Reviewerly/exHarmony huggingface dataset:
Prediction of Energy in Solar Powered Wireless Sensors Using Artificial Neural Network

Predicting of energy income into solar harvesters of a wireless sensor units is a necessary task to manage its operation. Thus, utilizing an accurate prediction algorithm leads finally to prolong the lifetime of that sensors, which is strongly required. The most common prediction algorithm, Exponentially Weighted Moving Average (EWMA), can be used only under consistent sequential days. This is attributed to the large prediction error that appears when weather changes unexpectedly from sunny to cloudy or vice versa. The other moving average algorithm, Weather Conditioned Moving Average (WCMA), still shows a considerable error at sunrise and sunset periods. Pro-Energy produces also a significant error. This paper presents Artificial Neural Network (ANN) as new prediction algorithm to reduce the prediction error caused. Additionally, it compares the results of applying this algorithm with the aforementioned conventional methods. For two weeks simulation periods, one in summer and another in winter, ANN shows respectively an average prediction error of (0.03% and 0.02%); while (EWMA, WCMA and Pro-Energy) show higher errors in overall. Thus, ANN is an accurate algorithm to be adopted for energy prediction in wireless sensors.
Technology Application In Education And Learning Process

Abstract Talking about education, firstly, education is a conscious effort in realizing the learning process in order to make students actively develop their potential to be able to have noble character, intelligence and personality that are good for themselves and society. Along with the development in the world of education that continues to change rapidly or develop rapidly which can change the mindset of educators, from being rigid to modern. That is very influential to education. The main purpose of education is to produce or make someone to have quality, knowledge and character, so that he has a broad view to get the desired ideals and to be able to adapt in environment. Education provides motivation to be a good person in all aspects of life. Education itself is a very important thing in human life. Therefore all citizens have right to get good quality of education. In education the role of a teacher is very important. A teacher is an educator, a teacher and an example for his students. Teaching is a very complicated matter, not as easy as we imagine when teacher is teaching, he must know each character of the students. In addition the teacher must also prepare designs before conducting the teaching and learning process. The teacher must think of ways that are used to increase students’ interest in learning and improve the quality of teaching. With the rapid development of technology, teachers must be able to use these technological developments in the teaching and learning process in the classroom. So that there will be no lag in that development. In the teaching and learning process the teacher only uses the monotonous lecture method that will reduce students’ interest in learning because students may feel bored or think it is not interesting for them. Therefore the teacher is able to apply the technology in learning. It is usually known as Educational Technology. Educational Technology is a medium that is used as a tool in education or the learning process to be more effective, and can be easily observed by students. The purpose of this Educational Technology is as a media to solve problems in learning that can improve learning performance. So that it can increase learning interest for students. Educational technology also help teachers in making media used in the learning process, so that students can attract the desire to learn. By Educational Technology, teachers can create media that enable them to apply the material presented with images on the internet. The material can be presented through videos or stories on YouTube. It is the same as making a TTS column from this technology the teacher can make questions through this TTS. So there is a touch of games in learning. This can increase students’ desire to learn. In addition, this can also make students more active and critical in learning activities.
Hierarchical stochastic graphlet embedding for graph-based pattern recognition

Despite being very successful within the pattern recognition and machine learning community, graph-based methods are often unusable because of the lack of mathematical operations defined in graph domain. Graph embedding, which maps graphs to a vectorial space, has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques. However, it is well known that graph embedding functions usually suffer from the loss of structural information. In this paper, we consider the hierarchical structure of a graph as a way to mitigate this loss of information. The hierarchical structure is constructed by topologically clustering the graph nodes, and considering each cluster as a node in the upper hierarchical level. Once this hierarchical structure is constructed, we consider several configurations to define the mapping into a vector space given a classical graph embedding, in particular, we propose to make use of the Stochastic Graphlet Embedding (SGE). Broadly speaking, SGE produces a distribution of uniformly sampled low to high order graphlets as a way to embed graphs into the vector space. In what follows, the coarse-to-fine structure of a graph hierarchy and the statistics fetched by the SGE complements each other and includes important structural information with varied contexts. Altogether, these two techniques substantially cope with the usual information loss involved in graph embedding techniques, obtaining a more robust graph representation. This fact has been corroborated through a detailed experimental evaluation on various benchmark graph datasets, where we outperform the state-of-the-art methods.
Deep Image Demosaicing for Submicron Image Sensors

