As the application of image emotion recognition technology grows increasingly widespread, emotional data faces potential privacy risks during transmission and storage. Images depicting negative emotions are particularly prone to revealing an individual is psychological state and sensitive information. Therefore, effective security protection for such images is of practical importance. This paper design a chaotic system and use CNN-based recognition to select the images requiring enhanced protection. First, a novel second-order memristor is designed and coupled with a neuron model to construct a chaotic system (SOM-Rulkov). Analysis of its phase diagram, bifurcation diagram, and Lyapunov exponent spectrum indicates that SOM-Rulkov exhibits rich dynamical characteristics, which can provide a pseudo-random key stream with good performance for encryption algorithms. Then, using a CNN to recognise emotions in images, employing the identified negative emotion images as encryption images to enhance data security during transmission and storage. During the encryption process, This paper proposes an enhanced diffusion structure with a parallel pool mechanism that enables four-directional diffusion to improve encryption efficiency. Experimental results show that the proposed scheme achieves strong security and high speed for emotional image privacy protection.

To solve the problem of resource consumption and information security during color image transmission, multiple color images encryption scheme according to a three-dimensional discrete chaotic map with pseudo-random number signal generator and discrete wavelet transform (DWT) compression is proposed. Firstly, dynamics of three-dimensional discrete chaotic map is analyzed and found to provide better randomness for encryption schemes. Next, multiple color images of different sizes are compressed to 1/4 of the original size after DWT processing. The multiple compressed images are merged into a plaintext cube, and the plaintext parameters associated with the cube are generated. The generated plaintext parameters are combined with the chaotic map to form the key. A series of sequences are generated by iteration for image confusion and diffusion to get cipher images. The final simulation results show that recovered plaintext image is still clearly visible even with lossy DWT compression. The security analysis results indicate that this scheme has high level of security protection for color images.

Adversarial text defense is a significant strategy to protect modern NLP models from being attacked.Typical text defense methods usually enhance the model's robustness by model retraining or equipping it with a data preprocessing step, aiming to eliminate the non-robust features and preserve the robust ones.Although some efforts have been made to recognize the robust features, e.g., by the information bottleneck (IB) technique, how to fully disentangle the robust and non-robust representation remains a big challenge.To alleviate this problem, we propose a novel text defense method, named Disentangled Information Bottleneck (DisIB), with two major merits.Firstly, we separate the robust features and non-robust features with a disentangled two-line framework rather than the one-line compression network in IB.This prevents the loss of robust features caused by information compression and produces complete robust features.Secondly, we design a discriminator network to approximate the minimum mutual information of the two lines, which sufficiently disentangles robust and non-robust features.To validate the effectiveness of our DisIB, we conduct a total of 96 defense experiments on four datasets by defending four popular attack methods.Experimental results elaborate that our method significantly outperforms six baselines, with accuracy improvements ranging from 3.8% to 20.7%.

In multi-target collaborative tracking, sensor resources allocation is a crucial yet complicated problem. A new sensor management algorithm using the Conditional Posterior Cramér-Rao Lower Bound (CPCRLB) as information metric and the Modified Closest Point of Approach (MCPA) algorithm to assess the threat of targets is proposed in this paper. To quantify the tracking performance, CPCRLB is computed based on Particle Filter and serves as the information metric for sensor management. The MCPA threat, which integrates the relative positions and velocities of the target with respect to the protected asset, is employed to guide the sequence of multi-target sensor allocation scheme in order to achieve high tracking accuracy in consideration of target threat. In order to meet the computation requirements, the Hierarchical Binary Particle Swarm Optimization (HBPSO) algorithm is used to search for the optimal sensor allocation scheme at each time step. By integrating a hierarchical architecture into the original Binary Particle Swarm Optimization algorithm, particles from different layers are used to search in different spaces to prevent the algorithm from getting stuck in locally optimal solutions. Simulations in both static and dynamic scenarios demonstrate that this new sensor management algorithm outperforms the existing algorithms, including the state-of-the-art algorithms based on Modified Particle Swarm Optimization (MPSO).

