Wireless Sensor Network (WSN) reliability evaluation is essential for ensuring the stable operation of network. Traditional methods usually focus on the network topology structure, and calculate the normal operation probability of WSNs. However, these methods usually ignore the energy consumption and network lifetime. In this paper, a novel reliability evaluation algorithm TLR is proposed, which calculates the network lifetime under dynamic network environment according to the pre-set network topology structure reliability threshold, and realizes the comprehensive reliability analysis of network topology and lifetime. In addition, as the basis for reliability evaluation, this paper proposes a new deep reinforcement learning network framework GNN-AC combining graph neural network and actor-critic network, which solves the challenge of constructing Virtual Backbone Network (VBN) in dynamically operating networks. Based on the self-defined fitness matrix and fitness value, the objective function is set to optimize the VBN construction scheme to accurately calculate the network lifetime, and the relationship between the reliability of network topology and network lifetime is discussed. Simulations are carried out for various sizes of WSNs to show the advantages and effectiveness of the proposed approach in estimating network lifetime and reliability evaluation.

Artificial Intelligence-empowered Industrial Control Networks coordinate massive heterogeneous devices and contain multi-node spatial-temporal information. Multivariate Time Series Anomaly Detection (MTS-AD) can discover data-fault behaviors for ensuring the security of distributed networks. However, existing studies tend to rely heavily on single temporal features or neglect the rich spatial-temporal correlations, which leads to the serious underutilization of interactive embeddings between the time and space domains. In this article, a novel Multiview Spatial-Temporal Interaction Attention Network (MSTIA-Net) scheme is proposed for the unsupervised MTS-AD task to better tackle these challenges. MSTIA-Net focuses on jointly modeling the comprehensive spatial-temporal dependencies by means of incorporating complex interactive contents and dynamic relations from multiview patterns. To fully leverage the content-oriented interactions, a spatial-temporal interactions aggregation module is presented to explicitly learn content-aware representations with a parallel-attention mechanism and a low-rank bilinear fusion manner. Simultaneously, considering the potential correlations among different variables as contextual cues, a spatial-temporal correlations learning module is developed to adaptively capture the relevant context for relation-aware representations. On this basis, both types of aware clues are further integrated by the dual attention-enhanced contrastive reconstruction, which can enrich the cross-aware fusion representations and generate the local and global outputs through a cross-view contrastive learning strategy. Experiments conducted on six benchmark datasets demonstrate the superiority of our MSTIA-Net over state-of-the-art baselines.

Empowered by the continuous integration of social multimedia and artificial intelligence, the application scenarios of Information Retrieval (IR) progressively tend to be diversified and personalized. Currently, User-Generated Content (UGC) systems have great potential to handle the interactions between large-scale users and massive media contents. As an emerging multimedia IR, Fashion Compatibility Modeling (FCM) aims to predict the matching degree of each given outfit and provide complementary item recommendation for user queries. Although existing studies attempt to explore the FCM task from a multi-modal perspective with promising progress, they still fail to fully leverage the interactions between multi-modal information or ignore the item–item contextual connectivities of intra-outfit. In this article, a novel FCM scheme is proposed based on Correlation-Aware Cross-Modal Attention Network. To better tackle these issues, our work mainly focuses on enhancing comprehensive multi-modal representations of fashion items by integrating the cross-modal collaborative contents and uncovering the contextual correlations. Since the multi-modal information of fashion items can deliver various semantic clues from multiple aspects, a modality-driven collaborative learning module is presented to explicitly model the interactions of modal consistency and complementarity via a co-attention mechanism. Considering the rich connections among numerous items in each outfit as contextual cues, a correlation-aware information aggregation module is further designed to adaptively capture significant intra-correlations of item–item for characterizing the content-aware outfit representations. Experiments conducted on two real-world fashion datasets demonstrate the superiority of our approach over state-of-the-art methods.

Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident Information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation.

Discrimination against different user groups has received growing attention in the recommendation field. To address this problem, existing works typically remove sensitive attributes that may cause discrimination through adversary learning to achieve fair recommendations. However, these approaches leverage all available interactions for learning user representations and overlook the fact that different interactions have varying relevance to users’ sensitive attributes. Ignoring this issue may weaken the effectiveness of adversary learning in removing sensitive attributes. To tackle this challenge, we propose a novel model called GS-FairRec, which distinguishes between user interactions to achieve better removal of sensitive attributes. The model consists of three modules: graph sampling-based representation learning, pseudo-user representation learning, and adversarial learning. Firstly, the graph sampling-based representation learning module removes some irrelevant neighbors from a user-item bipartite graph and employs a graph convolutional network (GCN) to learn user/item representations. Next, items that are relevant to a user’s sensitive information but do not match their preferences are defined as the user’s pseudo-interest items, which are leveraged to learn the pseudo-user representation. In the adversarial learning module, the user’s two kinds of representations are fused for adversarial learning to remove sensitive information. Additionally, we design a new metric to measure the model’s ability to remove sensitive attributes based on how a generated recommendation list discloses the user’s sensitive attributes. Finally, we conduct experiments on two real-world datasets, and our results demonstrate the superiority of our proposed model in fairness tasks.

