A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

Virtual Reality Based Process Integrated Simulation Platform in Refinery:Virtual Refinery and Its Application

With the combination between system simulation and virtual reality,we have established an integrated virtual refinery simulation platform,and analyzed the overall design and principal architecture.This paper introduces a simulation algorithm about a refinery based on virtual reality,and explains how the algorithm can be applied to the virtual refinery integrated simulation platform in detail.The virtual refinery simulation platform,which consists of a three-dimensional scene system,an integrated database system and a dynamic-static simulation system,has many applications,such as dynamic-static simulation of key process unit used as process control and oil tank blending simulation for scheduling.With the visualization and human-computer interaction for acquiring production and process data,this platform can provide effective supports on staff training related with monitoring,control and operation in refinery.Virtual refinery can also be web published through the internet and it is helpful for the distance training and education.

Building of cognizing semantic map in large-scale semi-unknown environment

The quick response code based artificial labels are applied to provide semantic concepts and relations of surroundings that permit the understanding of complexity and limitations of semantic recognition and scene only with robot's vision.By imitating spatial cognizing mechanism of human,the robot constantly received the information of artificial labels at cognitive-guide points in a wide range of structured environment to achieve the perception of the environment and robot navigation.The immune network algorithm was used to form the environmental awareness mechanism with "distributed representation".The color recognition and SIFT feature matching algorithm were fused to achieve the memory and cognition of scenario tag.Then the cognition-guide-action based cognizing semantic map was built.Along with the continuously abundant map,the robot did no longer need to rely on the artificial label,and it could plan path and navigate freely.Experimental results show that the artificial label designed in this work can improve the cognitive ability of the robot,navigate the robot in the case of semi-unknown environment,and build the cognizing semantic map favorably.

Deep learning for change detection in remote sensing:a review

A large number of publications have incorporated deep learning in the process of remote sensing change detection.In these Deep Learning Change Detection(DLCD)publications,deep learning methods have demonstrated their superiority over conventional change detection methods.However,the theoretical underpinnings of why deep learning improves the performance of change detection remain unresolved.As of today,few in-depth reviews have investigated the mechanisms of DLCD.Without such a review,five critical questions remain unclear.Does DLCD provide improved information representation for change detection?If so,how?How to select an appropriate DLCD method and why?How much does each type of change benefits from DLCD in terms of its performance?What are the major limitations of existing DLCD methods and what are the prospects for DLCD?To address these five questions,we reviewed according to the following strategies.We grouped the DLCD information assemblages into the four unique dimensions of remote sensing:spectral,spatial,temporal,and multi-sensor.For the extraction of information in each dimension,the difference between DLCD and conventional change detection methods was compared.We proposed a taxonomy of existing DLCD methods by dividing them into two distinctive pools:separate and coupled models.Their advantages,limitations,applicability,and performance were thoroughly investigated and explicitly presented.We examined the variations in performance between DLCD and conventional change detection.We depicted two limitations of DLCD,i.e.training sample and hardware and software dilemmas.Based on these analyses,we identified directions for future developments.As a result of our review,we found that DLCD’s advantages over conventional change detection can be attributed to three factors:improved information representation;improved change detection methods;and performance enhancements.DLCD has to surpass the limitations with regard to training samples and computing infrastructure.We envision this review can boost developments of deep learning in change detection applications.

Nondeterministic Temporal Relations in Multimedia Data

Synchronization is an important issue in multimedia systems which integrate a variety of temporally related media objects. One part of synchronization is the representation of temporal information. With the emerging illteractive multimedia, deterministic temporal models are replaced by nondeterministic ones with more expressiveness. This paper classifies temporal models by their expressiveness, and evaluates relevant nondeterministic temporal relations in multimedia data. Additionally, an intervalbased nondeterndnistic model based on a complete temporal operator set is proposed providing highlevel abstractions and a high degree of expressiveness for interactive multimedia systems.