Generative image steganography is a technique that directly generates stego images from secret infor-mation.Unlike traditional methods,it theoretically resists steganalysis because there is no cover image.Currently,th...Generative image steganography is a technique that directly generates stego images from secret infor-mation.Unlike traditional methods,it theoretically resists steganalysis because there is no cover image.Currently,the existing generative image steganography methods generally have good steganography performance,but there is still potential room for enhancing both the quality of stego images and the accuracy of secret information extraction.Therefore,this paper proposes a generative image steganography algorithm based on attribute feature transformation and invertible mapping rule.Firstly,the reference image is disentangled by a content and an attribute encoder to obtain content features and attribute features,respectively.Then,a mean mapping rule is introduced to map the binary secret information into a noise vector,conforming to the distribution of attribute features.This noise vector is input into the generator to produce the attribute transformed stego image with the content feature of the reference image.Additionally,we design an adversarial loss,a reconstruction loss,and an image diversity loss to train the proposed model.Experimental results demonstrate that the stego images generated by the proposed method are of high quality,with an average extraction accuracy of 99.4%for the hidden information.Furthermore,since the stego image has a uniform distribution similar to the attribute-transformed image without secret information,it effectively resists both subjective and objective steganalysis.展开更多
With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interf...With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interference,which leads to great differences of same object between UAV images.Overcoming the discrepancy difference between UAV images is crucial to improving the accuracy of change detection.To address this issue,a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model(GFLICM)was proposed in this study.Within this method,the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV images.The local variation coefficient was introduced and a new fuzzy factor was reconstructed,after which the GFLICM algorithm was used to analyze difference images.Finally,change detection results were analyzed qualitatively and quantitatively.To measure the feasibility and robustness of the proposed method,experiments were conducted using two data sets from the cities of Yangzhou and Nanjing.The experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.展开更多
This paper discusses the mathematical description of hybrid event systems, consisting of continuous, discrete, even logic-judgment and inference-decision event systems. The description takes events as its core, and in...This paper discusses the mathematical description of hybrid event systems, consisting of continuous, discrete, even logic-judgment and inference-decision event systems. The description takes events as its core, and information interconnection as its tie. In this paper, an event, instead of a process, is regarded as an analyzed element. The hybrid event systems are described in mathematical models with the following tools: chains of time, generalized space and interconnecting information.展开更多
This paper describes a non-linear information dynamics model for integrated risk assessment of complex disaster system from an evolution perspective. According to the occurrence and evolution of natural disaster syste...This paper describes a non-linear information dynamics model for integrated risk assessment of complex disaster system from an evolution perspective. According to the occurrence and evolution of natural disaster system with complicated and nonlinear characteristics, a non-linear information dynamics mode is introduced based on the maximum flux principle during modeling process to study the integrated risk assessment of complex disaster system. Based on the non-equilibrium statistical mechanics method, a stochastic evolution equation of this system is established. The integrated risk assessment of complex disaster system can be achieved by giving reasonable weights of each evaluation index to stabilize the system. The new model reveals the formation pattern of risk grade and the dynamics law of evolution. Meanwhile, a method is developed to solve the dynamics evolution equations of complex system through the self-organization feature map algorithm. The proposed method has been used in complex disaster integrated risk assessment for 31 provinces, cities and autonomous regions in China mainland. The results have indicated that the model is objective and effective.展开更多
Optical fber communication networks play an important role in the global telecommunication network.However,nonlinear efects in the optical fber and transceiver noise greatly limit the performance of fber communication...Optical fber communication networks play an important role in the global telecommunication network.However,nonlinear efects in the optical fber and transceiver noise greatly limit the performance of fber communication systems.In this paper,the product of mutual information(MI)and communication bandwidth is used as the metric of the achievable information rate(AIR).The MI loss caused by the transceiver is also considered in this work,and the bit-wise MI,generalized mutual information(GMI),is used to calculate the AIR.This loss is more signifcant in the use of higher-order modulation formats.The AIR analysis is carried out in the QPSK,16QAM,64QAM and 256QAM modulation formats for the communication systems with diferent communication bandwidths and transmission distances based on the enhanced Gaussian noise(EGN)model.The paper provides suggestions for the selection of the optimal modulation format in diferent transmission scenarios.展开更多
In this paper, we propose a hypothesis testing approach to checking model mis-specification in continuous-time stochastic diffusion model. The key idea behind the development of our test statistic is rooted in the gen...In this paper, we propose a hypothesis testing approach to checking model mis-specification in continuous-time stochastic diffusion model. The key idea behind the development of our test statistic is rooted in the generalized information equality in the context of martingale estimating equations. We propose a bootstrap resampling method to implement numerically the proposed diagnostic procedure. Through intensive simulation studies, we show that our approach is well performed in the aspects of type I error control, power improvement as well as computational efficiency.展开更多
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 o...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.展开更多
基金supported in part by the National Natural Science Foundation of China(Nos.62202234,62401270)the China Postdoctoral Science Foundation(No.2023M741778)the Natural Science Foundation of Jiangsu Province(Nos.BK20240706,BK20240694).
