Algorithms and data structures are principal parts of computer science education. For many students, however, it is not easy to master them due to their diversity and inherent complexity. The application of algorithm visualizations is a widely adopted approach, which can help to mitigate this difficulty. Within this work, we aim to improve the efficiency of the learning process in the field of algorithms and data structures. The main directions we use in this work to reach this goal are the introduction of comparative algorithm visualization and the implementation of the visualization tool based on contemporary standards. We analyze and compare several of the available solutions for algorithms and data structure visualization and evaluate them according to the provided functionalities. Further, we define a list of requirements, including the capability to compare selected algorithms visually. The practical outcome of this work is a web application that allows us to visualize and compare different algorithms and data structures in terms of their operation and efficiency. At the end of the paper, the proposed solution is evaluated in several ways.

Weibo has emerged as a vital platform for Chinese netizens to share information, but it has also given rise to numerous rumors. Real-time detection methods that do not rely on propagation features are the most effective way to curb the spread of these rumors. Currently, real-time detection methods that mine semantic features of rumor text based on deep learning lack sufficient generalization ability. Therefore, we propose a real-time rumor detection method integrating multiple user and content features. In addition to standard user basic features, our approach utilizes the user's historical posting data to extract two deep-level features: user rationality and professionalism. Regarding content features, in addition to standard statistical features, we use a graph attention network that considers edge weights to learn deep semantic features of the content. The user and content features are concatenated and fed into a multi-layer perceptron for classification. The experimental results on a real Weibo dataset show that the accuracy of the proposed method achieves 92.6%, which outperforms all the compared baseline methods.