Cross-view geo-localization plays a critical role in Unmanned Aerial Vehicle (UAV) localization and navigation. However, significant challenges arise from the drastic viewpoint differences and appearance variations between images. Existing methods predominantly rely on semantic features from RGB images, often neglecting the importance of spatial structural information in capturing viewpoint-invariant features. To address this issue, we incorporate geometric structural information from normal images and introduce a Joint perception network to integrate RGB and Normal images (JRN-Geo). Our approach utilizes a dual-branch feature extraction framework, leveraging a Difference-Aware Fusion Module (DAFM) and Joint-Constrained Interaction Aggregation (JCIA) strategy to enable deep fusion and joint-constrained semantic and structural information representation. Furthermore, we propose a 3D geographic augmentation technique to generate potential viewpoint variation samples, enhancing the network's ability to learn viewpoint-invariant features. Extensive experiments on the University-1652 and SUES-200 datasets validate the robustness of our method against complex viewpoint variations, achieving state-of-the-art performance.

Visual Place Recognition (VPR) plays a vital role in mobile robotics and autonomous navigation by retrieving reference images from a pre-established database. However, VPR systems frequently encounter performance degradation due to environmental variations. To overcome these challenges, we propose a re-ranking based VPR framework incorporating two key components: (1) A Visual Mamba Embedding (VME) module that optimizes spatial-channel feature interactions to generate discriminative global descriptors; and (2) A Spatial Graph Attentional Network (SGAN) that replaces conventional RANSAC-based verification with an efficient graph attention mechanism, improving matching accuracy while reducing computation. Comprehensive evaluations across multiple benchmark datasets demonstrate that the proposed method achieves superior performance compared to existing state-of-the-art methods, while maintaining advantages in computational efficiency and storage requirements.

Uncertainty estimation is a critical component of building safe and reliable machine learning models. Accurate estimation of uncertainties is essential for identifying and mitigating potential risks and ensuring that machine learning systems operate reliably in real-world scenarios. Various approaches, such as ensemble and Bayesian neural networks have been developed by sampling probability predictions from submodels, which is computatinally expensive. At present, these techniques are incapable of precisely delineating the boundary separating in-distribution (ID) and out-of-distribution (OOD) data. To fill up this research gap, this paper presents a normalizing flow based framework to directly predict parameters of prior distributions over the probability with a neural network, the proposed model is able to effectively differentiate between ID and OOD data in regression problems. The posterior distributions learned by the model precisely represent uncertainties for OOD data based solely on ID data, without the need for OOD data during training. This approach has shown promising results in a number of applications, including image depth estimation and image adversarial attacks.

Summary Limited number of labeled data of surveillance video causes the training of supervised model for pedestrian re‐identification to be a difficult task. Besides, applications of pedestrian re‐identification in pedestrian retrieving and criminal tracking are limited because of the lack of semantic representation. In this paper, a data‐driven pedestrian re‐identification model based on hierarchical semantic representation is proposed, extracting essential features with unsupervised deep learning model and enhancing the semantic representation of features with hierarchical mid‐level ‘attributes’. Firstly, CNNs, well‐trained with the training process of CAEs, is used to extract features of horizontal blocks segmented from unlabeled pedestrian images. Then, these features are input into corresponding attribute classifiers to judge whether the pedestrian has the attributes. Lastly, with a table of ‘attributes‐classes mapping relations’, final result can be calculated. Under the premise of improving the accuracy of attribute classifier, our qualitative results show its clear advantages over the CHUK02, VIPeR, and i‐LIDS data set. Our proposed method is proved to effectively solve the problem of dependency on labeled data and lack of semantic expression, and it also significantly outperforms the state‐of‐the‐art in terms of accuracy and semanteme.

Summary Information systems have been widely used to realize business processes. The business processes can be described by formal or semi‐formal models. In practice, there are a number of realistic processes involving loop semantics. As an insufficient design or improvement of the information system, event logs generated from these systems may not be consistent with the existing models. By comparing an existing process model with the event logs, we can detect inconsistencies called deviations and verify and extend the business process model. In this paper, some abnormal activities in business processes are formally defined based on Petri nets. An efficient approach to detect deviations between the process model with loop structures and event logs is proposed. A clinical process in a healthcare information system is used as a case study to illustrate our work. Experimental results show the effectiveness and efficiency of the presented approach.

Summary In order to deal with action recognition for large‐scale video data, we present a spatio‐temporal auto‐combination deep network, which is able to extract deep features from short video segments by making full use of temporal contextual correlation of corresponding pixels among successive video frames. Based on conventional sparse encoding, we further consider the representative features in adjacent nodes of the hidden layers according to activation states similarities. A sparse auto‐combination strategy is applied to multiple input maps in each convolution stage. An information constraint of the representative features of hidden layer nodes is imposed to handle the adaptive sparse encoding of the topology. As a result, the learned features can represent the spatio‐temporal transition relationships better and the number of hidden nodes can be restricted to a certain range. We conduct a series of experiments on two public data sets. The experimental results show that our approach is more effective and robust in video action recognition compared with traditional methods.

