Dynamic Datasets Research Articles

Groundwater environmental effects risk evaluation in mountain tunnel construction: A dynamic decision support system based on fuzzy two-dimensional cloud probability model

The underground water environment in mountain tunnels constitutes a complex system. During tunnel construction, the interaction between tunnel and underground water environment can lead to chain construction accidents and environmental damage. Identifying comprehensive risks of underground water environment effects and making rational decisions are crucial. Considering the diversity, complexity, uncertainty, and dynamics of groundwater environmental effects, this study develops a risk decision support system for tunnel construction. Firstly, to reflect the complexity and diversity of groundwater environmental effects, a disaster chain is established, and two indicator systems are extracted. Secondly, dynamic datasets are generated using a combination of expert system knowledge base and multi-source data fusion. Finally, based on the theories of fuzzy mathematic and two-dimensional cloud model, along with probabilistic algorithm, this study proposes a Fuzzy Two-Dimensional Cloud Probability Model (FTDCPM) to quantify the uncertainty of groundwater environmental effects. This engineering system can provide reference in similar projects.

The dynamic Colombian sign language dataset for basic conversation LSC70.

Sign language is a form of non-verbal communication used by people with hearing disability. This form of communication relies on the use of signs, gestures, facial expressions, and more. Considering that in Colombia, the population with hearing impairments is around half a million, a database of dynamic, alphanumeric signs and commonly used words was created to establish a basic conversation. For this purpose, 70 non-expert volunteers in Colombian Sign Language participated, and they were recorded against a clear background under uncontrolled lighting and clothing conditions. The dataset was named LSC70 and includes a six-frame transition in JPG format. It is organized into three parts: The first part contains alphanumeric signs at a resolution of 640×480 pixels; the second part includes word signs at a size of 640×480 pixels; and the third part focuses on the dominant hand in alphanumeric signs at 120×120 pixels. Through filtering that removed similar signs, the dataset now contains 35,208 frames with a total of 47 different signs. These images have been validated by experts at the University of Cauca and are available for free use within the research community.

Spatially constrained hyperpolarized 13C MRI pharmacokinetic rate constant map estimation using a digital brain phantom and a U-Net.

Fitting rate constants to Hyperpolarized [1-13C]Pyruvate (HP C13) MRI data is a promising approach for quantifying metabolism in vivo. Current methods typically fit each voxel of the dataset using a least-squares objective. With these methods, each voxel is considered independently, and the spatial relationships are not considered during fitting. In this work, we use a convolutional neural network, a U-Net, with convolutions across the 2D spatial dimensions to estimate pyruvate-to-lactate conversion rate, kPL, maps from dynamic HP C13 datasets. We designed a framework for creating simulated anatomically accurate brain data that matches typical HP C13 characteristics to provide large amounts of data for training with ground truth results. The U-Net is initially trained with the digital phantom data and then further trained with in vivo datasets for regularization. In simulation where ground-truth kPL maps are available, the U-Net outperforms voxel-wise fitting with and without spatiotemporal denoising, particularly for low SNR data. In vivo data was evaluated qualitatively, as no ground truth is available, and before regularization the U-Net predicted kPL maps appear oversmoothed. After further training with in vivo data, the resulting kPL maps appear more realistic. This study demonstrates how to use a U-Net to estimate rate constant maps for HP C13 data, including a comprehensive framework for generating a large amount of anatomically realistic simulated data and an approach for regularization. This simulation and architecture provide a foundation that can be built upon in the future for improved performance.

Spatio-Temporal Transformer with Kolmogorov–Arnold Network for Skeleton-Based Hand Gesture Recognition

