Existing multimodal sentiment analysis models are effective at capturing sentiment commonalities across different modalities and discerning emotions. However, these models still face significant challenges when analyzing samples with sentiment polarity differences across modalities. Neural networks struggle to process such divergent sentiment samples, particularly when they are scarce within datasets. While larger datasets could help address this limitation, collecting and annotating them is resource-intensive. To overcome this challenge, we proposeTriagedMSA, a multimodal sentiment analysis model with triage capability. Our model introduces theSentiment Disagreement Triage Network, which identifies sentiment disagreement between modalities within a sample. This triage mechanism reduces mutual influence by learning to distinguish between samples of sentiment agreement and disagreement. To process these two sample types, we develop theSentiment Selection Attention Networkand theSentiment Commonality Attention Network, both of which enhance modality interaction learning. Furthermore, we propose theAdaptive Polarity Detection (APD) algorithm, which ensures the generalizability of our model across different datasets, regardless of whether unimodal labels are available. The APD algorithm adaptively determines sentiment polarity disagreement or agreement between modalities. We conduct experiments on three multimodal sentiment analysis datasets:CMU-MOSI,CMU-MOSEIandCH-SIMS.v2. The results demonstrate that our proposed methodology outperforms existing state-of-the-art approaches.

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system’s potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme with the 3D-MCM for image encryption. Experimental results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks.

Currently, discrete memristors are a focal point in the study of chaotic maps. Similar to memristors, memcapacitors-another type of memory circuit component-have not received widespread attention in the design of chaotic maps. In this article, we propose a 4-D memcapacitive hyperchaotic logistic map (4D-MHLM) by integrating memcapacitors with the logistic map. The dynamical behavior of the 4D-MHLM is analyzed using Lyapunov exponent analysis, and the impact of different parameters on system performance is discussed. The complexity of generating pseudo-random sequences with the 4D-MHLM is investigated through complexity analysis, including spectral entropy complexity and C0 complexity. Notably, attractor analysis reveals a unique phenomenon of infinite coexisting attractors within the 4D-MHLM. Finally, the chaotic attractor generated by the 4D-MHLM is successfully implemented on a hardware platform. Theoretical analysis and digital circuit implementation results indicate that the 4D-MHLM exhibits rich dynamical behavior and higher complexity, offering significant value for practical applications.

Chaos systems find extensive applications in cryptography and pseudorandom number generation due to their ability to generate pseudo-random signals. This paper focuses on enhancing the complexity of chaotic systems by introducing the memristor, a nonlinear component. We propose a novel map called the 2D memristive Cubic map (2D-MCM), which integrates the memristor with the Cubic map to create a discrete mapping. The 2D-MCM exhibits rich dynamical behavior and a broad parameter space. Notably, the 2D-MCM displays boosting bifurcation behavior. As the control parameters increase, the 2D-MCM demonstrates an expanded range of values, indicating its ability to generate a larger number of pseudo-random sequences. To validate its performance, we establish a hardware platform to physically capture the attractors of the 2D-MCM. To verify the performance of the 2D-MCM in generating pseudorandom sequences, we designed a video encryption algorithm based on the 2D-MCM. This algorithm selectively encrypts specific areas within the video, with correlation coefficients of the encrypted video in the horizontal, vertical, and diagonal directions being 0.0002, -0.0005, and 0.0004, respectively. Through simulation experiments and security analysis, we demonstrate that the 2D-MCM performs well in video encryption tasks.

To provide robust security measures for audio data during transmission, this article has developed a novel dual-channel audio encryption scheme based on chaos theory. Specifically, a new 2-D chaotic system called 2-D infinite collapse with logistic map (2-D-ICLM) is designed in this article. Compared to traditional 2-D chaotic systems, the 2-D-ICLM exhibits a larger parameter space, complexity, and richness, along with high unpredictability and randomness. These characteristics provide potential advantages and applications in the field of encryption. In the proposed encryption scheme, audio information serves as input to a hash function, which generates the initial values and parameters for the 2-D-ICLM, producing the keystream for the cryptographic system. Considering the correlation between the two channels of audio information, the information from the left and right channels is fused to create a new audio signal for encryption. Scrambling and diffusion processes are performed synchronously in the encryption algorithm, with the ciphertext information from the left channel utilized in the encryption of the right channel audio. The experimental results prove the effectiveness of the suggested audio encryption technique, effectively countering various conventional attack methods and showcasing its robust security features. The correlation of adjacent elements of ciphertext audio is 0.0013, the NSCR and UACI is around 0.9960 and 0.3345, and the efficiency is 0.0003 s/KB.

An asynchronous updating Boolean network is employed to simulate and analyze the gene expression of a particular tissue or species, revealing the life activity process from a system perspective to reveal the disease mechanism and treat the disease. Therefore, to ensure the safe transmission of the asynchronous updating Boolean network in the network, we designed an asynchronous updating Boolean network encryption algorithm based on chaos (ABNEA). First, a novel 2D chaotic system (2D-FPSM) is designed. This system has better performance than the classical 2D chaotic system. It is very suitable for cryptographic systems to generate key streams. Second, an encoding rule is designed to convert the asynchronous updating Boolean network to a Boolean matrix and propagate it on the network as an image. The receiver and sender jointly save the encoding rule. Last, to protect the safe propagation of the Boolean network matrix on the network, the method of synchronous scrambling-diffusion is adapted to encrypt the Boolean network matrix based on the 2D-FPSM. Simulation experiments and security analysis show that the average correlation of adjacent pixels of ciphertext are 0.0010, -0.0010, -0.0020, and the average information entropy is 7.9984. The ABNEA can complete the encryption tasks of asynchronously updating Boolean networks and exhibits good security characteristics.

Indoor scene classification is an important topic in computer vision, which is challenging due to the variability of decoration. Human vision system, on the other hand, is marvelous in adaptively recognizing scene categories with excellent performance and can be used for reference. Although bio-inspired computer vision algorithms have proven their effectiveness in classification applications, nowadays few researches on indoor scene classification algorithms attempt to model human vision system, restricting further improvement of performance and making it difficult to achieve adaptive scene understanding. To deal with this problem, in this paper we attempt to model the human vision system and achieve scene classification according to the cognitive theory, by dividing the problem into low-level objection annotation and high-level knowledge inference respectively on a macro perspective. Inspired by the biotical perception principle, a novel cognitive hybrid motivation framework is proposed, including empirical based annotation and inference over knowledge base, which is a simple yet effective framework based on techniques of object detection and classification. For a given indoor scene, objects of indoor scene are first annotated, then knowledge base is utilized to infer the category, reducing the effect of variable background. Environmental context is also utilized to assist classification. The proposed framework are evaluated on popular indoor scene dataset, and its effectiveness is proved by experimental results.