Reviewerly/exHarmony huggingface dataset:
Multi-fidelity orbit uncertainty propagation

To reduce computation time while limiting loss in accuracy when propagating an orbit state probability density function, this work seeks to develop an adaptive approach to multi-fidelity uncertainty propagation for applications in astrodynamics. Using the method of stochastic collocation, a set of particles produced via a low-fidelity solver defines a basis used in the surrogate over the space of propagated states. This basis allows for identifying a subset of important samples that are re-propagated using a high-fidelity propagator, which defines a correction for the original basis. This approach reduces computation time for propagating a particle ensemble or a Gaussian mixture model via the unscented transform. This paper demonstrates the efficacy of this method for several Earth-orbit test cases, and provides a means for merging general and special perturbation theories to produce a posterior probability density function more statistically consistent with a precise estimated state.
A deep learning method for image super-resolution based on geometric similarity

A single image super-resolution (SR) algorithm that combines deep convolutional neural networks (CNNs) with multi-scale similarity is presented in this work. The aim of this method is to address the incapability of the existing CNN methods in digging the potential information in the image itself. In order to dig these information, the image patches that look similar within the same scale and across the different scales are firstly searched inside the input image. Subsequently, a spatial transform networks (STNs) are embedded into the CNNs to make the similar patches well aligned. The STNs allow the CNNs to have the ability of spatial manipulation of data. Finally, when SR is performing through the proposed pyramid-shaped CNNs, the high-resolution (HR) image will be predicted gradually according to the complementary information provided by these aligned patches. The experimental results confirm the effectiveness of the proposed method and demonstrate it can be compared with state-of-the-art approaches for single image SR.
Adaptive Texture-Preserving Denoising Method Using Gradient Histogram and Nonlocal Self-Similarity Priors

Natural image priors play an important role in image denoising, and various prior-based methods have been widely proposed for noise removal. However, these methods tend to smooth the fine image textures while suppressing noise, degrading the image visual quality. To address this problem, in this paper, we propose an adaptive texture-preserving denoising method. In contrast to most existing prior-based denoising methods, two types of priors [gradient histogram matching priors and nonlocal self-similarity (NSS) priors] are proposed, and their combination is used for image denoising. We introduce a hyper-Laplacian distribution of the gradient histogram matching prior, which enforces the gradient histogram of the denoised image to be as close as possible to the estimated reference histogram from the original image. Meanwhile, the proposed model obtained by introducing the NSS priors effectively preserves fine image details and generates sharp image edges. To improve the accuracy of the method, a content-adaptive parameter selection scheme based on edge detection filters is proposed. Moreover, the optimization problem with two types of priors and the content-adaptive parameter added into the objective function becomes a challenging non-convex optimization problem. To effectively solve this problem, we have developed a new numerical solution based on augmented Lagrangian multipliers and alternating minimization scheme. The experimental results demonstrate that the proposed method effectively preserves the texture features of the denoised images and outperforms several variational methods and other state-of-the-art methods in terms of various evaluation indices and visual quality, especially at medium and high noise levels.
New Techniques for Obfuscating Conjunctions

A conjunction is a function $$f(x_1,\dots ,x_n) = \bigwedge _{i \in S} l_i$$ where $$S \subseteq [n]$$ and each $$l_i$$ is $$x_i$$ or $$\lnot x_i$$ . Bishop et al. (CRYPTO 2018) recently proposed obfuscating conjunctions by embedding them in the error positions of a noisy Reed-Solomon codeword and placing the codeword in a group exponent. They prove distributional virtual black box (VBB) security in the generic group model for random conjunctions where $$|S| \ge 0.226n$$ . While conjunction obfuscation is known from LWE [31, 47], these constructions rely on substantial technical machinery. In this work, we conduct an extensive study of simple conjunction obfuscation techniques.
Joint simulation of compositional and categorical data via direct sampling technique – Application to improve mineral resource confidence

