- Ruyue Xin;Jiang Zhang;Yitong Shao;
Classifying large-scale networks into several categories and distinguishing them according to their fine structures is of great importance to several real-life applications.However, most studies on complex networks focus on the properties of a single network and seldom on classification, clustering, and comparison between different networks, in which the network is treated as a whole.Conventional methods can hardly be applied on networks directly due to the non-Euclidean properties of data.In this paper, we propose a novel framework of Complex Network Classifier(CNC) by integrating network embedding and convolutional neural network to tackle the problem of network classification.By training the classifier on synthetic complex network data, we show CNC can not only classify networks with high accuracy and robustness but can also extract the features of the networks automatically.We also compare our CNC with baseline methods on benchmark datasets, which shows that our method performs well on large-scale networks.
2020年04期 v.25 447-457页 [查看摘要][在线阅读][下载 6885K] [下载次数：177 ] |[网刊下载次数：0 ] |[引用频次：98 ] |[阅读次数：0 ] - Ruyue Xin;Jiang Zhang;Yitong Shao;
Classifying large-scale networks into several categories and distinguishing them according to their fine structures is of great importance to several real-life applications.However, most studies on complex networks focus on the properties of a single network and seldom on classification, clustering, and comparison between different networks, in which the network is treated as a whole.Conventional methods can hardly be applied on networks directly due to the non-Euclidean properties of data.In this paper, we propose a novel framework of Complex Network Classifier(CNC) by integrating network embedding and convolutional neural network to tackle the problem of network classification.By training the classifier on synthetic complex network data, we show CNC can not only classify networks with high accuracy and robustness but can also extract the features of the networks automatically.We also compare our CNC with baseline methods on benchmark datasets, which shows that our method performs well on large-scale networks.
2020年04期 v.25 447-457页 [查看摘要][在线阅读][下载 6885K] [下载次数：177 ] |[网刊下载次数：0 ] |[引用频次：98 ] |[阅读次数：0 ] - Ruyue Xin;Jiang Zhang;Yitong Shao;
Classifying large-scale networks into several categories and distinguishing them according to their fine structures is of great importance to several real-life applications.However, most studies on complex networks focus on the properties of a single network and seldom on classification, clustering, and comparison between different networks, in which the network is treated as a whole.Conventional methods can hardly be applied on networks directly due to the non-Euclidean properties of data.In this paper, we propose a novel framework of Complex Network Classifier(CNC) by integrating network embedding and convolutional neural network to tackle the problem of network classification.By training the classifier on synthetic complex network data, we show CNC can not only classify networks with high accuracy and robustness but can also extract the features of the networks automatically.We also compare our CNC with baseline methods on benchmark datasets, which shows that our method performs well on large-scale networks.
2020年04期 v.25 447-457页 [查看摘要][在线阅读][下载 6885K] [下载次数：177 ] |[网刊下载次数：0 ] |[引用频次：98 ] |[阅读次数：0 ] - Li Chen;Nan Ma;Patrick Wang;Jiahong Li;Pengfei Wang;Guilin Pang;Xiaojun Shi;
The development of autonomous driving has brought with it requirements for intelligence, safety, and stability.One example of this is the need to construct effective forms of interactive cognition between pedestrians and vehicles in dynamic, complex, and uncertain environments.Pedestrian action detection is a form of interactive cognition that is fundamental to the success of autonomous driving technologies.Specifically, vehicles need to detect pedestrians, recognize their limb movements, and understand the meaning of their actions before making appropriate decisions in response.In this survey, we present a detailed description of the architecture for pedestrian action recognition in autonomous driving, and compare the existing mainstream pedestrian action recognition techniques.We also introduce several commonly used datasets used in pedestrian motion recognition.Finally, we present several suggestions for future research directions.
2020年04期 v.25 458-470页 [查看摘要][在线阅读][下载 1215K] [下载次数：97 ] |[网刊下载次数：0 ] |[引用频次：30 ] |[阅读次数：0 ] - Li Chen;Nan Ma;Patrick Wang;Jiahong Li;Pengfei Wang;Guilin Pang;Xiaojun Shi;
The development of autonomous driving has brought with it requirements for intelligence, safety, and stability.One example of this is the need to construct effective forms of interactive cognition between pedestrians and vehicles in dynamic, complex, and uncertain environments.Pedestrian action detection is a form of interactive cognition that is fundamental to the success of autonomous driving technologies.Specifically, vehicles need to detect pedestrians, recognize their limb movements, and understand the meaning of their actions before making appropriate decisions in response.In this survey, we present a detailed description of the architecture for pedestrian action recognition in autonomous driving, and compare the existing mainstream pedestrian action recognition techniques.We also introduce several commonly used datasets used in pedestrian motion recognition.Finally, we present several suggestions for future research directions.
