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共 135251 条记录，以下是1561-1570 订阅

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Enabling Tactile Feedback for Robotic Strawberry Handling Using AST Skin 25th

Enabling Tactile Feedback for Robotic Strawberry Handling U...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Rajendran, S. Vishnu Nazari, Kiyanoush Parsons, Simon Ghalamzan, E. Amir Lincoln Institute of Agri-Food Technology University of Lincoln Lincoln United Kingdom School of Computer Science University of Lincoln Lincoln United Kingdom University of Surrey Guildford United Kingdom

ISBN: (纸本)9783031720581

Acoustic Soft Tactile (AST) skin is a novel sensing technology which derives tactile information from the modulation of acoustic waves travelling through the skin’s embedded acoustic channels. A generalisable data-driven calibration model maps the acoustic modulations to the corresponding tactile information in the form of contact forces with their contact locations and contact geometries. AST skin technology has been highlighted for its easy customisation. As a case study, this paper discusses the possibility of using AST skin on a custom-built robotic end effector finger for strawberry handling. The paper delves into the design, prototyping, and calibration method to sensorise the end effector finger with AST skin. A real-time force-controlled gripping experiment is conducted with the sensorised finger to handle strawberries by their peduncle. The finger could successfully grip the strawberry peduncle by maintaining a preset force of 2 N with a maximum Mean Absolute Error (MAE) of 0.31 N over multiple peduncle diameters and strawberry weight classes. Moreover, this study sets confidence in the usability of AST skin in generating real-time tactile feedback for robot manipulation tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： End effectors

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TeMME: Temporal Knowledge Graph Completion Using Multi-grade Multivector Embeddings 21st

TeMME: Temporal Knowledge Graph Completion Using Multi-grade...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Lu, Heng-Yang Yu, Hao-Kun Fan, Chenyou Zhan, Qianyi Fang, Wei Wu, Xiao-Jun School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China School of Artificial Intelligence South China Normal University Guangzhou China

ISBN: (纸本)9789819601240

Temporal Knowledge Graph Completion (TKGC) aims to discover missing factual knowledge from a given temporal knowledge graph. Previous works usually design Temporal Knowledge Graph Embedding (TKGE) models for the TKGC task. However, we have observed that the same relations can connect different timestamps and the embeddings of the same relation at different timestamps are similar. This poses challenges for TKGE in accurately predicting the entities linked by the same relations at different timestamps, especially when the given entities are similar or even the same. To tackle this challenge, we propose a Temporal Multi-grade Multivector Embeddings (TeMME) method, which contains a new score function with One-grade multivector and Two-grade multivector to learn temporal sensitive knowledge graph embeddings. Experimental results show that TeMME has significantly outperformed SOTA models in TKGC tasks. The MRR metrics in each of the four widely used benchmark datasets achieved a significant improvement: ICEWS14 (6.9%), ICEWS05-15 (6.4%), YAGO11k (4.5%), and Wikidata12k (8.3%). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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EBUD: Evolving Disaster Burst Detection over Social Streams 25th

EBUD: Evolving Disaster Burst Detection over Social Streams

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Qiao, Xiyu Zhou, Xiangmin Zhou, Changjun Wang, Hua Zhang, Yanchun Institute for Sustainable Industries and Liveable Cities Victoria University MelbourneVIC3011 Australia School of Computing Technologies RMIT University MelbourneVIC3000 Australia School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

ISBN: (纸本)9789819605668

The rapid rise of social media platforms such as Twitter and Weibo has heightened the growing significance of crisis warning and situational awareness. The nature of bursts or events on these platforms has become more complex, as information spreads rapidly and unpredictably, often involving changes in time and location. Current research is insufficient in detecting these complex, evolving bursts, especially during the early stages of incidents. To address these challenges, we propose an Evolving Disaster Burst Detection (EBUD) framework over social streams, which enables early identification and tracking of bursts, facilitating quick response and decision-making. Specifically, we propose a novel Adaptive Entropy Distance based active learning strategy for user to label the most representative data, while the model is tasked with labelling the rest of the data, effectively improving the accuracy of data labelling. Finally, a two-stage sketch algorithm is proposed to monitor the frequency of each label in real-time, enabling swift detection of sudden surges that may signify emerging events. Comprehensive evaluations on social data streams have been carried out to show the effectiveness and efficiency of our proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Active Learning Burst Detection Social Networks

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DBFIA: Diffusion-Based Face Image Anonymization 24th

DBFIA: Diffusion-Based Face Image Anonymization

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xue, Hanyu Yuan, Xin Liu, Bo Ding, Ming Australian Artificial Intelligence Institute School of Computer Science University of Technology Sydney Ultimo Australia Data61 CSIRO Eveleigh Australia

