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In 3D building models, a large number of texture maps with different sizes increase the number of model data loading and drawing batches, which greatly reduces the drawing efficiency of the model. Therefore, this paper proposes a texture set mapping method based on vertex importance. Firstly, based on the 2D space boxing algorithm, the texture maps are merged and a series of Mipmap texture maps are generated, and then the vertex curvature, texture variability and location information of each vertex are calculated, normalized, and weighted to get the importance of each vertex, and then finally, different Mipmap-level textures are remapped according to the importance of the vertices. The experiment proves that the algorithm in this paper can reduce the amount of texture data on the one hand, and avoid the rendering pressure brought by the still large amount of data after merging on the other hand, so as to improve the rendering efficiency of the model. [ABSTRACT FROM AUTHOR]

The problem of finding a maximum size matching in a graph (known as the maximum matching problem) is one of the most classical problems in computer science. Despite a significant body of work dedicated to the study of this problem in the data stream model, the state-of-the-art single-pass semi-streaming algorithm for it is still a simple greedy algorithm that computes a maximal matching, and this way obtains 1 / 2 -approximation. Some previous works described two/three-pass algorithms that improve over this approximation ratio by using their second and third passes to improve the above mentioned maximal matching. One contribution of this paper continues this line of work by presenting new three-pass semi-streaming algorithms that work along these lines and obtain improved approximation ratios of 0.6111 and 0.5694 for triangle-free and general graphs, respectively. Unfortunately, a recent work Konrad and Naidu (Approximation, randomization, and combinatorial optimization. Algorithms and techniques, APPROX/RANDOM 2021, August 16–18, 2021. LIPIcs, vol 207, pp 19:1–19:18, 2021. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.19) shows that the strategy of constructing a maximal matching in the first pass and then improving it in further passes has limitations. Additionally, this technique is unlikely to get us closer to single-pass semi-streaming algorithms obtaining a better than 1 / 2 -approximation. Therefore, it is interesting to come up with algorithms that do something else with their first pass (we term such algorithms non-maximal-matching-first algorithms). No such algorithms were previously known, and the main contribution of this paper is describing such algorithms that obtain approximation ratios of 0.5384 and 0.5555 in two and three passes, respectively, for general graphs. The main significance of our results is not in the numerical improvements, but in demonstrating the potential of non-maximal-matching-first algorithms. [ABSTRACT FROM AUTHOR]

In industrial vision, the lack of defect samples is one of the key constraints of depth vision quality inspection. This paper mainly studies defect detection under a small training set, trying to reduce the dependence of the model on defect samples by using normal samples. Therefore, we propose a Knowledge-Embedding Relational Network. We propose a Knowledge-Embedding Relational Network (KRN): firstly, unsupervised clustering and convolution features are used to model the knowledge of normal samples; at the same time, based on CNN feature extraction assisted by image segmentation, the conv feature is obtained from the backbone network; then, we build the relationship between knowledge and prediction samples through covariance, embed the knowledge, further mine the correlation using gram operation, normalize the power of the high-order features obtained by covariance, and finally send them to the prediction network. Our KRN has three attractive characteristics: (I) Knowledge Modeling uses the unsupervised clustering algorithm to statistically model the standard samples so as to reduce the dependence of the model on defect data. (II) Covariance-based Knowledge Embedding and the Gram Operation capture the second-order statistics of knowledge features and predicted image features to deeply mine the robust correlation. (III) Power Normalizing suppresses the burstiness of covariance module learning and the complexity of the feature space. KRN outperformed several advanced baselines in small training sets on the DAGM 2007, KSDD, and Steel datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a newnetwork structure, namely the ProNet network. Retinal medical image segmentation can help clinical diagnosis of related eye diseases and is essential for subsequent rational treatment. The baselinemodel of the ProNet network is UperNet (Unified perceptual parsing Network), and the backbone network is ConvNext (Convolutional Network). A network structure based on depth-separable convolution and 1 x 1 convolution is used, which has good performance and robustness. We further optimise ProNet mainly in two aspects. One is data enhancement using increased noise and slight angle rotation, which can significantly increase the diversity of data and help the model better learn the patterns and features of the data and improve the model's performance. Meanwhile, it can effectively expand the training data set, reduce the influence of noise and abnormal data in the data set on the model, and improve the accuracy and reliability of the model. Another is the loss function aspect, and we finally use the focal loss function. The focal loss function is well suited for complex tasks such as object detection. The function will penalise the loss carried by samples that the model misclassifies, thus enabling better training of the model to avoid these errors while solving the category imbalance problem as a way to improve image segmentation density and segmentation accuracy. From the experimental results, the evaluation metrics mIoU (mean Intersection overUnion) enhanced by 4.47%, and mDice enhanced by 2.92% compared to the baseline network. Better generalization effects and more accurate image segmentation are achieved. [ABSTRACT FROM AUTHOR]

I draw on a substantial body of theoretical and empirical research on geospatial mapping and immersive 3D technologies, distributed decision and control algorithms, and automated speech recognition and virtual navigation tools. In this research, prior findings were cumulated indicating that spatial computing and remote sensing technologies, real-time data mining and tracking tools, and spatio-temporal fusion and 3D image processing algorithms configure extended reality environments. I carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout December 2022, with search terms including “Web3-powered metaverse worlds” + “haptic and biometric sensor technologies,” “spatial data mining and simulation modeling algorithms,” and “virtual navigation and decision intelligence tools.” As I analyzed research published in 2022 and 2023, only 178 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, I decided on 34, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]

