Reviewerly/exHarmony huggingface dataset:
Petrographic and Geochemical Analysis of Indus Sediments: Implications for Placer Gold Deposits, Peshawar Basin, NW Himalaya, Pakistan

Alluvial placer gold deposits that accumulate in streams across the globe are essentially allochthonous sedimentary deposits that are transported from their source to their location of deposition. The purpose of this study was to find placer gold deposits in alluvial sediments along the Indus River in the NW Himalaya of Pakistan. The investigated region was divided into five clusters based on river morphology. The research focused on mineralogical distribution, sediment source, gold transport distance, elemental composition, and gold concentration in river sediments. Throughout, during the deposition at point and channel bars, the mixed source of alluvial sediments was revealed by its mineralogical composition. However, the SEM analysis demonstrated that the gold grains had a high flatness index, indicating that they had been transported a significant distance. The elemental composition of several samples revealed a significant concentration of silicon dioxide, iron oxide, and alumina oxide transported from the Himalaya. The study of the trace elements indicated gold and related base metals with granitic provenance were transported from an acidic/granitic source. Based on gold concentration, it is demonstrated that the left riverbank (clusters C1, C2, and C3) has a higher concentration of gold deposits and higher potential for economic gold exploration than the right bank (clusters C4 and C5) due to the lower velocity of running water in the study area. The findings revealed that the studied area has good potential for commercial exploration of gold resources, and the workflow can be adopted in any region with a similar geological setting and morphology.
An Easy to Use Deep Reinforcement Learning Library for AI Mobile Robots in Isaac Sim

The use of mobile robots for personal and industrial uses is becoming popular. Currently, many robot simulators with high-graphical capabilities can be used by engineering to develop and test these robots such as Isaac Sim. However, using that simulator to train mobile robots with the deep reinforcement learning paradigm can be very difficult and time-consuming if one wants to develop a custom experiment, requiring an understanding of several libraries and APIs to use them together correctly. The proposed work aims to create a library that conceals configuration problems in creating robots, environments, and training scenarios, reducing the time dedicated to code. Every developed method is equivalent to sixty-five lines of code at maximum and five at minimum. That brings time saving in simulated experiments and data collection, thus reducing the time to produce and test viable algorithms for robots in the industry or academy.
RV-TMO: Large-Scale Dataset for Subjective Quality Assessment of Tone Mapped Images

Tone mapping operators (TMO) are functions that map high dynamic range (HDR) images to a standard dynamic range (SDR), while aiming to preserve the perceptual cues of a scene that govern its visual quality. Despite the increasing number of studies on quality assessment of tone mapped images, current subjective quality datasets have relatively small numbers of images and subjective opinions. Moreover, existing challenges in transferring laboratory experiments to crowdsourcing platforms put a barrier for collecting large-scale datasets through crowdsourcing. In this work, we address these challenges and propose the RealVision-TMO (RV-TMO), a large-scale tone mapped image quality dataset. RV-TMO contains 250 unique HDR images, their tone mapped versions obtained using four TMOs and pairwise comparison results from seventy unique observers for each pair. To the best of our knowledge, this is the largest dataset available in the literature for quality evaluation of TMOs by the number of tone mapped images and number of annotations. Furthermore, we provide a content selection strategy to identify interesting and challenging HDR images. We also propose a novel methodology for observer screening in pairwise experiments. Our work does not only provide annotated data to benchmark existing objective quality metrics, but also paves the path to building new metrics for tone mapping quality evaluation.
Blockchain analytics in healthcare: An Overview

We can’t consider blockchain only as a change, but as a fast phenomenon that is already on the move. It is a new wave of disturbances that has come to redesign the interactions that involve any form of exchange of values. It brings a new perspective on security, resilience, and effectiveness of systems. It initially became popular with the emergence of cryptocurrencies. Currently, it is more than just a base for this type of coins, since its materialization it has already made inroads in different areas and industries, which we can highlight the area of health and healthcare. It was originally designed to hold transaction data, however, there is growing interest in providing analytics capabilities. The stored data can be used to respond to organizational needs such as: providing provenance histories, predictive planning, fraud identification, regulatory compliance, etc. In this article, in section 2, an overview of blockchain technology will be presented. An introduction to the topic Business Analytics will be carried out in section 3, so that it can later be related to the topic under study. The Blockchain Analytics concept will be presented in general in section 4. In the following section we will present several applicability of this technology in the health sector and, finally, a small conclusion and the future work that we intend to follow will be presented.
The Application of Data Encryption Technology in Computer Network Communication Security

