Enriching one-class collaborative filtering with content information from social media

In recent years, recommender systems have become popular to handle the information overload problem of social media websites. The most widely used Collaborative Filtering methods make recommendations by mining users’ rating history. However, users’ behaviors in social media are usually implicit, where no ratings are available. This is a One-Class Collaborative Filtering (OCCF) problem with only positive examples. How to distinguish the negative examples from missing data is important for OCCF. Existing OCCF methods tackle this by the statistical properties of users’ historical behavior; however, they ignored the rich content information in social media websites, which provide additional evidence for profiling users and items. In this paper, we propose to improve OCCF accuracy by exploiting the social media content information to find the potential negative examples from the missing user-item pairs. Specifically, we get a content topic feature for each user and item by probabilistic topic modeling and embed them into the Matrix Factorization model. Extensive experiments show that our algorithm can achieve better performance than the state-of-art methods.     

On the tag localization of web video

Nowadays, numerous social videos have pervaded on the web. Social web videos are characterized with the accompanying rich contextual information which describe the content of videos and thus greatly facilitate video search and browsing. Generally, those contextual data such as tags are provided at the whole video level, without temporal indication of when they actually appear in the video, let alone the spatial annotation of object related tags in the video frames. However, many tags only describe parts of the video content. Therefore, tag localization, the process of assigning tags to the underlying relevant video segments or frames even regions in frames is gaining increasing research interests and a benchmark dataset for the fair evaluation of tag localization algorithms is highly desirable. In this paper, we describe and release a dataset called DUT-WEBV, which contains about 4,000 videos collected from YouTube portal by issuing 50 concepts as queries. These concepts cover a wide range of semantic aspects including scenes like “mountain”, events like “flood”, objects like “cows”, sites like “gas station”, and activities like “handshaking”, offering great challenges to the tag (i.e., concept) localization task. For each video of a tag, we carefully annotate the time durations when the tag appears in the video and also label the spatial location of object with mask in frames for object related tag. Besides the video itself, the contextual information, such as thumbnail images, titles, and YouTube categories, is also provided. Together with this benchmark dataset, we present a baseline for tag localization using multiple instance learning approach. Finally, we discuss some open research issues for tag localization in web videos.     

基于HodgeRank的人脸美貌度预测

随着计算机技术的迅速发展以及人脸识别技术的成熟,人脸美貌度受到越来越多的关注和研究.针对目前的研究方法中存在的对训练数据集的评分过多依赖人工操作,以及对人脸美貌度的预测结果不够详细等问题,本文提出基于HodgeRank的人脸美貌度预测系统,利用数据挖掘方法学习女性人脸的美貌度特征,构造一个模拟预测人脸美貌度的系统.明显区别于之前的研究,该系统训练和测试时采用的人脸数据集放宽了对姿态、光照以及所处环境等条件的限制,评分所需的人工操作大大减少,无需进行大量的人工标定,使用图像的原始像素或纹理特征作为输入,分别采用聚类和改进的BP网络的方法,得到更符合人类特征的美貌度预测结果.     

基于Unity3D的仓储可视化编辑器

近年来,随着仓储业的快速发展和仓库物资种类的不断增加,传统的人工管理远远不能满足现代仓库管理的需要. 而能够提供实时、直观、沉浸式管理的三维可视化管理系统越来越受到仓库管理人员的青睐. 任何三维可视化系统的开发都需要一款无缝对接的编辑器. 本文提出的仓储可视化编辑器是针对仓储可视化系统的开发人员,可帮助开发人员迅速搭建一个真实的三维场景,缩短可视化系统的开发周期. 本文实现了基于Unity3D的仓储可视化编辑器,首先,实现了编辑器的总体框架. 然后,实现了3D交互层、统一通信接口、模型操作面板等功能.     

