历年获奖名单

2016人民网奖学金获奖名单：

徐安民、唐翯祎
2016人民网优秀技术课题获奖者名单：
一等奖：赖泽祺、戴柠薇、王吉磊
二等奖：王竹凡、贾许亚、郭泽华、吴振未、
　　　　周琳钧
三等奖：吴松芮

优秀技术课题一等奖（2）

VR技术及其新闻应用前景研究: 本文首先介绍VR技术及其在新闻、出版行业的应用前景，并提出当今VR技术面临的三大重要挑战。为了让VR应用获得更好的用户体验，本文提出QuickView，一套运行于当今智能终端上的渲染迁移框架。QuickView将复杂而高功耗的渲染任务通过网络传递到强大的云端，由云端完成高计算量的图像渲染工作，最终返回终端并回显给用户。为了克服无线网络下高延迟，带宽有限的挑战，QuickView采用了两项新技术：基于网络状态的用户行为预测和低延迟带宽的渲染迁移网络协议。我们实现了QuickView原型系统，并且将QuickView和传统的本地渲染的VR应用进行对比。实验证明，QuickView可以提升大约59%的帧刷新率，降低68%的用户感知时延，降低90%的平均功耗。我们相信，未来QuickView渲染迁移框架将会为更多的新闻、出版类VR应用提升更加便携的使用，更优质的视频服务质量，以及保证更长时间的续航。; 详细 >>

Learning Chinese-Japanese Bilingual Word Embedding by Using Common Characters: Abstract. Bilingual word embedding, which maps word embedding of two languages into one vector space, has been widely applied in the domain of machine translation, word sense disambiguation and so on. However, no model has been universally accepted for learning bilingual word embedding. In this work, we propose a novel model named CJ- BOC to learn Chinese-Japanese word embeddings. Given Chinese and Japanese share a large portion of common characters, we exploit them in our training process. We demonstrated the effectiveness of such exploitation through theoretical and also experimental study. To evaluate the performance of CJ-BOC, we conducted a comprehensive experiment, which reveals its speed advantage, and high quality of acquired word embeddings as well.; 详细 >>

基于在线自适应多重外观模型的长期视频跟踪模型: 如何构建被跟踪目标的外观描述是进行长期鲁棒的目标跟踪的基本挑战。在最近的研究中，许多跟踪方法更注重更新跟踪目标的当前外观，通过采用特殊的视觉特征和学习方法来构建一个在线外观模型。然而，单个外观模型总是不足以描述历史出现信息，且无法应对照明变化和目标被切割的情况，对于长期跟踪任务这些情况会出现的更多。在本文中，我们提出在线自适应多重模型以提高目标跟踪性能。通过构建基于Dirichlet过程混合模型（DPMM）的外观模型集合，其可以动态地且以无监督的方式将跟踪目标的不同外观进行分组。尽管DPMM具有如上有点，但DPMM依赖于吉布斯取样器这个计算密集的推断过程。由于目标跟踪对逐帧处理的效率要求较高，吉布斯取样器因为时间成本高不适合跟踪。所以在原有的DPMM模型基础上，我们提出了一种在线贝叶斯学习算法，通过以流的方式通过顺序逼近从头开始可靠和有效地学习DPMM以适应新的跟踪目标。在多个具有挑战性的基准公共数据集上进行的实验证明了所提出的跟踪算法优于现有技术。; 详细 >>

Reducing and Balancing Flow Table Entries in Software-Defined Networks: Software-Defined Networking (SDN) allows flexible and efficient management of networks. However, the limited capacity of flow tables in SDN switches hinders the deployment of SDN. In this paper, we propose a novel routing scheme to improve the efficiency of flow tables in SDNs. To efficiently use the routing scheme, we formulate an optimization problem with the objective to maximize the number of flows in the network, constrained by the limited flow table space in SDN switches.The problem is NP-hard, and we propose the K Similar Greedy Tree (KSGT) algorithm to solve it. We evaluate the performance of KSGT against ``traditional" SDN solutions with real-world topologies and traffic. The results show that, compared to the existing solutions, KSGT can reduce about 60% of flow entries when processing the same amount of flows, and improve about 25% of the successful installation and forwarding flows under the same flow table space..; 详细 >>

跨平台的音乐推荐算法: 传统的音乐推荐算法往往是根据用户的听歌记录来对其进行推荐，但是对于一个新用户传统的推荐算法就无能为力了。在本文中，我们使用机器学习中典型关联分析（CCA）技术期望能够通过加入用户的社会属性（微博）来改进原始的推荐算法，从而更加精准的推荐给用户他们喜欢听的音乐，另外，相较于其他推荐算法，我们能够实现推荐的冷启动。即使用户之前没有听过任何音乐，我们也能够从他的微博数据中挖掘出他喜欢听的音乐类型。; 详细 >>

Incremental Switch Deployment for Hybrid Software-Defined Networks: Software-Defined Networking (SDN) brings excellent opportunities to improve network performance by flexible flow control with a centralized network view. However, due to budget constraints and technique limitations, ISPs can upgrade only a limited number of conventional switches in real backbone networks to SDN devices at one time. In this paper, we propose one heuristic scheme to increase the deployment of SDN switches in hybrid SDNs, which are composed of conventional and SDN switches. Our scheme works for two different cases: (1) maximizing the network control ability with a given upgrading budget constraint, and (2) minimizing the upgrading cost to achieve the best network control ability. The results show that our scheme can achieve 95\% of the number of flows controlled with only 10% upgrading cost. We evaluate our scheme in two topologies, the ISP 1755 and the ISP 3967 from the real network..; 详细 >>

基于密文的数据范围搜索算法设计与实现: 随着大数据的发展，大规模的数据越来越多的以密文的形式存储在云服务器中。本文重点讨论了在云服务器中，如何对已加密的数据进行快速的范围搜索。当可信任的数据持有者将加密的数据上传到云服务器后，云服务器支持大量的用户搜索数据，并在不可解密数据及查找范围的前提下，对数据搜索进行精确的匹配。本文提出了通过建立安全索引的方式对一维、二维数据进行范围查询的模型，并用百万的数据验证了方法的可行性与正确性。; 详细 >>