Latest trend in image sensor technology allowing submicron pixel size for high-end mobile devices comes at very high image resolutions and with irregularly sampled Quad Bayer color filter array (CFA). Sustaining image quality becomes a challenge for the image signal processor (ISP), namely for demosaicing. Inspired by the success of deep learning approach to standard Bayer demosaicing, we aim to investigate how artifacts-prone Quad Bayer array can benefit from it. We found that deeper networks are capable to improve image quality and reduce artifacts; however, deeper networks can be hardly deployed on mobile devices given very high image resolutions: 24MP, 36MP, 48MP. In this article, we propose an efficient end-to-end solution to bridge this gap&#x2014;a duplex pyramid network (DPN). Deep hierarchical structure, residual learning, and linear feature map depth growth allow very large receptive field, yielding better details restoration and artifacts reduction, while staying computationally efficient. Experiments show that the proposed network outperforms state of the art for standard and Quad Bayer demosaicing. For the challenging Quad Bayer CFA, the proposed method reduces visual artifacts better than state-of-the-art deep networks including artifacts existing in conventional commercial solutions. While superior in image quality, it is 2&#x2010;25 times faster than state-of-the-art deep neural networks and therefore feasible for deployment on mobile devices, paving the way for a new era of on-device deep ISPs.
Variational Mode Decomposition-Based Event Recognition in Perimeter Security Monitoring With Fiber Optic Vibration Sensor

Recognition of different kinds of human intrusions from the environmental disturbance with high efficiency is still a challenging task in perimeter security monitoring with fiber optic vibration sensor, since the vibration signals induced by these events are highly similar to each other, and it can not be directly discriminated by the signals. In this paper, an intelligent event recognition scheme is proposed to improve its performance in complicated environmental applications. In this event recognition scheme, a variational mode decomposition based kurtosis feature combined with a zero crossing rate feature is used as the input feature vectors. A support vector machine is used to classify the input feature vectors into the corresponding categories. A series of field tests show that the proposed scheme can accurately and rapidly classify wind disturbance and three typical patterns of human intrusions such as waggling the fence, climbing the fence and knocking the fence. The average identification rate of 100.0% and 96.9% are achieved for the wind disturbance and human intrusion events, respectively. The recognition processing time can be controlled less than 0.4 s. Thus, the intelligent event recognition scheme can fully satisfy the online monitoring requirements for practical applications.
Identifying microbial life in rocks: Insights from population morphometry

The identification of cellular life in the rock record is problematic, since microbial life forms, and particularly bacteria, lack sufficient morphologic complexity to be effectively distinguished from certain abiogenic features in rocks. Examples include organic pore-fillings, hydrocarbon-containing fluid inclusions, organic coatings on exfoliated crystals and biomimetic mineral aggregates (biomorphs). This has led to the interpretation and re-interpretation of individual microstructures in the rock record. The morphologic description of entire populations of microstructures, however, may provide support for distinguishing between preserved micro-organisms and abiogenic objects. Here, we present a statistical approach based on quantitative morphological description of populations of microstructures. Images of modern microbial populations were compared to images of two relevant types of abiogenic microstructures: interstitial spaces and silica-carbonate biomorphs. For the populations of these three systems, the size, circularity, and solidity of individual particles were calculated. Subsequently, the mean/SD, skewness, and kurtosis of the statistical distributions of these parameters were established. This allowed the qualitative and quantitative comparison of distributions in these three systems. In addition, the fractal dimension and lacunarity of the populations were determined. In total, 11 parameters, independent of absolute size or shape, were used to characterize each population of microstructures. Using discriminant analysis with parameter subsets, it was found that size and shape distributions are typically sufficient to discriminate populations of biologic and abiogenic microstructures. Analysis of ancient, yet unambiguously biologic, samples (1.0 Ga Angmaat Formation, Baffin Island, Canada) suggests that taphonomic effects can alter morphometric characteristics and complicate image analysis; therefore, a wider range of microfossil assemblages should be studied in the future before automated analyses can be developed. In general, however, it is clear from our results that there is great potential for morphometric descriptions of populations in the context of life recognition in rocks, either on Earth or on extraterrestrial bodies.
A Privacy-Preserving Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition

Recently, multi-task learning (MTL) has been extensively studied for various face processing tasks including face detection, landmarks localization, pose estimation and gender recognition, which endeavors to train a better model by exploiting the synergy among the related tasks. However, the raw face dataset used for training often contains sensitive and private information, which can be maliciously recovered by carefully analyzing the model and outputs. To address this problem, we propose a novel privacy-preserving multi-task learning approach, which utilizes the differential private stochastic gradient descent algorithm to optimize the end-to-end multi-task model, and weighs loss functions of multiple tasks to improve learning efficiency and prediction accuracy. Specifically, calibrated noise is added to the gradient of loss functions to preserve privacy of the training data during model training. Furthermore, we exploit the homoscedastic uncertainty to balance different learning tasks. The experiments demonstrate that the proposed approach yields the differential privacy guarantees without decreasing accuracy of HyperFace under a desirable privacy budget.
Transliteration of Arabizi into Arabic Script for Tunisian Dialect

