Reviewerly/exHarmony huggingface dataset:
Origin and evolution of gold-bearing fluids in a carbon-rich sedimentary basin: a case study of the Algamarca epithermal gold-silver-copper deposit, northern Peru

Sediment-hosted gold deposits account for the major part of economic gold in the Earth's crust. However, the origin of the gold-bearing fluid and its evolution in sedimentary basins in the presence of organic carbon and its metamorphosed products such as graphite are poorly known. In an attempt to clarify these issues, we performed an integrated mineralogical, geochemical, and fluid-inclusion study of the Algamarca epithermal Au-Ag-Cu deposit, hosted by Mesozoic sediments corresponding to an over-mature petroleum system within the Marañón fold and thrust belt (northern Peru). Results show that mineralization started with a pre-gold stage characterized by quartz veins containing gold-poor pyrite and chalcopyrite. Most gold was deposited afterwards, during the main gold stage in an “invisible” form within arsenian pyrite, followed by minor visible native gold with sulfosalts and chalcopyrite at a later stage. Fluid inclusions in quartz from the pre-gold and gold stages show features analogous to those observed in porphyry Cu-Au systems such vapor-liquid immiscibility, enrichment in K, Rb, Cu, As, and Sb, a wide range of salinity (5-35 wt % NaCl eq.), and similar elemental (atomic) ratios (Zn/Pb ∼4, 0.1<K/Na<5, Br/Cl ∼0.06), all consistent with a fluid of magmatic origin. In addition, the fluid inclusions from the pre-gold stage are highly enriched in CO2 (∼60 mol% in gas phase), CH4 (∼10 mol%) and H2S (∼30 mol%). Such high volatile contents are rather unusual for typical porphyry-epithermal systems and likely reflect reactions between the magmatic fluid and carbon-bearing sediments. This conclusion is independently supported by the temperature values of graphite metamorphic peak determined by Raman spectroscopy, which are similar to those derived by fluid-inclusion microthermometry in quartz veins. Our findings imply that strong interactions of magmatic fluid with carbonaceous matter favored gold transport through the sedimentary basin and its subsequent concentration in arsenian pyrite. Furthermore, our results point to a possible presence of porphyry-style mineralization beneath the sedimentary sequence hosting the epithermal Algamarca deposit, thereby providing new potential for exploration.
Answering Uncertain, Under-Specified API Queries Assisted by Knowledge-Aware Human-AI Dialogue

Developers’ API needs should be more pragmatic, such as seeking suggestive, explainable, and extensible APIs rather than the so-called best result. Existing API search research cannot meet these pragmatic needs because they are solely concerned with query-API relevance. This necessitates a focus on enhancing the entire query process, from query definition to query refinement through intent clarification to query results promoting divergent thinking about results. This paper designs a novel Knowledge-Aware Human-AI Dialog agent (KAHAID) which guides the developer to clarify the uncertain, under-specified query through multi-round question answering and recommends APIs for the clarified query with relevance explanation and extended suggestions (e.g., alternative, collaborating or opposite-function APIs). We systematically evaluate KAHAID. In terms of human-AI dialogue process, it achieves a high diversity of question options (the average diversity between any two options is 74.9%) and the ability to guide developers to find APIs using fewer dialogue rounds (no more than 3 rounds on average). For API recommendation, KAHAID achieves an MRR and MAP of 0.769 and 0.794, outperforming state-of-the-art API search approaches BIKER and CLEAR by at least 47% in MRR and 226.7% in MAP. For knowledge extension, KAHAID obtains an MRR and MAP of 0.815 and 0.864, surpassing state-of-the-art query clarification approaches by at least 42% in MRR and 45.2% in MAP. As the first of its kind, KAHAID opens the door to integrating the immediate response capability of API research and the interaction, clarification, explanation, and extensibility capability of social-technical information seeking.
Bayesian Networks for Interpretable Cyberattack Detection

The challenge of cyberattack detection can be illustrated by the complexity of the MITRE ATT&CKTM matrix, which catalogues >200 attack techniques (most with multiple sub-techniques). To reliably detect cyberattacks, we propose an evidence-based approach which fuses multiple cyber events over varying time periods to help differentiate normal from malicious behavior. We use Bayesian Networks (BNs) – probabilistic graphical models consisting of a set of variables and their conditional dependencies – for fusion/classification due to their interpretable nature, ability to tolerate sparse or imbalanced data, and resistance to overfitting. Our technique utilizes a small collection of expert-informed cyber intrusion indicators to create a hybrid detection system that combines data-driven training with expert knowledge to form a host-based intrusion detection system (HIDS). We demonstrate a software pipeline for efficiently generating and evaluating various BN classifier architectures for specific datasets and discuss explainability benefits thereof.
3D-aided image augmentation for occluded pedestrian detection based on parallel vision