2020年04期 v.25 458-470页 [查看摘要][在线阅读][下载 1215K] [下载次数：97 ] |[网刊下载次数：0 ] |[引用频次：30 ] |[阅读次数：0 ] - Li Chen;Nan Ma;Patrick Wang;Jiahong Li;Pengfei Wang;Guilin Pang;Xiaojun Shi;
The development of autonomous driving has brought with it requirements for intelligence, safety, and stability.One example of this is the need to construct effective forms of interactive cognition between pedestrians and vehicles in dynamic, complex, and uncertain environments.Pedestrian action detection is a form of interactive cognition that is fundamental to the success of autonomous driving technologies.Specifically, vehicles need to detect pedestrians, recognize their limb movements, and understand the meaning of their actions before making appropriate decisions in response.In this survey, we present a detailed description of the architecture for pedestrian action recognition in autonomous driving, and compare the existing mainstream pedestrian action recognition techniques.We also introduce several commonly used datasets used in pedestrian motion recognition.Finally, we present several suggestions for future research directions.
2020年04期 v.25 458-470页 [查看摘要][在线阅读][下载 1215K] [下载次数：97 ] |[网刊下载次数：0 ] |[引用频次：30 ] |[阅读次数：0 ] - Yun Yue;Zi Yang;Xin Pei;Hongxin Chen;Chao Song;Danya Yao;
Research into the impact of road accidents on drivers is essential to effective post-crash interventions.However, due to limited data and resources, the current research focus is mainly on those who have suffered severe injuries.In this paper, we propose a novel approach to examining the impact that being involved in a crash has on drivers by using traffic surveillance data.In traffic video surveillance systems, the locations of vehicles at different moments in time are captured and their headway, which is an important indicator of driving behavior,can be calculated from this information.It was found that there was a sudden increase in headway when drivers return to the road after being involved in a crash, but that the headway returned to its pre-crash level over time.We further analyzed the duration of the decay using a Cox proportional hazards regression model, which revealed many significant factors(related to the driver, vehicle, and nature of the accident) behind the survival time of the increased headway.Our approach is able to reveal the crash impact on drivers in a convenient and economical way.It can enhance the understanding of the impact of a crash on drivers, and help to devise more effective re-education programs and other interventions to encourage drivers who are involved in crashes to drive more safely in the future.
2020年04期 v.25 471-478页 [查看摘要][在线阅读][下载 878K] [下载次数：35 ] |[网刊下载次数：0 ] |[引用频次：0 ] |[阅读次数：0 ] - Yun Yue;Zi Yang;Xin Pei;Hongxin Chen;Chao Song;Danya Yao;
Research into the impact of road accidents on drivers is essential to effective post-crash interventions.However, due to limited data and resources, the current research focus is mainly on those who have suffered severe injuries.In this paper, we propose a novel approach to examining the impact that being involved in a crash has on drivers by using traffic surveillance data.In traffic video surveillance systems, the locations of vehicles at different moments in time are captured and their headway, which is an important indicator of driving behavior,can be calculated from this information.It was found that there was a sudden increase in headway when drivers return to the road after being involved in a crash, but that the headway returned to its pre-crash level over time.We further analyzed the duration of the decay using a Cox proportional hazards regression model, which revealed many significant factors(related to the driver, vehicle, and nature of the accident) behind the survival time of the increased headway.Our approach is able to reveal the crash impact on drivers in a convenient and economical way.It can enhance the understanding of the impact of a crash on drivers, and help to devise more effective re-education programs and other interventions to encourage drivers who are involved in crashes to drive more safely in the future.
