Reviewerly/exHarmony huggingface dataset:
Parameterization of the Ångström–Prescott formula based on machine learning benefit estimation of reference crop evapotranspiration with missing solar radiation data

Abstract Accurately estimated reference evapotranspiration (ET 0 ) is essential to regional water management. The FAO recommends coupling the Penman–Monteith (P‐M) model with the Ångström–Prescott (A‐P) formula as the standard method for ET 0 estimation with missing R s measurements. However, its application is usually restricted by the two fundamental coefficients ( a and b ) of the A‐P formula. This paper proposes a new method for estimating ET 0 with missing R s by combining machine learning with physical‐based P‐M models (PM‐ET 0 ). The benchmark values of the A‐P coefficients were first determined at the daily, monthly, and yearly scales, and further evaluated in R s and ET 0 estimates at 80 national R s measuring stations. Then, three empirical models and four machine‐learning methods were evaluated in estimating the A‐P coefficients. Machine learning methods were also used to estimate ET 0 (ML‐ET 0 ) to compare with the PM‐ET 0 . Finally, the optimal estimation method was used to estimate the A‐P coefficients for the 839 regular weather stations for ET 0 estimation without R s measurement for China. The results demonstrated a descending trend for coefficient a from northwest to southeast China, with larger values in cold seasons. However, coefficient b showed the opposite distribution as the coefficient a . The FAO has recommended a larger a but a smaller b for southeast China, which produced the region's largest R s and ET 0 estimation errors. Additionally, the A‐P coefficients calibrated at the daily scale obtained the best estimation accuracy for both R s and ET 0 , and slightly outperformed the monthly and yearly coefficients without significant difference in most cases. The machine learning methods outperformed the empirical methods for estimating the A‐P coefficients, especially for the sites with extreme values. Further, ML‐ET 0 outperformed the PM‐ET 0 with yearly A‐P coefficients but underperformed those with daily and monthly ones. This study indicates an exciting potential for combining machine learning with physical models for estimating ET 0 . However, we found that using the A‐P coefficients with finer time scales is unnecessary to deal with the missing R s measurements.
Blueprints from plane to space: outlook of next‐generation three‐dimensional histopathology

Abstract Here, we summarize the literature relevant to recent advances in three‐dimensional (3D) histopathology in relation to clinical oncology, highlighting serial sectioning, tissue clearing, light‐sheet microscopy, and digital image analysis with artificial intelligence. We look forward to a future where 3D histopathology expands our understanding of human pathophysiology and improves patient care through cross‐disciplinary collaboration and innovation.
Public order offences: how feasible is their applicability to online hate?

As an area of law in the UK, public order offences are almost entirely useless on social media. This set of offences (ss. 4, 4A and 5 of the Public Order Act 1986) was aimed to address any behaviour or expressive activities, either oral or written, carried out in a context of physical proximity to the victim. In principle, the foundational base of public order offences runs the risk of becoming blurred if we extend their applicability to hateful messages online and, therefore, to any impersonal way of acting. Consequently, only 13% and 14% of the hate crimes committed online in 2016/17 and 2017/18 in England &amp; Wales involved public order offences. Therefore, there is a certain resistance based on the adequacy of these offences to the online environment without requiring the message to be audible or visible to someone, as a matter of immediacy/proximity. We will explain how this glimmer of hope has lasted over time amid fierce opposition to broaden the scope of application of public order offences beyond traditional public forums, such as disturbances triggering in a city's main square.
App Crafting for An Educational Institution

In the modern world, internet tools like Google Classroom, Gmail, Google Forms, WhatsApp, and others have become more and more important for teacher-student communication and information exchange. We created an app that works with all platforms, including tablets, computers, and mobile phones, to meet the various needs of educational institutions. All devices are easily supported by the Android app for websites optimized for mobile and tablets. This app satisfies all of the standards of the school by offering extensive information on attendance, exam schedules, lecture notes, cost data, event notifications, and online assessments. The newest technologies, such as Flutter and Dart with Firebase integration, were used in the development of this application. We also developed a desktop-friendly web application. Both the app and this website are linked to the same
Meeting Transcriber

In a world where collaboration is increasingly digital, the efficiency and security of meetings have become paramount. Our project introduces a comprehensive online meeting platform extension that leverages advanced technologies to enhance the meeting experience. Using BERT for data classification, question answering, and summarization, along with an advanced Language Model (LLM) for data protection, we address common challenges such as information overload, language barriers, limited searchability, and a lack of actionable insights. This extension aims to revolutionize meetings, making them more productive, collaborative, and secure
Algorithm to satisfy <i>l</i>‐diversity by combining dummy records and grouping