With the development of computer network and the popularization of information system, the security of databases, as platforms for centralized storage and sharing of information system data, has increasingly become a serious problem in the field of information security, so data encryption technology has come into people’s sight. However, the current data encryption technology still has certain shortcomings, such as the inconsistency of the code text of the encryption and decryption technology, and the low efficiency of decoding and encryption. The purpose of this paper is to study the application of data encryption technology in computer network communication to provide better suggestions and explore better methods for improving network communication security system. This article, through the analysis of the database threat model and database application system structure, puts forward a design scheme of database encryption system model. A complete database encryption system can be divided into five logic modules: a key storage module, key engine module, key information module, key management module, and data storage module, and provides an in-depth analysis of the various parts and implementation details related to these modules. Finally, the designed encryption system model is programmed to be implemented; with the help of online examination system, the function and performance of the encryption system were tested; and the transmission efficiency of data encryption is maintained at more than 95%, which proves the effectiveness of the system.
Gesture Spotter: A Rapid Prototyping Tool for Key Gesture Spotting in Virtual and Augmented Reality Applications

In this paper we examine the task of key gesture spotting: accurate and timely online recognition of hand gestures. We specifically seek to address two key challenges faced by developers when integrating key gesture spotting functionality into their applications. These are: i) achieving high accuracy and zero or negative activation lag with single-time activation; and ii) avoiding the requirement for deep domain expertise in machine learning. We address the first challenge by proposing a key gesture spotting architecture consisting of a novel gesture classifier model and a novel single-time activation algorithm. This key gesture spotting architecture was evaluated on four separate hand skeleton gesture datasets, and achieved high recognition accuracy with early detection. We address the second challenge by encapsulating different data processing and augmentation strategies, as well as the proposed key gesture spotting architecture, into a graphical user interface and an application programming interface. Two user studies demonstrate that developers are able to efficiently construct custom recognizers using both the graphical user interface and the application programming interface.
Combining Multiple Classifiers using Ensemble Method for Anomaly Detection in Blockchain Networks: A Comprehensive Review

Blockchain is one of the most anticipated technology revolutions, with immense promise in various applications.It is a distributed and encrypted database that can address a range of challenges connected to online security and trust.While many people identify Blockchain with cryptocurrencies such as Bitcoin, it has a wide range of applications in supply chain management, health, Internet of Things (IoT), education, identity theft prevention, logistics, and the execution of digital smart contracts.Although Blockchain Technology (BT) has numerous advantages for Decentralized Applications (DApps), it is nevertheless vulnerable to abuse, smart contract failures, security, theft, trespassing, and other concerns.As a result, using Machine Learning (ML) models to detect anomalies is an excellent way to detect and safeguard blockchain networks from criminal activity.Adapting ensemble learning methods in ML to create better prediction outcomes is a viable approach for anomaly identification.Ensemble learning, as the name implies, refers to creating a stronger and more accurate classification by combining the prediction results of numerous weak models.As a result, an in-depth evaluation of ensemble learning methodologies for anomaly detection in the blockchain network ecosystem is applied in this paper.It comprises numerous ensemble methods (e.g., averaging, voting, stacking, boosting, bagging).The review collects data from three established databases, which are Scopus, Web of Science (WoS), and Google Scholar.Specific keywords are employed, such as Blockchain, Ethereum, Bitcoin, Anomaly Detection, and Ensemble Learning, employing advanced searching algorithms.The results of the search found 60 primary articles from 2017 to 2022 (30 from Scopus, 20 from the WoS, and 10 from Google Scholar).Based on these findings, we decided to divide our debate into three primary themes: (1) the fundamentals of Blockchain Technology (BT), (2) the overview of ensemble learning, and (3) the integration and analysis of ensemble learning in blockchain networks for anomaly detection.In terms of awareness and knowledge, the results are also discussed in terms of what they mean and where future research should go.
Distributed Momentum-Based Frank-Wolfe Algorithm for Stochastic Optimization

This paper considers distributed stochastic optimization, in which a number of agents cooperate to optimize a global objective function through local computations and information exchanges with neighbors over a network. Stochastic optimization problems are usually tackled by variants of projected stochastic gradient descent. However, projecting a point onto a feasible set is often expensive. The Frank-Wolfe (FW) method has well-documented merits in handling convex constraints, but existing stochastic FW algorithms are basically developed for centralized settings. In this context, the present work puts forth a distributed stochastic Frank-Wolfe solver, by judiciously combining Nesterov's momentum and gradient tracking techniques for stochastic convex and nonconvex optimization over networks. It is shown that the convergence rate of the proposed algorithm is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(k^{-\frac{1}{2}})$</tex> for convex optimization, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(1/\log_{2}(k))$</tex> for nonconvex optimization. The efficacy of the algorithm is demonstrated by numerical simulations against a number of competing alternatives.
Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer’s Disease