2020年04期 v.25 471-478页 [查看摘要][在线阅读][下载 878K] [下载次数：35 ] |[网刊下载次数：0 ] |[引用频次：0 ] |[阅读次数：0 ] - Yun Yue;Zi Yang;Xin Pei;Hongxin Chen;Chao Song;Danya Yao;
Research into the impact of road accidents on drivers is essential to effective post-crash interventions.However, due to limited data and resources, the current research focus is mainly on those who have suffered severe injuries.In this paper, we propose a novel approach to examining the impact that being involved in a crash has on drivers by using traffic surveillance data.In traffic video surveillance systems, the locations of vehicles at different moments in time are captured and their headway, which is an important indicator of driving behavior,can be calculated from this information.It was found that there was a sudden increase in headway when drivers return to the road after being involved in a crash, but that the headway returned to its pre-crash level over time.We further analyzed the duration of the decay using a Cox proportional hazards regression model, which revealed many significant factors(related to the driver, vehicle, and nature of the accident) behind the survival time of the increased headway.Our approach is able to reveal the crash impact on drivers in a convenient and economical way.It can enhance the understanding of the impact of a crash on drivers, and help to devise more effective re-education programs and other interventions to encourage drivers who are involved in crashes to drive more safely in the future.
2020年04期 v.25 471-478页 [查看摘要][在线阅读][下载 878K] [下载次数：35 ] |[网刊下载次数：0 ] |[引用频次：0 ] |[阅读次数：0 ] - Jianhui Han;Zhaolin Li;Weimin Zheng;Youhui Zhang;
Inspired by real biological neural models, Spiking Neural Networks(SNNs) process information with discrete spikes and show great potential for building low-power neural network systems.This paper proposes a hardware implementation of SNN based on Field-Programmable Gate Arrays(FPGA).It features a hybrid updating algorithm,which combines the advantages of existing algorithms to simplify hardware design and improve performance.The proposed design supports up to 16 384 neurons and 16.8 million synapses but requires minimal hardware resources and archieves a very low power consumption of 0.477 W.A test platform is built based on the proposed design using a Xilinx FPGA evaluation board, upon which we deploy a classification task on the MNIST dataset.The evaluation results show an accuracy of 97.06% and a frame rate of 161 frames per second.
2020年04期 v.25 479-486页 [查看摘要][在线阅读][下载 786K] [下载次数：143 ] |[网刊下载次数：0 ] |[引用频次：10 ] |[阅读次数：0 ] - Jianhui Han;Zhaolin Li;Weimin Zheng;Youhui Zhang;
Inspired by real biological neural models, Spiking Neural Networks(SNNs) process information with discrete spikes and show great potential for building low-power neural network systems.This paper proposes a hardware implementation of SNN based on Field-Programmable Gate Arrays(FPGA).It features a hybrid updating algorithm,which combines the advantages of existing algorithms to simplify hardware design and improve performance.The proposed design supports up to 16 384 neurons and 16.8 million synapses but requires minimal hardware resources and archieves a very low power consumption of 0.477 W.A test platform is built based on the proposed design using a Xilinx FPGA evaluation board, upon which we deploy a classification task on the MNIST dataset.The evaluation results show an accuracy of 97.06% and a frame rate of 161 frames per second.
2020年04期 v.25 479-486页 [查看摘要][在线阅读][下载 786K] [下载次数：143 ] |[网刊下载次数：0 ] |[引用频次：10 ] |[阅读次数：0 ] - Jianhui Han;Zhaolin Li;Weimin Zheng;Youhui Zhang;
Inspired by real biological neural models, Spiking Neural Networks(SNNs) process information with discrete spikes and show great potential for building low-power neural network systems.This paper proposes a hardware implementation of SNN based on Field-Programmable Gate Arrays(FPGA).It features a hybrid updating algorithm,which combines the advantages of existing algorithms to simplify hardware design and improve performance.The proposed design supports up to 16 384 neurons and 16.8 million synapses but requires minimal hardware resources and archieves a very low power consumption of 0.477 W.A test platform is built based on the proposed design using a Xilinx FPGA evaluation board, upon which we deploy a classification task on the MNIST dataset.The evaluation results show an accuracy of 97.06% and a frame rate of 161 frames per second.
