Global Data Distribution Research Articles

Convolutional rule inference network based on belief rule-based system using an evidential reasoning approach

With the development of artificial intelligence, we pay widespread attention to the high reliability and interpretability of resulting architectures and algorithms. As one of the main methods of machine learning, the “black-box” mechanism of neural networks lacks interpretability. In order to alleviate the low interpretability, based on belief rule-based inference methodology using the evidential reasoning approach (RIMER), we propose a convolutional rule inference network (CRIN), which is interpretable by involving rules in belief rule base (BRB) under various types of uncertain knowledge representation. Considering the influence of global data distribution on attribute weights, the determination of each attribute may be affected by all other attributes, based on the sigmoid activation function, we determine optimized attribute weights to make sure that each attribute weight is formed by all attribute values. The derivation functions with optimized attribute weights based on evidential reasoning theory also showed to ensure the feasibility of the following learning algorithm. For expressing the influence of local rules input on final output in the network, the network framework and learning algorithm of CRIN based on convolution strategy are proposed. The inference process in RIMER is used as the feedforward mechanism to ensure the interpretability of the network. Meanwhile, the gradient descent method is used as back propagation algorithm to adjust the parameters of rule base to establish a more reasonable BRB. The experimental results and comparative analysis demonstrate that the proposed CRIN has advantages in terms of interpretability and learning capability. This study suggests new trends for interpretable network and uncertain classification issues in the reasoning approach.

Solar Climate Features Taking into Account the Morphometric Conditions of the Area and the Possibility of Using Them in Heliotherapy on the Example of the Cieplice and Kołobrzeg Health Resorts (Poland)

Global solar radiation is an important atmospheric stimulus affecting the human body and has been used in heliotherapy for years. In addition to environmental factors, the effectiveness of global solar radiation is increasingly influenced by human activity. This research was based on the use of heliographic and actinometric data (1996–2015) and the model distribution of global solar radiation to determine the possibility of heliotherapy with the example of two health resorts: Cieplice and Kołobrzeg (Poland). The solar features of health resorts (sunshine duration and global solar radiation) were characterized, and they were correlated with the spatial distribution of global solar radiation data obtained with the use of remote sensing techniques (System for Automated Geoscientific Analyzes-SAGA), including COoRdination and INformation on the Environment (CORINE) land cover (CLC) data. Using the maximum entropy model (MaxEnt), a qualitative and quantitative relationship between morphometric parameters and solar climate features was demonstrated for individual land cover types. Studies have shown that the period of late spring and summer, due to the climate’s solar features, is advisable for the use of heliotherapy. The human activity that determines the land cover is the main element influencing the spatial differentiation of the possibilities of using this form of health treatment. It also affects topographic indicators shown as significant in the MaxEnt predictive model. In general, areas with high openness were shown as predisposed for health treatment using global solar radiation, which is not consistent with areas commonly used for heliotherapy. The conducted research has shown the need for an interdisciplinary approach to the issue of heliotherapy, which will contribute to the optimization of the use of this form of health treatment from the perspective of climate change and human pressure.

Archaeomagnetic results from Cambodia in Southeast Asia: Evidence for possible low-latitude flux expulsion

Extensive spatial and temporal distribution of high-quality data are essential for understanding regional and global behaviors of the geomagnetic field. We carried out chronological and archaeomagnetic studies at the Angkor-era iron-smelting site of Tonle Bak in Cambodia in Southeast Asia, an area with no data available to date. We recovered high-fidelity full-vector geomagnetic information from the 11th to 14th century for this region, which fill gaps in the global distribution of data and will significantly improve the global models. These results reveal a sharp directional change of the geomagnetic field between 1200 and 1300 CE, accompanied by an intensity dip between 1100 and 1300 CE. The fast geomagnetic variation recorded by our data provides evidence for the possible existence of low-latitude flux expulsion. Related discussions in this paper will inspire a new focus on detailed geomagnetic research in low-latitude areas around the equator, and exploration of related dynamic processes.

