SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks

Anomaly detection on attributed networks aims to find the nodes whose behaviors are significantly different from other majority nodes. Generally, network data contains information about relationships between entities, and the anomaly is usually embodied in these relationships. Therefore, how to comprehensively model complex interaction patterns in networks is still a major focus. It can be observed that anomalies in networks violate the homophily assumption. However, most existing studies only considered this phenomenon obliquely rather than explicitly. Besides, the node representation of normal entities can be perturbed easily by the noise relationships introduced by anomalous nodes. To address the above issues, we present a novel contrastive learning framework for anomaly detection on attributed networks, \textbf{SCALA}, aiming to improve the embedding quality of the network and provide a new measurement of qualifying the anomaly score for each node by introducing sparsification into the conventional method. Extensive experiments are conducted on five benchmark real-world datasets and the results show that SCALA consistently outperforms all baseline methods significantly.Comment: 9 pages, 14 figure

The Quantitative Relationship among the Number of the Pacing Cells Required, the Dimension, and the Diffusion Coefficient

The purpose of the paper is to derive a formula to describe the quantitative relationship among the number of the pacing cells required (NPR), the dimension i, and the diffusion coefficient D (electrical coupling or gap junction G). The relationship between NPR and G has been investigated in different dimensions, respectively. That is, for each fixed i, there is a formula to describe the relationship between NPR and G; and three formulas are required for the three dimensions. However, there is not a universal expression to describe the relationship among NPR, G, and i together. In the manuscript, surveying and investigating the basic law among the existed data, we speculate the preliminary formula of the relationship among the NPR, i, and G; and then, employing the cftool in MATLAB, the explicit formulas are derived for different cases. In addition, the goodness of fit (R2) is computed to evaluate the fitting of the formulas. Moreover, the 1D and 2D ventricular tissue models containing biological pacemakers are developed to derive more data to validate the formula. The results suggest that the relationship among the NPR, i, and the G (D) could be described by a universal formula, where the NPR scales with the i (the dimension) power of the product of the square root of G (D) and a constant b which is dependent on the strength of the pacing cells and so on

Hierarchical Object-Oriented Petri Net Modeling Method based on Ontology

This paper presents a Hierarchical Object-Oriented Petri Net(HOOPN) modeling method based on Ontology that should not only enable sharing Petri nets models on the Semantic Web but also present a high level Petri net. Previous work on formal methods for representing Petri nets mainly focuses on modeling and analyzing aspects or formats for Petri net model interchange. However, such efforts do not provide a suitable model description for using Petri nets on the Semantic Web. This paper uses the HOOPN with the Ontology concepts as a starting point for implementing the Petri net ontology. Moreover this paper uses HOOPN as the Petri net model method. HOOPN supports a wide range of Object-Oriented features including abstract, encapsulated and modularized objects, object interaction by message passing, inheritance, and polymorphism. Keywords Hierarchical object-oriented Petri net; Ontology; modeling method 1

Ship Flooding Time Prediction Based on Composite Neural Network

When a ship sailing on the sea encounters flooding events, quickly predicting the flooding time of the compartments in the damaged area is beneficial to making evacuation decisions and reducing losses. At present, decision-makers obtain flooding data through various sensors arranged on board to predict the time of compartment flooding. These data help with the calculation of the flooding time in emergency situations. This paper proposes a new approach to obtaining the compartment flooding time. Specifically in damage scenarios, based on Convolutional Neural Network and Recurrent Neural Network (CNN-RNN), using a composite neural network framework estimates the time when the compartment’s flooding water reaches the target height. The input of the neural network is the flooding images of the damaged compartment. Transfer learning is utilized in the paper. The ResNet18 model in Pytorch is used to extract the spatial information from the flooding images. The Long Short-Term Memory (LSTM) model is then applied to predict when the compartment flooding water reaches the target height. Experimental results show that, for the damaged compartment, the flooding time predicted by the neural network is 85% accurate while the others’ accuracy is more than 91%. Intuitively, when it comes to the actual flooding event, the composite neural network’s average prediction error for compartment flooding time is approximately 1 min. To summarize, these results suggest that the composite neural network proposed above can provide flooding information to assist decision-makers in emergency situations

Project Gradient Descent Adversarial Attack against Multisource Remote Sensing Image Scene Classification

Deep learning technology (a deeper and optimized network structure) and remote sensing imaging (i.e., the more multisource and the more multicategory remote sensing data) have developed rapidly. Although the deep convolutional neural network (CNN) has achieved state-of-the-art performance on remote sensing image (RSI) scene classification, the existence of adversarial attacks poses a potential security threat to the RSI scene classification task based on CNN. The corresponding adversarial samples can be generated by adding a small perturbation to the original images. Feeding the CNN-based classifier with the adversarial samples leads to the classifier misclassify with high confidence. To achieve a higher attack success rate against scene classification based on CNN, we introduce the projected gradient descent method to generate adversarial remote sensing images. Then, we select several mainstream CNN-based classifiers as the attacked models to demonstrate the effectiveness of our method. The experimental results show that our proposed method can dramatically reduce the classification accuracy under untargeted and targeted attacks. Furthermore, we also evaluate the quality of the generated adversarial images by visual and quantitative comparisons. The results show that our method can generate the imperceptible adversarial samples and has a stronger attack ability for the RSI scene classification

DFDP: A Distributed Algorithm for Finding Disjoint Paths in Wireless Sensor Networks with Correctness Guarantee

In wireless sensor networks, routing messages through multiple (node) disjoint paths between two sensor nodes is a promising way to increase robustness, throughput, and load balance. This paper proposes an efficient distributed algorithm named distributedly finding disjoint paths (DFDP) to find k disjoint paths connecting two given nodes s and t . A set of paths connecting s and t are disjoint if any two of them do not have any common nodes except s and t . Unlike the existing distributed algorithms, DFDP guarantees correctness; that is, it will output k disjoint paths if there exist k disjoint paths in the network or the maximum number of disjoint paths otherwise. Compared with the centralized algorithms which also guarantee correctness, DFDP is shown to have much better efficiency and load balance by theory analysis and simulation results