Reviewerly/exHarmony huggingface dataset:
Single-Image Deraining via Recurrent Residual Multiscale Networks

Existing deraining approaches represent rain streaks with different rain layers and then separate the layers from the background image. However, because of the complexity of real-world rain, such as various densities, shapes, and directions of rain streaks, it is very difficult to decompose a rain image into clean background and rain layers. In this article, we develop a novel single-image deraining method based on residual multiscale pyramid to mitigate the difficulty of rain image decomposition. To be specific, we progressively remove rain streaks in a coarse-to-fine fashion, where heavy rain is first removed in coarse-resolution levels and then light rain is eliminated in fine-resolution levels. Furthermore, based on the observation that residuals between a restored image and its corresponding rain image give critical clues of rain streaks, we regard the residuals as an attention map to remove rains in the consecutive finer level image. To achieve a powerful yet compact deraining framework, we construct our network by recurrent layers and remove rain with the same network in different pyramid levels. In addition, we design a multiscale kernel selection network (MSKSN) to facilitate our single network to remove rain streaks at different levels. In this manner, we reduce 81% of the model parameters without decreasing deraining performance compared with our prior work. Extensive experimental results on widely used benchmarks show that our approach achieves superior deraining performance compared with the state of the art.
Smooth dendrite morphological neurons

A typical feature of hyperbox-based dendrite morphological neurons (DMN) is the generation of sharp and rough decision boundaries that inaccurately track the distribution shape of classes of patterns. This feature is because the minimum and maximum activation functions force the decision boundaries to match the faces of the hyperboxes. To improve the DMN response, we introduce a dendritic model that uses smooth maximum and minimum functions to soften the decision boundaries. The classification performance assessment is conducted on nine synthetic and 28 real-world datasets. Based on the experimental results, we demonstrate that the smooth activation functions improve the generalization capacity of DMN. The proposed approach is competitive with four machine learning techniques, namely, Multilayer Perceptron, Radial Basis Function Network, Support Vector Machine, and Nearest Neighbor algorithm. Besides, the computational complexity of DMN training is lower than MLP and SVM classifiers.
Deep Domain Adaptation Based Multi-Spectral Salient Object Detection

Salient Object Detection (SOD) plays an important role in many image-related multimedia applications. Although there are many existing research works about the salient object detection in traditional RGB (visible-light spectrum) images, there are still many complex situations that regular RGB images cannot provide enough cues for the accurate SOD, such as the shadow effect, similar appearance between background and foreground, strong or insufficient illumination, etc. Because of the success of near-infrared spectrum in many computer vision tasks, we explore the multi-spectral SOD in the synchronized RGB images and near-infrared (NIR) images for the both simple and complex situations. We assume that the RGB SOD in the existing RGB image datasets could provide references for the multi-spectral SOD problem. In this paper, we mainly model this research problem as a deep learning based domain adaptation from the traditional RGB image data (source domain) to the multi-spectral data (target domain), and an adversarial deep domain adaptation model is proposed. We first collect and will publicize a large multi-spectral dataset, RGBN-SOD dataset, including 780 synchronized RGB and NIR image pairs for the multi-spectral SOD problem in the simple and complex situations. Intensive experimental results show the effectiveness and accuracy of the proposed deep domain adaptation for the multi-spectral SOD. Besides, due to the absence of research on the field of multi-spectral co-saliency detection, we also collect 200 synchronized RGB and NIR image pairs in addition to explore the multi-spectral co-saliency detection.
Mixed Noise Removal Using Adaptive Median Based Non-Local Rank Minimization

In this paper, we present an innovative mechanism for image restoration problems in which the image is corrupted by a mixture of additive white Gaussian noise (AWGN) and impulse noise (IN). Mixed noise removal is much more challenging problem in contrast to the problems where either only one type of noise model (either Gaussian or impulse) is involved. Several well-known and efficient algorithms exist to effectively remove either Gaussian noise or Impulse noise, independently. However, in practice, noise may occur as a mixture of such noise models. Thus, the existing techniques devised to handle individual types of noise may not perform well. Moreover, the complexity of the problem hinges on the fact that the removal of either type of noise from the given image affects the noise statistics in the residual image. Therefore, a rigorous mechanism is required which not only infers altered noise statistics but also removes the residual noise in an effective manner. In this regard, an innovative approach is introduced to restore the underlying image in three key steps. Firstly, the intensity values, affected by impulsive noise, are identified by analyzing noise statistics with the help of adaptive median filtering. The identified intensity values are then aggregated by exploiting nonlocal data redundancy prior. Thus the first step enables the remaining noise to follow the zero mean Gaussian distribution in the median filtered image. Secondly, we estimate Gaussian noise in the resulting image, which acts as a key parameter in the subsequent singular value thresholding process for rank minimization. Finally, a reduced rank optimization applied to the pre-processed image obtained in the first step. The experimental results indicate that the proposed AMNLRA (Adaptive Median based Non-local Low Rank Approximation) approach can effectively tackle mixed noise complexity as compared to numerous state of the art algorithms.
Automatic Modulation Classification for Short Burst Underwater Acoustic Communication Signals Based on Hybrid Neural Networks

