Recent advances in single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for reconstruction gene regulation networks (GRNs). At present, many different models have been proposed to inf...
Recent advances in single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for reconstruction gene regulation networks (GRNs). At present, many different models have been proposed to infer GRN from a large number of RNA-seq data, but most deep learning models use a priori gene regulatory network to infer potential GRNs. It is a challenge to reconstruct GRNs from scRNA-seq data due to the noise and sparsity introduced by the dropout effect. Here, we propose GAALink, a novel unsupervised deep learning method. It first constructs the gene similarity matrix and then refines it by threshold value. It then learns feature representations of genes through a graphical attention autoencoder that propagates information across genes with different weights. Finally, we use gene feature expression for matrix completion such that the GRNs are reconstructed. Compared with seven existing GRNs reconstruction methods, GAALink achieves more accurate performance on seven scRNA-seq dataset with four ground truth networks. GAALink can provide a useful tool for inferring GRNs for scRNA-seq expression data.
Lattice Boltzmann method (LBM) has become a powerful method in computational fluid dynamics and has drawn more and more attention in high-performance computing due to its particulate nature and local dynamics, especia...
详细信息
Mixed-type data with both categorical and numerical features are ubiquitous in network security, but the existing methods are minimal to deal with them. Existing methods usually process mixed-type data through feature...
详细信息
ISBN:
(纸本)9781665408783
Mixed-type data with both categorical and numerical features are ubiquitous in network security, but the existing methods are minimal to deal with them. Existing methods usually process mixed-type data through feature conversion, whereas their performance is downgraded by information loss and noise caused by the transformation. Meanwhile, existing methods usually superimpose domain knowledge and machine learning in which fixed thresholds are used. It cannot dynamically adjust the anomaly threshold to the actual scenario, resulting in inaccurate anomalies obtained, which results in poor performance. To address these issues, this paper proposes a novel Anomaly Detection method based on Reinforcement Learning, termed ADRL, which uses reinforcement learning to dynamically search for thresholds and accurately obtain anomaly candidate sets, fusing domain knowledge and machine learning fully and promoting each other. Specifically, ADRL uses prior domain knowledge to label known anomalies and uses entropy and deep autoencoder in the categorical and numerical feature spaces, respectively, to obtain anomaly scores combining with known anomaly information, which are integrated to get the overall anomaly scores via a dynamic integration strategy. To obtain accurate anomaly candidate sets, ADRL uses reinforcement learning to search for the best threshold. Detailedly, it initializes the anomaly threshold to get the initial anomaly candidate set and carries on the frequent rule mining to the anomaly candidate set to form the new knowledge. Then, ADRL uses the obtained knowledge to adjust the anomaly score and get the score modification rate. According to the modification rate, different threshold modification strategies are executed, and the best threshold, that is, the threshold under the maximum modification rate, is finally obtained, and the modified anomaly scores are obtained. The scores are used to re-carry out machine learning to improve the algorithm's accuracy for anomalo
Machine learning engineering is an important technology that has attracted the attention of academia and industry in the past two years. For AI to become a productivity of enterprises, it must be engineered to solve t...
详细信息
Script is the structured knowledge representation of prototypical real-life event *** the commonsense knowledge inside the script can be helpful for machines in understanding natural language and drawing commonsensibl...
详细信息
Script is the structured knowledge representation of prototypical real-life event *** the commonsense knowledge inside the script can be helpful for machines in understanding natural language and drawing commonsensible *** learning is an interesting and promising research direction,in which a trained script learning system can process narrative texts to capture script knowledge and draw ***,there are currently no survey articles on script learning,so we are providing this comprehensive survey to deeply investigate the standard framework and the major research topics on script *** research field contains three main topics:event representations,script learning models,and evaluation *** each topic,we systematically summarize and categorize the existing script learning systems,and carefully analyze and compare the advantages and disadvantages of the representative *** also discuss the current state of the research and possible future directions.
Deep reinforcement learning(RL)has become one of the most popular topics in artificial intelligence *** has been widely used in various fields,such as end-to-end control,robotic control,recommendation systems,and natu...
详细信息
Deep reinforcement learning(RL)has become one of the most popular topics in artificial intelligence *** has been widely used in various fields,such as end-to-end control,robotic control,recommendation systems,and natural language dialogue *** this survey,we systematically categorize the deep RL algorithms and applications,and provide a detailed review over existing deep RL algorithms by dividing them into modelbased methods,model-free methods,and advanced RL *** thoroughly analyze the advances including exploration,inverse RL,and transfer ***,we outline the current representative applications,and analyze four open problems for future research.
Payload anomaly detection can discover malicious beliaviors tiidden in network packets. It is liard to liandle payload due to its various possible characters and complex semantic context, and tlius identifying abnorma...
详细信息
China is a big agricultural county with more than 500 million rural population. In China, farmers usually loan from rural commercial banks or rural credit cooperatives. It is crucial for the national economic developm...
详细信息
Improving the transferability of adversarial examples for the purpose of attacking unknown black-box models has been intensively studied. In particular, feature-level transfer-based attacks, which destroy the intermed...
详细信息
Improving the transferability of adversarial examples for the purpose of attacking unknown black-box models has been intensively studied. In particular, feature-level transfer-based attacks, which destroy the intermediate feature outputs of source models, are proven to generate more transferable adversarial examples. However, existing state-of-the-art feature-level attacks only destroy a single intermediate layer, this severely limits the transferability of adversarial examples. And all of these attacks have a vague distinction between positive and negative features. By contrast, we propose the Multi-layer Feature Division Attack (MFDA), which aggregates multi-layer feature information on the basis of feature division to attack. Extensive experimental evaluation demonstrates that MFDA can significantly boost the adversarial transferability and quantitatively distinguish the effects of positive and negative features on transferability. Compared to the state-of-the-art feature-level attacks, our improvement methods with MFDA increase the average success rate by 2.8% against normally trained models and 3.0% against adversarially trained models.
Current research shows that the privacy of FL is threatened by an honest-but-curious server. However, existing research focus on privacy attacks against the malicious server while overlooking that it could also compro...
Current research shows that the privacy of FL is threatened by an honest-but-curious server. However, existing research focus on privacy attacks against the malicious server while overlooking that it could also compromise the shared model's integrity by introducing poisoning attacks. In this work, we propose a novel data-free backdoor attack (DaBA) against FL via malicious server to bridge the gap. Specifically, we utilize global model inversion to obtain a dummy dataset on the server side, then add backdoor triggers to a portion of the inputs in the dummy dataset and replace their labels with the target label, and finally retrain part of the global model on the poisoned dummy dataset. Our experimental results show that DaBA can achieve a high attack success rate on poisoned samples and high prediction accuracy on clean samples, which means the effectiveness and stealthiness of DaBA, respectively. For example, in the experiment of the MNIST dataset, DaBA can achieve a 99.6% attack success rate and 96.3% accuracy rate. We also discuss possible defense strategies against our attack. Our research reveals a significant security risk of FL.
暂无评论