Abstract Universities and corporations frequently use personal information databases for diverse objectives, such as research and marketing. The use of these databases inherently intersects with privacy issues, which have been the subject of extensive research. Traditional anonymization techniques predominantly focus on removing or altering identifiers and quasi‐identifiers (QIDs), the latter of which, although not unique, are closely correlated with individuals. However, this modification of QIDs can often impede data analysis. In this study, we introduce an innovative anonymization algorithm that combines the dummy‐record addition technique with a grouping method while circumventing the modification of QIDs. This fusion reduces the number of dummy records required for effective anonymization. The principal contribution of this study is the algorithm's ability to reduce the number of added dummy records. The proposed algorithm not only retains a high degree of data usefulness but also successfully adheres to the ‐diversity standard, which is a critical metric in privacy security. The experimental findings demonstrate that the proposed method offers a more equitable balance between safety and utility than existing technological solutions.
Quantum computer based feature selection in machine learning

Abstract The problem of selecting an appropriate number of features in supervised learning problems is investigated. Starting with common methods in machine learning, the feature selection task is treated as a quadratic unconstrained optimisation problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. The different results in small problem instances are compared. According to the results of the authors’ study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, the authors compare the convergence behaviour of the QUBO methods via quantum computing with classical stochastic optimisation methods. Due to persisting error rates, the classical stochastic optimisation methods are still superior.
Explainable artificial intelligence-based evidential inferencing on process faults in plasma etching

Abstract The fault detection and classification (FDC) modeling proposed in this study is a research approach that is intended to improve the performance of plasma process models by leveraging optical emission spectroscopy (OES) data containing plasma information (PI) and enhancing model interpretability using explainable artificial intelligence (XAI) algorithms. Status variable identification data that included normal and abnormal states of bias power, pressure, SF 6 gas flow, and O 2 gas flow were collected during a silicon etching process with SF 6 , O 2 gas plasma. Additional variables were derived from the OES data and included additional PI, such as O and F radicals, which were computed using actinometry, and electron temperature and electron density computed using the line ratio method. By building a high-performance FDC model and interpreting its results using XAI algorithms, we propose solutions to the limitations of the FDC model in semiconductor plasma processes.
Reference‐Frame‐Independent Mode‐Pairing Quantum Key Distribution with Advantage Distillation

Abstract The coordination between distance and the secure key rate is one of the main challenges in the practical application of quantum key distribution (QKD). Mode‐pairing quantum key distribution is one of the schemes that can surpass the secret key capacity for repeaterless QKD. However, the protocol utilizes phase to encode the information, which leads to the problem of active stabilization in the interferometer. In this paper, a reference‐frame‐independent mode‐pairing quantum key distribution (RFI‐MP‐QKD) is proposed as an effective scheme to solve this problem. Moreover, the performance of the RFI‐MP‐QKD protocol is improved by applying the Advantage Distillation (AD) method in data post‐processing, which separates the highly correlated raw key bits from the weakly correlated information. The simulation results show that the secure key rate of RFI‐MP‐QKD has almost no degradation for reference frame deviation angles of . Compared to RFI‐MP‐QKD without AD method, the AD method decreases the quantum bit error rate from 0.04 to 0.012 and increases the maximum transmission distance from 406 to 450 km. The scheme proposed is expected to facilitate the practical implementation of RFI‐MP‐QKD, especially in cases of concerning reference frame alignment and high channel loss.
A Journey Through Open Access Publishing in Aquatic Sciences

Abstract Publishing scientific work is as important as conducting research. The rise of online Open Access (OA) publications that are immediately available to readers everywhere free of charge has transformed the publishing landscape in the past decades. I offer a translation of this article into Brazilian Portuguese as my own attempt to improve access to scientific content (Supplementary Information, SI). In this article, I introduce OA publishing in the context of the aquatic sciences. More specifically, I talk about how OA came to be, the different types of OA publications and copyright licenses, the benefits and pitfalls of OA publishing, and how to pay for gold OA publications.
Adaptive micro- and macro-knowledge incorporation for hierarchical text classification