Automatic modulation classification (AMC) is challenging for short burst underwater acoustic (UWA) communication signals.Difficulties include but are not limited to the poor UWA channels, impulsive noise, and data scarcity.To address these problems, a method based on hybrid neural networks (HNNs) is proposed in this paper.First, an impulsive noise preprocessor is adopted to mitigate the impulse in the received signals.Subsequently, an HNN consisting of an attention aided convolutional neural network (Att-CNN) and a sparse auto-encoder is built to extract features from the temporal waveforms and square spectra of the preprocessed signals after burst detection.Finally, a late fusion is made to combine the prediction results of the two sub-networks.To overcome the variable signal duration relative to the fixed input size of the Att-CNN, a data-reusing approach is proposed to perform dimension preprocessing on the waveforms.Moreover, a transfer learning strategy is introduced to resolve the issue of insufficient training data from the testing channel.The results of simulation experiments and practical signal tests both demonstrate that the proposed method is robust against UWA channels and ambient noise.Our approach significantly outperforms existing deep learning-based methods in dealing with short and weak signal bursts, while requiring less training data from the testing channel.
Image Captioning Using Deep Convolutional Neural Networks (CNNs)

Abstract Earth is challenging to label satellite image clips with atmospheric conditions and various classes of land cover and land use. We proposed an algorithms to help the global community for a better understanding that where, how, and why deforestation take place all over the world. Upcoming development in satellite imaging technology have set grow to new opportunities for more precise investigation of both broad and minute changes occurring on Earth, including deforestation. Since 40 years, almost a fifth of the Amazon rain forest has been cut down. To estimate and analysis the forest this application is developed. Satellite images are trained on deep convolutional neural networks (CNNs) to learn image features and used multiple classification frameworks including gate recurrent unit label captioning and sparse_cross_entropy to predict multiclass, multi-label images. By fine-tuning an architecture consisting of the encoder of pre-trained VGG-19 parameters trained on ImageNet data together with the GRU decoder.
More on Pipelined Dynamic Scheduling of Big Data Streams

An important as well as challenging task in modern applications is the management and processing with very short delays of large data volumes. It is quite often, that the capabilities of individual machines are exceeded when trying to manage such large data volumes. In this regard, it is important to develop efficient task scheduling algorithms, which reduce the stream processing costs. What makes the situation more difficult is the fact that the applications as well as the processing systems are prone to changes during runtime: processing nodes may be down, temporarily or permanently, more resources may be needed by an application, and so on. Therefore, it is necessary to develop dynamic schedulers, which can effectively deal with these changes during runtime. In this work, we provide a fast and fair task migration policy while maintaining load balancing and low latency times. The experimental results have shown that our scheme offers better load balancing and reduces the overall latency compared to the state of the art strategies, due to the stepwise communication and the pipeline based processing it employs.
Parallel versus cascaded logistic regression trained single-hidden feedforward neural network for medical data

An important step towards a better healthcare system is fast and accurate diagnosis. In the last decade, the application of intelligent systems in healthcare has led to impressive results. The goal of this paper is to extend the LogSLFN (single-hidden layer feedforward neural network trained using logistic regression) algorithm, which has been deployed successfully in the past for the case of a two-class decision problem, to the case of multiple classes. We have considered and statistically analyzed two approaches: a parallel LogSLFN, and a cascaded LogSLFN. According to the universal approximation theorem, a single-hidden layer feedforward neural network has the ability to approximate arbitrarily closely continuous functions of several real variables under certain reasonable assumptions. Essentially, a single hidden layer containing a finite fixed number of neurons is sufficient to provide an arbitrarily well approximation to a given training set of inputs and a desired target output represented by a continuous function. Parallel LogSLFN and cascaded LogSLFN are two novel approaches that can be applied to multiple-class decision problems. Both methods are extensions of the LogSLFN, which uses logistic regression to compute the weights between the input and hidden layer of a single-hidden layer feedforward network. No error correction is needed, the weights between the hidden and the output layer being computed using the Moore-Penrose pseudoinverse matrix. The proposed models have been tested on two medical datasets regarding cancer diagnosis and liver fibrosis staging. Experimental results and the subsequent statistical analysis have proved the robustness of the proposed models with other machine learning techniques reported in literature. The experimental results showed that the Parallel approach surpasses the Cascaded one. Still, both models are competitive to the other state-of-the-art techniques. The LogSFLN algorithm can be successfully extended to multiple-class decision problems. By embedding knowledge extracted from the data into the architecture, we obtained a raise by 20% in accuracy when applied on the liver fibrosis dataset.
Unsupervised Attributed Network Embedding via Cross Fusion

