/exHarmony huggingface dataset:
Predict passive seismic events with a convolutional neural network

The ample size of time lapse data requires tremendous event detection and source locating capabilities, especially in areas like shale gas exploration regions where a large number of passive seismic events are often recorded.In many cases, the realtime monitoring and locating of these events are essential to production decisions.Conventional methods face considerable drawbacks.For example, traveltime based methods require traveltime picking; migration methods, on the other hand, require many wavefield modeling applications.These human interaction based pickings or wavefield simulations face severe issues when too many passive sources need to be located, which is common in shale gas explorations.A purely automatic method with no human interactions and less computational cost is necessary.Recently, machine learning has been utilized for this task, whether to identity passive seismic events, or to locate their sources once they are identified and picked.We propose to use an artificial neural network to directly map seismic data, without any picking, to locations of potential seismic events.A visual geometry group (VGG) neural network is trained on synthetic acoustic data, corresponding to known sources, to provide predictions of new passive source locations, and other source features such as source wavelet peak frequencies, amplitudes and the number of sources within the data segment.To reduce the size of the input-to-the-network seismic data, we correlate the traces with the central trace to allow the network to focus on the curvature of the input data.We train the network to handle both single and double events that might be included in the correlation window.An initial application of the approach on a simple V (z) model demonstrates its potential.
Exploring the relationship between dockerfile quality and project characteristics

Dockerfile plays an important role in the Docker-based software development process, but many Dockerfile codes are infected with quality issues in practice. Previous empirical studies showed the existence of association between code quality and project characteristics. However, the relationship between Dockerfile quality and project characteristics has never been explored. In this paper, we seek to empirically study this relation through a large dataset of 6,334 projects. Using linear regression analysis, when controlled for various variables, we statistically identify and quantify the relationship between Dockerfile quality and project characteristics.
DecAug: Augmenting HOI Detection via Decomposition

Human-object interaction (HOI) detection requires a large amount of annotated data. Current algorithms suffer from insufficient training samples and category imbalance within datasets. To increase data efficiency, in this paper, we propose an efficient and effective data augmentation method called DecAug for HOI detection. Based on our proposed object state similarity metric, object patterns across different HOIs are shared to augment local object appearance features without changing their states. Further, we shift spatial correlation between humans and objects to other feasible configurations with the aid of a pose-guided Gaussian Mixture Model while preserving their interactions. Experiments show that our method brings up to 3.3 mAP and 1.6 mAP improvements on V-COCO and HICO-DET dataset for two advanced models. Specifically, interactions with fewer samples enjoy more notable improvement. Our method can be easily integrated into various HOI detection models with negligible extra computational consumption.
Mining Cardinality Restrictions in OWL

Abstract We present an approach to mine cardinality restriction axioms from an existing knowledge graph, in order to extend an ontology describing the graph. We compare frequency estimation with kernel density estimation as approaches to obtain the cardinalities in restrictions. We also propose numerous strategies for filtering obtained axioms in order to make them more available for the ontology engineer. We report the results of experimental evaluation on DBpedia 2016-10 and show that using kernel density estimation to compute the cardinalities in cardinality restrictions yields more robust results that using frequency estimation. We also show that while filtering is of limited usability for minimum cardinality restrictions, it is much more important for maximum cardinality restrictions. The presented findings can be used to extend existing ontology engineering tools in order to support ontology construction and enable more efficient creation of knowledge-intensive artificial intelligence systems.
3D Fetal Pose Estimation with Adaptive Variance and Conditional Generative Adversarial Network

Fetal motion is the dominant challenge to reliable performance and diagnostic quality of fetal magnetic resonance imaging (MRI). The fetus can move unpredictably and rapidly, leading to severe image artifacts. Consequently, MR acquisitions are largely limited to so-called single-shot techniques in an attempt to “freeze” fetal motion through fast imaging, while the problem due to motion occur between slices still exists. In this work, we propose a deep learning method for fetal pose estimation from MR volumes using the paradigm of conditional generative adversarial network which consists of two networks, a generator and a discriminator. The generator is responsible for estimating keypoint heatmaps from input MRI and the discriminator tries to learn the features of plausible fetal pose and distinguish ground-truth heatmaps from generated ones. With this adversarial training scheme, the generator can robustly produce realistic heatmaps for fetal pose inference. Besides, we use adaptive variance to model the difference in intensity of motion of different keypoints. Evaluation shows that the proposed method can improve the performance of pose estimation in 3D MRI, achieving quantitatively an average error of 2.64 mm and 98.31% accuracy (with error less than 10 mm). The proposed method can process volumes with latency less than 300 ms, potentially enabling low-latency online tracking of fetal pose during MR scans.
Anaphora Resolution in Chinese for Analysis of Medical Q&amp;A Platforms