ISBN: (纸本)9789819615247

This paper presents a novel approach to address the issue of identity protection in facial image datasets. Our goal is to prevent any violation of privacy for the individuals depicted in the dataset while ensuring that the dataset is still useful for downstream training tasks and maintains the quality of the images. Previous methods have predominantly used Generative Adversarial Network (GAN)-based models to anonymize facial datasets. However, such models suffer from distortion and loss of detail when processing real-life images. To overcome these challenges, we propose a method for anonymizing faces using a diffusion model. Our approach retains the original facial attributes and produces anonymized images with high-quality image details. These attributes are essential when using anonymized images for downstream tasks. Our proposed framework optimizes the latent space vectors of the conditional Denoising diffusion probabilistic model (DDPM) to maintain a certain distance between the identity features (in the feature space of ArcFace) of the anonymized images and the original identity features while preserving the facial attributes. Through qualitative and quantitative experiments, we demonstrate that our method can effectively anonymize the identity of facial images while preserving image details and facial attributes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anonymity

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From Data to Insights: Constructing and Evaluating a Hospitality Dataset for Quadruple Aspect-Based Sentiment Analysis 25th

From Data to Insights: Constructing and Evaluating a Hosp...

引用

25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Alharbi, Marwah Yin, Jiao Miao, Yuan Cao, Jinli Institute for Sustainable Industries and Liveable Cities Victoria University MelbourneVIC3011 Australia Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia

ISBN: (纸本)9789819605781

Recent advances in Quadruple Aspect-Based Sentiment Analysis (Quad-ABSA) have heavily relied on datasets from SemEval challenges, which raises concerns regarding the generalizability of these models across diverse domains. This study addresses this limitation by introducing a novel dataset specifically tailored to the hospitality sector, offering a unique benchmark to evaluate Quad-ABSA models. Our experiments reveal a notable gap: while existing models perform well with SemEval datasets, they fail to maintain their effectiveness when applied to our new domain-specific dataset. This underperformance highlights the critical importance of understanding dataset nuances, including domain-specific characteristics and annotation quality, which significantly influence model performance and generalization. Our research highlights the necessity for broader evaluations of Quad-ABSA models, advocating for the development and utilization of diverse and high-quality datasets to ensure robust and versatile sentiment analysis solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dataset construction Hospitality Sector Model evaluation Quadruple aspect-based sentiment analysis

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It’s Safe to Play While Driving: From a Spatial Traffic Logic Towards Traffic Games 2nd

It’s Safe to Play While Driving: From a Spatial Traffic L...

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2nd International Conference on Bridging the Gap Between AI and Reality, AISoLA 2024

作者： Schwammberger, Maike Hamarneh, Qais Karlsruhe Institute of Technology Karlsruhe76131 Germany

ISBN: (纸本)9783031754333

Managing complex driving situations is a non-trivial task for autonomous traffic agents. To guarantee desirable system properties of an agent’s driving decisions, formal methods offer a wide range of approaches. In previous work, we introduced Urban Multi-lane Spatial Logic (UMLSL) to prove the safety of local driving decisions of Autonomous Traffic Agents (ATAs), e.g. for turning left at an intersection. We now propose to integrate UMLSL into a Traffic Game model, allowing us to verify properties of driving strategies. We use Strategy Logic to compare different driving strategies w.r.t. diverse metrics, e.g. energy-consumption or timeliness. We currently intensify our work on extensions and applications of the UMLSL universe and we also reflect on some recent and future challenges for that in this paper. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer circuits

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Recommendation-Aware Collaborative Edge Caching Strategy in the Internet of Vehicles 18th

Recommendation-Aware Collaborative Edge Caching Strategy in...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Ou, Pingjie Chen, Ningjiang Huang, Zizhan School of Computer and Electronic Information Guangxi University Guangxi Nanning530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Guangxi University Guangxi Nanning530004 China

ISBN: (纸本)9783031714696

Due to the limited resources of edge networks, the heterogeneity of user content requests, high-cost caching from direct resource hits, and redundancy in resource retention time hinder system performance. Traditional caching methods based on learning and popularity tend to encounter data skew when handling vehicle requests, leading to untimely cache data updates. To address these issues, this paper proposes an efficient edge cooperative caching strategy with recommendation awareness. Firstly, a time series prediction model based on a multi-dense multilayer perceptron is designed. This model segments historical request information into sub-sequence level slivers to enhance positional correlations between information, predicting user requests through historical time series and covariates. Secondly, to optimize cache utilization, comprehensively considers user mobility and contribution to enable recommendation awareness. Additionally, an optimized lifecycle is introduced to design a cache replacement strategy. Simulation experiments demonstrate that, compared to caching schemes based on content popularity and deep reinforcement learning, the proposed method achieves lower caching costs and higher cache hit rates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep reinforcement learning

