Reviewerly/exHarmony huggingface dataset:
Inner matrix norms in evolving Cauchy possibilistic clustering for classification and regression from data streams

This paper presents the unification and generalization of different evolving clustering methods based on Cauchy density. This can be done by introducing different inner matrix norms to obtain different functionalities of the algorithm. This unified approach with a general inner matrix norm is called eCauchy recursive clustering. The well-known possibilistic c-means clustering (PCM) can be seen as a special example of the proposed eCauchy algorithm. The main motivation of the proposed method is to solve and overcome the problems of modelling the nonlinear data streams in highly noisy environments with frequently appearing outliers. By introducing the different inner matrix into the density metric, the algorithm can be modified in different ways to deal with different clustering problems, from classical classification to the preprocessing for solving regression problems. The evolving nature of the algorithm and simple computation also make it appropriate for dealing with big-data problems. The described eCauchy algorithm needs just a few initial parameters such as minimal and maximal density. The algorithm incrementally changes the structure of the model based on the flow of samples from the data stream, more specifically it evolves the structure of the model during the operation by adding, merging, splitting and removing the clusters. This approach allows the identification of very different clusters in size and shape and is also quite insensitive to the outliers and significant noise. In the paper, the universality of the proposed algorithm is shown on various examples.
Multi-Fidelity Automatic Hyper-Parameter Tuning via Transfer Series Expansion

Automatic machine learning (AutoML) aims at automatically choosing the best configuration for machine learning tasks. However, a configuration evaluation can be very time consuming particularly on learning tasks with large datasets. This limitation usually restrains derivative-free optimization from releasing its full power for a fine configuration search using many evaluations. To alleviate this limitation, in this paper, we propose a derivative-free optimization framework for AutoML using multi-fidelity evaluations. It uses many lowfidelity evaluations on small data subsets and very few highfidelity evaluations on the full dataset. However, the lowfidelity evaluations can be badly biased, and need to be corrected with only a very low cost. We thus propose the Transfer Series Expansion (TSE) that learns the low-fidelity correction predictor efficiently by linearly combining a set of base predictors. The base predictors can be obtained cheaply from down-scaled and experienced tasks. Experimental results on real-world AutoML problems verify that the proposed framework can accelerate derivative-free configuration search significantly by making use of the multi-fidelity evaluations.
Energy-Efficient Gabor Kernels in Neural Networks with Genetic Algorithm Training Method

Deep-learning convolutional neural networks (CNNs) have proven to be successful in various cognitive applications with a multilayer structure. The high computational energy and time requirements hinder the practical application of CNNs; hence, the realization of a highly energy-efficient and fast-learning neural network has aroused interest. In this work, we address the computing-resource-saving problem by developing a deep model, termed the Gabor convolutional neural network (Gabor CNN), which incorporates highly expression-efficient Gabor kernels into CNNs. In order to effectively imitate the structural characteristics of traditional weight kernels, we improve upon the traditional Gabor filters, having stronger frequency and orientation representations. In addition, we propose a procedure to train Gabor CNNs, termed the fast training method (FTM). In FTM, we design a new training method based on the multipopulation genetic algorithm (MPGA) and evaluation structure to optimize improved Gabor kernels, but train the rest of the Gabor CNN parameters with back-propagation. The training of improved Gabor kernels with MPGA is much more energy-efficient with less samples and iterations. Simple tasks, like character recognition on the Mixed National Institute of Standards and Technology database (MNIST), traffic sign recognition on the German Traffic Sign Recognition Benchmark (GTSRB), and face detection on the Olivetti Research Laboratory database (ORL), are implemented using LeNet architecture. The experimental result of the Gabor CNN and MPGA training method shows a 17–19% reduction in computational energy and time and an 18–21% reduction in storage requirements with a less than 1% accuracy decrease. We eliminated a significant fraction of the computation-hungry components in the training process by incorporating highly expression-efficient Gabor kernels into CNNs.
Defect detection using windowed Fourier spectrum analysis in diffraction phase microscopy

This paper introduces a technique to identify defects from fringe patterns for optical non-destructive testing and metrology. The technique relies on computation of the windowed Fourier spectrum of the fringe pattern at a given spatial frequency, and subsequent application of automated spectrum thresholding to localize the defect. The technique offers the advantages of high robustness against noise, fast implementation, high throughput and minimal operator intervention. The performance of the proposed technique is demonstrated for identifying defects of different types and sizes under varying levels of noise using numerical simulations, and practical validity is tested using experimental interferograms obtained in diffraction phase microscopy.
A Fourier Descriptor of 2D Shapes Based on Multiscale Centroid Contour Distances Used in Object Recognition in Remote Sensing Images

