Reviewerly/exHarmony huggingface dataset:
An Improved Perceptual Hash Algorithm Based on U-Net for the Authentication of High-Resolution Remote Sensing Image

Data security technology is of great significance for the effective use of high-resolution remote sensing (HRRS) images in GIS field. Integrity authentication technology is an important technology to ensure the security of HRRS images. Traditional authentication technologies perform binary level authentication of the data and cannot meet the authentication requirements for HRRS images, while perceptual hashing can achieve perceptual content-based authentication. Compared with traditional algorithms, the existing edge-feature-based perceptual hash algorithms have already achieved high tampering authentication accuracy for the authentication of HRRS images. However, because of the traditional feature extraction methods they adopt, they lack autonomous learning ability, and their robustness still exists and needs to be improved. In this paper, we propose an improved perceptual hash scheme based on deep learning (DL) for the authentication of HRRS images. The proposed method consists of a modified U-net model to extract robust feature and a principal component analysis (PCA)-based encoder to generate perceptual hash values for HRRS images. In the training stage, a training sample generation method combining artificial processing and Canny operator is proposed to generate robust edge features samples. Moreover, to improve the performance of the network, exponential linear unit (ELU) and batch normalization (BN) are applied to extract more robust and accurate edge feature. The experiments have shown that the proposed algorithm has almost 100% robustness to format conversion between TIFF and BMP, LSB watermark embedding and lossless compression. Compared with the existing algorithms, the robustness of the proposed algorithm to lossy compression has been improved, with an average increase of 10%. What is more, the algorithm has good sensitivity to detect local subtle tampering to meet the high-accuracy requirements of authentication for HRRS images.
Estimation of Viterbi path in Bayesian hidden Markov models

The article studies different methods for estimating the Viterbi path in the Bayesian framework. The Viterbi path is an estimate of the underlying state path in hidden Markov models (HMMs), which has a maximum joint posterior probability. Hence it is also called the maximum a posteriori (MAP) path. For an HMM with given parameters, the Viterbi path can be easily found with the Viterbi algorithm. In the Bayesian framework the Viterbi algorithm is not applicable and several iterative methods can be used instead. We introduce a new EM-type algorithm for finding the MAP path and compare it with various other methods for finding the MAP path, including the variational Bayes approach and MCMC methods. Examples with simulated data are used to compare the performance of the methods. The main focus is on non-stochastic iterative methods and our results show that the best of those methods work as well or better than the best MCMC methods. Our results demonstrate that when the primary goal is segmentation, then it is more reasonable to perform segmentation directly by considering the transition and emission parameters as nuisance parameters.
Constructing likelihood functions for interval‐valued random variables

Abstract There is a growing need for flexible methods to analyze interval‐valued data, which can provide efficient data representations for very large data sets. However, the existing descriptive frameworks to achieve this ignore the process by which interval‐valued data are typically constructed, namely, by the aggregation of real‐valued data generated from some underlying process. In this paper, we develop the foundations of likelihood‐based statistical inference for intervals that directly incorporates the underlying data generating procedure into the analysis. That is, it permits the direct fitting of models for the underlying real‐valued data given only the interval‐valued summaries. This generative approach overcomes several problems associated with existing methods, including the rarely satisfied assumption of within‐interval uniformity. The new methods are illustrated by simulated and real data analyses.
Marginal singularity and the benefits of labels in covariate-shift

Transfer Learning addresses common situations in Machine Leaning where little or no labeled data is available for a target prediction problem—corresponding to a distribution Q, but much labeled data is available from some related but different data distribution P. This work is concerned with the fundamental limits of transfer, that is, the limits in target performance in terms of (1) sample sizes from P and Q, and (2) differences in data distributions P, Q. In particular, we aim to address practical questions such as how much target data from Q is sufficient given a certain amount of related data from P, and how to optimally sample such target data for labeling. We present new minimax results for transfer in nonparametric classification (i.e., for situations where little is known about the target classifier), under the common assumption that the marginal distributions of covariates differ between P and Q (often termed covariate-shift). Our results are first to concisely capture the relative benefits of source and target labeled data in these settings through information-theoretic limits. Namely, we show that the benefits of target labels are tightly controlled by a transfer-exponent γ that encodes how singular Q is locally with respect to P, and interestingly paints a more favorable picture of transfer than what might be believed from insights from previous work. In fact, while previous work rely largely on refinements of traditional metrics and divergences between distributions, and often only yield a coarse view of when transfer is possible or not, our analysis—in terms of γ—reveals a continuum of new regimes ranging from easy to hard transfer. We then address the practical question of how to efficiently sample target data to label, by showing that a recently proposed semi-supervised procedure—based on k-NN classification, can be refined to adapt to unknown γ and, therefore, requests target labels only when beneficial, while achieving nearly minimax-optimal transfer rates without knowledge of distributional parameters. Of independent interest, we obtain new minimax-optimality results for vanilla k-NN classification in regimes with nonuniform marginals.
CNNs Based Viewpoint Estimation for Volume Visualization