Hierarchical text classification (HTC) aims to classify a text into multiple categories organized in a hierarchical structure. The state-of-the-art HTC methods usually employ graph networks, where label graphs are constructed and label representation is learned to interact with text representations for classification. In general, label graphs are built on the intrinsic label hierarchy, label semantic similarity, or label co-occurrence. Such graphs have been proven to be effective, but they only exploit knowledge from training data or simple label descriptions, without considering the vast external knowledge in the open sources. Actually, external knowledge from open sources could bring in complementary information to enhance the label graph’s representation power. Motivated by the above considerations, we explore the use of external knowledge for improving HTC in this paper. We categorize knowledge into micro-knowledge and macro-knowledge, which are defined as the fundamental concepts related to a single class label and the correlations among class labels, respectively. For tailor-made incorporation of the two types of knowledge into representation learning and classification, we propose Adaptive Micro- and Macro-Knowledge Incorporation for Hierarchical Text Classification (AMKI-HTC) model in this paper. The micro-knowledge incorporation helps capture class-relevant keywords in the text and hence produce discriminative representations While the macro-knowledge incorporation improves the accuracy of label graphs. finally a confidence maximization fusion strategy is developed for adaptive aggregation of multi-view features. extensive experiments on three benchmark HTC datasets demonstrate that AMKI-HTC consistently outperforms state-of-the-art models.
Research on industrial risk early warning algorithms based on edge computing and multimodal data

Abstract To achieve real‐time and effective prediction of industrial site risks, this paper proposes an industrial risk prediction framework for multimodal data based on edge computing. First, the authors gather and annotate industrial risk multimodal data that consist of text descriptions, images, and videos. Then, the authors transfer the data to the edge server, and apply deep learning models such as Bidirectional Encoder Representations from Transformers (BERT), ResNet etc., to extract features and learn representations for text, image, and video data respectively. The authors input the fused feature data into an enhanced long short term memory (LSTM) model and train it on the dataset. Finally, the authors perform the risk prediction based on the collected multimodal data. The experimental results demonstrate that the method proposed in this paper exhibits superior performance, achieving a 1.4% enhancement in predictive accuracy.
Approximate Bayesian computational methods to estimate the strength of divergent selection in population genomics models

Statistical estimation of parameters in large models of evolutionary processes is often too computationally inefficient to pursue using exact model likelihoods, even with single-nucleotide polymorphism (SNP) data, which offers a way to reduce the size of genetic data while retaining relevant information. Approximate Bayesian Computation (ABC) to perform statistical inference about parameters of large models takes the advantage of simulations to bypass direct evaluation of model likelihoods. We develop a mechanistic model to simulate forward-in-time divergent selection with variable migration rates, modes of reproduction (sexual, asexual), length and number of migration-selection cycles. We investigate the computational feasibility of ABC to perform statistical inference and study the quality of estimates on the position of loci under selection and the strength of selection. To expand the parameter space of positions under selection, we enhance the model by implementing an outlier scan on summarized observed data. We evaluate the usefulness of summary statistics well-known to capture the strength of selection, and assess their informativeness under divergent selection. We also evaluate the effect of genetic drift with respect to an idealized deterministic model with single-locus selection. We discuss the role of the recombination rate as a confounding factor in estimating the strength of divergent selection, and emphasize its importance in break down of linkage disequilibrium (LD). We answer the question for which part of the parameter space of the model we recover strong signal for estimating the selection, and determine whether population differentiation-based summary statistics or (LD)–based summary statistics perform well in estimating selection.
AI Fitness Model using Deep Learning

The project “AI Fitness Model Using Deep Learning (YOLOv5)” aims inorder to transform the fitness business by leveraging state-of-the-art deep learning techniques, specifically YOLOv5 (You Only Look Once version 5), to develop an advanced and efficient fitness tracking system. The model is designed to accurately detect and analyze human poses, movements, and exercise routines in real-time using computer vision. By employing YOLOv5’s object detection capabilities, the AI fitness model can identify key body points, track exercise execution, and provide personalized feedback to users, enhancing their workout experience. This creative strategy not only facilitates automated performance monitoring but also enables the creation of adaptive and dynamic fitness routines tailored to individual needs, fostering improved engagement and results in the realm of health and wellness
Using Stochastic Point Pattern Analysis to Track Regional Orientations of Magmatism During the Transition to Cenozoic Extension and Rio Grande Rifting, Southern Rocky Mountains