The evolution of information and communication technology has markedly influenced communication between correspondents. This evolution has facilitated the transmission of information and has engendered new forms of written communication (email, chat, SMS, comments, etc.). Most of these messages and comments are written in Latin script, also called Arabizi . Moreover, the language used in social media and SMS messaging is characterized by the use of informal and non-standard vocabulary, such as repeated letters for emphasis, typos, non-standard abbreviations, and nonlinguistic content like emoticons. Since the Tunisian dialect suffers from the unavailability of basic tools and linguistic resources compared to Modern Standard Arabic, we resort to the use of these written sources as a starting point to build large corpora automatically. In the context of natural language processing and to benefit from these networks’ data, transliterating from Arabizi to Arabic script is a necessary step because most recently available tools for processing the Tunisian dialect expect Arabic script input. Indeed, the transliteration task can help construct and enrich parallel corpora and dictionaries for the Tunisian dialect and can be useful for developing various natural language processing applications such as sentiment analysis, opinion mining, topic detection, and machine translation. In this article, we focus on converting the Tunisian dialect text that is written in Latin script to Arabic script following the Conventional Orthography for Dialectal Arabic. Then, we propose two models to transliterate Arabizi into Arabic script for the Tunisian dialect, namely a rule-based model and a discriminative model as a sequence classification task based on conditional random fields). In the first model, we use a set of transliteration rules to convert the Tunisian dialect Arabizi texts to Arabic script. In the second model, transliteration is performed both at word and character levels. In the end, our models got a character error rate of 10.47%.
A Physics-Based DNI Model Assessing All-Sky Circumsolar Radiation

By investigating the long-term observations at Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), we find that the routinely used Beer-Bouguer-Lambert law and the models that empirically separate direct normal irradiance (DNI) from measurements of global horizontal irradiance (GHI) have dramatic and unexpected bias in computing cloudy-sky DNI. This bias has led to tremendous uncertainty in estimating the electricity generation by solar energy conversion systems. To effectively reduce the bias, this study proposes a physical solution of all-sky DNI that computes solar radiation in the infinite-narrow beam along the sun direction and the scattered radiation falls within the circumsolar region. In sharp contrast with the other DNI models, this method uses a finite-surface integration algorithm that computes solar radiation in differential solid angles and efficiently infers its contribution to a surface perpendicular to the sun direction. The new model substantially reduces the uncertainty in DNI by a factor of 2–7.
A Fast and Efficient CAD System for Improving the Performance of Malignancy Level Classification on Lung Nodules

Accurate malignancy level classification of lung nodules can reduce lung cancer mortality rate effectively. In this study, we present a fast and efficient CAD system to improve the performance of nodules malignancy level classification. Firstly, to reduce false positives (FPs), we propose a novel vessel segmentation method which measures vessel likelihood by tubular-like structures discriminating from multiple views. The method can recognize irregular vascular structures robustly and sensitively, and achieve fast vessel segmentation. In addition, a mathematical description for 3D pulmonary entities using neighbor centroids clustering is provided as a fundamental condition for spatial feature extraction. To optimize features extraction, we formulate a gray values cumulative function and a patches selection function based on the mathematical description, to generate axial spatial outline and spatial density distribution samples of the entities, respectively. Then, we use Edge Orientation Histogram (EOH) to extract edge features from the spatial outline and propose a multi-scale path LBP (MSPLBP) to extract the texture feature of the density distribution samples. Finally, the fused EOH and MSPLBP are classified into 6 malignancy levels by three state-of-the-art classifiers. The experimental results show that the vessel segmentation method achieves an average F1_Score of 78.14% and AUC value under PR curves of 0.8149. Moreover, our system reaches an average accuracy of 95.88% and consumes average 176.26 seconds for evaluating a CT set on malignancy level classification. These results indicate that the system can segment vessels exactly, and classify the malignancy level of nodules efficiently. Our system is the potential to be a powerful tool for early diagnosis of lung cancer.
Themis: Fair and Efficient {GPU} Cluster Scheduling