Ore deposits usually consist of ore materials with different discrete (e.g. rock and alteration types) and continuous (e.g. geochemical and mineral composition) features. Financial feasibility studies are highly dependent on the modelling of these features and their associated joint uncertainties. Few geostatistical techniques have been developed for the joint modelling of high-dimensional mixed data (continuous and categorical) or constrained data, such as compositional data. The compositional nature of the mineral and geochemical data induces several challenges for multivariate geostatistical techniques, because such data carry relative information and are known for spurious statistical and spatial correlation effects. This paper investigates the application of the direct sampling algorithm for joint modelling of compositional and categorical data. In some mining projects the amount of available data may be enormous in some parts of the deposit and if the density of measurements is sufficient, multivariate geospatial patterns can be derived from that data and be simulated (without model inference) at other undersampled areas of the deposit with similar characteristics. In this context, the direct sampling multiple-point simulation method can be implemented for this reconstruction process. The compositional nature of the data is addressed via implementing an isometric log-ratio transformation. The approach is illustrated through two case studies, one synthetic and one real. The accuracy of the results is checked against a set of validation data, revealing the potential of the proposed methodology for joint modelling of compositional and categorical information. The direct sampling technique can be considered as a smart move to assess the future risk and uncertainty of a resource by making use of all the information hidden within the early data.
A Spiral-Phase Rear Mounted Triple Masking for Secure Optical Image Encryption Based on Gyrator Transform

Background: A spiral phase rear mounted masked scheme is proposed based on Gyrator Transform (GT) to enhance the security contribution of second lens of the existing Double Random Phase Encoding (DRPE) system by modulating the phase of the output obtained in output plane. An additional third layer of Spiral Phase Mask (SPM) is included in the output plane in the same 4f system. Objective: To develop a symmetric cryptosystems to enhance the security potential of the second lens and to prevent the comfortable realization of the cipher-image in the transform domain. Methods: The original image is first scrambled using Arnold transform with frequency and then is convoluted with a secret random phase mask, RPM in GT and then obtained result is convoluted with another secret RPM in inverse GT. The obtained result is then finally convoluted with SPM. Results: It verifies the sensitivity and achieves better performance in terms of recovering a high quality image. Results show the security, performance and quality analysis on the basis of correlation coefficient, occlusion attack, key sensitivity and noise attack, entropy and histogram. Conclusion: It enhances the security potential of second lens in DRPE and introduces diffusion in the system. The system is simulated for binary and greyscale image and achieves better performance as compared to existing DRPE variants. Key sensitivity is more secure and cannot recover original image without knowing all the parameters. Correlation coefficient are also weakly correlated and does not reveals relevant information. Simulation result demonstrates the feasibility and robustness of cryptosystem.
Robust visual tracking via nonlocal regularized multi-view sparse representation

The multi-view sparse representation based visual tracking has attracted increasing attention because the sparse representations of different object features can complement with each other. Since the robustness of different object features is actually not the same in challenging video sequences, it may contain unreliable features (the features with low robustness) in multi-view sparse representation. In this case, how to highlight the useful information of unreliable features for proper multi-feature fusion has become a tough work. To solve this problem, we propose a multi-view discriminant sparse representation method for robust visual tracking, in which we firstly divide the multi-view observations into different groups, and then estimate the sparse representations of multi-view group projections for calculating the observation likelihood. The advantages of the proposed sparse representation method are two-folds: 1) It can properly fuse the observation groups with reliable and unreliable features by using an online updated discriminant matrix to explore the group similarity in multi-feature space. 2) It introduces a nonlocal regularizer to enforce the spatial smoothness among the sparse representations of different group projections, which can enhance the robustness of multi-view sparse representation. Experimental results show that our method can achieve a better tracking performance than state-of-the-art tracking methods do.
Adaptive weighted fuzzy interpolative reasoning based on representative values and similarity measures of interval type-2 fuzzy sets

In this paper, we propose an adaptive weighted fuzzy interpolative reasoning (AWFIR) method based on representative values (RVs) and similarity measures of interval type-2 polygonal fuzzy sets (IT2PFSs). The proposed AWFIR method can overcome the shortcomings of Cheng et al.’s adaptive fuzzy interpolative reasoning (AFIR) method (2016), Chen and Adam's AFIR method (2018), Yang and Shen's AFIR method (2011) and Yang et al.’s AFIR method (2017) to deal with the diarrheal disease prediction problem. The advantages of the proposed AWFIR method are (1) it produces higher degrees of consistency between the FIR results and (2) it deals with AWFIR using IT2PFSs rather than type-1 fuzzy sets (T1FSs).
An adaptive encoding learning for artificial bee colony algorithms