Attributed network embedding aims to learn low dimensional node representations by combining both the network's topological structure and node attributes. Most of the existing methods either propagate the attributes over the network structure or learn the node representations by an encoder-decoder framework. However, propagation based methods tend to prefer network structure to node attributes, whereas encoder-decoder methods tend to ignore the longer connections beyond the immediate neighbors. In order to address these limitations while enjoying the best of the two worlds, we design cross fusion layers for unsupervised attributed network embedding. Specifically, we first construct two separate views to handle network structure and node attributes, and then design cross fusion layers to allow flexible information exchange and integration between the two views. The key design goals of the cross fusion layers are three-fold: 1) allowing critical information to be propagated along the network structure, 2) encoding the heterogeneity in the local neighborhood of each node during propagation, and 3) incorporating an additional node attribute channel so that the attribute information will not be overshadowed by the structure view. Extensive experiments on three datasets and three downstream tasks demonstrate the effectiveness of the proposed method.
Noise robust in-domain children speech enhancement for automatic Punjabi recognition system under mismatched conditions

Success of any commercial Automatic Speech Recognition (ASR) system depends upon availability of its training data. Although, it's performance gets degraded due to absence of enough signal processing characteristics in less resource language corpora. Development of Punjabi Children speech system is one such challenge where zero resource conditions and variabilities in children speech occurs due to speaking speed and vocal tract length than that of adult speech. In this paper, efforts have been made to build Punjabi Children ASR system under mismatched conditions using noise robust approaches like Mel Frequency Cepstral Coefficient (MFCC) or Gammatone Frequency Cepstral Coefficient (GFCC). Consequently, acoustic and phonetic variations among adult and children speech are handled using gender based in-domain training data augmentation and later acoustic variability among speakers in training and testing sets are normalised using Vocal Tract Length Normalization (VTLN). We demonstrate that inclusion of pitch features with test normalized children dataset has significantly enhanced system performance over different environment conditions i.e clean or noisy. The experimental results show a relative improvement of 30.94% using adult female voice pooled with limited children speech over adult male corpus on noise based training data augmentation respectively.
Human Friendly Smart Trolley with Automatic Billing System

Shopping and buying is an integral part of our daily lives. Big mega markets have a wide variety of items and different stores can have different deliveries of goods. It is difficult for many consumers to stand in the long queue for the billing of goods purchased. This causes a wastage of money and a poor bill for the wrong customer. Trolleys are used in supermarkets or grocery stores to make shopping simpler. However, it is difficult for customers to control the trolley while shopping. An automated customer following trolley was implemented which calculates the total sum of grocery items carried in the trolley by the customers. This reduces the customer's effort to pull the trolley and keep the line for the payment of the grocery products. The customer who has a specific tag and a web camera installed in front of the trolley will recognize the tag and move the trolley to the customer. Using the RFID tag and the Raspberry Pi receiver, the item bill inserted has been obtained in the trolley. This results in an ideal solution to all these problems.
Transfer Learning Approach for the Classification of Conidial Fungi (Genus Aspergillus) Thru Pre-trained Deep Learning Models

The Aspergillus genus is deemed relevant for distinction and classification in the field of food, agriculture and medicine. As there are harmful and useful ones, it adds to the necessity of correct classification. Categorization of this conidial fungi is usually done through manual microscopical procedures which apparently has a degree of subjectiveness. In order to classify Aspergillus samples faster and more accurately, technology, specifically image processing and machine learning are incorporated in this study. Pre-trained deep learning models are employed in classifying 9 kinds of Aspergillus. The methodology is generally comprised of preprocessing, deep-learning (training) and performance evaluation. Performance evaluation pertains to the validation accuracy and running times of the system after training through visual display of graphs and tabulation of acquired data. This study achieved a 93.3333% testing accuracy proving that the transferred knowledge is accurate, compatible and reliable.
Industrial Augmented Reality: Concepts and User Interface Designs for Augmented Reality Maintenance Worker Support Systems

