Data Representation Research Articles

Augmented Reality Waste Accumulation Visualizations

The negative impact humans have on the environment is partly caused by thoughtless consumption leading to unnecessary waste. A likely contributing factor is the relative invisibility of waste: waste produced by individuals is either out of their sight or quickly taken away. Nevertheless, waste disposal systems sometimes break down, creating natural information displays of waste production that can have educational value. We take inspiration from such natural displays and introduce a class of situated visualizations we call augmented reality waste accumulation visualizations , or ARwav s, which are literal representations of waste data embedded in users’ familiar environment. We implemented examples of ARwav s and demonstrated them in feedback sessions with experts in pro-environmental behavior, and during a large tech exhibition event. We discuss general design considerations for ARwav s. Finally, we conducted a study with 20 participants suggesting that ARwav s yield stronger emotional responses than nonimmersive waste accumulation visualizations and plain numbers.

Thermodynamic signatures and phase transitions in high-energy Au–Au collision

In this study, we systematically investigate the dynamics of various hadrons namely π +, π −, K +, K −, p, p¯ , Λ, Λ¯ , Ξ− and Ξ¯+ produced in central Au–Au collisions. We analyze data of AGS and RHIC, which span a broad range of collision energies, ranging from sNN = 1.9 to 200 GeV. To analyze the transverse momentum (p T ) and transverse mass (m T ) distributions, we employ a two-component standard distribution function, achieving a very good representation of the experimental data across these energy regimes. We extract key thermodynamic parameters, including the effective temperature T, the mean transverse momentum 〈p T 〉, and the initial temperature T i , and analyze their dependence on the values of collision energy and particle mass. Our findings reveal a distinct transition behaviour around sNN=19.6GeV . Below sNN=19.6GeV , the values of T, 〈p T 〉, and T i increase monotonically for all hadrons due to higher energy transfer into the system. Above this energy threshold, these extracted parameters plateau, suggesting that the additional energy is utilized as latent heat for phase transition rather than increasing the system’s temperature. These observations delineate two distinct regions: a hadron-dominated region at lower energies and a parton-dominated region at higher energies, each potentially indicative of different phases of matter, with the latter possibly signalling the onset of a quark–gluon plasma. The study thus provides critical insights into the complex interplay of thermodynamics, phase transitions, and particle interactions in high-energy Au–Au collisions.

Towards Improved Privacy in Digital Marketing: A Unified Approach to User Modeling with Deep Learning on a Data Monetization Platform

This paper introduces an innovative method for safeguarding user privacy in digital marketing campaigns through the application of deep learning techniques on a data monetization platform. This framework empowers users to maintain authority over their personal data while enabling marketers to pinpoint suitable target audiences. The system consists of several key stages Data representation learning in hyperbolic space captures latent user interests across various data sources with hierarchical structures. Subsequently, Generative Adversarial Networks generate synthetic user interests from these embedding. To preserve user privacy, Federated Learning is utilized for decentralized user monetization, Data privacy, modeling training, ensuring data remains undisclosed to marketers. Lastly, a hyperbolic embedding, Federated learning targeting strategy, rooted in recommendation systems, utilizes learned user interests to identify optimal target audiences for digital marketing campaigns. In sum, this approach offers a comprehensive solution for privacy-preserving user modeling in digital marketing.

Knowledge Graph Analysis On English Wikipedia Pages Using A Deep Learning Algorithm

Analysis of social networks or online communities can be very difficult when working on large networks, as many measurements require expensive hardware. For example, identifying the community structure of a network is a very computationally expensive task. Embedded graph is a way to represent graphs with vectors, so that further analysis becomes easier. The purpose of this research is to analyze the knowledge graph from the wikipedia article data. This research aims to implement web scraping techniques on the wikipedia article search engine and display similar wikipedia pages and analyze them using a predetermined deep learning algorithm. Data collection in this research used scraping techniques to retrieve data from the unstructured wikipedia website and then processed it into structured data. The method used in this research is a standard cross-industry process for data mining by performing phases of data collection, data processing, proposed algorithms, testing and evaluation. The algorithm applied is deepwalk, kmeans, girvan newman. By doing this research, it is expected to provide knowledge about the deep learning approach for data representation of the wikipedia pages knowledge graph and can help users find similar wikipedia pages and enrich literacy on knowledge graph analysis.

Graph machine learning for integrated multi-omics analysis.