Manually crafted features often suffer from being subjective, having an inadequate accuracy, or lacking in robustness in recognition. Meanwhile, existing deep learning methods often overlook the structural and dynamic characteristics of the human hand, failing to fully explore the contextual information of joints in both the spatial and temporal domains. To effectively capture dependencies between the hand joints that are not adjacent but may have potential connections, it is essential to learn long-term relationships. This study proposes a skeleton-based hand gesture recognition framework, the ST-KT, a spatio-temporal graph convolution network, and a transformer with the Kolmogorov–Arnold Network (KAN) model. It incorporates spatio-temporal graph convolution network (ST-GCN) modules and a spatio-temporal transformer module with KAN (KAN–Transformer). ST-GCN modules, which include a spatial graph convolution network (SGCN) and a temporal convolution network (TCN), extract primary features from skeleton sequences by leveraging the strength of graph convolutional networks in the spatio-temporal domain. A spatio-temporal position embedding method integrates node features, enriching representations by including node identities and temporal information. The transformer layer includes a spatial KAN–Transformer (S-KT) and a temporal KAN–Transformer (T-KT), which further extract joint features by learning edge weights and node embeddings, providing richer feature representations and the capability for nonlinear modeling. We evaluated the performance of our method on two challenging skeleton-based dynamic gesture datasets: our method achieved an accuracy of 97.5% on the SHREC’17 track dataset and 94.3% on the DHG-14/28 dataset. These results demonstrate that our proposed method, ST-KT, effectively captures dynamic skeleton changes and complex joint relationships.

"Synthetic" DSC Perfusion MRI with Adjustable Acquisition Parameters in Brain Tumors Using Dynamic Spin-and-Gradient-Echo Echoplanar Imaging.

Normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) computed from dynamic susceptibility contrast (DSC) perfusion imaging are useful biomarkers for differential diagnosis and treatment response assessment in brain tumors. However, their measurements are dependent on DSC acquisition factors, and CBV-optimized protocols technically differ from PSR-optimized protocols. This study aimed to generate "synthetic" DSC data with adjustable synthetic acquisition parameters using dual-echo gradient-echo (GE) DSC datasets extracted from dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI). Synthetic DSC was aimed at: 1) simultaneously create nrCBV and PSR maps using optimal sequence parameters, 2) compare DSC datasets with heterogeneous external cohorts, and 3) assess the impact of acquisition factors on DSC metrics. Thirty-eight patients with contrast-enhancing brain tumors were prospectively imaged with dynamic SAGE-EPI during a non-preloaded single-dose contrast injection and included in this cross-sectional study. Multiple synthetic DSC curves with desired pulse sequence parameters were generated using the Bloch equations applied to the dual-echo GE data extracted from dynamic SAGE-EPI datasets, with or without optional preload simulation. Dynamic SAGE-EPI allowed for simultaneous generation of CBV-optimized and PSR-optimized DSC datasets with a single contrast injection, while PSR computation from guideline-compliant CBV-optimized protocols resulted in rank variations within the cohort (Spearman's ρ = 0.83-0.89, i.e. 31%-21% rank variation). Treatment-naïve glioblastoma exhibited lower parameter-matched PSR compared to the external cohorts of treatment-naïve primary CNS lymphomas (PCNSL) (p<0.0001), supporting a role of synthetic DSC for multicenter comparisons. Acquisition factors highly impacted PSR, and nrCBV without leakage correction also showed parameter-dependence, although less pronounced. However, this dependence was remarkably mitigated by post-hoc leakage correction. Dynamic SAGE-EPI allows for simultaneous generation of CBV-optimized and PSR-optimized DSC data with one acquisition and a single contrast injection, facilitating the use of a single perfusion protocol for all DSC applications. This approach may also be useful for comparisons of perfusion metrics across heterogeneous multicenter datasets, as it facilitates post-hoc harmonization.

Research on a Density-Based Clustering Method for Eliminating Inter-Frame Feature Mismatches in Visual SLAM Under Dynamic Scenes

