Vision-based hand pose estimation is important in human-computer interaction. While many recent works focus on full degree-of-freedom hand pose estimation, robust estimation of global hand pose remains a challenging problem. This paper presents a novel algorithm to optimize the leaf weights in a Hough forest to assist global hand pose estimation with a single depth camera. Different from traditional Hough forest, we propose to learn the vote weights stored at the leaf nodes of a forest in a principled way to minimize average pose prediction error, so that ambiguous votes are largely suppressed during prediction fusion. Experiments show that the proposed method largely improves pose estimation accuracy with optimized leaf weights on both synthesis and real datasets and performs favorably compared to state-of-the-art convolutional neural network-based methods. On real-world depth videos, the proposed method demonstrates improved robustness compared to several other recent hand tracking systems from both industry and academy. Moreover, we utilize the proposed method to build virtual/augmented reality applications to allow users to manipulate and examine virtual objects with bare hands.

As an essential system for protecting internal networks and valuable information, the firewall monitors and controls network traffic in terms of access control, authentication, logging, and auditing. In particular, it carries out both allowing and blocking communications between internal and external networks based on proper Access Control List (ACL). However, a complex ACL along with huge network environments lead to exposing vulnerabilities and communication problems, because of anomalies among policies. Even though various techniques and applications combined with visualization approaches have been proposed, there is still a lack of usability caused by not only the limitation of the text-based interface but also the complexity of practical use. In order to solve these problems, this work proposes a 3D-based hierarchical visualization method, namely F/Wvis, for intuitive ACL management and analysis. The F/Wvis, particularly, supports ACL management for a large-scale network as well as analysis of detail anomalies on policies by providing a drill-down user interface through the hierarchical visualization approach. Further, the implemented system is evaluated against popular tools by network security experts to identify the usability and effectiveness in real-world situations (a demonstration video is available at: https://bit.ly/34ooEDc ).

By using a bit vector and a set of hash functions to represent data set, bloom filter can query a given data effectively. Bloom filter can be used to determine an element belongs to data set or not. Split bloom filter is amelioration to the bloom filter, which use a S times N bit matrix to represent data set. In distributed systems, if the number of the elements increases continually, the increasing error rate of bloom filter will make the representation nonsensically. Split bloom filter can only weaken this problem. In this paper, a new kind of bloom filter, named as K-divided bloom filter, is presented. Compared with split bloom filter, it can reduce space and time spending and has a resembling or better performance. K-divided bloom filter gets better tradeoff among error rate, space and time.

A collaborative education system using networked virtual reality (VR) overcomes differences of time and space between its teachers and students. The system can give cyber experiences in a virtual environment to students. We propose a collaboration system for experiment and education in biology. The proposed system uses a social computing based VR system called SecondLife. Students can learn basic theories and experiments of biology by watching movies and manipulating molecular models in SecondLife. They can also learn experimental details and precise simulations through remote collaborative virtual reality workspaces. Teachers can discuss with their students in SecondLife and evaluate scores of their students through remote collaborative work spaces. The proposed system offers both teaching and learning experiences in cyberspace.