In medical Q&A platforms, patients share information about their diagnosis, give advice and consult with doctors, this creates a large amount of data that contains valuable knowledge on the side effects of drugs, patients’ actions and symptoms. This information is widely considered to be the most important in the field of computer-aided medical analysis. Nevertheless, messages on the Internet are difficult to analyze because of their unstructured form. Thus, the purpose of this study is to develop a program for anaphora resolution in Chinese and to implement it for analysis of user-generated content in the medical Q&A platform. The experiments are conducted on three models: BERT, NeuralCoref and BERT-Chinese+SpanBERT. BERT-Chinese+SpanBERT achieves the highest accuracy—68.5% on the OntoNotes 5.0 corpus. Testing the model that showed the highest result was carried out on messages from the medical Q&A platform haodf.com. The results of the study might contribute to improving the diagnosis of hereditary diseases.
S-CDA: A Smart Cloud Disk Allocation Approach in Cloud Block Storage System

Cloud disk provided to users by cloud providers has been a prevalent form of cloud storage. Assigning disk storage space for cloud disks among available data warehouses remains a challenging task, as the load characteristics of cloud disks are not known at the time of disk creation. Methods considering only subscribed capacity could be prone to result in resource under-utilization and load imbalance among warehouses. We propose a Smart Cloud Disk Allocation (S-CDA) approach, which uses clustering and classifying to predict the load information for new cloud disks, and then realizes multi-dimensional allocation based on Manhattan distance. Experimental results with realistic cloud workloads show that, compared with the existing one-dimensional allocation scheme, S-CDA increases the overall space/IOPS/disk bandwidth utilization, while decreasing the load imbalance.
Joint Total Variation ESTATICS for Robust Multi-parameter Mapping

Quantitative magnetic resonance imaging (qMRI) derives tissue-specific parameters – such as the apparent transverse relaxation rate \(R_2^\star \), the longitudinal relaxation rate \(R_1\) and the magnetisation transfer saturation – that can be compared across sites and scanners and carry important information about the underlying microstructure. The multi-parameter mapping (MPM) protocol takes advantage of multi-echo acquisitions with variable flip angles to extract these parameters in a clinically acceptable scan time. In this context, ESTATICS performs a joint loglinear fit of multiple echo series to extract \(R_2^\star \) and multiple extrapolated intercepts, thereby improving robustness to motion and decreasing the variance of the estimators. In this paper, we extend this model in two ways: (1) by introducing a joint total variation (JTV) prior on the intercepts and decay, and (2) by deriving a nonlinear maximum a posteriori estimate. We evaluated the proposed algorithm by predicting left-out echoes in a rich single-subject dataset. In this validation, we outperformed other state-of-the-art methods and additionally showed that the proposed approach greatly reduces the variance of the estimated maps, without introducing bias.
A novel image classification model based on adversarial training for pulsar candidate identification

Pulsars are highly magnetized, rotating neutron stars with small volume and high density. The discovery of pulsars is of great significance in the fields of physics and astronomy. With the development of artificial intelligent, image recognition models based on deep learning are increasingly utilized for pulsar candidate identification. However, pulsar candidate datasets are characterized by unbalance and lack of positive samples, which has contributed the traditional methods to fall into poor performance and model bias. To this end, a general image recognition model based on adversarial training is proposed. A generator, a classifier, and two discriminators are included in the model. Theoretical analysis demonstrates that the model has a unique optimal solution, and the classifier happens to be the inference network of the generator. Therefore, the samples produced by the generator significantly augment the diversity of training data. When the model reaches equilibrium, it can not only predict labels for unseen data, but also generate controllable samples. In experiments, we split part of data from MNIST for training. The results reveal that the model not only behaves better classification performance than CNN, but also has better controllability than CGAN and ACGAN. Then, the model is applied to pulsar candidate dataset HTRU and FAST. The results exhibit that, compared with CNN model, the F-score has increased by 1.99% and 3.67%, and the Recall has also increased by 6.28% and 8.59% respectively.
Masked-field Pre-training for User Intent Prediction

