Reviewerly/exHarmony huggingface dataset:
Mineral texture classification using deep convolutional neural networks: an application to zircons from porphyry copper deposits

The texture and morphology of igneous zircon indicates magmatic conditions during zircon crystallisation and can be used to constrain provenance. Zircons from porphyry copper deposits are typically prismatic, euhedral and strongly oscillatory zoned which may differentiate them from zircons associated with unmineralised igneous systems. Here, cathodoluminesence images of zircons from the Quellaveco porphyry copper district, Southern Peru, were collected to compare zircon textures between the unmineralised Yarabamba Batholith and the Quellaveco porphyry copper deposit. Quellaveco porphyry zircons are prismatic, euhedral and strongly oscillatory zoned, whereas the batholith zircons contain more variable morphologies and zoning patterns. We adopt a deep convolutional neural networks (CNNs) approach to demonstrate that a machine can classify porphyry zircons with a high success rate. We trial several existing CNN architectures to classify zircon images: LeNet-5, AlexNet and VGG, including a transfer learning approach where we used the weights of a VGG model pre-trained on the ImageNet dataset. The VGG model with transfer learning is the most effective approach, with accuracy and ROC-AUC scores of 0.86 and 0.93, indicating that a Quellaveco porphyry zircon CL image can be ranked higher than a batholith zircon with 93% probability. Visualising model layer outputs demonstrates that the CNN models can recognise crystal edges, zoning and mineral inclusions. We trial implementing trained CNN models as unsupervised feature extractors, which can empirically quantify crystal textures and morphology. Therefore, deep learning provides a powerful tool for the extraction of petrographic information from minerals which can be applied to constrain provenance in detrital studies.
Local stress field spatio-temporal variations at Campi Flegrei from crustal anisotropy measurements

We compiled a database for the Campi Flegrei seismic events that occurred from 2011 to 2018 at all stations available (merging permanent and temporary networks). Then we computed the two observables of the crustal anisotropy: time delay between fast and slow S-wave&amp;#8217;s arrivals, and polarization direction of the fast S-wave. These results provide useful information about the amount of crustal anisotropy and the main direction, respectively, with this latter representing a proxy for the local stress field. We could thus obtain a picture of their spatial and temporal distributions to be compared with other geophysical and geochemical observations. In particular we could identify common features, such as change points, to several time series. This helps us in building a more complete interpretation of the volcanic system changes that were occurring during the recent ongoing unrest phase, which started in 2005.&amp;#160;
Geochemical composition of agricultural soils and its link to plant Pb contents

The compositional peculiarities of the soil&amp;#8217;s chemical environment must be taken into account to study the possibility of toxic elements (TE) to be accumulated in plants. This research covered 7 provinces (marzes) of Armenia providing around 80.1% of the national total gross agricultural production. From June to October 2019, the sampling procedure was carried out as part of the national residue monitoring program. Pb geochemical associations in agricultural soils were investigated, revealing the link between these associations and Pb contents in plants, as well as determining the source-specific transfer of Pb from soil to plants, using both compositional data analysis (CoDa) and geospatial mapping. CoDa included the combination of the results of k-means clustering and CoDa-biplot and was applied to study the relationship between the TEs and identify their geochemical associations (CoDaPack v.2.02.21 and R statistics). In addition, a hierarchical cluster analysis (HCA) was used to study the links between food Pb contents and soil TE contents in each group of sub-samples identified by k-means clustering.The obtained results showed that the research area&amp;#8217;s unique geology and probable chemical element release sources influenced the soil&amp;#8217;s chemical composition. Using HCA, it was discovered that in every sub-sample, the Pb soil and plant contents were in the same cluster. Particularly, CoDa-biplot and k-means clustering enable the distinction of three distinct sub-samples. However, the geochemical associations of the elements in subsamples I and III showed that Pb plant contents were shown in a geochemical association (K, Rb, Pb, and Zn) typical of both fertilizers and potassium feldspar. In contrast, sub-sample II showed that Pb plant contents were in a geochemical association (K, Rb, Pb, and Zn) typical of carbonates. The transfer factor (TF) for the similarly higher values is observed for the sub-sample associated with the geochemical relationship of K, Rb, Pb, and Zn. Moreover, it has been demonstrated that carbonates had a negative impact on the availability of Pb in plants. This can be explained by the capacity of carbonates in sub-samples I and III to fix Pb and reduce its availability in plants. Based on the study&amp;#8217;s findings, it is important to emphasize that further research on compositional characteristics of chemical elements via the identification of geochemical associations can enable to reveal of possible connections between the elements in various media.
Improving Speaking Skill through Information Gap Activities at Second Semester of Management Study Program at Panca Marga University in 2021/2022 Academic Year

