Reviewerly/exHarmony huggingface dataset:
Online grooming detection: A comprehensive survey of child exploitation in chat logs

Social media platforms present significant threats against underage users targeted for predatory intents. Many early research works have applied the footprints left by online predators to investigate online grooming. While digital forensics tools provide security to online users, it also encounters some critical challenges, such as privacy issues and the lack of data for research in this field. Our literature review investigates all research papers on grooming detection in online conversations by looking at the psychological definitions and aspects of grooming. We study the psychological theories behind the grooming characteristics used by machine learning models that have led to predatory stage detection. Our survey broadly considers the authorship profiling research works used for grooming detection in online conversations, along with predatory conversation detection and predatory identification approaches. Various approaches for online grooming detection have been evaluated based on the metrics used in the grooming detection problem. We have also categorized the available datasets and used feature vectors to give readers a deep knowledge of the problem considering their constraints and open research gaps. Finally, this survey details the constraints that challenge grooming detection, unaddressed problems, and possible future solutions to improve the state-of-the-art and make the algorithms more reliable.
Relationship existence recognition-based social group detection in urban public spaces

In urban public spaces, a social group consists of two or more individuals who share some social relationships and interact based on mutual expectations. However, most existing studies found people's F-formations on a top view, which is hard to observe their social contexts and the top-view videos are not easily accessible in real urban life. Recently, some researchers turned to urban scenes and analysed front-view human behaviours for social group detection. But these methods still cannot grasp the nature of social groups, i.e., the relationships among individuals. It is the key to finding social groups to judge whether any two individuals belong to the same cluster. Therefore, this paper proposes a new paradigm: relationship existence recognition-based social group detection. Additionally, on top of the paradigm, we designed a new social group detection algorithm incorporated with the visual cue-based and non-visual cue-based components. Specifically, the former exploits the spatial interactions and the temporal information to recognise the existence of social relationships through supervised deep learning. The latter estimates the similarities of trajectory pairs using the unsupervised spatial–temporal position information. Social group detection achieves superior accuracy with the two components' complementary results. On Social-CAD (Social Collective Activity Dataset) and PLPS (Public Life in Public Space) datasets, extensive experiments demonstrate that our algorithm outperforms the state-of-the-art (SOTA) methods.
Dynamic concept-aware network for few-shot learning

Few-shot learning (FSL) aims to recognize novel classes with extremely limited samples via adapting the prior knowledge learned from base classes. Most of existing methods for feature alignment in FSL consider the correspondence of semantic concepts between support and query images. However, encoding task-specific information for query features has not been sufficiently explored. Therefore, in this paper, we propose a dynamic concept-aware network (DCAN), which efficiently encodes task-specific structural concepts and adaptively dynamic alignment. Concretely, this is achieved by dynamic prototype task awareness (DPTA) and cross-correlation dynamic alignment (CoDA). The DPTA module assigns concept weights to each pixel position feature of support images so that dynamically generate prototypes to encode task-specific information with attention mechanism. The CoDA module first calculates the co-attention maps between image representations, and adaptively learns channel-dependent and spatial-dependent dynamic meta-filters based on inputs. Combining two dynamic modules to obtain final embeddings that generalize well to novel categories. Extensive experiments demonstrate that DCAN outperforms the state-of-the-art methods on four FSL classification benchmarks. Our code is available at https://github.com/zjgans/dcan . • We design a Dynamic Prototype Task Awareness (DPTA) module to encode task-specific information with transferable structural concepts. • We propose a novel feature alignment strategy incorporating decoupled dynamic filter, which is task-adaptive and generalizes well to new scenarios. • Extensive experiments demonstrate that our method outperforms the state-of-the-art methods on four standard FSL benchmarks, and ablation studies verify the effectiveness of our proposed components.
HR-PrGAN: High-resolution story visualization with progressive generative adversarial networks

Generating a series of images to describe a story of multiple sentences is a challenging task in computer vision as it needs to consider both the image-level precision and story-level consistency. Existing methods usually focus on the consistency between images at the cost of the fine-grained object details. We propose a progressive adversarial learning algorithm, termed HR-PrGAN, to achieve high-resolution image sequences with rich details by decomposing the problem of generating into multiple stages. Specifically, HR-PrGAN has two stages, where the Coarse-grain Stage generates a series of coherent coarse-grained images from both the story and context embeddings, and an additional unconditional loss is proposed to restrict their deformation and preserve the object contours and layouts. Subsequently, the Refinement Stage further refines the series of coherent coarse-grained images by injecting the story-level text embeddings and preserving the image-level details via a Coarse-grained feature Supplementary Module (CSM). Moreover, two commonly-used datasets, i.e. the CLEVR-SV and PororoSV datasets, are applied to evaluate the proposed method. Extensive experiments demonstrate that the proposed model significantly outperforms state-of-the-art methods in terms of image anti-deformation, fine-grained feature synthesis and human perception based image quality evaluation.
The Taxonomy of Mineral Occurrence Rarity and Endemicity