The aim of this systematic review is to synthesize and analyze generative artificial intelligence algorithms articulating specific treatment recommendations, clinical decision-making, correct diagnoses, patient outcomes, medical practices, and healthcare equity. In this research, prior findings were cumulated indicating that generative artificial intelligence tools automatically identify and segment various structures in medical images and enhance diagnostic accuracy and surgical planning. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout April 2023, with search terms including "generative artificial intelligence-based diagnostic algorithms" + "disease risk detection," "personalized and targeted healthcare procedures," and "patient care safety and quality." As we analyzed research published in 2023, only 186 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), resul ts unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 32, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]

We draw on a substantial body of theoretical and empirical research on smart infrastructure sensors, deep learning-based autonomous driving and data processing technologies, and spatio-temporal fusion algorithms. In this research, prior findings were cumulated indicating that monitoring and sensing technologies, predictive maintenance and data mining tools, and computer vision and object detection algorithms optimize vehicular traffic flows and road safety. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout June 2022, with search terms including “socially responsible technologies” + “autonomous mobility systems” + “self-driving car control algorithms,” “virtual data modeling tools,” and “cognitive wireless sensor networks.” As we analyzed research in 2022, only 181 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 27, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]

Rural tourism has become an important force in implementing the rural revitalisation strategy and accelerating rural economic development. The hectic pace of life has made more and more city dwellers yearn for rural life, and travelling in the countryside has become their weekend choice. However, the current level of rural tourism informationization is low, the publicity is insufficient, the tourists' awareness is low, and the source of customers is seriously insufficient. To this end, this paper proposes a relatively novel multidata source fusion tourism recommendation algorithm, which adopts the idea of tensor orthogonal decomposition and fuses multisource data models to predict the target domain's for rating. The integrated consideration of multiple data sources under the do-it-yourself approach assists the target domain to discover the target user neighbourhood users more quickly and to discover the user's interest degree more accurately. It is worth pointing out that the recommendation algorithm proposed in this paper under the fusion of multiple data sources is not necessarily applicable to data sources with weak correlation, such as travel data sources and music data sources, which are relatively weakly correlated, and the algorithm is slightly weak in making predictions of user preferences. [ABSTRACT FROM AUTHOR]

The aim of this systematic review is to synthesize and analyze metaverse applications, technologies, and infrastructure in relation to predictive algorithms, real-time customer data analytics, and virtual navigation tools. With increasing evidence of metaverse live-video shopping events, there is an essential demand for comprehending whether consumer digital engagement as regards virtual assets improves immersive shopping journeys and experiences. In this research, prior findings were cumulated indicating that consumer journey analytics and data-driven decision making can determine purchase intentions across immersive 3D worlds and in virtual stores. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March 2022, with search terms including "metaverse" + "predictive algorithms," "real-time customer data analytics," and "virtual navigation tools." As we analyzed research published between 2021 and 2022, only 86 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 20, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]

We draw on a substantial body of theoretical and empirical research on behavioral analytics, immersive technologies, and machine vision algorithms in the Web3-powered metaverse world. With increasing evidence of data tracking, management, measurement, optimization, and analysis across metaverse worlds, there is an essential demand for comprehending whether technology-enabled live shopping can improve customer engagement and satisfaction in virtual and augmented reality-based immersive environments. In this research, prior findings were cumulated indicating that ambient scene detection and visual analytics can drive customer retention and acquisition in the virtual retail market, building brand awareness and hyper-personalization across e-commerce operations. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March 2022, with search terms including "metaverse" + "behavioral analytics," "immersive technologies," and "machine vision algorithms." As we analyzed research published in 2022, only 80 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 15, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]

The aim of this systematic review is to synthesize and analyze multisensor fusion technology, spatial simulation and environment mapping algorithms, and real-world connected vehicle data in smart sustainable urban mobility systems. With increasing evidence of autonomous vehicle planning algorithms, object localization and mapping tools, and spatial computing technology, there is an essential demand for comprehending whether road anomaly detection tools, blockchain and data fusion technologies, and trajectory estimation algorithms assist smart traffic planning and analytics. In this research, prior findings were cumulated indicating that automated collision avoidance systems, data monitoring algorithms, and virtual navigation tools reduce crash severities. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March 2022, with search terms including "smart sustainable urban mobility systems" + "multi-sensor fusion technology," "spatial simulation and environment mapping algorithms," and "real-world connected vehicle data." As we analyzed research published between 2019 and 2022, only 88 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 13, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]

The objective of this paper is to systematically review extended realitybased metaverse technologies, predictive geospatial modeling and simulation tools, and image processing computational and visual cognitive algorithms. The findings and analyses highlight that virtual reality-based immersive experiences can be attained by use of wearable haptic garments, haptic and sensory technologies, and movement and behavior tracking tools. Throughout January 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “immersive multisensory virtual spaces” + “extended reality-based metaverse technologies,” “predictive geospatial modeling and simulation tools,” and “image processing computational and visual cognitive algorithms.” As research published between 2022 and 2023 was inspected, only 172 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, I selected 33 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR. [ABSTRACT FROM AUTHOR]

