Knowledge graph completion aims to perform link prediction between entities. In this paper, we consider the approach of knowledge graph embeddings. Recently, models such as TransE and TransH build entity and relation embeddings by regarding a relation as translation from head entity to tail entity. We note that these models simply put both entities and relations within the same semantic space. In fact, an entity may have multiple aspects and various relations may focus on different aspects of entities, which makes a common space insufficient for modeling. In this paper, we propose TransR to build entity and relation embeddings in separate entity space and relation spaces. Afterwards, we learn embeddings by first projecting entities from entity space to corresponding relation space and then building translations between projected entities. In experiments, we evaluate our models on three tasks including link prediction, triple classification and relational fact extraction. Experimental results show significant and consistent improvements compared to state-of-the-art baselines including TransE and TransH.

Despite rapid development of adhesive hydrogels, the typical double-sided adhesives fail to adhere to wet tissues and concurrently prevent postoperative tissue adhesion, thus severely limiting their applications in repair of internal tissues. Herein, a negatively charged carboxyl-containing hydrogel is gradiently, electrostatically complexed with a cationic oligosaccharide by a one-sided dipping method to form a novel Janus hydrogel wet adhesive whose two-side faces demonstrate strikingly distinct adhesive and nonadhesive properties. The lightly complexed surface demonstrates instant robust adhesion to various wet biological tissues even under water since the phase separation induced by electrostatic complexation increases the hydrophobicity and water drainage capacity. Intriguingly, the highly complexed surface is non-adhesive due to complete neutralization of carboxyls in the hydrogels. The Janus hydrogel can be used to replace traditional sutures to treat gastric perforation of rabbits. Animal experiment outcomes reveal that one side of the Janus hydrogel is firmly glued to the stomach tissue, and other side facing outward can efficiently prevent the postoperative adhesion. Molecular simulation elucidates the importance for selecting cationic polymer species. It is believed that gradient polyelectrolyte complexation establish a new direction to create Janus adhesives for internal tissue/organ repair and simultaneous prevention of post-operative adhesion.

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Summary To protect assets and resources from being hacked, intrusion detection systems are widely implemented in organizations around the world. However, false alarms are one challenging issue for such systems, which would significantly degrade the effectiveness of detection and greatly increase the burden of analysis. To solve this problem, building an intelligent false alarm filter using machine learning classifiers is considered as one promising solution, where an appropriate algorithm can be selected in an adaptive way in order to maintain the filtration accuracy. By means of cloud computing, the task of adaptive algorithm selection can be offloaded to the cloud, whereas it could cause communication delay and increase additional burden. In this work, motivated by the advent of edge computing, we propose a framework to improve the intelligent false alarm reduction for DIDS based on edge computing devices. Our framework can provide energy efficiency as the data can be processed at the edge for shorter response time. The evaluation results demonstrate that our framework can help reduce the workload for the central server and the delay as compared to the similar studies.

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