6329 papers
This position paper argues that as wearable Augmented Reality evolves into Mediated Reality capable of altering visual scenes in safety-critical mobile contexts, it is essential to develop governance mechanisms that allow users to configure, inspect, and understand these perceptual modifications without compromising safety and trust.
This paper presents a model-based control framework for humanoid robots that utilizes trained tactile force estimators to dynamically redistribute forces across the torso, arm, wrist, and fingers, thereby maintaining stable contact with objects of varying mass or unstable configurations by minimizing the distance between the Center of Pressure and the contact polygon centroid.
This paper introduces MV-Fashion, a large-scale multi-view video dataset featuring 3,273 sequences with pixel-level annotations, ground-truth material properties, and paired flat/worn garment images, designed to bridge the realism and annotation gaps in existing datasets for virtual try-on and size estimation tasks.
Edged USLAM is a hybrid visual-inertial SLAM system that integrates an edge-aware front-end and a lightweight depth module to achieve robust, drift-minimized localization on UAVs, particularly excelling in slow or structured trajectories under challenging illumination where purely event-based or learning-based methods may struggle.
This paper proposes a novel spectrogram-based Convolutional Neural Network (CNN) approach for multilabel environmental sound classification that significantly outperforms traditional MFCC-based methods on the South Asian SAS-KIIT and UrbanSound8K datasets, offering a more robust solution for complex, overlapping acoustic environments.
This mixed factorial study of 140 older adults reveals that while an LLM-based voice assistant's agreeableness significantly influences perceptions of empathy and likeability without affecting perceived intelligence, highly agreeable users are particularly sensitive to low-agreeableness agents, highlighting the importance of personality congruence and context-aware explanations in assistive settings.
This paper proposes a novel explainable hybrid deep learning framework combining 1D-CNN and GRU architectures with interpretability techniques like IGs and SHAP to enhance fault detection, diagnosis, and root cause analysis in automotive software system validation while overcoming the limitations of traditional black-box models.
This paper introduces the Condition Insight Agent, a deployed decision-support framework that integrates heterogeneous industrial data sources through constrained, rule-verified LLM reasoning to generate evidence-grounded maintenance explanations and actionable advice while ensuring reliability and human oversight.
This paper introduces ESC, an Evolution Strategy-based calibration method that addresses the unique challenges of audio signal quantization by optimizing activation scaling, thereby achieving near-lossless performance for INT4 and full INT8 quantization across multiple speech tasks.
The paper introduces MERLIN, a novel training framework accompanied by the EM-100k dataset and EM-Bench benchmark, to overcome data scarcity, evaluation gaps, and low-SNR fragility in building robust Multimodal Large Language Models for electromagnetic signals.
This paper reveals that hidden states in end-to-end full-duplex speech models like SALM-Duplex and Moshi significantly leak speaker identity, and proposes two streaming anonymization methods using Stream-Voice-Anon that effectively mitigate this privacy risk while maintaining low-latency dialogue performance.
Using an interdisciplinary multi-agent model, this study demonstrates that while climate change has a minor impact on Senegalese artisanal fisheries, reducing fishing effort is critical to preventing fishery collapse and mass migration, thereby enabling a sustainable equilibrium that restores historical catch levels.
This paper presents a retrieval-augmented, multi-agent AI system developed in partnership with Hacon that accelerates agile regression testing by automatically generating executable scripts from validated specifications, thereby significantly increasing throughput and reducing manual effort while highlighting the continued necessity of human oversight and clear requirements for quality assurance.
This paper proposes a topological framework utilizing Hodge decomposition to analyze serverless service flows, distinguishing between locally correctable errors and globally persistent harmonic inefficiencies to derive actionable remediation strategies without requiring complete architectural restructuring.
Fusion-Poly is a novel spatial-temporal fusion framework for 3D multi-object tracking that effectively leverages asynchronous LiDAR and camera observations to enable higher-frequency state updates and achieve state-of-the-art performance on the nuScenes benchmark.
This paper details the experience and techniques involved in automatically converting an 800,000-line C++ monolith into a Java EE application running on WildFly, addressing specific architectural challenges and utilizing a clang-tool-based solution to continuously regenerate Java code alongside ongoing C++ development.
This paper proposes MM-TS, a novel framework for multi-modal contrastive learning that dynamically adjusts temperature and margin schedules based on local data distribution to address long-tail imbalances, unifying InfoNCE and max-margin objectives to achieve state-of-the-art performance across multiple image- and video-language datasets.
This paper proposes two novel fusion strategies, Registration-aware Guided Image Fusion (RGIF) and Reliability-Gated Modality-Attention Fusion (RGMAF), which effectively integrate heterogeneous thermal and visual sensor data to significantly enhance unmanned aerial vehicle detection performance across diverse perspectives and resolutions.
This paper addresses the multi-objective chance-constrained multiple-choice knapsack problem with implicit probability distributions by proposing an efficient order-preserving Monte Carlo evaluation method and a hybrid evolutionary algorithm (NHILS) that outperforms state-of-the-art optimizers in solving real-world 5G network configuration challenges.
The paper introduces Video2LoRA, a lightweight and scalable framework that utilizes a hypernetwork to generate personalized LoRA weights from reference videos, enabling efficient, zero-shot semantic-controlled video generation with strong generalization and minimal storage requirements.