For many applications, predicting the users' intents can help the system provide the solutions or recommendations to the users. It improves the user experience, and brings economic benefits. The main challenge of user intent prediction is that we lack enough labeled data for training, and some intents (labels) are sparse in the training set. This is a general problem for many real-world prediction tasks. To overcome data sparsity, we propose a masked-field pre-training framework. In pre-training, we exploit massive unlabeled data to learn useful feature interaction patterns. We do this by masking partial field features, and learning to predict them from other unmasked features. We then finetune the pre-trained model for the target intent prediction task. This framework can be used to train various deep models. In the intent prediction task, each intent is only relevant to partial features. To tackle this problem, we propose a Field-Independent Transformer network. This network generates separate representation for each field, and aggregates the relevant field representations with attention mechanism for each intent. We test our method on intent prediction datasets in customer service scenarios as well as several public datasets. The results show that the masked-field pre-training framework significantly improves the prediction precision for deep models. And the Field-Independent Transformer network trained with the masked-field pre-training framework outperforms the state-of-the-art methods in the user intent prediction.
Model Establishment of Decision Tree Algorithm and Its Application in Vehicle Fault Prediction Analysis

In this paper, the basic principle and optimization strategy of decision tree algorithm are described. The model is embedded into x software as an independent sub-module. On the premise of the analysis results of the previous failure law, the fault mode, grade and treatment mode of the vehicle are predicted through the setting of specific vehicle driving parameters. The results of the model test show that it can predict and analyze the immediate failure of the vehicle very well, and improve the precision and efficiency of vehicle support.
A Survey of Game Theoretical Privacy Preservation for Data Sharing and Publishing

Privacy preservation has been one of the biggest concerns in data sharing and publishing. The wide-spread application of data sharing and publishing contributes to the utilization of data, but brings a severe risk of privacy leakage. Although the corresponding privacy preservation techniques have been proposed, it is inevitable to decrease the accuracy of data. More importantly, it is a challenge to analyze the behaviors and interactions among different participants, including data owners, collectors and adversaries. For data owners and collectors, they need to select proper privacy preservation mechanisms and parameters to maximize their utility under a certain amount of privacy guarantee. For data adversaries, their objective is to get the sensitive information by various attack measurements. In this paper, we survey the related work of game theory-based privacy preservation under data sharing and publishing. We also discuss the possible trends and challenges of the future research. Our survey provides a systematic and comprehensive understanding about privacy preservation problems under data sharing and publishing.
Global community response to COVID-19 challenges in education

The article deals with the COVID-19 pandemic impact, that has swept the world in 2020, and has stricken a blow to the education systems of the countries. As the title implies the article describes the world community's response to pandemic challenges in education. It is especially noted that the pandemic has transformed the established learning formats and techniques at all levels of education. A mention should be made that international organizations have made a huge contribution to find out the countermeasures to COVID-19. The key international organizations acted as agents of the educational community, and proposed and implemented universal tools for learning in a coronavirus crisis. It should be emphasized that the authors have analyzed the published materials of international organizations (UNESCO, OECD, EU, World Bank, etc.), nongovernmental organizations (The National Center on Education and the Economy (NCEE), National Conference of State Legislatures (NCSL), academic works. The paper examines the activities that were implemented during February-May 2020. It is worth mentioning that many countries all over the world have introduced an integrated approach to organizing the learning process in the context of the COVID-19 pandemic, combining online learning with other traditional tools. An important part of the global community response to COVID-19 in education has been the allocation of targeted funding for the purchase and provision of computers and digital devices, as well as access to the Internet for students, especially from low-income families. Equally important is the direct financial support of low-income families by governments, as well as cooperation with parents. In the conclusions it is stressed the absence in some countries or underdevelopment of the legal and regulatory framework for the transformation of the educational process into online platforms; inflexibility of the authorities' response to changes in educational reality and delays in adapting the content of education; lack of ICT skills; lack of technical capabilities; complexity of some subjects transformation; parents' unwillingness to support children in online learning; negative impacts on students' healthcare.
Gaze Tracker Accuracy and Precision Measurements in Virtual Reality Headsets

To effectively utilize a gaze tracker in user interaction it is important to know the quality of the gaze data that it is measuring. We have developed a method to evaluate the accuracy and precision of gaze trackers in virtual reality headsets. The method consists of two software components. The first component is a simulation software that calibrates the gaze tracker and then performs data collection by providing a gaze target that moves around the headset's field-of-view. The second component makes an off-line analysis of the logged gaze data and provides a number of measurement results of the accuracy and precision. The analysis results consist of the accuracy and precision of the gaze tracker in different directions inside the virtual 3D space. Our method combines the measurements into overall accuracy and precision. Visualizations of the measurements are created to see possible trends over the display area. Results from selected areas in the display are analyzed to find out differences between the areas (for example, the middle/outer edge of the display or the upper/lower part of display).
Adaptive Content Frame Skipping for Wyner–Ziv-Based Light Field Image Compression