Viewpoint estimation from 2D rendered images is helpful in understanding how users select viewpoints for volume visualization and guiding users to select better viewpoints based on previous visualizations. In this article, we propose a viewpoint estimation method based on Convolutional Neural Networks (CNNs) for volume visualization. We first design an overfit-resistant image rendering pipeline to generate the training images with accurate viewpoint annotations, and then train a category-specific viewpoint classification network to estimate the viewpoint for the given rendered image. Our method can achieve good performance on images rendered with different transfer functions and rendering parameters in several categories. We apply our model to recover the viewpoints of the rendered images in publications, and show how experts look at volumes. We also introduce a CNN feature-based image similarity measure for similarity voting based viewpoint selection, which can suggest semantically meaningful optimal viewpoints for different volumes and transfer functions.
Vector quantization using the improved differential evolution algorithm for image compression

Vector quantization (VQ) is a popular image compression technique with a simple decoding architecture and high compression ratio. Codebook designing is the most essential part in vector quantization. Linde–Buzo–Gray (LBG) is a traditional method of generation of VQ codebook which results in lower PSNR value. A codebook affects the quality of image compression, so the choice of an appropriate codebook is a must. Several optimization techniques have been proposed for global codebook generation to enhance the quality of image compression. In this paper, a novel algorithm called IDE-LBG is proposed which uses improved differential evolution algorithm coupled with LBG for generating optimum VQ codebooks. The proposed IDE works better than the traditional DE with modifications in the scaling factor and the boundary control mechanism. The IDE generates better solutions by efficient exploration and exploitation of the search space. Then the best optimal solution obtained by the IDE is provided as the initial codebook for the LBG. This approach produces an efficient codebook with less computational time and the consequences include excellent PSNR values and superior quality reconstructed images. It is observed that the proposed IDE-LBG find better VQ Codebooks as compared to IPSO-LBG, BA-LBG and FA-LBG.
Bridging Machine Learning and Cryptography in Defence Against Adversarial Attacks

In the last decade, deep learning algorithms have become very popular thanks to the achieved performance in many machine learning and computer vision tasks. However, most of the deep learning architectures are vulnerable to so called adversarial examples. This questions the security of deep neural networks (DNN) for many security- and trust-sensitive domains. The majority of the proposed existing adversarial attacks are based on the differentiability of the DNN cost function. Defence strategies are mostly based on machine learning and signal processing principles that either try to detect-reject or filter out the adversarial perturbations and completely neglect the classical cryptographic component in the defence. In this work, we propose a new defence mechanism based on the second Kerckhoffs's cryptographic principle which states that the defence and classification algorithm are supposed to be known, but not the key. To be compliant with the assumption that the attacker does not have access to the secret key, we will primarily focus on a gray-box scenario and do not address a white-box one. More particularly, we assume that the attacker does not have direct access to the secret block, but (a) he completely knows the system architecture, (b) he has access to the data used for training and testing and (c) he can observe the output of the classifier for each given input. We show empirically that our system is efficient against most famous state-of-the-art attacks in black-box and gray-box scenarios.
Exploiting Web Images for Weakly Supervised Object Detection

In recent years, the performance of object detection has advanced significantly with the evolution of deep convolutional neural networks. However, the state-of-the-art object detection methods still rely on accurate bounding box annotations that require extensive human labeling. Object detection without bounding box annotations, that is, weakly supervised detection methods, are still lagging far behind. As weakly supervised detection only uses image level labels and does not require the ground truth of bounding box location and label of each object in an image, it is generally very difficult to distill knowledge of the actual appearances of objects. Inspired by curriculum learning, this paper proposes an easy-to-hard knowledge transfer scheme that incorporates easy web images to provide prior knowledge of object appearance as a good starting point. While exploiting large-scale free web imagery, we introduce a sophisticated labor-free method to construct a web dataset with good diversity in object appearance. After that, semantic relevance and distribution relevance are introduced and utilized in the proposed curriculum training scheme. Our end-to-end learning with the constructed web data achieves remarkable improvement across most object classes, especially for the classes that are often considered hard in other works.
Bilattice logic properly displayed

