Reviewerly/exHarmony huggingface dataset:
Cu isotopes in groundwater for hydrogeochemical mineral exploration: A case study using the world-class Mount Isa Cu–Pb–Zn deposit (Australia)

Copper is the crux resource in the transition to renewable energy sources, with green technologies such as solar panels, wind turbines and batteries all relying on this critical metal for their componentry, and more importantly for connection to electrical grids. While demand intensifies, copper discovery rates continue to fall due to increased scarcity of deposits that are outcropping and/or detectable by conventional means. This has engendered novel methods of detecting Cu-bearing ore under cover, such as hydrogeochemical analyses to trace ore mineral interaction with natural waters. This notably includes the development of Cu isotope systematics in natural waters, wherein proximal to Cu-bearing ore bodies enrichment of water in the heavier Cu isotope occurs (e.g. from oxidative weathering), thus providing a fingerprint of water interaction with Cu-bearing sulfides. Here, Cu isotope compositions for eighteen groundwater samples overlying and distal to the world-class, sediment-hosted stratiform Mount Isa Cu–Zn–Pb deposit were analyzed to assess the utility of groundwater Cu isotope compositions as an exploration tool for Cu-bearing ore under cover. A further 12 chalcopyrite Cu isotope compositions were determined from two drill cores directly overlying the main Cu-bearing ore body, to establish a baseline Cu isotope composition for the Mt Isa deposit. Cu isotope data were synthesized together with field water parameters and placed within a geological context to construct a framework for interpretation. When accounting for industrially impacted sites and underlying geology, results show a clear trend towards heavy Cu isotope enrichment in groundwater proximal to known mineralization, with enrichments of ∼1 per mil (‰) or more relative to distal groundwater and primary deposit chalcopyrite. These results corroborate and expand upon similar work from surface and groundwater samples around porphyry, exotic and IOCG Cu-bearing deposits. Moreover, these results strongly indicate that groundwater Cu isotope systematics for exploration under cover has great potential as a vectoring tool, illustrating that thus far the technique is applicable across deposit types.
Dual spatial constraints-based few-shot image classification

As one of the development directions of artificial intelligence in the future, few-shot learning has attracted more and more attention in recent years. How to make full use of the information of a small amount of samples is one of the main difficulties in the field of few-shot learning. Most of the research work utilizes the meta-learning mechanism to alleviate the negative impact of insufficient samples on model performance. However, the training between subtasks also makes it difficult for meta-learning models to obtain general feature representations between samples. Therefore, researchers are turning their research perspective to supervised learning, and they have drawn a conclusion that embedding models with good performance are simpler and more effective than complex meta-learning models. Recent research work has also proved the importance of feature representation. Based on the above view points and analysis, we propose a few-shot image classification method, which strengthens the difference of samples from different categories and the similarity of samples from the same category and realizes dual constraints in high-dimensional feature space and low-dimensional feature space. Experimental results on four public datasets demonstrate that the proposed method effectively improves the accuracy of image classification with few-shot learning.
Designing of an optical vortices phase mask and used in the frequency domain of linear canonical transform for double image encryption

We present an improved cryptographic system that utilizes optical vortex to implement double image encryption. Each of the grayscale images has been encrypted individually using DRPE. Phase mask incorporating optical vortexes (OV) and RPMs has been handed down in Fourier, space spectrum. Multiply the image to be encrypted by OV, and then perform direct and reverse LCT conversion on the resulting LCT to create the encrypted image. The resemblance can be recovered from the encrypted image by using the correct LCT and OV parameters. Digitalization has been performed using MATLAB (R2020a). In addition to the RPM, OV, and conversion sequence associated with LCT, are additional keys that cause difficulties for unauthorized users. The proposed scheme, therefore, is more secure than conventional technology. In addition, the RMSE between the restored image and the authentic image corroborates the efficacy of the strategy. Results of the numerical analysis demonstrate the effectiveness and safety of the system.
The Omnipresence of Metaverse With the Cultural Conventions

Reality always encompasses us with challenges and creates perplexities. We hanker for a life wherein everything happens according to our wants and desires. With the advancement in technology and the invention of artificial intelligence (AI), such grounds have been initiated through virtual reality wherein the Metaverse plays a significant role. Since the pandemic, the world has been more dependent on the digital world than the real one, and this has led to a major shift in the cultural conventions changing the structure of the society. People now have started to use more digital money and purchase through online stores in comparison to real ones. The omnipresence of the Metaverse will untether the potential changes and meet the desired advancements for the globalised world. It is required to understand the recent changes in the paradigms of the virtual world. The research will be a comprehensive study on how advanced technology has contributed to the dynamic changes in the cultural traits of the global society in this new era of transition.
GENERALIZED PARTIAL MEET AND KERNEL CONTRACTIONS

