Reviewerly/exHarmony huggingface dataset:
Orientation-aware leg movement learning for action-driven human motion prediction

The task of action-driven human motion prediction aims to forecast future human motion based on the observed sequence while respecting the given action label. It requires modeling not only the stochasticity within human motion but the smooth yet realistic transition between multiple action labels. However, the fact that most datasets do not contain such transition data complicates this task. Existing work tackles this issue by learning a smoothness prior to simply promote smooth transitions, yet doing so can result in unnatural transitions especially when the history and predicted motions differ significantly in orientations. In this paper, we argue that valid human motion transitions should incorporate realistic leg movements to handle orientation changes, and cast it as an action-conditioned in-betweening (ACB) learning task to encourage transition naturalness. Because modeling all possible transitions is virtually unreasonable, our ACB is only performed on very few selected action classes with active gait motions, such as “Walk” or “Run”. Specifically, we follow a two-stage forecasting strategy by first employing the motion diffusion model to generate the target motion with a specified future action, and then producing the in-betweening to smoothly connect the observation and prediction to eventually address motion prediction. Our method is completely free from the labeled motion transition data during training. To show the robustness of our approach, we generalize our trained in-betweening learning model on one dataset to two unseen large-scale motion datasets to produce natural transitions. Extensive experimental evaluations on three benchmark datasets demonstrate that our method yields the state-of-the-art performance in terms of visual quality, prediction accuracy, and action faithfulness.
Exploratory functional data analysis of multivariate densities for the identification of agricultural soil contamination by risk elements

Geochemical mapping of risk element concentrations in soils is performed in many countries around the world. It results in numerous large datasets of high analytical quality, which can be used to identify soils that violate individual legislative limits for safe food production. However, there is a lack of advanced data mining tools that would be suitable for sensitive exploratory data analysis of big data while respecting the natural variability of soil composition. To distinguish anthropogenic contamination from natural variations, the analysis of the entire data distribution for smaller subareas is key. In this article, we propose a new data mining methodology for geochemical mapping data based on functional data analysis of probability densities in the framework of Bayes spaces after post-stratification of a big dataset to smaller districts. The tools we propose allow us to analyse the entire distribution, going well beyond a superficial detection of extreme concentration anomalies. We illustrate the proposed methodology on a dataset gathered according to the Czech national legislation (1990–2009), whose information content has not yet been fully exploited. Taking into account specific properties of probability density functions and recent results for orthogonal decomposition of multivariate densities enabled us to reveal real contamination patterns that were so far only suspected in Czech agricultural soils. We process the above Czech soil composition dataset for Cu, Pb, and Zn by first compartmentalizing it into spatial units, the so-called districts, and by subsequently clustering these districts according to diagnostic features of their uni- and multivariate distributions at high concentration levels. These clusters were seen to correspond to compartments that show known features of contamination, such as historical metallurgy of non-ferrous metals and iron and steel production. Comparison between compartments, notably neighbouring districts with similar natural factors controlling soil variability, is key to the reliable distinction of diffuse contamination. In this work, we used soil contamination by Cu-bearing pesticides as an example for empirical testing of the proposed data mining approach. In general, there are no natural and justifiable thresholds of risk element concentrations that would be valid for geographical areas with too much natural heterogeneity. Therefore, national (or larger) soil geochemistry datasets cannot be processed as a whole. As we demonstrate in this paper, empirical knowledge and careful tailoring of statistical tools for the characteristic types of soil contamination are essential for unequivocal identification of the anthropogenic component in real datasets.
GRRLN: Gated Recurrent Residual Learning Networks for code clone detection

Abstract Code clone detection is a critical problem in software development and maintenance domains. It aims to identify functionally identical or similar code fragments within an application. Existing works formulate the code clone detection task as a binary classification problem which predicts a code pair as a clone or not based on a pre‐defined threshold. In reality, there are various types of code clone subject to the degree of how a pair of code fragments are similar to each other. To investigate the effect of different code clone detection manners on the clone detection result, we propose Gated Recurrent Residual Learning Networks (GRRLN), a novel neural network model for code clone detection. To train GRRLN, we first represent each code fragment as a statement‐level tree sequence derived from the whole abstract syntax tree (AST). Then, a gated recurrent neural network with residual connections is adopted to fully extract the semantics of all individual statement trees together with their dependency relationships across the input statement sequence. Finally, the output representations of code fragments by GRRLN are used for similarity calculation and clone detection. We evaluate GRRLN using two real‐world datasets for code clone detection and clone type classification. Experiments show that GRRLN achieves promising and compelling results and meanwhile needs significantly less time and memory consumption compared with the state‐of‐the‐art methods.
Expanding information behaviour boundaries: A study with religious leaders