2020年04期 v.25 479-486页 [查看摘要][在线阅读][下载 786K] [下载次数：143 ] |[网刊下载次数：0 ] |[引用频次：10 ] |[阅读次数：0 ] - Zhiyao Hu;Dongsheng Li;Deke Guo;
Apache Spark provides a well-known Map Reduce computing framework, aiming to fast-process big data analytics in data-parallel manners.With this platform, large input data are divided into data partitions.Each data partition is processed by multiple computation tasks concurrently.Outputs of these computation tasks are transferred among multiple computers via the network.However, such a distributed computing framework suffers from system overheads, inevitably caused by communication and disk I/O operations.System overheads take up a large proportion of the Job Completion Time(JCT).We observed that excessive computational resources incurs considerable system overheads, prolonging the JCT.The over-allocation of individual jobs not only prolongs their own JCTs, but also likely makes other jobs suffer from under-allocation.Thus, the average JCT is suboptimal,too.To address this problem, we propose a prediction model to estimate the changing JCT of a single Spark job.With the support of the prediction method, we designed a heuristic algorithm to balance the resource allocation of multiple Spark jobs, aiming to minimize the average JCT in multiple-job cases.We implemented the prediction model and resource allocation method in Re B, a Resource-Balancer based on Apache Spark.Experimental results showed that Re B significantly outperformed the traditional max-min fairness and shortest-job-optimal methods.The average JCT was decreased by around 10%–30% compared to the existing solutions.
2020年04期 v.25 487-497页 [查看摘要][在线阅读][下载 6925K] [下载次数：50 ] |[网刊下载次数：0 ] |[引用频次：12 ] |[阅读次数：0 ] - Zhiyao Hu;Dongsheng Li;Deke Guo;
Apache Spark provides a well-known Map Reduce computing framework, aiming to fast-process big data analytics in data-parallel manners.With this platform, large input data are divided into data partitions.Each data partition is processed by multiple computation tasks concurrently.Outputs of these computation tasks are transferred among multiple computers via the network.However, such a distributed computing framework suffers from system overheads, inevitably caused by communication and disk I/O operations.System overheads take up a large proportion of the Job Completion Time(JCT).We observed that excessive computational resources incurs considerable system overheads, prolonging the JCT.The over-allocation of individual jobs not only prolongs their own JCTs, but also likely makes other jobs suffer from under-allocation.Thus, the average JCT is suboptimal,too.To address this problem, we propose a prediction model to estimate the changing JCT of a single Spark job.With the support of the prediction method, we designed a heuristic algorithm to balance the resource allocation of multiple Spark jobs, aiming to minimize the average JCT in multiple-job cases.We implemented the prediction model and resource allocation method in Re B, a Resource-Balancer based on Apache Spark.Experimental results showed that Re B significantly outperformed the traditional max-min fairness and shortest-job-optimal methods.The average JCT was decreased by around 10%–30% compared to the existing solutions.
2020年04期 v.25 487-497页 [查看摘要][在线阅读][下载 6925K] [下载次数：50 ] |[网刊下载次数：0 ] |[引用频次：12 ] |[阅读次数：0 ] - Zhiyao Hu;Dongsheng Li;Deke Guo;
Apache Spark provides a well-known Map Reduce computing framework, aiming to fast-process big data analytics in data-parallel manners.With this platform, large input data are divided into data partitions.Each data partition is processed by multiple computation tasks concurrently.Outputs of these computation tasks are transferred among multiple computers via the network.However, such a distributed computing framework suffers from system overheads, inevitably caused by communication and disk I/O operations.System overheads take up a large proportion of the Job Completion Time(JCT).We observed that excessive computational resources incurs considerable system overheads, prolonging the JCT.The over-allocation of individual jobs not only prolongs their own JCTs, but also likely makes other jobs suffer from under-allocation.Thus, the average JCT is suboptimal,too.To address this problem, we propose a prediction model to estimate the changing JCT of a single Spark job.With the support of the prediction method, we designed a heuristic algorithm to balance the resource allocation of multiple Spark jobs, aiming to minimize the average JCT in multiple-job cases.We implemented the prediction model and resource allocation method in Re B, a Resource-Balancer based on Apache Spark.Experimental results showed that Re B significantly outperformed the traditional max-min fairness and shortest-job-optimal methods.The average JCT was decreased by around 10%–30% compared to the existing solutions.
2020年04期 v.25 487-497页 [查看摘要][在线阅读][下载 6925K] [下载次数：50 ] |[网刊下载次数：0 ] |[引用频次：12 ] |[阅读次数：0 ] - Liuyuan Deng;Ming Yang;Zhidong Liang;Yuesheng He;Chunxiang Wang;
This paper addresses the problem of the semantic segmentation of large-scale 3D road scenes by incorporating the complementary advantages of point clouds and images.To make full use of geometrical and visual information, this paper extracts 3D geometric features from a point cloud using a deep neural network for 3D semantic segmentation and extracts 2D visual features from images using a Convolutional Neural Network(CNN)for 2D semantic segmentation.In order to bridge the features of the two modalities, this paper uses superpoints as an intermediate representation to connect the 2D features with the 3D features.A superpoint-based pooling method is proposed to fuse the features from the two different modalities for joint learning.To evaluate the approach, the paper generates 3D scenes from the Virtual KITTI dataset.The results of the experiments demonstrate that the proposed approach is capable of segmenting large-scale 3D road scenes based on the compact and semantically homogeneous superpoints, and that it achieves considerable improvements over the 2D image and 3D point cloud semantic segmentation methods.
