2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)最新文献

Turning an open source project into a carrier grade vswitch for NFV: Vosyswitch challenges & results 将开源项目转变为NFV的运营商级虚拟交换机:Vosyswitch的挑战和结果

2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) Pub Date : 2016-09-01 DOI: 10.1109/ICNIDC.2016.7974529

Michele Paolino, J. Fanguede, Nikolay Nikolaev, D. Raho

引用次数: 7

The joint effect of semantic and syntactic word embeddings on sentiment analysis 语义词嵌入和句法词嵌入在情感分析中的联合作用

2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) Pub Date : 2016-09-01 DOI: 10.1109/ICNIDC.2016.7974598

Shu Chen, Guang Chen, Wei Wang

引用次数: 0

Saliency detection based on multi-cue and multi-scale with cellular automata 基于元胞自动机的多线索多尺度显著性检测

2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) Pub Date : 2016-09-01 DOI: 10.1109/ICNIDC.2016.7974563

Ling Huang, Songguang Tang, Jiani Hu, Weihong Deng

引用次数: 0

A data driven orchestration framework in software defined security 软件定义安全性中的数据驱动编排框架

2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) Pub Date : 2016-09-01 DOI: 10.1109/ICNIDC.2016.7974531

Weijia Wang, Xiaofeng Qiu, Li Sun, Rui Zhao

引用次数: 1

Cross-modal subspace learning for sketch-based image retrieval: A comparative study 基于草图的图像检索的跨模态子空间学习:比较研究

2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) Pub Date : 2016-09-01 DOI: 10.1109/ICNIDC.2016.7974625

Peng Xu, Ke Li, Zhanyu Ma, Yi-Zhe Song, Liang Wang, Jun Guo

{"title":"Cross-modal subspace learning for sketch-based image retrieval: A comparative study","authors":"Peng Xu, Ke Li, Zhanyu Ma, Yi-Zhe Song, Liang Wang, Jun Guo","doi":"10.1109/ICNIDC.2016.7974625","DOIUrl":"https://doi.org/10.1109/ICNIDC.2016.7974625","url":null,"abstract":"Sketch-based image retrieval (SBIR) has become a prominent research topic in recent years due to the proliferation of touch screens. The problem is however very challenging for that photos and sketches are inherently modeled in different modalities. Photos are accurate (colored and textured) depictions of the real-world, whereas sketches are highly abstract (black and white) renderings often drawn from human memory. This naturally motivates us to study the effectiveness of various cross-modal retrieval methods in SBIR. However, to the best of our knowledge, all established cross-modal algorithms are designed to traverse the more conventional cross-modal gap of image and text, making their general applicableness to SBIR unclear. In this paper, we design a series of experiments to clearly illustrate circumstances under which cross-modal methods can be best utilized to solve the SBIR problem. More specifically, we choose six state-of-the-art cross-modal subspace learning approaches that were shown to work well on image-text and conduct extensive experiments on a recently released SBIR dataset. Finally, we present detailed comparative analysis of the experimental results and offer insights to benefit future research.","PeriodicalId":439987,"journal":{"name":"2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126467049","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}

引用次数: 23

Two approaches towards EDZL scheduling for performance asymmetric multiprocessors 性能不对称多处理器的两种EDZL调度方法

2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) Pub Date : 2016-09-01 DOI: 10.1109/ICNIDC.2016.7974548

Peng Wu, Shakaiba Majeed, Minsoo Ryu

引用次数: 2

Design of an all-textile microwave absorber for indoor radar clear 用于室内雷达清除的全纺织微波吸收器的设计

2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) Pub Date : 2016-09-01 DOI: 10.1109/ICNIDC.2016.7974545

J. Tak, Jaehoon Choi

引用次数: 1

Clusteringfor coordinated zero-forcing beamformingin multi-user interference networks 多用户干扰网络中协调零强迫波束形成的聚类

2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) Pub Date : 2016-09-01 DOI: 10.1109/ICNIDC.2016.7974589

Weidong Gao, Shengjie He, Gang Chuai

引用次数: 2

UGC quality evaluation based on meta-learning and content feature analysis 基于元学习和内容特征分析的UGC质量评价

2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) Pub Date : 2016-09-01 DOI: 10.1109/ICNIDC.2016.7974624

Xiaoyue Cong, Lei Li

{"title":"UGC quality evaluation based on meta-learning and content feature analysis","authors":"Xiaoyue Cong, Lei Li","doi":"10.1109/ICNIDC.2016.7974624","DOIUrl":"https://doi.org/10.1109/ICNIDC.2016.7974624","url":null,"abstract":"With the fast development of Social Networking Services, there has been increasingly vast amount of information published by massive network users. Given this information explosion, how to analyze the quality of User Generated Contents (UGC) automatically becomes a challenging task for researchers. To solve the problem, we need to build an effective UGC quality evaluation system. In the light of our experience, we believe that the textual content of UGC is the key factor for its quality. Hence, we focus on textual content based quality evaluation and classification instead of using UGC publishing related data, such as times being commented and forwarded in this paper. We extract various features of the textual contents based on natural language processing technologies firstly, such as word segmentation, keywords, topic model, sentence parsing, distributed word representation etc. Secondly, we build several base-learning classifiers with different features and different machine learning algorithms to assign UGC contents with four different quality labels. Then, we create the global meta-learning model based on these base classifiers to generate the final quality labels for UGC contents. We have also implemented a series of experiments based on realistic data collected from Tianya Forum and use 10-fold cross-validation to test the model. Results have shown that our proposed meta-learning model performs much better.","PeriodicalId":439987,"journal":{"name":"2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131624532","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}

引用次数: 0

Adaptive resource allocation based on satisfaction for LTE 基于满意度的LTE自适应资源分配

2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) Pub Date : 2016-09-01 DOI: 10.1109/ICNIDC.2016.7974554

Xinyu Liang, Jiansong Miao

引用次数: 0