Reviewerly/exHarmony huggingface dataset:
Three-Dimensional Geological–Geophysical Modeling and Prospecting Indications of the Ashele Ore Concentration Area in Xinjiang Based on Irregular Sections

The Ashele ore concentration area is an important area for polymetallic ore concentration in Xinjiang, China. Scholars have made progress in understanding the ore-controlling structures, ore-bearing horizons, and metallogenic age of this area. However, there are still uncertainties about the 3D distributions of plutons, fault structures, and ore-bearing strata, which restrict the development of deep and peripheral ore prospecting and the discovery of new ore bodies in the area. This study proposes a geological–geophysical modeling method based on irregular sections and uses this method to establish a 3D geological–geophysical model based on physical property data, boreholes, surface geological maps, and geophysical data. The model shows that the study area has many hidden rock masses with various depths and shapes and fracture structures with complex shapes. The fault structure in the area is complex, and the ore bodies are controlled by the faults. The ore-bearing geological units (Ashele Formation) exhibit an obvious east–west-trending W-shaped fold structure. The deep part of the northern Ashele Formation extends northward slightly, and the southern Ashele Formation has thick strata, with depths generally greater than 2 km. Based on the information on deep structures provided by the model, three metallogenic prospective areas are predicted, which points out the direction for further prospecting work in the ore concentration area and shows that the adopted modeling method and process have good applicability for constructing 3D models of ore concentration areas with sparse data, large area, and complex geological structures. The proposed modeling method provides technical support for ore prospecting, particularly in the overburden area or ore concentration area with sparse data.
Time-Domain Universal Linear-Optical Operations for Universal Quantum Information Processing

We demonstrate universal and programmable three-mode linear-optical operations in the time domain by realizing a scalable dual-loop optical circuit suitable for universal quantum information processing (QIP). The programmability, validity, and deterministic operation of our circuit are demonstrated by performing nine different three-mode operations on squeezed-state pulses, fully characterizing the outputs with variable measurements, and confirming their entanglement. Our circuit can be scaled up just by making the outer loop longer and also extended to universal quantum computers by incorporating feed forward systems. Thus, our work paves the way to large-scale universal optical QIP.
Selective inference for clustering with unknown variance

In many modern statistical problems, the limited available data must be used both to develop the hypotheses to test, and to test these hypotheses—that is, both for exploratory and confirmatory data analysis. Reusing the same dataset for both exploration and testing can lead to massive selection bias, leading to many false discoveries. Selective inference is a framework that allows for performing valid inference even when the same data is reused for exploration and testing. In this work, we are interested in the problem of selective inference for data clustering, where a clustering procedure is used to hypothesize a separation of the data points into a collection of subgroups, and we then wish to test whether these data-dependent clusters in fact represent meaningful differences within the data. Recent work by Gao, Bien and Witten (2022) provides a framework for doing selective inference for this setting, where a hierarchical clustering algorithm is used for producing the cluster assignments, which was then extended to k-means clustering by Chen and Witten (2022). Both these works rely on assuming a known covariance structure for the data, but in practice, the noise level needs to be estimated—and this is particularly challenging when the true cluster structure is unknown. In our work, we extend this work to the setting of noise with unknown variance, and provide a selective inference method for this more general setting. Empirical results show that our new method is better able to maintain high power while controlling Type I error when the true noise level is unknown.
Mixed Fractional-Order and High-Order Adaptive Image Denoising Algorithm Based on Weight Selection Function

In this paper, a mixed-order image denoising algorithm containing fractional-order and high-order regularization terms is proposed, which effectively suppresses the staircase effect generated by the TV model and its variants while better preserving the edges and details of the image. Adding different regularization penalties in different regions is fundamental to improving the denoising performance of the model. Therefore, a weight selection function is designed using the structure tensor to achieve a more effective selection of regularization terms in different regions. In each iteration, the regularization parameters are adaptively adjusted according to the Morozov discrepancy principle to promote the performance of the algorithm. Based on the primal–dual theory, the original algorithm is improved by using the predictor–corrector scheme to obtain a more accurate approximate solution while ensuring the convergence of the algorithm. The effectiveness of the proposed algorithm is demonstrated through simulation experiments.
Multiscale Dynamic Graph Representation for Biometric Recognition With Occlusions