Abstract Two of the most well-known belief contraction operators are partial meet contractions (PMCs) and kernel contractions (KCs). In this paper we propose two new classes of contraction operators, namely the class of generalized partial meet contractions (GPMC) and the class of generalized kernel contractions (GKC), which strictly contain the classes of PMCs and of KCs, respectively. We identify some extra conditions that can be added to the definitions of GPMCs and of GKCs, which give rise to some interesting subclasses of those classes of functions, namely the classes of extensional and of uniform GPMCs/GKCs. In the context of contractions on belief sets the classes of partial meet contractions, uniform GPMCs and extensional GPMCs are all identical. Nevertheless, when considered as operations on belief bases, the class of uniform GPMCs coincides with the class of partial meet contractions, but the extensional GPMCs constitute a new kind of belief base contraction functions whose characterizing postulate of irrelevance of syntax is extensionality—the same postulate of irrelevance of syntax which occurs in the classical axiomatic characterization of partial meet contractions for belief sets—rather than the postulate of uniformity—which is the irrelevance of syntax postulate used in the axiomatic characterization for partial meet contractions on belief bases. Analogous results are obtained regarding the classes of extensional and of uniform GKCs. We present the interrelations in the sense of inclusion among all the new classes of operators presented in this paper and several well known classes of PMCs and of KCs.
Neural network models for predicting trends in VTuber Industry on Bilibili

Due to the paucity of data for this VTuber industry and the volatility of the data itself, in recent years the processing of datasets in the VTuber industry has remained at the statistical stage, and providing accurate forecasts of industry trends has been quite challenging. In this paper, a new way of thinking about dealing with the VTuber industry on Bilibili datasets is conceived, namely using Neural Networks (NN) and machine learning algorithms to implement predictions. Bilibili is a well-known video-sharing website and also the largest live streaming platform for VTubers in China. More specifically, the prediction of datasets in the VTuber industry using the Long Short-Term Memory (LSTM) model is done based on the TensorFlow machine learning framework. In short, the dataset is processed to match the dimensions of the TensorFlow library input by dividing and preprocessing the training and test datasets, and then the prediction is implemented by the LSTM model. Finally, the error metrics are calculated by Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). The experimental results show that the LSTM model achieves a very high accuracy match in predicting the VTuber industry dataset, achieves a more competitive performance in data processing, and can provide more effective industry predictions for those engaged in the VTuber industry.
Implementation of augmented reality to reduce paper usage in the learning process

Abstract The use of paper directly proportional to the growth in the number of people. The impact is in the form of environmental problems where there is logging of forest trees, and deforestation so that it affects the ecosystem and the natural environment. Trees are cut down and made into paper for human needs, including to make books as learning media. In the current technological era, the use of books as a medium of learning has an impact on students’ declining motivation to learn. To solve the problem, we proposes the application of Augmented Reality (AR) as a learning medium. This research uses Game Engine Unity and Vuforia which can display 3-dimensional objects, and uses Android as the basis of the system. In this paper, we introduce four equipments of athletic sports. The aim of this research is to reduce the use of paper as a learning medium and increase learning motivation. The result of the research is an Augmented Reality-based athletics introduction application that has been tested for its performance using blackbox testing using various conditions of its marker.
Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper, a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search (CS) algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm's ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search (PACS) with CS, Parallel Cuckoo Search (PCS), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Differential Evolution (DE) and Artificial Bee Colony (ABC) algorithms by using the CEC-2013 test function.The results show that PACS algorithm outperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm, this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect, and the material utilization rate is improved from 89.5% to 97.8% after optimization.
A new approach for SPN removal: nearest value based mean filter