This paper provides a systematic literature review of studies investigating deep learning-based sensing and extended reality technologies, virtual simulation and data acquisition tools, and spatial cognition and neural network algorithms configuring virtual urban environments. Throughout April 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “digital twin cities and immersive 3D environments” + “deep learning-based sensing and extended reality technologies,” “visual recognition and geospatial mapping tools,” and “virtual simulation and spatial cognition algorithms.” As research published in 2022 and 2023 was inspected, only 144 articles satisfied the eligibility criteria, and 21 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, MMAT, and ROBIS. [ABSTRACT FROM AUTHOR]

Tiled maps are one of the key GIS technologies used in the development and construction of WebGIS in the era of big data; there is an urgent need for high-performance tile map services hosted on big data GIS platforms. To address the current inefficiency of massive tile map data management and access, this paper proposes a massive tile map data access model that utilizes the jump consistent hash algorithm. Via the uniformity and consistency of a certain seed of a pseudo-random function, the algorithm can generate a storage slot for each tile data efficiently. By recording the slot information in the head of a row key, a uniform distribution of the tiles on the physical cluster nodes is achieved. This effectively solves the problem of hotspotting caused by the monotonicity of tile row keys in the data access process, thereby maximizing the random-access performance of a big data platform and greatly improving concurrent database access. Experiments show that this model can significantly improve the efficiency of tile map data access by more than 39% compared to a direct storage method, thereby confirming the model's advantages in accessing massive tile map data on a big data GIS platform. [ABSTRACT FROM AUTHOR]

The indoor positioning technology of an ultra-wideband (UWB) can play an excellent supplementary role in satellite navigation and has broad application prospects. However, if strong interference exists, the measurement data based on UWB will fluctuate abnormally, which seriously affects the accuracy of positioning. In view of the above problems, based on the combination of mathematical modeling, this paper starts from the subject data, and proposes a positioning method suitable for non-interference/interference conditions and an abnormal data identification method to improve the positioning accuracy. The specific scheme includes data preprocessing model establishment and solution, positioning model establishment and solution, migration application of positioning model, interfering data identification model establishment and solution, and movement track positioning model establishment and solution. [ABSTRACT FROM AUTHOR]

Monte Carlo simulation studies are among the primary scientific outputs contributed by methodologists, guiding application of various statistical tools in practice. Although methodological researchers routinely extend simulation study findings through follow-up work, few studies are ever replicated. Simulation studies are susceptible to factors that can contribute to replicability failures, however. This paper sought to conduct a meta-scientific study by replicating one highly cited simulation study (Curran et al., Psychological Methods, 1, 16-29, 1996) that investigated the robustness of normal theory maximum likelihood (ML)-based chi-square fit statistics under multivariate nonnormality. We further examined the generalizability of the original study findings across different nonnormal data generation algorithms. Our replication results were generally consistent with original findings, but we discerned several differences. Our generalizability results were more mixed. Only two results observed under the original data generation algorithm held completely across other algorithms examined. One of the most striking findings we observed was that results associated with the independent generator (IG) data generation algorithm vastly differed from other procedures examined and suggested that ML was robust to nonnormality for the particular factor model used in the simulation. Findings point to the reality that extant methodological recommendations may not be universally valid in contexts where multiple data generation algorithms exist for a given data characteristic. We recommend that researchers consider multiple approaches to generating a specific data or model characteristic (when more than one is available) to optimize the generalizability of simulation results. [ABSTRACT FROM AUTHOR]

In this paper, based on the Karush–Kuhn–Tucker (KKT) conditions of ℓ 0 regularization, we propose a communication-efficient distributed learning approach for high-dimensional and sparse generalized linear models with massive data sets stored across different machines. This proposed method is a support detection and root finding method for generalized linear models in a distributed form. In each round of the proposed method, the support set is first determined by the primal and dual information reduced to the master machine, then the reduced maximum likelihood estimator is obtained by the gradient descent method, among which it only suffices to calculate the gradient vectors on each machine and communicate them instead of the data. We give the optimal ℓ ∞ -norm error bound for the sequences generated by the proposed algorithm and show that this ℓ ∞ -norm error bound decays exponentially to the optimal order. Moreover, we show that the oracle estimator can be recovered if the target signal is not less than the detectable level. In addition, an adaptive version of the proposed algorithm is developed to estimate the sparsity level. Simulation studies illustrate the superior performance of the proposed methods. [ABSTRACT FROM AUTHOR]

As an important branch of machine learning, recommendation algorithms have attracted the attention of many experts and scholars. The current recommendation algorithms all more or less have problems such as cold start and single recommended items. In order to overcome these problems and improve the accuracy of personalized recommendation algorithms, this paper proposes a recommendation for multi-task learning based on directed graph convolutional network (referred to as MTL-DGCNR) and applies it to recommended areas for e-commerce. First, the user's micro-behavior is constructed and converted into directed graph structure data for model embedding. It can fully consider the embedding of first-order proximity nodes and second-order proximity nodes, which can effectively enhance the transformation ability of features. Secondly, this model adopts the multi-task learning method, and uses knowledge graph embedding to effectively deal with the one-to-many or many-to-many relationship between users and commodities. Finally, it is verified by experiments that MTL-DGCNR has a higher interpretability and accuracy in the field of e-commerce recommendation than other recommendation models. The ranking evaluation experiments, various training methods comparison experiments, and controlling parameter experiments are designed from multiple perspectives to verify the rationality of MTL-DGCNR. [ABSTRACT FROM AUTHOR]