2020年04期 v.25 498-507页 [查看摘要][在线阅读][下载 4552K] [下载次数：99 ] |[网刊下载次数：0 ] |[引用频次：18 ] |[阅读次数：0 ] - Liuyuan Deng;Ming Yang;Zhidong Liang;Yuesheng He;Chunxiang Wang;
This paper addresses the problem of the semantic segmentation of large-scale 3D road scenes by incorporating the complementary advantages of point clouds and images.To make full use of geometrical and visual information, this paper extracts 3D geometric features from a point cloud using a deep neural network for 3D semantic segmentation and extracts 2D visual features from images using a Convolutional Neural Network(CNN)for 2D semantic segmentation.In order to bridge the features of the two modalities, this paper uses superpoints as an intermediate representation to connect the 2D features with the 3D features.A superpoint-based pooling method is proposed to fuse the features from the two different modalities for joint learning.To evaluate the approach, the paper generates 3D scenes from the Virtual KITTI dataset.The results of the experiments demonstrate that the proposed approach is capable of segmenting large-scale 3D road scenes based on the compact and semantically homogeneous superpoints, and that it achieves considerable improvements over the 2D image and 3D point cloud semantic segmentation methods.
2020年04期 v.25 498-507页 [查看摘要][在线阅读][下载 4552K] [下载次数：99 ] |[网刊下载次数：0 ] |[引用频次：18 ] |[阅读次数：0 ] - Liuyuan Deng;Ming Yang;Zhidong Liang;Yuesheng He;Chunxiang Wang;
This paper addresses the problem of the semantic segmentation of large-scale 3D road scenes by incorporating the complementary advantages of point clouds and images.To make full use of geometrical and visual information, this paper extracts 3D geometric features from a point cloud using a deep neural network for 3D semantic segmentation and extracts 2D visual features from images using a Convolutional Neural Network(CNN)for 2D semantic segmentation.In order to bridge the features of the two modalities, this paper uses superpoints as an intermediate representation to connect the 2D features with the 3D features.A superpoint-based pooling method is proposed to fuse the features from the two different modalities for joint learning.To evaluate the approach, the paper generates 3D scenes from the Virtual KITTI dataset.The results of the experiments demonstrate that the proposed approach is capable of segmenting large-scale 3D road scenes based on the compact and semantically homogeneous superpoints, and that it achieves considerable improvements over the 2D image and 3D point cloud semantic segmentation methods.
2020年04期 v.25 498-507页 [查看摘要][在线阅读][下载 4552K] [下载次数：99 ] |[网刊下载次数：0 ] |[引用频次：18 ] |[阅读次数：0 ] - Baonan Wang;Feng Hu;Chao Wang;
With the slow progress of universal quantum computers, studies on the feasibility of optimization by a dedicated and quantum-annealing-based annealer are important.The quantum principle is expected to utilize the quantum tunneling effects to find the optimal solutions for the exponential-level problems while classical annealing may be affected by the initializations.This study constructs a new Quantum-Inspired Annealing(QIA) framework to explore the potentials of quantum annealing for solving Ising model with comparisons to the classical one.Through various configurations of the 1 D Ising model, the new framework can achieve ground state, corresponding to the optimum of classical problems, with higher probability up to 28% versus classical counterpart(22% in case).This condition not only reveals the potential of quantum annealing for solving the Ising-like Hamiltonian, but also contributes to an improved understanding and use of the quantum annealer for various applications in the future.
