Reviewerly/exHarmony huggingface dataset:
Closed-Form Optimal Two-View Triangulation Based on Angular Errors

In this paper, we study closed-form optimal solutions to two-view triangulation with known internal calibration and pose. By formulating the triangulation problem as L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> minimization of angular reprojection errors, we derive the exact closed-form solutions that guarantee global optimality under respective cost functions. To the best of our knowledge, we are the first to present such solutions. Since the angular error is rotationally invariant, our solutions can be applied for any type of central cameras, be it perspective, fisheye or omnidirectional. Our methods also require significantly less computation than the existing optimal methods. Experimental results on synthetic and real datasets validate our theoretical derivations.
Convolutional Neural Network Style Transfer Towards Chinese Paintings

Chinese painting is one of the most important cultural heritages. However, creating a Chinese painting usually requires specific skills, patience, and years of professional training. Moreover, most existing style transfer methods mainly focus on photograph or western painting, and there are intrinsic differences between Chinese and western paintings. To this end, we propose a novel algorithm of Chinese painting style transfer (CPST) towards transferring with unique ink and wash characteristics, which automatically generates Chinese paintings with machine learning technology. In this paper, firstly, comparing Chinese paintings with western works, we set up four key restrictions in the style transfer, i.e., with special considerations of typical ink and wash features, including brush stroke, space reservation, ink tone with diffusion and yellowing. In order to incorporate these restrictions into the transferring convolutional neural networks (CNN), we separate different layers of the CNN into layers of style and content, so as to faithfully reserve the style of the reference image. Secondly, as Chinese paintings can be divided into fine brushwork and freehand brushwork, to cope with diverse painting skills, we devise different strategies to transfer not only the ink tone but also the painting skills to the target images. Experiments show that taking the aesthetic characteristics of Chinese painting style as reference, our results are more visually satisfactory and successfully overcome spillovers.
Adaptive Feature Sampling for Recommendation with Missing Content Feature Values

Most recommendation algorithms mainly make use of user history interactions in the model, while these methods often suffer from the cold-start problem (user/item has no history information). On the other sides, content features help on cold-start scenarios for modeling new users or items. So it is essential to utilize content features to enhance different recommendation models. To take full advantage of content features, feature interactions such as cross features are used by some models and outperform than using raw features. However, in real-world systems, many content features are incomplete, e.g., we may know the occupation and gender of a user, but the values of other features (location, interests, etc.) are missing. This missing-feature-value (MFV) problem is harmful to the model performance, especially for models that rely heavily on rich feature interactions. Unfortunately, this problem has not been well studied previously.
Blockchain and GDPR: Application Scenarios and Compliance Requirements

Blockchain and the European General Data Protection Regulation (GDPR) are two topics that are currently highly discussed in academia and amongst professionals. The Blockchain technology is claimed to revolutionize how business is being conducted by its way of storing data and sharing it with others. The recently introduced GDPR has a huge impact on processing personal data because it brought major changes to privacy regulation. This might also affect the processing of personal data in Blockchain-based application scenarios. Based on literature analysis, the paper at hand provides an overview of the Blockchain technology, presents a decision model, and introduces two possible Blockchain application scenarios. It analyses relevant requirements of the GDPR in view of processing personal data and compares these with the fundamental principles of the Blockchain technology. The paper concludes that processing personal data in the Blockchain is violating the GDPR because it conflicts with fundamental specification of this regulation. This finding reveals the need for further research to propose concepts and frameworks for a GDPR-compliant processing of personal data using Blockchain technology.
Review of Machine Learning and Deep Learning Based Recommender Systems for Health Informatics

Recommender Systems have become essential in personalized healthcare as they provide meaningful information to the patients depending on the specific requirements and availability of health records. With the improvement of machine learning techniques, the recommender system brings about several opportunities to the medical science. Systems can perform more efficiently and solve complex problems using deep learning, even when data set is diverse and unstructured. Here we present a comprehensive overview of the challenges associated with the existing recommender systems. Machine learning and deep learning techniques that are generally applied for health recommender system are discussed in detail along with their application to health informatics.
3D Point Cloud Color Denoising Using Convex Graph-Signal Smoothness Priors