Deep learning algorithms rely heavily on diverse and high-quality datasets to excel in complex tasks. However, in intricate traffic scenarios, manual data collection often falls short in terms of diversity due to the challenges of replicating real-world scenes. While techniques such as Generative Adversarial Networks (GANs) have been used for image generation, they frequently overlook the impact of object relationships on data diversity. In this paper, our primary focus is on the perception of pedestrians, especially those who are occluded, within complex driving scenes. We introduce a novel 3D-aided image augmentation technique known as "Drive-CP," which is based on the principles of parallel vision. This innovative approach enriches the dataset by generating instances of occluded pedestrians within the original images, thereby significantly enhancing the diversity of the training data. Experimental results consistently demonstrate that detectors trained on our augmented datasets outperform those relying solely on the original data. This work contributes to strengthening the robustness of deep learning algorithms in complex traffic scenarios, providing a more reliable foundation for practical applications.
Convolutional Autoencoders-Based Image Watermarking Techniques

Digital image watermarking plays an important role in securing multimedia content against unauthorized use and manipulation. The realm of image watermarking focuses on its vulnerability to various attacks and the integration of Convolutional Neural Networks (CNNs) to bolster its robustness. This proposed paper illustrates an overview of common attacks such as geometric transformations, compression, and signal processing attacks, which can compromise watermark robustness and integrity. These findings highlight the effectiveness and reliability of the encoder, embedder, decoder, and extractor modules in achieving secure and visually appealing watermarking.
Foggy non-motor vehicle detection algorithm based on image enhancement and full-dimensional dynamic convolution

This paper proposes an improved foggy non-motorized vehicle detection algorithm YOLOv5s-X based on multi-scale image enhancement and the YOLOv5s algorithm. Firstly, the foggy road images are defogged by multi-scale image enhancement to reduce the occlusion of targets by fog. Secondly, full dimensional dynamic convolution is added to the original algorithm to improve the detection capability of the model in complex backgrounds and to optimize the NMS non-extreme suppression value a priori frame to reduce the missed detection rate of non-motorized targets. Finally, the SPPFCSPC module was introduced into the feature pyramid network to increase the perceptual field of the model and improve the feature representation capability. The experimental results show that the average accuracy of YOLOv5s-X in light and dense fog is 4.3 % and 6.3 % higher respectively than the original algorithm, effectively improving the accuracy of non-motorized vehicle detection in foggy conditions.
CombineNet: A Stereo Network Combining Local and Semi-global Aggregation

Cost aggregation is a critical component in the stereo matching task, as it largely determines the quality of the resulting disparity map. However, most existing stereo networks rely solely on 3D convolution, a local aggregation method. In this study, we propose incorporating the traditional semi-global aggregation method, which optimizes an energy function, as a layer in CNN models by backpropagating the gradient from the disparity map to the cost volume. This approach offers the advantage of not requiring any learned parameters in the layer. Furthermore, the same method can be used in the Winner-Take-All (WTA) strategy instead of soft-argmin for disparity prediction. We also introduce an end-to-end stereo network (CombineNet) that combines both local and semi-global aggregation. Our results demonstrate that this network achieves competitive performance on Scene Flow and KITTI datasets, and outperforms advanced networks for foreground disparity prediction.
A Comprehensive Comparative Analysis of Sentiment Analysis Models: CNN-LSTM vs. Hierarchical Attention Network (HAN)

As the digital world continues to evolve, sentiment analysis plays an increasingly crucial role in deciphering public opinion and influencing a company's image. This research delves deep into a comparative analysis of two powerful sentiment analysis models: Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and Hierarchical Attention Network (HAN). Through a meticulously crafted study utilizing the vast Sentiment140 dataset, we evaluate their performance, analyze their architecture, and delve into their computational efficiency and processing time. Our focus remains unwavering - to identify the most effective model for predicting sentiment towards a company's image, a key factor in shaping its success in today's competitive landscape.
Facial Landmark Detection Using Machine Learning

