It remains a challenging to capture inter-variable correlations in time series forecasting (TSF), which leads researchers to focus more on channel-independent TSF methods. However, it is unwise to overlook variable interactions in real-world applications. To address this issue, we propose a novel TSF model, named Recurrent Low-rank Cross-attention Network (RLCN), which integrates channel independence and variable interaction. RLCN adopts a dual-branch feature extraction strategy: one branch learns an interpretable symmetric low-rank core matrix to model inter-variable dependencies, while the other employs an RNN-based module to capture global temporal patterns within variables. Subsequently, a cross-attention encoding module integrates these features. Experimental results demonstrate that RLCN achieves high forecasting accuracy with low memory consumption. The ablation study verifies the indispensability of components. In the context of real-world wind forecasting tasks, we analyze the correlations encoded in the low-rank core matrix by integrating meteorological domain knowledge. The matrix reflects inter-variable correlations, identifies key factors influencing wind speed forecasting, endows the model with good interpretability.

With the rapid development of 5G communication technology, the data in the Internet of Medical Things (IoMT) application systems exhibits complex characteristics such as large volume, high dimensionality, nonlinearity, and diversity, which significantly affect the efficiency and detection performance of anomaly detection tasks. How to efficiently extract nonlinear features from high-dimensional data in the context of the IoMT while minimizing information distortion in data objects are challenging problems in recent academic research. A novel adaptive nonlinear feature extraction method via fruit fly olfactory neural network (Fly dimension expansion projection and remain main components by PCA, FDEPCA) is proposed, where 1) the data are mean-centered; 2) a binary sparse random projection matrix is used for dimension expansion projection; and 3) PCA is used to extract principal component information. The proposed method overcomes the problems of present nonlinear feature extraction in the face of high-dimensional outliers where the intrinsic geometric structure of the data is severely distorted and computationally expensive. The dataset after nonlinear feature extraction by the FDEPCA algorithm is applied to specific anomaly detection models, using ROC curves and AUC as evaluation metrics for classification performance. Extensive comparison experiments are conducted on eight publicly available datasets, and experimental results show that compared with the popular nonlinear feature extraction algorithms, the FDEPCA algorithm has better classification performance and projection time advantage. When applied to proximity-based, probability-based, and ensemble-based different anomaly detection models respectively, the FDEPCA algorithm exhibits strong applicability in different types of anomaly detection classifiers.

Abstract With the rapid development of communication technology, various complex heterogeneous sensor network applications produce a large number of high‐dimensional dynamic data streams, which results in more difficult to anomaly detection than ever before. So, anomaly detection for high‐dimensional dynamic data streams is of a more and more challenging problem. This paper proposes a novel method for detecting anomalies in high‐dimensional dynamic data streams by utilizing several components. Firstly, it uses a stacked habituation autoencoder with habituation physiological mechanism to detect similarity anomalies more easily and capture feature relationships. Secondly, a union kernel density estimator with micro‐cluster is designed to improve online anomaly detection accuracy by estimating the data density. Lastly, candidate anomaly sets and a delayed processing approach are utilized to cope with conceptual drift and evolution in the data stream, allowing the system to adapt to changes in the data over time. Extensive experiments on four high‐dimensional dynamic data streams of the Internet of Things show that the proposed method is very effective.

Summary Anonymization is a practical solution for preserving user's identity privacy before data publishing. There are various anonymity techniques can be applied to maintain data utility of micro‐data and social networks, however these methods lead to a high runtime or low anonymous graph utility. In this article, an efficient, utility‐preserving approach has been proposed to reduce anonymization runtime as well as the amount of information loss incurred by graph anonymization. We craft our anonymization algorithm by combining greedy partition‐based aggregating with multi‐dimensional sorting as main heuristic tools. The proposed algorithm generates a partial order of the vertices so that the vertex at top rank and another vertex at bottom rank can never be aggregated in the same group, the runtime is reduced. Greedy partition‐based aggregating is employed to create ‐anonymous clusters which minimizing information loss. Experimental results on real‐world datasets show the proposed method has good performance and is superior to the existing methods.

With recent advancements in Industrial Internet of Things (IIoT), the stream data generated in IIoT applications presents new characteristics: huge amount of data, ultrahigh computational complexity and large memory consumption, and the existence of concept drift leading to the ineffective distinction between real drift and anomalies. It is difficult for the current mainstream methods to cope with the above problems effectively. In this article, we propose an efficient Gaussian kernel microcluster real-time-clustering method for IIoT data streams (GKMC).The method uses a microcluster sketch structure instead of individual data sample points to participate in clustering directly, which solves the problem of not being able to store unlimited data in limited memory; it uses a Gaussian kernel function to calculate the local density of microclusters to enhance the detection of anomalies; in addition, using the gravity energy function recursively to update the microcluster online and using the relearning strategy to improve the detection ability of whether the outdated microcluster belongs to abnormal microcluster or has real concept drift, ensuring that the current microcluster is always the latest microcluster most closely related to the cluster. The theoretical analysis and sufficient comparison experiments on three data sets show that the proposed algorithm has a better clustering effect than the current mainstream stream clustering algorithms.

