Aspect-level sentiment classification is a finegrained task in sentiment analysis. Since it provides more complete and in-depth results, aspect-level sentiment analysis has received much attention these years. In this paper, we reveal that the sentiment polarity of a sentence is not only determined by the content but is also highly related to the concerned aspect. For instance, "The appetizers are ok, but the service is slow.", for aspect taste, the polarity is positive while for service, the polarity is negative. Therefore, it is worthwhile to explore the connection between an aspect and the content of a sentence. To this end, we propose an Attention-based Long Short-Term Memory Network for aspect-level sentiment classification. The attention mechanism can concentrate on different parts of a sentence when different aspects are taken as input. We experiment on the SemEval 2014 dataset and results show that our model achieves state-ofthe-art performance on aspect-level sentiment classification.
This paper presents the generic concept of using cloud-based intelligent car parking services in smart cities as an important application of the Internet of Things (IoT) paradigm. This type of services will become an integral part of a generic IoT operational platform for smart cities due to its pure business-oriented features. A high-level view of the proposed middleware is outlined and the corresponding operational platform is illustrated. To demonstrate the provision of car parking services, based on the proposed middleware, a cloud-based intelligent car parking system for use within a university campus is described along with details of its design, implementation, and operation. A number of software solutions, including Kafka/Storm/Hbase clusters, OSGi web applications with distributed NoSQL, a rule engine, and mobile applications, are proposed to provide ‘best’ car parking service experience to mobile users, following the Always Best Connected and best Served (ABC&S) paradigm.
In this paper, we present an unstructured peer-to-peer network called GridMedia for live media streaming employing a push-pull approach. Each node in GridMedia randomly selects its neighbors in the overlay and uses push-pull method to fetch data from the neighbors. The pull mode in the unstructured overlay which is inherently robust can work well with the high churn rate in P2P environment while the push mode can efficiently reduce the accumulated latency observed at user nodes. A practical system based on this framework has been developed. And the performance evaluation of our system which is established on PlanetLab [8] demonstrates that the pull-push method in GridMedia achieves good qualities even in high group change rate. Furthermore, our system was adopted by CCTV to broadcast the Gala Evening for Spring Festival 2005 through the Internet and attracted more than 500,000 users all over the world at that night with the incredibly maximum concurrent users of 15,239.
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