A shape descriptor is an effective tool for describing the shape feature of an object in remote sensing images. Researchers have put forward a lot of excellent descriptors. The discriminability of some descriptors is very strong in the experiments, but usually their computational cost is large, which makes them unsuitable to be used in practical applications. This paper proposes a new descriptor-FMSCCD (Fourier descriptor based on multiscale centroid contour distance)—which is a frequency domain descriptor based on the CCD (centroid contour distance) method, multiscale description, and Fourier transform. The principle of FMSCCD is simple, and the computational cost is very low. What is commendable is that its discriminability is still strong, and its compatibility with other features is also great. Experiments on three databases demonstrate its strong discriminability and operational efficiency.
Privacy Regulation Aware Process Mapping in Geo-Distributed Cloud Data Centers

Recently, various applications including data analytics and machine learning have been developed for geo-distributed cloud data centers. For those applications, the ways of mapping parallel processes to physical nodes (i.e., “process mapping”) could significantly impact the performance of the applications because of non-uniform communication cost in geo-distributed environments. What's more, the different data privacy requirements in geo-distributed data centers pose additional constraints on process mapping solutions. While process mapping has been widely studied in grid/cluster environments, few of the existing studies have considered the problem in geo-distributed cloud environment, which is a challenging task due to the multi-level data privacy constraints, heterogeneous network performance and process failures. In this paper, we introduce the special privacy requirements in geo-distributed data centers and formulate the geo-distributed process mapping problem as an optimization problem with multiple constraints. We develop a new method to efficiently find good process mapping solutions to the problem. Experimental results on real clouds (including Amazon EC2 and Windows Azure) and simulations demonstrate that our proposed approach can achieve significant performance improvement compared to the state-of-the-art algorithms.
3D Behavior Recognition Based on Multi-Modal Deep Space-Time Learning

This paper proposes a dual-stream 3D space-time convolutional neural network action recognition framework. The original depth map sequence data is set as the input in order to study the global space-time characteristics of each action category. The high correlation within the human action itself is considered in the time domain, and then the deep motion map sequence is introduced as the input to another stream of the 3D space-time convolutional network. Furthermore, the corresponding 3D skeleton sequence data is set as the third input of the whole recognition framework. Although the skeleton sequence data has the advantage of including 3D information, it is also confronted with the problems of the existence of rate change, temporal mismatch and noise. Thus, specially designed space-time features are applied to cope with these problems. The proposed methods allow the whole recognition system to fully exploit and utilize the discriminatory space-time features from different perspectives, and ultimately improve the classification accuracy of the system. Experimental results on different public 3D data sets illustrate the effectiveness of the proposed method.
Label-removed generative adversarial networks incorporating with K-Means

Generative Adversarial Networks (GANs) have achieved great success in generating realistic images. Most of these are conditional models, although acquisition of class labels is expensive and time-consuming in practice. To reduce the dependence on labeled data, we propose an un-conditional generative adversarial model, called K-Means-GAN (KM-GAN), which incorporates the idea of updating centers in K-Means into GANs. Specifically, we redesign the framework of GANs by applying K-Means on the features extracted from the discriminator. With obtained labels from K-Means, we propose new objective functions from the perspective of deep metric learning (DML). Distinct from previous works, the discriminator is treated as a feature extractor rather than a classifier in KM-GAN. Meanwhile, the utilization of K-Means makes features of the discriminator more representative. Experiments are conducted on various datasets, such as MNIST, Fashion-10, CIFAR-10 and CelebA, and show that the quality of samples generated by KM-GAN is comparable to some conditional generative adversarial models.
HI2Rec: Exploring Knowledge in Heterogeneous Information for Movie Recommendation

Online movies' recommender systems aim to address the information explosion of movies and make the personalized recommendation for users. Recently, knowledge graphs have been proven to be highly effective to recommender systems, because they are able to fuse various recommendation models and can handle the issues of data sparsity and cold start to improve recommendation performance. However, less consideration is given to the information about the user's properties than the item's properties in the existing knowledge graph recommendation methods, which leads to some limitations in the recommendation results. In this paper, we propose HI2Rec, which integrates multiple information to learn the user's and item's vector representations for top-N recommendation to address the above-mentioned issues. We extract the movie-related information from the Linked Open Data and then leverage the knowledge representation learning approach to embed this information as well as real-world datasets' information of recommender systems to a unified vector space. These vector representations are further calculated to generate a preliminary recommendation list. Finally, we utilize a collaborative filter approach to generate a precision recommendation list. The experimental results on the real-world datasets demonstrate that HI2Rec gives substantial performance improvements against the state-of-the-art recommendation models.
Dynamics of quantum discord of two coupled spin-1/2’s subjected to time-dependent magnetic fields