Writer retrieval is crucial in document forensics and historical document analysis. However, due to the difference in syntactic structure between Chinese and other languages, the existing methods may not be directly applied to Chinese writer retrieval. Previous work on Chinese writer retrieval does not overcome the performance degradation problem when the number of samples grows. In this paper, we propose a novel compound uniform encoding algorithm (CUE) for Chinese writer retrieval, which mainly consists of a combined feature extraction module (CFE) and a prototype substitution module (PS). The CFE module combines two complementary features from image filter response and character contour. It counts local symmetries and edge co-occurrence pairs. PS module substitutes the outliers with the class prototypes to alleviate the influence of the outliers. Finally, the weighted Chi-square distance is applied to measure the similarity between writer and text. To verify the superiority of our proposed method, experiments are conducted on four public datasets and our built dataset. The results validate that CUE outperforms the state-of-the-art algorithms on mAP metric.

With the tremendous development in hardware computing and the widespread use of mobile terminal devices, there are increasingly more people who prefer to share their lives and opinions on social media. Though social media plat-forms allow everyone to express their opinions freely, they create convenience for rumor propagation in the meantime, which brings huge negative influence on the public and makes rumor detection extremely necessary. Currently, the most effective methods regard rumor propagation network as a graph and adopt graph convolutional networks (GCN) to detect rumor automatically. Such methods achieve promising performance in rumor detection, however, we argue that they have two critical defects: 1) they neglect the position contributions of rumor nodes in a graph, reducing the accuracy of rumor detection results; 2) they are inadequate in dealing with imbalanced data, which also indicates the inflexibility and the poor generalization ability of the model. To overcome these issues, we incorporate Katz centrality into spectral-domain graph convolution and propose a novel model named Out-In-Degree Graph Convolutional Networks (OID-GCN). Specifically, besides enhancing accuracy, Katz centrality can efficiently capture the position information of nodes, while the rest structure of OID-GCN shows a superb ability in dealing with imbalanced data. Comprehensive experimental results on two real-world datasets Twitter-15 and Twitter-16 demonstrate our OID-GCN outperforms existing methods.

Some work in information spreading show that the attention plays an important role in explaining human behaviors and the attention decay exists in the process of information spreading. However, few researchers take the attention decay into consideration when studying the spreading dynamics of information. In this paper, we propose a susceptible-received-accepted-immune (SRAI) information spreading model to explore the attention decay's effect on the spread dynamics of information, integrating the memory, the social reinforcement and the attention decay. We simulate the model in different complex networks and verify the impacts of the attention decay on the information spreading process. Particularly, simulation results show that in some situations, the effect of the attention decay will decrease with the increasement of the network's randomness. Our work can provide insights to the understanding of the role of the attention decay in information spreading.

MapReduce is a widely adopted computing framework for data-intensive applications running on clusters. We propose an approach to exploit data parallelisms in XML processing using MapReduce in Hadoop. Our solution seamlessly integrates data storage, labelling, indexing, and parallel queries to process a massive amount of XML data. Specifically, we introduce an SDN labelling algorithm and a distributed hierarchical index using DHTs, we develop an efficient data retrieval approach called B-SLCA. More importantly, we design an advanced two-phase MapReduce solution that is able to efficiently address the issues of labelling, indexing, and query processing on big XML data. We implemented our solution on a real-world Hadoop cluster processing the real-world datasets. Our experimental results show that SDN outperforms NCIM by up to a factor of 1.36 with an average of 1.17, our BSLCA outperforms BwdSLCA by up to a factor of 1.96 with an average of 1.2.

With the booming of online social networks, social trust has been used to cluster users in recommender systems. It has been proven to improve the recommendation accuracy when trust communities are integrated into memory-based collaborative filtering algorithms. However, existing trust community mining methods only consider the trust relationships, regardless of the distrust information. In this paper, considering both the trust and distrust relationships, a SVD signs based community mining method is proposed to process the trust relationship matrix in order to discover the trust communities. A modified trust metric which considers a given user's expertise level in a community is presented to obtain the indirect trust values between users. Then some missing ratings of the given user are complemented by the weighted average preference of his/her trusted neighbors selected in the same community during the random walk procedures. Finally, the prediction for a given item is generated by the conventional collaborative filtering. The comparison experiments on Epinions data set demonstrate that our approach outperforms other state-of-the-art methods in terms of RMSE and RC.