Reviewerly/exHarmony huggingface dataset:
An Efficient SPV Power Forecasting using Hybrid Wavelet and Genetic Algorithm based LSTM Deep Learning Model

Solar photovoltaics (SPV) are widely favoured energy generation system that has seen its rapidly growing installed capacity in the power system structure from past few decades. This grows concern of operation and control due to its high stochastic nature and dependence on weather variables such as temperature, irradiance, humidity etc,. This makes SPV power forecasting necessary in order to manage and plan to get useful insights. This article proposes a hybrid wavelet and genetic algorithm (GA)-based long short term memory (LSTM) deep neural network model to forecast SPV output power of a 58 MW utility scale SPV plant installed in the Florida state. The data are obtained from publicly available NREL database with 5-min resolution. Temperature and relative humidity along with historical SPV output power has been used as input features to the neural network model. Discrete wavelet transform is applied in order to denoise the data and due to its inconstancy, which increases the data dimension and helps in improving forecasting accuracy. GA has been combined with LSTM to find the optimized window size and LSTM units. The proposed method is then compared with different benchmark methods such as persistent/naive, state vector regression (SVR) and long short term memory-deep neural network (LSTM-DNN) model structure. The results shows an improvement of accuracy in terms of performance metrics most commonly used in machine learning such as mean squared error, root mean squared error, mean absolute error and r-squared values.
Development of Instructional Videos for The Principles of 3D Computer Animation

Abstract Understanding the principles of animation is an important factor and determines the quality of animation that can be produced. However, explanation of the principles of animation in many book are still specifically to traditional animation, not 3D computer animation that mostly used today. Students have difficulty applying traditional animation principles to 3D computer animation principles. Therefore, the purpose of this research is to develop thirteen instructional videos for the topic of Principle of 3D Computer Animation and to determine the effectiveness of these videos on student understanding. Video development uses the Research and Development Four-D model (Define, Design, Develop, and Disseminate). Based on the results of product testing, these thirteen learning videos were declared “very good”. The paired t-test results showed that the product developed was effective in increasing students’ understanding of the Principle of 3D Computer Animation.
Real-time foreground object segmentation networks using long and short skip connections

Foreground object segmentation is an important task with various applications in outdoor surveillance and navigation. Most existing methods focus on accuracy and therefore, are computationally expensive and low in speed, making them difficult to use in actual applications. In this study, we aim to address the issue of accuracy and efficiency trade-off. In particular, in contrast with existing methods that use fine-tuning routine on heavyweight pretrained models and/or optimization techniques to enhance results, we propose a lightweight end-to-end network that can be trained from scratch effectively and efficiently. First, long and short skip connections are used among convolutional blocks and within the bottleneck block. By doing so, information flow within the networks is enhanced during the training stage, and thus, the use rate of parameters in the model is increased, allowing a more compact and efficient network design. Second, we use feature fusions based on element-wise summing before each up-sampling layer to reduce the size of the decoder, accelerate the up-sampling process, and stabilize training convergence. Our proposed method is tested rigorously. In particular, we achieved 1000 times higher speed compared with state-of-the-art methods on CD2014 and SBI2015 datasets with comparable accuracy.
Adaptation of Population Size in Sine Cosine Algorithm

Sine Cosine Algorithm (SCA) is a newly proposed competent population-based metaheuristic, which has gained a multi-disciplinary interest in solving optimization problems.Like other metaheuristics, the performance of SCA is sensitive to the settings of its parameters and one such parameter is Population Size (PS).There is no one population size that fits all; problem uniqueness requires a matching strategy of parameter selection and adaptation.However, in standard SCA and its variants, PS is treated as a usercontrolled parameter and no study has explored the effect of PS adaptation on SCA's performance.To fill this research gap, this study investigates and compares the impact of promising strategies for setting and controlling population size, from other metaheuristics, on the performance of the standard SCA and five of its variants in terms of fitness, run-time, and convergence characteristics.Finding the best PS setting for a metaheuristic is a challenging problem since it depends on both the nature of the algorithm used and the problem being solved.Leading PS adaptation techniques considered in this study are linear staircase reduction, iterative halving, reinitialization and incrementation, pulse wave, population diversity, and three parent crossover strategies.A classic set of 23 well-known benchmark functions has been utilized for a fixed number of evaluations to assess the impact of each PS adaptation strategy on the performance of SCA and its variants.Also, non-parametric Wilcoxon's rank sum test is performed to provide a comprehensive view of various PS adaptation strategies' performance with respect to each other in terms of fitness and run-time.Simulation results reveal that proper selection of PS adaptation strategy can further enhance the exploration and exploitation capabilities of SCA and its variants.
Fast Light Field Angular Resolution Enhancement Using Convolutional Neural Network