Structural breaks are often encountered in empirical studies with large panels. This paper considers the estimation of multiple breaks in the mean of panel data model based on a modified screening and ranking algorithm. This algorithm satisfies symmetry and is suitable for both cases where the jump size of break points is positive and negative. The break points are first initially screened based on the adaptive Fisher's statistic, followed by further screening of the break points using the threshold criterion, and finally the final break points are screened using the information criterion. Furthermore, the consistency of the break point estimators is proved. The Monte Carlo simulation results show that the proposed method performs well even if the error terms are serially correlated or cross-sectionally correlated. Finally, two empirical examples illustrate the use of this method. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) had shown excellent performance in complex graph data modelings such as node classification, link prediction and graph classification. However, GNNs are vulnerable to adversarial attacks resulting in severe performance degradation, which brings many security and privacy issues. Such vulnerability of GNNs limits its application in safety–critical fields such as finance and transportation. Studying the principles and implementation of graph adversarial attacks and their countermeasures can provide insight into the reason behind the vulnerability of GNNs and consequently improve the robustness and generalization of the model. This paper introduces the related concepts of existing graph adversarial attack and defense algorithms and analyzes the basic idea and implementation of each algorithm. Moreover, we compare the strategies, target tasks, advantages and disadvantages of typical algorithms. Through the summary of the state of the art, the limitations and possible development directions of graph adversarial attacks and defenses are analyzed, which provides a useful reference for the further developing relative researches. [ABSTRACT FROM AUTHOR]

Based on an in-depth survey of the literature, the purpose of the paper is to explore digital twin modeling, multi-sensor data fusion techniques, and geospatial mapping and virtual reality simulation tools. In this research, previous findings were cumulated showing that urban big data analytics harnesses spatial computing technologies, image processing techniques, and modeling and simulation algorithms, and we contribute to the literature by indicating that immersive virtual worlds develop on digital twin simulations, mobile wireless sensor networks, and urban monitoring systems. Throughout June 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “the geopolitics of smart city digital twins” + “urban sensing and immersive virtual technologies,” “spatio-temporal fusion algorithms,” and “visualization modeling tools.” As research published between 2021 and 2022 was inspected, only 170 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 24 mainly empirical sources. [ABSTRACT FROM AUTHOR]

The objective of this paper is to systematically review unattainable fashionable beauty ideals integrating virtual accessories of face-perfecting filters. The findings and analyses highlight that digital self-tracking devices and beauty apps articulate idealized facial images, body ideal internalization, and sociocultural pressures. Throughout May 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including "beauty apps and filters in visual digital cultures" + "perceived sociocultural pressures," "self-rated emotional expressiveness," and "image processing algorithms." As I inspected research published between 2017 and 2022, only 166 articles satisfied the eligibility criteria. By removing controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 32, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, MMAT, and SRDR. [ABSTRACT FROM AUTHOR]

The objective of this paper is to systematically review connected vehicle data, spatial recognition technologies, and machine learning and computer vision algorithms. The findings and analyses highlight that autonomous mobility technologies leverage vehicle and pedestrian detection tools, road environment data, and environment mapping and predictive control algorithms. Throughout May 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “social ethics” + “autonomous vehicle routing and navigation” + “spatial recognition technologies,” “environment mapping algorithms,” and “mobility simulation tools.” As research published in 2022 was inspected, only 188 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, I selected 35 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR. [ABSTRACT FROM AUTHOR]

Based on an in-depth survey of the literature, the purpose of the paper is to explore Internet of Things-sensing tools, data processing and smart mobility technologies, and self-driving car control algorithms. In this research, previous findings were cumulated showing that self-driving cars develop on route detection and environment mapping algorithms, deep neural network technology, and image processing tools, and I contribute to the literature by indicating that connected car data, algorithm-driven sensing devices, and spatial data visualization tools reduce preventable road injuries. Throughout May 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “social justice” + “intelligent transportation systems” + “deep learning-based autonomous driving technologies,” “cooperative navigation algorithms,” and “vehicle and pedestrian detection tools.” As research published in 2022 was inspected, only 187 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, I selected 34 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR. [ABSTRACT FROM AUTHOR]

This paper provides a systematic literature review of studies investigating frictionless customer engagement processes across immersive decentralized networking and virtual environments. The analysis highlights that consumer journey analytics deploys spatial cognition algorithms, deep learning-based sensing technologies, and virtual navigation tools throughout immersive hyper-connected spaces. Throughout March 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including "metaverse" + "haptic and biometric sensor technologies," "visual imagery and geospatial mapping tools," and "cognitive data mining algorithms." As I inspected research published between 2021 and 2022, only 186 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 32, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, MMAT, and ROBIS. [ABSTRACT FROM AUTHOR]

In Multi-Label Classification (MLC), each data sample is characterized by multiple labels. In ML, there is no restriction on the number of classes a data sample could belong to. Various methods are proposed to construct a model to map data samples to the class labels. Transforming the MLC problem into several binary classification problems is the baseline approach, also called the binary relevance technique. As an alternative solution, the label powerset approach transforms the MLC problem into a multi-class classification problem by building a binary classifier for every label composition. However, in Ensemble Methods (EMs), multiple classifiers are consolidated to form a multi-label ensemble classifier. In this case, each classifier predicts a class label. Thereupon, the predicted class labels are joined using an ensemble method. This paper presents a neoteric EM classifier using the Ridge Regression-based Principal Label Space Transformation (RR-PLST). In the proposed model, the PLST utilizes SVD to discover correlations between the labels and data samples. Dimensionality reduction is a well-known technique to enhance the model's precision and diminish execution time. Consequently, the OPLS technique is recruited to reduce data dimensions in the proposed model. Moreover, in the proposed model, a Q-Learning method is used to amend the initially predicted labels. To demonstrate the effectiveness of the proposed model, vast experiments are conducted on the various widely used multi-label datasets. The proposed algorithm results are compared with similar algorithms by the AP, EM, HS, Macro, and Micro F1 metrics. The experimental results demonstrate the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