Modern distributed machine learning (ML) training workloads benefit significantly from leveraging GPUs. However, significant contention ensues when multiple such workloads are run atop a shared cluster of GPUs. A key question is how to fairly apportion GPUs across workloads. We find that established cluster scheduling disciplines are a poor fit because of ML workloads’ unique attributes: ML jobs have long-running tasks that need to be gang-scheduled, and their performance is sensitive to tasks’ relative placement. We propose THEMIS, a new scheduling framework for ML training workloads. It’s GPU allocation policy enforces that ML workloads complete in a finish-time fair manner, a new notion we introduce. To capture placement sensitivity and ensure efficiency, THEMIS uses a two-level scheduling architecture where ML workloads bid on available resources that are offered in an auction run by a central arbiter. Our auction design allocates GPUs to winning bids by trading off efficiency for fairness in the short term, but ensuring finish-time fairness in the long term. Our evaluation on a production trace shows that THEMIS can improve fairness by more than 2.25X and is 5% to 250% more cluster efficient in comparison to state-of-the-art schedulers.
Fast and Accurate 3D Hand Pose Estimation via Recurrent Neural Network for Capturing Hand Articulations

3D hand pose estimation from a single depth image plays an important role in computer vision and human-computer interaction. Although recent hand pose estimation methods using convolution neural network (CNN) have shown notable improvements in accuracy, most of them have a limitation that they rely on a complex network structure without fully exploiting the articulated structure of the hand. A hand, which is an articulated object, is composed of six local parts: the palm and five independent fingers. Each finger consists of sequential-joints that provide constrained motion, referred to as a kinematic chain. In this paper, we propose a hierarchically-structured convolutional recurrent neural network (HCRNN) with six branches that estimate the 3D position of the palm and five fingers independently. The palm position is predicted via fully-connected layers. Each sequential-joint, i.e. finger position, is obtained using a recurrent neural network (RNN) to capture the spatial dependencies between adjacent joints. Then the output features of the palm and finger branches are concatenated to estimate the global hand position. HCRNN directly takes the depth map as an input without a time-consuming data conversion, such as 3D voxels and point clouds. Experimental results on public datasets demonstrate that the proposed HCRNN not only outperforms most 2D CNN-based methods using the depth image as their inputs but also achieves competitive results with state-of-the-art 3D CNN-based methods with a highly efficient running speed of 285 fps on a single GPU.
Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks

Medical image reconstruction is often an ill-posed inverse problem. In order to address such ill-posed inverse problems, prior knowledge of the sought after object property is usually incorporated by means of regularization. For example, sparsity-promoting regularization in a suitable transform domain is widely used to reconstruct images with diagnostic quality from noisy and/or incomplete medical data. However, sparsity-promoting regularization may not be able to comprehensively describe the actual prior information of the objects being imaged. Deep generative models, such as generative adversarial networks (GANs) have shown great promise in learning the underlying distribution of images. Prior distributions for images estimated using GANs have been employed as a means of regularization with impressive results in several linear inverse problems in computer vision that are also relevant to medical imaging. However, in practice, it can be difficult for a GAN to comprehensively describe prior distributions, which can potentially lead to a lack of fidelity between the reconstructed image and the observed data. Recently, an image-adaptive GAN-based reconstruction method (IAGAN) was proposed to guarantee stronger data consistency by adapting the trained generative model parameters to the observed measurements. In this work, for the first time, we apply the IAGAN method to reconstruct images from undersampled magnetic resonance imaging (MRI) measurements. A state-of-the-art GAN model called Progressive Growing of GANs (ProGAN) was trained on a large number of ground truth images from the NYU fastMRI dataset, and the learned generator was subsequently employed in the IAGAN framework to reconstruct high fidelity images from retrospectively undersampled experimental k-space data in the validation dataset. It is demonstrated that by use of the GAN-based reconstruction method with noisy and/or incomplete measurements, we can potentially recover fine structures in the object that are relevant for medical diagnosis that may be difficult to achieve using traditional reconstruction methods relying on sparsity-promoting penalties.
AI Data Driven Approach-Based Endogenous Inputs for Global Solar Radiation Forecasting

Controlling the random nature of renewable energy sources such as solar radiation at ground, allows electric grid operators to better integrate it.In this paper, an intelligent datadriven model based on artificial neural network with autoregressive input sequence is developed to forecast the global solar radiation (GSR) time series on a half hour resolution in the site of Agdal, Marrakesh, Morocco.The database that is used to create this model was divided into two subsets.The first subset is used for training the proposed model on the data measured during the year 2008 by adopting three efficient optimizers (levenbergmarquardt, resilient backpropagation, and scaled conjugate gradient).The second subset is used for testing the efficiency and the robustness of the developed model to generate accurate predictions during the next six years (from 2009 to 2014).The obtained results demonstrate the accuracy and the stability of the proposed data-driven model to perform prediction in case of GSR measurements intermittence or sensor damage.