Facial landmark detection is a pivotal task in the field of computer vision, holding significant implications across various applications, including facial analysis, augmented reality, facial recognition, and emotion detection. In visual communication, the face serves as a primary means of conveying information, and humans possess an innate ability to discern a wealth of information about another person's intentions, identity, and emotions merely by observing their facial expressions. The process of facial landmark detection using machine learning involves training a model on a dataset comprising real-time facial images, where each image is meticulously annotated with the coordinates of crucial facial landmarks. These landmarks typically include points representing the eyes, nose, mouth, and other facial features. Once trained, the model becomes proficient in recognizing and predicting the precise locations of these landmarks in new, unseen images and videos. The primary objective of this work is to predict facial landmarks in real-time using a live webcam input. This task is achieved through a two-step process. First, a Convolutional Neural Network (CNN) is trained on a comprehensive dataset, such as the 300W-LP dataset available on platforms like Kaggle. This dataset consists of facial images meticulously annotated with 68 distinct facial landmarks. The CNN learns to extract intricate facial features and landmark relationships from this training data, enabling it to recognize these landmarks with high accuracy. With the trained CNN in place, the next step is to extend its capabilities to perform facial landmark detection on live video streams. This involves integrating face detection techniques to identify and isolate faces within the live video feed. Once a face is detected, the CNN can then be applied to predict the locations of the 68 facial landmarks in real-time, enhancing our ability to understand and interpret facial expressions and movements in dynamic scenarios. In summary, this work underscores the significance of facial landmark detection in various computer vision applications. By training a CNN on a dataset of annotated facial images and integrating it with live video feeds, we aim to empower machines with the ability to understand and interpret human facial expressions and gestures in real-time, opening up new possibilities for applications in fields like human-computer interaction, emotion analysis, and beyond.
Clothing Classification using Corner Features in Pedestrian Attribute Recognition Framework

The recognition of pedestrian attributes has become increasingly important in ensuring community safety. It replaces the outdated and cumbersome method of identifying criminal characteristics with a more advanced, efficient, and accurate framework. With the widespread use of Closed-Circuit Television (CCTV) and the emergence of Big Data, an advanced analytic tool can now dissect and understand massive collections of video footage for multiple purposes. To identify pedestrians, this paper focuses on upper-body and lower-body clothing classification using the P-DESTRE dataset which provides multiple attributes for pedestrians. Prior to feature extraction, pre-processing steps using DeepLab for background removal and AlphaPose for body parts recognition are performed. The framework then classifies the collar, upper-body clothing, and lower-body clothing type by utilising a combination of Features from Accelerated Segment Test (FAST), FAST with Non-Maximal Suppression (FASTNMS), and Shi-Tomasi corner detectors. The findings indicate a classification rate of over 90% for all three elements, demonstrating the effectiveness of the method and establishing a framework for recognizing a pedestrian based on upper and lower body clothing.
Book Review: Urban Groundwater

GroundwaterEarly View Book Review Book Review: Urban Groundwater John M. Sharp Jr., Corresponding Author John M. Sharp Jr. [email protected] Department of Earth and Planetary Sciences, University of Texas at Austin, 2275 Speedway Stop C9000, Austin, Texas, 78712-1722 USASearch for more papers by this author John M. Sharp Jr., Corresponding Author John M. Sharp Jr. [email protected] Department of Earth and Planetary Sciences, University of Texas at Austin, 2275 Speedway Stop C9000, Austin, Texas, 78712-1722 USASearch for more papers by this author First published: 26 December 2023 https://doi.org/10.1111/gwat.13381Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Reference Howard, K. 2023. Urban Groundwater. The Groundwater Project, Guelph, Canada. https://gw-project.org/books/urban-groundwater/ (accessed December 4, 2023). Google Scholar Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation
Identifying the probability of genetic mutations in lung cancer using predictive and prognostic biomarkers from histopathological images

Background: Lung cancer is the highest deadliest disease and second largest disease being diagnosed worldwide. In the age of precision medicine, determining a patient’s genetic status is critical. Finding the percentage of gene mutation of a particular biomarker will help in targeted therapy of a patient at an early stage. Objective: Histopathology images are larger in size which needs to be converted into smaller tiles for the computational purpose. Deep Learning Techniques could be applied on this huge number of histopathological images to derive the probability of gene mutation occurrence in predictive and prognostic biomarkers of lung cancer. Methods: In this work, a deep learning convolutional neural network (CNN) model (InceptionV3) is trained on histopathology images obtained from The Cancer Genome Atlas (TCGA) to accurately predict the mutated genes in lung adenocarcinoma. The convolutional neural network-based model predicts 10 major genetic mutations percentage, i.e., EGFR, FAT1, FAT4, KEAP1, KRAS, LRP1B, NF1, SETBP1, STK11, TP53. Results: InceptionV3 predicted the probability of gene mutation from the histopathology images and categorized the genes as predictive and prognostic. InceptionV3 yielded an accuracy of 82.36% and cross entropy of 37.62%. Conclusion: InceptionV3 was trained on histopathology images to predict gene mutations with an accuracy of 82%. Prediction of gene mutations with different CNN models like AlexNet and ResNet can be explored further.
Document Classification Using Lightweight Neural Network