文摘Generative image steganography is a technique that directly generates stego images from secret infor-mation.Unlike traditional methods,it theoretically resists steganalysis because there is no cover image.Currently,the existing generative image steganography methods generally have good steganography performance,but there is still potential room for enhancing both the quality of stego images and the accuracy of secret information extraction.Therefore,this paper proposes a generative image steganography algorithm based on attribute feature transformation and invertible mapping rule.Firstly,the reference image is disentangled by a content and an attribute encoder to obtain content features and attribute features,respectively.Then,a mean mapping rule is introduced to map the binary secret information into a noise vector,conforming to the distribution of attribute features.This noise vector is input into the generator to produce the attribute transformed stego image with the content feature of the reference image.Additionally,we design an adversarial loss,a reconstruction loss,and an image diversity loss to train the proposed model.Experimental results demonstrate that the stego images generated by the proposed method are of high quality,with an average extraction accuracy of 99.4%for the hidden information.Furthermore,since the stego image has a uniform distribution similar to the attribute-transformed image without secret information,it effectively resists both subjective and objective steganalysis.
基金National Natural Science Foundation of China(No.62101219)Natural Science Foundation of Jiangsu Province(Nos.BK20201026,BK20210921)+1 种基金Science Foundation of Jiangsu Normal University(No.19XSRX006)Open Research Fund of Jiangsu Key Laboratory of Resources and Environmental Information Engineering(No.JS202107)。
文摘With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interference,which leads to great differences of same object between UAV images.Overcoming the discrepancy difference between UAV images is crucial to improving the accuracy of change detection.To address this issue,a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model(GFLICM)was proposed in this study.Within this method,the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV images.The local variation coefficient was introduced and a new fuzzy factor was reconstructed,after which the GFLICM algorithm was used to analyze difference images.Finally,change detection results were analyzed qualitatively and quantitatively.To measure the feasibility and robustness of the proposed method,experiments were conducted using two data sets from the cities of Yangzhou and Nanjing.The experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.
文摘This paper discusses the mathematical description of hybrid event systems, consisting of continuous, discrete, even logic-judgment and inference-decision event systems. The description takes events as its core, and information interconnection as its tie. In this paper, an event, instead of a process, is regarded as an analyzed element. The hybrid event systems are described in mathematical models with the following tools: chains of time, generalized space and interconnecting information.
基金supported by the National Twelfth Five-year Technology Support Projects of China (Grant Nos. 2009BAJ28B04, 2011BAK07B01,2011BAJ08B03, and 2011BAJ08B05)the National Natural Science Foundation of China (Grant No. 51208017)+1 种基金Beijing Postdoctoral Research Foundation (Grant No. 2012ZZ-17)China Postdoctoral Science Foundation Funded Project (Grant No. 2011M500199)
文摘This paper describes a non-linear information dynamics model for integrated risk assessment of complex disaster system from an evolution perspective. According to the occurrence and evolution of natural disaster system with complicated and nonlinear characteristics, a non-linear information dynamics mode is introduced based on the maximum flux principle during modeling process to study the integrated risk assessment of complex disaster system. Based on the non-equilibrium statistical mechanics method, a stochastic evolution equation of this system is established. The integrated risk assessment of complex disaster system can be achieved by giving reasonable weights of each evaluation index to stabilize the system. The new model reveals the formation pattern of risk grade and the dynamics law of evolution. Meanwhile, a method is developed to solve the dynamics evolution equations of complex system through the self-organization feature map algorithm. The proposed method has been used in complex disaster integrated risk assessment for 31 provinces, cities and autonomous regions in China mainland. The results have indicated that the model is objective and effective.
基金supported by the National Key Research and Development Program of China(No.2022YFE0202100)EU Horizon 2020 MSCA Grant 101008280 and UK Royal Society Grant(IES\R3\223068).
文摘Optical fber communication networks play an important role in the global telecommunication network.However,nonlinear efects in the optical fber and transceiver noise greatly limit the performance of fber communication systems.In this paper,the product of mutual information(MI)and communication bandwidth is used as the metric of the achievable information rate(AIR).The MI loss caused by the transceiver is also considered in this work,and the bit-wise MI,generalized mutual information(GMI),is used to calculate the AIR.This loss is more signifcant in the use of higher-order modulation formats.The AIR analysis is carried out in the QPSK,16QAM,64QAM and 256QAM modulation formats for the communication systems with diferent communication bandwidths and transmission distances based on the enhanced Gaussian noise(EGN)model.The paper provides suggestions for the selection of the optimal modulation format in diferent transmission scenarios.
基金Supported by the Quantitative Finance Foundation of Southwestern University of Finance and Economics
文摘In this paper, we propose a hypothesis testing approach to checking model mis-specification in continuous-time stochastic diffusion model. The key idea behind the development of our test statistic is rooted in the generalized information equality in the context of martingale estimating equations. We propose a bootstrap resampling method to implement numerically the proposed diagnostic procedure. Through intensive simulation studies, we show that our approach is well performed in the aspects of type I error control, power improvement as well as computational efficiency.
基金supported by the National Key Project of Scientific and Technical Supporting Programs of China(2014BAK15B01)
文摘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.