Visual SLAM relies on the motion information of static feature points in keyframes for both localization and map construction. Dynamic feature points interfere with inter-frame motion pose estimation, thereby affecting the accuracy of map construction and the overall robustness of the visual SLAM system. To address this issue, this paper proposes a method for eliminating feature mismatches between frames in visual SLAM under dynamic scenes. First, a spatial clustering-based RANSAC method is introduced. This method eliminates mismatches by leveraging the distribution of dynamic and static feature points, clustering the points, and separating dynamic from static clusters, retaining only the static clusters to generate a high-quality dataset. Next, the RANSAC method is introduced to fit the geometric model of feature matches, eliminating local mismatches in the high-quality dataset with fewer iterations. The accuracy of the DSSAC-RANSAC method in eliminating feature mismatches between frames is then tested on both indoor and outdoor dynamic datasets, and the robustness of the proposed algorithm is further verified on self-collected outdoor datasets. Experimental results demonstrate that the proposed algorithm reduces the average reprojection error by 58.5% and 49.2%, respectively, when compared to traditional RANSAC and GMS-RANSAC methods. The reprojection error variance is reduced by 65.2% and 63.0%, while the processing time is reduced by 69.4% and 31.5%, respectively. Finally, the proposed algorithm is integrated into the initialization thread of ORB-SLAM2 and the tracking thread of ORB-SLAM3 to validate its effectiveness in eliminating feature mismatches between frames in visual SLAM.

A Novel Data Sanitization Method Based on Dynamic Dataset Partition and Inspection Against Data Poisoning Attacks

A Novel Data Sanitization Method Based on Dynamic Dataset Partition and Inspection Against Data Poisoning Attacks

GPS Phase Integer Ambiguity Resolution Based on Eliminating Coordinate Parameters and Ant Colony Algorithm.

Correctly fixing the integer ambiguity of GNSS is the key to realizing the application of GNSS high-precision positioning. When solving the float solution of ambiguity based on the double-difference model epoch by epoch, the common method for resolving the integer ambiguity needs to solve the coordinate parameter information, due to the influence of limited GNSS phase data observations. This type of method will lead to an increase in the ill-posedness of the double-difference solution equation, so that the fixed success rate of the integer ambiguity is not high. Therefore, a new integer ambiguity resolution method based on eliminating coordinate parameters and ant colony algorithm is proposed in this paper. The method eliminates the coordinate parameters in the observation equation using QR decomposition transformation, and only estimates the ambiguity parameters using the Kalman filter. On the basis that the Kalman filter will obtain the float solution of ambiguity, the decorrelation processing is carried out based on continuous Cholesky decomposition, and the optimal solution of integer ambiguity is searched using the ant colony algorithm. Two sets of static and dynamic GPS experimental data are used to verify the method and compared with conventional least squares and LAMBDA methods. The results show that the new method has good decorrelation effect, which can correctly and effectively realize the integer ambiguity resolution.

A network-enabled pipeline for gene discovery and validation in non-model plant species.

Identifying key regulators of important genes in non-model crop species is challenging due to limited multi-omics resources. To address this, we introduce the network-enabled gene discovery pipeline NEEDLE, a user-friendly tool that systematically generates coexpression gene network modules, measures gene connectivity, and establishes network hierarchy to pinpoint key transcriptional regulators from dynamic transcriptome datasets. After validating its accuracy with two independent datasets, we applied NEEDLE to identify transcription factors (TFs) regulating the expression of cellulose synthase-like F6 (CSLF6), a crucial cell wall biosynthetic gene, in Brachypodium and sorghum. Our analyses uncover regulators of CSLF6 and also shed light on the evolutionary conservation or divergence of gene regulatory elements among grass species. These results highlight NEEDLE's capability to provide biologically relevant TF predictions and demonstrate its value for non-model plant species with dynamic transcriptome datasets.

Fast online feature selection in streaming data

The challenge of getting big amounts of high-quality labeled data is compounded by the fact that data labeling is often subjective and requires significant human effort. In many cases, the quality of the labeled data depends entirely on the expertise and experience of human annotators, making it challenging to ensure labeling accuracy in large and dynamic datasets. Moreover, there may be a significant delay between the arrival of a new instance and its manual labeling. This paper explores the use of fully unsupervised feature selection algorithms in non-stationary data streams, where the importance of features may change over time. We introduce a novel feature selection algorithm called Online Fast FEa-ture SELection-OFFESEL, which calculates the feature importance scores in each incoming window based on their mean normalized values and without using any class labels. We evaluate OFFESEL on 17 benchmark data streams, both stationary and non-stationary, using popular online classifiers like PerceptronMask, VFDT, Online Boosting, and Linear SVM. We compare OFFESEL to several other feature selection algorithms, including state-of-the-art supervised ones like FIRES and ABFS, as well as popular unsupervised ones like MCFS, LS, and Max Variance, which we adapted to data streams. Our results indicate that OFFESEL outperforms all supervised and unsupervised feature selection algorithms in terms of classification accuracy. Specifically, OFFESEL preserves the accuracy level of the supervised FIRES algorithm, which proved more accurate than ABFS in our experiments, while maintaining the accuracy level achieved by the unsupervised Max Variance algorithm. Moreover, OFFESEL requires even less computation time than Max Variance and shows high stability on stationary datasets. Overall, our study demonstrates the potential benefits of using unlabeled data for feature ranking and selection in dynamic data streams.

