A Pseudo DNA Cryptography Method

The DNA cryptography is a new and very promising direction in cryptography research. DNA can be used in cryptography for storing and transmitting the information, as well as for computation. Although in its primitive stage, DNA cryptography is shown to be very effective. Currently, several DNA computing algorithms are proposed for quite some cryptography, cryptanalysis and steganography problems, and they are very powerful in these areas. However, the use of the DNA as a means of cryptography has high tech lab requirements and computational limitations, as well as the labor intensive extrapolation means so far. These make the efficient use of DNA cryptography difficult in the security world now. Therefore, more theoretical analysis should be performed before its real applications. In this project, We do not intended to utilize real DNA to perform the cryptography process; rather, We will introduce a new cryptography method based on central dogma of molecular biology. Since this method simulates some critical processes in central dogma, it is a pseudo DNA cryptography method. The theoretical analysis and experiments show this method to be efficient in computation, storage and transmission; and it is very powerful against certain attacks. Thus, this method can be of many uses in cryptography, such as an enhancement insecurity and speed to the other cryptography methods. There are also extensions and variations to this method, which have enhanced security, effectiveness and applicability.Comment: A small work that quite some people asked abou

Robust Stereoscopic Crosstalk Prediction

We propose a new metric to predict perceived crosstalk using the original images rather than both the original and ghosted images. The proposed metrics are based on color information. First, we extract a disparity map, a color difference map, and a color contrast map from original image pairs. Then, we use those maps to construct two new metrics (Vdispc and Vdlogc). Metric Vdispc considers the effect of the disparity map and the color difference map, while Vdlogc addresses the influence of the color contrast map. The prediction performance is evaluated using various types of stereoscopic crosstalk images. By incorporating Vdispc and Vdlogc, the new metric Vpdlc is proposed to achieve a higher correlation with the perceived subject crosstalk scores. Experimental results show that the new metrics achieve better performance than previous methods, which indicate that color information is one key factor for crosstalk visible prediction. Furthermore, we construct a new data set to evaluate our new metrics

Temporal Aware Mixed Attention-based Convolution and Transformer Network (MACTN) for EEG Emotion Recognition

Emotion recognition plays a crucial role in human-computer interaction, and electroencephalography (EEG) is advantageous for reflecting human emotional states. In this study, we propose MACTN, a hierarchical hybrid model for jointly modeling local and global temporal information. The model is inspired by neuroscience research on the temporal dynamics of emotions. MACTN extracts local emotional features through a convolutional neural network (CNN) and integrates sparse global emotional features through a transformer. Moreover, we employ channel attention mechanisms to identify the most task-relevant channels. Through extensive experimentation on two publicly available datasets, namely THU-EP and DEAP, our proposed method, MACTN, consistently achieves superior classification accuracy and F1 scores compared to other existing methods in most experimental settings. Furthermore, ablation studies have shown that the integration of both self-attention mechanisms and channel attention mechanisms leads to improved classification performance. Finally, an earlier version of this method, which shares the same ideas, won the Emotional BCI Competition's final championship in the 2022 World Robot Contest

Matten: Video Generation with Mamba-Attention

In this paper, we introduce Matten, a cutting-edge latent diffusion model with Mamba-Attention architecture for video generation. With minimal computational cost, Matten employs spatial-temporal attention for local video content modeling and bidirectional Mamba for global video content modeling. Our comprehensive experimental evaluation demonstrates that Matten has competitive performance with the current Transformer-based and GAN-based models in benchmark performance, achieving superior FVD scores and efficiency. Additionally, we observe a direct positive correlation between the complexity of our designed model and the improvement in video quality, indicating the excellent scalability of Matten