Point cloud is a collection of 3D coordinates and associated color information, which are discrete samples of an object's 2D surfaces. Imperfection in the acquisition process means that point clouds are often corrupted with noise in both geometric and color spaces. Building on recent advances in graph signal processing, we design two algorithms for 3D point cloud color denoising. Specifically, we develop a smoothness notion for 3D color point clouds using graph Laplacian regularizer (GLR) or graph total variation (GTV) priors defined on the RGB space to promote piecewise smoothness (PWS) of RGB values. Using GLR or GTV as signal prior, we formulate the point cloud color denoising problem as a maximum a posteriori (MAP) estimation problem. For the GLR prior, the MAP formulation leads to an unconstrained quadratic programming (QP) problem, which can be efficiently computed using conjugate gradient (CG). For the GTV prior, the MAP formulation results in an ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> cost function; we minimize it using alternating direction method of multipliers (ADMM) and proximal gradient descent, where a good step size is chosen via Lipschitz continuity analysis. Extensive experiments show satisfactory denoising performance using our proposed algorithms.
Human Detection and Tracking on Surveillance Video Footage Using Convolutional Neural Networks

Safety is one of basic human needs so we need a security system that able to prevent crime happens. Commonly, we use surveillance video to watch environment and human behaviour in a location. However, the surveillance video can only used to record images or videos with no additional information. Therefore we need more advanced camera to get another additional information such as human position and movement. This research were able to extract those information from surveillance video footage by using human detection and tracking algorithm. The human detection framework is based on Deep Learning Convolutional Neural Networks which is a very popular branch of artificial intelligence. For tracking algorithms, channel and spatial correlation filter is used to track detected human. This system will generate and export tracked movement on footage as an additional information. This tracked movement can be analysed furthermore for another research on surveillance video problems.
Machine learning-based improvement of empiric models for an accurate estimating process of global solar radiation

The change of the solar radiation reaching the earth depending on specific conditions brings the execution of system planning meticulously and optimally by solar power researchers to the fore. For the estimation of the solar radiation, the most frequently used model is the Angtröm-Prescott model. In this model, sunshine ratio plays an important role. In the study, it is attempted to enhance the annual and semi-annual models developed for the city of Muğla, Turkey and to congregate the semi-annual models in a single model by using the Artificial Bee Colony (ABC) algorithm. The results obtained have revealed that in the multiple model relying on only the sunshine duration, the statistical error values were not reduced to very low levels. In order to cope with this problem, the multiple model relying on both the sunshine duration and the sunset-sunrise hour angle has been proposed. In this way, the statistical errors are found to be reduced by about 40% using the ABC algorithm and the multiple model. It was seen that the models recommended are superior to all the models especially in summer and spring months when there is plenty of sunshine.
Impact of Method Noise on SAR Image Despeckling

This article introduces the concept, use and implementation of method noise in the field of synthetic aperture radar (SAR) image despeckling. Method noise has the capability to enhance the efficiency and performance of any despeckling algorithm. It is easy, efficient and enhanced way of improving the results. The difference between speckled image and despeckled image contains some residual image information which is due to the inefficiency of the denoising algorithm. This article will compare the results of some standard methods with and without the use of method noise and prove its efficiency and validity. It also shows its best use in different ways of denoising. The results will be compared on the basis of performance metrics like PSNR and SSIM. The concept of method noise is not restricted to only SAR images. It has vast usage and application. It can be used in any denoising procedure such as medical images, optical image etc. but this paper shows the experimental results only on the SAR images.
A Survey of Light-Weight Cryptographic Algorithm for Information Security and Hardware Efficiency In Resource Constrained Devices

Cryptography is that apply and analysis of techniques for protected communication system in the interruption of trusted third parties (called arbiter). It is the technique of transforming data to create them secure and proof against attack. In cryptography, the plain text is encrypted to create cipher text at sender side and decrypted to create plain text at receiver. Traditional cryptography algorithms are slow, complex and energy-consuming for handling resource forced devices. Some of the resource forced devices are RFID tags, health care devices and environment with smart objects which have restricted storage device, limited power and fewer data processing capabilities. Light-weight cryptography [1] is employed for resource forced devices. The face of light-weight cipher has been active analysis topic for the past several decades. In this paper, comparative evaluation of recent hardware implementation based on 0.18μm CMOS technology of symmetric lightweight cryptographic algorithm is presented. In addition to this, the survey provides the various attacks which affect the data confidentiality, data integrity and availability.
Web Data Extraction using Hybrid Program Synthesis: A Combination of Top-down and Bottom-up Inference

