Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets

We present attribute bagging (AB), a technique for improving the accuracy and stability of classifier ensembles induced using random subsets of features. AB is a wrapper method that can be used with any learning algorithm. It establishes an appropriate attribute subset size and then randomly selects subsets of features, creating projections of the training set on which the ensemble classifiers are built. The induced classifiers are then used for voting. This article compares the performance of our AB method with bagging and other algorithms on a hand-pose recognition dataset. It is shown that AB gives consistently better results than bagging, both in accuracy and stability. The performance of ensemble voting in bagging and the AB method as a function of the attribute subset size and the number of voters for both weighted and unweighted voting is tested and discussed. We also demonstrate that ranking the attribute subsets by their classification accuracy and voting using only the best subsets further improves the resulting performance of the ensemble.     

Cooperative security in distributed networks

We consider a distributed network in which faulty nodes can pose serious threats as they can subvert the correct operation of basic functionalities, such as, routing or data aggregation. As a setoff to such nodes, we suggest that trust management between nodes is an essential part of a distributed system. In particular, benign nodes shall communicate with trusted nodes only and misbehaving nodes must be rapidly removed from the system.     

ASPP-DF-PVNet: Atrous Spatial Pyramid Pooling and Distance-Filtered PVNet for occlusion resistant 6D object pose estimation

Detecting objects and estimating their 6D poses from a single RGB image is quite challenging under severe occlusions. Recently, vector-field based methods have shown certain robustness to occlusion and truncation. Based on the vector-field representation, applying voting strategy to localize 2D keypoints can further reduce the influence of outliers. To improve the effectiveness of vector-field based deep network and voting scheme, we propose Atrous Spatial Pyramid Pooling and Distance-Filtered PVNet (ASPP-DF-PVNet), an occlusion resistant framework for 6D object pose estimation. ASPP-DF-PVNet utilizes the effective Atrous Spatial Pyramid Pooling (ASPP) module of Deeplabv3 to capture multi-scale features and encode global context information, which improves the accuracy of segmentation and vector-field prediction comparing to the original PVNet, especially under severe occlusions. Considering that the distances between pixels and keypoint hypotheses will affect the voting deviations, we then present a distance-filtered voting scheme which takes the voting distances into consideration to filter out the votes with large deviations. Experiments demonstrate that our method outperforms the state-of-the-art methods by a considerable margin without using pose refinement, and obtains competitive results against the methods with refinement on the LINEMOD and Occlusion LINEMOD datasets.     

Social Pressure on Social Media: Using Facebook Status Updates to Increase Voter Turnout

The widespread adoption of the Internet offers tangible potential for increasing political participation through disseminating digital reminders to vote. This study presents three experiments in which confederates mobilize members of their networks to vote by tagging them in Facebook status updates. Relying on the technological affordances of Facebook, treatments publicize individuals' past participation or failure to vote in an ongoing election. The results show substantial increases in turnout greater than that which is usually produced by face‐to‐face methods. Findings suggest that digital media offer citizens the potential to generate tremendous gains in voter participation, and address concerns that our increasingly digitally networked society may prove harmful to democracy.     

一个基于ASP的流行语投票系统

利用ASP可以直接在HTML文件中嵌入用JavaScript或VBScript脚本语言编写的脚本命令和ADO组件,对任何与ODBC兼容的数据库或OLE DB数据源的高性能连接的优点,结合数据库结构化查询语言(SQL),构建了一个动态交互的投票系统,完成流行语的网上投票,该系统可以修改泛化应用于各种类型的网上投票或调查系统。