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- A Spammer Identification Method for Class Imbalanced Weibo Datasets21
By analyzing the characteristics of spammers in Weibo, an ensemble learning method is used to combine multiple base classifiers for improving the learning performance and demonstrates that compared with the existing state-of-the-art methods, the recall rate of the proposed approach increases by 6.5% and reaches the precision value of 87.53% when used to deal with real-world Weibo datasets.
- Corn tassel detection based on image processing20
The corn tassel identification and location method was studied based on image processing and automated technology guidance information was provided for the actual production of corn emasculation operation to provide theoretical basis guidance for corn intelligent detasseling machine.
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- Cooperative collision avoidance in multirobot systems using fuzzy rules and velocity obstacles15
A distributed and hybrid motion planning method, named Fuzzy-VO, is proposed for multirobot systems that contains two basic components: fuzzy rules, which can deal with linguistic requirements and compute motion efficiently, and velocity obstacles (VOs), which can generate collision-free motion effectively.
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- DiMSOD: A Diffusion-Based Framework for Multi-Modal Salient Object Detection12
DiMSOD is efficient, only requiring fine-tuning of newly introduced modules on the existing stable diffusion, which not only reduces the fine-tuning cost, making it more viable for practical use, but also enhances the integration of multi-modal conditional controls.
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- Moral Testing of Autonomous Driving Systems3
A set of moral meta-principles derived from existing moral experiments and well-established social science theories are extracted, aiming to capture widely recognized and common-sense moral values for ADSs, and a metamorphic testing framework is proposed to systematically identify potential moral issues.
- Robust motion planning for mobile robots under attacks against obstacle localization3
A robust motion planning method ObsGAN-DRL, integrating a generative adversarial network (GAN) into DRL models to mitigate OLAs in the environment and can leverage the state-of-the-art DRL methods to compute collision-free motion commands efficiently.
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- BrainMem: Brain-Inspired Evolving Memory for Embodied Agent Task Planning2
BrainMem, a training-free hierarchical memory system that equips embodied agents with working, episodic, and semantic memory inspired by human cognition, is proposed, highlighting evolving memory as a promising and scalable mechanism for generalizable embodied intelligence.
- Work Zones challenge VLM Trajectory Planning: Toward Mitigation and Robust Autonomous Driving2
This work conducts the first systematic study of VLMs for work zone trajectory planning, revealing that mainstream VLMs fail to generate correct trajectories in 68.0% of cases, and proposes REACT-Drive, a trajectory planning framework that integrates VLMs with Retrieval-Augmented Generation (RAG).
- Shedding Light on VLN Robustness: A Black-box Framework for Indoor Lighting-based Adversarial Attack2
This work proposes Indoor Lighting-based Adversarial Attack (ILA), a black-box framework that manipulates global illumination to disrupt VLN agents, and designs two attack modes: Static Indoor Lighting-based Attack (SILA), where the lighting intensity remains constant throughout an episode, and Dynamic Indoor Lighting-based Attack (DILA), where lights are switched on or off at critical moments to induce abrupt illumination changes.
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- Semantic Intelligence: A Bio-Inspired Cognitive Framework for Embodied Agents1
The Semantic Intelligence-Driven Embodied (SIDE) agent framework is introduced, which integrates a hierarchical semantic cognition architecture with a semantic-driven decision-making process that enables agents to reason about and interact with the physical world in a contextually adaptive manner.
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- VLAMotor: Test-Guided Enhancement of Vision-Language-Action Models via Agent-BasedData Synthesis–
VLAMotor, the first analysis framework for VLA enhancement, is proposed, which integrates distance-aware model testing for failure exposure and agent-based data synthesis for model finetunning and combines uncertainty ranking with redundancy elimination to build compact test sets that expose diverse failures.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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