Speaking is the highest target in English language because speaking as the basis of communication which becomes a function in learning. There are many kinds of technique to improve speaking skill, such as information gap technique. The researcher wants to find out whether using information gap activities can improve the students’ speaking skills and analysing the classroom situation when this technique is implemented in speaking class. This research used collaborative classroom action research and composed for three cycles following the procedures of action research, namely planning, action, observation, and reflection. The researcher uses instrument of observations, lists, tests, and questionnaire to collect the data. The data obtained was presented in two ways, they are qualitative and quantitative. The findings showed that IGT activities were effective to improve the speaking skill of the learners in class activities. It can be seen from each cycle by the rise in the students’ mean scores in the test result, 59.8 in the first cycle, 69 in the second cycle, and 81, 8 in the third cycle. In other hand, the improvements also based on the results of the observation that showed positivity and good responses from the learners. From the questionnaire results. it was gotten that the means score for the students’ perception was 3,72. It means that the students give positive response for the implementation of this technique.
Asymptotic Convergence Rate of Dropout on Shallow Linear Neural Networks

We analyze the convergence rate of gradient flows on objective functions induced by Dropout and Dropconnect, when applying them to shallow linear Neural Networks(NN) ---which can also be viewed as doing matrix factorization using a particular regularizer. Dropout algorithms such as these are thus regularization techniques that use 0,1-valued random variables to filter weights during training in order to avoid coadaptation of features. By leveraging a recent result on nonconvex optimization and conducting a careful analysis of the set of minimizers as well as the Hessian of the loss function, we are able to obtain (i) a local convergence proof of the gradient flow and (ii) a bound on the convergence rate that depends on the data, the dropout probability, and the width of the NN. Finally, we compare this theoretical bound to numerical simulations, which are in qualitative agreement with the convergence bound and match it when starting sufficiently close to a minimizer.
White Blood Cells Detection Using Spectral Tresholding

The study of the morphology of White Blood Cells (WBCs) further contributes to the clinical diagnosis of blood diseases.In this research paper, we come up with an image segmentation enhancement by combining Fourier Fast Transform on smear blood capture and classical thresholding.The Fast Fourier Transform (FFT) is a very powerful tool in image processing and it was used to segment and extract the WBCs.Our image processing method uses a Fast Fourier Transform combined with filtering and an Inverse Fast Fourier Transform for the extraction and visualization of the high frequency region of the image.In order to remove residual Red Blood Cells acting as noise in the expected result, a final thresholding step is added at the end of the processing.The results presented in this article report the tests performed using our mathematical implementation.Moreover, we were able to detect and differentiate the sub-families of WBCs.
LDA-BASED COSMETIC SATISFACTION FACTORS MINING

