Reviewerly/exHarmony huggingface dataset:
Cyclegean: Cycle Generative Enhanced Adversarial Network for Voice Conversion

Cycle Generative Adversarial Network (CycleGAN) for voice conversion (VC) task only used discriminators to identify whether the input voice is generated or real. It means the confrontational does not check the similarity with the target voice, leading the generated voice not much similar to the target. In this paper, instead of vocal checking, we propose to enhance the confrontation to target similarity checking that addresses this problem. A Cycle Generative Enhanced Adversarial Network (CycleGEAN) was introduced to make the original two discriminators to target classifier and non-target classifier. The target classifier aims to identify whether the target speaks the input voice or not. Similarly, the non-target classifier identifies the non-target voice. Furthermore, we add a gradient reversal layer with different operations for target and non-target. Then in each GAN, we used both classifiers. One is the discriminator, and the other is trained for using in another GAN. In experiments, the proposed method compare to CycleGAN improves Mean Opinion Score (MOS) of 0.1 and Voice Similarity Score (VSS) of 0.2 on the Voice Conversion Challenge 2018 (VCC2018) dataset.
Seeking anonymity on the Internet: The knowledge accumulation process and global usage of the Tor network

The Onion Router (Tor) network is one of the most prominent technologies for accessing online resources while preserving anonymity. Effectively employing the technology is not a trivial process and involves the following steps: (1) motivated by needs, (2) becoming aware of and learning the technology, and (3) realizing desired purposes by usage. Using country-level panel data, this study examines the knowledge accumulation process through which motivated users eventually employ Tor. The results suggest that Tor is often searched in less free countries for censorship circumvention, while it is employed for Dark Web activities in more free countries. There is also an indirect relationship between being aware of the technology and its usage through how-to knowledge accumulation. This study is the first attempt to understand the role of knowledge accumulation in the global usage of Tor. The findings provide insights into the worldwide concerns of online privacy and Dark Web regulation.
Learning MLatent Representations for Generalized Zero-Shot Learning

In generative adversarial network (GAN) based zero-shot learning (ZSL) approaches, the synthesized unseen visual features are inevitably prone to seen classes since the feature generator is merely trained on seen references, which causes the inconsistency between visual features and their corresponding semantic attributes. This visual-semantic inconsistency is primarily induced by the non-preserved semantic-relevant components and the non-rectified semantic-irrelevant low-level visual details. Existing generative models generally tackle the issue by aligning the distribution of the two modalities with an additional visual-to-semantic embedding, which tends to cause the hubness problem and ruin the diversity of visual modality. In this paper, we propose a novel generative model named learning modality-consistent latent representations GAN (LCR-GAN) to address the problem via embedding the visual features and their semantic attributes into a shared latent space. Specifically, to preserve the semantic-relevant components, the distributions of the two modalities are aligned by maximizing the mutual information between them. And to rectify the semantic-irrelevant visual details, the mutual information between original visual features and their latent representations is confined within an appropriate range. Meanwhile, the latent representations are decoded back to both modalities to further preserve the semantic-relevant components. Extensive evaluations on four public ZSL benchmarks validate the superiority of our method over other state-of-the-art methods.
Compositional Graph Convolutional Networks for 3D Human Pose Estimation

3D human pose estimation (HPE) from a single image can be decomposed into two subtasks, i.e., 2D HPE followed by 2D-to-3D pose lifting. Despite recent success in 2D HPE, 3D pose regression from 2D detections remains challenging due to the substantial depth ambiguity. Recently, graph convolutional networks (GCNs) have been exploited to model the relationships among body joints and demonstrate promising results. In this paper, we go one step further along this direction and propose a novel framework, termed Compositional GCN, for 3D HPE. It learns compositional relationships among body parts of different semantic levels and then exploits multilevel structural reasoning to reduce the depth uncertainty. Furthermore, we introduce a novel part-aware graph convolution. It not only disentangles self and neighbor transformations but also captures different relational patterns between each part and their respective neighbors. Experimental results demonstrate the effectiveness of the proposed approach.
Concept of annual solar window and simple calculation for optimal monthly tilt angle to maximize solar power generation