Cyanobacteria hot spot detection integrating remote sensing data with convolutional and Kolmogorov-Arnold networks.

Prompt and accurate monitoring of cyanobacterial blooms is essential for public health management and understanding aquatic ecosystem dynamics. Remote sensing, in particular satellite observations, presents a good alternative for continuous monitoring. This study employs multispectral images from the Sentinel-2 constellation alongside ERA5-Land to enable broad-scale data acquisition. A simple deep convolutional neural network (CNN) architecture was proposed to analyze cyanobacteria (CB) concentration dynamics in Pigeon Lake, Canada, over five years. The model achieved an R2 value of 0.81 and an RMSE score of 0.03 for the training set and 0.15 for the testing set, demonstrating high predictive accuracy. Using the Local Getis-Ord statistic, we identified and analyzed trends in hot and cold spots under the null hypothesis that such spots are randomly distributed, observing changes in their distribution and the median CB concentration in hot spots over time. Additionally, a Kolmogorov-Arnold Network (KAN) and dense neural networks (NN) with a single hidden layer were trained to classify sections of the lake shoreline into hot and no hot spots using the Dynamic World dataset within a 500m radius of the lake. The KAN achieved a recall metric of 0.83 for detecting hot spots.

Deep deterministic policy gradients with a self-adaptive reward mechanism for image retrieval

Traditional image retrieval methods often face challenges in adapting to varying user preferences and dynamic datasets. To address these limitations, this research introduces a novel image retrieval framework utilizing deep deterministic policy gradients (DDPG) augmented with a self-adaptive reward mechanism (SARM). The DDPG-SARM framework dynamically adjusts rewards based on user feedback and retrieval context, enhancing the learning efficiency and retrieval accuracy of the agent. Key innovations include dynamic reward adjustment based on user feedback, context-aware reward structuring that considers the specific characteristics of each retrieval task, and an adaptive learning rate strategy to ensure robust and efficient model convergence. Extensive experimentation with the three distinct datasets demonstrates that the proposed framework significantly outperforms traditional methods, achieving the highest retrieval accuracy having 3.38%, 5.26%, and 0.21% improvement overall as compared to the mainstream models over DermaMNIST, PneumoniaMNIST, and OrganMNIST datasets, respectively. The findings contribute to the advancement of reinforcement learning applications in image retrieval, providing a user-centric solution adaptable to various dynamic environments. The proposed method also offers a promising direction for future developments in intelligent image retrieval systems.

A Dynamic Characteristic Aware Index Structure Optimized for Real-world Datasets

Many datasets in real life are complex and dynamic, that is, their key densities are varied over the whole key space and their key distributions change over time. It is challenging for an index structure to efficiently support all key operations for data management, in particular, search, insert, and scan, for such dynamic datasets. In this paper, we present DyTIS (Dynamic dataset Targeted Index Structure), an index that targets dynamic datasets. DyTIS, though based on the structure of Extendible hashing, leverages the CDF of the key distribution of a dataset, and learns and adjusts its structure as the dataset grows. The key novelty behind DyTIS is to group keys by the natural key order and maintain keys in sorted order in each bucket to support scan operations within a hash index. We also define what we refer to as a dynamic dataset and propose a means to quantify its dynamic characteristics. Our experimental results show that DyTIS provides higher performance than the state-of-the-art learned index for the dynamic datasets considered. We also analyze the effects of the dynamic characteristics of datasets, including sequential datasets, as well as the effect of multiple threads on the performance of the indexes.