The aim of this systematic review is to synthesize and analyze interconnected monitoring devices, interoperable digital avatars, and employee monitoring software. In this research, prior findings were cumulated indicating that generative artificial intelligence and virtual connectivity tools further talent acquisition and management, employee effectiveness and productivity, and virtual team performance and processes. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout May 2023, with search terms including "immersive interconnected 3D worlds" + "image processing computational algorithms," "multimodal behavioral analytics," and "generative artificial intelligence and ambient scene detection tools." As we analyzed research published in 2023, only 168 papers met the eligibility criteria, and 53 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]

The huge amount of data produced by the Internet of Things need to be validated and curated to be prepared for the selection of relevant data in order to prototype models, train them, and serve the model. On the other side, blockchains and open data are also important data sources that need to be integrated into the proposed integrative models. It is difficult to find a sufficiently versatile and agnostic architecture based on the main machine learning frameworks that facilitate model development and allow continuous training to continuously improve them from the data streams. The paper describes the conceptualization, implementation, and testing of a new architecture that proposes a use case agnostic processing chain. The proposed architecture is mainly built around the Apache Submarine, an unified Machine Learning platform that facilitates the training and deployment of algorithms. Here, Internet of Things data are collected and formatted at the edge level. They are then processed and validated at the fog level. On the other hand, open data and blockchain data via Blockchain Access Layer are directly processed at the cloud level. Finally, the data are preprocessed to feed scalable machine learning algorithms. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a technique that involves multiple participants who update their local models with private data and aggregate these models using a central server. Unfortunately, central servers are prone to single-point failures during the aggregation process, which leads to data leakage and other problems. Although many studies have shown that a blockchain can solve the single-point failure of servers, blockchains cannot identify or mitigate the effect of backdoor attacks. Therefore, this paper proposes a blockchain-based FL framework for defense against backdoor attacks. The framework utilizes blockchains to record transactions in an immutable distributed ledger network and enables decentralized FL. Furthermore, by incorporating the reverse layer-wise relevance (RLR) aggregation strategy into the participant's aggregation algorithm and adding gradient noise to limit the effectiveness of backdoor attacks, the accuracy of backdoor attacks is substantially reduced. Furthermore, we designed a new proof-of-stake mechanism that considers the historical stakes of participants and the accuracy for selecting the miners of the local model, thereby reducing the stake rewards of malicious participants and motivating them to upload honest model parameters. Our simulation results confirm that, for 10% of malicious participants, the success rate of backdoor injection is reduced by nearly 90% compared to Vanilla FL, and the stake income of malicious devices is the lowest. [ABSTRACT FROM AUTHOR]

Based on an in-depth survey of the literature, the purpose of the paper is to explore retail data measurement tools, cognitive artificial intelligence algorithms, and metaverse live shopping analytics in immersive hyper-connected virtual spaces. In this research, previous findings were cumulated showing that artificial intelligence chatbot customer service can boost customer engagement and hyper-realistic personalized interactive experiences by use of visual analytics, shopper behavioral data, and location data, and we contribute to the literature by indicating that digital interactive performance in relation to virtual products and possessions shapes customer habits by harnessing location data. Throughout February 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including "metaverse" + "live shopping analytics," "retail data measurement tools," "cognitive artificial intelligence algorithms," and "immersive hyper-connected virtual spaces." As research published in 2022 was inspected, only 87 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 18 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR. [ABSTRACT FROM AUTHOR]

The objective of this paper is to systematically review remote sensing data fusion techniques, autonomous vehicle driving perception algorithms, and mobility simulation tools in smart transportation systems. The findings and analyses highlight that visual perception algorithms, sensing and computing technologies, and route planning and control tools configure networked digital infrastructures. Throughout March 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including "smart transportation systems" + "remote sensing data fusion techniques," "autonomous vehicle driving perception algorithms," and "mobility simulation tools." As research published between 2019 and 2022 was inspected, only 92 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 15 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR. [ABSTRACT FROM AUTHOR]

This paper provides a systematic literature review of studies investigating autonomous vehicle interaction control software, urban sensing and computing technologies, and trajectory planning and route detection algorithms in networked transport systems. The analysis highlights that intelligent transportation infrastructures develop on mobility simulation and geospatial data visualization tools and blockchain and extended reality technologies. Throughout April 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including "networked transport systems" + "autonomous vehicle interaction control software," "urban sensing and computing technologies," and "trajectory planning and route detection algorithms." As I inspected research published between 2021 and 2022, only 93 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 13, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, MMAT, and ROBIS. [ABSTRACT FROM AUTHOR]