Current light field angular super-resolution algorithm based on deep learning has excessive computation cost and low operational efficiency, for sequentially up-sampling on each lenslet region of the light field image.In this paper, we propose a novel convolutional neural network to fastly enhance the angular resolution, via wholesale up-sampling lenslet regions.Firstly, the network simultaneously extracts the angular information of all lenslet regions on the input light field image.Then, from the extracted angular information, four feature maps are predicted.Especially, the angular resolution of each feature map is the same as that of the input light field image.Finally, to enhance the angular resolution, we integrate four feature maps into one image, by referring to angular information arrangement in lenslet regions.The experimental results verify the effectiveness of our proposed method.We only need 11.95s to enhance(actually double) the angular resolution of one light field image with 2562 × 3724 pixels, which surpasses 20 times faster than the state-of-the-art method.Meanwhile, our method also achieves average PSNR gains of 0.39 dB.
A novel structured sparse fully connected layer in convolutional neural networks

Abstract Convolutional Neural Networks (CNNs) are one of the factors supporting the rapid development of artificial intelligent techniques. However, as the ability of the network increases, the size of the network becomes larger. Thus far, several works related to reduction of the network size have been tackled. In many cases, these approaches produce an unstructured network which prevents efficient parallel computation. To avoid this problem, we propose a novel structured sparse fully connected layer (FCL) in the CNNs. The aim of our proposed approach is reduction of the number of network parameters in the FCLs which occupy a large part of network parameters. Unlike the general FCLs used in the popular CNNs such as VGG‐16, the proposed approach reduces the connection between the last convolutional layer and the first FCL. In addition, we show an implementation for the proposed sparse FCLs on the GPU using cuBLAS. As a result for ILSVRC‐2012 dataset, the proposed approach achieves a 21.3 times compression with 0.68% top‐1 accuracy and 0.31% top‐5 accuracy decreases for VGG‐16. The implementation of the proposed FCLs achieves speed‐up factor 14.97 and 16.67 for forward and backward propagation compared to that for the noncompressed FCLs, respectively.
Point Location and Active Learning: Learning Halfspaces Almost Optimally

Given a finite set X ⊂ R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> and a binary linear classifier c: R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> → {0,1}, how many queries of the form c(x) are required to learn the label of every point in X? Known as point location, this problem has inspired over 35 years of research in the pursuit of an optimal algorithm. Building on the prior work of Kane, Lovett, and Moran (ICALP 2018), we provide the first nearly optimal solution, a randomized linear decision tree of depth Õ(dlog(|X|)), improving on the previous best of Õ(d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> log(|X|)) from Ezra and Sharir (Discrete and Computational Geometry, 2019). As a corollary, we also provide the first nearly optimal algorithm for actively learning halfspaces in the membership query model. En route to these results, building on the work of Carlen, Lieb, and Loss (J. Geometric Analysis 2004), as well as Dvir, Saraf, and Wigderson (STOC 2014), we prove a novel characterization of Barthe's Theorem (Inventiones Mathematicae, 1998) of independent interest. In particular, we show that X may be transformed into approximate isotropic position if and only if there exists no k-dimensional subspace with more than a k/d-fraction of X, and provide a similar characterization for exact isotropic position. The below is an extended abstract. The full work can be found at https://arxiv.org/abs/2004.11380.
Face recognition technology based on CNN, XGBoost, model fusion and its application for safety management in power system

Abstract The safety maintenance of operating site is important for safety management in power system. With the development of artificial intelligence (AI) and popularization of monitoring camera, face recognition technology is widely utilized in operating site of power system. To improve the capability for safety management of operating site in power system, a face recognition model based on convolutional neural network (CNN), eXtreme gradient boosting (XGBoost) and model fusion is built. Firstly, pre-processed images are input into CNN to obtain the recognition probabilities of various face and the extracted face features. Secondly, the extracted features of CNN are input into XGBoost to obtain the recognition probabilities of various face recognized by XGBoost. Finally, above two groups of probabilities are weighted by model fusion technology to obtain the final recognition probabilities of various face, and the final face recognition results are output. Simulation results show that CNN has better capability of feature extraction, and the proposed face recognition model has the highest recognition accuracy among those advanced face recognition models. In addition, this paper describes the application based on the proposed face recognition model in non-working personnel recognition, trajectory tracking of operators, etc, so as to greatly improve the safety management of operating site in power system, which not only ensures safety, but also reduces unnecessary management expenses.
An Ensemble Neural Network for Scene Relighting with Light Classification

