Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mo...Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mobile traffic offloaded from cellular networks.We introduce a gossip-style social cascade(GSC) model to model the epidemic-like information diffusion process in MSNets.For static-case and mobile-case networks,we establish an equivalent view and a temporal mapping of the information diffusion process,respectively.We further prove the submodularity in the information diffusion and propose a greedy algorithm to choose the seed users for traffic offloading,yielding a sub-optimal solution to the NP-hard traffic offloading maximization(TOM) problem.Experiments are carried out to study the offloading performance,illustrating that the greedy algorithm significantly outperforms the heuristic and random algorithms,and user mobility can help further reduce cellular load.展开更多
This paper proposed a novel text representation and matching scheme for Chinese text retrieval. At present, the indexing methods of Chinese retrieval systems are either character-based or word-based. The character-bas...This paper proposed a novel text representation and matching scheme for Chinese text retrieval. At present, the indexing methods of Chinese retrieval systems are either character-based or word-based. The character-based indexing methods, such as bi-gram or tri-gram indexing, have high false drops due to the mismatches between queries and documents. On the other hand, it's difficult to efficiently identify all the proper nouns, terminology of different domains, and phrases in the word-based indexing systems. The new indexing method uses both proximity and mutual information of the word pairs to represent the text content so as to overcome the high false drop, new word and phrase problems that exist in the character-based and word-based systems. The evaluation results indicate that the average query precision of proximity-based indexing is 5.2% higher than the best results of TREC-5.展开更多
基金supported by the National Basic Research Program of China(973 Program) through grant 2012CB316004the Doctoral Program of Higher Education(SRFDP)+1 种基金Research Grants Council Earmarked Research Grants(RGC ERG) Joint Research Scheme through Specialized Research Fund 20133402140001National Natural Science Foundation of China through grant 61379003
文摘Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mobile traffic offloaded from cellular networks.We introduce a gossip-style social cascade(GSC) model to model the epidemic-like information diffusion process in MSNets.For static-case and mobile-case networks,we establish an equivalent view and a temporal mapping of the information diffusion process,respectively.We further prove the submodularity in the information diffusion and propose a greedy algorithm to choose the seed users for traffic offloading,yielding a sub-optimal solution to the NP-hard traffic offloading maximization(TOM) problem.Experiments are carried out to study the offloading performance,illustrating that the greedy algorithm significantly outperforms the heuristic and random algorithms,and user mobility can help further reduce cellular load.
文摘This paper proposed a novel text representation and matching scheme for Chinese text retrieval. At present, the indexing methods of Chinese retrieval systems are either character-based or word-based. The character-based indexing methods, such as bi-gram or tri-gram indexing, have high false drops due to the mismatches between queries and documents. On the other hand, it's difficult to efficiently identify all the proper nouns, terminology of different domains, and phrases in the word-based indexing systems. The new indexing method uses both proximity and mutual information of the word pairs to represent the text content so as to overcome the high false drop, new word and phrase problems that exist in the character-based and word-based systems. The evaluation results indicate that the average query precision of proximity-based indexing is 5.2% higher than the best results of TREC-5.