Information behaviour is a very productive research area in information science, although some study contexts are still little explored, such as religious and spiritual practices, particularly related to minority religions. Thus, this study investigated the information behaviour of religious leaders of Santo Daime, a religion originating in the Brazilian Amazon. Exploratory research with a qualitative approach and semi-structured interviews with four leaders of the doctrine were carried out. The most used sources of information were documents about the Daime religion, personal experience, contact with other leaders and the institution. Information was sought to guide the participants in their rituals, solve administrative problems, improve their knowledge and strengthen their faith. Used in a critical, reflective and creative way, it seeks coherence with the Daimist principles. The findings may contribute to the understanding of the information behaviour of this little-studied social group and the appreciation of cultural and religious diversity in society.
From NeurODEs to AutoencODEs: A mean-field control framework for width-varying neural networks

Abstract The connection between Residual Neural Networks (ResNets) and continuous-time control systems (known as NeurODEs) has led to a mathematical analysis of neural networks, which has provided interesting results of both theoretical and practical significance. However, by construction, NeurODEs have been limited to describing constant-width layers, making them unsuitable for modelling deep learning architectures with layers of variable width. In this paper, we propose a continuous-time Autoencoder, which we call AutoencODE, based on a modification of the controlled field that drives the dynamics. This adaptation enables the extension of the mean-field control framework originally devised for conventional NeurODEs. In this setting, we tackle the case of low Tikhonov regularisation, resulting in potentially non-convex cost landscapes. While the global results obtained for high Tikhonov regularisation may not hold globally, we show that many of them can be recovered in regions where the loss function is locally convex. Inspired by our theoretical findings, we develop a training method tailored to this specific type of Autoencoders with residual connections, and we validate our approach through numerical experiments conducted on various examples.
Modification of the wave algorithm for Euclidean distances calculation in transport applications

Many engineering problems could be either reduced to or directly involve the mathematical problem of finding the shortest path in some confined space with obstacles. To solve this problem, graph theory and dynamic programming offers quite a lot of exact and heuristic algorithms that work with directed and undirected, weighted and unweighted, connected and disconnected graphs. All these methods are based on their own techniques of calculating the distance between the vertices, the choice of which is dictated by the convenience of implementation and methodological considerations. As a result, the resulted paths found by the algorithms usually are characterized by a certain abstract length, which is often difficult to convert into Euclidean one. Nevertheless, spatial problems in engineering practice often require an answer in terms of real physical distances. Specifically, such a task fully arises in engineering applications related to the design of seaports, namely, in the laying of routes of intra-terminal transport, power supply networks, in the design of approach channels. A modification of the most common method for finding the shortest paths, the wave orthogonal-diagonal algorithm, is described in the paper. It allows you to include the geometric characteristics of its individual sections in the calculation of the length of the found route, which makes it possible to accurately estimate the length in the sense of the Euclidean distance.
The ecology of online hate speech: Mapping expert perspectives on the drivers for online hate perpetration with the Delphi method

Abstract Online hate speech (OHS) is a prevalent issue on social media with harmful consequences for individuals and society. Counteracting this issue requires a better understanding of the factors that drive behaviors such as posting hate speech or making hateful comments. The present study contributes to this need by investigating the personal, social, and digital determinants for OHS perpetration. By conducting a two‐round Delphi study, data was collected from an interdisciplinary sample of experts. Practitioners were included in this sample to bridge gaps between research and fieldwork on OHS. The first survey collected information through open‐ended questions, which was then organized in different themes and lists of determinants in a thematic analysis. These determinants were rated for relevance in a follow‐up survey. Data analysis of the second survey used nonparametric statistics. From a total list of 129 determinants, the participants reached a consensus on 27 determinants as most relevant (median &gt; 4) for facilitating the production and dissemination of OHS. An ecological perspective was applied to interpret these findings. As a result, we propose an ecological model of personal, social, and digital determinants for online hate perpetration to illustrate their influence on individual behavior as well as their possible connections. The model provides a comprehensive overview of factors facilitating OHS perpetration, which can help to identify points for intervention.
One-step Bayesian example-dependent cost classification: The OsC-MLP method

