Chen X.,Fuzhou University |
Chen X.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing |
Li A.,Fuzhou University |
Li A.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing |
And 6 more authors.
Frontiers of Computer Science | Year: 2015
The internet of things (IoT) attracts great interest in many application domains concerned with monitoring and control of physical phenomena. However, application development is still one of the main hurdles to a wide adoption of IoT technology. Application development is done at a low level, very close to the operating system and requires programmers to focus on low-level system issues. The underlying APIs can be very complicated and the amount of data collected can be huge. This can be very hard to deal with as a developer. In this paper, we present a runtime model based approach to IoT application development. First, the manageability of sensor devices is abstracted as runtime models that are automatically connected with the corresponding systems. Second, a customized model is constructed according to a personalized application scenario and the synchronization between the customized model and sensor device runtime models is ensured through model transformation. Thus, all the application logic can be carried out by executing programs on the customized model. An experiment on a real-world application scenario demonstrates the feasibility, effectiveness, and benefits of the new approach to IoT application development. © 2015, Higher Education Press and Springer-Verlag Berlin Heidelberg.
Zhou L.,Chinese University of Hong Kong |
Zhou L.,Key Laboratory of High Confidence Software Technologies Ministry of Education |
Li B.,Chinese University of Hong Kong |
Li B.,Key Laboratory of High Confidence Software Technologies Ministry of Education |
And 6 more authors.
EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference | Year: 2011
Polarity classification of opinionated sentences with both positive and negative senti-ments1 is a key challenge in sentiment analysis. This paper presents a novel unsuper-vised method for discovering intra-sentence level discourse relations for eliminating polarity ambiguities. Firstly, a discourse scheme with discourse constraints on polarity was defined empirically based on Rhetorical Structure Theory (RST). Then, a small set of cue-phrase-based patterns were utilized to collect a large number of discourse instances which were later converted to semantic sequential representations (SSRs). Finally, an unsuper-vised method was adopted to generate, weigh and filter new SSRs without cue phrases for recognizing discourse relations. Experimental results showed that the proposed methods not only effectively recognized the defined discourse relations but also achieved significant improvement by integrating discourse information in sentence-level polarity classification. © 2011 Association for Computational Linguistics.