In this study, a new adaptive filter is proposed to eliminate salt and pepper noise (SPN). The basis of the proposed method consists of two-stages. (1) Changing the noisy pixel value with the closest pixel value or assigning their average to the noisy pixel in case there is more than one pixel with the same distance; (2) the updating of the calculated noisy pixel values with the average filter by correlating them with the noise ratio. The method developed was named as Nearest Value Based Mean Filter (NVBMF), because of using the pixel value which the closest distance in the first stage. Results obtained with the proposed method: it has been compared with the results obtained with the Adaptive Frequency Median Filter, Adaptive Riesz Mean Filter, Improved Adaptive Weighted Mean Filter, Adaptive Switching Weight Mean Filter, Adaptive Weighted Mean Filter, Different Applied Median Filter, Iterative Mean Filter, Two-Stage Filter, Multistage Selective Convolution Filter, Different Adaptive Modified Riesz Mean Filter, Stationary Framelet Transform Based Filter and A New Type Adaptive Median Filter methods. In the comparison phase, nine different noise levels were applied to the original images. Denoised images were compared using Peak Signal-to-Noise Ratio, Image Enhancement Factor, and Structural Similarity Index Map image quality metrics. Comparisons were made using three separate image datasets and Cameraman, Airplane images. NVBMF achieved the best result in 52 out of 84 comparisons for PSNR, best in 47 out of 84 comparisons for SSIM, and best in 36 out of 84 comparisons for IEF. In addition, values nearly to the best result were obtained in comparisons where the best result could not be reached. The results obtained show that the NVBMF can be used as an effective method in denoising SPN.
Fake Products, Real Effects: Evidence from Special 301 Actions

Abstract We study how the U.S. government’s anti-counterfeiting enforcement actions through Special 301 Reports influence U.S. businesses. We show that anti-counterfeiting enforcement in foreign countries improves U.S. firms’ sales, profitability, and valuations. Firms significantly reduce capital and research and development investments when their brands and products are protected from counterfeiting activities. Anti-counterfeiting enforcement measures also improve brand asset value, brand profitability, brand inventiveness, market penetration, and customer loyalty.
Adaptation of Young Specialists As a Method of Solving Intergenerational Contra-dictions in the Work Collective

The article analyzes the experience of introducing a program for adapting young special-ists to the working environment on the example of a separate enterprise, assesses the ef-fectiveness of the program, identifies its strengths and weaknesses, as well as the causes of contradictions in the team in the process of interaction between novice specialists and experienced co-workers. Recommendations are formulated on how to get out of the most controversial situations of intergenerational interaction in an organization associat-ed with differences in the level of development of communication skills and the ability to work in a team among representatives of different generations. The prospects for fur-ther use of adaptation programs are outlined in order to form a human resource at enterprises, in industries and society as a whole.
Progressive Interpretation Synthesis: Interpreting Task Solving by Quantifying Previously Used and Unused Information

A deep neural network is a good task solver, but it is difficult to make sense of its operation. People have different ideas about how to interpret its operation. We look at this problem from a new perspective where the interpretation of task solving is synthesized by quantifying how much and what previously unused information is exploited in addition to the information used to solve previous tasks. First, after learning several tasks, the network acquires several information partitions related to each task. We propose that the network then learns the minimal information partition that supplements previously learned information partitions to more accurately represent the input. This extra partition is associated with unconceptualized information that has not been used in previous tasks. We manage to identify what unconceptualized information is used and quantify the amount. To interpret how the network solves a new task, we quantify as meta-information how much information from each partition is extracted. We implement this framework with the variational information bottleneck technique. We test the framework with the MNIST and the CLEVR data set. The framework is shown to be able to compose information partitions and synthesize experience-dependent interpretation in the form of meta-information. This system progressively improves the resolution of interpretation upon new experience by converting a part of the unconceptualized information partition to a task-related partition. It can also provide a visual interpretation by imaging what is the part of previously unconceptualized information that is needed to solve a new task.
Software code refactoring based on deep neural network‐based fitness function

Summary Refactoring is extensively recognized for enhancing the internal structure of object‐oriented software while preserving its external behavior. However, determining refactoring opportunities is challenging for designers and researchers alike. In recent years, machine learning algorithms have shown a great possibility of resolving this issue. This study proposes a deep neural network‐based fitness function (DNNFF) to resolve the software refactoring issue. This study suggests an effective learning technique that automatically featured extracted from the trained models and predicted code clones to recommend which category to refactor. The software engineers automatically assess the recommended refactoring solutions using Genetic Algorithms (GA) for minimum iterations. A Deep Neural Networks (DNN) utilizes these training instances to assess the refactoring solutions for the residual iterations. The refactoring process primarily depends on software designers' skills and perceptions. The simulation findings demonstrate that the suggested DNNFF model enhances the code change score of 98.7%, automatic refactoring score of 97.3%, defect correlation ratio of 96.9%, refactoring precision ratio of 95.9%, flaw detection ratio of 94.4%, and reduces the execution time of 10.2% compared to other existing methods.
Suppressing residual vibration caused in objects carried by robots using a heuristic algorithm