This paper investigates the implementation of virtual networks on the SDN data plane modeled by a graph of physical connections between network nodes. A virtual network is defined as a set of ordered host pairs (sender and receiver) and is implemented by a set of host–host paths that uniquely determine the settings of switches. A set of paths is said to be perfect if any subset of its paths can be implemented loop-free, i.e., without endless movement of packets in a loop, without duplicate paths (when the host receives the same packet several times), and without unintended paths (when the host receives a packet directed to some other host). In the case where the switchgraph is a complete graph, sufficient conditions for the existence of the largest perfect set of paths connecting all pairs of different hosts are established. Algorithms for constructing this largest perfect set are proposed and their complexity is estimated. The paper also reports preliminary results of conducted computer experiments, which suggest that the proposed sufficient conditions are not necessary ones. [ABSTRACT FROM AUTHOR]

Adaptive lasso penalized generalized linear models (GLMs) are a powerful tool for analyzing the high-dimensional sparse data where the classical linear or normal assumption is not met. In non-distributed environments, the estimation problem of adaptive lasso penalized GLMs is often solved by the coordinate descent based algorithm developed in Friedman, Hastie, and Tibshirani (2010), which has been well implemented in the R package glmnet. However, when applied to distributed big data, this algorithm is usually inflexible or even infeasible due to its non-parallel implementation, especially when the communication costs between the central and local machines are expensive, or the storage and computing capabilities of the central machine are insufficient. In this paper, we propose a new method, QAGLM-alasso, for the adaptive lasso penalized GLMs problem in distributed big data by applying the quadratic approximation representation of GLMs, and further develop a path-following algorithm for its estimation based on the Least Angle Regression (LARS). Theoretical analyses show that, under mild regularity conditions, the QAGLM-alasso enjoys the oracle property, and the obtained estimator is asymptotically equivalent to the original adaptive lasso. Simulation studies demonstrate that the new algorithm has similar estimation accuracy with glmnet, but is significantly faster than glmnet in distributed environments. We further illustrate the practical performance of the proposed method by analyzing a supersymmetric (SUSY) benchmark data set. [ABSTRACT FROM AUTHOR]

Stochastic configuration network (SCN) is a mathematical model of incremental generation under a supervision mechanism, which has universal approximation property and advantages in data modeling. However, the efficiency of SCN is affected by some network parameters. An optimized searching algorithm for the input weights and biases is proposed in this paper. An optimization model with constraints is first established based on the convergence theory and inequality supervision mechanism of SCN; Then, a hybrid bat-particle swarm optimization algorithm (G-BAPSO) based on gradient information is proposed under the framework of PSO algorithm, which mainly uses gradient information and local adaptive adjustment mechanism characterized by pulse emission frequency to improve the searching ability. The algorithm optimizes the input weights and biases to improve the convergence rate of the network. Simulation results over some datasets demonstrate the feasibility and validity of the proposed algorithm. The training RMSE of G-BAPSO-SCN increased by 5.57 × 10 − 5 and 3.2 × 10 − 3 compared with that of SCN in the two regression experiments, and the recognition accuracy of G-BAPSO-SCN increased by 0.07% on average in the classification experiments. [ABSTRACT FROM AUTHOR]

In the inversion of geophysical data, an attempt is made to obtain a model with the best fit on the observed data. Unfortunately, the results are usually accompanied by non-uniqueness and ambiguity. These inversion problems can be reduced by inverting different geophysical datasets. Sequential inversion is one of the most common ways to integrate two or more geophysical datasets, to obtain a model that is compatible with all geophysical data, thus reducing the amount of ambiguity. This paper presents separate inversions of DC resistivity and magnetic data and sequential inversion of DC resistivity constrained by magnetic data. Here, the inverse model of magnetic data is considered the initial model for the sequential inversion of DC resistivity data. At first, the algorithm is applied to a synthetic model composed of four conductive and magnetized bodies, and the results show notable improvement for the resistivity model after sequential inversion, compared with the separate resistivity inversion model. Finally, encouraged by the results obtained in the synthetic case, the algorithm was applied to DC resistivity and magnetic datasets that were collected in the archeological area of old Pompeii city nearby Naples, Italy. The result of the sequential resistivity inversion model was notably superior to the corresponding resistivity model obtained from standard separate inversion. [ABSTRACT FROM AUTHOR]

This paper draws on a substantial body of theoretical and empirical research on generative artificial intelligence and spatial data mining algorithms enabling virtual employee engagement, human capital development, and cognitive analytics management. A quantitative literature review of ProQuest, Scopus, and the Web of Science was carried out throughout August 2023, with search terms including “generative artificial intelligence and haptic and biometric sensor tech - nologies” + “spatial data mining and image processing computational algorithms,” “virtual team performance and behavior tracking tools,” and “immersive extended reality environments.” As research published in 2023 was inspected, only 170 articles satisfied the eligibility criteria, and 53 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]

This paper presents a lossless image compression method with a fast decoding time and flexible adjustment of coder parameters affecting its implementation complexity. A comparison of several approaches for computing non-MMSE prediction coefficients with different levels of complexity was made. The data modeling stage of the proposed codec was based on linear (calculated by the non-MMSE method) and non-linear (complemented by a context-dependent constant component removal block) predictions. Prediction error coding uses a two-stage compression: an adaptive Golomb code and a binary arithmetic code. The proposed solution results in 30% shorter decoding times and a lower bit average than competing solutions (by 7.9% relative to the popular JPEG-LS codec). [ABSTRACT FROM AUTHOR]