We propose a novel framework for Alzheimer's disease (AD) detection using brain MRIs. The framework starts with a data augmentation method called Brain-Aware Replacements (BAR), which leverages a standard brain parcellation to replace medically-relevant 3D brain regions in an anchor MRI from a randomly picked MRI to create synthetic samples. Ground truth "hard" labels are also linearly mixed depending on the replacement ratio in order to create "soft" labels. BAR produces a great variety of realistic-looking synthetic MRIs with higher local variability compared to other mix-based methods, such as CutMix. On top of BAR, we propose using a soft-label-capable supervised contrastive loss, aiming to learn the relative similarity of representations that reflect how mixed are the synthetic MRIs using our soft labels. This way, we do not fully exhaust the entropic capacity of our hard labels, since we only use them to create soft labels and synthetic MRIs through BAR. We show that a model pre-trained using our framework can be further fine-tuned with a cross-entropy loss using the hard labels that were used to create the synthetic samples. We validated the performance of our framework in a binary AD detection task against both from-scratch supervised training and state-of-the-art self-supervised training plus fine-tuning approaches. Then we evaluated BAR's individual performance compared to another mix-based method CutMix by integrating it within our framework. We show that our framework yields superior results in both precision and recall for the AD detection task.
Whole Slide Cervical Cancer Screening Using Graph Attention Network and Supervised Contrastive Learning

Cervical cancer is one of the primary factors that endanger women's health, and Thin-prep cytologic test (TCT) has been widely applied for early screening. Automatic whole slide image (WSI) classification is highly demanded, as it can significantly reduce the workload of pathologists. Current methods are mainly based on suspicious lesion patch extraction and classification, which ignore the intrinsic relationships between suspicious patches and neglect the other patches apart from the suspicious patches, and therefore limit their robustness and generalizability. Here we propose a novel method to solve the problem, which is based on graph attention network (GAT) and supervised contrastive learning. First, for each WSI, we extract and rank a large number of representative patches based on suspicious cell detection. Then, we select the top-K and bottom-K suspicious patches to construct two graphs seperately. Next, we introduce GAT to aggregate the features from each node, and use supervised contrastive learning to obtain valuable representations of graphs. Specifically, we design a novel contrastive loss so that the latent distances between two graphs are enlarged for positive WSIs and reduced for negative WSIs. Experimental results show that the proposed GAT method outperforms conventional methods, and also demonstrate the effectiveness of supervised contrastive learning.
Improved recurrent generative model for reconstructing large-size porous media from two-dimensional images

Modeling the three-dimensional (3D) structure from a given 2D image is of great importance for analyzing and studying the physical properties of porous media. As an intractable inverse problem, many methods have been developed to address this fundamental problems over the past decades. Among many methods, the deep learning--(DL) based methods show great advantages in terms of accuracy, diversity, and efficiency. Usually, the 3D reconstruction from the 2D slice with a larger field-of-view is more conducive to simulate and analyze the physical properties of porous media accurately. However, due to the limitation of reconstruction ability, the reconstruction size of most widely used generative adversarial network--based model is constrained to ${64}^{3}$ or ${128}^{3}$. Recently, a 3D porous media recurrent neural network based method (namely, 3D-PMRNN) (namely 3D-PMRNN) has been proposed to improve the reconstruction ability, and thus the reconstruction size is expanded to ${256}^{3}$. Nevertheless, in order to train these models, the existed DL-based methods need to down-sample the original computed tomography (CT) image first so that the convolutional kernel can capture the morphological features of training images. Thus, the detailed information of the original CT image will be lost. Besides, the 3D reconstruction from a optical thin section is not available because of the large size of the cutting slice. In this paper, we proposed an improved recurrent generative model to further enhance the reconstruction ability (${512}^{3}$). Benefiting from the RNN-based architecture, the proposed model requires only one 3D training sample at least and generates the 3D structures layer by layer. There are three more improvements: First, a hybrid receptive field for the kernel of convolutional neural network is adopted. Second, an attention-based module is merged into the proposed model. Finally, a useful section loss is proposed to enhance the continuity along the $Z$ direction. Three experiments are carried out to verify the effectiveness of the proposed model. Experimental results indicate the good reconstruction ability of proposed model in terms of accuracy, diversity, and generalization. And the effectiveness of section loss is also proved from the perspective of visual inspection and statistical comparison.
MEA-Net: A Lightweight SAR Ship Detection Model for Imbalanced Datasets

