Due to the significant Influence of social networks, information diffusion prediction, which aims to study the spread of messages among users, has become a crucial objective in various scenarios. Existing works have m...
Due to the significant Influence of social networks, information diffusion prediction, which aims to study the spread of messages among users, has become a crucial objective in various scenarios. Existing works have mostly attempted to integrate multiple source features such as social relationships and user preferences for prediction. Although it can improve the accuracy of predictions to some extent, there are still issues with complex models, low computational efficiency, and limited prediction performance. To address these challenges, this paper proposes a Gaussian-Noise-based information Diffusion Prediction Model (GNIDP). As we know, we are the first to model the information diffusion in the social network as Gaussian noise diffusion, specifically, GNIDP utilizes Gaussian noise to simulate information diffusion patterns during information propagation, while capturing the evolving diffusion patterns over time across different time slots. Consequently, our model does not rely on social topology to achieve prediction, while improving the efficiency and accuracy of predictions. Experiments on two real social network datasets demonstrates that GNIDP achieves 13.54% relative gains over the best baseline on average. Therefore, the proposed model can be applied in scenarios where social topology is missing or difficult to obtain particularly in large-scale datasets requiring efficient diffusion prediction.
This paper considers waveform design for MIMO radar to synthesize a desired beampattern under similarity and constant modulus constraints. Generally, the constructed framework is a complex nonconvex optimization probl...
This paper considers waveform design for MIMO radar to synthesize a desired beampattern under similarity and constant modulus constraints. Generally, the constructed framework is a complex nonconvex optimization problem, which is difficult to solve directly. To tackle this problem, we convert it into a neural network-based learning problem. In particular, an objective function is developed to characterize the similarity constraint that makes the design waveform have good characteristics similar to the reference waveform. Then, we design a joint loss function for optimizing the transmit beampattern and waveform similarity, which allows the designed waveform to have better detection performance. Numerical simulation results show that the proposed method has better performance than the existing state-of-the-art method.
Weak target recognition, tracking and track management with a low signal-to-noise ratio (SNR) are always tricky problems. Probability hypothesis density (PHD) filtering propagates the first-order multi-target moment t...
Weak target recognition, tracking and track management with a low signal-to-noise ratio (SNR) are always tricky problems. Probability hypothesis density (PHD) filtering propagates the first-order multi-target moment to obtain the best Poisson approximation to multi-target density. The PHD filtering does not consider explicit associations between measurements and targets, which is computationally efficient. But it cannot distinguish different targets or extract the time series of track states. Based on track-before-detect (TBD) strategies, this paper proposes labeled PHD (LPHD) filtering and derives its close-form solution, which identifies targets with a unique label. It is derived based on rigorous Bayes criteria, finite set statistics and Kullback-Leibler divergence minimization approximation. The separable TBD-based observation likelihood is conjugate to the Poisson mixture prior for LPHD filtering. Under the point-target assumption, the multi-hypothesis assignments of pixel-to-target are implemented with Murty’s K-shortest path algorithm for LPHD filtering. Additionally, sequential Monte Carlo (SMC) implementations under the nonlinear non-Gaussian assumption are devised. Finally, simulations exhibit good performance in low SNR scenarios.
This paper addresses the partial coherent target detection problem for over-the-horizon (OTH) radar in Gaussian noise and clutter environment. The Gaussian distribution is adopted to model the noise and clutter based ...
This paper addresses the partial coherent target detection problem for over-the-horizon (OTH) radar in Gaussian noise and clutter environment. The Gaussian distribution is adopted to model the noise and clutter based on the low range resolution characteristic of OTH radar. The novel detectors are proposed in the presence of partial coherent signals. To be more concrete, by using the likelihood ratio test (LRT), we develop the partial coherent detector, coherent detector, and noncoherent detector. The performance of the proposed detectors is evaluated and compared by using the simulated partial coherent signals data. The numerical results show that the partial coherent detector performs better than the coherent detector and noncoherent detector.
Due to the complexity of urban building environments and the reduced signal-to-noise ratio (SNR) of electromagnetic (EM) waves after multiple reflections, it is difficult to detect targets in urban environments using ...
Due to the complexity of urban building environments and the reduced signal-to-noise ratio (SNR) of electromagnetic (EM) waves after multiple reflections, it is difficult to detect targets in urban environments using the reflection of EM waves. At this time, if the target is detected based on detection before tracking (DBT), it is more likely to cause a false alarm or missed detection. Inspired by the idea of joint multi-frame accumulation detection targets in tracking before detection (TBD), we propose a new detection method for the NLOS target. Compared to DBT, this method ensures the detection probability of the target at low SNR while reducing the false alarm probability by judging the accumulated results of multiple frames. Furthermore, several experimental results show that when the target is behind a building corner, the algorithm can effectively reduce the generation of false targets while ensuring the probability of target detection.