Occlusion is a common problem with biometric recognition in the wild. The generalization ability of CNNs greatly decreases due to the adverse effects of various occlusions. To this end, we propose a novel unified framework integrating the merits of both CNNs and graph models to overcome occlusion problems in biometric recognition, called multiscale dynamic graph representation (MS-DGR). More specifically, a group of deep features reflected on certain subregions is recrafted into a feature graph (FG). Each node inside the FG is deemed to characterize a specific local region of the input sample, and the edges imply the co-occurrence of non-occluded regions. By analyzing the similarities of the node representations and measuring the topological structures stored in the adjacent matrix, the proposed framework leverages dynamic graph matching to judiciously discard the nodes corresponding to the occluded parts. The multiscale strategy is further incorporated to attain more diverse nodes representing regions of various sizes. Furthermore, the proposed framework exhibits a more illustrative and reasonable inference by showing the paired nodes. Extensive experiments demonstrate the superiority of the proposed framework, which boosts the accuracy in both natural and occlusion-simulated cases by a large margin compared with that of baseline methods.
Despeckling of SAR Images Using Residual Twin CNN and Multi-Resolution Attention Mechanism

The despeckling of synthetic aperture radar images using two different convolutional neural network architectures is presented in this paper. The first method presents a novel Siamese convolutional neural network with a dilated convolutional network in each branch. Recently, attention mechanisms have been introduced to convolutional networks to better model and recognize features. Therefore, we propose a novel design for a convolutional neural network using an attention mechanism for an encoder–decoder-type network. The framework consists of a multiscale spatial attention network to improve the modeling of semantic information at different spatial levels and an additional attention mechanism to optimize feature propagation. Both proposed methods are different in design but they provide comparable despeckling results in subjective and objective measurements in terms of correlated speckle noise. The experimental results are evaluated on both synthetically generated speckled images and real SAR images. The methods proposed in this paper are able to despeckle SAR images and preserve SAR features.
Online Hate: Is Hate an Infectious Disease? Is Social Media a Promoter?

ABSTRACT Our time is marked by a resurgence of hate that threatens to increase oppression. Social media has contributed to this by acting as a medium through which hate speech is spread. How should we model the spread of hate? This article considers two models. First, I consider a simple contagion model. In this model, hate spreads like a virus through a social network. This model, however, fails to capture the fact that people do not acquire hatred from a single infectious contact. Instead, it builds up in a person's beliefs and attitudes through time until the infection reaches a level where the subject themselves becomes a generator of hate speech. Second, to accommodate this, I consider an alternative model known as complex contagion . I argue that not only is a complex contagion model more explanatory and predictive, but it can be used to explain why certain features of social media cause it to be a promoter of hate. I conclude by sketching some mitigation strategies.
AUIE–GAN: Adaptive Underwater Image Enhancement Based on Generative Adversarial Networks

Underwater optical imaging devices are often affected by the complex underwater environment and the characteristics of the water column, which leads to serious degradation and distortion of the images they capture. Deep learning-based underwater image enhancement (UIE) methods reduce the reliance on physical parameters in traditional methods and have powerful fitting capabilities, becoming a new baseline method for UIE tasks. However, the results of these methods often suffer from color distortion and lack of realism because they tend to have poor generalization and self-adaptation capabilities. Generating adversarial networks (GANs) provides a better fit and shows powerful capabilities on UIE tasks. Therefore, we designed a new network structure for the UIE task based on GANs. In this work, we changed the learning of the self-attention mechanism by introducing a trainable weight to balance the effect of the mechanism, improving the self-adaptive capability of the model. In addition, we designed a feature extractor based on residuals with multi-level residuals for better feature recovery. To further improve the performance of the generator, we proposed a dual path discriminator and a loss function with multiple weighted fusions to help model fitting in the frequency domain, improving image quality. We evaluated our method on the UIE task using challenging real underwater image datasets and a synthetic image dataset and compared it to state-of-the-art models. The method ensures increased enhancement quality, and the enhancement effect of the model for different styles of images is also relatively stable.
Nonparametric Mixture MLEs Under Gaussian-Smoothed Optimal Transport Distance