ABSTRACT Nearly a half of known IMA-approved minerals (as of November 2021) are reported from four localities or fewer and so may be considered rare mineral species. These minerals form a continuum with more common species (e.g., rock-forming minerals), all of which constitute important constituents of Earth and contributors to its dynamics. To better understand the taxonomy of mineral rarity, evaluations have been made on the basis of k-means clustering and kernel density estimation of one-dimensional data on mineral occurrence metrics. Results from second- and third-degree polynomial regression analyses indicate the presence of a divergence between the observed number of endemic minerals discovered since 2000 and those that are likely to represent “true” endemic species. The symmetry index, calculated using the approach of Urusov for each rarity cluster, reveals a gradual decrease from ubiquitous to endemic from 0.64 to 0.47. A network analysis of element co-occurrences within each rarity cluster suggests the existence of at least three different communities having similar geochemical affinities; the latter may reflect the relative abundance of minerals their elements tend to form. The analysis of element co-occurrence matrices within each group indicates that crustal abundance is not the only factor controlling the total number of minerals each element tends to form. Other significant factors include: (1) the geochemical affinity to the principal element in the group (i.e., sulfur for chalcophile and oxygen for lithophile elements) and (2) dispersion of the principal element through geochemical processes. There is a positive correlation between the lithophile element group's abundance in the Earth's crust and the number of common minerals they tend to form, but a negative correlation with the number of rare species.
The Virtual Quantum Optics Laboratory

We present a web-based software tool, the Virtual Quantum Optics Laboratory (VQOL), that may be used for designing and executing realistic simulations of quantum optics experiments. A graphical user interface allows one to rapidly build and configure a variety of different optical experiments, while the runtime environment provides unique capabilities for visualization and analysis. All standard linear optical components are available as well as sources of thermal, coherent, and entangled Gaussian states. A unique aspect of VQOL is the introduction of non-Gaussian measurements using detectors modeled as deterministic devices that "click" when the amplitude of the light falls above a given threshold. We describe the underlying theoretical models and provide several illustrative examples. We find that VQOL provides a a faithful representation of many experimental quantum optics phenomena and may serve as both a useful instructional tool for students as well as a valuable research tool for practitioners.
Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities

Electronic health records (EHRs) have been successfully used in data science and machine learning projects. However, most of these data are collected for clinical use rather than for retrospective analysis. This means that researchers typically face many different issues when attempting to access and prepare the data for secondary use. We aimed to investigate how raw EHRs can be accessed and prepared in retrospective data science projects in a disciplined, effective, and efficient way. We report our experience and findings from a large-scale data science project analyzing routinely acquired retrospective data from the Kepler University Hospital in Linz, Austria. The project involved data collection from more than 150,000 patients over a period of 10 years. It included diverse data modalities, such as static demographic data, irregularly acquired laboratory test results, regularly sampled vital signs, and high-frequency physiological waveform signals. Raw medical data can be corrupted in many unexpected ways that demand thorough manual inspection and highly individualized data cleaning solutions. We present a general data preparation workflow, which was shaped in the course of our project and consists of the following 7 steps: obtain a rough overview of the available EHR data, define clinically meaningful labels for supervised learning, extract relevant data from the hospital's data warehouses, match data extracted from different sources, deidentify them, detect errors and inconsistencies therein through a careful exploratory analysis, and implement a suitable data processing pipeline in actual code. Only few of the data preparation issues encountered in our project were addressed by generic medical data preprocessing tools that have been proposed recently. Instead, highly individualized solutions for the specific data used in one's own research seem inevitable. We believe that the proposed workflow can serve as a guidance for practitioners, helping them to identify and address potential problems early and avoid some common pitfalls.
To adopt or not to adopt? The determinants of cloud computing adoption in information technology sector

