In news stories, event mentions denote real-world events of different spatial and temporal granularity. Narratives in news stories typically describe some real-world event of coarse spatial and temporal granularity al...
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ISBN:
(纸本)9782951740884
In news stories, event mentions denote real-world events of different spatial and temporal granularity. Narratives in news stories typically describe some real-world event of coarse spatial and temporal granularity along with its subevents. In this work, we present HiEve, a corpus for recognizing relations of spatiotemporal containment between events. In HiEve, the narratives are represented as hierarchies of events based on relations of spatiotemporal containment (i.e., superevent-subevent relations). We describe the process of manual annotation of HiEve. Furthermore, we build a supervised classifier for recognizing spatiotemporal containment between events to serve as a baseline for future research. Preliminary experimental results are encouraging, with classifier performance reaching 58% F1-score, only 11% less than the inter-annotator agreement.
The multi-tier architecture is prevalently adopted by cloud applications, such as the three-tier web application. It is highly desirable for both tenants and providers to provide virtual networks in an efficient and e...
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ISBN:
(纸本)9781467375887
The multi-tier architecture is prevalently adopted by cloud applications, such as the three-tier web application. It is highly desirable for both tenants and providers to provide virtual networks in an efficient and elastic way, where tenant applications can automatically scale in or out with varying workloads and providers can accommodate as many requests as possible in the underlying network. However, due to potential conflicts between efficiency and elasticity, it is challenging to achieve these two goals simultaneously in abstracting tenant requirements and designing corresponding provisioning algorithms. In this paper, we propose an efficient and elastic virtual network provisioning solution called Easy Alloc, which is comprised of an elasticity-aware abstraction model and a virtual network provisioning algorithm. To accurately capture the tenant requirement and maintain the provisioning simplicity for providers, the elasticity-aware model enables two types of decoupling, i.e., Always-on VMs for normal load and on-demand VMs for dynamic scaling, and the bandwidth requirement of each VM for intra- and inter-tier communications. Then we formulate the virtual network provisioning as an overhead minimization problem, where the objective simultaneously considers the bandwidth and elasticity overhead. Due to the NP-completeness of this problem, we leverage two heuristics, slot reservation and tier iteration, to obtain an efficient algorithm. Extensive simulation results show that compared with a typical elasticity-agnostic method under a heavy load, Easy Alloc enables a 9% increase of request acceptance rate and a 16.8% improvement of the successful extension rate. To the best of our knowledge, this is the first work targeting at the elastic virtual network provisioning.
Web Spam is the result of a number of methods to deceive search engine algorithms so as to obtain higher ranks in the search results. Advanced spammers use keyword and link stuffing methods to create farms of spam pag...
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In developing multi-UAV(Unmanned Aerial Vehicle) system, a simulation environment is essential to verify the functionalities of the whole system with higher productivity and reduced risks of accidents. Simulations of ...
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In this paper hierarchical cluster and the competitive learning cluster are compared by using molecular data of large size sets. We construct a reproducible matrix to evaluate the quality of clustering, and dead nodes...
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In this paper hierarchical cluster and the competitive learning cluster are compared by using molecular data of large size sets. We construct a reproducible matrix to evaluate the quality of clustering, and dead nodes problem of the competitive learning network is solved by the conscience mechanism. The experimental results show that the hierarchical clustering can represent a multi-level hierarchy which show the tree relation of cluster distance, the competitive learning network has a good clustering reproducible and indicate the effectiveness of clusters for molecular data
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