Automatic synthesis of web data extraction programs has been explored in a variety of settings, but in practice there remain various robustness and usability challenges. In this work we present a novel program synthesis approach which combines the benefits of deductive and enumerative synthesis strategies, yielding a semi-supervised technique with which concise programs expressible in standard languages can be synthesized from very few examples. We demonstrate improvement over existing techniques in terms of overall accuracy, number of examples required, and program complexity. Our method has been deployed as a web extraction feature in the mass market Microsoft Power BI product.
DSConv: Efficient Convolution Operator

Quantization is a popular way of increasing the speed and lowering the memory usage of Convolution Neural Networks (CNNs). When labelled training data is available, network weights and activations have successfully been quantized down to 1-bit. The same cannot be said about the scenario when labelled training data is not available, e.g. when quantizing a pre-trained model, where current approaches show, at best, no loss of accuracy at 8-bit quantizations. We introduce DSConv, a flexible quantized convolution operator that replaces single-precision operations with their far less expensive integer counterparts, while maintaining the probability distributions over both the kernel weights and the outputs. We test our model as a plug-and-play replacement for standard convolution on most popular neural network architectures, ResNet, DenseNet, GoogLeNet, AlexNet and VGG-Net and demonstrate state-of-the-art results, with less than 1% loss of accuracy, without retraining, using only 4-bit quantization. We also show how a distillation-based adaptation stage with unlabelled data can improve results even further.
HandSense

Head-mounted devices (HMD) for Augmented Reality (AR) are gaining traction thanks to a growing number of applications in the areas of image guided therapy, computer aided design, cargo packing, manufacturing and digital field service. However, providing an always available, intuitive and user friendly input for these devices remains a challenging problem. This paper explores recognizing dynamic, micro finger gestures using capacitive coupling for interacting with a head-mounted device. Electrodes are attached to fingertips of users gloves and capacitive coupling among all pairs of electrodes is measured quickly to infer the real-time spatial relationship between fingers. The system is able to recognize fine, low-effort finger gestures, such as swiping, sliding, tap, double-tap. We evaluated our prototype with 14 gestures executed by 10 subjects and found a 97% accuracy of gesture recognition.
Single-rotor UAV flow field simulation using generative adversarial networks

In recent years, with the large-scale application of unmanned aerial vehicles (UAV) in agricultural plant protection, various shortcomings of have been identified; for example, the rotor flow field of the UAV will cause drift of the droplets, resulting in waste and secondary disaster. Therefore, digital simulation has become a necessity. However, due to the complexity of the rotor flow field of the rotor UAV and the operating environment of the UAV, digital simulation is associated with a large workload and great computational costs. It is urgent to explore new models using deep learning to identify the law of the rotor flow field. To address this problem, deep learning, in combination with flow field methods, is used to explore new models in this paper. A generative adversarial network (GAN) prediction model is proposed in this paper. The GAN includes a generation network and a discrimination network. In this paper, the features of the flow field are learned by the generative network to identify deep features of the flow field. The discrimination network distinguishes between true and false pictures by extracting features during training to realize adversarial training. This model can predict the flow field by identifying features of the flow-field distribution in training samples to build a predictive model. The compression effects of the computational fluid dynamics (CFD) model and the GAN model are compared in this paper. The GAN model outperforms the CFD model in predicting the flow field and compressing the data.
Discrete Wavelet Transform on static hand gesture recognition

Abstract The technology in present-day is very useful for human-physical recognition and the hand gesture recognition is one of them. Nevertheless, that technology still had various weakness, for example in the image brightness, contrast, recognition time, and accuracy rate. The objective of this paper was to construct hand gesture recognition system using RGB images. Pre-processing is wrought by resizing the image, separate the hand area, and pick the specific layer. This experiment used the YCbCr because its derived directly from RGB and had a higher contrast compared to other layers. The feature value was gathered from feature extraction on Discrete Wavelet Transform (DWT) using Low-Low sub-band and 2 nd level decomposition. The current sub-band had smoothest contours in comparison with other sub-band. The final process was gesture classification using Hidden Markov Models (HMM) and K-Nearest Neighbor (KNN). The amount of training and testing data used were 150 and 100 images respectively, divided into five gestures with accuracy using HMM and KNN consecutively was 58% and 100%. The research novelty was that the classification impacted positively on accuracy level.
CHAOSA: Chaotic map based random number generator on Arduino platform