This research is mainly focused on the adoption of cloud computing in the information technology (IT) industry of a developing country, Pakistan by using the theoretical lens of technology acceptance model (TAM) and Elaboration Likelihood Model (ELM). Cloud computing, being one of the latest technologies in the field of IT, has been recently adopted by organizations around the globe, although developing nations have recently started using this technology in their supply chain processes. This study involved the employees of IT industry working in the capital city of Pakistan, i.e. Islamabad. Sample respondents consisted of 213 employees of the IT based organization. Data was collected online by employing structured questionnaires based on past literature. The results revealed that there exists a positive and significant relationship between perceived security, argument advantage, source credibility and perceived usefulness. Additionally, the outcome of the study supported the significant relationships between perceived usefulness and attitude towards adoption of cloud computing, perceived usefulness and intention to adopt cloud computing, and attitude towards cloud computing and intention to adopt cloud computing. The research study has managerial and practical implications. It is one of first of its kind that explores some of the factors leading to adoption of cloud computing in of IT companies in Pakistan.
Time-varying external archive differential evolution algorithm with applications to parallel mechanisms

In this work, a time-varying external archive differential evolution algorithm is proposed, which can obtain the multiple roots for system of nonlinear equations for the forward displacement of parallel mechanisms. In time-varying external archive differential evolution three improvements are presented. At first, a time-varying subpopulation strategy is proposed to consider both global and local searching capabilities. Then, an individual judgment criterion is proposed, which employs the weighted basis vector mutation strategy for ordinary individuals and a hybrid mutation operator for locally optimal individuals. Finally, the external archive strategy of neighborhood-based crowding differential evolution is improved to enhance the domain searching ability of the algorithm. To evaluate the performance of time-varying external archive differential evolution, twenty benchmark functions are selected as the test suite. Moreover, the solutions for forward displacements of four strongly coupled parallel mechanisms with different degrees of freedom are derived to test the generality of this method. From the acquired experimental results, it is demonstrated that fairly-competitive results can be provided compared to other methods, in both benchmark functions and forward displacement solutions of parallel mechanisms.
Digital Competence of Higher Education Students as a Predictor of Academic Success

The purpose of the present study is to analyze the digital competence of Higher Education students, as a function of their academic performance (have either repeated or a not previously), as well as to predict its significant predictors. For this, an ex-post factor and a sample of 17301 students from Chile (Latin America) were utilized. A questionnaire composed of a total of 30 items, classified into five endogenous and exogenous dimensions through a causal model, adapted from the works of Gutiérrez-Castillo (2013), Gutiérrez-Castillo & Cabero (2016) and Gutiérrez-Castillo et al. (2017), was administered via online to the students. The results showed statistically significant differences in digital competence, as a function of repeating an academic year or not, with better scores for the latter. Also, factors such as the number of digital resources utilized for the teaching-learning process, the previous preparation for managing their studies, as well as the level of education of the parents, mainly the father, significantly had an influence on both types of students. This study highlights the importance of the development of an adequate digital competence which has an influence on the learning of the students and their posterior class promotion.
Principal component analysis of infinite variance functional data

Principal Components Analysis is a widely used approach of multivariate analysis. Over the past 30 years, it has gained renewed attention in the context of functional data, chiefly as a commonly used tool for dimension reduction or feature extraction. Consequently, a large body of statistical theory has been developed to justify its application in various contexts. This theory focuses on the convergence of sample PCs to their population counterparts in a multitude of statistical models and under diverse data collection assumptions. What such results have in common is the assumption that the population covariance operator exists.This paper is concerned with multivariate and functional data that have infinite variance and, consequently, for which the population covariance operator is not defined. However, the sample covariance operator and its eigenfunctions are always defined. It has been unknown what the asymptotic behavior of these important statistics is in the context of infinite variance multivariate or functional data. We derive suitable large sample theory. In particular, we specify normalizing sequences and conditions for suitably defined consistency. We study multivariate models in which explicit limits can be derived. These examples show that definitions, results and intuition developed for multivariate and functional data with finite variance need not apply in the setting we consider.
Ensemble hybrid model for Hindi COVID-19 text classification with metaheuristic optimization algorithm

A SARS-CoV-2 virus has spread around the globe since March 2020. Millions of people infected worldwide with coronavirus. People from every country expressed their sentiments about coronavirus on social media. The aim of this work is to determine the general public opinion of Indian Twitter users about coronavirus. The Hindi tweets posted about COVID-19 is used as input data for sentiment analysis. The natural language processing is applied on input data for feature extraction. Further, the optimal features are selected from the pre-processed data using the metaheuristic based Grey wolf optimization technique. Finally, a hybrid of convolution neural network(CNN) and a long short-term memory (LSTM) model pair is employed to categorize the sentiments as positive, negative, and neutral. The outcome of the proposed model is compared with other machine learning techniques, namely, Random Forest, Decision Tree, K-Nearest Neighbor, Naive Bayes, Support vector machine (SVM), CNN, LSTM, LSTM-CNN, and CNN-LSTM. The highest accuracy of 87.75%, 88.41%, 87.89%, 85.54%, 89.11%, 91.46%, 88.72%, 91.54%, and 92.34% is obtained by Random Forest, Decision Tree, K-Nearest Neighbor, Naive Bayes, SVM, CNN, LSTM, LSTM-CNN, and CNN-LSTM, respectively. The proposed ensemble hybrid model gives the highest 95.54%, 91.44%, 89.63%, and 90.87% classification accuracy, precision, recall, and F-score, respectively.
Performance of Two Variable Machine Learning Models to Forecast Monthly Mean Diffuse Solar Radiation across India under Various Climate Zones