We introduce a proper multi-type display calculus for bilattice logic (with conflation) for which we prove soundness, completeness, conservativity, standard subformula property and cut elimination. Our proposal builds on the product representation of bilattices and applies the guidelines of the multi-type methodology in the design of display calculi.
PASiam: Predicting Attention Inspired Siamese Network, for Space-Borne Satellite Video Tracking

Tracking a moving target of interests from a space-borne satellite video is really challenging. The difficulty lies in that the target usually occupies only several pixels, so that its features are very difficult to obtain. Besides, appearance features of the target would be unobvious when it is occluded, suffers from illumination variation influence or moves to similarity surroundings. In this paper, we propose a PREDICTING ATTENTION Inspired SIAMESE NETWORK (PASiam) for space-borne satellite video tracking, which constructs a fully convolutional Siamese network with shallow-layer features to obtain fine-grained appearance features. Moreover, a predicting attention is proposed to deal with occlusion and obscure. It employs Gaussian mixture models (GMM) to detect the target's motion status, and Kalman filter to predict and correct the target's location. Quantitative evaluations are performed on three real satellite video datasets. The results show our approach outperforms the state-of-the-art tracking methods while running at 54.83 FPS.
Improving Robustness of Heterogeneous Serverless Computing Systems via Probabilistic Task Pruning

Cloud-based serverless computing is an increasingly popular computing paradigm. In this paradigm, different services have diverse computing requirements that justify deploying an inconsistently Heterogeneous Computing (HC) system to efficiently process them. In an inconsistently HC system, each task needed for a given service, potentially exhibits different execution times on each type of machine. An ideal resource allocation system must be aware of such uncertainties in execution times and be robust against them, so that Quality of Service (QoS) requirements of users are met. This research aims to maximize the robustness of an HC system utilized to offer a serverless computing system, particularly when the system is oversubscribed. Our strategy to maximize robustness is to develop a task pruning mechanism that can be added to existing task-mapping heuristics without altering them. Pruning tasks with a low probability of meeting their deadlines improves the likelihood of other tasks meeting their deadlines, thereby increasing system robustness and overall QoS. To evaluate the impact of the pruning mechanism, we examine it on various configurations of heterogeneous and homogeneous computing systems. Evaluation results indicate a considerable improvement (up to 35%) in the system robustness.
Experimental Study on 164 Algorithms Available in Software Tools for Solving Standard Non-Linear Regression Problems

In the specialized literature, researchers can find a large number of proposals for solving regression problems that come from different research areas. However, researchers tend to use only proposals from the area in which they are experts. This paper analyses the performance of a large number of the available regression algorithms from some of the most known and widely used software tools in order to help non-expert users from other areas to properly solve their own regression problems and to help specialized researchers developing well-founded future proposals by properly comparing and identifying algorithms that will enable them to focus on significant further developments. To sum up, we have analyzed 164 algorithms that come from 14 main different families available in 6 software tools (Neural Networks, Support Vector Machines, Regression Trees, Rule-Based Methods, Stacking, Random Forests, Model trees, Generalized Linear Models, Nearest Neighbor methods, Partial Least Squares and Principal Component Regression, Multivariate Adaptive Regression Splines, Bagging, Boosting, and other methods) over 52 datasets. A new measure has also been proposed to show the goodness of each algorithm with respect to the others. Finally, a statistical analysis by non-parametric tests has been carried out over all the algorithms and on the best 30 algorithms, both with and without bagging. Results show that the algorithms from Random Forest, Model Tree and Support Vector Machine families get the best positions in the rankings obtained by the statistical tests when bagging is not considered. In addition, the use of bagging techniques significantly improves the performance of the algorithms without excessive increase in computational times.
Deep Reinforcement Learning in Match-3 Game

An increasing number of algorithms in deep reinforcement learning area creates new challenges for environments, particularly, for their comprehensive analysis and searching application areas. The key purpose of this article is to provide an extensible environment for researches. We consider a Match-3 game, which has simple gameplay, but challenging game design for engaging players. The article provides metrics for evaluation of agents and corresponding baselines in different scenarios.
Activities and Operations with Cryptocurrencies and Their Taxation Implications: The Spanish Case

