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A Multi-modal Framework to Counter Hate Speeches 27th

A Multi-modal Framework to Counter Hate Speeches

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Bhesra, Kirtilekha Agarwal, Akshay C.V Raman Global University Bhubaneswar India Indian Institute of Science Education and Research Bhopal Bhopal India

ISBN: (纸本)9783031781186

The proliferation of offensive, hateful, and toxic content on social media platforms has reached unprecedented levels. These deleterious expressions not only tarnish the fabric of online interactions but also pose significant threats to individual well-being, potentially precipitating mental health issues such as depression. Manifesting in various modalities including audio and text, this digital toxicity exerts a corrosive influence, leaving enduring impacts on the psyche of individuals. The literature has begun addressing this issue through the lens of natural language processing. However, conventional toxic language detection systems often exhibit biases, particularly in misidentifying text featuring mentions of minority groups as harmful. Furthermore, the overreliance on spurious correlations undermines the efficacy of these systems in detecting implicitly toxic language. Notably, existing benchmark datasets such as ToxiGen predominantly comprise text-based content. Thus, to address this gap, this study presents a pioneering effort in assembling an audio-based hate speech dataset. Subsequently, a multimodal hate speech detection algorithm integrating audio and text inputs is proposed, demonstrating a significant performance enhancement over conventional text-based models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Natural language processing systems

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Repeated Measures Latent Dirichlet Allocation for Longitudinal Microbiome Analysis 12th

Repeated Measures Latent Dirichlet Allocation for Longitudi...

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12th International Conference on Computational Advances in Bio and Medical sciences, ICCABS 2023

作者： Pais, Namitha Viona Ravishanker, Nalini Rajasekaran, Sanguthevar Weinstock, George Department of Statistics University of Connecticut StorrsCT United States Department of Computer Science and Engineering University of Connecticut StorrsCT United States Jackson Laboratory for Genomic Medicine FarmingtonCT United States

ISBN: (纸本)9783031827679

Topic modeling algorithms generally examine a set of documents, referred to as a corpus in Natural Language Processing (NLP), and analyze the words observed in a document to uncover themes that run through each document in a collection. In the microbiome framework, they are used to identify co-occurring microbial species and reveal hidden patterns or relationships within the microbial communities. Longitudinal microbiome data analysis provides a robust framework for studying microbiome compositions over time. By collecting multiple samples from the same individuals at different time points, researchers can capture the temporal variation within an individual’s microbiome and evaluate its impact on the subjects’ health status during each of their visits. This paper extends the Latent Dirichlet Allocation (LDA) modeling technique to a repeated measures framework. We propose Repeated Measures Latent Dirichlet Allocation (RM-LDA) where each document (subject) is assumed to be a collection of multiple sub-documents (visits associated with a given subject). In this study, we examine microbiome data on subjects making multiple visits to a medical facility to provide data on their microbiome counts. Our model allows us to analyze hidden patterns in the microbiome data over multiple visits, estimate the latent topic correlation structure within each subject, and study their association with the individual’s health status over each visit. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： health status latent topic microbiome counts repeated measures topic models

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VEAGLE: Eye Gaze-Assisted Guidance for Video Browser Showdown 31st

VEAGLE: Eye Gaze-Assisted Guidance for Video Browser Showdow...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Nguyen-Ho, Thang-Long Huynh, Viet-Tham Kongmeesub, Onanong Tran, Minh-Triet Nie, Dongyun Healy, Graham Gurrin, Cathal Dublin City University Dublin Ireland Software Engineering Laboratory University of Science VNU-HCM Ho Chi Minh Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819620739

In this work, we focus on assisting users in finding information that may have been unintentionally overlooked. Our system supports not only experienced users but also newcomers to Video Browser Showdown systems, enabling them to search for information more quickly and accurately. During the querying process, users might unintentionally miss important images, including those they are specifically looking for. By leveraging eye-tracking technology, our system records the user’s gaze duration on each image and optimises the presentation of potentially relevant images, which should lead to better performance when using interactive systems under time-pressure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Search engines

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Efficient Object Detection via Fine-Grained Regularization with Global Initialization 27th

Efficient Object Detection via Fine-Grained Regularization ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Chen, Binhan Wu, Qiaojun Chen, Song Kang, Yi University of Science and Technology of China Anhui China