The paper describes the concept of annual solar window that is like an imaginary umbrella over the solar PV panel, which is based on physical year-round observations of sunrise from a common observatory location. The observatory location and the window corners have been fixed with the help of solar declination angle in a year. It has been calculated that the solar window may have a physical dimension of roughly 0.2x30km over the panel in a year; the smaller dimension represents the sunrise (and sunset) location points gathered in a year as a straight line with December and June being the extreme points and March and September located in-between almost overlapping with each other. The bigger dimension is the daily solar path from east to west. This paper also calculates the optimum monthly tilt angle for the solar panel at equator based on declination angle of equator, which is then extrapolated to any other location by substituting latitude of the location. Additionally, the theoretically calculated monthly tilt angle has been verified and validated with the experimental results. It is also proved that there would be more than 12% power loss, if there is no monthly tilt is provided to the solar panel. This leads to suggesting a simple hand operated mechanism to change the tilt angle at least every month, which could be done by the service personnel who go for cleaning of the solar panels at regular intervals. These calculations are size (power capacity) independent of the power plant.
CiFi: Versatile Analysis of Class and Field Immutability

Reasoning about immutability is important for preventing bugs, e.g., in multi-threaded software.So far, static analysis to infer immutability properties has mostly focused on individual objects and references.Reasoning about fields and entire classes, while significantly simpler, has gained less attention.Even a consistently used terminology is missing, which makes it difficult to implement analyses that rely on immutability information.We propose a model for class and field immutability that unifies terminology for immutability flavors considered by previous work and covers new levels of immutability to handle lazy initialization and immutability dependent on generic type parameters.Using the OPAL static analysis framework, we implement CiFi, a set of modular, collaborating analyses for different flavors of immutability, inferring the properties defined in our model.Additionally, we propose a benchmark of representative test cases for class and field immutability.We use the benchmark to showcase CiFi's precision and recall in comparison to state of the art and use CiFi to study the prevalence of immutability in real-world libraries, showcasing the practical quality and relevance of our model.
Multi-Scale Application of Advanced ANN-MLP Model for Increasing the Large-Scale Improvement of Digital Data Visualisation Due to Anomalous Lithogenic and Anthropogenic Elements Distribution

The main objective of this paper is to compare and improve spatial distributions models for Pb and Cu in air and soil using the universal kriging and ANN-MLP at the macro regional scale. For this purpose, both models have been applied for visualization of a spatial distribution of lead (Pb) and copper (Cu) in a morphologically and geologically complex area. Two river basins in the eastern part of North Macedonia, have been selected as the main research region due to the extensive anthropogenic impact of long-lasting mining activities, with emphasis on the specific geochemistry of the area. Two environmental media (soil and moss) have been selected as they are much more available as space from biospheres submitted for destruction processes globally. Surface soil and moss as bio-indicator element measurements were submitted in correlation with geospatial data obtained from DEM, land cover data, and remote sensing, and are incorporated into spatial distribution mapping using an advanced prediction modeling technique, ANN-MPL. Both methods have been further compared and evaluated. The comparative data outputs have led to the general conclusion that ANN-MPL gives more realistic, reliable, and comprehensive results than the universal kriging method for the reconstruction of main distribution pathways. The more the factors influencing the process of distribution of the elements increase, the more the use of ANN-MPL improves.
Visual and semantic similarity norms for a photographic image stimulus set containing recognizable objects, animals and scenes

We collected visual and semantic similarity norms for a set of photographic images comprising 120 recognizable objects/animals and 120 indoor/outdoor scenes. Human observers rated the similarity of pairs of images within four categories of stimuli-inanimate objects, animals, indoor scenes and outdoor scenes-via Amazon's Mechanical Turk. We performed multidimensional scaling (MDS) on the collected similarity ratings to visualize the perceived similarity for each image category, for both visual and semantic ratings. The MDS solutions revealed the expected similarity relationships between images within each category, along with intuitively sensible differences between visual and semantic similarity relationships for each category. Stress tests performed on the MDS solutions indicated that the MDS analyses captured meaningful levels of variance in the similarity data. These stimuli, associated norms and naming data are made available to all researchers, and should provide a useful resource for researchers of vision, memory and conceptual knowledge wishing to run experiments using well-parameterized stimulus sets.
An Empirical Method for Causal Inference of Constructs for QoE in Haptic–Audiovisual Communications

This article proposes an empirical method for inferring causal directions in multidimensional Quality of Experience (QoE) in multimedia communications, noting that causation in QoE is perceptual. As an example for modeling framework, we pick up a Bayesian structural equation model (SEM) previously built for haptic audiovisual interactive communications. The SEM includes three constructs (Audiovisual quality, Haptic quality, and User experience quality), which are latent variables each representing a group of observed variables with similar characteristics. In the SEM, the causal directions of the constructs were assumed by resorting to the domain knowledge. This article aims at proposing a methodology for inferring causal directions of constructs in general by verifying the assumption of causal directions in the SEM through their observed data alone. For that purpose, we compare six SEMs each with different causal directions of constructs, one of which is the one from the domain knowledge. The proposed method is based on QoE prediction by a Bayesian approach with Markov chain Monte Carlo (MCMC) simulation. Setting observed scores to the indicators of exogenous variables in each SEM, we predict values of all the indicators; we then assess the mean square error (MSE) between predicted QoE and mean opinion score (MOS) from observed scores and estimate the probability distribution of the MSE in each SEM. We can compare any two SEMs to find which is more plausible by examining the probability that the MSE for one SEM is smaller than or equal to that for the other. These probabilities are estimated with MCMC simulation. The method indicates that the causal directions thus inferred for the haptic audiovisual interactive communications adequately support the original ones drawn from the domain knowledge. In addition, we demonstrate that QoE can behave like the “impact-perceive-adapt” model of the effects of delayed haptic and visual feedback on performance in a collaborative environment, which Jay, Glencross, and Hubbold proposed in 2007, and that it accompanies reversal of plausible causal directions like a flip–flop.
The Art of Data Privacy