With the rapid development of Internet technology, the convenience of the information age makes it possible for consumers to buy satisfactory products without leaving home. Compared with offline purchase, online purchases of cosmetic products is more convenient and has more kinds of products for users to choose from, and the price and shopping cost of the products are also lower, but the quality, branding, and after-sales service of online products are often criticized by people, and these problems seriously affect user satisfaction. In this paper, we use a text mining method to crawl online reviews of lipsticks as a data source using python language to explore the factors affecting consumer satisfaction, taking a typical experience-based product, lipstick, as an example. Then the LDA topic model is used to cluster the processed text, and the factors affecting consumer satisfaction are summarized and summarized according to the results of text mining, to obtain the composition of factors affecting consumer satisfaction. The research shows that this study not only improves the reliability of determining key factors, but also provides management implications for cosmetic e-commerce enterprises in four aspects: product traits, usage experience, brand value, and purchase intention, respectively. The study shows that this study not only improves the reliability of determining key factors, but also provides management implications for the improvement of cosmetic e-commerce enterprises in four aspects: product traits, usage experience, brand value, and purchase intention.
Energy Efficient and Secure Neural Network–based Disease Detection Framework for Mobile Healthcare Network

Adopting mobile healthcare network (MHN) services such as disease detection is fraught with concerns about the security and privacy of the entities involved and the resource restrictions at the Internet of Things (IoT) nodes. Hence, the essential requirements for disease detection services are to (i) produce accurate and fast disease detection without jeopardizing the privacy of health clouds and medical users and (ii) reduce the computational and transmission overhead (energy consumption) of the IoT devices while maintaining the privacy. For privacy preservation of widely used neural network– (NN) based disease detection, existing literature suggests either computationally heavy public key fully homomorphic encryption (FHE), or secure multiparty computation, with a large number of interactions. Hence, the existing privacy-preserving NN schemes are energy consuming and not suitable for resource-constrained IoT nodes in MHN. This work proposes a lightweight, fully homomorphic, symmetric key FHE scheme (SkFhe) to address the issues involved in implementing privacy-preserving NN. Based on SkFhe, widely used non-linear activation functions ReLU and Leaky ReLU are implemented over the encrypted domain. Furthermore, based on the proposed privacy-preserving linear transformation and non-linear activation functions, an energy-efficient, accurate, and privacy-preserving NN is proposed. The proposed scheme guarantees privacy preservation of the health cloud’s NN model and medical user’s data. The experimental analysis demonstrates that the proposed solution dramatically reduces the overhead in communication and computation at the user side compared to the existing schemes. Moreover, the improved energy efficiency at the user is accomplished with reduced diagnosis time without sacrificing classification accuracy.
Enhancing the emotion task classification in the imbalanced dataset of YouTube© using synthetic minority over-sampling technique

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Shakti Kundu, Anurag Gupta, Ajay Rastogi; Enhancing the emotion task classification in the imbalanced dataset of YouTube© using synthetic minority over-sampling technique. AIP Conf. Proc. 27 February 2023; 2427 (1): 020042. https://doi.org/10.1063/5.0125068 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Blockchain used in apparel supply and textile

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Chitrangada Chaubey; Blockchain used in apparel supply and textile. AIP Conf. Proc. 27 February 2023; 2427 (1): 020035. https://doi.org/10.1063/5.0116490 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Machine learning for data fusion

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Anurag Gupta, Rupal Gupta, Vineet Saxena; Machine learning for data fusion. AIP Conf. Proc. 27 February 2023; 2427 (1): 020104. https://doi.org/10.1063/5.0125054 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
The integrated development environment (IDE) for application development: Android studio and its tools

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Chitrangada Chaubey, Anuska Sharma; The integrated development environment (IDE) for application development: Android studio and its tools. AIP Conf. Proc. 27 February 2023; 2427 (1): 020087. https://doi.org/10.1063/5.0116494 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Android app development using Kotlin programming language

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Anuska Sharma; Android app development using Kotlin programming language. AIP Conf. Proc. 27 February 2023; 2427 (1): 020099. https://doi.org/10.1063/5.0130782 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
The nature of insight from data and implications for automated decisioning: predictive and prescriptive models, decisions, and actions