Multi-omics experiments at bulk or single-cell resolution facilitate the discovery of hypothesis-generating biomarkers for predicting response to therapy, as well as aid in uncovering mechanistic insights into cellular and microenvironmental processes. Many methods for data integration have been developed for the identification of key elements that explain or predict disease risk or other biological outcomes. The heterogeneous graph representation of multi-omics data provides an advantage for discerning patterns suitable for predictive/exploratory analysis, thus permitting the modeling of complex relationships. Graph-based approaches-including graph neural networks-potentially offer a reliable methodological toolset that can provide a tangible alternative to scientists and clinicians that seek ideas and implementation strategies in the integrated analysis of their omics sets for biomedical research. Graph-based workflows continue to push the limits of the technological envelope, and this perspective provides a focused literature review of research articles in which graph machine learning is utilized for integrated multi-omics data analyses, with several examples that demonstrate the effectiveness of graph-based approaches.

A review on evaluating mental stress by deep learning using EEG signals

AbstractMental stress is a common problem that affects individuals all over the world. Stress reduces human functionality during routine work and may lead to severe health defects. Early detection of stress is important for preventing diseases and other negative health-related consequences of stress. Several neuroimaging techniques have been utilized to assess mental stress, however, due to its ease of use, robustness, and non-invasiveness, electroencephalography (EEG) is commonly used. This paper aims to fill a knowledge gap by reviewing the different EEG-related deep learning algorithms with a focus on Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the evaluation of mental stress. The review focuses on data representation, individual deep neural network model architectures, hybrid models, and results amongst others. The contributions of the paper address important issues such as data representation and model architectures. Out of all reviewed papers, 67% used CNN, 9% LSTM, and 24% hybrid models. Based on the reviewed literature, we found that dataset size and different representations contributed to the performance of the proposed networks. Raw EEG data produced classification accuracy around 62% while using spectral and topographical representation produced up to 88%. Nevertheless, the roles of generalizability across different deep learning models and individual differences remain key areas of inquiry. The review encourages the exploration of innovative avenues, such as EEG data image representations concurrently with graph convolutional neural networks (GCN), to mitigate the impact of inter-subject variability. This novel approach not only allows us to harmonize structural nuances within the data but also facilitates the integration of temporal dynamics, thereby enabling a more comprehensive assessment of mental stress levels.

Adaptation of state-of-the-art neural network architectures to interference fringe reduction in absorption spectroscopy

State-of-the-art neural network architectures in image classification and natural language processing were applied to interference fringe reduction in absorption spectroscopy by interpreting the data structure accordingly. A model was designed for temporal interpolation of background spectra and a different model was created for gas concentration fitting. The networks were trained on experimental data provided by a wavelength modulation spectroscopy instrument and the best performing architectures were analyzed further to evaluate generalization performance, robustness and transferability. A BERT-styled fitter achieved the best performance on the validation set and reduced the mean squared error of fitted amplitude by 99.5%. However, analysis of the de-noising behavior showed large biases. A U-Net styled convolutional neural network reduced the mean squared error of the interpolation by 93.2%. Evaluation on a test set provided evidence that the combination of model interpolation and linear fitting was robust and the detection limit was improved by 52.4%. Transferring the trained interpolator model to a different spectrometer setup showed no chaotic out-of-distribution effects. Additional fine-tuning further increased the performance. Neural network architectures cannot be generally applied to all absorption spectroscopy tasks. However, given the right task and the data representation, robust performance increase is achievable.

The Internet Doesn't Exist in the Sky: Literacy, AI, and the Digital Middle Passage

AbstractThis article utilizes speculative and visual storytelling alongside interdisciplinary research on artificial intelligence (AI) and algorithmic oppression to engage in a thought experiment on how literacy studies might refuse the oppressionist logics currently undermining the possibilities of AI in literacy education. As technological advancements in education will only continue to increase and as society is yet to ascertain the parameters of an ethical AI system, it is paramount to analyze the past and present and contemplate potential futures, especially those that do not result in violence against Black and Brown peoples. To engage in speculation, we employ Endarkened Storywork (Toliver, 2022) to present an empirically driven, futuristic, science fiction narrative from two perspectives: (1) a US, Black girl who is forced to participate in AI‐structured secondary schooling and (2) a Black girl in Haiti who is forced to live in a country polluted by technological byproduct. This narrative, which is grounded in academic research and news editorials, is accompanied by comic art and followed by a companion analysis detailing the theoretical backdrop of the story. By utilizing multiple methods of scholarly distribution, we provide multiple entry points for readers to engage with this work. We offer a means for readers to see—via story, art, and scholarship—the potential impacts of AI on Black people globally. Additionally, by situating this article in the creative and scholarly realms, we strategically deconstruct traditional forms and methods of knowledge production that have constrained academic research and rendered invisible alternative forms of data representation.

