The visualization of streaming high-dimensional data often needs to consider the speed in dimensionality reduction algorithms, the quality of visualized data patterns, and the stability of view graphs that usually change over time with new data. Existing methods of streaming high-dimensional data visualization primarily line up essential modules in a serial manner and often face challenges in satisfying all these design considerations. In this research, we propose a novel parallel framework for streaming high-dimensional data visualization to achieve high data processing speed, high quality in data patterns, and good stability in visual presentations. This framework arranges all essential modules in parallel to mitigate the delays caused by module waiting in serial setups. In addition, to facilitate the parallel pipeline, we redesign these modules with a parametric non-linear embedding method for new data embedding, an incremental learning method for online embedding function updating, and a hybrid strategy for optimized embedding updating. We also improve the coordination mechanism among these modules. Our experiments show that our method has advantages in embedding speed, quality, and stability over other existing methods to visualize streaming high-dimensional data.

We propose a contrastive dimensionality reduction approach (CDR) for interactive visual cluster analysis. Although dimensionality reduction of high-dimensional data is widely used in visual cluster analysis in conjunction with scatterplots, there are several limitations on effective visual cluster analysis. First, it is non-trivial for an embedding to present clear visual cluster separation when keeping neighborhood structures. Second, as cluster analysis is a subjective task, user steering is required. However, it is also non-trivial to enable interactions in dimensionality reduction. To tackle these problems, we introduce contrastive learning into dimensionality reduction for high-quality embedding. We then redefine the gradient of the loss function to the negative pairs to enhance the visual cluster separation of embedding results. Based on the contrastive learning scheme, we employ link-based interactions to steer embeddings. After that, we implement a prototype visual interface that integrates the proposed algorithms and a set of visualizations. Quantitative experiments demonstrate that CDR outperforms existing techniques in terms of preserving correct neighborhood structures and improving visual cluster separation. The ablation experiment demonstrates the effectiveness of gradient redefinition. The user study verifies that CDR outperforms t-SNE and UMAP in the task of cluster identification. We also showcase two use cases on real-world datasets to present the effectiveness of link-based interactions.

The popularization of 5G and the development of cloud computing further promote the application of images. The storage of images in an untrusted environment has a great risk of privacy leakage. This paper outlines a design for a lightweight image encryption algorithm based on a message-passing algorithm with a chaotic external message. The message-passing (MP) algorithm allows simple messages to be passed locally for the solution to a global problem, which causes the interaction among adjacent pixels without additional space cost. This chaotic system can generate high pseudorandom sequences with high speed performance. A two-dimensional logistic map is utilized as a pseudorandom sequence generator to yield the external message sets of edge pixels. The external message can affect edge pixels, and then adjacent pixels interact with each other to produce an encrypted image. A MATLAB simulation shows the cipher-image performs fairly uniform distribution and has acceptable information entropy of 7.996749. The proposed algorithm reduces correlation coefficients from plain-image 1 to its cipher-image 0, which covers all of the plain-image characters with high computational efficiency (speed = 18.200374 Mbit/s). Theoretical analyses and experimental results prove the proposed algorithm’s persistence to various existing attacks with low cost.