Illumination is a very important environmental condition. Objects in different illumination environments will present different light and shadow effects. Different kinds of illumination sources will cause different brightness and colors on the surface of the object. The conversion of illumination in two pictures is an interesting and challenging new task, which will be useful in the fields of photography and computer graphics. To solve this problem, we propose a novel solution with three stages: illumination classification, One-to-One Relighting, and Any-to-Any Relighting. Our solution can accurately classify the illumination condition of the input image and can change the direction of the illumination source from any direction to another. We evaluate our methods on VIDIT, a rendered dataset of artificial scenes. The proposed solution produces good results under different light conditions.
Is it Easier to Prove Theorems that are Guaranteed to be True?

Consider the following two fundamental open problems in complexity theory: ; Does a hard-on-average language in NP imply the existence of one-way functions? : Does a hard-on-average language in NP imply a hard-on-average problem in TFNP (i.e., the class of total NP search problem)? Our main result is that the answer to (at least) one of these questions is yes. Both one-way functions and problems in TFNP can be interpreted as promise-true distributional NP search problems-namely, distributional search problems where the sampler only samples true statements. As a direct corollary of the above result, we thus get that the existence of a hard-on-average distributional NP search problem implies a hard-on-average promise-true distributional NP search problem. In other words, It is no easier to find witnesses (a.k.a. proofs) for efficiently-sampled statements (theorems) that are guaranteed to be true. This result follows from a more general study of interactive puzzles-a generalization of average-case hardness in NP- and in particular, a novel round-collapse theorem for computationally-sound protocols, analogous to Babai-Moran's celebrated round-collapse theorem for information-theoretically sound protocols. As another consequence of this treatment, we show that the existence of O(1)-round public-coin non-trivial arguments (i.e., argument systems that are not proofs) imply the existence of a hard-on-average problem in NP/poly.
Noisy Low-Light Image Enhancement using Reflectance Similarity Prior

Images captured in low-light conditions, such as night vision, shadow or backlit, often suffer from low contrast and color distortion, which lose details and naturalness. In this paper, a noisy low-light image enhancement algorithm is presented using reflectance similarity prior. In order to preserve the hue and saturation, the proposed method is performed on the value channel of the HSV color space. First, we propose the reflectance similarity prior by exploring the characteristic of the reflectance component. Then, according to the proposed prior, the image is decomposed into reflectance and illumination components simultaneously based on the Retinex model. This step constrains the reflectance component from color, structure and texture. Finally, the illumination is adjusted by the Gamma correction. Compared with the existing state-of-the-art methods, the proposed method produces high-quality enhanced images, which achieve better subjective and objective assessments.
Robust Adaptive Filtering Based on Exponential Functional Link Network: Analysis and Application

The exponential functional link network (EFLN) has been recently investigated and applied to nonlinear filtering. This brief proposes an adaptive EFLN filtering algorithm based on a novel inverse square root (ISR) cost function, called the EFLN-ISR algorithm, whose learning capability is robust under impulsive interference. The steady-state performance of EFLN-ISR is rigorously derived and then confirmed by numerical simulations. Moreover, the validity of the proposed EFLN-ISR algorithm is justified by the actually experimental results with the application to hysteretic nonlinear system identification.
Improved Modeling of 3D Shapes with Multi-view Depth Maps

We present a simple yet effective general-purpose framework for modeling 3D shapes by leveraging recent advances in 2D image generation using CNNs. Using just a single depth image of the object, we can output a dense multi-view depth map representation of 3D objects. Our simple encoder-decoder framework, comprised of a novel identity encoder and class-conditional viewpoint generator, generates 3D consistent depth maps. Our experimental results demonstrate the two-fold advantage of our approach. First, we can directly borrow architectures that work well in the 2D image domain to 3D. Second, we can effectively generate high-resolution 3D shapes with low computational memory. Our quantitative evaluations show that our method is superior to existing depth map methods for reconstructing and synthesizing 3D objects and is competitive with other representations, such as point clouds, voxel grids, and implicit functions. Code and other material will be made available at http://multiview-shapes. umiacs.io.
3D Analytical Modeling and Iterative Solution for High Performance Computing Clusters