Visual analysis of meteorological satellite data via model-agnostic meta-learning

Satellites detect the distribution of meteorological data worldwide. However, due to the orbital constraints, the satellite can only reach the same area again after one orbiting cycle. The interval between two detections in the same area is long, and the variation of meteorological data between the two detections is unknown. Moreover, meteorological satellite data are only located near the orbit in one cycle, while the global distribution of meteorological data is unknown. Our method allows to train a regression model with only few meteorological satellite data by taking advantage of the recent advances in deep learning. In detail, we train a model-agnostic meta-learning (MAML) model with data from ground stations instead of meteorological satellites and get the initial network parameters. Based on the initial network parameters trained by MAML, we train the regression models again for different areas. We sample the regression curves of all areas by time and get a time series of global meteorological data distribution. Through case studies conducted together with domain experts, we validate the effectiveness of our method.

Global Data Distribution Weighted Synthetic Oversampling Technique for Imbalanced Learning

Imbalanced learning is a common problem in data mining. There is a different distribution of data samples among other classes in the imbalanced datasets. It's a challenge for standard algorithms designed for balanced class distributions. Although there are various strategies to solve this problem, generating artificial data to achieve a relatively balanced class distribution is universal rather than directly modifying specific classification algorithms. The oversampled data can be combined with any user-specified algorithm without any restrictions. In this paper, we present a novel oversampling method, Global Data Distribution Weighted Synthetic Oversampling Technique (GDDSYN). By applying clustering, optimizing the selection criteria of the minority class samples that are used to generate synthetic samples, avoiding generating more noise samples. GDDSYN assigns weights for the number of synthetic samples to tackle the within-class imbalance and between-class imbalance simultaneously, according to the informative level of the sample and the sparsity of the cluster to which the sample belongs. The use of scores with Silhouette Coefficient and Mutual Information helps the k-means algorithm set a reasonable number of clusters for the minority and majority classes respectively so that the clustering effect can be guaranteed. Next, by using clustering information, synthetic samples' generation path is improved to avoid class overlap. Additionally, GDDSYN has been evaluated extensively on 10 artificial and 10 real-world data sets. The empirical results show that our method is outperforms or comparable with some other existing methods in terms of assessment metrics when artificial data generated by GDDSYN are used.

Center-aligned domain adaptation network for image classification

For a target task where the labeled data are unavailable, unsupervised domain adaptive learning performs transfer learning from labeled source data to unlabeled target data. Previous deep domain adaption methods mainly learned the global domain shift between different domains, the global distributions are aligned without considering the correspondence information between the same class data of different domains. Recently, more and more researchers pay attention to semantic alignment that focuses on accurately aligning the distributions of the same class data from different domains. However, most of them ignore two points: the learning of the global distribution of the target domain data; the compactness of intra-class domain data and the discrimination of inter-class domain data, which lead to unsatisfying transfer learning performance. To resolve this problem, we propose a Center-aligned Domain Adaptation Network (CenterDA) to facilitate the semantic alignment, In this study, for each class in label space, we learn a common class center for all data with the same class label in the source and target domains, which allows us to learn the global distribution of the target domain data under the supervised learning of the source domain data. Furthermore, we minimize the distance between the deep features and its common class center to compact the feature representations of data. In this manner, we achieve the desired goals: The global distribution of the target domain data is learned by common class center. Second, the source and the target domain data of the same class are aligned near the common center. Third, we model the intra-class compactness and the inter-class separability modeling. Extensive experiments on three datasets show that our method achieves remarkable results on image classification and has comparable performance with the latest methods.

Elastic Resilience for Software-Defined Satellite Networking: Challenges, Solutions, and Open Issues

Satellite networks have long been regarded as a key enabler for ubiquitous Internet access and global data distribution. However, since they are highly dynamic and much more vulnerable to various failures, how to detour traffic around fault satellites and interrupted links becomes an important but challenging issue. Thanks to the emerging software-defined networking, great controllability can be introduced to the satellite networks for agile management and automation. Hence, in this article, we focus on elastic resilience for software-defined satellite networking, and propose a preliminary solution to cope with the related fundamental challenges in guarantees of controller reachability, collections of network status, and failure detection and recovery. We also discuss several key open issues to be urgently addressed, hoping to shed some light on this promising land.

