Secure and efficient service authentication is vital for trustable interactions between users and service nodes in decentralized metaverse environments. However, conventional PKI-based authentication methods face limitations such as high query latency, privacy leakage risks, and centralized trust dependencies, making them unsuitable for large-scale, real-time metaverse services. To address these challenges, we propose ESAChain, a novel blockchain-based authentication framework that e nsures lightweight, decentralized, and privacy-preserving identity verification. Specifically, we propose a Mutually Exclusive Cuckoo Filter (MECF) integrated with a Filter Hash Chain (FHC), which provides lightweight data structures and efficient querying capabilities for certificate status. Furthermore, we design a trust-decay-based Delegated Proof-of-Stake (TD-DPoS) consensus mechanism to maintain the integrity and reliability of certificate status data by dynamically adjusting node trust values and decaying votes to prevent single-node dominance. We also incorporate a blind-signature-based authentication mechanism to enhance privacy-preserving identity authentication by preventing tracking of certificate verification requests. Extensive simulation experiments and security analyses demonstrate that ESAChain significantly reduces query latency and data transmission overhead, enhances consensus robustness, and provides an efficient, trustworthy, and privacy-preserving authentication solution for secure metaverse services.

Multimodal magnetic resonance (MR) images are important for accurate analysis of the type and extent of lesions. However, obtaining high-quality multimodal MRI images remains challenging due to factors such as equipment and personnel. To solve this problem, we propose a multimodal fusion generation network (MF-Net) for high-quality MR image synthesis, which can synthesize target modality images from two existing modality images. The network has two core modules: The multi-scale Residual Atrous (MRA) module for feature extraction and the Parallel Fusion Attention (PFA) module for attention mechanisms. The MRA module is designed to efficiently extract contextual features at different scales, while the PFA module enhances and fuses features from different modalities effectively. Extensive experiments on BraTS2019 indicate that MF-Net can effectively synthesize target modality images and outperform other single-modality synthesis methods.

Soil salinity is a significant environmental indicator, directly reflecting the land quality and its productivity. In this work, dual-polarized synthetic aperture radar (PolSAR) data were exploited to retrieve the soil salinity in saline-affected fields based on the deep neural network (DNN) theory. Two polarimetric features gotten from Cloude polarization decomposition, together with six other features, were calculated and selected from ten features according to the feature importance. The DNN regression model for soil salinity (DNNR-S) based on one-dimensional convolutional neural network, quantifying the relationship between the eight feature parameters and soil salinity, was established to retrieve the soil salinity in northeastern China. Compared with other five traditional machine learning regression methods [logistic regression (LOR), random forest regression (RFR), support vector regression (SVR), decision tree regression (DTR), and multilayer perceptron regression (MPR)], DNNR-S is the best-performing model with the performance of root mean square error (RMSE) = 0.28, mean absolute error (MAE) = 0.21, and$r = 0.74$. Using the proposed model, the soil salinity was retrieved, and 52.37% was found to be affected by salinization which was in general agreement with the government report. Due to the training criteria in the proposed model, it was a little more computationally intensive than all the others, but the DNNR-S had the advantage of easy spread to other study areas.

Anomaly detection in surveillance videos aims at identifying abnormal event under specific scenarios and it is widely applied in public security, smart city, and pedestrian surveillance. In the weakly-supervised setting, most existing anomaly detection approaches are formulated as the classic multiple-instance learning problem. In this paper, we provide a unique perspective that selection biased positive and unlabeled learning. In such a viewpoint, as long as estimating the label frequency from training set, we can effectively apply supervised classifier tow eakly supervised anomaly detection, and take greater advantage of these well-developed classifiers. For this purpose, we present a novel method to estimate label frequency from the attribute subdomains with large label probability. In the test phase, we only use the label frequency to modify the supervised classifier. Comprehensive experiments are performed on different scales datasets. Our method provides superior on all dataset which demonstrate the effectiveness.

The existence of outliers will reduce the reliability of original data streams, thus adversely affecting data-based operations. Through the interpretation and analysis of outliers, users can clarify the difference between the normal data and outliers, thus clearing outliers to improve the reliability of original data streams. However, there are few explanations and analyses on the reasons of outliers. The contribution of this paper is that we have designed a visual analysis system for outlier detection of data streams based on association, VooddS, which can reasonably explain and analyze outliers from two aspects of algorithm operation process and data distribution, and better show users why data instances are judged as outliers. VooddS system starts from the running process of the algorithm and designs a visual forms according to the running process of outlier detection algorithm, so as to reasonably explain the reason for determining outliers to users; From the point of view of data distribution, a combination of box graph, broken line and histogram is designed, and the correlation between data and outlier degree is analyzed from two levels of data attribute and data tuple, which helps users to explore the influence of context data on outlier judgment. In addition, the effectiveness of the system is verified by real data sets, and the results show that VooddS can reasonably explain abnormal values, and the interactive mode of the system can bring better experience to users.

