Reviewerly/exHarmony huggingface dataset:
Beyond dynasties and binary alternations: a diachronic corpus study of four-way variability in Chinese theme-recipient constructions

Abstract Despite the vast body of literature on the historical development of the theme-recipient alternation (also known as the “dative” alternation) in Chinese, most studies that have been conducted so far are limited to philological recounts of the binary choice between the prepositional dative and the ditransitive dative across dynasties, which usually spanned centuries. Against this backdrop, we conduct a state-of-the-art variationist analysis of the four variants, utilizing a large and richly annotated diachronic dataset based on a corpus of Chinese texts (1300s–1900s). Using conditional inference trees and conditional random forest analysis, we demonstrate that end-weight effects are the most stable linguistic constraint on variation, while definiteness and animacy of the theme constituent tend to be more fluid. Supplementary distinctive collexeme analysis reveals a strong collostructional interplay between verbs and the variants, including changes involving the prototypical verb of GIVING 给 gĕi .
Interpretable ensembles of hyper-rectangles as base models

A new extremely simple ensemble-based model with the uniformly generated axis-parallel hyper-rectangles as base models (HRBM) is proposed. Two types of HRBMs are studied: closed rectangles and corners. The main idea behind HRBM is to consider training examples inside and outside each rectangle. It is proposed to incorporate HRBMs into the gradient boosting machine (GBM) to construct effective ensemble-based models and to avoid overfitting. A simple method for calculating optimal regularization parameters of the model, which can be modified in the explicit way at each iteration of GBM, is considered. Moreover, a new regularization called the “step height penalty” is studied in addition to the L1 and L2 regularizations. An extremely simple approach to the proposed ensemble-based model prediction interpretation by using the well-known method SHAP is proposed. It is shown that GBM with HRBM can be regarded as a model extending a set of interpretable models for explaining black-box models. Numerical experiments with real datasets illustrate the proposed GBM with HRBMs for regression and classification problems. The best p-values in the t-test comparing the proposed model with the well-known ensemble-based regression and classification models are 0.004 and 0.0031, respectively. Experiments also illustrate computational efficiency of the proposed SHAP modifications. The code of proposed algorithms implementing GBM with HRBM is publicly available.
International and Russian Legal Regulation of the Turnover of Crypto-assets: Conceptual-Terminological Correlation

Objective : to assess the Russian legislation for its compliance with the international-legal approaches to shaping symmetrical regulation of crypto-assets and possibility to complement it with new internationallegal categories reflecting the in-depth changes in the global economy and structure of international finance, determined by the broad introduction of new financial technologies based on distributed ledger technologies. Methods : the methodological basis of the research is a set of general scientific methods of scientific cognition, among which of utmost importance are special-legal (formal-legal and comparative-legal) methods, complemented with risk-oriented approach, legal modeling and juridical forecasting. Applied integrally, they allowed comprehending the architecture, “letter and “spirit” of the modern international financial law and national legislation in their conceptual-terminological correlation and to forecast further development and adjustment of the legal regulation of crypto-assets turnover. Results : it was found that there appears a stable trend in the crypto-assets turnover regulation, according to which “soft” law dominates among the law sources (this is especially notable in the sphere of international financial law compared, for example, with conventions or international treaties); at the same time, there is a strengthening trend of “fragmentation” of international law with regard to crypto-assets turnover; the authors mark inconsistency of the conceptual framework contained in international acts and in the Russian legislation, as well as the gaps in the regime of crypto-assets turnover at the level of national law; the trends and forecasts are presented referring to the development of international-legal regulation of the sphere of crypto-assets. Scientific novelty : consists, first of all, in a complex comparison, based on, among other aspects, the fundamentally new concepts of regulation of such progressive international-legal categories as cryptoasset, virtual asset, cryptocurrency, stablecoin, etc., some of them rarely used in the Russian legal discourse and actually never applied in legislation. Practical significance : the scientifically grounded proposals are formulated, aimed at improving the conceptual-terminological framework of the Russian legislation in the sphere of crypto-assets turnover, implementation of which will allow constructing a common legal space with the technologically most advanced states, will help to improve investment climate and financial attraction of the state; will improve the national-legal regime of crypto-assets turnover from the viewpoint of not only actual market demands, but also state security interests and improving competitiveness of the Russian legislation.
Real-Time Pose Estimation Based on ResNet-50 for Rapid Safety Prevention and Accident Detection for Field Workers