An Analysis of the Supply of Open Government Data

An index of the release of open government data, published in 2016 by the Open Knowledge Foundation, shows that there is significant variability in the country’s supply of this public good. What explains these cross-country differences? Adopting an interdisciplinary approach based on data science and economic theory, we developed the following research workflow. First, we gather, clean, and merge different datasets released by institutions such as the Open Knowledge Foundation, World Bank, United Nations, World Economic Forum, Transparency International, Economist Intelligence Unit, and International Telecommunication Union. Then, we conduct feature extraction and variable selection founded on economic domain knowledge. Next, we perform several linear regression models, testing whether cross-country differences in the supply of open government data can be explained by differences in the country’s economic, social, and institutional structures. Our analysis provides evidence that the country’s civil liberties, government transparency, quality of democracy, efficiency of government intervention, economies of scale in the provision of public goods, and the size of the economy are statistically significant to explain the cross-country differences in the supply of open government data. Our analysis also suggests that political participation, sociodemographic characteristics, and demographic and global income distribution dummies do not help to explain the country’s supply of open government data. In summary, we show that cross-country differences in governance, social institutions, and the size of the economy can explain the global distribution of open government data.

Analyzing User-Level Privacy Attack Against Federated Learning

Federated learning has emerged as an advanced privacy-preserving learning technique for mobile edge computing, where the model is trained in a decentralized manner by the clients, preventing the server from directly accessing those private data from the clients. This learning mechanism significantly challenges the attack from the server side. Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i.e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client. To analyze the privacy leakage of federated learning, this paper gives the first attempt to explore user-level privacy leakage by the attack from a malicious server. We propose a framework incorporating GAN with a multi-task discriminator, called multi-task GAN - Auxiliary Identification (mGAN-AI), which simultaneously discriminates category, reality, and client identity of input samples. The novel discrimination on client identity enables the generator to recover user specified private data. Unlike existing works interfering the federated learning process, the proposed method works “invisibly” on the server side. Furthermore, considering the anonymization strategy for mitigating mGAN-AI, we propose a beforehand linkability attack which re-identifies the anonymized updates by associating the client representatives. A novel siamese network fusing the identification and verification models is developed for measuring the similarity of representatives. The experimental results demonstrate the effectiveness of the proposed approaches and the superior to the state-of-the-art.

Federated Data Cleaning: Collaborative and Privacy-Preserving Data Cleaning for Edge Intelligence

As an important driving factor of emerging Internet-of-Things (IoT) applications, machine learning algorithms are currently facing the challenge of how to “clean” data noise, that is introduced during the training process (e.g., asynchronous execution and lossy data compression and quantization). In an attempt to guarantee data quality, various data cleaning approaches have been proposed to filter out abnormal data entries based on the global data distribution. However, most existing data cleaning approaches are based on a centralized paradigm and thus cannot be applied to future edge-based IoT applications, where each edge node (EN) has only a limited view of the global data distribution. Moreover, the increasing demand for privacy preservation largely prevents ENs from combining their data for centralized cleaning. In this study, we propose a federated data cleaning protocol, coined as FedClean, for edge intelligence (EI) scenarios that is designed to achieve data cleaning without compromising data privacy. More specifically, different ENs first generate Boolean shares of their data and distribute them to two noncolluding servers. These two servers then run the FedClean protocol to privately and efficiently compute the attribute value frequency (AVF) scores of the collected data entries, which are then sorted in ascending order via a bitonic sorting network without revealing their values. As a result, data entries with lower AVF scores are considered as abnormal and filtered out. The security, efficiency, and effectiveness of the proposed approach are then demonstrated via concrete security analysis and comprehensive experiments.

Sulfur dioxide from the atmospheric chemistry experiment (ACE) satellite

The version 4.0 dataset from the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) on SCISAT, released in March of 2019, has sulfur dioxide (SO2) volume mixing ratio (VMR) profiles as a routine data product. From this dataset, global SO2 distributions between the altitudes of 10.5 km and 23.5 km are analyzed. The global distribution of all SO2 VMR data by altitude is broken down into 30° and 5° latitude zones. Seasonality of the global SO2 distribution is explored. Volcanic SO2 plumes are isolated in the dataset and compared with extinction data from the ACE-FTS Imager.