The purpose of this chapter is to explore in some detail how modern artificial intelligence (AI) and machine learning (ML) technologies can facilitate new insights and inform better decisions in healthcare. More specifically, the discussion will focus on the nature of insights, causal inference, and explainability. It is widely understood that correlation does not imply causation. AI and ML methods are (almost always) applied to historical data, in order to detect “relationships,” that is, correlations. Does that mean that AI/ML models do not lend themselves to be explained, drive insight, and support better informed decisions? Actually, they do. The current chapter first discusses how much of human expertise to “do-things” (e.g., that of an expert surgeon, radiologist, etc.) is acquired through interaction with complex historical and correlational data. Next it will provide an overview of methods and algorithms now commonly in use to explain results and predictions from AI/ML models.
Designing an Interpretability Analysis Framework for Deep Reinforcement Learning (DRL) Agents in Highway Automated Driving Simulation

Explainability is a key requirement for users to effectively understand, trust, and manage artificial intelligence applications, especially those concerning safety. We present the design of a framework aimed at supporting a quantitative explanation of the behavioural planning performed in automated driving (AD) highway simulations by a high-level decision making agent trained through deep reinforcement learning (DRL). The framework relies on the computation of SHAP values and keeps into consideration a neural architecture featuring an attention layer. The framework is particularly devoted to study the relationship between attention and interpretability, and how to represent, analyze and compare attention and SHAP values in a 2D spatial highway environment. The framework features three main visualization areas, that are obtained by processing quantities such as attention, SHAP values, vehicular observations: Episode view, plotting quantities on an episode’s timeline; Frame view, reporting the measurement values step by step; Aggregated view, showing, also on 2D maps, statistical values from the aggregation of several simulation episodes. To the best of our knowledge, this is the first in-depth explainability analysis of a DRL-based decision-making AD system, also including a statistical analysis. We hope that the upcoming open source release of the designed framework will be useful to further advance research in the field.
Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory

The firing accuracy of the projectile has a positive relation with aerodynamic parameters. Due to the complex dynamic characteristics of projectiles, there is an overfitting risk when a single extreme learning machine (ELM) is used to identify the aerodynamic parameters of the projectile, and the identification results oscillate transonic region. To obtain the aerodynamic parameters of the projectile accurately, an aerodynamic parameter identification model based on ensemble learning theory and ELM optimized by improved particle swarm optimization is proposed. The improved particle swarm optimization algorithm (IPSO) with an adaptive update strategy is used to optimize the weight and threshold of ELM. Combined with the ensemble learning theory, the improved ELM neural network is regarded as a weak learner to generate a strong learner. The structural parameters of the strong learner were continuously optimized through training, and an aerodynamic parameter identification model of projectile based on ensemble learning theory is obtained. The simulation results show that the introduction of the IPSO and ensemble learning theory enables the model to exhibit excellent generalization ability. The proposed identification model can accurately describe the variation of aerodynamic parameters with the Mach number.
ePMLF: Efficient and Privacy-Preserving Machine Learning Framework Based on Fog Computing

With the continuous improvement of computation and communication capabilities, the Internet of Things (IoT) plays a vital role in many intelligent applications. Therefore, IoT devices generate a large amount of data every day, which lays a solid foundation for the success of machine learning. However, the strong privacy requirements of the IoT data make its machine learning very difficult. To protect data privacy, many privacy-preserving machine learning schemes have been proposed. At present, most schemes only aim at specific models and lack general solutions, which is not an ideal solution in engineering practice. In order to meet this challenge, we propose an efficient and privacy-preserving machine learning training framework (ePMLF) in a fog computing environment. The ePMLF framework can let the software service provider (SSP) perform privacy-preserving model training with the data on the fog nodes. The security of the data on the fog nodes can be protected and the model parameters can only be obtained by SSP. The proposed secure data normalization method in the framework further improves the accuracy of the training model. Experimental analysis shows that our framework significantly reduces the computation and communication overhead compared with the existing scheme.