Example-dependent cost classification problems are those where the decision costs depend not only on the true and the attributed classes but also on the sample features. Discriminative algorithms that carry out such classification tasks must take this dependence into account. In some applications, the decision costs are known for the training set but not in production, which complicates the problem. In this paper, we introduce a new one-step Bayesian formulation to train Neural Networks and solve the above limitation for binary cases with one-step Learning Machines, avoiding the drawbacks that unknown analytical forms of the example-dependent costs create. The formulation is based on defining an artificial likelihood ratio by using the available training classification costs in its definition, and proposes a test that does not require the values of the costs for unseen samples. Furthermore, it also includes Bayesian rebalancing mechanisms to combat the negative effects of class imbalance. Experimental results support the consistency and effectiveness of the corresponding algorithms.
Life cycle, education, and statistical cognitive ability

High statistical cognitive ability is an essential factor to achieve high-quality development in the era of artificial intelligence and big data. In this research, we use the machine learning local weighted regression algorithm to analyze the change curve of Chinese statistical cognitive ability throughout the life cycle, as well as the impact of individual education and parental education on statistical cognitive ability of 26,000 individuals from different groups of gender, age, educational background, and family background. All the data analyzed is from the China Family Panel Studies (CFPS). We find that the statistical cognitive ability curve is inverted U-shaped throughout the life cycle, and the years of education, parental education and individual are proportional to statistical cognitive ability. Keywords: statistical cognitive ability, machine learning, robust locally weighted and smoothing scatterplots, education, life cycle.
Research on denoising and enhancing methods of medical images based on convolutional neural networks

Summary In the process of modern medical diagnosis, medical image‐assisted diagnosis plays a very important role. However, the process of medical image acquisition, will be affected by various types and degrees of noise, and there will be a certain probability of producing strip artifacts, which will interfere with the doctor's diagnosis, analysis, and treatment of diseases to a certain extent. However, the traditional medical image denoising method will cause problems such as image edge blurring and detail loss, and it is difficult to achieve the balance between noise removal and detail information retention. Therefore, denoising medical images and improving the accuracy of denoising as much as possible have very important scientific research significance and clinical application value. Based on this, this article proposes a medical image denoising method based on a double residual convolutional neural network and compares it with traditional medical images denoising methods such as K‐SVD, BM3D, and PNLM3. Experimental results show that the medical image denoising method based on the double residual convolutional neural network proposed in this article has excellent performance.
Fast simulation for multi-photon, atomic-ensemble quantum model of linear optical systems addressing the curse of dimensionality

Abstract Photons are elementary particles of light in quantum mechanics, whose dynamics can be difficult to gain detailed insights, especially in complex systems. Simulation is a promising tool to resolve this issue, but it must address the curse of dimensionality, namely, that the number of bases increases exponentially in the number of photons. Here we mitigate this dimensionality scaling by focusing on optical systems composed of linear optical objects, modeled as an ensemble of two-level atoms. We decompose the time evolutionary operator on multiple photons into a group of time evolution operators acting on a single photon. Since the dimension of a single-photon time evolution operator is exponentially smaller than that of a multi-photon one in the number of photons, the decomposition enables the multi-photon simulations to be performed at a much lower computational cost. We apply this method to basic single- and multi-photon phenomena, such as Hong–Ou–Mandel interference and violation of the Bell-CHSH inequality, and confirm that the calculated properties are quantitatively comparable to the experimental results. Furthermore, our method visualizes the spatial propagation of photons hence provides insights that aid experiment designs for quantum-enabled technologies.
Online and Mobile-Assisted Language Learning

Online language classes and mobile-assisted language learning applications (Apps) are non-traditional methods of education that differ from traditional classes, which involve face-to-face instruction and teacher-led lessons. Web or mobile-assisted language learning applications cater to the changing world, making them convenient and affordable for those with limited time. These applications can be used at the learners’ convenience, making them a qualitative leap in the field of language learning. There is a world full of language applications some of which are dedicated to the education of the English Language as the Global language and others have many languages to learn. In the explorations of language applications, the Google Bundle is one of the best for online classes as it is the most comprehensive bundle and Duolingo is the best language application, if we considered the increasing number of its downloads and its continuously increasing revenue. Most of the applications have free and Pro or Premium features, and some only offer a free trial of one lesson or a seven-day trial. Each application has different teaching methods; some use gamification, interactive exercises, communicative approach, spaced repetition, contextual learning, audio-visual learning, and personalization. Moreover, most of the applications employ Artificial intelligence (AI) for immediate feedback.
Quasi-Dense Matching for Oblique Stereo Images through Semantic Segmentation and Local Feature Enhancement