We describe the dynamics of quantum discord of two interacting spin-1/2’s subjected to controllable time-dependent magnetic fields. The exact time evolution of discord is given for various input mixed states consisting of classical mixtures of two Bell states. The quantum discord manifests a complex oscillatory behaviour in time and is compared with that of quantum entanglement, measured by concurrence. The interplay of the action of the time-dependent magnetic fields and the spin-coupling mechanism in the occurrence and evolution of quantum correlations is examined in detail.
Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method

Accurate probabilistic forecasts of renewable generation are drivers for operational and management excellence in modern power systems and for the sustainable integration of green energy. The combination of forecasts provided by different individual models may allow increasing the accuracy of predictions; however, in contrast to point forecast combination, for which the simple weighted averaging is often a plausible solution, combining probabilistic forecasts is a much more challenging task. This paper aims at developing a new ensemble method for photovoltaic (PV) power forecasting, which combines the outcomes of three underlying probabilistic models (quantile k-nearest neighbors, quantile regression forests, and quantile regression) through a weighted quantile combination. Due to the challenges in combining probabilistic forecasts, the paper presents different combination strategies; the competing strategies are based on unconstrained, constrained, and regularized optimization problems for estimating the weights. The competing strategies are compared to individual forecasts and to several benchmarks, using the data published during the Global Energy Forecasting Competition 2014. Numerical experiments were run in MATLAB and R environments; the results suggest that unconstrained and Least Absolute Shrinkage and Selection Operator (LASSO)-regularized strategies exhibit the best performances for the datasets under study, outperforming the best competitors by 2.5 to 9% of the Pinball Score.
Multimodal Fusion Object Detection System for Autonomous Vehicles

In order for autonomous vehicles to safely navigate the road ways, accurate object detection must take place before safe path planning can occur. Currently, general purpose object detection convolutional neural network (CNN) models have the highest detection accuracies of any method. However, there is a gap in the proposed detection frameworks. Specifically, those that provide high detection accuracy necessary for deployment but do not perform inference in realtime, and those that perform inference in realtime but detection accuracy is low. We propose multimodel fusion detection system (MFDS), a sensor fusion system that combines the speed of a fast image detection CNN model along with the accuracy of light detection and range (LiDAR) point cloud data through a decision tree approach. The primary objective is to bridge the tradeoff between performance and accuracy. The motivation for MFDS is to reduce the computational complexity associated with using a CNN model to extract features from an image. To improve efficiency, MFDS extracts complimentary features from the LiDAR point cloud in order to obtain comparable detection accuracy. MFDS is novel by not only using the image detections to aid three-dimensional (3D) LiDAR detection but also using the LiDAR data to jointly bolster the image detections and provide 3D detections. MFDS achieves 3.7% higher accuracy than the base CNN detection model and is able to operate at 10 Hz. Additionally, the memory requirement for MFDS is small enough to fit on the Nvidia Tx1 when deployed on an embedded device.
Geochemical mapping and background concentrations of iron and potentially toxic elements in active stream sediments from Carajás, Brazil – implication for risk assessment

The Companhia de Pesquisa de Recursos Minerais (CPRM - Geological Survey of Brazil) has conducted geochemical surveys in the south-central region of the Itacaiúnas River watershed (IRW) in the Carajás region. The surveyed area covers 9,650 km2 and the data obtained by CPRM is the basis for the present study. A total of 833 active stream sediment samples, including 144 duplicates were collected. The <0.177 mm fraction of all samples was digested by aqua regia and 53 elements were analyzed by inductively coupled plasma atomic emission spectroscopy (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS). From the results made available by CPRM, 14 elements, including Fe and some potentially toxic elements (PTEs, namely Ba, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Sn, V, and Zn) are used in our study to define the geochemical signature of stream sediments in the area. A multivariate statistics was employed to evaluate major associations among elements and GIS techniques to derive interpolated geochemical distribution maps. Geochemical background values were calculated by multiple statistical methods, and based on that a preliminary environmental risk assessment was undertaken. Strong evidence of natural control in the spatial distribution of studied elements were observed. Large differences in the spatial distribution of many elements are observed between the central and southern region of the IRW and are related to contrasts in local geological settings. Background values were established in three different sets of geochemical sample locations, one for the whole study area, a second and third for the Central and Southern regions of the IRW, respectively. These background values were compared to existing stream sediment guideline values for (eco)toxicological effects of the same elements. Each applied statistical method to derive background values yielded different results. This difference is mainly influenced by the central criterion of the statistical method (median, mean, 75th, 95th, 98th percentiles). The Median ±2*(Median Absolute Deviation) furnished the most consistent and realistic results. Six environmental indicators were calculated to evaluate potential areas of risk. The results indicated that active stream sediments of the study area are generally uncontaminated. Occasionally local natural contamination sources were identified in the area of the recently created Ferruginous Fields National Park (FFNP). Moreover, there is no a clear impact of the changes in land use and land cover, as well as mining activities (e.g. Sossego copper mine), on the geochemistry of stream sediments. At the sampling scale, the variation in the background concentrations of all investigated elements in the stream sediment samples is apparently much larger than any anthropogenic impact.
An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models