Are we advancing universal health coverage through cataract services? Protocol for a scoping review

IntroductionUniversal health coverage (UHC) includes the dimensions of equity in access, quality services that improve health and protection against financial hardship. Cataract continues to be the leading cause of blindness...

Characteristics of Mainland Coastline Changes in Southeast Asia during the 21st Century

Song, Y.; Li, D., and Hou, X., 2020. Characteristics of mainland coastline changes in Southeast Asia during the 21st century. Journal of Coastal Research, 36(2), 261–275. Coconut Creek (Florida), ISSN 0749-0208.Based on multitemporal Landsat images covering the entire coastal zone, a visual interpretation method was adopted to extract the coastlines of mainland Southeast Asia (MSA) in 2000 and 2015 referencing Google Earth images and global distribution of mangrove data. Coastline structures, coastline fractal dimensions, coastline change rates, and patterns of land–sea interchange were analyzed to reveal the spatial–temporal characteristics of mainland coastline changes in the 21st century. The results showed that (1) during the past 15 years, the length and percentage of natural coastline decreased from 15,440.24 km (83.33%) to 14,909.75 km (78.91%) and artificial coastlines increased at an average growth rate of 29.04% from 3088.79 to 3985.89 km; (2) the fractal dimensions of most areas in MSA tended to increase under interference driven by human activities, and the coastline morphology became more complex overall; (3) coastlines had obvious evolution, in which 18.54% of the coastline advanced toward the sea and 11.25% retreated toward land at average rates of +20.36 and –15.41 m.a–1, respectively; and (4) the number of patches representing land–sea interchange reached 6607 so that the net land expansion area was 534.25 km2 and among these regions, those larger than 1 km2 accounted for only a small percentage (2.54%) of the total number but 79.41% of the total area. These hot spots were mainly concentrated in Myanmar, followed by Vietnam and Malaysia. Overall, the coastline of MSA was characterized by rapid artificial growth but still had a high percentage of naturalization. When it comes to developing the marine economy and addressing the ecological risk, the intensity of utilization and hardening of the coastline will likely increase.

Distribution-free data density estimation in large-scale networks

Estimating the global data distribution in large-scale networks is an important issue and yet to be well addressed. It can benefit many applications, especially in the cloud computing era, such as load balancing analysis, query processing, and data mining. Inspired by the inversion method for random variate (number) generation, in this paper, we present a novel model called distribution-free data density estimation for large ring-based networks to achieve high estimation accuracy with low estimation cost regardless of the distribution models of the underlying data. This model generates random samples for any arbitrary distribution by sampling the global cumulative distribution function and is free from sampling bias. Armed with this estimation method, we can estimate data densities over both one-dimensional and multidimensional tuple sets, where each dimension could be either continuous or discrete as its domain. In large-scale networks, the key idea for distribution-free estimation is to sample a small subset of peers for estimating the global data distribution over the data domain. Algorithms on computing and sampling the global cumulative distribution function based on which the global data distribution is estimated are introduced with a detailed theoretical analysis. Our extensive performance study confirms the effectiveness and efficiency of our methods in large ring-based networks.

Several robust extensions of collaborative representation for image classification

Collaborative representation (CR) is one of the well-known representation methods and has been widely used in computer vision and pattern recognition. The collaborative representation-based classification (CRC) and its extension called the probabilistic collaborative representation-based classification (PCRC) have obtained promising performance in image classification. However, the representation fidelity is usually measured by the ℓ2-norm , which is not robust to outliers. Moreover, CRC and PCRC only consider the global distribution of data and ignore the locality of data. To overcome those problems, in this paper, we propose the residual-based extensions and the weighted version of CRC and PCRC. Specifically, for the residual-based extensions of CRC and PCRC, we measure the representation fidelity by jointing ℓ1-norm and the ℓ2-norm of coding residuals. For the weighted extensions of CRC and PCRC with different coding residuals, we constrained the representation coefficients with locality of data. To verify the effectiveness of the proposed methods, we conduct extensive experiments on six popular face databases and three image databases. Experimental results have demonstrated that the ℓ1-norm of coding residual, jointing both the ℓ1-norm and the ℓ2-norm of coding residuals and the locality constraint of representation coefficients can enhance pattern discrimination effectively.

