A framework of force of information influence and application for C4KISR system

The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.

Spatio-temporal Fragmentation of Leisure Activities in Information Era： Empirical Evidence from Nanjing, China

Activity fragmentation provides a new approach for understanding the transformation of urban space and function in the information era. Numerous theoretical and empirical studies have been conducted on activity fragmentation, but few studies have focused on the fragmentation of leisure activities. This study was intended to extend the extant literature by: 1) analysing the spatio-temporal fragmentation of physical and virtual leisure activities by using a dataset collected in Nanjing, China, and 2) evaluating the reasons of leisure activity fragmentation, as well as the potential spatial effect of activity fragmentation. The results indicated that virtual leisure activities are more fragmented than physical leisure activities, but the fragmentation of physical and virtual leisure activities varies on weekday and weekend, as well as in various locations and urban districts. In addition, the results suggested that sociodemographic factors and information and communication technology(ICT) variables distinctly affect the fragmentation of leisure activities. Meanwhile, the fragmentation of virtual leisure activities may enhance the transformation of traditional urban space by reallocating leisure activity times and locations.

Network-Based Synthesis Evaluation Model

With the explosion of information, people are receiving numerous of messages at any moment. How to explore the relationships between everything you know？ And how to make wise decisions while stuck in a huge information net？

Joint model of user check-in activities for point-of-interest recommendation

With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.