Light field (LF) imaging introduces attractive possibilities for digital imaging, such as digital focusing, post-capture changing of the focal plane or view point, and scene depth estimation, by capturing both spatial and angular information of incident light rays. However, LF image compression is still a great challenge, not only due to light field imagery requiring a large amount of storage space and a large transmission bandwidth, but also due to the complexity requirements of various applications. In this paper, we propose a novel LF adaptive content frame skipping compression solution by following a Wyner–Ziv (WZ) coding approach. In the proposed coding approach, the LF image is firstly converted into a four-dimensional LF (4D-LF) data format. To achieve good compression performance, we select an efficient scanning mechanism to generate a 4D-LF pseudo-sequence by analyzing the content of the LF image with different scanning methods. In addition, to further explore the high frame correlation of the 4D-LF pseudo-sequence, we introduce an adaptive frame skipping algorithm followed by decision tree techniques based on the LF characteristics, e.g., the depth of field and angular information. The experimental results show that the proposed WZ-LF coding solution achieves outstanding rate distortion (RD) performance while having less computational complexity. Notably, a bit rate saving of 53% is achieved compared to the standard high-efficiency video coding (HEVC) Intra codec.
Supervised Hyperparameter Estimation for Anomaly Detection

The detection of anomalies, i.e. of those points found in a dataset but which do not seem to be generated by the underlying distribution, is crucial in machine learning. Their presence is likely to make model predictions not as accurate as we would like; thus, they should be identified before any model is built which, in turn, may require the optimal selection of the detector hyperparameters. However, the unsupervised nature of this problem makes that task not easy. In this work, we propose a new estimator composed by an anomaly detector followed by a supervised model; we can take then advantage of this second model to transform model estimation into a supervised problem and, as a consequence, the estimation of the detector hyperparameters can be done in a supervised setting. We shall apply these ideas to optimally hyperparametrize four different anomaly detectors, namely, Robust Covariance, Local Outlier Factor, Isolation Forests and One-class Support Vector Machines, over different classification and regression problems. We will also experimentally show the usefulness of our proposal to estimate in an objective and automatic way the best detector hyperparameters.
Practical seed-recovery for the PCG Pseudo-Random Number Generator

The Permuted Congruential Generators (PCG) are popular conventional (non-cryptographic) pseudo-random generators designed in 2014. They are used by default in the NumPy scientific computing package. Even though they are not of cryptographic strength, their designer stated that predicting their output should nevertheless be "challenging".In this article, we present a practical algorithm that recovers all the hidden parameters and reconstructs the successive internal states of the generator. This enables us to predict the next “random” numbers, and output the seeds of the generator. We have successfully executed the reconstruction algorithm using 512 bytes of challenge input; in the worst case, the process takes 20 000 CPU hours.This reconstruction algorithm makes use of cryptanalytic techniques, both symmetric and lattice-based. In particular, the most computationally expensive part is a guessand-determine procedure that solves about 252 instances of the Closest Vector Problem on a very small lattice.
Rare Earth Elements Assessment in the Granitoids of Part of Southwestern Nigeria

The Rare Earth Elements (REE) composition of granitoids in and around Ila-Orangun area Southwestern Nigeria was assessed in order to ascertain their potential for possible exploitation. Detailed lithological mapping of the area was undertaken followed by whole rock geochemical analysis of representative samples of the granitoids using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) technique. Petrographic study of the samples was carried out as well as the interpretation of the geochemical data using diverse geochemical discrimination plots.&#x0D; The rock units mapped were biotite granite gneiss, granite gneiss and hornblende biotite granite. Biotite hornblende gneiss, quartzite, talc-chlorite-tremolite-schist, mica schist and pegmatites were the surrounding country rocks.&#x0D; The REE concentrations (in ppm) revealed higher concentrations of the light REEs compared to the heavy REEs. The fractionation ratio, (La/Yb)N ranged from 4.35-15.04 (granite gneiss) and 13.78-18.48 (hornblende biotite granite) indicating enrichment in LREEs over the HREEs. The spider plot for the REEs also showed that the granitoids are LREE-enriched and HREE-depleted suggesting fractional crystallisation and a distinct negative Eu anomaly indicating plagioclase fractionation. Enrichment plot also revealed that the REEs in the granitoids are significantly enriched. Comparison with other areas showed that the granitoids of the study area especially the hornblende biotite granite has higher concentrations of REEs and may be a possible pointer of REE mineralisation.