The annual dynamic dataset of high-resolution crop water use in China from 1991 to 2019

Accurately quantifying agricultural water use is essential for protecting agricultural systems from the risk of water scarcity and promoting sustainable water management. While previous studies have innovatively provided spatially explicit analyses or datasets, they tend to have relatively coarse resolution (~8.3 km), and inadequately considered precise localization parameters. Here, we produced annual blue and green water use for 15 main crops with a resolution of 1 km for the years 1991–2019 in China. Firstly, we estimated the yearly crop blue and green water use at the site scale by incorporating more localized input parameters using a dynamic water balance model. Then, the random forest model was combined with site-scale simulation results to generate spatial predictions of blue and green water for each crop from 1991 to 2019. The resulting maps showed a high correlation with locally observed values at field stations (R2 = 0.95), statistics (R2 = 0.77), and exhibited some strengths compared with existing datasets that covered various scales. This dataset can play a key role in devising sustainable water management strategies.

Depth Prediction Improvement for Near-Field iToF Lidar in Low-Speed Motion State.

Current deep learning-based phase unwrapping techniques for iToF Lidar sensors focus mainly on static indoor scenarios, ignoring motion blur in dynamic outdoor scenarios. Our paper proposes a two-stage semi-supervised method to unwrap ambiguous depth maps affected by motion blur in dynamic outdoor scenes. The method trains on static datasets to learn unwrapped depth map prediction and then adapts to dynamic datasets using continuous learning methods. Additionally, blind deconvolution is introduced to mitigate the blur. The combined use of these methods produces high-quality depth maps with reduced blur noise.

DIAFM: An Improved and Novel Approach for Incremental Frequent Itemset Mining

Traditional approaches to data mining are generally designed for small, centralized, and static datasets. However, when a dataset grows at an enormous rate, the algorithms become infeasible in terms of huge consumption of computational and I/O resources. Frequent itemset mining (FIM) is one of the key algorithms in data mining and finds applications in a variety of domains; however, traditional algorithms do face problems in efficiently processing large and dynamic datasets. This research introduces a distributed incremental approximation frequent itemset mining (DIAFM) algorithm that tackles the mentioned challenges using shard-based approximation within the MapReduce framework. DIAFM minimizes the computational overhead of a program by reducing dataset scans, bypassing exact support checks, and incorporating shard-level error thresholds for an appropriate trade-off between efficiency and accuracy. Extensive experiments have demonstrated that DIAFM reduces runtime by 40–60% compared to traditional methods with losses in accuracy within 1–5%, even for datasets over 500,000 transactions. Its incremental nature ensures that new data increments are handled efficiently without needing to reprocess the entire dataset, making it particularly suitable for real-time, large-scale applications such as transaction analysis and IoT data streams. These results demonstrate the scalability, robustness, and practical applicability of DIAFM and establish it as a competitive and efficient solution for mining frequent itemsets in distributed, dynamic environments.

Two-Terminal Neuromorphic Devices for Spiking Neural Networks: Neurons, Synapses, and Array Integration.

The ever-increasing volume of complex data poses significant challenges to conventional sequential global processing methods, highlighting their inherent limitations. This computational burden has catalyzed interest in neuromorphic computing, particularly within artificial neural networks (ANNs). In pursuit of advancing neuromorphic hardware, researchers are focusing on developing computation strategies and constructing high-density crossbar arrays utilizing history-dependent, multistate nonvolatile memories tailored for multiply-accumulate (MAC) operations. However, the real-time collection and processing of massive, dynamic data sets require an innovative computational paradigm akin to that of the human brain. Spiking neural networks (SNNs), representing the third generation of ANNs, are emerging as a promising solution for real-time spatiotemporal information processing due to their event-based spatiotemporal capabilities. The ideal hardware supporting SNN operations comprises artificial neurons, artificial synapses, and their integrated arrays. Currently, the structural complexity of SNNs and spike-based methodologies requires hardware components with biomimetic behaviors that are distinct from those of conventional memristors used in deep neural networks. These distinctive characteristics required for neuron and synapses devices pose significant challenges. Developing effective building blocks for SNNs, therefore, necessitates leveraging the intrinsic properties of the materials constituting each unit and overcoming the integration barriers. This review focuses on the progress toward memristor-based spiking neural network neuromorphic hardware, emphasizing the role of individual components such as memristor-based neurons, synapses, and array integration along with relevant biological insights. We aim to provide valuable perspectives to researchers working on the next generation of brain-like computing systems based on these foundational elements.

