When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective

Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces crowdsourcing methods for mobile sensing. Inspired by social network, one can establish trust among participatory agents to leverage the wisdom of crowds for mobile sensing. A prototype of social network inspired mobile multimedia and sensing application is presented for illustrative purpose. Numerical experiments on real-world datasets show improved performance of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect to Internet layers are discussed.Comment: To appear in Oct. IEEE Communications Magazine, feature topic on "Social Networks Meet Next Generation Mobile Multimedia Internet

Highly efficient coherent optical memory based on electromagnetically induced transparency

Quantum memory is an important component in the long-distance quantum communication system based on the quantum repeater protocol. To outperform the direct transmission of photons with quantum repeaters, it is crucial to develop quantum memories with high fidelity, high efficiency and a long storage time. Here, we achieve a storage efficiency of 92.0(1.5)\% for a coherent optical memory based on the electromagnetically induced transparency (EIT) scheme in optically dense cold atomic media. We also obtain a useful time-bandwidth product of 1200, considering only storage where the retrieval efficiency remains above 50\%. Both are the best record to date in all kinds of the schemes for the realization of optical memory. Our work significantly advances the pursuit of a high-performance optical memory and should have important applications in quantum information science.Comment: 5 pages, 5 figures, supplementary materials: 12 pages, 4 figure

Supervised Collective Classification for Crowdsourcing

Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e.g., labels of an item) provided by labelers. Current crowdsourcing algorithms are mainly unsupervised methods that are unaware of the quality of crowdsourced data. In this paper, we propose a supervised collective classification algorithm that aims to identify reliable labelers from the training data (e.g., items with known labels). The reliability (i.e., weighting factor) of each labeler is determined via a saddle point algorithm. The results on several crowdsourced data show that supervised methods can achieve better classification accuracy than unsupervised methods, and our proposed method outperforms other algorithms.Comment: to appear in IEEE Global Communications Conference (GLOBECOM) Workshop on Networking and Collaboration Issues for the Internet of Everythin

A Study of Developing a System Dynamics Model for the Learning Effectiveness Evaluation

[[abstract]]This study used the research method of system dynamics and applied the Vensim software to develop a learning effectiveness evaluation model. This study developed four cause-and-effect chains affecting learning effectiveness, including teachers’ teaching enthusiasm, family involvement, school’s implementation of scientific activities, and creative teaching method, as well as the system dynamics model based on the four cause-and-effect chains. Based on the developed system dynamic model, this study performed simulation to investigate the relationship among family involvement, learning effectiveness, teaching achievement, creative teaching method, and students’ learning interest. The results of this study verified that there are positive correlations between family involvement and students’ learning effectiveness, as well as students’ learning effectiveness and teachers’ teaching achievements. The results also indicated that the use of creative teaching method is able to increase students’ learning interest and learning achievement.[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]電子版[[countrycodes]]US