Reviewerly/exHarmony huggingface dataset:
A customized nonlocal restoration schemes with adaptive strength of smoothening for magnetic resonance images

Nonlocal Means (NLM) filter is a nonlinear filter exclusively suitable for images comprising redundant patterns at spatially distinct locations, like Magnetic Resonance (MR) images. However, its performance heavily depends on the selection of one of its arbitrary operational parameters termed as smoothening strength (ξ). Improper selection of ξ may lead to over-blurring or partial denoising. An image adaptive algorithm, to determine the optimum value of ξ, meant for customizing the NLM for MR images is proposed. In the proposed scheme for initializing ξ, its value is increased in proportion to the standard deviation of noise σ^n, according to a linear model ξ=β σ^n. The optimum value of the arbitrary parameter β is identified through an iterative search, using a composite metric termed as Optimum Denoising Index (ODI), which objectively accounts for the noise suppression and edge preservation capability of the filter, as target function. Assuming the noise to be Gaussian distributed with zero mean, its standard deviation is computed using a ‘difference of Laplacian’ kernel. The techniques available in literature for the selection of β are (i) Coupe’s model with β derived out of minimum error sense, (ii) simple linear model ξ=βσ with empirically decided value β = 10 and global optimum of β modified locally in proportion to either (iii) local noise statistics or (iv) edge strength. ODI exhibited by the above techniques and the proposed iterative search are 0.3145 ± 0.0347, 0.2509 ± 0.0149, 0.1210 ± 0.0143, 0.1790 ± 0.0511 and 0.3678 ± 0.0022, respectively. Peak Signal to Noise Ratio (PSNR) between the denoised and the noise-free ground truth images exhibited by Kuwahara Filter, Total Variation (TV) Filter, Anisotropic Diffusion (AD) Filter, Bilateral Filter, SUSAN Filter and the proposed NLM Filter are 21.8739 ± 4.7310, 20.9596 ± 5.0518, 22.1553 ± 5.3369, 22.2142 ± 5.1275, 28.5628 ± 0.02 and 28.9967 ± 0.13, on 30 standard images. The iterative search is superior to methods available in literature with regard to the sharpness of the true morphological edges and smoothness of the homogenous regions in the denoised image. The proposed scheme of NLM is found to be superior to Kuwahara, TV, AD, Bilateral and SUSAN Filters.
A climatology of solar irradiance and its controls across the United States: Implications for solar panel orientation

The National Solar Radiation Database (NSRDB), which is a spatially-dense modeled dataset intended to accurately represent long-term statistics, was used to construct a 15-year climatology of variations in the direct-beam and diffuse radiation fields across the United States (US). Continental scale variations in the radiation field vary predominantly by latitude during the winter and longitude during the summer. Eight sub-regions, defined by similar climate forcing and geography, demonstrate that the largest seasonal and year-to-year variations in the diffuse-to-direct ratio are in the Northwest and Southwest, while the smallest are in the Northeast and Southeast. The year-to-year variability across the western US is attributed to large scale climate signals, such as the El Niño Southern Oscillation (ENSO). A preponderance of diffuse radiation during the summer in the Midwest, South-Central, Southeast and Northeast is demonstrated. It is suggested that harvesting procedures be tuned to accommodate this preponderance. Orienting a fraction of fixed panels to optimize the diffuse beam during the summer in these regions is suggested to increase energy yield. Adopting a seasonal and year-to-year approach in the harvesting procedure in the Northwest and Southwest that is tuned to large scale climate signals is likely to optimize harvest yield in those regions.
Mineral recognition of single particles in ore slurry samples by means of multispectral image processing

Mineral processing relies heavily on the differential behaviour of particles in slurries. In order to monitor the performance of separation units such as flotation cells or hydrocyclones it is helpful to develop on stream characterization tools providing information on particle size distribution and mineralogy. This paper describes an experimental set-up designed for the multispectral imaging of particles in water suspensions. The imaging system enables the acquisition of multispectral images. Images are processed to provide analysis on hundreds of particles in a few seconds. The prototype was used to acquire images of a series of pure mineral samples. Pure minerals powdered samples were suspended in water in order to mimic mineral slurries. The data are processed by means of machine learning methods. It is demonstrated that selected minerals can be discriminated from one another based on their optical properties. It is also shown that the optical properties measured with the system are somehow related to well-documented optical features such as specular and diffuse reflectance. The technology developed in this work sets the basis for on line monitoring of ore slurries with reasonably simple mineralogy.
Investigating the managerial practices' effect on Employee-Perceived Service Quality with the moderating role of supportive leadership behavior