The present study proposes a Real-Time Pose Estimation technique using OpenPose based on ResNet-50 that enables rapid safety prevention and accident detection among field workers. Field workers perform tasks in high-risk environments, and accurate Pose Estimation is a crucial aspect of ensuring worker safety. However, it is difficult for Real-Time Pose Estimation to be conducted in such a way as to simultaneously meet Real-Time processing requirements and accuracy in complex environments. To address these issues, the current study uses the OpenPose algorithm based on ResNet-50, which is a neural network architecture that performs well in both image classification and feature extraction tasks, thus providing high accuracy and efficiency. OpenPose is an algorithm specialized for multi-human Pose Estimation that can be used to estimate the body structure and joint positions of a large number of individuals in real time. Here, we train ResNet-50-based OpenPose for Real-Time Pose Estimation and evaluate it on various datasets, including actions performed by real field workers. The experimental results show that the proposed algorithm achieves high accuracy in the Real-Time Pose Estimation of field workers. It also provides stable results while maintaining a fast image processing speed, thus confirming its applicability in real field environments.
Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities

Earthquake Disaster Assessment (EDA) plays a critical role in earthquake disaster prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in image processing, signal recognition, and object detection, has facilitated scientific research in EDA. This paper analyses 204 articles through a systematic literature review to investigate the status quo, development, and challenges of DL for EDA. The paper first examines the distribution characteristics and trends of the two categories of EDA assessment objects, including earthquakes and secondary disasters as disaster objects, buildings, infrastructure, and areas as physical objects. Next, this study analyses the application distribution, advantages, and disadvantages of the three types of data (remote sensing data, seismic data, and social media data) mainly involved in these studies. Furthermore, the review identifies the characteristics and application of six commonly used DL models in EDA, including convolutional neural network (CNN), multi-layer perceptron (MLP), recurrent neural network (RNN), generative adversarial network (GAN), transfer learning (TL), and hybrid models. The paper also systematically details the application of DL for EDA at different times (i.e., pre-earthquake stage, during-earthquake stage, post-earthquake stage, and multi-stage). We find that the most extensive research in this field involves using CNNs for image classification to detect and assess building damage resulting from earthquakes. Finally, the paper discusses challenges related to training data and DL models, and identifies opportunities in new data sources, multimodal DL, and new concepts. This review provides valuable references for scholars and practitioners in related fields.
Temporal–Spatial Distributions and Influencing Factors of Heavy Metals As, Cd, Pb, and Zn in Alluvial Soils on a Regional Scale in Guangxi, China

Understanding the temporal–spatial distribution and influencing factors of heavy metals on a regional scale is crucial for assessing the anthropogenic impacts and natural variations in elemental geochemical behavior. This study evaluated the spatial distributions of the heavy metals As, Cd, Pb, and Zn as well as the driving mechanisms over the past 31 years in Guangxi, China, using three geochemical baseline projects (the Environmental Geochemical Monitoring Network Project (EGMON) project 1992–1996; the Geochemical Baseline (CGB) 1 project 2008–2012; and the CGB2 project 2015–2019). By calculating the variable importance using the random forest algorithm, it was found that natural factors are the primary drivers of the spatial distribution of heavy metals in the EGMON project, especially precipitation for As, the digital elevation model (DEM) for Cd and Pb, and temperature for Zn. Surface alluvial soils showed obvious heavy metal enrichment in the CGB1 project, with the gross domestic product (GDP) driving the spatial distribution of all heavy metals. In addition, the anomalous intensity and range of heavy metals in the CGB2 project decreased significantly compared with the CGB1 project, especially owing to the normalized difference vegetation index (NDVI) as a positive anthropogenic factor that improves the degree of rocky desertification, thus reducing the heavy metal contents of As and Pb, and the precipitation promoting the decomposition of Fe–Mn concretions and thus the migration of Cd and Zn. This research promotes an understanding of anthropogenic and natural influences on the spatiotemporal distribution of heavy metals and is of great significance for environmental monitoring and governance.
Multi-Feature Fusion-Guided Low-Visibility Image Enhancement for Maritime Surveillance