A deep learning model was proposed to simultaneously integrate the functions of demodulation and denoising for the phase-sensitive optical time domain reflectometry ( $\Phi$ -OTDR), resulting in low-noise reconstructi...
A deep learning model was proposed to simultaneously integrate the functions of demodulation and denoising for the phase-sensitive optical time domain reflectometry ( $\Phi$ -OTDR), resulting in low-noise reconstruction of the phase curves.
The utilization of machine learning techniques has greatly improved the accuracy of micro-Doppler(m-D) signatures-based radar signal recognition. However, the "catastrophic forgetting" problem commonly exist...
The utilization of machine learning techniques has greatly improved the accuracy of micro-Doppler(m-D) signatures-based radar signal recognition. However, the "catastrophic forgetting" problem commonly exists in data-driven algorithms severely limits the adaptability of recognition algorithms in real-world applications, as models cannot incrementally train and learn new categories. In this paper, we propose an incremental learning method, "Boundary Transfer and Uncertainty Augmentation (BTUA)" for continuous learning of m-D signatures. BTUA utilizes boundary transfer(BT) to generate pseudo-decision boundaries for old categories and avoid the forgetting problem. It also employs an uncertainty augmentation(UA) algorithm to enhance the model’s generalization and improve the correctness of feature extraction. Finally, the validation demonstrates the advantages of our algorithm in terms of both accuracy and practicality.
Monocular depth estimation is a significant task in computer vision, which can be widely used in Simultaneous Localization and Mapping (SLAM) and navigation. However, the current unsupervised approaches have limitatio...
Monocular depth estimation is a significant task in computer vision, which can be widely used in Simultaneous Localization and Mapping (SLAM) and navigation. However, the current unsupervised approaches have limitations in global information perception, especially at distant objects and the boundaries of the objects. To overcome this weakness, we propose a global-aware attention model called GlobalDepth for depth estimation, which includes two essential modules: Global Feature Extraction (GFE) and Selective Feature Fusion (SFF). GFE considers the correlation among multiple channels and refines the encoder feature by extending the receptive field of the network. Furthermore, we restructure the skip connection by employing SFF between the low-level and the high-level features in element wise, rather than simply concatenation or addition at the feature level. Our model excavates the key information and enhances the ability of global perception to predict details of the scene. Extensive experimental results demonstrate that our method reduces the absolute relative error by 10.32% compared with other state-of-the-art models on KITTI datasets.
Due to the lack of centralized regulatory authorities, the cryptocurrency trading market has witnessed an increase in illicit activities. Recently, more and more researchers have started exploring the application of m...
详细信息
ISBN:
(数字)9798350358261
ISBN:
(纸本)9798350358278
Due to the lack of centralized regulatory authorities, the cryptocurrency trading market has witnessed an increase in illicit activities. Recently, more and more researchers have started exploring the application of machine learning techniques to achieve anomaly detection in these transaction networks. However, in this transaction network, it’s hard to find the abnormal nodes because the labeling cost is high, without enough labeled and imbalanced data, which greatly limits the performance of the detection model. Therefore, this paper proposes a Minimal Substitution-based Label Propagation (MSLP) model to provide more labeled data to balance the graph data and complement the sample for downstream anomalous transaction detection in the blockchain networks. Specifically, MSLP is the first method that utilizes the minimal substitution theory from the social computing field to find more minority nodes from the unlabeled nodes. This approach has the potential to obtain more high-quality labeled data with minimal computational cost by utilizing a small amount of labeled graph data. Then, a label evaluation mechanism is proposed to decide the number of samples to be adopted for each class, ensuring the performance of downstream graph neural networks. Finally, extensive experiments were conducted and the proposed model improve the F1 score of illegal transaction node identification by 2.6% to 8.2%.
Target spawning and extended shape estimation are important problems in group target tracking. In this paper, we propose a Gaussian mixture cardinalized probability hypothesis density (GMCPHD) filter for group targets...
Target spawning and extended shape estimation are important problems in group target tracking. In this paper, we propose a Gaussian mixture cardinalized probability hypothesis density (GMCPHD) filter for group targets with spawning and irregular shape based on star-convex Random Hypersurface Model (RHM). In order to solve the problem of irregular group shape, we use star-convex RHM to describe the distribution of measurement sources. Besides, we use the distance division method to realize the division of measurement sets and the judgment of group splitting. On this basis, the real-time tracking of the motion state and extended shape is realized under the framework of GMCPHD. The performance of this algorithm is showcased by comparison with the elliptical RHM-based GMCPHD filter, and the results show that the proposed algorithm can improve the estimation accuracy of group shape and motion state effectively.
暂无评论