A Novel Kernel for Text Classification Based on Semantic and Statistical Information

In text categorization, a document is usually represented by a vector space model which can accomplish the classification task, but the model cannot deal with Chinese synonyms and polysemy phenomenon. This paper presents a novel approach which takes into account both the semantic and statistical information to improve the accuracy of text classification. The proposed approach computes semantic information based on HowNet and statistical information based on a kernel function with class-based weighting. According to our experimental results, the proposed approach could achieve state-of-the-art or competitive results as compared with traditional approaches such as the k-Nearest Neighbor (KNN), the Naive Bayes and deep learning models like convolutional networks

The Antecedents and Consequences of Crowdfunding Investors’ Citizenship Behaviors – an Empirical Research on Motivations and Stickiness

This study investigates the antecedents (internal and external motivations) and consequences (stickiness intentions) of crowdfunding investors’ citizenship behavior. In addition, this study examines the moderating effects of investors’ perceived project novelty on the relationships between motivations and citizenship behavior. Based on a sample of 226 crowdfunding investors, results indicate that internal and external motivations significantly influence investors’ citizenship behavior, which further affect investors’ stickiness intentions. Furthermore, results show that investors’ perceived project novelty moderates the relationships between internal/ external motivation and citizenship behavior

Endothelial toll-like receptor 4 maintains lung integrity via epigenetic suppression of p16INK4a.

We previously reported that the canonical innate immune receptor toll-like receptor 4 (TLR4) is critical in maintaining lung integrity. However, the molecular mechanisms via which TLR4 mediates its effect remained unclear. In the present study, we identified distinct contributions of lung endothelial cells (Ec) and epithelial cells TLR4 to pulmonary homeostasis using genetic-specific, lung- and cell-targeted in vivo methods. Emphysema was significantly prevented via the reconstituting of human TLR4 expression in the lung Ec of TLR4-/- mice. Lung Ec-silencing of TLR4 in wild-type mice induced emphysema, highlighting the specific and distinct role of Ec-expressed TLR4 in maintaining lung integrity. We also identified a previously unrecognized role of TLR4 in preventing expression of p16INK4a , a senescence-associated gene. Lung Ec-p16INK4a -silencing prevented TLR4-/- induced emphysema, revealing a new functional role for p16INK4a in lungs. TLR4 suppressed endogenous p16INK4a expression via HDAC2-mediated deacetylation of histone H4. These findings suggest a novel role for TLR4 in maintaining of lung homeostasis via epigenetic regulation of senescence-related gene expression

Rendering Secure and Trustworthy Edge Intelligence in 5G-Enabled IIoT using Proof of Learning Consensus Protocol

Industrial Internet of Things (IIoT) and fifth generation (5G) network have fueled the development of Industry 4.0 by providing an unparalleled connectivity and intelligence to ensure timely (or real time) and optimal decision making. Under this umbrella, the edge intelligence is ready to propel another ripple in the industrial growth by ensuring the next generation of connectivity and performance. With the recent proliferation of blockchain, edge intelligence enters a new era, where each edge trains the local learning model, then interconnecting the whole learning models in a distributed blockchain manner, known as blockchain-assisted federated learning. However, it is quiet challenging task to provide secure edge intelligence in 5G-enabled IIoT environment alongside ensuring latency and throughput. In this paper, we propose a Proof-of-Learning (PoL) consensus protocol that considers the reputation opinion for edge blockchain to ensure secure and trustworthy edge intelligence in IIoT. This protocol fetches each edge's reputation opinion by executing a smart contract, and partly adopts the winner's learning model according to its reputation opinion. By quantitative performance analysis and simulation experiments, the proposed scheme demonstrates the superior performance in contrast to the traditional counterparts