Low-visibility maritime image enhancement is essential for maritime surveillance in extreme weathers. However, traditional methods merely optimize contrast while ignoring image features and color recovery, which leads to subpar enhancement outcomes. The majority of learning-based methods attempt to improve low-visibility images by only using local features extracted from convolutional layers, which significantly improves performance but still falls short of fully resolving these issues. Furthermore, the computational complexity is always sacrificed for larger receptive fields and better enhancement in CNN-based methods. In this paper, we propose a multiple-feature fusion-guided low-visibility enhancement network (MFF-Net) for real-time maritime surveillance, which extracts global and local features simultaneously to guide the reconstruction of the low-visibility image. The quantitative and visual experiments on both standard and maritime-related datasets demonstrate that our MFF-Net provides superior enhancement with noise reduction and color restoration, and has a fast computational speed. Furthermore, the object detection experiment indicates practical benefits for maritime surveillance.
Exploring Evaluation Methods for Interpretable Machine Learning: A Survey

In recent times, the progress of machine learning has facilitated the development of decision support systems that exhibit predictive accuracy, surpassing human capabilities in certain scenarios. However, this improvement has come at the cost of increased model complexity, rendering them black-box models that obscure their internal logic from users. These black boxes are primarily designed to optimize predictive accuracy, limiting their applicability in critical domains such as medicine, law, and finance, where both accuracy and interpretability are crucial factors for model acceptance. Despite the growing body of research on interpretability, there remains a significant dearth of evaluation methods for the proposed approaches. This survey aims to shed light on various evaluation methods employed in interpreting models. Two primary procedures are prevalent in the literature: qualitative and quantitative evaluations. Qualitative evaluations rely on human assessments, while quantitative evaluations utilize computational metrics. Human evaluation commonly manifests as either researcher intuition or well-designed experiments. However, this approach is susceptible to human biases and fatigue and cannot adequately compare two models. Consequently, there has been a recent decline in the use of human evaluation, with computational metrics gaining prominence as a more rigorous method for comparing and assessing different approaches. These metrics are designed to serve specific goals, such as fidelity, comprehensibility, or stability. The existing metrics often face challenges when scaling or being applied to different types of model outputs and alternative approaches. Another important factor that needs to be addressed is that while evaluating interpretability methods, their results may not always be entirely accurate. For instance, relying on the drop in probability to assess fidelity can be problematic, particularly when facing the challenge of out-of-distribution data. Furthermore, a fundamental challenge in the interpretability domain is the lack of consensus regarding its definition and requirements. This issue is compounded in the evaluation process and becomes particularly apparent when assessing comprehensibility.
A Multimodal Deep Learning Model Using Text, Image, and Code Data for Improving Issue Classification Tasks

Issue reports are valuable resources for the continuous maintenance and improvement of software. Managing issue reports requires a significant effort from developers. To address this problem, many researchers have proposed automated techniques for classifying issue reports. However, those techniques fall short of yielding reasonable classification accuracy. We notice that those techniques rely on text-based unimodal models. In this paper, we propose a novel multimodal model-based classification technique to use heterogeneous information in issue reports for issue classification. The proposed technique combines information from text, images, and code of issue reports. To evaluate the proposed technique, we conduct experiments with four different projects. The experiments compare the performance of the proposed technique with text-based unimodal models. Our experimental results show that the proposed technique achieves a 5.07% to 14.12% higher F1-score than the text-based unimodal models. Our findings demonstrate that utilizing heterogeneous data of issue reports helps improve the performance of issue classification.
Augmented Reality: Current and New Trends in Education

The educational landscape is an environment prone to change due to the volatile and ever-changing nature of the digital society in which we all live. Although the world moves at different speeds and any generalization is bound to have some exceptions, there is evidence from research conducted in different places and contexts that educational methods are becoming increasingly digitized and driven by technological innovation. Among the technological trends fueled in many cases by the COVID-19 pandemic and the need to stay at home but online, augmented reality solutions received an additional boost as a valid and versatile educational technology worth exploring and eventually integrating into several teaching methods already in use. Although the technology still faces problems related to affordability, accessibility, and the technical skills required of users, some ongoing projects have already provided evidence that using augmented reality solutions as teaching and learning tools can improve teacher and student learning outcomes by increasing engagement and interactivity. The same issues arose when personal computers, tablets, and smartphones were first discussed as valuable tools for education and have now found their way into most classrooms. This paper reviews some of the key concepts related to augmented reality, as well as some current trends, benefits, and concerns related to its integration into educational contexts in areas such as life sciences, engineering, and health. The work conducted and presented in this paper provides an interesting insight into a technology that has given rise to global phenomena such as Pokémon Go, and continues to improve in terms of portability, usability, and overall user experience. Throughout the paper and in the conclusion section, we discuss the relevance of using the best features of augmented reality and how they can contribute to positive educational outcomes.
A Job Recommendation Method Based on Attention Layer Scoring Characteristics and Tensor Decomposition