Abstract Statistics are vital for understanding society, but they can pose a risk to the privacy of individuals who contribute their data. Claire McKay Bowen illustrates some of the methods used to minimise that risk – with the aid of a famous artwork
Modifying the SMOTE and Safe-Level SMOTE Oversampling Method to Improve Performance

Synthetic Minority Oversampling Technique (SMOTE) is a sampling method that oversamples the minority class by computing median feature vectors between nominal feature samples and its potential nearest neighbours by Euclidean distance of standard deviations. The objective of SMOTE is therefore to generate a new data instance between the two existing data instances of the minority class. However, the challenge is that the existing SMOTE and Safe-Level SMOTE techniques do not always generate new data instances between two given minority class data instances, which leads to the problem of the two algorithms violating the objective of the SMOTE algorithm. The Safe-Level SMOTE is a family of SMOTE oversampling techniques that generate a new data instance between two minority data instances at the safe level of the minority class. In this study, the SMOTE and Safe-Level SMOTE techniques were re-designed to always meet the objectives of the SMOTE while preserving or improving the capabilities of the algorithms. The study shows that the original Safe-Level SMOTE oversampling technique performs better than the SMOTE oversampling technique. Furthermore, the study shows that the rectified SMOTE oversampling technique performs better than Safe-Level SMOTE oversampling technique. The performances of the original SMOTE and Safe-Level SMOTE algorithms and the proposed algorithms were compared using artificial neural network, support vector machine, naïve Bayesian, decision tree and k-Nearest Neighbour machine learning techniques.
Real-Time Variants of Vertical Synchrosqueezing: Application to Radar Remote Sensing

This article presentsthorough research on using high-order vertical synchrosqueezing (VSS) in different radar remote sensing applications. The method well established in the literature is examined and compared to the novel form of third-order VSS and first-order VSS using an enhanced estimator of the instantaneous frequency, both proposed by the authors. An investigation shows that the two introduced variants of VSS are characterized by preserved capabilities (understood as the possibility to concentrate the time-frequency distribution and its reconstruction) with significantly reduced computation cost. The research shows that in practical radar remote sensing applications, high-order VSS can be successfully replaced by the approach proposed in this article with a lower computational burden. Furthermore, the methods are validated under numerical experiments, both simulated and real-life, which showed the efficiency of the proposed methods in radar signal processing, particular component extraction, and signal decomposition. Moreover, the authors developed the real-time graphical-processing-unit-based implementation of the proposed techniques and presented its efficiency in practical conditions.
Fast and optimal joint decision and estimation by quantized data via noisy channels of sensor networks

Joint decision and estimation (JDE) problems arise in a wide range of practical applications. This work addresses the JDE problem in a distributed scenario using quantized data from noisy channels to achieve optimized decision and estimation at fusion center. Most previous works mainly focus on JDE from measurement acquired by single sensor, and certain class of JDE algorithms suffer from the high computational complexity for involving numerical search. To address these issues, we first propose a distributed JDE model incorporating the essential factors, e.g., spatial correlation of measurement noise between neighboring nodes, information loss due to necessary quantization for limited bandwidth, and unavoidable transmission errors caused by noisy channels. Secondly, we theoretically investigate the distributed JDE model, then propose an optimal JDE algorithm. Thirdly, we propose an equality constrained linear programming (ECLP) algorithm for solving optimal decision rule. The core idea is using the equality constraints of detection error probabilities to simplify the decision-dependent estimation cost, such that the expensive numerical search can be eliminated. Extensive simulations verify that, the proposed distributed JDE algorithm along with ECLP achieves a lower cost and more stable estimation in terms of worst-case mean square error, and faster than the state-of-the-art JDE algorithms.
A parallel sampling algorithm for some nonlinear inverse problems