Abstract Advanced knowledge engineering (KE), represented by knowledge graph (KG), drives the development of various fields and engineering technologies and provides various knowledge fusion and knowledge empowerment interfaces. At the same time, advanced system engineering (SE) takes model-based system engineering (MBSE) as the core to realize formal modeling and process analysis of the whole system. The two complement each other and are the key technologies for the transition from 2.0 to 3.0 in the era of artificial intelligence and the transition from perceptual intelligence to cognitive intelligence. This survey summarizes an advanced information fusion system, from model-driven to knowledge-enabled. Firstly, the concept, representative methods, key technologies and application fields of model-driven system engineering are introduced. Then, it introduces the concept of knowledge-driven knowledge engineering, summarizes the architecture and construction methods of advanced knowledge engineering and summarizes the application fields. Finally, the combination of advanced information fusion systems, development opportunities and challenges are discussed.

To satisfy the demand for automated product configuration in manufacturing enterprises, a product configuration recommendation algorithm based on knowledge graph optimization is proposed. Firstly, the historical configuration information of the same product, material and component is mined through FP-Tree, and the inventory data of the same configuration relationship is compressed into a single normalized weight data. Secondly, combined with the weight data, the knowledge graph is constructed by using the a priori relationships of the same product, material and component stored in the structured database. On this basis, the recall of associated materials and components is completed by using the graph query of product entity, and then the FM algorithm is used to achieve detailed sorting recommendations based on the normalized weight data. Validated using production data, the accuracy rate of product configuration recommendation reaches 74.54%.

The conventional fuzzy C-means (FCM) is sensitive to the initial cluster centers and outliers, which may cause the centers deviate from the real centers when the algorithm converges. To improve the performance of FCM, a method of initializing the cluster centers based on probabilistic suppression is proposed and an improved local outlier factor is integrated into the model of FCM. Firstly, the probability of an object as cluster center is defined by its local density, and all initial centers are obtained by the cluster center’s probability and probability suppression function incrementally. Next, an improved local outlier factor is reconstructed according to the local distribution of an object, and its reciprocal is regarded as the contribution degree of an object to cluster center. Then, the improved local outlier factor is integrated into FCM to alleviate the negative effect caused by outliers. Finally, experiments on synthetic and real-world datasets are provided to demonstrate the clustering performance and anti-noise ability of proposed method.

Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs. Some recent work started to study the pre-training of GNNs. However, none of them provide theoretical insights into the design of their frameworks, or clear requirements and guarantees towards their transferability. In this work, we establish a theoretically grounded and practically useful framework for the transfer learning of GNNs. Firstly, we propose a novel view towards the essential graph information and advocate the capturing of it as the goal of transferable GNN training, which motivates the design of EGI (Ego-Graph Information maximization) to analytically achieve this goal. Secondly,when node features are structure-relevant, we conduct an analysis of EGI transferability regarding the difference between the local graph Laplacians of the source and target graphs. We conduct controlled synthetic experiments to directly justify our theoretical conclusions. Comprehensive experiments on two real-world network datasets show consistent results in the analyzed setting of direct-transfering, while those on large-scale knowledge graphs show promising results in the more practical setting of transfering with fine-tuning.

Face alignment is an important process in facial analysis. Cascaded linear regression approaches have shown the capability to achieve the state-of-the-art accuracy on numerous face alignment datasets. However, most of these approaches only learn to map coordinate offsets of the key points from image features. This regression strategy can be easily trapped in local optima. We propose a novel regression strategy by introducing affine transformation. First, the best affine-transformation parameters between the initial mean shape and the ground truth are estimated by Procrustes analysis. Subsequently, we base the mapping from image features on the best affine-transformation parameters. Experimental results indicate that this strategy can reduce the offsets between two shapes significantly. Combined with coordinate-offset regression strategy, the hybrid approach produces a remarkably performance in term of accuracy, training time, prediction rate, and the model size. Moreover, the affine-transformation parameter regression strategy can be considered as a shape-initialization method that can be combined with other initial shape-based face alignment algorithms to improve the face alignment accuracy.