2020年04期 v.25 508-515页 [查看摘要][在线阅读][下载 488K] [下载次数：52 ] |[网刊下载次数：0 ] |[引用频次：3 ] |[阅读次数：0 ] - Baonan Wang;Feng Hu;Chao Wang;
With the slow progress of universal quantum computers, studies on the feasibility of optimization by a dedicated and quantum-annealing-based annealer are important.The quantum principle is expected to utilize the quantum tunneling effects to find the optimal solutions for the exponential-level problems while classical annealing may be affected by the initializations.This study constructs a new Quantum-Inspired Annealing(QIA) framework to explore the potentials of quantum annealing for solving Ising model with comparisons to the classical one.Through various configurations of the 1 D Ising model, the new framework can achieve ground state, corresponding to the optimum of classical problems, with higher probability up to 28% versus classical counterpart(22% in case).This condition not only reveals the potential of quantum annealing for solving the Ising-like Hamiltonian, but also contributes to an improved understanding and use of the quantum annealer for various applications in the future.
2020年04期 v.25 508-515页 [查看摘要][在线阅读][下载 488K] [下载次数：52 ] |[网刊下载次数：0 ] |[引用频次：3 ] |[阅读次数：0 ] - Baonan Wang;Feng Hu;Chao Wang;
With the slow progress of universal quantum computers, studies on the feasibility of optimization by a dedicated and quantum-annealing-based annealer are important.The quantum principle is expected to utilize the quantum tunneling effects to find the optimal solutions for the exponential-level problems while classical annealing may be affected by the initializations.This study constructs a new Quantum-Inspired Annealing(QIA) framework to explore the potentials of quantum annealing for solving Ising model with comparisons to the classical one.Through various configurations of the 1 D Ising model, the new framework can achieve ground state, corresponding to the optimum of classical problems, with higher probability up to 28% versus classical counterpart(22% in case).This condition not only reveals the potential of quantum annealing for solving the Ising-like Hamiltonian, but also contributes to an improved understanding and use of the quantum annealer for various applications in the future.
2020年04期 v.25 508-515页 [查看摘要][在线阅读][下载 488K] [下载次数：52 ] |[网刊下载次数：0 ] |[引用频次：3 ] |[阅读次数：0 ] - Zhuo Zhang;Guangyuan Fu;Rongrong Ni;Jia Liu;Xiaoyuan Yang;
Traditional steganography is the practice of embedding a secret message into an image by modifying the information in the spatial or frequency domain of the cover image.Although this method has a large embedding capacity, it inevitably leaves traces of rewriting that can eventually be discovered by the enemy.The method of Steganography by Cover Synthesis(SCS) attempts to construct a natural stego image, so that the cover image is not modified; thus, it can overcome detection by a steganographic analyzer.Due to the difficulty in constructing natural stego images, the development of SCS is limited.In this paper, a novel generative SCS method based on a Generative Adversarial Network(GAN) for image steganography is proposed.In our method, we design a GAN model called Synthetic Semantics Stego Generative Adversarial Network(SSS-GAN) to generate stego images from secret messages.By establishing a mapping relationship between secret messages and semantic category information, category labels can generate pseudo-real images via the generative model.Then, the receiver can recognize the labels via the classifier network to restore the concealed information in communications.We trained the model on the MINIST, CIFAR-10, and CIFAR-100 image datasets.Experiments show the feasibility of this method.The security, capacity, and robustness of the method are analyzed.
2020年04期 v.25 516-527页 [查看摘要][在线阅读][下载 1206K] [下载次数：44 ] |[网刊下载次数：0 ] |[引用频次：22 ] |[阅读次数：0 ] - Zhuo Zhang;Guangyuan Fu;Rongrong Ni;Jia Liu;Xiaoyuan Yang;
Traditional steganography is the practice of embedding a secret message into an image by modifying the information in the spatial or frequency domain of the cover image.Although this method has a large embedding capacity, it inevitably leaves traces of rewriting that can eventually be discovered by the enemy.The method of Steganography by Cover Synthesis(SCS) attempts to construct a natural stego image, so that the cover image is not modified; thus, it can overcome detection by a steganographic analyzer.Due to the difficulty in constructing natural stego images, the development of SCS is limited.In this paper, a novel generative SCS method based on a Generative Adversarial Network(GAN) for image steganography is proposed.In our method, we design a GAN model called Synthetic Semantics Stego Generative Adversarial Network(SSS-GAN) to generate stego images from secret messages.By establishing a mapping relationship between secret messages and semantic category information, category labels can generate pseudo-real images via the generative model.Then, the receiver can recognize the labels via the classifier network to restore the concealed information in communications.We trained the model on the MINIST, CIFAR-10, and CIFAR-100 image datasets.Experiments show the feasibility of this method.The security, capacity, and robustness of the method are analyzed.