The purpose of this article was to attempt to shed light on the taxation of activities or actions related to cryptocurrencies. For this purpose, a small analysis was carried out on the nature, operation, and characteristics of cryptocurrencies. Subsequently, from the point of view of the Spanish tax system, the tax implications of the use, actions, and operations carried out with this virtual medium were discussed. The most recent information derived from the binding inquiries issued by the general directorate of taxes in Spain, an institution under the Ministry of Finance, was reviewed. This article analyzed whether the activities related to Bitcoin should be declared for the purposes of personal income tax, property tax, inheritance tax and finally, in the tax on transmissions of assets and documented legal acts. Finally, special mention was made of activities, such as the mining of Bitcoins in “Bitcoin farms” and the exploitation of websites for buying and selling cryptocurrencies and vending machines. Other collateral situations were also analyzed, such as the system to be used.
Multiple-information security system using key image phase and chaotic random phase encoding in Fresnel transform domain

A novel multiple information security system based on key image phase and chaotic random phase encoding is proposed. First, a key color image is separated into R, G, and B channels. Each channel of key image is scrambled by using a chaotic permutation method based on the two-coupled logistic maps and then key phase mask is produced. Second, secret color image is split into R, G, and B channels. Each channel of secret image is phase encoded and modulated by corresponding key phase mask and chaotic random phase mask. Each modulated channel of authenticated users is combined independently together to get three resultant channels, which are Fresnel transformed to obtain corresponding encrypted channels. The parameters of key phase mask and chaotic random phase mask are exploited as decryption keys, which provide nonlinearity to proposed system. Consequently, it circumvents potential attacks. The remarkable advantage of the proposed system is introduction of key phase mask for an individual user, as the key phase mask having multiple decryption keys is constructed by scrambled key image of an individual user. Moreover, the storage, transmission and management of decryption keys are convenient. The proposed system can be executed by using a simple hybrid optoelectronic system. Numerical simulation results demonstrate the viability and efficiency of the proposed system.
Feature Learning for Offline Handwritten Signature Verification Using Convolutional Neural Network

In biometrics, handwritten signature verification can be considered as an important topic. In this article, the authors' proposed method to verify handwritten signatures are based on deep convolution neural network (CNN), which is s bio-inspired network that works as if there exists human brain. Deep CNN extracts features from the studied images, which is followed by cubic support vector machine for classification. To evaluate their proposed work, the authors have tested on three different datasets: GPDS, BME2 and SVC20, and have received encouraging results.
EMBDN: An Efficient Multiclass Barcode Detection Network for Complicated Environments

This article presents a novel method for efficient barcodes detection in real and complicated environments using a convolutional neural network (CNN)-based model. The method is developed as a preprocess-module of existing decoders to enhance decoding rates. Our method is trained as an end-to-end model to determine accurate locations of four barcode vertexes. Our method consists of four modules: 1) base net module; 2) region proposals generator; 3) classification and regression module; and 4) distortion removal module. The feature of barcodes extracted from the base net is fed to the next module. Region proposals are generated and selected as region of interest (ROI). Then the ROI are forward propagated to the classification and regression module to determine the positions and shapes of the barcodes. Finally, the distortion removal module is used to remove the geometric distortion according to regression parameters acquired from the previous step. The accurate position and distorted barcodes shape can be determined and corrected by our method. We validate our method on a challenging large-scale dataset in experiments. Compared with the previous methods, our method provides an end-to-end solution to determine accurate locations of barcode vertexes, which shows an excellent performance on detection accuracy. In addition, our method can enhance decoding rate through distortion removal.
MOOC-KG: A MOOC Knowledge Graph for Cross-Platform Online Learning Resources

The exponential growth of Massive Open Online Courses (MOOCs) provides increasing learning options along with distinguishing difficulties in choosing courses or finding learning paths for learners. To improve online learning resource utilization, a knowledge graph, MOOC-KG, collecting and formulating the information of online courses on major platforms was constructed. The aim of MOOC-KG is to not only provide users with a new MOOC resource organization but also supply an easy approach for learning resource using among several platforms. In our work, an acknowledged educational ontology was adopted to model the knowledge of online learning resources and the web information extraction methods were developed to collect the metadata on major platforms. The entity disambiguation and conflict resolution methods were adopted to integrate the resources among different platforms. As a result, MOOC-KG is the biggest MOOC-related knowledge graph, which represents and stores 28591 instances with their corresponding relations, including 4 platforms, 604 universities, 18671 teachers and 9312 courses until now. Related valuable data analyses illustrate the fundamental information and reveal explorable parts of MOOC-KG. We have released the related project on GitHub to facilitates further improvement and extensive use.