Abstract Objective We aim to evaluate the effectiveness of advanced deep learning models (eg, capsule network [CapNet], adversarial training [ADV]) for single-domain and multidomain relation extraction from electronic health record (EHR) notes. Materials and Methods We built multiple deep learning models with increased complexity, namely a multilayer perceptron (MLP) model and a CapNet model for single-domain relation extraction and fully shared (FS), shared-private (SP), and adversarial training (ADV) modes for multidomain relation extraction. Our models were evaluated in 2 ways: first, we compared our models using our expert-annotated cancer (the MADE1.0 corpus) and cardio corpora; second, we compared our models with the systems in the MADE1.0 and i2b2 challenges. Results Multidomain models outperform single-domain models by 0.7%-1.4% in F1 (t test P &amp;lt; .05), but the results of FS, SP, and ADV modes are mixed. Our results show that the MLP model generally outperforms the CapNet model by 0.1%-1.0% in F1. In the comparisons with other systems, the CapNet model achieves the state-of-the-art result (87.2% in F1) in the cancer corpus and the MLP model generally outperforms MedEx in the cancer, cardiovascular diseases, and i2b2 corpora. Conclusions Our MLP or CapNet model generally outperforms other state-of-the-art systems in medication and adverse drug event relation extraction. Multidomain models perform better than single-domain models. However, neither the SP nor the ADV mode can always outperform the FS mode significantly. Moreover, the CapNet model is not superior to the MLP model for our corpora.
Robust Mixture Modeling Based on Two-Piece Scale Mixtures of Normal Family

In this paper, we examine the finite mixture (FM) model with a flexible class of two-piece distributions based on the scale mixtures of normal (TP-SMN) family components. This family allows the development of a robust estimation of FM models. The TP-SMN is a rich class of distributions that covers symmetric/asymmetric and light/heavy tailed distributions. It represents an alternative family to the well-known scale mixtures of the skew normal (SMSN) family studied by Branco and Dey (2001). Also, the TP-SMN covers the SMN (normal, t, slash, and contaminated normal distributions) as the symmetric members and two-piece versions of them as asymmetric members. A key feature of this study is using a suitable hierarchical representation of the family to obtain maximum likelihood estimates of model parameters via an EM-type algorithm. The performances of the proposed robust model are demonstrated using simulated and real data, and then compared to other finite mixture of SMSN models.
Domain-independent severely noisy image segmentation via adaptive wavelet shrinkage using particle swarm optimization and fuzzy C-means

Noisy image segmentation is a hot topic in natural, medical, and remote sensing image processing. It is among the non-trivial problems of computer vision having to address denoising and segmentation at the same time. Fuzzy C-means (FCM) is a clustering algorithm that has been shown to be effective at dealing with both segmentation-oriented denoising and segmentation at the same time. Moreover, with a high level of noise and other imaging artifacts, FCM loses its ability to perform image segmentation effectively. This paper introduces a Particle Swarm Optimization (PSO)-based feature enhancement approach in the wavelet domain for noisy image segmentation. This approach applies adaptive wavelet shrinkage using FCM clustering performance as an evaluation mechanism and also as the segmentation algorithm. The PSO-based process helps to enhance intensity features for clustering-based denoising, and also provides adaptivity for the system that performs well on a range of real, synthetic, and simulated noisy images with different noise levels and range/spatial properties. Furthermore, the algorithm applies edge enhancement based on Canny edge detector in order to further improve accuracy. Experiments are presented using three different datasets each degraded with different types of common noise. The presented algorithms show effective and consistent performance over a range of severe noise levels without the need for any parameter tuning.