Semi-supervised learning constructs the predictive model by learning from a few labeled training examples and a large pool of unlabeled ones. It has a wide range of application scenarios and has attracted much attention in the past decades. However, it is noteworthy that although the learning performance is expected to be improved by exploiting unlabeled data, some empirical studies show that there are situations where the use of unlabeled data may degenerate the performance. Thus, it is advisable to be able to exploit unlabeled data safely. This article reviews some research progress of safe semi-supervised learning, focusing on three types of safeness issue: data quality, where the training data is risky or of low-quality;model uncertainty, where the learning algorithm fails to handle the uncertainty during training; measure diversity, where the safe performance could be adapted to diverse measures.
Elastic simulation plays an important role in computer graphics and has been widely applied to film and game industries. It also has a tight relationship to virtual reality and computational fabrication applications. The balance between accuracy and performance are the most important challenge in the design of an elastic simulation algorithm. This survey will begin with the basic knowledge of elastic simulation, and then investigate two major acceleration techniques for it. From the viewpoint of deformation energy, we introduce typical linearization and reduction ideas for accelerating. We also introduce some recent progress in projective and position-based dynamics, which mainly rely on special numerical methods. Besides, optimal control for elastic objects and typical collision resolving techniques are discussed. Finally, we discuss several possible future works on integrating elastic simulation into virtual reality and 3D printing applications.
In recent times, mobile Internet has witnessed the explosive growth of video applications, embracing user-generated content, Internet Protocol television (IPTV), live streaming, video-on-demand, video conferencing, and FaceTime-like video communications. The exponential rise of video traffic and dynamic user behaviors have proved to be a major challenge to video resource sharing and delivery in the mobile environment. In this article, we present a survey of state-of-the-art video distribution solutions over the Internet. We first discuss the challenges of mobile peer-to-peer (MP2P)-based solutions and categorize them into two groups. We discuss the design idea, characteristics, and drawbacks of solutions in each group.We also give a reviewfor solutions of video transmission in wireless heterogeneous networks. Furthermore, we summarize the information-centric networking (ICN)-based video solutions in terms of in-network caching and name-based routing. Finally, we outline the open issues for mobile video systems that require further studies.
The evolution of social network and multimedia technologies encourage more and more people to generate and upload visual information, which leads to the generation of large-scale video data. Therefore, preeminent compression technologies are highly desired to facilitate the storage and transmission of these tremendous video data for a wide variety of applications. In this paper, a systematic review of the recent advances for large-scale video compression (LSVC) is presented. Specifically, fast video coding algorithms and effective models to improve video compression efficiency are introduced in detail, since coding complexity and compression efficiency are two important factors to evaluate video coding approaches. Finally, the challenges and future research trends for LSVC are discussed.
The development of wireless sensor networking, social networking, and wearable sensing techniques has advanced the boundaries of research on understanding social dynamics. Collaborative sensing, which utilizes diversity sensing and computing abilities across different entities, has become a popular sensing and computing paradigm. In this paper, we first review the history of research in collaborative sensing, which mainly refers to single space collaborative sensing that consists of physical, cyber, and social collaborative sensing. Afterward, we extend this concept into cross-space collaborative sensing and propose a general reference framework to demonstrate the distinct mechanism of cross-space collaborative sensing. We also review early works in cross-space collaborative sensing, and study the detail mechanism based on one typical research work. Finally, although cross-space collaborative sensing is a promising research area, it is still in its infancy. Thus, we identify some key research challenges with potential technical details at the end of this paper.
Program slicing is an effective technique for analyzing concurrent programs. However, when a conventional closure-based slicing algorithmfor sequential programs is applied to a concurrent interprocedural program, the slice is usually imprecise owing to the intransitivity of interference dependence. Interference dependence arises when a statement uses a variable defined in another statement executed concurrently. In this study, we propose a global dependence analysis approach based on a program reachability graph, and construct a novel dependence graph calledmarking-statement dependence graph (MSDG), in which each vertex is a 2-tuple of program state and statement. In contrast to the conventional program dependence graph where the vertex is a statement, the dependence relation in MSDG is transitive. When traversing MSDG, a precise slice will be obtained. To enhance the slicing efficiency without loss of precision, our slicing algorithm adopts a hybrid strategy. The procedures containing interaction statements between threads are inlined and sliced by the slicing algorithm based on program reachability graphs while allowing other procedures to be sliced as sequential programs. We have implemented our algorithm and three other representative slicing algorithms, and conducted an empirical study on concurrent Java programs. The experimental results show that our algorithm computes more precise slices than the other algorithms. Using partial-order reduction techniques, which are effective for reducing the size of a program reachability graph without loss of precision, our algorithm is optimized, thereby improving its performance to some extent.