The existing synthetic aperture radar (SAR) ship datasets have an imbalanced number of inshore and offshore ship targets, and the number of small, medium and large ship targets differs greatly. At the same time, the existing SAR ship detection models in the application have a huge structure and require high computing resources. To solve these problems, we propose a SAR ship detection model named mask efficient adaptive network (MEA-Net), which is lightweight and high-accuracy for imbalanced datasets. Specifically, we propose the following three innovative modules. Firstly, we propose a mask data balance augmentation (MDBA) method, which solves the imbalance of sample data between inshore and offshore ship targets by combining mathematical morphological processing and ship label data to greatly improve the ability of the model to detect inshore ship targets. Secondly, we propose an efficient attention mechanism (EAM), which effectively integrates channel features and spatial features through one-dimensional convolution and two-dimensional convolution, to improve the feature extraction ability of the model for SAR ship targets. Thirdly, we propose an adaptive receptive field block (ARFB), which can achieve more effective multi-scale detection by establishing the mapping relationship between the size of the convolution kernel and the channel of feature map, to improve the detection ability of the model for ship targets of different sizes. Finally, MEA-Net is deployed on the Jeston Nano edge computing device of the 2 GB version. We conducted experimental validation on the SSDD and HRSID datasets. Compared with the baseline, the AP of MEA-Net increased by 2.18% on the SSDD dataset and 3.64% on the HRSID dataset. The FLOPs and model parameters of MEA-Net were only 2.80 G and 0.96 M, respectively. In addition, the FPS reached 6.31 on the Jeston Nano, which has broad application prospects.
An investment and banking driven growth and development - virtuous mark of finances in economy

This paper aims to establish the impact of both fiscal and monetary aspects on the growth and development of the Indian economy. The causality between GDP and FDI and also between FDI and all dimensions of development is evaluated. Findings provide a comprehensive image of how FDI has impacted a developing country like India in terms of growth and development. The paper also aims to establish the impact of banking parameters (financialisation) such as broad money, commercial bank branches, domestic credit to the private sector, automated teller machines, lending interest rate, listed domestic companies, total market capitalisation of listed domestic sector companies and stocks traded on both growth and development. The primary findings portray the positive impact of commercial bank branches, domestic credit to the private sector, lending interest rate, and market capitalisation of listed domestic sector companies on the overall development of India.
Magnetic object recognition with magnetic gradient tensor system heading-line surveys based on kernel extreme learning machine and sparrow search algorithm

We found that single heading-line surveys from magnetic gradient tensor system (MGTS) can be used to realize pattern recognition of magnetic objects, such as shape and posture, which can greatly improve the target detection efficiency compared with the two-dimensional grid measurement. Abandoning complex mathematical process, we measure and learn several training routes in advance, and use kernel extreme learning machine (KELM) and sparrow search algorithm (SSA) to recognize the target. The magnetic gradient tensor and its derived variables are analyzed for the sensitivity of the magnetization direction, and two types of characteristic attributes suitable for the target posture and shape categories are summarized. Time-domain waveform feature extraction from continuously sampled signals helps build datasets with corresponding category labels. Principal component analysis (PCA) is used to reduce feature dimensionality and improve classifier efficiency. Both simulation and experiment dataset have achieved 100% accurate recognition of the target posture and shape categories.
Augmented Multi-Party Computation Against Gradient Leakage in Federated Learning

Multi-Party Computation (MPC) provides an effective cryptographic solution for distributed computing systems so that local models with sensitive information are encrypted before sending to the centralized servers for aggregation. Though direct local knowledge leakages are eliminated in MPC-based algorithms, we observe the server can still obtain the local information indirectly in many scenarios, or even reveal the groundtruth images through methods like Deep Leakage from Gradients (DLG). To eliminate such possibilities and provide stronger protections, we propose an augmented MPC approach by encrypting local models with two rounds of decomposition before transmitting to the server. The proposed solution allows us to remove the constraint that servers must be honest in the general federated learning settings since the true global model is hidden from the servers. Specifically, the augmented MPC algorithm encodes local models into multiple secret shares in the first round, then each share is furthermore split into a public share and a private share. Consequences of such a two-round decomposition are that the augmented algorithm fully inherits the advantages of standard MPC by providing lossless encryption and decryption while simultaneously rendering the global model invisible to the central server. Both theoretical analysis and experimental verification demonstrate that such an augmented solution can provide stronger protections for the security and privacy of the training data, with minimal extra communication and computation costs incurred.