ISBN: (纸本)9783031781216

The improvement in accuracy of object detection networks has been progressive in facilitating real vision applications. However, the increase in accuracy comes with the cost of increasing memory footprint and computations and it has presented ever increasing challenges for deployment of updated object detection networks. To address these challenges, we propose a structured pruning method called Fine-grained Regularization with Global Initialization (FRGI). Considering the varying impact of filters on the subsequent layers that process the same L1-norm but different means, FRGI introduces a mean-aware sparsity term during the global sparsification to promote near-equal means of the filters, then initializes the global pruned filters by prioritizing them based on the L1-norm. The expressive power on the pruned structures selected in the initialization is transferred with minimum loss in accuracy by applying fine-grained regularization. Moreover, for the residual blocks commonly found in object detection networks, FRGI averages the L1-norm of related filters. We show through extensive validations on the MS-COCO dataset that FRGI leads to more efficient object detection models for all sizes of object detection networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Object recognition

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AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV Detection 24th

AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV De...

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

作者： Yang, Runjie Guo, Yongde Yan, Jun Yang, Haonan Dai, Zifeng Faculty of Data Science City University of Macau SAR Macau China School of Data Science Qingdao University of Science and Technology Qingdao266000 China Zhuhai Aerospace Microchips Science and Technology Co. Ltd. Zhuhai519000 China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100039 China

ISBN: (纸本)9789819615445

Effective and accurate detection of unmanned aerial vehicles (UAVs) is crucial for combating malicious UAV systems. However, adverse weather conditions, such as haze or low light, often degrade the quality of captured UAV images. Traditional target detection models applied to these low-quality UAV images usually suffer from decreased accuracy. To address this challenge, this paper proposes AW-YOLOv9, a UAV target detection method designed for adverse weather conditions. Specifically, an image enhancement processing (IEP) module with adaptive learning parameters is introduced to suppress weather-related interference in the images and restore hidden target information. The DP module embedded within YOLOv9 was designed to reduce the network’s parameter count while improving the detection speed. We established a dataset of UAV images under adverse weather conditions and utilized this dataset to validate the superiority of AW-YOLOv9. The experiments demonstrate that the proposed method achieves a 3.30% improvement in mean average precision compared to SOTA methods, effectively enhancing the detection accuracy of UAV targets under adverse weather conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aircraft detection

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Hybrid Method for Linear Programming 15th

Hybrid Method for Linear Programming

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15th International Conference on Optimization and Applications, OPTIMA 2024

作者： Pirau, Vitali Hildebrand, Roland Institutskiy per. 9 Moscow Region Dolgoprudny141701 Russia Skolkovo Institute of Science and Technology Skolkovo Innovation Center Bolshoy Boulevard 30 P.1 Moscow121205 Russia

ISBN: (纸本)9783031791185

We propose a new approach to solving linear programs which combines the advantages of interior point methods and active set methods. Methods of the first type, in particular the Nesterov-Todd interior-point method for self-concordant barriers [8] solve the problem in polynomial time, running Onlog1Ε iterations, however, it can be shown that it converges to accuracy ΕM when implemented on machines with ΕM accuracy, which is unacceptable for some practical problems, such as bank scheduling [14]. On the other hand, the simplex method [4] avoids the argument accuracy problem, since its trajectory traverses only the vertices of the feasible polyhedron. However, the simplex method is known to spend at worst exponential time to find the answer, and in practice the interior point method runs much faster and is preferred. The approach proposed in this paper is a hybrid algorithm which generates iterations as in an interior point method, but at some steps it additionally analytically solves an auxiliary minimization problem on a cone approximating the orthant with marginal additional cost. This allows us to find exactly-zero solution components and lower the dimensionality of the problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Polynomial approximation

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Is Your Style Transfer Doing Anything Useful? An Investigation into Hippocampus Segmentation and the Role of Preprocessing 7th

Is Your Style Transfer Doing Anything Useful? An Investigati...

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7th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Kalabizadeh, Hoda Griffanti, Ludovica Yeung, Pak-Hei Voets, Natalie Gillis, Grace Mackay, Clare Namburete, Ana IL Dinsdale, Nicola K. Kamnitsas, Konstantinos Department of Computer Science University of Oxford Oxford United Kingdom Department of Psychiatry University of Oxford Oxford United Kingdom College of Computing and Data Science Nanyang Technological University Singapore Singapore Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9783031787607