Numerical computation of maximum likelihood estimates (MLE) is one of the most common problems encountered in applied statistics. Even if there exist many algorithms considered as performing, they can suffer in some cases for one or many of the following criteria: global convergence (capacity of an algorithm to converge to the true unknown solution from all starting guesses), numerical stability (ascent property), implementation feasibility (for example, algorithms requiring matrix inversion cannot be implemented when the involved matrices are not invertible), low computation time, low computational complexity, and capacity to handle high dimensional problems. The reality is that, in practice, no algorithm is perfect, and for each problem, it is necessary to find the most performing of all existing algorithms or even develop new ones. In this paper, we consider the computing of the maximum likelihood estimate of the vector parameter of a statistical model of crash frequencies. We split the parameter vector, and we develop a new estimation algorithm using the profile likelihood principle. We provide an automatic starting guess for which convergence and numerical stability are guaranteed. We study the performance of our new algorithm on simulated data by comparing it to some of the most famous and modern optimization algorithms. The results suggest that our proposed algorithm outperforms these algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the data-driven consensus tracking problem for heterogeneous multi-agent systems. All of the heterogeneous dynamics, disturbances and measurement noises are considered for the output tracking consensus of nonlinear multi-agent network under a data-driven scheme. A dynamic linearisation method is introduced to deal with the nonlinear nonaffine structures of the agents and a heterogeneous linear data model for the agent is obtained due to the heterogeneous dynamics of the agent itself. On this basis, a distributed model-free adaptive predictive control (DMFAPC) algorithm is constructed by the use of consensus errors and the robustness analysis is conducted in the presence of disturbances. Further, the results are modified by using measured outputs to replace the system outputs, and thus a measured output-based DMFAPC is presented to deal with the omnipresent measurement noises in practical applications. The two proposed DMFAPC methods are data driven since no mechanistic model information is required wherein. And thus, no unmodelled dynamics affects the consensus performance. Instead, they can improve the control performance by utilising additional predictive information. The two proposed DMFAPC methods are verified using numerical simulations. [ABSTRACT FROM AUTHOR]

As a specific data hiding technique, reversible data hiding (RDH) has recently received extensive attention. By this technique, both the embedded data and the original cover image can be exactly extracted from the marked image. In our previous work, a general expansion-shifting model for RDH is proposed by introducing the so-called reversible embedding function (REF). With REF, RDH can be designed and the corresponding rate-distortion formulations can be established, providing an approach to optimize the reversible embedding performance. In this paper, by extending our previous work, optimal REF for one-dimensional histogram is investigated, and all optimal REF are derived in this case when the maximum modification to the cover pixel is limited as a small value. Moreover, based on the derived optimal REF for one-dimensional histogram and multiple histograms modification, a practical RDH scheme is presented and it is experimental verified better than some state-of-the-art algorithms in terms of capacity-distortion performance. [ABSTRACT FROM AUTHOR]

Chinese Ludo, also known as Aeroplan Chess, has been a very popular board game for several decades. However, there is no mature algorithm existing for human–machine gambling. The major challenge is the high randomness of the dice rolls, where the algorithm must ensure that the machine is smarter than a human in order to guarantee that the owner of the game machines makes a profit. This paper presents a fast Chinese Ludo algorithm (named "Threat Matrix") that we have recently developed. Unlike from most chess programs, which rely on high performance computing machines, the evaluation function in our program is only a linear sum of four factors. For fast and low-cost computation, we innovatively construct the concept of the threat matrix, by which we can easily obtain the threat between any two dice on any two positions. The threat matrix approach greatly reduces the required amount of calculations, enabling the program to run on a 32-bit 80 × 86 SCM with a 100 MHz CPU while supporting a recursive algorithms to search plies. Statistics compiled from matches against human game players show that our threat matrix has an average win rate of 92% with no time limit, 95% with a time limit of 10 s, and 98% with a time limit of 5 s. Furthermore, the threat matrix can reduce the computation cost by nearly 90% compared to real-time computing; memory consumption drops and is stable, which increases the evaluation speed by 58% compared to real-time computing. [ABSTRACT FROM AUTHOR]

With the implementation of national strategies such as sports power and national fitness, the sports economy has become an important element of high-quality national development, and the demand for sports economy and management talents is greatly increased. Particularly in the new area with big data as the typical feature, the teaching content, teaching method, and teaching mode of sports economics and management majors have put forward new requirements. The continuous progress of storage and network technology has prompted the generation of massive multisource spatiotemporal data in various fields. The advantage of association analysis algorithms is that they are easy to code and implement. The relationships found by association analysis can take two forms: frequent itemsets or association rules. We use correlation analysis methods to perform correlation learning between sports economy and related big data and thus improve the development of sports economy. Mining and analyzing the relevant big data can precisely reveal the problems of sports economic development and can realize the fine management of sports, thus contributing to the healthy development of sports. Mastering the skills of acquiring, analyzing, and applying big data is the core content of sports economic analysis. The sports economy has refined and intelligent management means, and its adoption of virtual reality reflects the current situation and development trend of the sports business, which further highlights the status and role of multisource big data in the sports economy. Based on these, this paper proposed a sports economy mining algorithm in view of the correlation analysis and big data model. Then, we verified the effectiveness of the model through experiments, which laid the foundation for the development of the sports economy. [ABSTRACT FROM AUTHOR]