The current study investigates the effects of employee empowerment (EE) and work environment on Employee-Perceived Service Quality (EPSQ) and investigate the role of supportive leadership behavior (SLB) as a moderator. Particularly, the study addressed the mediating effect of job satisfaction (JS) on the relationship between (EE), work environment with (EPSQ). A survey distributed on a sample of 208 frontline employees working in the five-star hotels in Jordan. A statistical analysis was conducted using structural equation modelling with Smart-Partial Least Squares (PLS). The results reveal that (EE), work environment, and (JS) significantly impact on Employee-Perceived Service Quality. Also, results reveal that (JS) has a mediator role in this relationship. Quite surprisingly, we found no significant direct relationships between supportive leadership and (EPSQ) as a moderator. The practical and research implications of these findings are discussed as well as the justification of the rejected hypothesis.
A Novel Simplified AES Algorithm for Lightweight Real-Time Applications: Testing and Discussion

Background: Lightweight cryptographic algorithms have been the focus of many researchers in the past few years. This has been inspired by the potential developments of lightweight constrained devices and their applications. These algorithms are intended to overcome the limitations of traditional cryptographic algorithms in terms of exaction time, complex computation and energy requirements. Methods: This paper proposes LAES, a lightweight and simplified cryptographic algorithm for constricted environments. It operates on GF(24), with a block size of 64 bits and a key size of 80-bit. While this simplified AES algorithm is impressive in terms of processing time and randomness levels. The fundamental architecture of LAES is expounded using mathematical proofs to compare and contrast it with a variant lightweight algorithm, PRESENT, in terms of efficiency and randomness level. Results: Three metrics were used for evaluating LAES according to the NIST cryptographic applications statistical test suite. The testing indicated competitive processing time and randomness level of LAES compared to PRESENT. Conclusion: The study demonstrates that LAES achieves comparable results to PRESENT in terms of randomness levels and generally outperform PRESENT in terms of processing time.
Open shell electronic state calculations on quantum computers: A quantum circuit for the preparation of configuration state functions based on Serber construction

Full configuration interaction (full-CI) calculations can be executed efficiently on quantum computers (QCs) by utilizing a quantum phase estimation algorithm (QPEA). In the QPEA-based full-CI on QCs, the preparation of the initial guess wave functions having large overlap with the full-CI root is crucial. Recently, we proposed a quantum circuit to prepare spin symmetry-adapted configuration state functions (CSFs) toward open shell electronic structure calculations of molecules on QCs. Here, we propose an improved quantum circuit, based on Serber construction of spin eigenfunctions, enabling us to prepare CSFs with much less and easier-to-implement quantum gates than the previously proposed one.
Improving classification with semi-supervised and fine-grained learning

In this paper, we propose a novel and efficient multi-stage approach, which combines both semi-supervised learning and fine-grained learning to improve the performance of classification model learned only from a few samples. The fine-grained category recognition process utilized in our method is dubbed as MSR. In this process, we cut images into multi-scaled parts to feed into the network to learn more fine-grained features. By assigning these image cuts with dynamic weights, we can reduce the negative impact of background information and thus achieve a more accurate prediction. Furthermore, we present the voted pseudo label (VPL) which is an efficient method of semi-supervised learning. In this approach, for unlabeled data, VPL picks up the classes with non-confused labels verified by the consensus prediction of different classification models. These two methods can be applied to most neural network models and training methods. Inspired from classifier-based adaptation, we also propose a mix deep CNN architecture (MixDCNN). Both the VPL and MSR are integrated with the MixDCNN. Comprehensive experiments demonstrate the effectiveness of VPL and MSR. Without bottles and jars, we achieve the state-of-the-art or even better performance in two fine-grained recognition tasks on the datasets of Stanford Dogs and CUB Birds, with the accuracy of 95.6% and 85.2%, respectively.
Multi-Gram CNN-Based Self-Attention Model for Relation Classification

Relation classification is a crucial ingredient in numerous information-extraction systems and has attracted a great deal of attention in recent years. Traditional approaches largely rely on feature engineering and suffer from the limitations of the domain adaption and the error propagation. To overcome the above-mentioned problems, many deep neural network-based methods have been proposed; however, these methods cannot effectively locate and utilize the relation trigger features. To locate the relation trigger features and make full use of them, we propose a novel multi-gram convolution neural network-based self-attention model with a recurrent neural network framework. The multi-gram conventional neural network attention model can learn the adaptive relational semantics of inputs based on the fact that a relation can be totally defined by the shortest dependency path between its two entities. With the learned relational semantics, we can obtain the corresponding importance distribution over input sentences and locate the relation trigger features. For effective information propagation and integration, we utilize a bidirectional gated recurrent unit to encode the high-level features during recurrent propagation. The experimental results on two benchmark datasets demonstrate that the proposed model outperforms most of the state-of-the-art models.
Method name suggestion with hierarchical attention networks

