Abstract JavaScript code is often included in web applications to implement various functionalities. However, namespace is absent in JavaScript(JS), and all JavaScript code in a same frame shares a common namespace. The absence of namespace may lead to mutual interference among JavaScript code, which results in abnormal program execution. In this paper, we investigate the issue of global identifier conflicts in JavaScript code that cause anomalies across entire web pages. Unlike existing dynamic detection methods like JSOBSERVER which introduce significant runtime performance overhead and can only detect conflicts in executed code paths, our approach avoids execution dependency and performance penalty. Aimed to this issue, we develop a static analysis tool, called DetecJS, to analyze dependencies and conflict relationships among JavaScript code. It can be used to assists developers in identifying global identifier conflicts in the program early during development without executing the code. Based on DetecJS, we identify 2618 global identifier conflicts across 1000 websites. Additionally, we conduct a performance evaluation of DetecJS, the results indicated that the tool exhibits high performance, with an average analysis time of only 5.56 s per web page and conflict detection taking just 15.15 ms.

Lane detection is a hot topic in the field of autonomous driving, providing essential assistance to vehicles. Due to the lack of effective integration among hierarchical features and insufficient adaptability to variations of lanes under diverse background conditions, accurate lane detection remains challenging. To address the aforementioned issues, we propose BANet, an efficient lane detection network based on bidirectional feature aggregation and adaptive multi-scene perception, aiming to improve the lane detection accuracy under different backgrounds. Firstly, we propose a Bidirectional Feature Aggregation Module (BFAM) that, via the proposed composition built from PG2f, effectively integrates complementary information across scales, improving anchor localization. Secondly, we propose an Adaptive Multi-scene Perception Module (AMPM) that learns scene-aware spatial information to enhance lane-relevant cues, addressing the no-visual-clue problem. Finally, we propose an Edge Refinement Attention (ERA), which models spatial and channel information in parallel to refine lane representations. The experimental results on the CULane, CurveLanes, and TuSimple datasets show that the proposed network outperforms existing methods and exhibits excellent performance in the most challenging scenes.

The generation of talking avatars has achieved significant advancements in precise audio synchronization. However, crafting lifelike talking head videos requires capturing a broad spectrum of emotions and subtle facial expressions. Current methods face fundamental challenges: a) the absence of frameworks for modeling single basic emotional expressions, which restricts the generation of complex emotions such as compound emotions; b) the lack of comprehensive datasets rich in human emotional expressions, which limits the potential of models. To address these challenges, we propose the following innovations: 1) the Mixture of Emotion Experts (MoEE) model, which decouples six fundamental emotions to enable the precise synthesis of both singular and compound emotional states; 2) the DH-FaceEmoVid-150 dataset, specifically curated to include six prevalent human emotional expressions as well as four types of compound emotions, thereby expanding the training potential of emotion-driven models. Furthermore, to enhance the flexibility of emotion control, we propose an emotion-to-latents module that leverages multimodal inputs, aligning diverse control signals—such as audio, text, and labels—to ensure more varied control inputs as well as the ability to control emotions using audio alone. Through extensive quantitative and qualitative evaluations, we demonstrate that the MoEE framework, in conjunction with the DH-FaceEmoVid-150 dataset, excels in generating complex emotional expressions and nuanced facial details, setting a new benchmark in the field. These datasets will be publicly released.

While large language models (LLMs) have advanced procedural planning for embodied AI systems through strong reasoning abilities, the integration of multimodal inputs and counterfactual reasoning remains underexplored. To tackle these challenges, we introduce LLaPa, a vision-language model framework designed for multimodal procedural planning. LLaPa generates executable action sequences from textual task descriptions and visual environmental images using vision-language models (VLMs). Furthermore, we enhance LLaPa with two auxiliary modules to improve procedural planning. The first module, the Task-Environment Reranker (TER), leverages task-oriented segmentation to create a task-sensitive feature space, aligning textual descriptions with visual environments and emphasizing critical regions for procedural execution. The second module, the Counterfactual Activities Retriever (CAR), identifies and emphasizes potential counterfactual conditions, enhancing the model's reasoning capability in counterfactual scenarios. Extensive experiments on ActPlan-1K and ALFRED benchmarks demonstrate that LLaPa generates higher-quality plans with superior LCS and correctness, outperforming advanced models. The code and models are available https://github.com/sunshibo1234/LLaPa.