Modeling Relation Paths for Representation Learning of Knowledge Bases

Representation learning of knowledge bases (KBs) aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths. (2) We represent relation paths via semantic composition of relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text.Comment: 10 page

Determine Factors of NFC Mobile Payment Continuous Adoption in Shopping Malls:Evidence From Indonesia

Near Field Communication (NFC) mobile payment systems allow users to utilize services through smartphones. There is insufficient literature exploring the adoption of NFC with payment scenarios in developing countries. This study aims to explore the influential factors of consumer adoption of NFC, taking payment behaviors through NFC in Indonesia as an example. One hundred forty-seven participants were enrolled in the 5-point Likert scale survey, and 124 valid samples were analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that trust mediates the effect of context on consumers’ continuous intention to use NFC mobile payment. Additionally, trust mediates the effect of perceived risk on consumers’ continuous intention to use. The perceived ease of use and perceived usefulness have no effects on consumers’ continuous intention to use. The mediating effect of religiosity has not been observed in this study. The findings can enbale service providers and local governments to offer better mobile payment services

Synthesis and Application of Dendriticlinear Polymer PAMAM-Si for Leather Fatliquoring Process

Content: Environmental pollution caused by leather making is the primary concern in the development of leather industry. The use of safe, effective and multi-functional green chemical products has the advantages of reducing leather operations, increasing chemicals utilization, decreasing the environmental burden, improving leather quality. In this study, dendritic-linear polymers of PAMAM-Si 1G and PAMAM-Si 2G were applied to fatliquoring process, which were prepared by branching polysiloxane on the dendritic polyamide-amine (PAMAM). Then the emulsion properties, fatliquoring properties and fatliquoring mechanism were studied by EDS, SEM, XRD, TG and washing experiments. The conclusion was drawn that PAMAM-Si are weak alkali products with high emulsion stability. The particle size of PAMAM-Si 1G was 35.8 nm, and that of PAMAM-Si 2G was 26.7 nm. They can improve the softness, shrinkage temperature and physical and mechanical properties of leather. The softness of leather with PAMAM-Si 1G and PAMAM-Si 2G increased by 115.6% and 104.7% respectively. The shrinkage temperature of leather with PAMAM-Si 2G increased by 2.9℃. The Breaking elongation of leather with PAMAM-Si 1G and PAMAM-Si 2G increased by 38.6% and 32.4% respectively. At the same time, PAMAM-Si not only increased the distance and disorder of fiber but combined with collagen fiber through hydrogen bond, a certain amount of physical adsorption and covalent bond. Take-Away: 1. The dendritic-linear polymers of PAMAM-Si 1G and PAMAM-Si 2G were prepared by branching polysiloxane on the dendritic polyamide-amine (PAMAM). 2. PAMAM-Si can improve the softness, shrinkage temperature and physical and mechanical properties of leather. 3. PAMAM-Si not only increased the distance and disorder of fiber but combined with collagen fiber through hydrogen bond, a certain amount of physical adsorption and covalent bond

Blind Quality Assessment for in-the-Wild Images via Hierarchical Feature Fusion and Iterative Mixed Database Training

Image quality assessment (IQA) is very important for both end-users and service-providers since a high-quality image can significantly improve the user's quality of experience (QoE) and also benefit lots of computer vision algorithms. Most existing blind image quality assessment (BIQA) models were developed for synthetically distorted images, however, they perform poorly on in-the-wild images, which are widely existed in various practical applications. In this paper, we propose a novel BIQA model for in-the-wild images by addressing two critical problems in this field: how to learn better quality-aware feature representation, and how to solve the problem of insufficient training samples in terms of their content and distortion diversity. Considering that perceptual visual quality is affected by both low-level visual features (e.g. distortions) and high-level semantic information (e.g. content), we first propose a staircase structure to hierarchically integrate the features from intermediate layers into the final feature representation, which enables the model to make full use of visual information from low-level to high-level. Then an iterative mixed database training (IMDT) strategy is proposed to train the BIQA model on multiple databases simultaneously, so the model can benefit from the increase in both training samples and image content and distortion diversity and can learn a more general feature representation. Experimental results show that the proposed model outperforms other state-of-the-art BIQA models on six in-the-wild IQA databases by a large margin. Moreover, the proposed model shows an excellent performance in the cross-database evaluation experiments, which further demonstrates that the learned feature representation is robust to images with diverse distortions and content. The code will be released publicly for reproducible research