In the field of job recruitment, a classic recommendation system consists of users, positions, and user ratings on positions. Its key task is to predict the unknown rating data of users on positions and then recommend positions that users are interested in. However, traditional recommendation methods only rely on user rating data for jobs and provide recommendation services for recruiters and candidates through simple information matching. This simple recommendation strategy not only causes a lot of information waste but also cannot effectively utilize the multi-source heterogeneous data information in the field of job recruitment. Therefore, this paper proposes a job recommendation model based on users’ attention levels and tensor decomposition for specific recruitment positions. This model puts forward assumptions based on browsing time for the special behaviors and habits of users in the field of job recruitment, defines corresponding label values for different interactive behaviors, and establishes a grading method based on the attention of job seekers, thus constructing a three-dimensional tensor of “job seeker user-position-attention layered”. Then, a recommendation model is constructed by decomposing the three-dimensional tensor. The effectiveness of the model is verified by comparative experiments with other recommendation algorithms.
Critical behavior of the Ising model by preparing the thermal state on a quantum computer

We simulate the critical behavior of the Ising model utilizing a thermal state prepared using quantum computing techniques. The preparation of the thermal state is based on the variational quantum imaginary time evolution (QITE) algorithm. The initial state of QITE is prepared as a classical product state, and we propose a systematic method to design the variational ansatz for QITE. We calculate the specific heat and susceptibility of the long-range interacting Ising model and observe indications of the Ising criticality on a small lattice size. We find the results derived by the quantum algorithm are well consistent with the ones from exact diagonalization, in both the neighborhood of the critical temperature and the low-temperature region.
Research Institute Scientists E-Books Usage in India: A Study

The study is all about using e-books by scientists in research institutes in India. To identify gender-wise usage of e-books by scientists, to understand the purchase and usage of e-books and e-resources, to observe the way of access and usage to e-books, to understand the frequency of the usage of e-books, to detect the type of problems faced by scientists while accessing e-books and to know the reasons for not attending the training program/orientation on using e-resources. The questionnaire method was used to gather the data. An entire 860 questionnaires were distributed, and 700 filled questionnaires were received, with a response rate of 81.39%. The study results show that the maximum number of respondents are male (59.15%) scientists, and 65.7% of RRI scientists use to purchase and use e-books. A maximum number of scientists access e-books purchased by their research institute. 52.8% of scientists feel that many e-books could be more beneficial for their research. More than 50% of scientists feel they need no training to access e-books. It is very clear from the study that research institute libraries must subscribe to e-books useful to their scientists.
Anomaly Detection in Medical Time Series with Generative Adversarial Networks: A Selective Review

Anomaly detection in medical data is often of critical importance, from diagnosing and potentially localizing disease processes such as epilepsy to detecting and preventing fatal events such as cardiac arrhythmias. Generative adversarial networks (GANs) have since their inception shown promise in various applications and have been shown to be effective in cybersecurity, data denoising, and data augmentation, and have more recently found a potentially important place in the detection of anomalies in medical time series. This chapter provides a selective review of this novel use of GANs, in the process highlighting the nature of anomalies in time series, special challenges related to medical time series, and some general issues in approaching time series anomaly detection with deep learning. We cover the most frequently applied GAN models and briefly detail the current landscape of applying GANs to anomaly detection in two commonly used medical time series, electrocardiography (ECG) and electroencephalography (EEG).
User Profiling and Vulnerability Introduction Prediction in Social Coding Repositories: A Dynamic Graph Embedding Approach

Social Coding Repositories (SCRs) such as GitHub host open-source code that is significant to the global economy. However, open-source code is especially vulnerable, with most vulnerabilities being introduced due to human error. An important mitigation strategy is preventing the introduction of vulnerabilities. One of the ways this can be achieved is through targeted developer security training, which necessitates the identification of high-risk actors and predicting the introduction of vulnerabilities. In this study, we propose a novel framework for predicting the introduction of vulnerabilities in SCRs by users, with a novel dynamic graph representation learning model, security continuous propagation, and evolution (seCoPE). The proposed seCoPE framework addresses the limitations of existing methods by incorporating the relative influence of nodes on the propagation of information to generate high-quality nodal embeddings. We systematically evaluate seCoPE against prevailing Recurrent Neural Network (RNN) based and Attention-based models on a vulnerability introduction dataset. The proposed framework has important implications for cybersecurity providers, firms, and software developers.