With the rapid expansion of global logistics networks, container terminals, as critical nodes in the logistics chain, exert significant influence on the overall performance of supply chains. In terminal operations, container stacking strategies and equipment configuration are core factors determining operational efficiency. However, due to the complexity of terminal operations and the existence of multi-layered feedback mechanisms, there is currently a lack of systematic and quantitative evaluation methods to analyze the advantages and disadvantages of stacking strategies and equipment configurations. To address this gap, this study proposes a data-driven analytical framework. By processing real-world terminal operation data, the framework constructs a digital yard feature model, extracts key spatiotemporal operational features, and integrates the spatial feature extraction capability of 3D Convolutional Neural Networks (3DCNN) with the advantages of Boosting algorithms in handling non-linear relationships and feature importance. The study introduces a novel Deep-Tree Cascaded Regression (DTCR) algorithm to predict vessel handling efficiency, a highly uncertain and nonlinear indicator, to quantitatively assess the practical impact of different stacking strategies and equipment configurations. Experimental results demonstrate that the proposed model accurately captures the key correlations between container stacking and terminal operations, predicts vessel handling efficiency within a reasonable accuracy range, and realistically reflects the terminal’s operational processes. These findings provide a scientific basis for optimizing yard stacking strategies and equipment operation workflows, effectively improving overall terminal operational efficiency. Additionally, this research offers technical support and practical insights for the development of smart ports.

Creating and animating 3D biped cartoon characters is crucial and valuable in various applications. Compared with geometry, the diverse texture design plays an important role in making 3D biped cartoon characters vivid and charming. Therefore, we focus on automatic texture design for cartoon characters based on input instructions. This is challenging for domain-specific requirements and a lack of high-quality data. To address this challenge, we propose Make-It-Vivid, the first attempt to enable high-quality texture generation from text in UV space. We prepare a detailed text-texture paired data for 3D characters by using vision-question-answering agents. Then we customize a pretrained text-to-image model to generate texture map with template structure while preserving the natural 2D image knowledge. Furthermore, to enhance fine-grained details, we propose a novel adversarial learning scheme to shorten the domain gap between original dataset and realistic texture domain. Extensive experiments show that our approach outperforms current texture generation methods, resulting in efficient character texturing and faithful generation with prompts. Besides, we showcase various applications such as out of domain generation and texture stylization. We also provide an efficient generation system for automatic text-guided textured character generation and animation.

Logs are prevalent in modern cloud systems and serve as a valuable source of information for system maintenance. Over the years, a lot of research and industrial efforts have been devoted to the field of log-based anomaly detection. Through analyzing the limitations of existing approaches, we find that most of them still suffer from practical issues and are thus hard to be applied in real-world scenarios. For example, supervised approaches are dependent on a large amount of labeled log data for training, which can require much manual labeling effort. Besides, log instability, which is a pervasive issue in real-world systems, poses great challenge to existing methods, especially under the presence of many dissimilar new log events. To overcome these problems, we propose LogOnline, which is a semi supervised anomaly detector aided with online learning mechanism. The semi-supervised nature of LogOnline makes it able to get rid of the erroneous and time-consuming manual labeling of log data. Based on our proposed online learning mechanism, LogOnline can learn the normal sequence patterns continuously as new log sequences emerge, thus staying robust to unstable log data. Unlike previous works, the proposed online learning mechanism requires no labeled log data nor human intervention in the process. We have evaluated LogOnline on two widely used public datasets, and the experimental results demonstrate the effectiveness of LogOnline. In particular, LogOnline achieves a comparable result with the studied supervised approaches, outperforming all semi-supervised counterparts. When the log instability issue is more common, LogOnline exhibits the best performance over all compared approaches, further confirming its practicability.

Log parsing, which extracts log templates and parameters, is a critical prerequisite step for automated log analysis techniques. Though existing log parsers have achieved promising accuracy on public log datasets, they still face many challenges when applied in the industry. Through studying the characteristics of real-world log data and analyzing the limitations of existing log parsers, we identify two problems. Firstly, it is non-trivial to scale a log parser to a vast number of logs, especially in real-world scenarios where the log data is extremely imbalanced. Secondly, existing log parsers overlook the importance of user feedback, which is imperative for parser fine-tuning under the continuous evolution of log data. To overcome the challenges, we propose SPINE, which is a highly scalable log parser with user feedback guidance. Based on our log parser equipped with initial grouping and progressive clustering,we propose a novel log data scheduling algorithm to improve the efficiency of parallelization under the large-scale imbalanced log data. Besides, we introduce user feedback to make the parser fast adapt to the evolving logs. We evaluated SPINE on 16 public log datasets. SPINE achieves more than 0.90 parsing accuracy on average with the highest parsing efficiency, which outperforms the state-of-the-art log parsers. We also evaluated SPINE in the production environment of Microsoft, in which SPINE can parse 30million logs in less than 8 minutes under 16 executors, achieving near real-time performance. In addition, our evaluations show that SPINE can consistently achieve good accuracy under log evolution with a moderate number of user feedback.