Mobile Cloud Computing enables the migration of services to the edge of Internet. Therefore, high performance computing clusters are widely deployed to improve computational capabilities of such environments. However, they are prone to failures and need analytical models to predict their behavior in order to deliver desired quality-of-service and quality-of-experience to mobile users. This article proposes a 3D analytical model and a problem-solving approach for sustainability evaluation of high-performance computing clusters. The proposed solution uses an iterative approach to obtain performance measurements to overcome the state space explosion problem. The availability modeling and evaluation of master and computing nodes are performed using a multi-repairman approach. The optimum number of repairmen is also obtained to get realistic results and reduce the overall cost. The proposed model is validated using discrete event simulation. The analytical approach is much faster and in good agreement with the simulations. The analysis focuses on mean queue length, throughput and mean response time outputs. The maximum differences between analytical and simulation results in the considered scenarios of up to a billion states are less than 1.149, 3.82, and 3.76 percent, respectively. These differences are well within the 5 percent of confidence interval of the simulation and the proposed model.
VRHAZE: The Simulation of Synthetic Haze Based on Visibility Range for Dehazing Method in Single Image

Outdoor images are typically degraded by light scattering and absorption from aerosols, such as dust, mist, and smoke in the atmosphere. Because of poor visibility, dimmed brightness, low contrast, and colour distortion, these phenomena affect the captured image. Therefore, it is a critical challenge to recover pictures taken in a haze condition, which is called image dehazing. The primary aim of image dehazing is to improve details on visibility, edge, and texture and retain the image structure and colours without data loss. There are no proven benchmarks for their assessment, despite the many algorithms suggested for single image dehazing. In previous publications, arbitrary comparisons were mostly focused on a small number of images, with different publications using different sets of images. This paper presents VRHAZE, a new dataset that includes eight image pairs of hazy and corresponding outdoor images that are haze-free (ground-truth). Most of the current hazy database presented in a single image simulated synthetic haze indicated complicated calculation of the depth map. Unlike most of the current dehazing databases, a synthetic haze, which is determined by the atmospheric scattering algorithm derived from the actual distance from the camera to the scene object, has simulated hazy images. In the separate range, the synthetic haze derivation referred from the meteorological range explicitly based on haze conditions. On a clear day as referred to as a low Air Pollutant Index, this experiment simulated synthetic haze in the Malaysian outdoor scene. The haze simulation illustrates how this VRHAZE approach can lead to better outcomes in the measurement of image quality than the current state-of-the-art dehazing method.
Swarm-based painting of an area cluttered with obstacles

This article studies the problem of painting of a rectangular region cluttered with horizontal obstacles, by a swarm of mobile robots. Initially, the robots are deployed randomly within the target area. From the initial configuration, the robots are assembled on the left boundary of the region. Upon assembling on the left boundary, the robots first explore the entire area collectively to make a virtual partition of it and then collectively paint the target region in a subsequent phase. The proposed algorithm assumes that robots work in look–compute–move model. The robots follow a completely distributed algorithm to paint the region. The robots are either synchronous or semi-synchronous. The outcome of the proposed algorithm is a complete painting of an area without any repetition and collision.
Salient object detection via a boundary-guided graph structure

Graph-based salient object detection methods have gained more and more attention recently. However, existing works fail to separate effectively salient object and background in some challenging scenes. Inspired by this observation, we propose an effective salient object detection method based on a novel boundary-guided graph structure. More specifically, the input image is firstly segmented into a series of superpixels. Then we integrate two prior cues to generate the coarse saliency map, a novel weighting mechanism is proposed to balance the proportion of two prior cues according to their performance. Secondly, we propose a novel boundary-guided graph structure to explore deeply the intrinsic relevance between superpixels. Based on the proposed graph structure, an iterative propagation mechanism is constructed to refine the coarse saliency map. Experimental results on four datasets show adequately the superiority of the proposed method than other state-of-the-art methods.
Interaction Styles in Context: Comparing Drag-and-Drop, Point-and-Touch, and Touch in a Mobile Spelling Game

The choice of appropriate interaction style for children’s application has been a divisive subject of debate. For example, drag-and-drop is often said to perform worse than point-and-click in educational applications – and vice versa. In this article, we argue for the need to choose the interaction style in context, considering a range of factors. We compare drag-and-drop, point-and-touch, and simple touch for selecting letters to form words in a spelling line as part of an educational spelling game. We evaluate the perceived workload, user experience, preference, and writing times of twenty-five children (8–11 years), eight of whom were dyslexic. We found that touch received better ratings and was ranked highest most often on all subscales compared to drag-and-drop and point-and-touch. Children needed less time using touch and 68% chose it as their favorite interaction style. We also found small advantages for drag-and-drop over point-and-touch, which runs counter to some recent recommendations. This becomes particularly clear when using ranking responses, which support a particularly fine-grained picture.