Transferability of Machine Learning Models for Geogenic Contaminated Groundwaters.

Machine learning models show promise in identifying geogenic contaminated groundwaters. Modeling in regions with no or limited samples is challenging due to the need for large training sets. One potential solution is transferring existing models to such regions. This study explores the transferability of high fluoride groundwater models between basins in the Shanxi Rift System, considering six factors, including modeling methods, predictor types, data size, sample/predictor ratio (SPR), predictor range, and data informing. Results show that transferability is achieved only when model predictors are based on hydrochemical parameters rather than surface parameters. Data informing, i.e., adding samples from challenging regions to the training set, further enhances the transferability. Stepwise regression shows that hydrochemical predictors and data informing significantly improve transferability, while data size, SPR, and predictor range have no significant effects. Additionally, despite their stronger nonlinear capabilities, random forests and artificial neural networks do not necessarily surpass logistic regression in transferability. Lastly, we utilize the t-SNE algorithm to generate low-dimensional representations of data from different basins and compare these representations to elucidate the critical role of predictor types in transferability.

Comprehensive Signal Analysis: Unraveling Mean Frequency, Power, and Bandwidth

Objective: The power spectral density (PSD) serves as a fundamental tool in signal analysis, revealing valuable insights into the distribution of power across frequencies. This paper explores the concept of PSD, which emerges from the Fourier transform of a signal’s autocorrelation function. By examining power distribution, PSD provides a comprehensive understanding of a signal’s spectral characteristics. Methods: We generate a chirp signal with specific parameters, including initial and final frequencies, and introduce white Gaussian noise. The combination of these signals forms a composite signal, enabling the calculation of its PSD. Results: Visualizing the PSD allows for discernment of the frequency distribution, with the median frequency providing insights into the central tendency of the distribution. Conclusion: Furthermore, the customization of plots enhances the visual representation of data, tailoring it to the requirements of the specific application. Adjustments to color schemes, line styles, and annotations improve clarity and aid in conveying complex information effectively.

Join multiple Riemannian manifold representation and multi‐kernel non‐redundancy for image clustering

AbstractImage clustering has received significant attention due to the growing importance of image recognition. Researchers have explored Riemannian manifold clustering, which is capable of capturing the non‐linear shapes found in real‐world datasets. However, the complexity of image data poses substantial challenges for modelling and feature extraction. Traditional methods such as covariance matrices and linear subspace have shown promise in image modelling, and they are still in their early stages and suffer from certain limitations. However, these include the uncertainty of representing data using only one Riemannian manifold, limited feature extraction capacity of single kernel functions, and resulting incomplete data representation and redundancy. To overcome these limitations, the authors propose a novel approach called join multiple Riemannian manifold representation and multi‐kernel non‐redundancy for image clustering (MRMNR‐MKC). It combines covariance matrices with linear subspace to represent data and applies multiple kernel functions to map the non‐linear structural data into a reproducing kernel Hilbert space, enabling linear model analysis for image clustering. Additionally, the authors use matrix‐induced regularisation to improve the clustering kernel selection process by reducing redundancy and assigning lower weights to identical kernels. Finally, the authors also conducted numerous experiments to evaluate the performance of our approach, confirming its superiority to state‐of‐the‐art methods on three benchmark datasets.

Air pollution and mortality for cancer of the respiratory system in Italy: an explainable artificial intelligence approach

Respiratory system cancer, encompassing lung, trachea and bronchus cancer, constitute a substantial and evolving public health challenge. Since pollution plays a prominent cause in the development of this disease, identifying which substances are most harmful is fundamental for implementing policies aimed at reducing exposure to these substances. We propose an approach based on explainable artificial intelligence (XAI) based on remote sensing data to identify the factors that most influence the prediction of the standard mortality ratio (SMR) for respiratory system cancer in the Italian provinces using environment and socio-economic data. First of all, we identified 10 clusters of provinces through the study of the SMR variogram. Then, a Random Forest regressor is used for learning a compact representation of data. Finally, we used XAI to identify which features were most important in predicting SMR values. Our machine learning analysis shows that NO, income and O3 are the first three relevant features for the mortality of this type of cancer, and provides a guideline on intervention priorities in reducing risk factors.