The Gaussian-smoothed optimal transport (GOT) framework, pioneered by Goldfeld et al. and followed up by a series of subsequent papers, has quickly caught attention among researchers in statistics, machine learning, information theory, and related fields. One key observation made therein is that, by adapting to the GOT framework instead of its unsmoothed counterpart, the curse of dimensionality for using the empirical measure to approximate the true data generating distribution can be lifted. The current paper shows that a related observation applies to the estimation of nonparametric mixing distributions in discrete exponential family models, where under the GOT cost the estimation accuracy of the nonparametric MLE can be accelerated to a polynomial rate. This is in sharp contrast to the classical sub-polynomial rates based on unsmoothed metrics, which cannot be improved from an information-theoretical perspective. A key step in our analysis is the establishment of a new Jackson-type approximation bound of Gaussian-smoothed Lipschitz functions. This insight bridges existing techniques of analyzing the nonparametric MLEs and the new GOT framework.
Hyper Coupled Map Lattices for Hiding Multiple Images

The concept of a hyper coupled map lattice (CML) is presented in this paper. The complexity of the lattice is increased not by adding another spatial dimension of the lattice but by replacing scalar nodal variables by multidimensional square matrices of iterative variables. The proposed scheme exploits the nonlinear effects of the spatiotemporal divergence induced by nilpotent nodal matrices to generate separate secret images at different discrete moments of time during the evolution of the CML. The time variable plays the primary role in the decoding stage of the scheme. The carrying capacity of the proposed scheme is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>n</mi> <mo>−</mo> <mn>1</mn> </math> different dichotomous digital images, where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>n</mi> </math> is the dimension of the nilpotent nodal matrices. Computational experiments are used to demonstrate the efficacy of the proposed scheme.
Blockchain for Healthcare Games Management

More and more serious games have been developed in recent years for patients with aging, rehabilitation, mental, and other healthcare needs. Often, such healthcare games have different experiments or evaluations associated. Rapid advancements in healthcare games see increased regulatory concerns. Unfortunately, there is no authority like the Food and Drug Administration (FDA) in United States for regulatory approvals of healthcare games. Yet, it is not appropriate to use the traditional pharmaceutical FDA approval, which is a tedious and time-consuming process, for healthcare games. We propose a Healthcare Game Blockchain (HGB) to support game developers, healthcare providers, healthcare authorities, and patients. For healthcare game developers, HGB is a common platform to deposit healthcare game prototypes or products. HGB can collect feedback data for analysis similar to drug clinical trials on efficacy and toxicity. Volunteers can be recruited for clinical trials using the healthcare games available on HGB. Once the healthcare games are accepted with a recommended usage, patient gamers can leverage the same HGB platform for treatments following the instructions of healthcare providers. HGB allows healthcare professionals to track the patient responses during their healthcare game medication. Big data analytics can be developed to monitor patient outcomes and healthcare game efficacy. Security and privacy issues are critical in healthcare data handling. HGB has the potential to resolve these limitations and inefficiencies improving data collection, data sharing, and data security by leveraging on the intrinsic properties that blockchain technology provides. This paper explores the possibility of integrating blockchain and healthcare games supporting clinical trials and treatment monitoring.
Business Language for Information Security

Prominent standards and frameworks for information security clearly state that business aspects on the one side, and technical aspects on the other, are equally important for the management of cyber security. Organisations with a relatively low maturity level in security management typically consider information security primarily as a technological issue. For those organisations, information security might not get the necessary support from top-level management because they are predominantly focused on business aspects, and are blind to the role information security plays for business. To obtain support from top-level management the information security practitioners need the skills to influence and help relevant stakeholders to understand how information security can support business objectives. In this debate, it is often argued that it is important to speak the language of management. This means that information security practitioners should learn how to translate technical terms to a business context, so top-level management can understand what it means for them. However, this debate has mostly focused on the importance of speaking the "Business Language for Information Security (BLIS)" but has not elaborated on what this language consists of and how to learn it. This paper proposes BLIS and a framework for how to learn it. By mastering BLIS, security professionals can articulate arguments that top-executive management can easily understand and act on. Therefore, we argue that taking a learning module on BLIS will be valuable and useful for the next generation of students in information security. Said briefly, learning BLIS will help students understand how information security can support business, and also how this can be explained to others.
An Efficient Image Encryption Algorithm Based on Innovative DES Structure and Hyperchaotic Keys