Brain atrophy assessment in MRI, particularly of the hippocampus, is commonly used to support diagnosis and monitoring of dementia. Consequently, there is a demand for accurate automated hippocampus quantification. Most existing segmentation methods have been developed and validated on research datasets and, therefore, may not be appropriate for clinical MR images and populations, leading to potential gaps between dementia research and clinical practice. In this study, we investigated the performance of segmentation models trained on research data that were style-transferred to resemble clinical scans. Our results highlighted the importance of intensity normalisation methods in MRI segmentation, and their relation to domain shift and style-transfer. We found that whilst normalising intensity based on min and max values, commonly used in generative MR harmonisation methods, may create a need for style transfer, Z-score normalisation effectively maintains style consistency, and optimises performance. Moreover, we show for our datasets spatial augmentations are more beneficial than style harmonisation. Thus, emphasising robust normalisation techniques and spatial augmentation significantly improves MRI hippocampus segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clinical research

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Accelerate K-Mode Algorithms Using The Triangle Inequality 16th

Accelerate K-Mode Algorithms Using The Triangle Inequality

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16th International Conference on Scalable Uncertainty Management, SUM 2024

作者： Nguyen, Vu-Linh Nguyen-Mau, Toan Huynh, Van-Nam Heudiasyc Laboratory University of Technology of Compiègne Compiègne France Japan Advanced Institute of Science and Technology Nomi Japan

ISBN: (纸本)9783031762345

Clustering is an unsupervised machine learning task that aims to discover natural groups in the given dataset. K-mode algorithms, which are adaptions of K-mean algorithms for continuous data, are among the most popular algorithms for discovering clusters in categorical data. In this paper, we present some results on how to accelerate them using the triangle inequality, while still always computing exactly the same result as the original K-mode algorithms. We also provide some empirical evidence to illustrate the potential gains provided by leveraging the triangle inequality. Finally, we envision future work aimed at providing a comprehensive understanding of the use of triangle inequality in accelerating other clustering algorithms for categorical data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Movie Retrieval Systems Using Genre-Guided Multimodal Learning Techniques 31st

Movie Retrieval Systems Using Genre-Guided Multimodal Learni...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Huang, Wei-Lun Hidayati, Shintami Chusnul Pan, Tse-Yu National Taiwan University of Science and Technology Taipei Taiwan Sepuluh Nopember Institute of Technology Surabaya Indonesia

ISBN: (纸本)9789819620739

Given the rapid development of streaming platforms in recent years, movie retrieval has become an important and challenging research topic. However, compared to short video clips, the complex narrative structure of movies makes it difficult for models to understand audiovisual content, resulting in suboptimal movie representation learning. To address this issue, we propose a multimodal alignment-based movie retrieval method that integrates audiovisual features with textual information, such as plot summaries and genre descriptions, to more accurately reflect the themes, plots, and styles of movies. Our method trains audiovisual encoders to align with pre-trained text encoders, thereby extracting highly semantically rich movie audiovisual representations. Validated on the LVU benchmark [6], our approach demonstrates superior performance in movie retrieval tasks, effectively retrieving movie clips based on user descriptions or genre tags. Additionally, we have designed a visualization interface for the retrieval results, enhancing the user experience and showcasing the advantages of our method. Our demo video can be accessed through the following link: https://***/dIoFOm0Sy48 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Content based retrieval

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Towards Communication-Efficient Collaborative Perception: Harnessing Channel-Spatial Attention and Knowledge Distillation 18th

Towards Communication-Efficient Collaborative Perception: Ha...

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

作者： Wang, Fei Dai, Penglin Li, Chuzhao Meng, Zhangjie Liu, Kai College of Computer Science Chongqing University Chongqing China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China China Automotive Engineering Research Institute Company Ltd. Chongqing China

ISBN: (纸本)9783031714696

Collaborative perception holds the promise of enhancing the accuracy of 3D object detection over single-vehicle perception. However, existing collaborative perception methods face persistent challenges, including overly complex fusion strategies, noise introduced by directly replicating full-size features, and excessive communication overhead. To tackle these issues, this paper proposes an innovative framework for collaborative perception based on knowledge distillation, an early-fusion-based teacher model guides an intermediate-fusion-based student model for efficient deployment. Within the teacher model, we introduce the Channel-Spatial Attention Adaptive (CSAA) module, which refines features into enriched representations conducive to collaboration. Additionally, in the student model, we employ a feature filter to identify valuable regions, alongside feature compression techniques by quantization and entropy coding to further reduce transmission volume. Moreover, we introduce an adaptive knowledge distillation method focusing on target-centric areas, embedding crucial clues into the student network to enhance perception performance. Extensive experiments conducted on the V2XSim2.0 dataset demonstrate the superiority of our method, achieving a 10.3% improvement in accuracy over the state-of-the-art collaborative perception method, while reducing 46× communication volume. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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