Dual-path multi-scale attention residual network for fault diagnosis of rolling bearings under complex operating conditions

Rolling bearing faults inevitably occur during the long-term continuous operation of rotating machinery. Therefore, fault diagnosis is greatly important for ensuring the normal and safe operation of rolling bearings. However, the complexity and diversity of working conditions of rolling bearings present a significant challenge in extracting fault characteristics accurately, which further affects the ultimate fault diagnosis results. In this article, we propose a new model, called dual-path multi-scale attention residual network (DPMARN), for diagnosing bearing faults under complex operating conditions. DPMARN can effectively capture the feature-feature correlation information at different scales, which is more beneficial for fusing fault features at different scales to improve the model’s performance. The main contributions of this work are summarized as follows: (1) the designed dual-path network model which incorporates parallel multi-scale branches of convolutional kernels and serially connects skip-layer multi-scale branches can integrate both low-frequency and high-frequency information and enhance the multi-scale feature extraction and complex data representation abilities. (2) The squeeze-and-excitation attention mechanism is embedded into the residual blocks to improve the ability of learning feature correlations and utilizing feature information effectively, which is helpful for extracting important fault characteristics. Extensive experiments conducted on two public bearing datasets demonstrate the superior performance of the DPMARN model for addressing the complex fault diagnosis problem. These results indicate that our proposed approach provides an effective solution for fault diagnosis of rolling bearings under complex operating conditions.

Early sport specialization impact on rates of injury in collegiate and professional sport participation: A systematic review and meta-analysis

Early sport specialization (ESS) is prevalent among adolescent athletes aspiring for elite status. However, the impact of ESS on sports injury (SI) rates during their collegiate and professional careers remains unclear. This systematic review and meta-analysis evaluate the effect of ESS on SI rates during high-level sport participation. Data sources Search included PubMed, Web of Science, SPORTDiscus, EMBASE, Cochrane, CINAHL, ERIC, and Medline. Study selection PRISMA guidelines were followed to identify peer-reviewed articles published until 01 August 2023. Eligible studies reported numbers of athletes in collegiate and professional teams, including national, in ESS and their counterpart category, along with the portion with at least one SI. Studies on concussion or non-contact injuries, or without explicit injury reports during elite sport participation were excluded. Of 526 studies, 13 met the criteria, and 5 were included in the quantitative analysis. These studies reported SIs on collegiate and professional athletes. A total of 3087 athletes were analyzed (ESS = 45%). The overall effect showed that ESS athletes had a lower odds ratio (OR = 0.7) of a SI during high-level sport participation, but this finding was not statistically significant (p > 0.05). Results demonstrated that ESS athletes had a lower odds ratio (OR = 0.7) of a SI during elite sport participation, but the effect was not statistically significant. Articles with clear reports are scarce, and thus limit the impact of the findings in this study. High heterogeneity was evident in this analysis, largely due to lack of standardized terminology, evaluation, and data representation. Prospective studies that consider diverse athlete population of the same sport are warranted.

What do Australians affected by cancer think about oncology researchers sharing research data? A cross-sectional survey.

Previous research has shown patients and the public in Australia generally support medical researchers in making de-identified research data available to other scientists. However, this research has focussed on certain types of data and recipients. We surveyed Australians affected by cancer to characterize their attitudes toward the sharing of research data with multiple third parties, including the public. A short, anonymous online survey of Australians with a previous diagnosis of cancer was advertised between October 27, 2022, and February 27, 2023. Quantitative responses were analyzed with descriptive statistics. Free-text responses were coded deductively and summarised using content analysis. In total, 551 respondents contributed data to the survey. There was strong support for cancer researchers sharing non-human and de-identified human research data with clinicians (90% and 95%, respectively) and non-profit researchers (both 94%). However, fewer participants supported sharing data with for-profit researchers (both 64%) or publicly (both 61%). When asked if they would hypothetically consent to researchers at their treatment location using and sharing their de-identified data publicly, only half agreed. In contrast, after being shown a visual representation of the de-identified survey data, 80% of respondents supported sharing it publicly. Australians affected by cancer support the sharing of research data, particularly with clinicians and non-profit researchers. Our results also imply that visualization of the data to be shared may enhance support for making it publicly available. These results should help alleviate any concerns about research participants' attitudes toward data sharing, as well as boost researchers' motivation for sharing.

