Call graph reachability analysis is essential for vulnerability detection, dependency conflict analysis, and compatibility checks. However, modern software systems, particularly those developed within integrated development environments (IDEs), often rely on third-party libraries (TPLs), which significantly increase the analysis cost. This article introduces ReachCheck, a compositional library-aware analysis for method pair reachability in the IDEs. Specifically, ReachCheck summarizes TPL reachability via offline transitive closure and integrates the summaries with application code on-demand, eliminating redundant analysis. Additionally, we use matrix representations for call graphs and employ fast matrix multiplication for transitive closure, further improving efficiency. We have implemented our approach as a prototype and evaluated it upon real-world projects. Compared to online traversal, function summary approaches and three state-of-the-art graph reachability approaches ( Ferrari , BL and BFL), ReachCheck achieves 237.75 \(\times\) , 78.55 \(\times\) , 84.86 \(\times\) , 4,369.09 \(\times\) , and 80.91 \(\times\) speedup, respectively. For downstream clients like dependency conflict detection and CVE risk detection, ReachCheck completes analysis in 0.61 and 0.35 s, yielding 537.59 \(\times\) and 519.03 \(\times\) speedup over existing techniques.

In urban planning, traffic flow prediction, a core component of Intelligent Transportation Systems, has made significant progress with the development of deep learning. The key problem of traffic flow prediction lies in capturing the complex spatio-temporal correlations in traffic flow. In recent years, more and more research has tended to apply Transformer-based models to solve this problem. However, Transformer-based models have two major limitations for traffic flow prediction: i) Most methods only focus on extracting data features within the attention head, while ignoring the correlation between these heads, making it difficult to integrate the multi-features of traffic data; ii) Most methods do not recognize the unique impact of nodes that serve as pivotal traffic hubs in the traffic networks, which cause the Transformer to excessively focus on the influence of non-pivotal nodes. In this study, we propose a novel Transformer-based model, the Multi-Feature Fusion Transformer (MF2former), aimed at addressing the above limitations of traffic flow prediction. MF2former incorporates the Augmented Synergistic Transformer Module to achieve a comprehensive multi-feature fusion of traffic data by enhancing the information capacity within self-attention heads and performing information fusion between these heads. Additionally, our model incorporates the Pivotal Node Module, which extracts pivotal nodes from all nodes and masks the global receptive field of the Transformer to enhance its focus on pivotal nodes in the traffic network. Our model is evaluated using two real-world traffic datasets, demonstrating superior performance compared to existing methods. This study provides a stable framework for accurate traffic flow prediction, offering valuable insights for urban planners and commuters.

Despite the benefits of using third-party libraries (TPLs), the misuse of TPL functions raises quality and security concerns. Using traditional static analysis to detect bugs caused by TPL function is non-trivial. One promising solution would be to automatically generate and persist the summaries of TPL functions offline and then reuse these summaries in compositional static analysis online. However, when dealing with millions of lines of TPL code, the summaries designed by existing studies suffer from an unresolved paradox. That is, a highly precise form of summary leads to an unaffordable space and time overhead, while an imprecise one seriously hurts its precision or recall.

Long-term monitoring and recognition of underwater organism objects are of great significance in marine ecology, fisheries science and many other disciplines. Traditional techniques in this field, including manual fishing-based ones and sonar-based ones, are usually flawed. Specifically, the method based on manual fishing is time-consuming and unsuitable for scientific researches, while the sonar-based one, has the defects of low acoustic image accuracy and large echo errors. In recent years, the rapid development of deep learning and its excellent performance in computer vision tasks make vision-based solutions feasible. However, the researches in this area are still relatively insufficient in mainly two aspects. First, to our knowledge, there is still a lack of large-scale datasets of underwater organism images with accurate annotations. Second, in consideration of the limitation on hardware resources of underwater devices, an underwater organism detection algorithm that is both accurate and lightweight enough to be able to infer in real time is still lacking. As an attempt to fill in the aforementioned research gaps to some extent, we established the Multiple Kinds of Underwater Organisms (MKUO) dataset with accurate bounding box annotations of taxonomic information, which consists of 10,043 annotated images, covering eighty-four underwater organism categories. Based on our benchmark dataset, we evaluated a series of existing object detection algorithms to obtain their accuracy and complexity indicators as the baseline for future reference. In addition, we also propose a novel lightweight module, namely Sparse Ghost Module, designed especially for object detection networks. By substituting the standard convolution with our proposed one, the network complexity can be significantly reduced and the inference speed can be greatly improved without obvious detection accuracy loss. To make our results reproducible, the dataset and the source code are available online at https://cslinzhang.github.io/MKUO-and-Sparse-Ghost-Module/ .