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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Wang, Zhou Zhihao, Liu Ying, Tian Yujun, Yang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province LuZhou China College of Computer and Artificial Intelligence Software College Huaihua University Huaihua China

ISBN: (纸本)9798331519254

UAV (Unmanned Aerial Vehicle) navigation can be considered as the process of robots that determine how to successfully and quickly reach the target location. Specifically, in order to complete the scheduled task successfully, the UAV must accurately understand its status, such as position, navigation speed, heading, as well as the starting point and target position. Moreover, it mainly depends on the current environment and location. This article generally focuses on the research on visual SLAM (Simultaneous localization and mapping) for UAV positioning and navigation. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Robust Motion-Guided Frame Sampler With Interpretive Evaluation for Video Action Recognition

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6197-6208页

作者： Bai, Jing Zhang, Yuxiang Wang, Yiran Xiao, Zhu Xiong, Yong Jiao, Licheng Xidian University School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xi’an710071 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Shenzhen Research Institute Hunan University Shenzhen518055 China Hunan Lianzhi Technology Company Ltd Changsha410200 China

Due to the presence of redundancy and interference, frame sampling is a promising but challenging solution to mitigate the expensive computation of video action recognition. Although the motion prior has shown great potential for frame selection, existing motion-based strategies suffer from limitations in terms of robustness and interpretive evaluation. In this paper, we devise a robust frame sampling strategy called positive motion guided sampler (PMGSampler). It consists of two procedures, local motion capture and global motion statistics. At the local level, we propose two concepts about inter-frame motion amplitude and motion continuity, which helps to perceive the movement of subjects and identify abnormal events that may generate negative pseudo-motion information. Then, through a global analysis of the obtained local motions, the sampler becomes more sensitive to informative frames and robust to outliers. The proposed sampler can be applied to most existing models for improving recognition accuracy. We conduct extensive experiments on four widely-used benchmarks to demonstrate the superiority of our PMGSampler over other methods of the same type. In addition, to analyse how sampled frames influence action recognition, we present a visual interpretation method for video models, termed as spatio-temporal class activation map (STCAM). By introducing spatial and temporal branches, our STCAM is able to visualise the salience of spatio-temporal features. With the help of STCAM, we can further intuitively evaluate the performance of different sampling strategies. © 2002-2012 IEEE.

关键词： Motion capture

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A Federated Learning Approach for Intrusion Detection System using Deep Neural Network

A Federated Learning Approach for Intrusion Detection System...

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2025 International Symposium on Innovative Informatics of Biskra, ISNIB 2025

作者： Abdeldjalil, Tedjini Mustapha, Chenine Kamel, Benkaddour Mohammed Malika, Abid Fntic Faculty University of Kasdi Merbah Department of Computer Science and Information Technology Ouargla Algeria University of Kasdi Merbah Artificial Intelligence and Information Technology Laboratory (LINATI) Department of Computer Science and Information Technology Ouargla 30000 Algeria

ISBN: (纸本)9798331509576

Network technology advancements have significantly increased the risk of device and data compromise, particularly during data sharing and distribution. Machine learning (ML) and deep learning (DL) based intrusion detection systems (IDS) present effective solutions for mitigating these threats. However, in networks with numerous devices, sharing data with a central server for model training poses serious privacy and security risks. Federated Learning (FL) presents a viable solution by eliminating the need to share data with the server for training. Instead, it enables local model training, with only model parameters transmitted to the server for aggregation in a continuous learning process. This paper addresses privacy and security challenges in intrusion detection by leveraging federated learning with Convolutional Neural Networks(CNN). The proposed approach is tested using the UNSW-NB15 dataset under both independent and identically distributed (IID) and non-independent and non-identically distributed (Non-IID) data distributions, providing competitive results compared to centralized architectures, while ensuring data privacy. © 2025 IEEE.

关键词： CNN FedAvg Federated learning IDS NonIID UNSW-NB15

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Multi-Request Data Cache Optimization Strategy in Edge Computing 26

Multi-Request Data Cache Optimization Strategy in Edge Compu...

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26th ACIS International Winter Conference on Software Engineering, artificial intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Wang, Futian Wu, Zhimin Tang, Jin Li, Chenglong Zhang, Cheng School of Computer Science and Technology Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei China School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Anhui University Hefei China

ISBN: (纸本)9798350345865

With the rapid growth in the number of mobile devices, more and more data is created and requested by users. By caching the data to the edge server, users can obtain the content they request in a closer place, which reduces the network load and improves the user experience. However, existing articles only focus on a single request of users, while users often request multiple content at one time, which leads to more complex caching scenarios. In this paper, we will study an edge data cache scenario with multiple requests, and design a cost function to optimize the cache results. In this paper, we formulate the data cache problem as a mixed integer programming problem and minimize our cost function. Extensive experiments are conducted on a real-world dataset that contains the locations of edge servers and mobile users, and the results reveal that our approach significantly outperform the baseline approaches. © 2023 IEEE.