Summary Due to the characteristic of risk diversification in non–life insurance industry, the company's financial risk early warning is very important. In order to reasonably predict the financial status of non–life insurance company, the evaluation system of financial risk indicator is constructed from the aspects of solvency, profitability, and growth ability. Taking data of non–life insurance companies in past years as sample, the evaluation indicators are weighted objectively using the entropy method. The RBF neural network model is improved with the genetic algorithm, and the early warning model is established. The empirical results show that the prediction accurate rate of RBF neural network model based on genetic algorithm is increased.

Summary MapReduce has become a major programming model for data‐intensive applications in cloud computing environments. Hadoop, an open source implementation of MapReduce, has been adopted by an increasingly wide user community. However, Hadoop suffers from task scheduling performance degradation in heterogeneous contexts because of its homogeneous design focus. This paper presents gSched, a resource‐aware Hadoop scheduler that takes into account both the heterogeneity of computing resources and provisioning charges in task allocation in cloud computing environments. gSched is initially evaluated in an experimental Hadoop cluster and demonstrates enhanced performance compared with the default Hadoop scheduler. Further evaluations are conducted on the Amazon EC2 cloud that demonstrates the effectiveness of gSched in task allocation in heterogeneous cloud computing environments. Copyright © 2016 John Wiley & Sons, Ltd.

Summary By the reason of the variability of light and pedestrians' appearance, it is hard for a camera to obtain a clear human figure. Person re‐identification with different cameras is a difficult visual recognition task. In this paper, a novel approach called attribute learning based on distributed deep convolutional neural network model is proposed to address person re‐identification task. It shows how attributes, namely the mid‐level medium between classes and features, are obtained automatically and how they are employed to re‐identify person with semantics when an author‐topic model is used to mapping category. Besides, considering the ability to operate on raw pixel input without the need to design special features, deep convolutional neural network is employed to generate features without supervision for attributes learning model. To overcome the model's weakness in computing speed, parallelized implementations such as distributed parameter manipulation and attributes learning are employed in attribute learning based on distributed deep convolutional neural network model. Experiments show that the proposed approach achieves state‐of‐the‐art recognition performance in the VIPeR data set and is with a good semantic explanation. Copyright © 2016 John Wiley & Sons, Ltd.

Summary At present MapReduce computing model‐based Hadoop framework has gradually become the most famous distributed computing framework because of its remarkable features such as scalability, fault tolerance, data security, and powerful IO ability. However, Hadoop framework only supports limited load balancing policies, which may result in performance deterioration in heterogeneous clusters. Additionally Hadoop does not have advanced dynamic load balancing mechanism in enabling its optimal performance in dynamic environment. This paper presents a sliding window‐based dynamic load balancing algorithm, which specially aims at balancing the load among the heterogeneous nodes during the Hadoop job processing. The presented algorithm is evaluated in both simulated and physical environments. The experimental results show that the performances in terms of efficiency of Hadoop cluster can be significantly improved. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Visual surveillance is playing an ever increasing role in criminal detection because of a rapid deployment of surveillance cameras. Motion detection, which refers to the process of detecting a change in the position of an object in relation to the background or the change in the background in relation to the object, has become one of the enabling techniques to facilitate visual surveillance. This paper parallelizes a motion detection algorithm using a cluster of inexpensive computing devices. Custom region of interest is implemented to enhance the performance and accuracy of the motion detection algorithm. The performance of the parallelized algorithm is evaluated from both the scalability in computation and the accuracy in motion detection. Performance evaluation results show that the enhanced algorithm achieves higher accuracy in motion detection with reduced execution times in computation. Copyright © 2016 John Wiley & Sons, Ltd.

Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision.

P2P networks facilitate people belonging to a community to share resources of interest. However, discovering resources in a large scale P2P network poses a number of challenges. Although distributed hash table (DHT) structured P2P networks have shown enhanced scalability in routing messages, they only support key based exact matches. This paper presents DIndex, a distributed indexing component that can be used in P2P networks in support of range queries. DIndex introduces the concept of search dimensions for partitioning a search space, and it organizes peer nodes in a three-layered structure. Experimental results show that, for aP2P network with N number of peers, the average number of hops per message is less than log(N).

Abstract This paper presents WSOWG, a Web‐services‐oriented wrapper generator for automatically wrapping non‐networked legacy codes as Web services for reuse in distributed problem‐solving environments. Using WSOWG, a finite element based computational fluid dynamics (CFD) legacy code has been wrapped as a Web service. A problem‐solving environment for simulating incompressible Navier–Stokes flows has also been implemented. A user makes use of the CFD service through a Web page without knowing the exact implementation of the service. In this way, a user's computing environment can be extended to a heterogeneous distributed computing environment. Performance evaluation shows that the overhead to invoke the CFD Web service generated by WSOWG using Simple Object Access Protocol (SOAP) and CORBA Internet Inter‐ORB Protocol (IIOP) is reasonable compared with that of invoking another CFD Web service manually wrapped from the CFD legacy code using SOAP only. Copyright © 2004 John Wiley & Sons, Ltd.