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It becomes more and more common for people to have multiple mobile devices. This opens the opportunity of multi-surface computing in which users interact with an app using multiple devices simultaneously. Recently, a system called FLUID was developed, which can distribute User Interface (UI) elements of an app to multiple devices to support multi-surface computing. FLUID enables general, flexible and transparent multi-device interaction, which cannot be achieved by previous approaches such as screen mirroring, app migration, and customized app development on multiple devices. However, the practicality of FLUID is still severely limited because it requires that (1) the app source codes must be available and (2) the same app is pre-installed on all devices. This paper presents PRUID, a UI distribution system that is free from the above-mentioned limitations of FLUID. PRUID captures and extracts relevant information about UI elements to be distributed completely at run time, without requiring the app source code. An app-independent UI agent is designed to dock and render the UI components distributed to the guest device, so pre-installation of the app on guest devices is not required. We developed representative use cases to demonstrate the usage and evaluate the performance of PRUID. The evaluation results show that the extra overhead incurred due to the UI information extraction at run time is marginal and PRUID provides a smooth user experience.

Knowledge Graph (KG) embedding has begun to be widely used in various fields, such as search engine, semantic search, and knowledge inference. In the traditional model, because an effective time period of knowledge is not taken into account, it leads to a wrong knowledge construction. And their robustness is not excellence after adding new knowledge elements. In most existing KG representation learning models, repeated calculations are performed by synthesizing a negative sample set with the correct sample set, which also leads to an increase in the amount of calculation. At the same time, because there are many-to-many situations for entities and relationships in the KG, the vector representation obtained when using the previous model for representation learning is not unique. So it leads to an increase in computational complexity. Therefore, in this paper we propose a knowledge representation learning model based on softmax, which calculates the most suitable representation vector by the conditional probability of the entity and relation vector. The experimental results show that our model has improved significantly in the knowledge completion compared with the traditional method.

This paper presents a method for measuring the semantic similarity between words. The previous work on words semantic similarity measure have focused on either knowledge based or word embedding based. However semantic similarity between words do not perform well within specific domains. Therefore in this paper we introduce a word semantic similarity measure namely KVEC that was created by the synergistic union of word embedding based and knowledge graph based semantic similarity methods. Through experiments performed on proverbial word similarity dataset, we show that KVEC semantic similarity method whose maximum correlation coefficient reached 0.818, which outperform both, knowledge based method and word embedding method training on corpus.