AUTOSAR enhances the management of complex automotive electrical and electronic architectures by improving the reusability and interchangeability of software modules between OEMs and suppliers. However, existing AUTOSAR modeling tools need help with non-intuitive representation and complicated architectural relationship modeling processes. In this paper, we propose a visual architecture modeling language to represent based on model-driven development of the system architecture design of vehicles. Our approach addresses these issues by implementing multi-dimensional visualization capabilities, incorporating two-dimensional graphical representations and detailed one-dimensional tabular displays. Furthermore, we introduce a practical'AUTOSAR meeting in the middle modeling method, which allows for separate modeling at different levels. This approach effectively harnesses the expertise of detailed bottom-level designers and high-level architects, improving efficiency in automotive system design. A detailed case study and evaluation substantiate the effectiveness of our modeling language in describing the system architecture.

With the trend of intelligence, automobile archi-tecture has become more complex. It is necessary to predict and discover reliability-related issues to reduce the cost of correction in the later period. In AUTOSAR-based automotive architecture design, software and hardware interaction, mid-dleware platform behavior, physical environment, and system usage profile affect the system's reliability. It is necessary to comprehensively consider these factors to predict the system's reliability reasonably. However, existing methods often overlook the influence of some factors, especially oversimplifying the control flow of the middleware platform in the system. Resulting in difficulty in effectively modeling the behavior of the AUTOSAR middleware platform in error propagation and its impact on system failure behavior. To analyze the impact of middleware platforms on failure behavior, this paper analyzes the impact of the AutoSAR middleware platform on application software faults based on error propagation methods. Then, expand the AUTOSAR meta-model to model reliability parameters and automatically convert the architecture model into a formal model for reliability prediction. Finally, the effectiveness of considering unreliable platform behavior modeling was verified through a car headlight design case study.

Repackaged Android apps are the major source of Android malware, which not only compromise the pecuniary profit of original authors, but also pose threat to security and privacy of mobile users. Although a large number of birthmark based approaches have been proposed for Android repackaging detection, the majority of them heavily rely on the code instruction details, thus suffering from the following two limitations: (1) subject to code/resource obfuscation technologies; (2) fail to large scale repackaging detection. In this paper, we propose a novel behavior based approach for Android repackaging detection to meet scalability and obfuscation-resilience at the same time. As the repackaged app always keeps the basic functionalities of the original one for leveraging its popularity, they usually have similar behaviors. This observation inspires us to design the new behavior based birthmark for Android repackaging detection, namely, API dependency graph. To further improve the detection performance, we also introduce a system dependency summary graph based ADG extraction approach for high efficiency birthmark construction. We implement a prototype system named ACFinder and evaluate our system using 13,917 apps of 22 categories collected from APK-DL. Experiments show that ACFinder can extract behavior birthmark efficiently (average 52.9s per app), and that our behavior birthmark is resilient to complex code obfuscation technologies (average app similarity all are 1.0 for 11 code obfuscation algorithms) and capable to large scale detection (average 0.37s per app pair).

With the increasing use of GPS-enabled mobile phones, geo-tagging, which refers to adding GPS information to media such as micro-blogging messages or photos, has seen a surge in popularity recently. This enables us to not only browse information based on locations, but also discover patterns in the location-based behaviors of users. Many techniques have been developed to find the patterns of people's movements using GPS data, but latent topics in text messages posted with local contexts have not been utilized effectively. In this paper, we present a latent topic-based clustering algorithm to discover patterns in the trajectories of geo-tagged text messages. We propose a novel probabilistic model to capture the semantic regions where people post messages with a coherent topic as well as the patterns of movement between the semantic regions. Based on the model, we develop an efficient inference algorithm to calculate model parameters. By exploiting the estimated model, we next devise a clustering algorithm to find the significant movement patterns that appear frequently in data. Our experiments on real-life data sets show that the proposed algorithm finds diverse and interesting trajectory patterns and identifies the semantic regions in a finer granularity than the traditional geographical clustering methods.