&lt;p&gt;In recent years, OCR data has been used for learning and analyzing document classification. In addition, some neural networks have used image recognition for training, such as the network published by the ImageNet Large Scale Visual Recognition Challenge for document image training, AlexNet, GoogleNet, and MobileNet. Document image classification is important in data extraction processes and often requires significant computing power. Furthermore, it is difficult to implement image classification using general computers without a graphics processing unit (GPU). Therefore, this study proposes a lightweight neural network application that can perform document image classification on general computers or the Internet of Things (IoT) without a GPU. Plustek Inc. provided 3065 receipts belonging to 58 categories. Three datasets were considered as test samples while the remaining were considered as training samples to train the network to obtain a classifier. After the experiments, the classifier achieved 98.26% accuracy, and only 3 out of 174 samples showed errors.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Antique Theater Mask on a Sarmatian Type Buckle: Manufacturing Technology

The work is devoted to studying the technological features of a eight-shaped buckle with a theatrical mask from a stone crypt at the site of the Panticapaeum necropolis (near the modern Maly Lane in the city of Kerch). The study of the elemental composition of the buckle is carried out by scanning electron microscopy in combination with energy-dispersive X-ray microanalysis (SEM/EDX). The study of the internal structure of the object is carried out by the methods of X-ray and neutron tomography. The conducted studies make it possible to determine that the product has a homogeneous internal structure and almost similar elemental composition of all parts. The revealed manufacturing technology of the buckle, as well as the specific features of its external appearance, suggest that this object could be a replica made on the model of another buckle.
Identifying Hate Speech through Syntax Dependency Graph Convolution and Sentiment Knowledge Transfer

In recent years, hate speech spread on the Internet has seriously affected society’s harmony, stability, and development. A way to quickly identify hate speech from the vast amount of data on the Internet is urgent. In this paper, different from previous traditional methods, we explore a novel scenario of constructing a syntax dependency graph for each instance based on the syntactical information retrieved from an external tool. We propose a model called the Dependency Graph Convolutional and Sentiment Knowledge Transfer (DGCSKT) network. DGCSKT utilizes syntactic dependency graphs and dependency graph convolutional operations to enhance the model’s ability to perceive contextual information. Additionally, we introduce sentiment resources that are data-homogeneous as an auxiliary task at the bottom level of the model to share effective sentiment features and improve recognition performance. Then, we propose the Dynamic Normalized Weighting (DNW) method to weight the training information of different tasks and thus improve the model’s generalization ability. Compared to the current state-of-the-art methods, our proposed approach improves the Macro-F1 by 3.88% and 0.54% in OLID and HateEval respectively.
A study on learner perceptions of general English classes utilizing the YouTube platform

This study examines the effects of using YouTube for general English classes on learner perceptions. The study was to find out if using YouTube contents could assist both in learning English and achieving the objectives of general education. Specifically, the research aimed to examine the effects on English proficiency, critical thinking skills, and understanding of other cultures. In this study, 107 university students were taught in general English classes using YouTube videos selected by the researcher. Their perceptions were investigated using online questionnaire. The result showed using YouTube had positive impacts on learners' satisfaction, interest, concentration, understanding, participation, and confidence in English learning. These were due to characteristics of YouTube as a medium and the diversity of content. Additionally, this study found using YouTube had a positive effect on English proficiency, especially on listening and speaking skills. This was attributed to exposure to real conversations and colloquial language. Finally, the study found positive effects on achieving the goals of general education. This was confirmed by the diversity of contents. Also, it was found that the effect could be enhanced when instructors select appropriate contents. According to the results, this study suggests using YouTube helps general English class in many aspects.
Discussion on Statistical Monitoring and Evaluation of High-Quality Development of County Economy

The county economy serves as the fundamental unit in China’s economic development and construction. There exists a significant correlation between the quality and level of county economic development and the overall economic construction level of the country. In recent years, China’s economy has experienced rapid growth, contributing to an improved living environment and substantial economic income for its citizens. However, this progress has also brought to light certain issues, such as an unbalanced industrial structure and inefficient resource utilization, leading to problems such as low efficiency and severe environmental pollution. Therefore, it becomes imperative to enhance the statistical monitoring and evaluation of high-quality county economic development. This approach aims to gather insights into the development status of the county economy and provide essential data support for decision-making in county economic development. Consequently, this paper proposes development suggestions for the statistical monitoring and evaluation of high-quality county economic development, serving as a crucial starting point for future initiatives.