Method Rename has been a widely used refactoring operation that improves program comprehension and maintenance. Descriptive method names that summarize functionalities of source code can facilitate program comprehension. Much research has been done to suggest method names through source code summarization. However, unlike natural language, a code snippet consists of basic blocks organized by complicated structures. In this work, we observe a hierarchical structure --- tokens form basic blocks and basic blocks form a code snippet. Based on this observation, we exploit a hierarchical attention network to learn the representation of methods. Specifically, we apply two-level attention mechanism to learn the importance of each token in a basic block and that of a basic block in a method respectively. We evaluated our approach on 10 open source repositories and compared it against three state-of-the-art approaches. The results on these open-source data show the superiority of our hierarchical attention networks in terms of effectiveness.
A learning approach to image-based visual servoing with a bagging method of velocity calculations

Visual servoing allows accurate control of positioning and motion relative to a stationary or moving target using vision and is the subject of many studies. Most servoing gains for image-based visual servoing methods are selected heuristically or empirically so accuracy is affected. This study uses reinforcement learning to adaptively tune the proportional servoing gain over sequences of image based visual servoing, instead of using a constant gain. The system is used to control hovering and tracking for a stationary or slowly moving target. An image Jacobian matrix with four dimensions is constructed because a quad-rotor drone features under-actuated dynamics. The Moore-Penrose pseudo inverse method is usually used to calculate the inverse image Jacobian matrix, but this study uses a bagging approach to calculate the inverse kinematics. The desired velocity is obtained from time-varying image errors, which gives greater robustness. An adaptive method to calculate the servoing gain method is proposed, whereby the selection of an appropriate servo gain over time is regarded as a reinforcement learning problem. The proposed visual servoing control system is implemented and tested experimentally using a quad-rotor drone system. The experimental results demonstrate that the proposed method is more robust and converges faster than other methods.
Recovering 6D object pose from RGB indoor image based on two-stage detection network with multi-task loss

Object pose estimation from an RGB image has recently become a common problem owing to its widespread application. The advent of convolutional neural networks has impelled significant progress in object detection. However, most available methods do not involve a category-level pose estimation approach. This study presents an end-to-end 6D category-level pose estimation based on a two-stage bounding-box recognition backbone architecture. Our network directly outputs the 6D pose without requiring multiple stages or additional post-processing such as a Perspective-n-Point (PnP). The two-stage CNN architecture and our loss function render multi-task joint training effective and efficient. We improve the pose estimation accuracy by replacing fully connected layers with fully convolutional layers. Fully convolutional networks require fewer parameters and are less susceptible to overfitting. Moreover, we transform the pose estimation problem into classification and regression tasks using our network; these are called Pose-cls and Pose-reg, respectively. We also present qualitative and quantitative results on real data from the SUN RGB-D dataset. The experiments demonstrate the effectiveness of our algorithms compared to other state-of-the-art methods.
Object Detection Network Based on Feature Fusion and Attention Mechanism

In recent years, almost all of the current top-performing object detection networks use CNN (convolutional neural networks) features. State-of-the-art object detection networks depend on CNN features. In this work, we add feature fusion in the object detection network to obtain a better CNN feature, which incorporates well deep, but semantic, and shallow, but high-resolution, CNN features, thus improving the performance of a small object. Also, the attention mechanism was applied to our object detection network, AF R-CNN (attention mechanism and convolution feature fusion based object detection), to enhance the impact of significant features and weaken background interference. Our AF R-CNN is a single end to end network. We choose the pre-trained network, VGG-16, to extract CNN features. Our detection network is trained on the dataset, PASCAL VOC 2007 and 2012. Empirical evaluation of the PASCAL VOC 2007 dataset demonstrates the effectiveness and improvement of our approach. Our AF R-CNN achieves an object detection accuracy of 75.9% on PASCAL VOC 2007, six points higher than Faster R-CNN.
Digital Video Copy Detection Using Steganography Frame Based Fusion Techniques

An effective and exact technique for copying video location in a huge dataset is the utilization of video pictures. We have exactly picked the shading format description, a minimal and powerful casing-based description to make pictures which are additionally determined by vector quantization (VQ). We recommend a new nonmetric length measure to discover the likeness among the question and a dataset video picture and tentatively demonstrate its better execution over other length measures for exact copy identification. The effective look cannot be executed for high-dimensional information utilizing a nonmetric distance measure with accessible ordering systems. Consequently, we create a novel search algorithm in the view of precompiled distances and new dataset reduce systems yielding reduced recovery times. We perform different things with colossal dataset recordings. For singular questions with a normal span of 60 s (around half of the normal dataset video duration), the copy videos are recovered in 0.032 s, on Intel Xeon with CPU 2.33 GHz, with a high exactness of 98.5%.
Sentiment Analysis on a Set of Movie Reviews Using Deep Learning Techniques