Abstract Locality‐sensitive hashing (LSH) is widely used in the context of nearest neighbor search of large‐scale high‐dimensions. However, there are serious imbalance problems between the efficiency of data index structure construction and the query accuracy of LSH methods. In this article, a novel higher‐entropy‐hyperplane clusters LSH (HEHC‐LSH) algorithm is proposed, which we improve vector quantization to preprocess the data and greatly shortens the preprocessing time; We innovatively integrate the maximum entropy principle into the distribution estimation algorithm to construct a novel hash function cluster method, also incorporate bootstrap aggregating of ensemble learning, and adopt the parallel index dictionary to improve the generalization performance of the index structure. And in the query stage, we realize the comprehensive filtering of index set using integrated learning idea, which not only avoids a lot of distance calculation, but also improves the quality of query results. We also analyze the rationality and effectiveness of the proposed method. Finally, extensive experiment results show that HEHC‐LSH can achieve more higher precision and efficiency simultaneously comparing to current methods, and reflect the strong robustness on different datasets.

With the advent of the 5G era, wireless multimedia sensor networks (WMSNs) will be more widely used in security monitoring, environmental monitoring, intelligent transportation, and other applications of the Internet of Things (IoT). Data collected by WMSNs, part of IoT systems, are high-dimensional, multilevel, unstructured, and large-scale complex data. Effective nearest neighbor searching faces the “dimension curse” problem. To this end, by incorporating the neighborhood rough set (NRS) approach, this article proposes a novel and effective locality-sensitive hashing (LSH) method for high-dimensional WMSN data based on the neighborhood (NLSH). This method innovatively combines the neighborhood knowledge representation method with the LSH mechanism and extends the indexing ability of LSH. Moreover, this method does not require any prior knowledge and has good universality. The indexing method of neighborhood bucket building can reduce the number of searches and improve the response speed of the WMSN system. Extensive experiments have been carried out on several real-world multimedia data sets. The results show that a large-scale high-dimensional NLSH search based on the NRS approach can efficiently query the target point and produce very accurate results. The NLSH algorithm based on the NRS approach outperforms other advanced mainstream LSH algorithms.

Wireless multimedia sensor networks (WMSNs) have been widely used in environmental monitoring, intelligent transportation, and other scenarios; however, their data have high-dimensional, large-scale, and multitype properties, as well as other characteristics. It is technically difficult to construct an appropriate index structure and query strategy while we perform a highly accurate search. This paper proposes a novel locality-sensitive hashing (LSH) fast Johnson-Lindenstrauss transform (FJLT)-fly locality-sensitive hashing (FLSH) algorithm for WMSN Internet of Things search. In this method, the projection method of FJLT and the winner-takes-all feature selection strategy in the fruity FLSH are considered. The method provides a new solution for the nearest neighbor search of high-dimensional data. We also discuss the distance-keeping property of our algorithm, and prove theoretically that the method proposed in this paper has better distance keeping performance than the traditional dimensionality reduction method. The experimental results show that the proposed algorithm has better generalization, accuracy of the search results, and time efficiency when using the Drosophila olfactory nerve to simulate the LSH process. This method effectively solves the problem of the approximate neighbor query of high-dimensional big data and can be effectively applied to search application on WMSN system.

Architecture-based software performance optimization can not only significantly save time but also reduce cost. A few rule-based performance optimization approaches at software architecture (SA) level have been proposed in recent years. However, in these approaches, the number of rules being used and the order of application of each rule are uncertain in the optimization process and these uncertainties have not been fully considered so far. As a result, the search space for performance improvement is limited, possibly excluding optimal solutions. Aiming to solve this problem, we propose an evolutionary algorithm for rule-based performance optimization at SA level named EA4PO. First, the rule-based software performance optimization at SA level is abstracted into a mathematical model called RPOM. RPOM can precisely characterize the mathematical relation between the usage of rules and the optimal solution in the performance improvement space. Then, a framework named RSEF is designed to support the execution of rule sequences. Based on RPOM and RSEF, EA4PO is proposed to find the optimal performance improvement solution. In EA4PO, an adaptive mutation operator is designed to guide the search direction by fully considering heuristic information of rule usage during the evolution. Finally, the effectiveness of EA4PO is validated by comparing EA4PO with a typical rule-based approach. The results show that EA4PO can explore a relatively larger space and get better solutions.

In this paper, a visual ontology modeling tool called VOEditor and an architecture based on the tool aimed at collaborative querying of Semantic Web resources are presented. A visual environment for ontology construction is deployed, providing a convenient mechanism for ontology engineers to create and edit their ontologies in a graphical mode. Search and coordination activities are carried out by a system of multi-agent designed for such environments. Finally, the tool and the architecture are demonstrated in the Tourism Recommendation System which is based on a tourism domain ontology created by the VOEditor.

Although there can be some value in accessing a single Web service, the greater value is derived from assembling Web services into more powerful composite Web services. Web service flows are composite Web services based on process. The Web service flows specified by current specifications are static that can not dynamically adapt themselves to often changing business environment and have the problem of code scattering and tangling relating to crosscutting concerns that cut across the process boundaries. In this paper, we apply AOP concept to support the dynamic adaptation of Web service flows. We give our approach for describing aspect as extension to BPEL, considering how to reuse the definition of an advice that maybe has different type at different pointcuts. And we present an approach for verifying the correctness of the web service flows weaved with aspects before they are deployed.