Graph model has been widely applied in docu- ment summarization by using sentence as the graph node, and the similarity between sentences as the edge. In this paper, a novel graph model for document summarization is p...
详细信息
Graph model has been widely applied in docu- ment summarization by using sentence as the graph node, and the similarity between sentences as the edge. In this paper, a novel graph model for document summarization is presented, that not only sentences relevance but also phrases relevance information included in sentences are utilized. In a word, we construct a phrase-sentence two-layer graph structure model （PSG） to summarize document（s） . We use this model for generic document summarization and query-focused sum- marization. The experimental results show that our model greatly outperforms existing work.
Delta-based accumulative iterative computation （DAIC） model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAIC model and the asynchronou...
详细信息
Delta-based accumulative iterative computation （DAIC） model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAIC model and the asynchronous DAIC model only satisfy some given conditions, respectively, and perform poorly under other conditions either for high synchronization cost or for many redundant activations. As a result, the whole performance of both DAIC models suffers from the serious network jitter and load jitter caused by multi- tenancy in the cloud. In this paper, we develop a system, namely Hyblter, to guarantee the performance of iterative algorithms under different conditions. Through an adaptive execution model selection scheme, it can efficiently switch between synchronous and asynchronous DAIC model in order to be adapted to different conditions, always getting the best performance in the cloud. Experimental results show that our approach can improve the performance of current solutions up to 39.0%.
GPU cluster is important for high performance computing with its high performance/cost ratio. However, it is still very hard for application developers to write parallel codes on GPU. MPI is mostly used for parallel p...
详细信息
It has been recognized that cellular network interfaces are not energy efficient because of tail energy after each transmission. Although many research efforts have been made to reduce tail energy, they ignore the dyn...
详细信息
It has been recognized that cellular network interfaces are not energy efficient because of tail energy after each transmission. Although many research efforts have been made to reduce tail energy, they ignore the dynamic of link quality caused by user mobility or network congestion, which would lead to limited improvement without quality-of-experience guarantee. In this paper, we study to minimize energy consumption of the cellular network interface with a sequence of download/upload requests. Given accurate estimation of achievable link rate, we design a dynamic-programming (DP) based algorithm to obtain the optimal solution. Without the knowledge of dynamic link quality and future requests, an online algorithm is proposed to approximate the optimal solution. Finally, we conduct extensive simulations using real traces to evaluate the performance of our proposals, and the results show that 29% energy can be saved by using our algorithm under typical network settings.
Outsourced data in cloud and computation results are not always trustworthy because data owners lack physical possession and control over the data as a result of virtualization, replication, and migration techniques. ...
详细信息
ISBN:
(纸本)9781467387163
Outsourced data in cloud and computation results are not always trustworthy because data owners lack physical possession and control over the data as a result of virtualization, replication, and migration techniques. Protecting outsourced data from security threats has become a challenging and potentially formidable task in cloud computing; hence, many schemes have focused on ameliorating this problem and on enabling public auditability for cloud data storage security. These schemes drop into two categories: total computation cost and burden on client side. Researchers have used bilinear map technology with public key cryptography. Although this technology is highly efficient, computation time is long and overhead cost is high. The client needs to perform numerous computations to ensure the integrity of data storage. To reduce auditing cost, we propose an efficient and robust scheme to maintain data integrity in cases that involve public auditing. Our scheme adopts modern cipher cryptography with a cryptographic hash function. We consider allowing a third party auditor to preprocess data on behalf of cloud users before uploading them to cloud service providers and then verifying data integrity afterward. Our proposed scheme has important security characteristics, such as privacy, key management, low cost computation, key exchange, low overhead cost, no burden on client side, inability of cloud service providers to create correct verifier respond without data, and one-time key. Finally, efficiency analysis shows that our scheme is faster and more cost-efficient than the bilinear map-based scheme.
Multi-tenant service-based systems (SBSs) have become a major paradigm in software engineering in the cloud environment. Instead of serving a single end-user, a multitenant SBS provides multiple tenants with similar a...
详细信息
Multi-tenant service-based systems (SBSs) have become a major paradigm in software engineering in the cloud environment. Instead of serving a single end-user, a multitenant SBS provides multiple tenants with similar and yet customised functionalities with potentially different quality-of service (QoS) values. Thus, existing approaches to service selection for single-tenant SBSs are no longer suitable. Furthermore, the target multi-tenancy maturity level also needs to be considered in the service selection approach for an SBS. In this paper, we propose three novel QoS-aware service selection approaches for composing multi-tenant SBSs that achieve three different multi-tenancy maturity levels. Extensive and comprehensive experiments are conducted and the experimental results show that our approaches outperform the existing approach in both effectiveness and efficiency.
Linear classification is useful in many applications, but training large-scale data remains an important research issue. Recent advances in linear classification have shown that distributed methods can be efficient in...
详细信息
ISBN:
(纸本)9781479979363
Linear classification is useful in many applications, but training large-scale data remains an important research issue. Recent advances in linear classification have shown that distributed methods can be efficient in improving the training time. However, for most of the existing training methods,based on MPI or Hadoop, the communication between nodes is the bottleneck. To shorten the communication between nodes, we propose and analyze a method for distributed support vector machine and implement it on an iterative MapReduce framework. Through our distributed method, the local SVMs are generic and can make use of the state-of-the-art SVM solvers. Unlike previous attempts to parallelize SVMs the algorithm does not make assumptions on the density of the support vectors, i.e., the efficiency of the algorithm holds also for the “difficult” cases where the number of support vectors is very high. The performance of the our method is evaluated in an experimental environment. By partitioning the training dataset into smaller subsets and optimizing the partitioned subsets across a cluster of computers, we reduce the training time significantly while maintaining a high level of accuracy in both binary and multiclass classifications.
Cloud is an emerging computing *** has drawn extensive attention from both academia and *** its security issues have been considered as a critical obstacle in its rapid *** data owners store their data as plaintext in...
详细信息
Cloud is an emerging computing *** has drawn extensive attention from both academia and *** its security issues have been considered as a critical obstacle in its rapid *** data owners store their data as plaintext in cloud,they lose the security of their cloud data due to the arbitrary accessibility,specially accessed by the un-trusted *** order to protect the confidentiality of data owners’cloud data,a promising idea is to encrypt data by data owners before storing them in ***,the straightforward employment of the traditional encryption algorithms can not solve the problem well,since it is hard for data owners to manage their private keys,if they want to securely share their cloud data with others in a fine-grained *** this paper,we propose a fine-grained and heterogeneous proxy re-encryption(FHPRE)system to protect the confidentiality of data owners’cloud *** applying the FH-PRE system in cloud,data owners’cloud data can be securely stored in cloud and shared in a fine-grained ***,the heterogeneity support makes our FH-PRE system more efficient than the previous ***,it provides the secure data sharing between two heterogeneous cloud systems,which are equipped with different cryptographic primitives.
Running MapReduce in a shared cluster has become a recent trend to process large-scale data analytical applications while improving the cluster utilization. However, the network sharing among various applications can ...
详细信息
Silicosis remains one of the most harmful occupational respiratory diseases. It threatens the workers exposed to dust environment. Chest radiograph is the main available image source for silicosis diagnosis according ...
详细信息
暂无评论