Abstract We derive a parallel sampling algorithm for computational inverse problems that present an unknown linear forcing term and a vector of nonlinear parameters to be recovered. It is assumed that the data are noisy and that the linear part of the problem is ill-posed. The vector of nonlinear parameters ${m} $ is modeled as a random variable. A dilation parameter $\alpha $ is used to scale the regularity of the linear unknown and is also modeled as a random variable. A posterior probability distribution for $({m}, \alpha )$ is derived following an approach related to the maximum likelihood (ML) regularization parameter selection (Galatsanos, N. P. &amp; Katsaggelos, A. K. (1992). Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation. IEEE Trans. Image Process., 1, 322–336). A major difference in our approach is that, unlike in Galatsanos, N. P. &amp; Katsaggelos, A. K. (1992, Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation. IEEE Trans. Image Process., 1, 322–336), we do not limit ourselves to the maximum likelihood value of $\alpha $. We then derive a parallel sampling algorithm where we alternate computing proposals in parallel and combining proposals to accept or reject them as in Calderhead, B. (2014, A general construction for parallelizing metropolis- hastings algorithms. Proc. Natl Acad Sci, 111, 17408–17413). This algorithm is well suited to problems where proposals are expensive to compute. We then apply it to an inverse problem in seismology. We show how our results compare favorably to those obtained from the ML, the Generalized Cross Validation and the Constrained Least Squares algorithms.
Image classification with parallel KPCA‐PCA network

Abstract Principal component analysis (PCA) is widely used in computer vision for object detection. In this article, we take advantage of the algorithms of PCA and kernel principal component analysis (KPCA) to construct a deep learning model named parallel KPCA‐PCA network (PK‐PCANet). In the proposed model, both of the given PCA and KPCA algorithm are aiming to calculate the filters that will be used in the following convolution layers. The extracted features from PCANet and KPCANet are fused by the strategy of parallel feature fusion. With the aim of reducing the dimensionality of the learned features, the algorithm of compressed sensing is incorporated in the proposed network. According to the cooperative advantages of deep learning network and compressed sensing, the proposed PK‐PCANet model obtains some improvements in several visual recognition tasks. Extensively experiments are performed on face recognition, hand‐written digit recognition and object classification, and the experimental results on various image classification benchmarks such as Extended Yale B, AR, MNIST, CIFAR‐10, and VOC 2007 validated the efficiency of the proposed method of PK‐PCANet.
FeatInter: Exploring fine-grained object features for video-text retrieval

In this paper, we target the challenging task of video-text retrieval. The common way for this task is to learn a text-video joint embedding space by cross-modal representation learning, and compute the cross-modality similarity in the joint space. As videos typically contain rich information, how to represent videos in a joint embedding space is crucial for video-text retrieval. The majority of works typically depend on pre-extracted frame-level features or clip-level features for video representation, which may cause fine-grained object information in videos to be ignored. To alleviate it, we explicitly introduce more fine-grained object-level features to enrich video representation. In order to exploit the potential of the object-level features, we propose a new model named FeatInter, which jointly considers the visual and semantic features of objects. Besides, a visual-semantic interaction and a cross-feature interaction are proposed to mutually enhance object features and frame features. Extensive experiments on two challenging video datasets, i.e., MSR-VTT and TGIF, demonstrate the effectiveness of our proposed model. Moreover, our model achieves a new state-of-the-art on TGIF. While the state-of-the-art methods use seven video features on MSR-VTT, our model with just three features obtains comparable performance.
Animal face classification based on deep learning

Using deep learning to automatically identify and classify wildlife can greatly improve the efficiency of wildlife protection strategies. Based on the public data set of Animal Faces-HQ, this essay analyzed methods to classify different types of animals. Then, this essay developed the four means to predict animal species. First two adopts traditional Convolutional neural networks, while later two construct the deep-learning model based on Auto Encoder. Experimental results showed that our four methods is accuracy and effectiveness while the model in which SVM is superimposed on neural network feature extractor performs slightly better. To verify the applicability of our model, this research applied this model in additional test set of data, and it also achieved satisfactory fitting results.
Application of graph domination to defend medical information networks against cyber threats

The principal and supreme concern in medical information systems is the issue of safe guarding the electronic health records of patients. Defending a health care industry's computer network against attacks on its nodes and links requires placing mobile guards on the nodes of a network. Bloom graph topologies are attractive networks that are potential structures for massively parallel computers. This article focuses on the computation of exact value of the parameters which gives the minimum number of guards required to protect the bloom networks with a linear time algorithm. This research highlights the benefits of locating domination sets in locating the minimum number of detection devices or cyber security employees (mobile guards) to be deployed on the significant servers (nodes) of the bloom's topology (healthcare network) which is essential for securing the network to fight against cyber threats.