The tracing system of agricultural products plays a role in guaranteeing their quality and safety. It has multiple modules to accomplish traceability. Its reliability is the premise to ensure the normal operation of the tracing system. Because the modules of the system run concurrently with each other, the unverified system cannot guarantee the rationality of the design for the relationship among the modules, such as the hierarchy of the system modules and the concurrent coupling of the modules when the system runs, which is one of the reasons leading to the reliability problem of the system. Color Petri net is a kind of improved Petri net tools. This paper analyzes the operation of the modules of agricultural product traceability system. It uses colored Petri net of agricultural products tracing system for modeling, and uses the hierarchical petri net to design the method of reducing the size of the model to ensuring that the system module design of the border. with a color set represents the transmission of information between different modules. Finally, CPN Tools are used to verify the model. The results shows that this model detection method can ensure the reliability of the tracing system of agricultural products.

The degradation of salinized land in western Jilin Province and its negative impact on the local and global environment have led to increasing interest in salinization restoration. Satellite remote sensing technology may provide up-to-date information on large-scale land salinization, and provide support for land protection and monitoring planning. In this case, the fusion of optical and radar remote sensing data may be precious because most of the land salinization degradation is located in areas with high cloud cover, which limiting the use of optical data. Radar data can “see-through” clouds, but the experience so far shows that it can't distinguish some types of land cover very well. At present, there are many optical and radar data fusion methods, but there is rare information about how to mapping land salinity using data fusion. To evaluate data fusion's potential, we applied classification method to sentinel-1 and sentinel-2 images for mapping the land salinity cover in Da'an City, Western Jilin Province. Based on our research results, we provide a beneficial method to help people involved in salinized land restoration to utilize the potential of big data.

Anomaly detection plays a critical role in intelligent video surveillance. However, real-world video data obtained always contains large numbers of normal video data, along with large numbers of unlabeled data. A promising solution with one-class classification and semi-supervised learning may not be satisfactory as they fail to make good use of unlabeled data with only normal data available. In this paper, we introduced a new framework, called Positive Unlabeled learning-based Anomaly event Detection (PU-AD), to exploit the weakly-supervised information. To the best of our knowledge, this is the first work that introduces the PU idea and achieves detecting abnormal events with a limited number of partially labeled data. Experiments on real-world surveillance videos show that the proposed method outperforms the existing state-of-the-art methods.

In order to achieve the fastest fire-fighting purpose, warehouse autonomous mobile fire-fighting robots need to make an overall optimal planning based on the principle of the shortest time for their traveling path. A ∗ algorithm is considered as a very ideal shortest path planning algorithm, but the shortest path is not necessarily the optimal path for robots. Furthermore, the conventional A ∗ algorithm is affected by the search neighborhood restriction and the theoretical characteristics, so there are many problems, which are closing to obstacles, more inflection points, more redundant points, larger total turning angle, etc. Therefore, A ∗ algorithm is improved in eight ways, and the inflection point prior strategy is adopted to compromise Floyd algorithm and A ∗ algorithm in this paper. According to the criterion of the inflection point in this paper, the path inflection point arrays are constructed and traveling all path nodes are replaced by traveling path inflection points for the conventional Floyd algorithm backtracking, so it greatly reduces the backtracking time of the smooth path. In addition, this paper adopts the method of the extended grid map obstacle space in path planning safety distance. According to the relationship between the actual scale of the warehouse grid map and the size of the robot body, the different safe distance between the planning path and the obstacles is obtained, so that the algorithm can be applied to the safe path planning of the different size robots in any map environments. Finally, compared with the conventional A ∗ algorithm, the improved algorithm reduces by 7.846% for the path length, reduces by 71.429% for the number of the cumulative turns, and reduces by 75% for the cumulative turning angle through the experiment. The proposed method can ensure robots to move fast on the planning path and ultimately achieve the goal of reducing the number of inflection points, reducing the cumulative turning angle, and reducing the path planning time.

Performing multiple instances of one activity enables workflow management system to be flexible on handling workflow process. When handling multiple instances the main problem is the synchronization of workflow activity instances. After analyzing the assignment and the join of multiple instances, this paper describes the patterns of multiple instances, and classifies the basic patterns into four types. Then from the point of view control of instances joining, instance splitting and triggering the next activity instance, we pick four elements up that affect the process's progress. The four elements can be used as the uniform denotation to show the characteristics of multiple instances. The formal presentation of activity attributes of multiple instances enables a workflow engine to handle the activity instances simple and unifiable