智能宽带信号检测系统

为了能够准确的测量信号的参数,设计了一种宽带交直流信号检测系统,该系统的控制芯片采用的是STC12C5A32S2单片机.首先,信号采集电路采集到的信号经交直流测量电路进行处理;然后,通过分压和自动增益放大电路等处理后,在频率和相位检测电路中测量信号的频率和相位;最后,在液晶显示器上显示待测信号的幅度、相位和频率等信息.在实验中,测量了频率为0-20KHz的不同幅度信号,实验结果表明,在低频情况下测量误差可以忽略,在高频信号测量时的测量误差小于1%.     

基于联合特征匹配的多视角三维重建方法

本研究提出了一种基于角点与直线联合特征的三阶段匹配算法,依次经过相关匹配、松弛迭代匹配和最小平方中值法匹配三个步骤,并与此同时加入限定图像匹配区域、添加手工匹配点对以及局部直线匹配三个人机交互环节,将局部坐标系中得到的数据点进行三维数据融合到一个坐标系中,能较好解决视差不连续区域和遮挡区域的误匹配问题.实验证明该算法具有良好的运行效率和稳定性,能够对非平面物体进行精确三维重建并实现多视角显示.     

Starting open source collaborative innovation: the antecedents of network formation in community source

Specific needs in the area of enterprise applications have led to a new type of collaborative open source innovation development across institution borders: community source. We use the Kuali community source network, a jointly managed, border‐spanning organization that supplies the institutions that created it, to describe how community source works. This study builds a theoretical basis for understanding the individual and institutional factors affecting community source network formation and the decision by organizations to join a community source network. We identify eight antecedents of decisions about forming or joining community source initiatives: motives, learning, trust, norms and monitoring, institutional similarity, external funding, hostile external environment and information technology. © 2016 John Wiley & Sons Ltd     

Finding users preferences from large-scale online reviews for personalized recommendation

Along with the growth of Internet and electronic commerce, online consumer reviews have become a prevalent and rich source of information for both consumers and merchants. Numerous reviews record massive consumers’ opinions on products or services, which offer valuable information about users’ preferences for various aspects of different entities. This paper proposes a novel approach to finding the user preferences from free-text online reviews, where a user-preference-based collaborative filtering approach, namely UPCF, is developed to discover important aspects to users, as well as to reflect users’ individual needs for different aspects for recommendation. Extensive experiments are conducted on the data from a real-world online review platform, with the results showing that the proposed approach outperforms other approaches in effectively predicting the overall ratings of entities to target users for personalized recommendations. It also demonstrates that the approach has an advantage in dealing with sparse data, and can provide the recommendation results with desirable understandability.     

互连网络的m层二进制图模型

超立方体、交叉立方体、Mbius立方体以及折叠立方体等都是著名的互连网络。它们有一个共同的弱点:其结点度随着网络规模(结点数)的增大而增大。这意味着依此互连网络设计出的超级计算机的扩展性很差。能否构建出既能保持它们已有特性又能使结点度固定的互连网络呢？现提出互连网络的m层二进制图模型,并依此模型设计了分别由超立方体、交叉立方体、Mbius立方体以及折叠立方体等生成的m层超立方体、m层交叉立方体、m层Mbius立方体以及m层折叠立方体。特别地,m层超立方体有一个特点:结点度可以不随网络规模的增大而增大,而且具有超立方体的特性。另外,还提出了由已知图生成m层图的概念。     

财务报销单据智能审批系统

随着市场竞争日趋激烈,成本控制对企业的重要性日益增加.财务报销系统是企业成本支出的重要窗口,对控制企业成本起着关键性的作用.传统的财务报销管理软件侧重于业务处理,对报销支出的管控较弱,而且各级管理者在审批报销单据时常常因为缺乏相关信息而只能走过场式审批.本文目标是通过构建财务报销单据智能审批系统,为审批人提供审批决策支持,从而提高审批效率和审批质量,降低企业成本.本文对财务报销单据智能审批系统的基本功能和应用流程进行了详细介绍.