2020年04期 v.25 516-527页 [查看摘要][在线阅读][下载 1206K] [下载次数：44 ] |[网刊下载次数：0 ] |[引用频次：22 ] |[阅读次数：0 ] - Zhuo Zhang;Guangyuan Fu;Rongrong Ni;Jia Liu;Xiaoyuan Yang;
Traditional steganography is the practice of embedding a secret message into an image by modifying the information in the spatial or frequency domain of the cover image.Although this method has a large embedding capacity, it inevitably leaves traces of rewriting that can eventually be discovered by the enemy.The method of Steganography by Cover Synthesis(SCS) attempts to construct a natural stego image, so that the cover image is not modified; thus, it can overcome detection by a steganographic analyzer.Due to the difficulty in constructing natural stego images, the development of SCS is limited.In this paper, a novel generative SCS method based on a Generative Adversarial Network(GAN) for image steganography is proposed.In our method, we design a GAN model called Synthetic Semantics Stego Generative Adversarial Network(SSS-GAN) to generate stego images from secret messages.By establishing a mapping relationship between secret messages and semantic category information, category labels can generate pseudo-real images via the generative model.Then, the receiver can recognize the labels via the classifier network to restore the concealed information in communications.We trained the model on the MINIST, CIFAR-10, and CIFAR-100 image datasets.Experiments show the feasibility of this method.The security, capacity, and robustness of the method are analyzed.
2020年04期 v.25 516-527页 [查看摘要][在线阅读][下载 1206K] [下载次数：44 ] |[网刊下载次数：0 ] |[引用频次：22 ] |[阅读次数：0 ] - Bo Liu;Shijiao Tang;Xiangguo Sun;Qiaoyun Chen;Jiuxin Cao;Junzhou Luo;Shanshan Zhao;
The user-generated social media messages usually contain considerable multimodal content.Such messages are usually short and lack explicit sentiment words.However, we can understand the sentiment associated with such messages by analyzing the context, which is essential to improve the sentiment analysis performance.Unfortunately, majority of the existing studies consider the impact of contextual information based on a single data model.In this study, we propose a novel model for performing context-aware user sentiment analysis.This model involves the semantic correlation of different modalities and the effects of tweet context information.Based on our experimental results obtained using the Twitter dataset, our approach is observed to outperform the other existing methods in analysing user sentiment.
2020年04期 v.25 528-541页 [查看摘要][在线阅读][下载 2962K] [下载次数：126 ] |[网刊下载次数：0 ] |[引用频次：11 ] |[阅读次数：0 ] - Bo Liu;Shijiao Tang;Xiangguo Sun;Qiaoyun Chen;Jiuxin Cao;Junzhou Luo;Shanshan Zhao;
The user-generated social media messages usually contain considerable multimodal content.Such messages are usually short and lack explicit sentiment words.However, we can understand the sentiment associated with such messages by analyzing the context, which is essential to improve the sentiment analysis performance.Unfortunately, majority of the existing studies consider the impact of contextual information based on a single data model.In this study, we propose a novel model for performing context-aware user sentiment analysis.This model involves the semantic correlation of different modalities and the effects of tweet context information.Based on our experimental results obtained using the Twitter dataset, our approach is observed to outperform the other existing methods in analysing user sentiment.
2020年04期 v.25 528-541页 [查看摘要][在线阅读][下载 2962K] [下载次数：126 ] |[网刊下载次数：0 ] |[引用频次：11 ] |[阅读次数：0 ] - Bo Liu;Shijiao Tang;Xiangguo Sun;Qiaoyun Chen;Jiuxin Cao;Junzhou Luo;Shanshan Zhao;
The user-generated social media messages usually contain considerable multimodal content.Such messages are usually short and lack explicit sentiment words.However, we can understand the sentiment associated with such messages by analyzing the context, which is essential to improve the sentiment analysis performance.Unfortunately, majority of the existing studies consider the impact of contextual information based on a single data model.In this study, we propose a novel model for performing context-aware user sentiment analysis.This model involves the semantic correlation of different modalities and the effects of tweet context information.Based on our experimental results obtained using the Twitter dataset, our approach is observed to outperform the other existing methods in analysing user sentiment.