In order to solve the problem of cost cloud data and hole repair efficiency and accuracy, this article offers a study of integrated cloud network hole algorithm research based on optimal neural network. This paper first introduces a common point cloud hole-filling algorithm, provides a neural network-based point cloud blank filling algorithm, and introduces hotspot problems in a given algorithm, combined with the technology related to high-performance computing, the parallel optimization technology based on OpenMP and CUDA is adopted to accelerate the algorithm accordingly. Experiments have shown that the accuracy of pre- and postoptimization of CUDA-based algorithms varies. As the model and input point cloud data increase, the accuracy of the algorithm decreases slightly. However, when the data increases to 104 orders of magnitude, the rate of accuracy decline meets an inflection point and finally stabilizes to about 96.8% in the environment of 132457 data of monk model. The point cloud hole-filling algorithm based on the optimal neural network given in this article is highly predictable, can well repair incomplete point cloud holes, has good repair effect on point cloud holes, and can obtain high acceleration ratio, which can provide reference for practical engineering application. [ABSTRACT FROM AUTHOR]

While the classical knapsack problem has been the object to be solved by optimization algorithm proposals for many years, another version of this problem, discounted {0-1} knapsack problem, is gaining a lot of attention recently. The original knapsack problem requires selecting specific items from an item set to maximize the total benefit while ensuring that the total weight does not exceed the knapsack capacity. Meanwhile, discounted {0-1} knapsack problem has more stringent requirements in which items are divided into groups, and only up to one item from a particular group can be selected. This constraint, which does not exist in the original knapsack problem, makes discounted {0-1} knapsack problem even more challenging. In this paper, we propose a new algorithm based on salp swarm algorithm in the form of four different variants to resolve the discounted {0-1} knapsack problem. In addition, we also make use of an effective data modeling mechanism and a greedy repair operator that helps overcome local optima when finding the global optimal solution. Experimental and statistical results show that our algorithm is superior to currently available algorithms in terms of solution quality, convergence, and other statistical criteria. [ABSTRACT FROM AUTHOR]

In recent years, with the development of Big Data and dynamic modeling technology, people have gradually deepened the research on the efficient construction of music audio database. Based on this background, this paper first realizes the construction of multilevel music audio database using Big Data technology and collaborative filtering algorithm. Then, through the Big Data analysis technology based on collaborative filtering algorithm, the QRE (quick reaction estimate) model is constructed and the data query system is formed. Finally, experiments are designed to verify whether the music audio database based on collaborative filtering algorithm can correctly retrieve the target music audio data. The experimental results show that compared with the traditional single ∗ input stacked database, the music and audio database constructed by the discrete Big Data dynamic modeling technology based on collaborative filtering algorithm has faster search rate and higher accuracy and can accurately locate the data nodes in the database. This research uses Big Data and collaborative filtering algorithm technology to establish a new multilevel music audio database with discrete dynamic modeling characteristics of complex systems, which can greatly improve the data structure of each level of the database, so as to improve the efficiency and accuracy of retrieving various types of music and audio files in the database. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth analysis and study of the interactive flipped classroom model for a digital micro-video for a big data English course. To improve the learning efficiency of English courses and reduce the learning pressure of students, the thesis also uses certain techniques to apply audiovisual language to the production of specific micro-class videos, broadcast the successfully recorded micro-class courses to students, and then use the questionnaire to randomly distribute the designed audiovisual language use questionnaire. Micro-classes earnestly perform data statistics for students and finally conduct data analysis to summarize and verify the effects of micro-class audiovisual language use. The improved algorithm can effectively reduce the fluctuation of the consumption of various resources in the cluster and make the services in the cluster more stable. The new distributed interprocess communication based on protocol and serialization technology is more efficient than traditional communication based on protocol standards, reduces bandwidth consumption in the cluster, and improves the throughput of each node in the cluster. The content design and scripting of micro-video teaching resources are based on this. Then, the production process of micro-video teaching resources is explained, according to the selection of tools, the preparation, recording, editing, and generation of materials. [ABSTRACT FROM AUTHOR]

The growth of georeferenced data sources calls for advanced matching methods to improve the reliability of geospatial data processing, such as map conflation. Existing matching methods mainly focus on similarity measures at the entity scale or area scale. A measure that combines entity-scale and area-scale similarities can provide sound matching results under various circumstances. In this paper, we propose a georeferenced-graph model that integrates multiscale similarities for data matching. Specifically, a match of correspondent data objects is identified by the entity-scale measure under the constraint of the area-scale measure. Nodes in the proposed georeferenced graph model represent polygons by their centroids, whereas the links in the graph connect the nodes (i.e. centroids) according to pre-defined rules. Then, we develop an algorithm to identify many-to-many matches. We demonstrate the proposed graph model and algorithm in real-world experiments using OpenStreetMap data. The experimental results show that the proposed georeferenced-graph model can effectively integrate the context and the location-and-form distance of geospatial data matches across different datasets. [ABSTRACT FROM AUTHOR]

With the development of 3D technology, the construction of 3D motion model has become an important research issue in many fields, such as virtual reality, animation and robot vision. In this paper, a 3D motion model construction method based on simulation algorithm is proposed. Firstly, the gravity simulation algorithm is used to simulate the trajectory of the object, and the position data of multiple moments with numerical accuracy of 10cm are obtained. Then, the interpolation algorithm is used to interpolate these position data into continuous curves, and the object trajectory with 1cm accuracy is obtained. Finally, we integrate the obtained trajectory data with 3D modeling software to generate a 3D motion model with real motion effect. The experimental results show that this method has high accuracy and reliability in constructing 3D motion model, and is suitable for many fields. [ABSTRACT FROM AUTHOR]