In fact, as a traditional encryption method, DES has been certified as an unsuitable tool for ciphering due to its smaller key space. Further, in concern of the real-time encryption in the current fast communication era, such as 5G, long-time as well as large computational level processes are not gotten into the consideration. As a result, an innovative encryption structure with hyperchaotic keys for efficient encryption is constructed, where the frame of DES structure is applied, the plain image is shuffled through row and column directions in the first round, and then rearranged to be 64 blocks to fit into the frame of DES structure for 4 rounds ciphering with hyperchaotic subkeys. Also, in order to encrypt the content of the image at the block level, a set of alternative S-box has been produced in this article as well. The simulation results indicate that the proposed scheme is feasible and reliable for digital image encrypting, not only a large key space can be obtained, but also the low correlation of the adjacent contents can be achieved, and further, in comparison of several existing approaches, less-computational resource can be proven as well. In particular, due to the innovative DES structure, the computational speed is significantly faster than the original DES algorithm and many other chaos-based image ciphering schemes.
A newly-developed multi-algorithm integration technique for mapping the potentially mineralized alteration zones

The present work introduces a newly-developed mathematical-based Multi-Algorithm Integration (MAI) technique, GIS weighted overlay model, and field investigations for delineating the potentially mineralized alteration zones. The introduced technique was tested on Semna area in Egypt as a case study. Three different algorithms were utilized to map the abundance of six alteration indicator minerals. The introduced MAI technique enabled combining the outputs of the utilized mapping algorithms allowing the delineation of only the localities that were successfully mapped as the potential for alteration zones occurrence by all the three algorithms together. Localities delineated by only one or even two techniques are not included in the output of the MAI technique, giving rise to a higher accuracy than that of individual techniques. The structural linear features were automatically depicted from a hill-shade image generated from a high-accuracy Digital Elevation Model (DEM). A Geographic Information System (GIS) Weighted Overlay Model (WOM) was then implemented to integrate the alteration maps resulting from the MAI technique with the density map of lineaments. A final hydrothermal-related mineralization potentiality map was obtained and validated through fieldwork and spectral analyses for confirming the occurrence of alteration indicator minerals. Samples were collected from the delineated potentially mineralized localities. The spectral signatures of the collected samples were measured, and spectral analyses were performed to reveal the identity of the existing alteration minerals. Various index alteration minerals were recorded in the measured samples including; kaolinite, montmorillonite, sericite, chlorite, carbonates (dolomite), and iron oxides/hydroxide minerals.
Forensic investigation framework for cryptocurrency wallet in the end device

Cryptocurrency is increasingly being utilized in criminal activities such as money laundering and the acquisition of illicit goods from darknet markets, due in part to its pseudonymous and decentralized nature. Despite this, there is currently no widely accepted digital forensic framework to assist in the investigation of cryptocurrency-related crimes. To address this gap, we propose a three-phase framework for the forensic examination of cryptocurrency wallets. This framework consists of three distinct stages: Forensic Readiness, Acquisition, and Investigation. The aim of this study is to offer investigators a standardized methodology for conducting cryptocurrency investigations. To assess the validity of our proposed framework, we conducted experiments using three well-known cryptocurrency wallets. The results of these experiments indicate that the proposed framework holds significant potential as a general framework for cryptocurrency forensics.
Masked transformer through knowledge distillation for unsupervised text style transfer

Abstract Text style transfer (TST) aims at automatically changing a text’s stylistic features, such as formality, sentiment, authorial style, humor, and complexity, while still trying to preserve its content. Although the scientific community has investigated TST since the 1980s, it has recently regained attention by adopting deep unsupervised strategies to address the challenge of training without parallel data. In this manuscript, we investigate how relying on sequence-to-sequence pretraining models affects the performance of TST when the pretraining step leverages pairs of paraphrase data. Furthermore, we propose a new technique to enhance the sequence-to-sequence model by distilling knowledge from masked language models. We evaluate our proposals on three unsupervised style transfer tasks with widely used benchmarks: author imitation, formality transfer, and polarity swap. The evaluation relies on quantitative and qualitative analyses and comparisons with the results of state-of-the-art models. For the author imitation and the formality transfer task, we show that using the proposed techniques improves all measured metrics and leads to state-of-the-art (SOTA) results in content preservation and an overall score in the author imitation domain. In the formality transfer domain, we paired with the SOTA method in the style control metric. Regarding the polarity swap domain, we show that the knowledge distillation component improves all measured metrics. The paraphrase pretraining increases content preservation at the expense of harming style control. Based on the results reached in these domains, we also discuss in the manuscript if the tasks we address have the same nature and should be equally treated as TST tasks.