This article presents a novel random number generation algorithm, called CHAOSA, which is based on Ikeda maps and XOR function. The novel system is realized by the Arduino microcontroller. The quality of output sequence is evaluated by key space analysis and ENT, DIEHARD, and NIST test packages. The results show excellent performance of the proposed generator.
Gradient-Guided Convolutional Neural Network for MRI Image Super-Resolution

Super-resolution (SR) technology is essential for improving image quality in magnetic resonance imaging (MRI). The main challenge of MRI SR is to reconstruct high-frequency (HR) details from a low-resolution (LR) image. To address this challenge, we develop a gradient-guided convolutional neural network for improving the reconstruction accuracy of high-frequency image details from the LR image. A gradient prior is fully explored to supply the information of high-frequency details during the super-resolution process, thereby leading to a more accurate reconstructed image. Experimental results of image super-resolution on public MRI databases demonstrate that the gradient-guided convolutional neural network achieves better performance over the published state-of-art approaches.
CFA Video Denoising and Demosaicking Chain via Spatio-Temporal Patch-Based Filtering

Demosaicking and denoising are key steps in the camera imaging chain for both images and videos. The reconstruction errors during these stages will have undesirable effects on the final result if not handled properly. Demosaicking provokes the spatial and color correlation of noise, which is afterwards enhanced by the processing pipeline. This structured noise generally degrades the image quality and, for dark scenes with low signal to noise ratio, prevents the correct interpretation of the image. When trying to mitigate such structured noise on already processed data, denoising methods attenuate details and texture. We present a video processing chain, consisting of a novel strategy for the removal of noise at the camera sensor and a novel video demosaicking algorithm. In both cases, a spatio-temporal patch-based filter with motion compensation is introduced. The experimental results, including real examples, illustrate the performance of the proposed chain, avoiding the creation of interpolation artefacts and colored spots.
Domain adaptive representation learning for facial action unit recognition

Learning robust representations for applications with multiple modalities of input can have a significant impact on improving performance. Traditional representation learning methods rely on projecting the input modalities to a common subspace to maximize agreement amongst the modalities for a particular task. We propose a novel approach to representation learning that uses a latent representation decoder to reconstruct the target modality and thereby employ the target modality purely as a supervision signal for discovering correlations between the modalities. Through cross modality supervision, we demonstrate that the learnt representation is able to improve upon the performance of the task of facial action unit (AU) recognition over modality specific representations and even their fused counterparts. As an extension, we explore a new transfer learning technique to adapt the learnt representation to the target domain. We also present a shared representation based feature fusion methodology to improve the performance of any multi-modal system. Our experiments on three AU recognition datasets - MMSE, BP4D and DISFA, show strong performance gains producing state-of-the-art results in spite of the absence of data from a modality.
Selecting Efficient Cloud Resources for HPC Workloads

Constant advances in CPU, storage, and network virtualization are enabling high-performance computing (HPC) applications to be efficiently executed on cloud computing systems. In this computing model, users pay only for what they use, with no need to acquire nor maintain expensive computing infrastructure. Moreover, users have at their disposal multiple kinds of computing resources and are able to assemble computing infrastructures that fit the application needs. Nonetheless, the available computing resources vary in price and performance and selecting the proper resources to execute the applications is of utmost importance to optimize cost and performance. In this work, we discuss the performance and cost implications of selecting different kinds of cloud resources to execute HPC workloads and show that the best resources for executing a given application depend not only on the application itself but also on the input dataset being processed. We also propose a methodology to support the selection of efficient cloud resources for these applications and show that is was able to select the best of 11 different cloud infrastructure configurations to execute 8 different benchmarks by executing just a few seconds of each application on each one of the configurations.
On the error estimate of the randomized double block Kaczmarz method

In this paper, we consider the convergence analysis of the randomized double block Kaczmarz method and improve the upper bound of the error estimate in expectation of the randomized double block Kaczmarz method. Numerical experiments are given to demonstrate the theoretical results and to show a large gap between the new and the old bound.