Deep learning signifies feature selection in a neural network with numerous layers of nodes amidst input and output layers. The feature selection includes various preprocessing and processing stages, just as the human brain. Nowadays, deep learning models such as deep neural networks and convolution neural networks are being utilized for categorizing reactions, cross-lingual problems as well as product review analysis. Sentiment analysis distinguishes the judgment of a text which may be generalized into a vast depository of emotions. As understanding of the sentiment comes naturally for a human, the real challenge is to train a machine to be able to do that. The huge repository of information present in the social media today helps achieve real-time opinionated knowledge about any product. This content acts as a rich resource for all those who are concerned with general attitudes, opinions, or views. The chapter presented here consists of analyzing the sentiment of 50,000 IMDB movie reviews collected online. Two algorithms, namely, Google's Word2Vec and Doc2Vec have been applied for text classification of the reviews. Different types of clustering algorithms as well as classifiers have been used to make the predictions. The performance is hence evaluated based on the reviews collected.
A novel discrete particle swarm optimization algorithm for solving bayesian network structures learning problem

Bayesian network is an effective representation tool to describe the uncertainty of the knowledge in artificial intelligence. One important method to learning Bayesian network from data is to employ a search procedure to explore the space of networks and a scoring metric to evaluate each candidate structure. In this paper, a novel discrete particle swarm optimization algorithm has been designed to solve the problem of Bayesian network structures learning. The proposed algorithm not only maintains the search advantages of the classical particle swarm optimization but also matches the characteristics of Bayesian networks. Meanwhile, mutation and neighbor searching operators have been used to overcome the drawback of premature convergence and balance the exploration and exploitation abilities of the particle swarm optimization. The experimental results on benchmark networks illustrate the feasibility and effectiveness of the proposed algorithm, and the comparative experiments indicate that our algorithm is highly competitive compared to other algorithms.
A model-free Bayesian classifier

When using the naïve Bayesian classifier (NBC), it is necessary to assume that the attributes are independent. Although the relationship between the attributes can be analyzed by the Bayesian network (BN), the network structure and parameters need to be trained. Meanwhile, each continuous attribute is given a priori probability distribution for both the NBC and the BN. If the actual probability distribution of the samples is inconsistent with the priori probability distribution, the classification result is poor. In this paper, a model-free Bayesian classifier (MFBC) is proposed to cope with the drawbacks of the NBC and the BN. The information of the sample space and the joint probability density can be obtained based on the nearest neighbor (NN) strategy. The proposed MFBC is able to cope with discrete or continuous attributes even if there is no a priori probability distribution of the attributes. It is also not necessary to build a network structure to obtain a unitive calculation framework for the attributes. Moreover, the probability distribution for limited amounts of samples is approximated using the NN method. Then, the classification label of a sample can be predicted by the MFBC. The sensitivity analysis of the MFBC is obtained using the UCI data sets. Compared with some classical and ensemble classifiers, the analysis results verify the effectiveness and convergence performance of the MFBC.
Analysis of cause-effect inference by comparing regression errors

We address the problem of inferring the causal direction between two variables by comparing the least-squares errors of the predictions in both possible directions. Under the assumption of an independence between the function relating cause and effect, the conditional noise distribution, and the distribution of the cause, we show that the errors are smaller in causal direction if both variables are equally scaled and the causal relation is close to deterministic. Based on this, we provide an easily applicable algorithm that only requires a regression in both possible causal directions and a comparison of the errors. The performance of the algorithm is compared with various related causal inference methods in different artificial and real-world data sets.
Detection method of TFe content of iron ore based on visible-infrared spectroscopy and IPSO-TELM neural network

As the main material for industrial production, the TFe content of iron ore determines the grade and quality of iron ore. The existing methods for measuring the TFe content of iron ore either have large errors or take a long time. Therefore, this paper proposes a method for detecting TFe content of iron ore based on IPSO-TELM (two hidden layer extreme learning machine optimized by the improved particle swarm optimization) algorithm using visible-infrared spectroscopy. The IPSO (improved particle swarm optimization) is used to optimize the first hidden layer parameters and the number of the hidden layer nodes in the TELM network. We first obtained iron ore samples from the Anshan mining area. Then, measured the spectral data through the spectral analysis instrument and spectral features are analyzed by PCA (principal component analysis). Finally, we applied the IPSO-TELM algorithm to establish a detection model for TFe. Experiments shows that the IPSO-TELM model has higher detection accuracy and better generalization ability than the TELM and PSO-TELM models. Compared with traditional chemical analysis methods and instrumental analysis methods, this method has great advantages in economy, speed and accuracy.