{"title":"A Dynamic CPU Resources Scheduling Method for Xen Virtual Machine","authors":"Xiaodong Liu, Miao Wang","doi":"10.1109/NGCIT.2015.8","DOIUrl":"https://doi.org/10.1109/NGCIT.2015.8","url":null,"abstract":"In order to make full use of the underlying physical resources of Xen virtual machine, this paper presents a dynamic CPU resources scheduling method(DRS). DRS uses the allocated credits and consumed credits to diagnose the CPU resources requirements of VMs. VMs are divided into three resources statuses according to their resources requirements and run information. DRS dynamically schedules CPU resources according to resources statuses. The evaluation results show that the CPU resources overall utilization is improved.","PeriodicalId":228304,"journal":{"name":"2015 4th International Conference on Next Generation Computer and Information Technology (NGCIT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124309920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Efficient Approach to Summarizing Events from Microblogs","authors":"Tong Cui, Jie Zhao, Peiquan Jin","doi":"10.1109/NGCIT.2015.12","DOIUrl":"https://doi.org/10.1109/NGCIT.2015.12","url":null,"abstract":"In this paper, we present an efficient approach to summarizing events from microblogs. As many events are first reported on microblogging platforms, it is valuable to extract events from microblogs. However, current solutions on event extraction can only provide a course-grained description of events. We propose to generate summarizations for microblog-based events. Particularly, we use a set of short sentences to describe an event and propose a graph model to generate the set of short sentences for describing an event. We conduct experiments on a data set consisting of 14 events crawled from a well-known microblogging platform. The experiment results suggest the effectiveness of the proposed approach.","PeriodicalId":228304,"journal":{"name":"2015 4th International Conference on Next Generation Computer and Information Technology (NGCIT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125350213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Balanced Collaborative Filtering Approach Incorporating with Conformity","authors":"Lei Ren","doi":"10.1109/NGCIT.2015.10","DOIUrl":"https://doi.org/10.1109/NGCIT.2015.10","url":null,"abstract":"Collaborative filtering can estimate users' ratings for unvisited items based on the opinions about items implied in their observed ratings. The issue of sparsity induced by the insufficiency of rating is a key factor impacting the recommendation accuracy. Aiming at the issue of sparsity, a balanced collaborative filtering approach is proposed in this work. According to the conformity of users, the proposed approach employs the target item's general rating and personalized rating to predict the rating for it, with adjusting importance of both types of rating.","PeriodicalId":228304,"journal":{"name":"2015 4th International Conference on Next Generation Computer and Information Technology (NGCIT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114995662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Time-Enhanced Collaborative Filtering Approach","authors":"Lei Ren","doi":"10.1109/NGCIT.2015.9","DOIUrl":"https://doi.org/10.1109/NGCIT.2015.9","url":null,"abstract":"Collaborative filtering can predict an active user's interests for unrated items based on his observed ratings, and the issue of concept drift exists in most of recommender systems. Aiming at the issue of concept drift, a time-enhanced collaborative filtering approach is proposed in this work, in which a time weight is introduced into the framework of collaborative filtering. As the experimental results show, the proposed approach improves the recommendation accuracy in contrast with the basic collaborative filtering.","PeriodicalId":228304,"journal":{"name":"2015 4th International Conference on Next Generation Computer and Information Technology (NGCIT)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117252427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}