Abstract The southern Rocky Mountains in Colorado and northern New Mexico hosted intracontinental magmatism that developed during a tectonic transition from shortening (Laramide orogeny, ca. 75 to 40 Ma) through extension and rifting. We present a novel approach that uses stochastic weighted bootstrap simulations of a large set of new and historical geochronology data to better understand how regional anisotropies responsible for focusing magma emplacement evolved through time. This technique can detect subtle trends in directional distributions, including multi‐modal orientations, and can be filtered from regional to local scales. Our results indicate that magmatism followed first the northeast trend of the Colorado mineral belt between 75 and 40 Ma and deviated afterward. These deviations vary depending on the scale of the analysis. At the smallest scale we evaluated (&lt;75 km), the orientation of magmatism from 45 to 30 Ma rotated counter‐clockwise before aligning with the north‐south trend of the modern Rio Grande rift. Larger, regional‐scale analyses indicate magma centers between 40 to 35 Ma and 25 to 20 Ma were dominantly oriented southwest‐northeast, whereas magmatism between 35 and 25 Ma had north‐south orientation. The large areal footprint of magmatism and shifting regional patterns suggest that ancient zones of weakness in the North American lithosphere accommodated magma flow at different moments in time, rather than controlled by a retreating interface of the Farallon and North American plates.
Revolutionizing Travel: An Introduction to a Voice-Enabled Flight Booking System Powered by Machine Learning

Numerous websites exist that enable flight booking with various customizations for a particular user. Technology is used to make things easier and convenient at every turn. So, the developments in this field aim at being user friendly and accessible to the user by providing certain features that ensure user interaction occurs at ease. One of these features at disposal is a voice-controlled input system for an online flight reservation website or online travel agencies. This feature is added using voice-to-text system and a machine learning algorithm is used to extract relevant information such as destination, dates and preferences from the user’s voice input and show relevant results for the same. This trait aids in understanding user preferences and provide personalized recommendations, streamlining the user experience by presenting relevant content
Adaptive Trajectories’ Constant False Alarm Rate Mirror Filters and Elevation Angle Evaluation for Multiple-Input Multiple-Output Radar-Based Hand Gesture Recognition

Gesture recognition technology has been quickly developed in the field of human–computer interaction. The multiple-input multiple-output (MIMO) radar is popular in gesture recognition because of its notable spatial resolution. This work proposes a MIMO radar-based hand gesture recognition algorithm with low complexity. We leverage low-complexity adaptive signal processing to extract trajectory information and minimize noise to create a system that can be applied in real-world applications with small training datasets. First, a spectrum analysis is utilized on range-Doppler maps (RDMs), and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness of noise. Then, the features related to the distance, speed, direction, and elevation angle of the moving object are determined using the proposed adaptive signal analysis techniques. For classification, the random forest algorithm is implemented. The proposed system can precisely distinguish and identify eight gestures, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anti-clockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.
Navigating the digital world: development of an evidence-based digital literacy program and assessment tool for youth

Abstract The rapid expansion of digital connectivity has provided youth with wide-ranging access to digital platforms for communication, entertainment, and education. In light of this profound shift, there have been growing concerns about online safety, data privacy, and cybersecurity. A critical factor influencing the ability of youth to responsibly navigate digital platforms is digital literacy. While digital literacy programs have been implemented in various regions worldwide, significant disparities remain not only in overall digital literacy levels, but also the assessment of digital literacy initiatives. To address these challenges, an environmental scan and literature review were conducted to identify existing digital literacy programs in Canada developed specifically for youth, as well as digital literacy assessment tools, respectively. The search encompassed peer-reviewed articles, organizational curricula, and assessment measures indexed in various databases and organization websites. The environmental scan identified 15 programs targeting key components of digital literacy such as data safety, cyberbullying, and digital media. The literature review identified 12 digital literacy assessment tools. Based on the findings, data were synthesized from shortlisted programs and assessment tools to inform the development of both a new digital literacy program and assessment tool to complement the youth-focused program. The new program focuses on four key components: (1) digital fluency, (2) digital privacy and safety, (3) ethics and empathy, and (4) consumer awareness. A 15-item assessment tool was also developed consisting of 4–5 questions specific to each program component. Given the growing importance of digital competencies, a youth-focused program and assessment tool are crucial for understanding and addressing digital literacy among this vulnerable cohort. This program's adaptability allows for customization across sociodemographic target groups, including culturally diverse and geographically remote communities—an aspect that has the potential to enhance digital literacy across settings. Implementing digital literacy programs can better prepare youth for an increasingly digital world, while minimizing potential risks associated with technology use.