2020年04期 v.25 528-541页 [查看摘要][在线阅读][下载 2962K] [下载次数：126 ] |[网刊下载次数：0 ] |[引用频次：11 ] |[阅读次数：0 ] - Jiashuai Zhang;Wenkai Li;Min Zeng;Xiangmao Meng;Lukasz Kurgan;Fang-Xiang Wu;Min Li;
Proteins drive virtually all cellular-level processes.The proteins that are critical to cell proliferation and survival are defined as essential.These essential proteins are implicated in key metabolic and regulatory networks,and are important in the context of rational drug design efforts.The computational identification of the essential proteins benefits from the proliferation of publicly available protein interaction datasets.Scientists have developed several algorithms that use these interaction datasets to predict essential proteins.However, a comprehensive web platform that facilitates the analysis and prediction of essential proteins is missing.In this study, we design,implement, and release Net EPD: a network-based essential protein discovery platform.This resource integrates data on Protein–Protein Interaction(PPI) networks, gene expression, subcellular localization, and a native set of essential proteins.It also computes a variety of node centrality measures, evaluates the predictions of essential proteins, and visualizes PPI networks.This comprehensive platform functions by implementing four activities, which include the collection of datasets, computation of centrality measures, evaluation, and visualization.The results produced by Net EPD are visualized on its website, and sent to a user-provided email, and they are available to download in a parsable format.This platform is freely available at http://bioinformatics.csu.edu.cn/netepd.
2020年04期 v.25 542-552页 [查看摘要][在线阅读][下载 952K] [下载次数：29 ] |[网刊下载次数：0 ] |[引用频次：3 ] |[阅读次数：0 ] - Jiashuai Zhang;Wenkai Li;Min Zeng;Xiangmao Meng;Lukasz Kurgan;Fang-Xiang Wu;Min Li;
Proteins drive virtually all cellular-level processes.The proteins that are critical to cell proliferation and survival are defined as essential.These essential proteins are implicated in key metabolic and regulatory networks,and are important in the context of rational drug design efforts.The computational identification of the essential proteins benefits from the proliferation of publicly available protein interaction datasets.Scientists have developed several algorithms that use these interaction datasets to predict essential proteins.However, a comprehensive web platform that facilitates the analysis and prediction of essential proteins is missing.In this study, we design,implement, and release Net EPD: a network-based essential protein discovery platform.This resource integrates data on Protein–Protein Interaction(PPI) networks, gene expression, subcellular localization, and a native set of essential proteins.It also computes a variety of node centrality measures, evaluates the predictions of essential proteins, and visualizes PPI networks.This comprehensive platform functions by implementing four activities, which include the collection of datasets, computation of centrality measures, evaluation, and visualization.The results produced by Net EPD are visualized on its website, and sent to a user-provided email, and they are available to download in a parsable format.This platform is freely available at http://bioinformatics.csu.edu.cn/netepd.
2020年04期 v.25 542-552页 [查看摘要][在线阅读][下载 952K] [下载次数：29 ] |[网刊下载次数：0 ] |[引用频次：3 ] |[阅读次数：0 ] - Jiashuai Zhang;Wenkai Li;Min Zeng;Xiangmao Meng;Lukasz Kurgan;Fang-Xiang Wu;Min Li;
Proteins drive virtually all cellular-level processes.The proteins that are critical to cell proliferation and survival are defined as essential.These essential proteins are implicated in key metabolic and regulatory networks,and are important in the context of rational drug design efforts.The computational identification of the essential proteins benefits from the proliferation of publicly available protein interaction datasets.Scientists have developed several algorithms that use these interaction datasets to predict essential proteins.However, a comprehensive web platform that facilitates the analysis and prediction of essential proteins is missing.In this study, we design,implement, and release Net EPD: a network-based essential protein discovery platform.This resource integrates data on Protein–Protein Interaction(PPI) networks, gene expression, subcellular localization, and a native set of essential proteins.It also computes a variety of node centrality measures, evaluates the predictions of essential proteins, and visualizes PPI networks.This comprehensive platform functions by implementing four activities, which include the collection of datasets, computation of centrality measures, evaluation, and visualization.The results produced by Net EPD are visualized on its website, and sent to a user-provided email, and they are available to download in a parsable format.This platform is freely available at http://bioinformatics.csu.edu.cn/netepd.
2020年04期 v.25 542-552页 [查看摘要][在线阅读][下载 952K] [下载次数：29 ] |[网刊下载次数：0 ] |[引用频次：3 ] |[阅读次数：0 ] 下载本期数据