Search

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]

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]

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]

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]

NoSQL databases have moved from theoretical solutions to exceed relational databases limits to a practical and indisputable application for storing and manipulation big data. In term of variety, NoSQL databases store heterogeneous data without being obliged to respect a predefined schema such as the case of relational and object-relational databases. Those solutions, also surpass the traditional databases in storage capacity; we consider MongoDB for example, which is a document-oriented database capable of storing unlimited number of documents with a maximal size of 32TB depending on the machine that runs the database and also the operating system. Also, in term of velocity, many researches compared the execution time of different transactions and proved that NoSQL databases are the perfect solution for real-time applications. This paper presents an algorithm to store data modeled by graphs as NoSQL documents, the purpose of this study is to exploit the high amount of data stored in SQL databases and to make such data usable by recent clustering algorithms and other data science tools. This study links relational data to document datastores by defining an effective algorithm for reading relational data, modelling those data as graphs and storing those data as NoSQL documents. [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]

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]

The ID3 algorithm is a key and important method in existing data mining, and its rules are simple and easy to understand and have high application value. If the decision tree algorithm is applied to the online data migration of sports competition actions, it can grasp the sports competition rules in the relationship between massive data to guide sports competition. This paper analyzes the application performance of the traditional ID3 algorithm in online data migration of sports competition actions; realizes the application steps and data processing process of the traditional ID3 algorithm, including original data collection, original data preprocessing, data preparation, constructing a decision tree, data mining, and making a comprehensive evaluation of the traditional ID3 algorithm; and clarifies the problems of the traditional ID3 algorithm. Mainly, the problems of missing attributes and overfitting are clarified, which provide directions for the subsequent algorithm optimization. Then, this paper proposes a k -nearest neighbor-based ID3 optimization algorithm, which selects values similar to k -nearest neighbors to fill in the missing values for the attribute missing problem of the traditional ID3 algorithm. Based on this, the improved algorithm is applied to the online data migration of sports competition actions, and the application effect is evaluated. The results show that the performance of the k -nearest neighbor-based ID3 optimization algorithm is significantly improved, and it can also solve the overfitting problem existing in the traditional ID3 algorithm. For the overall classification problem of six types of samples of travel patterns, the experimental data samples have the characteristics of high data quality, a considerable number of samples, and obvious sample differentiation. Therefore, this paper also uses the deep factorization machine algorithm based on deep learning to classify the six classes of travel patterns of sports competition action data using the previously extracted relevant features. The research in this paper provides a more accurate method and a higher-performance online data migration model for sports competition action data mining. [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]

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]

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]

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]

In order to achieve a fair distribution of benefits in federated learning, an indicator that quantifies the contribution of each data provider to the global model is critical. Aiming at the problems of privacy leakage, opacity and dependence on central server in existing contribution evaluation schemes, this paper proposed a transparent federated learning contribution evaluation scheme based on blockchain. Firstly, this paper proposed an improved Paillier secure aggregation algorithm, which avoided the inference of user local data in the model aggregation stage by joint decryption. Secondly, this paper proposed a method to approximate the contribution based on the gradient of user cumulative submission, which solved the problem of privacy leakage in the existing contribution evaluation scheme. In addition, it integrated the evaluation of the contribution into the consensus process of the blockchain, making the evaluation results auditable. Finally, experiments based on MNIST dataset show that the proposed method can effectively evaluate the contribution. [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]

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]

Covert channel of the packet ordering is a hot research topic. Encryption technology is not enough to protect the security of both sides of communication. Covert channel needs to hide the transmission data and protect content of communication. The traditional methods are usually to use proxy technology such as tor anonymous tracking technology to achieve hiding from the communicator. However, because the establishment of proxy communication needs to consume traffic, the communication capacity will be reduced, and in recent years, the tor technology often has vulnerabilities that led to the leakage of secret information. In this paper, the covert channel model of the packet ordering is applied into the distributed system, and a distributed covert channel of the packet ordering enhancement model based on data compression (DCCPOEDC) is proposed. The data compression algorithms are used to reduce the amount of data and transmission time. The distributed system and data compression algorithms can weaken the hidden statistical probability of information. Furthermore, they can enhance the unknowability of the data and weaken the time distribution characteristics of the data packets. This paper selected a compression algorithm suitable for DCCPOEDC and analyzed DCCPOEDC from anonymity, transmission efficiency, and transmission performance. According to the analysis results, it can be seen that DCCPOEDC optimizes the covert channel of the packet ordering, which saves the transmission time and improves the concealment compared with the original covert channel. [ABSTRACT FROM AUTHOR]

Reducing energy consumption has become a critical issue in today data centers. Reducing the number of required physical and Virtual Machines results in energy-efficiency. In this paper, to avoid the disadvantages of VM migration, a static VM placement algorithm is proposed which places VMs on hosts in a Worst-Fit-Decreasing (WFD) fashion. To reduce energy consumption further, the effect of job scheduling policy on the number of VMs needed for maintaining QoS requirements is studied. Each VM is modeled by an M/M/* queue in space-shared, time-shared, and hybrid job scheduling policies, and energy consumption of real-time as well as non-real-time applications is analyzed. Numerical results show that the hybrid policy outperforms space-shared and time-shared policies, in terms of energy consumption as well as Service Level Agreement (SLA) violations. Moreover, our non-migration method outperforms three different algorithms which use VM migration, in terms of reducing both energy consumption and SLA Violations. [ABSTRACT FROM AUTHOR]

In this paper, a novel 3D roaming algorithm considering collision detection and interaction is proposed that adopts a triangle mesh to organize and manage massive spatial data and uses a customized bounding box intersector to rapidly obtain the potential collided triangles. The proposed algorithm can satisfy the requirements of timeliness and practicability during complicated large 3D scene collision detection. Moreover, we designed a method to calculate the collision point coordinates according to the spatial position relation and distance change between the virtual collision detection sphere and triangles, with the triangle edges and three vertices being considered. Compared to the methods that use the native intersector of OpenSceneGraph (OSG) to obtain the collision point coordinates, the calculation efficiency of the proposed method is greatly improved. Usually, when there is a big split/pit in the scene, the viewpoints will fly off the scene due to the fall of the collision detection sphere, or the region interior cannot be accessed when the entrance of some local region (e.g., internal grotto) of the scene is too small. These problems are solved in this paper through 3D scene-path training and by self-adaptively adjusting the radius of the virtual collision detection sphere. The proposed 3D roaming and collision detection method applicable for massive spatial data overcomes the limitation that the existing roaming and collision detection methods are only applicable to 3D scenes with a small amount of data and simple models. It provides technical supports for freewill browsing and roaming of indoor/outdoor and overground/underground of the 3D scene in cases of massive spatial data. [ABSTRACT FROM AUTHOR]

Because assessing an application-specific prior is difficult and, for scientific inference, often not even desirable, quick and standardized approaches to generating priors, like that suggested in Gallant's paper, are useful. However, his argument that his procedure can be given a Bayesian interpretation is flawed. In general, his method requires adjusting the prior and model after seeing the data. It cannot be interpreted as delivering inference based on a prior independent of the data. Variants on his method might have small-sample advantages over the straightforward approach of inverting the frequentist asymptotic distribution to interpret it as a posterior, but it is not at all clear that there are any advantages. [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]

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 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]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]