Recently, the improvements of artificial bee colony (ABCs) have attracted increasing interest in the studies of single optimization problems. However, most existing work of ABC aims to design new solution search equations and is still challenged when solving optimization problems with variable linkages. To overcome this limit, an adaptive encoding learning for ABCs (AEL + ABCs) is proposed in this paper. In AEL + ABCs, the solution search equations are encoded in both natural coordinate system and eigen coordinate system guided by covariance matrix learning. The purpose of the former is to maintain the diversity of population, while the latter aims at directing the evolution of population toward the promising directions by identifying the properties of fitness landscape. In addition, an adaptive selection mechanism is used to achieve a good tradeoff between convergence and diversity. For the comparison purposes, the proposed AEL strategy is applied to eight ABCs and their performance is tested on 30 CEC2014 benchmark functions. Experiment results show that the proposed AEL + ABCs can significantly improve the performance of the state-of-the-art ABCs in the majority of the benchmark functions.
A 68-mw 2.2 Tops/w Low Bit Width and Multiplierless DCNN Object Detection Processor for Visually Impaired People

Deep convolutional neural network (DCNN) object detection is a powerful solution in visual perception, but it requires huge computation and communication costs. We proposed a fast and low-power always-on object detection processor that allows visually impaired people to understand their surroundings. We designed an automatic DCNN quantization algorithm that successfully quantizes the data to 8-bit fix-points with 32 values and uses 5-bit indexes to represent them, reducing hardware cost by over 68% compared to the 16-bit DCNN, with negligible accuracy loss. A specific hardware accelerator is designed, which uses reconfigurable process engines to realize multi-layer pipelines to significantly reduce or eliminate the off-chip temporary data transfer. A lookup table is used to implement all multiplications in convolutions to reduce the power significantly. The design is fabricated in SMIC 55-nm technology, and the post-layout simulation shows only 68-mw power at 1.1-v voltage with 155 Go/s performance, achieving 2.2 Top/w energy efficiency.
Word spotting and recognition via a joint deep embedding of image and text

This work addresses three important yet challenging problems of handwritten text understanding: word recognition, query-by-example (QBE) word spotting and query-by-string (QBS) word spotting. In most existing approaches, these related tasks are considered independently. We propose a single unified framework based on deep learning to solve all three tasks efficiently and simultaneously. In this framework, an end-to-end deep neural network architecture is used for the joint embedding of handwritten word texts and images. Word images are embedded via a convolution neural network (CNN), which is trained to predict a representation modeling character-level information. The output of the last convolutional layer is considered as representation in the joint embedding subspace. Likewise, a recurrent neural network (RNN) is used to map a sequence of characters to the joint subspace representation. Finally, a model based on multi-layer perceptrons is proposed to predict the matching probability between two embedding vectors. Experiments on five databases of documents written in three languages show our method to yield state-of-the-art performance for QBE and QBS word spotting. The proposed method also obtains competitive results for word recognition, when compared against approaches tailored specifically for this task.
An effective EM algorithm for mixtures of Gaussian processes via the MCMC sampling and approximation

The Mixture of Gaussian Processes (MGP) is a powerful statistical model for characterizing multimodal data, but its conventional Expectation-Maximization (EM) algorithm (Dempster et al., 1977) is computationally intractable because of its time complexity. To solve this problem, some approximation techniques have been proposed in the conventional EM algorithm. However, these approximate EM algorithms are ineffective or limited in some situations. To implement the EM algorithm more effectively, we approximate the EM algorithm with simulated samples of latent variable via the Monte Carlo Markov Chain (MCMC) sampling, and design an MCMC EM algorithm. Experiments on both synthetic and real-world data sets demonstrate that our MCMC EM algorithm is more effective than the state-of-the-art EM algorithms on classification and prediction problems.
Chasing the Chatbots

Big tech-players have been successful in pushing the chatbots forward. Investments in the technology are growing fast, as well as the number of users and applications available. Instead of driving investments towards a successful diffusion of the technology, user-centred studies are currently chasing the popularity of chatbots. A literature analysis evidences how recent this research topic is, and the predominance of technical challenges rather than understanding users' perceptions, expectations and contexts of use. Looking for answers to interaction and design questions raised in 2007, when the presence of clever computers in everyday life had been predicted for the year 2020, this paper presents a panorama of the recent literature, revealing gaps and pointing directions for further user-centred research.
Stereo-Matching Network for Structured Light

Recently, deep learning has been widely applied in binocular stereo matching for depth acquisition, which has led to an immense increase of accuracy. However, little attention has been paid to the structured light field. In this letter, a network for structured light is proposed to extract effective matching features for depth acquisition. The proposed network promotes the Siamese network by considering receptive fields of different scales and assigning proper weights to the corresponding features, which is achieved by combining pyramid-pooling structure with the squeeze-and-excitation network into the Siamese network for feature extraction and weight calculations, respectively. For network training and testing, a structured-light dataset with amended ground truths is generated by projecting a random pattern into the existing binocular stereo dataset. Experiments demonstrate that the proposed network is capable of real-time depth acquisition, and it provides superior depth maps using structured light.
Toy user interfaces: Systematic and industrial mapping