This paper proposes a quasi-dense feature matching algorithm that combines image semantic segmentation and local feature enhancement networks to address the problem of the poor matching of image features because of complex distortions, considerable occlusions, and a lack of texture on large oblique stereo images. First, a small amount of typical complex scene data are used to train the VGG16-UNet, followed by completing the semantic segmentation of multiplanar scenes across large oblique images. Subsequently, the prediction results of the segmentation are subjected to local adaptive optimization to obtain high-precision semantic segmentation results for each planar scene. Afterward, the LoFTR (Local Feature Matching with Transformers) strategy is used for scene matching, enabling enhanced matching for regions with poor local texture in the corresponding planes. The proposed method was tested on low-altitude large baseline stereo images of complex scenes and compared with five classical matching methods. Results reveal that the proposed method exhibits considerable advantages in terms of the number of correct matches, correct rate of matches, matching accuracy, and spatial distribution of corresponding points. Moreover, it is well-suitable for quasi-dense matching tasks of large baseline stereo images in complex scenes with considerable viewpoint variations.
Self-organizing maps: a novel approach to identify and map business clusters

Business cluster identification is an essential topic for helping understand regional and global supply chains and establishing economic policies and logistics. This work aims to leverage the benefits of self-organizing maps (SOM), combined with traditional clustering algorithms and image processing techniques, to identify business clusters that are described by high-dimensionality feature vectors. It is advantageous over previous work because the algorithm is unsupervised and makes no assumptions about the number of clusters for a given feature set. The proposed algorithm was evaluated using recent datasets for US metropolitan cities from the Indiana Business Research Center (Innovation 2.0) and the Occupational Employment Statistics Survey. Data involving innovation metrics, education levels, economic well-being, connectivity, local GDP, and STEM are aggregated to demonstrate the effectiveness of the proposed neural network. The clustering results are compared to traditional approaches, including K-means clustering, both quantitatively and qualitatively. The unsupervised nature of the proposed SOM approach, and the acceptable computational complexity of the overall algorithm, suggests that self-organizing maps offer several advantages over traditional methods. In this work, we present a novel architecture coupling a SOM model with processing techniques for automatically identifying business clusters derived from high-dimensionality feature vectors, the first use case of SOMs in business cases affecting supply chains and other economic decisions. Preliminary results confirm the viability of architecture as an unsupervised approach for identifying business clusters.
Accessing the artwork in covid-19: loss, recovery and reimagination

Being ‘there’ and present within a physical space has been a long-accepted part of any art museum visitor experience. Once the sole access route, the emergence of digitisation and the Internet has since offered a new flexibility for visitors. Such online provision is often considered supplementary, ancillary, yet the year 2020 was witness to a seismic shift, when museums were compelled to unilaterally close their physical doors in response to Covid-19. This paper explores the sense of loss engendered by the abrupt removal of a direct interaction with physical artworks and examines the online initiatives that took its place. The theme of loss is explored through phenomenological experience of artworks/objects, an experience which cannot be recaptured virtually, in relation to the interchange between the physical and the virtual at both an individual artwork level and within a curated museum space. Advances in digital access, exciting new realities and future possibilities are examined, considering how this sense of loss might be accommodated in lieu, mitigated online. It is argued that a multiplicity of approaches that seek to redefine rather than replicate will engage new audiences experientially and offer a refreshed look at access between the physical and virtual art museum space.
Extended Reality as an Educational Resource in the Primary School Classroom: An Interview of Drawbacks and Opportunities

The use of Extended Reality in Primary Education classrooms has emerged as a transformative element that enhances the teaching and learning process of students. In this context, examining the various effects that this tool can generate is essential to identify both the opportunities and limitations that teachers face when incorporating this technology into their practices. The aim of this research is to analyse the impact of the use of Extended Reality as an educational resource in Primary Education, focusing on teachers’ perceptions. The information was collected through semi-structured interviews with 36 active teachers in Primary Education. The analysis of the data obtained identifies the benefits and functionalities offered by the implementation of Extended Reality in Primary Education classrooms, as well as the uncertainties and concerns that teachers have with the implementation of Extended Reality. The results highlight the significant opportunities that Extended Reality offers in the teaching–learning process, provided that teachers are adequately trained. Furthermore, this study offers valuable recommendations to guide future teachers and researchers in the successful integration of this technology into the educational process.