When industrial robots transfer objects at high speeds, residual vibrations occur at the target positions. Recently, autonomous mobile robots such as AGV and AMR have become popular. However, the vibrations of the objects to be transported occur when the robot stops moving. Waiting until the residual vibration converges is necessary during the installation and assembly of transported objects. Thus, the overall work time increases even though the robots operate and move at a high speed. If the trajectory, which is the change in the position and attitude of the output point of a robot with time, can be adjusted such that the robot can move and operate within the same operation time by suppressing the residual vibration caused in transported objects, the work time can be significantly reduced. However, suppressing vibration is not easy because it usually requires solving complex equations of motion to consider the dynamics of robots. Therefore, in this study, based on the behavior of an object to be transported, which is measured by actually operating a robot, we propose a method for determining a trajectory that can suppress residual vibration using a heuristic algorithm without performing a kinetic analysis. The trajectories determined by this method reduced the settling time by approximately 90% with only a few dozen robot operations. The proposed method can be widely applied to commercial robots because appropriate trajectories are generated using a simple program that determines the variables used in the robot language without kinetic analysis.
Prevention and Control of Financial Fraud: a Scoping Review

As awareness has grown internationally of the harms caused by financial fraud, there has been a concomitant increase in efforts to prevent and control it. Despite this growing awareness, the literature addressing measures against financial fraud has not yet been systematically reviewed or synthesised. On the basis of a scoping literature review, this study provides a structured overview of the literature on prevention and control measures targeting financial fraud (n = 159). We classify these measures on the basis of four functions specified by Maesschalck and Bertók (2009): “determining and defining,” “guiding,” “monitoring,” and “enforcing.” Looking at a period of just over 20 years, we see that a growing number of studies have been published on this topic, with most focusing on “monitoring” (k = 98) and the least on “enforcing” (k = 37). With 11.3% and 12.0% of the studies respectively, both criminology and Europe are less represented in the literature than expected. Most studies are cross-sectional, many are methodologically weak, and they all provide only limited information on the effectiveness or the costs of the anti-fraud measures. The literature is thus not up to the challenge of facing what has become one of the most frequent and threatening crimes worldwide. In the conclusion, we sketch an agenda for future research to help address this gap.
AdaFI-FCN: an adaptive feature integration fully convolutional network for predicting driver’s visual attention

Visual Attention Prediction (VAP) is widely applied in GIS research, such as navigation task identification and driver assistance systems. Previous studies commonly took color information to detect the visual saliency of natural scene images. However, these studies rarely considered adaptively feature integration to different geospatial scenes in specific tasks. To better predict visual attention while driving tasks, in this paper, we firstly propose an Adaptive Feature Integration Fully Convolutional Network (AdaFI-FCN) using Scene-Adaptive Weights (SAW) to integrate RGB-D, motion and semantic features. The quantitative comparison results on the DR(eye)VE dataset show that the proposed framework achieved the best accuracy and robustness performance compared with state-of-the-art models (AUC-Judd = 0.971, CC = 0.767, KL = 1.046, SIM = 0.579). In addition, the experimental results of the ablation study demonstrated the positive effect of the SAW method on the prediction robustness in response to scene changes. The proposed model has the potential to benefit adaptive VAP research in universal geospatial scenes, such as AR-aided navigation, indoor navigation, and street-view image reading.
Human visual sensitivity based optimal local backlight dimming methodologies under different viewing conditions

An adaptive local backlight dimming algorithm is proposed considering human visual sensitivities under various viewing conditions. Local dimming amount is maximized based on contrast sensitivity function and local image characteristics to minimize perceived luminance and contrast losses maintaining image qualities. Average luminance, contrast and spatial frequency are evaluated as local image characteristics. As dimming based on local characteristics has blocking artifacts, a compensation method using edge luminance difference is proposed considering human responses. The proposed algorithms reduce backlight power consumption by 39.69% on average, with an average SSIM of 0.995. It can be increased by 5.66% with varying viewing conditions, resulting in a 45.35% power consumption reduction on average. The outperformances of the proposed algorithm in power consumption reduction and maintaining image quality are verified with various image quality metrics compared to recent dimming algorithms.