Inference of dynamic hypergraph representations in temporal interaction data

Inference of dynamic hypergraph representations in temporal interaction data

CMVT: ConVit Transformer Network Recombined with Convolutional Layer

Vision transformers are deep neural networks applied to image classification based on a self-attention mechanism and can process data in parallel. Aiming at the structural loss of Vision transformers, this paper combines ConViT and Convolutional Neural Network (CNN) and proposes a new model Convolution Meet Vision Transformers (CMVT). This model adds a convolution module to the ConViT network to solve the structural loss of the transformer. By adding hierarchical data representation, the ability to gradually extract more image classification features is improved. We have conducted comparative experiments on multiple dataset, and all of them have been enhanced to improve the efficiency and performance of the model.

Manifold attention-enhanced multi-domain convolutional network for decoding motor imagery intention

Research in brain-computer interface (BCI), particularly in brain intention decoding, has made significant strides relying on the remarkable capabilities of deep learning (DL). However, many current DL framework-based models struggle to effectively utilize complementary information present in various domains of electroencephalographic (EEG) signals. This limitation may lead to suboptimal performance, particularly exacerbated by the presence of noise and artifacts. Therefore, this study proposed a manifold attention-based multi-domain convolutional neural network, known as MAMCNet, to benefit from complementary strengths across different domains of EEG signals. Specifically, the EEG signals were divided into multi-band data representations based on frequency. Multiple spatiotemporal convolutional blocks were then devised to simultaneously extract the spatiotemporal feature maps from these sub-band components. To enhance the model's robustness, these maps were mapped onto the Riemannian manifold. Additionally, manifold attention was utilized to emphasize salient spatiotemporal feature maps with attention focused on the target. A frequency convolutional block was specifically designed to merge optimized feature maps from different bands, extracting temporospatiofrequency features in EEG signals comprehensively. The experimental results on two datasets demonstrated that the proposed model outperformed the mainstream DL architecture-based model. These results suggest that the proposed model could offer an alternative scheme for facilitating BCI applications.

Efficient distributed association management method of data, model, and knowledge for digital twin railway

ABSTRACT Digital twin railway is a pivotal foundation for the intelligent construction and maintenance of railway engineering projects within extensive open spaces. Its essence is the integrated representation and association management of multi-granularity spatiotemporal data, executable analysis models, and professional knowledge. These elements are characterized by the prominent characteristics of multi-source, heterogeneity, and massive volume. However, current decentralized and independent management strategies often neglect the dynamic coupling relationships between them, and numerous multi-path joins and conversion aggregation operations exist across various spatial scale applications. Consequently, this results in challenges such as the inability to dynamically couple data-model-knowledge and conduct global association retrieval, thereby limiting the potential for real-time analysis and intelligent application capabilities. To address these problems, we first constructed a tripartite graph model ( DMK − Graph model ) that explicitly associates temporal, spatial, and interactive relationships. Subsequently, an association management architecture was proposed, accompanied by a global association graph index ( DMK − Graph index ) and a global-local indexing mechanism. Finally, a prototype system for railway data-model-knowledge association management was developed. The effectiveness of the distributed association management method was demonstrated by employing a case study of high-temperature safety risk analysis in railway tunnel engineering with multi-physics field coupling.

Latent Representation Learning for Geospatial Entities

Representation learning has been instrumental in the success of machine learning, offering compact and performant data representations for diverse downstream tasks. In the spatial domain, it has been pivotal in extracting latent patterns from various data types, including points, polylines, polygons, and networked structures. However, existing approaches often fall short of explicitly capturing both semantic and spatial information, relying on proxies and synthetic features. This paper presents GeoNN, a novel graph neural network-based model designed to learn spatially-aware embeddings for geospatial entities. GeoNN leverages edge features generated from geodesic functions, dynamically selecting relevant features based on relative locations. It introduces both transductive (GeoNN-T) and inductive (GeoNN-I) models, ensuring effective encoding of geospatial features and scalability with entity changes. Extensive experiments demonstrate GeoNN’s effectiveness in location-sensitive superpixel-based graphs and real-world points of interest, outperforming baselines across various evaluation measures.