Network representation learning (RL) aims to transform the nodes in a network into low-dimensional vector spaces while preserving the inherent properties of the network. Though network RL has been intensively studied, most existing works focus on either network structure or node attribute information. In this paper, we propose a novel framework, named ANRL, to incorporate both the network structure and node attribute information in a principled way. Specifically, we propose a neighbor enhancement autoencoder to model the node attribute information, which reconstructs its target neighbors instead of itself. To capture the network structure, attribute-aware skip-gram model is designed based on the attribute encoder to formulate the correlations between each node and its direct or indirect neighbors. We conduct extensive experiments on six real-world networks, including two social networks, two citation networks and two user behavior networks. The results empirically show that ANRL can achieve relatively significant gains in node classification and link prediction tasks.
Radiomics is an objective method for extracting quantitative information from medical images. However, in radiomics, standardization, overfitting, and generalization are major challenges to be overcome. Test–retest experiments can be used to select robust radiomic features that have minimal variation. Currently, it is unknown whether they should be identified for each disease (disease specific) or are only imaging device-specific (computed tomography [CT]-specific). Here, we performed a test–retest analysis on CT scans of 40 patients with rectal cancer in a clinical setting. Correlation between radiomic features was assessed using the concordance correlation coefficient (CCC). In total, only 9/542 features have a CCC > 0.85. Furthermore, results were compared with the test–retest results on CT scans of 27 patients with lung cancer with a 15-minute interval. Results show that 446/542 features have a higher CCC for the test–retest analysis of the data set of patients with lung cancer than for patients with rectal cancer. The importance of controlling factors such as scanners, imaging protocol, reconstruction methods, and time points in a radiomics analysis is shown. Moreover, the results imply that test–retest analyses should be performed before each radiomics study. More research is required to independently evaluate the effect of each factor.
The rapid development and popularization of the network have brought many problems to network security. Intrusion detection technology is often used as an effective security technology to protect the network. The deep belief network (DBN), as a classic model of deep learning, has good classification performance and is often used in the field of intrusion detection. However, the network structure of DBN is generally set through practical experience. For the optimization problem of the DBN-based intrusion detection classification model (DBN-IDS), this paper proposes a new joint optimization algorithm to optimize the DBN's network structure. First, we design a particle swarm optimization (PSO) based on the adaptive inertia weight and learning factor. Second, we use the fish swarm behavior of cluster, foraging, and other behaviors to optimize the PSO to find the initial optimization solution. Then, based on the initial optimization solution, we use the genetic operators with self-adjusting crossover probability and mutation probability to optimize the PSO to search the global optimization solution. Finally, the global optimization solution constructed by the above-mentioned joint optimization algorithm is used as the network structure of the intrusion detection classification model. The experimental results show that compared with other DBN-IDS optimization algorithms, our algorithm shortens the average detection time by at least 24.69% on the premise of increasing the average training time by 6.9%; compared with the tested classification algorithms, our DBN-IDS improves the average classification accuracy by at least 1.3% and up to 14.80% in the five-category classification, which is proved to be an efficient DBN-IDS optimization method. INDEX TERMS Intrusion detection, deep belief network, particle swarm optimization, artificial fish swarm algorithm, genetic algorithm.
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