Maintenance departments of producing companies in most industrial countries are facing challenges originating from an aging workforce, increasing product variety, and the pressure to increase productivity. We present the concepts and the user interface (UI) designs for two Augmented Reality (AR) applications, which help to tackle these issues. An AR Guidance System will allow new and unexperienced staff to perform medium to highly complex maintenance tasks, which they currently incapable to. The AR Remote Service System enables technicians at the machine to establish a voice/video stream with an internal or external expert. The video stream can be augmented with 3D models and drawings so that problems can be solved remotely and more efficiently. A qualitative assessment with maintenance managers and technicians from three producing companies rated the AR application concept as beneficial and the UI designs as very usable.
Monocular Depth Estimation with Joint Attention Feature Distillation and Wavelet-Based Loss Function

Depth estimation is a crucial component in many 3D vision applications. Monocular depth estimation is gaining increasing interest due to flexible use and extremely low system requirements, but inherently ill-posed and ambiguous characteristics still cause unsatisfactory estimation results. This paper proposes a new deep convolutional neural network for monocular depth estimation. The network applies joint attention feature distillation and wavelet-based loss function to recover the depth information of a scene. Two improvements were achieved, compared with previous methods. First, we combined feature distillation and joint attention mechanisms to boost feature modulation discrimination. The network extracts hierarchical features using a progressive feature distillation and refinement strategy and aggregates features using a joint attention operation. Second, we adopted a wavelet-based loss function for network training, which improves loss function effectiveness by obtaining more structural details. The experimental results on challenging indoor and outdoor benchmark datasets verified the proposed method’s superiority compared with current state-of-the-art methods.
Data-First Visualization Design Studies

We introduce the notion of a data-first design study which is triggered by the acquisition of real-world data instead of specific stakeholder analysis questions. We propose an adaptation of the design study methodology framework to provide practical guidance and to aid transferability to other data-first design processes. We discuss opportunities and risks by reflecting on two of our own data-first design studies. We review 64 previous design studies and identify 16 of them as edge cases with characteristics that may indicate a data-first design process in action.
Genetic Algorithm-based Dark Channel Prior Parameters Selection for Single Underwater Image Dehazing

Dehazing through Dark Channel Prior (DCP), originally developed for land-based images, has translated its potential for improving the quality of underwater images. However, the DCP default parameters, which are just adapted from land-based applications, may not be applicable for underwater images. Such constraint limits the capability of this restoration algorithm to improve the quality of an underwater image; the values of these parameters must be searched for each underwater image. A proposed approach on the parameter values assignment problem is to conduct an optimized search based on Genetic Algorithm. The presentation of this proposed approach focuses on the Genetic Algorithm processes: chromosome encoding, fitness function development, and selection, mutation, and crossover, to perform an effective search of the best solution out of a pool of possible solutions. Qualitative and quantitative evaluations show that utilization of optimized combination of DCP parameters, achieves images of higher quality in comparison to the utilization of established default DCP parameters.
Optimal Randomness for Stochastic Configuration Network (SCN) with Heavy-Tailed Distributions

Stochastic Configuration Network (SCN) has a powerful capability for regression and classification analysis. Traditionally, it is quite challenging to correctly determine an appropriate architecture for a neural network so that the trained model can achieve excellent performance for both learning and generalization. Compared with the known randomized learning algorithms for single hidden layer feed-forward neural networks, such as Randomized Radial Basis Function (RBF) Networks and Random Vector Functional-link (RVFL), the SCN randomly assigns the input weights and biases of the hidden nodes in a supervisory mechanism. Since the parameters in the hidden layers are randomly generated in uniform distribution, hypothetically, there is optimal randomness. Heavy-tailed distribution has shown optimal randomness in an unknown environment for finding some targets. Therefore, in this research, the authors used heavy-tailed distributions to randomly initialize weights and biases to see if the new SCN models can achieve better performance than the original SCN. Heavy-tailed distributions, such as Lévy distribution, Cauchy distribution, and Weibull distribution, have been used. Since some mixed distributions show heavy-tailed properties, the mixed Gaussian and Laplace distributions were also studied in this research work. Experimental results showed improved performance for SCN with heavy-tailed distributions. For the regression model, SCN-Lévy, SCN-Mixture, SCN-Cauchy, and SCN-Weibull used less hidden nodes to achieve similar performance with SCN. For the classification model, SCN-Mixture, SCN-Lévy, and SCN-Cauchy have higher test accuracy of 91.5%, 91.7% and 92.4%, respectively. Both are higher than the test accuracy of the original SCN.