Toys are play products designed for leisure and social play activities. Today, play products increasingly incorporate hardware and software computation, often connecting to online services and other computing devices, thus being commonly referred to as “smart toys.” Accordingly, such products can also be categorized as user interfaces as they allow human interaction with digital contents using the physical toys’ inputs and outputs. Here we propose the idea of Toy-User-Interfaces (ToyUI) and relate them to the domains of human-computer interaction, mixed reality, and the Internet of Things. We surveyed and categorized a multitude of ToyUIs, gathering information from research papers and toy companies by performing both a systematic mapping (covering research from 2008 to 2017) and an industrial mapping (covering releases from 2012 to 2017). The resulting 297 items were then analyzed according to our classification model, being divided into four categories, eight genres, and 22 setups, considering the play features and the interface features of such items. The classification model and the analysis of the results allowed us to identify six trends in design and technology for ToyUI. Ultimately, our research findings may guide the future of ToyUI projects for researchers, designers, educators, therapists, and end-users.
BIP: A dimensionality reduction for image indexing

Searching on internet is one of the daily task done by millions of users around the Globe. There is an urge for effective indexing scheme for unstructured data, which provide better search results. The image, content report, and site pages are said to be unstructured database. These information are not sorted out so the recovery rates of these information are purposely low when contrasted with indexed database. These information are indexed concerning their cues is one of the challenging areas to be explored. For the client require, the data recovery segment endeavors to coordinate the client question with the indexed database will provide effective recovery rates. In this paper a new image indexing scheme called Basic intrinsic pattern (BIP) is elaborated, which uses the image intensity as the key variable for image indexing. The algorithm is evaluated with respect to the size of the feature vector and the retrieval rate.
Classification of scaled texture patterns with transfer learning

Classification of texture patterns with large scale variations poses a great challenge for expert and intelligent systems. A pure learning approach addresses this issue by including texture patterns at all scales in the training dataset. This approach makes the construction of an expert system quite costly and unrealistic given the large variations in real-world texture scales and patterns. We propose a transfer learning approach where the full range of texture scales is available only for a small subset of the texture classes. Such a subset is used to learn the scaling map through partial least-square regression or coupled dictionary learning. Experimental results on classifiers equipped with the learned maps show promising reduction in training data scale variability with improved classification accuracy compared to the data-intensive pure learning approach. The proposed approach can be followed to build image-based expert systems of reasonable accuracy and limited data requirements.
Spatial variability of selected metals using auxiliary variables in agricultural soils

Spatial estimation of the content of soil heavy metals in agriculture is critical for monitoring farmland contamination and ensuring sustainable eco-agriculture. In this study, a hyperspectral image (115 bands) and 17 environmental parameters were used as auxiliary variables to predict the concentrations of As, Cd, Cr, Pb and Zn in agricultural soils. The study also combined hybrid geostatistical methods, such as regression-ordinary kriging (ROK) and back propagation neural network-ordinary kriging (BPNNOK). To this end, a total of 100 topsoil (0–20 cm) samples were collected in Zengcheng in the Pearl River Delta (PRD), China. The results showed that the BPNNOK with input principal components (PCs) from auxiliary variables had the best prediction performance for As (RMSE = 5.02 mg kg−1 and R2 = 64.21%), Cd (RMSE = 0.014 mg kg−1 and R2 = 61.82%), Cr (RMSE = 3.78 mg kg−1 and R2 = 54.39%), Pb (RMSE = 4.09 mg kg−1 and R2 = 60.92%), and Zn (RMSE = 2.86 mg kg−1 and R2 = 59.31%) in comparison with simple kriging (SK), ordinary kriging (OK) and ROK. The effects of hyperspectral remote sensing data exhibited the best sensitivity and responses for all soil heavy metals by the multiple linear regression (MLR) and back propagation neural network (BPNN) models, which were obviously superior to environmental parameters. It was concluded that information from hyperspectral remote sensing data is promising, and that the efficient use of auxiliary variables for monitoring sustainable pollution prevention and control of soil heavy metals, which improve mapping quality of soil heavy metals, is the primary process in non-point source variations of soil heavy metals.