In representation learning for Web temporal knowledge bases, each node in Web knowledge bases carries a specific contextual meaning. Existing services often neglect the implicit semantic Web knowledge behind entities and relations, thus failing to effectively capture the knowledge representation of temporal Web knowledge bases. To address this issue, this paper develops an autocompletion service for temporal Web knowledge bases, which is based on multisource semantic feature learning and feature fusion. We construct a semantic model oriented toward external semantic Web repositories to supplement entity-relation descriptions, and our service leverages the pretrained language model BERT, effectively learning semantic knowledge features. Additionally, our service captures the textual features of quadruples using a recurrent neural network, constructs a historical sparse timestamp matrix, and generates a mask tensor, successfully obtaining the weights of potentially correct entities, and thereby capturing the historical features of quadruples. Furthermore, our service integrates complementary features from different modules through an attention mechanism. Experimental validation shows that our service outperforms existing approaches in terms of four evaluation metrics: mean reciprocal rank (MRR), Hits@1, Hits@3, and Hits@10. The results also exhibit that it improves the accuracy and performance for autocompletion service for temporal Web knowledge bases.

In multi-view environment, it would yield missing observations due to the limitation of the observation process. The most current representation learning methods struggle to explore complete information by lacking either cross-generative via simply filling in missing view data, or solidative via inferring a consistent representation among the existing views. To address this problem, we propose a deep generative model to learn a complete generative latent representation, namely Complete Multi-view Variational Auto-Encoders (CMVAE), which models the generation of the multiple views from a complete latent variable represented by a mixture of Gaussian distributions. Thus, the missing view can be fully characterized by the latent variables and is resolved by estimating its posterior distribution. Accordingly, a novel variational lower bound is introduced to integrate view-invariant information into posterior inference to enhance the solidative of the learned latent representation. The intrinsic correlations between views are mined to seek cross-view generality, and information leading to missing views is fused by view weights to reach solidity. Benchmark experimental results in clustering, classification, and cross-view image generation tasks demonstrate the superiority of CMVAE, while time complexity and parameter sensitivity analyses illustrate the efficiency and robustness. Additionally, application to bioinformatics data exemplifies its practical significance.

In an era of more diverse data modalities, multi-view clustering has become a fundamental tool for comprehensive data analysis and exploration. However, existing multi-view unsupervised learning methods often rely on strict assumptions on semantic consistency among samples. In this paper, we reformulate the multi-view clustering problem from an information-theoretic perspective and propose a general theoretical model. In particular, we define three desiderata under multi-view unsupervised learning in terms of mutual information, namely, comprehensiveness, concentration, and cross-diversity. The multi-view variational lower bound is then obtained by approximating the samples' high-dimensional mutual information. The Kullback–Leibler divergence is utilized to deduce sample assignments. Ultimately the information-based multi-view clustering model leverages deep neural networks and Stochastic Gradient Variational Bayes to achieve representation learning and clustering simultaneously. Extensive experiments on both synthetic and real datasets with wide types demonstrate that the proposed method exhibits a more stable and superior clustering performance than state-of-the-art algorithms.

In this letter, we propose a novel weighted sparse graph regularization (SGR) (WSGR) model by incorporating the spatial information both in pre and postprocessing stages. In the preprocessing stage, we generate adaptive patch features to represent the spatial information. Traditional patch feature extraction methods rarely consider the quality of the neighboring pixels within a patch since the central pixel and its neighbors may belong to different classes. To solve this issue, a Gaussian-kernel-based weighting scheme that can adaptively model the neighborhood information is introduced into the original SGR model. Experimental results, conducted with two popular hyperspectral data sets, indicate that the proposed method outperforms other related methods with an overall accuracy higher than 90% when only 20 labeled samples per class are used for training.

This paper presents a real-time long short-term memory (LSTM) recurrent neural network (RNN) to trace and predict the GPS positioning errors within next one to several seconds, offering an enhance GPS positioning. The proposed LSTM prediction model was further verified over extensive experimental data captured in cities and metropolitans, urbans and highways across several middle and eastern States of the United States. The prediction accuracy of the proposed real-time LSTM can be within less than 1-3% of its ground true values outperforms those results gained by conventional statistics and linear prediction models.

There has been significant progress in the development and deployment of peer-to-peer (P2P) live video streaming systems. However, there has been little study on the security aspect in such systems. Our prior experiences in Anysee exhibit that existing systems are largely vulnerable to intermediate attacks, in which the content pollution is a common attack that can significantly reduce the content availability, and consequently impair the playback quality. This paper carries out a formal analysis of content pollution and discusses its implications in P2P live video streaming systems. Specifically, we establish a probabilistic model to capture the progress of content pollution. We verify the model using a real implementation based on Anysee system; we evaluate the content pollution effect through extensive simulations. We demonstrate that (1) the number of polluted peers can grow exponentially, similar to random scanning worms. This is vital that with 1% polluters, the overall system can be compromised within minutes; (2) the effective bandwidth utilization can be sharply decreased due to the transmission of polluted packets; (3) Augmenting the number of polluters does not imply a faster progress of content pollution, in which the most influential factors are the peer degree and access bandwidth. We further examine several techniques and demonstrate that a hash-based signature scheme can be effective against the content pollution, in particular when being used during the initial phase.

In Peer-to-Peer (P2P) file sharing systems, the attributes of resource description can influence the user behavior, especially on resource selection. However, this has been only qualitatively speculated but lacks of quantitative analysis. In this paper, we carry out a systematically quantitative stu