Stall prediction model based on deep learning network in axial flow compressor

To predict stall and surge in advance that make the aero-engine compressor operate safely, a stall prediction model based on deep learning theory is established in the current study. The Long Short-Term Memory (LSTM) originating from the recurrent neural network is used, and a set of measured dynamic pressure datasets including the stall process is used to learn what determines the weight of neural network nodes. Subsequently, the structure and function hyperparameters in the model are deeply optimized, and a set of measured pressure data is used to verify the prediction effects of the model. On this basis of the above good predictive capability, stall in low- and high-speed compressor are predicted by using the established model. When a period of non-stall pressure data is used as input in the model, the model can quickly complete the prediction of subsequent time series data through the self-learning and prediction mechanism. Comparison with the real-time measured pressure data demonstrates that the starting point of the predicted stall is basically the same as that of the measured stall, and the stall can be predicted more than 1 s in advance so that the occurrence of stall can be avoided. The model of stall prediction in the current study can make up for the uncertainty of threshold selection of the existing stall warning methods based on measured data signal processing. It has a great application potential to predict the stall occurrence of aero-engine compressor in advance and avoid the accidents.

PQKELP: Projected Quantum Kernel Embedding based Link Prediction in dynamic networks

In dynamic networks, where the network’s topology is constantly changing, link prediction is a challenging issue. The two major challenges in link prediction within a time-varying network are accuracy and efficiency. Although random walk techniques have introduced promising embedding-based approaches, they fall short of optimization. Quantum computing, on the other hand, enhances performance in high-dimensional spaces, yet faces concerns about the limited efficiency of few qubit simulators. Addressing these issues, Projected Quantum Kernels (PQK) presents an elegant solution to achieve quantum advantage by simple quantum projection using the kernel trick, followed by a projection back to the classical state with relabeled data. In this work, we propose Projected Quantum Kernel Embedding based Link Prediction (PQKELP), a projected quantum kernel (PQK) approach on random walk embedding-based features to solve the link prediction problem. Thereby achieving a two-fold improvement by combining embedding generation and quantum projection, resulting in quantum-enhanced embedded features that enhance link prediction performance. Extensive experiments and analyses were done with a set of well-known random walk methods, i.e., Node2Vec, DeepWalk, and Walklets, also with six classical machine learning techniques and on five well-known dynamic network datasets. The results including several performance metrics like accuracy, AUC, F1-score, and precision show that our proposed model is better than traditional link prediction methods, classical machine learning approaches, and even the most cutting-edge methods available currently.

Enhancing information fusion and feature selection efficiency via the PROMETHEE method for multi-source dynamic decision data sets

With the surge in big data, the complexity of synthesizing information from multiple sources has become a critical challenge for feature selection methodologies. Feature selection is the process of reducing the number of attributes in data. Traditional single-source centric approaches are inefficient, requiring extensive preprocessing for multi-source data consolidation prior to feature selection. At the same time, an information fusion method is needed to transform the multi-source information system with selected features into a single-source information system. This paper introduces a novel multi-source information fusion and feature selection approach that seamlessly integrates the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) with a dynamic adaptation mechanism. This method is adept at addressing the complexities introduced by the evolving nature of feature and information source dimensions. The Attribute Evaluation Matrix (AEM) and the Attribute Preference Degree Matrix (APDM) are proposed to systematically assess and rank the significance of attributes within a static decision-making framework. Following this, an information fusion method using the source center is proposed. The dynamic feature selection and information fusion methods are proposed to deal with the condition when number of attributes and samples change. Extensive experimental validation confirms that this method not only reduces the computational overhead associated with multi-source feature selection but also significantly enhances the efficiency as the volume and variety of data sources increase.