Semi-supervised modality-dependent cross-media retrieval

In this paper, we propose a modality-dependent cross-media retrieval approach under semi-supervised conditions. The approach utilizes both labeled samples and unlabeled ones to obtain two couples of projection matrices and uses feature distance to represent the semantic information of unlabeled samples in the optimization process, so as to fully utilize the data structural information. Different from supervised modality-dependent cross-media retrieval approaches which use labeled samples and fixed semantic information, the proposed approach makes full use of the global data distribution property and the semantic information of both labeled and unlabeled samples. Experiments on benchmark datasets show its superiority over the compared methods.

Interactive Regression Lens for Exploring Scatter Plots

AbstractData analysis often involves finding models that can explain patterns in data, and reduce possibly large data sets to more compact model‐based representations. In Statistics, many methods are available to compute model information. Among others, regression models are widely used to explain data. However, regression analysis typically searches for the best model based on the global distribution of data. On the other hand, a data set may be partitioned into subsets, each requiring individual models. While automatic data subsetting methods exist, these often require parameters or domain knowledge to work with. We propose a system for visual‐interactive regression analysis for scatter plot data, supporting both global and local regression modeling. We introduce a novel regression lens concept, allowing a user to interactively select a portion of data, on which regression analysis is run in interactive time. The lens gives encompassing visual feedback on the quality of candidate models as it is interactively navigated across the input data. While our regression lens can be used for fully interactive modeling, we also provide user guidance suggesting appropriate models and data subsets, by means of regression quality scores. We show, by means of use cases, that our regression lens is an effective tool for user‐driven regression modeling and supports model understanding.

Distributed Vector Quantization Based on Kullback-Leibler Divergence

The goal of vector quantization is to use a few reproduction vectors to represent original vectors/data while maintaining the necessary fidelity of the data. Distributed signal processing has received much attention in recent years, since in many applications data are dispersedly collected/stored in distributed nodes over networks, but centralizing all these data to one processing center is sometimes impractical. In this paper, we develop a distributed vector quantization (VQ) algorithm based on Kullback-Leibler (K-L) divergence. We start from the centralized case and propose to minimize the K-L divergence between the distribution of global original data and the distribution of global reproduction vectors, and then obtain an online iterative solution to this optimization problem based on the Robbins-Monro stochastic approximation. Afterwards, we extend the solution to apply to distributed cases by introducing diffusion cooperation among nodes. Numerical simulations show that the performances of the distributed K-L–based VQ algorithm are very close to the corresponding centralized algorithm. Besides, both the centralized and distributed K-L–based VQ show more robustness to outliers than the (centralized) Linde-Buzo-Gray (LBG) algorithm and the (centralized) self-organization map (SOM) algorithm.

Citizen-Based Litter and Marine Debris Data Collection and Mapping

Marine debris and plastic in our oceans is a global issue of increasing concern. However, the monitoring of litter and debris is challenging at the global scale because of disconnected local organizations and the use of paper and pen for documentation. The Marine Debris Tracker mobile app and citizen science program allow for the collection of global standardized data at a scale, speed, and efficiency that wasn't previously possible. The app also serves as an outreach and education tool, with a Web portal that instantly shows the data that users have logged. User engagement through a top tracker competition and social media keeps people interested in the Marine Debris Tracker community. More than 400,000 items have been tracked, with plastic being the most common category noted; maps provide both global and local distribution of data. The Marine Debris Tracker community and dataset continue to grow daily.

Deciphering global signal features of high-throughput array data from cancers

Normalization of array data relies on the assumption that most genes are not altered, which means that the signals for different samples should be scaled to have similar median or average values. However, accumulating evidence suggests that gene expression could be widely up-regulated in cancers. Our previous results and subsequent findings have shown that violation of the assumption led to erroneous interpretation of microarray data. To decipher the global signal features of microarray data from cancer samples, we empirically evaluated a large collection of gene and miRNA expression profiles and copy-number variation arrays. Our results showed that, at the transcriptomic level, genes and miRNAs are widely over-expressed in a large proportion of cancers. In contrast, at the genomic level, global raw signal intensities for methylation and copy number variation show negligible differences between cancer and normal samples. These results force us to re-evaluate the proper use of normalization procedures under different experimental conditions and for different array platforms.