For the various climatic zones of India, machine learning (ML) models are created in the current work to forecast monthly-average diffuse solar radiation (DSR). The long-term solar radiation data are taken from Indian Meteorological Department (IMD), Pune, provided for 21 cities that span all of India’s climatic zones. The diffusion coefficient and diffuse fraction are the two groups of ML models with dual input parameters (sunshine ratio and clearness index) that are built and compared (each category has seven models). To create ML models, two well-known ML techniques, random forest (RF) and k-nearest neighbours (KNN), are used. The proposed ML models are compared with well-known models that are found in the literature. The ML models are ranked according to their overall and within predictive power using the Global Performance Indicator (GPI). It is discovered that KNN models generally outperform RF models. The results reveal that in diffusion coefficient models perform well than diffuse fraction models. Moreover, functional form 2 is the best followed by form 6. The ML models created here can be effectively used to accurately forecast DSR in various climates.
MEANet: Magnitude estimation via physics-based features time series, an attention mechanism, and neural networks

The traditional magnitude estimation method, which establishes a linear relationship between a single warning parameter and the magnitude, exhibits considerable scatter and underestimation. In addition, the extraction of features from raw waveforms by a deep learning network is a black box. To provide a more robust magnitude estimation and to construct a deep learning network with an interpretable input, in light of deep learning and earthquake rupture physics, we have established a magnitude estimation network model (MEANet) via the physics-based features time series, an attention mechanism, and neural networks. We use events with 4 ≤ M ≤ 7.5 that occur in Japan and the Sichuan-Yunnan region, China, to train and validate MEANet, and then use MEANet to test additional events. Our results find that MEANet has a more robust magnitude estimation than the traditional [Formula: see text] and [Formula: see text] methods, with a standard deviation of error of ±0.25 magnitude units at a single station with a 3 s P-wave time window. Within 10 s after the first station is triggered, based on the weighted average of the triggered stations, MEANet provides robust magnitude estimation without underestimation for events with 4 ≤ M ≤ 7.5. Our finding implies that the final magnitude is to some degree deterministic by the combination of deep learning and physics-based features. Meanwhile, MEANet might have potential for earthquake early warning.
Apparent age prediction from faces: A survey of modern approaches

Apparent age estimation via human face image has attracted increased attention due to its numerous real-world applications. Predicting the apparent age has been quite difficult for machines and humans. However, researchers have focused on machine estimation of "age as perceived" to a high level of accuracy. To further improve the performance of apparent age estimation from the facial image, researchers continue to examine different methods to enhance its results further. This paper presents a critical review of the modern approaches and techniques for the apparent age estimation task. We also present a comparative analysis of the performance of some of those approaches on the apparent facial aging benchmark. The study also highlights the strengths and weaknesses of each approach used for apparent age estimation to guide in choosing the appropriate algorithms for future work in the field. The work focuses on the most popular algorithms and those that appear to have been the most successful for apparent age estimation to improve on the existing state-of-the-art results. We based our evaluations on three facial aging datasets, including looking at people (LAP)-2015, LAP-2016, and APPA-REAL, the most popular and publicly available datasets benchmark for apparent age estimation.
Addressing the uncertainty interaction problem in software-intensive systems

Software-intensive systems are increasingly used to support tasks that are typically characterized by high degrees of uncertainty. The modeling notations employed to design, verify, and operate such systems have increasingly started to capture different types of uncertainty, so that they can be explicitly considered when systems are developed and deployed. While these modeling paradigms consider different sources of uncertainty individually, these sources are rarely independent, and their interactions affect the achievement of system goals in subtle and often unpredictable ways. This vision paper describes the problem of uncertainty interaction in software-intensive systems, illustrating it on examples from relevant application domains. We then identify key open challenges and define desiderata that future modeling notations and model-driven engineering research should consider to address these challenges.