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MCANet: Multimodal Caption Aware Training-Free Video Anomaly Detection via Large Language Model 27th

MCANet: Multimodal Caption Aware Training-Free Video Anomaly...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Dev, Prabhu Prasad Hazari, Raju Das, Pranesh Machine Learning Laboratory Department of Computer Science and Engineering National Institute of Technology Calicut India

ISBN: (纸本)9783031781247

Towards Video Anomaly Detection (VAD), existing methods require labor-intensive data collection and model retraining, making them costly and domain-specific. The proposed method, termed as Multi-modal Caption Aware Network (MCANet), introduces a novel paradigm that identifies anomalies in video sequences without requiring prior domain knowledge. This training-free VAD approach dynamically generates and analyzes textual descriptions of video frames by utilizing off-the-shelf vision-language model (VLM), audio-language model (ALM) and large language model (LLM). MCANet has four primary modules. The first module utilizes image-text similarities to clean noisy captions generated by the image captioning model, while the second module applies audio-text similarities to refine noisy captions produced by the audio captioning model. The third module employs a LLM to consolidate scene dynamics over time. Finally, the fourth module enhances the results by aggregating scores from semantically similar frames based on video-text similarity. To validate the effectiveness of the proposed method, experiments are conducted on two large-scale benchmark datasets (UCF-Crime and XD-Violence). Experimental results demonstrate that MCANet surpasses existing unsupervised and one-class approaches without requiring any training or data collection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Multi-modal Information Multi-angle Mining for Multimedia Recommendation 31st

Multi-modal Information Multi-angle Mining for Multimedia Re...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Zhu, Yijie Li, Mingyong College of Computer and Information Science Chongqing Normal University Chongqing401331 China

ISBN: (纸本)9789819620630

Unlike traditional recommendation systems that rely solely on historical user-item interactions for recommendations, multimodal recommendation systems integrate multimodal features of items to enhance the accuracy of recommended results. In multimodal recommendation systems, generating multimodal embeddings is crucial for accurately recommending products of interest to target users. In many early models, the generation of multimodal embeddings lacked rigor and contained significant modal noise, resulting in underutilization of the effective information from the modalities. Therefore, we propose the Multi-modal Information Multi-Angle Mining (MIMM) for multimedia recommendation method to address this issue. Specifically, we have designed a novel multimodal embedding generator aimed at better aligning with historical interaction data and multimodal information. It consists of two main components: a similarity matrix that captures relationships between items, and a semantic fusion unit that integrates multimodal information. These components effectively capture the semantic essence of multimodal data, filter out noise, and enhance the precision of recommendations. In addition, we have developed a multimodal latent semantic miner that can identify unique semantics from latent modalities that exist outside of common modalities, emphasizing the independence of each modality. We validated the effectiveness of the model on three real-world public datasets, and found that it is better in recommendation performance than all existing advanced methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Miners

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Interpretative Attention Networks for Structural Component Recognition 27th

Interpretative Attention Networks for Structural Component R...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Uniyal, Abhishek Mandal, Bappaditya Puhan, Niladri B. Bera, Padmalochan School of Electrical Sciences Indian Institute of Technology Bhubaneswar India School of Computer Science and Mathematics Keele University Keele United Kingdom

ISBN: (纸本)9783031784439

Bridges are essential for enabling movement during environmental disasters and serve as crucial links for rescue and aid delivery. Effective bridge inspection and maintenance are more critical than ever due to increasing severity and frequency of environmental disasters. Although current state-of-the-art deep learning models have achieved good performance many challenges still exist, such as their performance on challenging datasets and their opaque-box nature makes it difficult to understand their decision-making process and identify potential biases. This research work proposes a novel architecture that incorporates innovative parallel twin attention module, synchronous amplification module, aggregated multi-feature attention module and squeeze and excitation blocks, that helps to focus on specific regions of the image plane automatically resulting in improved structural component recognition accuracy. Its parallelism helps to capture long-range dependencies enabling the model to use contextual information encompassing spatial and channel information when segmenting bridge components. Experimental results and ablation studies show that our proposed architecture outperforms the current state-of-the-art methodologies in the challenging bridge component classification dataset. We also examine our models through XAI methods to provide insights into its decision-making process and making it more trustable by highlighting the importance of different features for various similar recognition/segmentation tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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