关键词： Cost functions

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning

Analyzing and Reducing Catastrophic Forgetting in Parameter ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Xinlong Ren, Weijieying Qin, Wei Wang, Lei Zhao, Tianxiang Hong, Richang AHU-IAI AI Joint Laboratory AnHui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China College of Information Sciences and Technology Pennsylvania State University Pennsylvania United States School of Computer Science Hefei Univeristy of Technolog Hefei China School of Computer Science and Technology Singapore Management University Singapore

ISBN: (纸本)9798350368741

Existing continual learning works explored strategies like memory replay, regularization, and parameter isolation, but little analysis was conducted on the optimization behavior of LLMs' continual fine-tuning. In this work, we investigate the geometric connections of different minima along the continual LLM fine-tuning trajectories. We validate this phenomenon on LLMs and propose a new method called Interpolation-based LoRA (I-LoRA). I-LoRA can strike a balance between plasticity and stability through parameter interpolation, which constructs a dual-memory experience replay framework based on LoRA. Experiments on eight domain-specific benchmarks demonstrate that I-LoRA consistently shows significant improvement over previous approaches with up to 11% performance gains. Our code is available at https://***/r/LLMCL-3823. © 2025 IEEE.

关键词： catastrophic forgetting continual learning

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Cross-Modal Retrieval for Motion and Text via DropTriple Loss 5

Cross-Modal Retrieval for Motion and Text via DropTriple Los...

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5th ACM International Conference on Multimedia in Asia, MMAsia 2023

作者： Yan, Sheng Liu, Yang Wang, Haoqiang Du, Xin Liu, Mengyuan Liu, Hong School of Artificial Intelligence Chongqing University of Technology China College of Computer Science Sichuan University China Key Laboratory of Machine Perception Shenzhen Graduate School Peking University China

ISBN: (纸本)9798400702051

Cross-modal retrieval of image-text and video-text is a prominent research area in computer vision and natural language processing. However, there has been insufficient attention given to cross-modal retrieval between human motion and text, despite its wide-ranging applicability. To address this gap, we utilize a concise yet effective dual-unimodal transformer encoder for tackling this task. Recognizing that overlapping atomic actions in different human motion sequences can lead to semantic conflicts between samples, we explore a novel triplet loss function called DropTriple Loss. This loss function discards false negative samples from the negative sample set and focuses on mining remaining genuinely hard negative samples for triplet training, thereby reducing violations they cause. We evaluate our model and approach on the HumanML3D and KIT Motion-Language datasets. On the latest HumanML3D dataset, we achieve a recall of 62.9% for motion retrieval and 71.5% for text retrieval (both based on R@10). The source code for our approach is publicly available at https://***/eanson023/rehamot. © 2023 Copyright held by the owner/author(s).

关键词： Semantics

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Fusion of Dynamic Hypergraph and Clinical Event for Sequential Diagnosis Prediction 29

Fusion of Dynamic Hypergraph and Clinical Event for Sequenti...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhang, Xin Peng, Xueping Guan, Hongjiao Zhao, Long Qiao, Xinxiao Lu, Wenpeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

ISBN: (纸本)9798350330717

Sequential diagnosis prediction (SDP) is a challenging task, aiming to predict patients' future diagnoses based on their historical medical records. While methods based on graph neural networks (GNNs) have proven successful for this task, they typically focus on modeling pairwise diseases using a global disease combination graph. However, these approaches neglect the fine-grained higher-order relations among persistent and emerging diseases within a single visit, which may contain crucial clues to predict the next diagnosis. Additionally, they fail to fully leverage patient-related clinical information present in electronic health records (EHRs). To address these challenges, this paper proposes a novel approach called the fusion of Dynamic Hypergraph and Clinical Event (DHCE) for sequential diagnosis prediction. The proposed method aims to exploit the fine-grained higher-order relations among diagnoses within a visit and leverage clinical event information from EHRs to improve the accuracy of predicting the next diagnosis. Specifically, DHCE categorizes diagnoses within a single visit in a fine-grained granularity into persistent and emerging categories based on a patient's historical diagnoses. It then constructs dynamic hypergraphs to capture higher-order disease relations within each visit. Next, we design a transition function to extract the transitional context from previous visits in order to generate the visit representation. Furthermore, to fully leverage patient-related clinical events in a visit, we utilize Bio-Clinical BERT to encode them and generate the clinical event representation for each visit. Finally, we combine the visit representation and event representation to generate a comprehensive patient representation, which is then used to predict the patient's next diagnosis. Experimental results on two benchmark datasets consistently demonstrate that DHCE outperforms state-of-the-art methods. © 2023 IEEE.

关键词： Bio-Clinical BERT clinical event dynamic hypergraph sequential diagnosis prediction

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Incomplete Multi-view Clustering via Diffusion Contrastive Generation 39

Incomplete Multi-view Clustering via Diffusion Contrastive G...

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39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Zhang, Yuanyang Lin, Yijie Yan, Weiqing Yao, Li Wan, Xinhang Li, Guangyuan Zhang, Chao Ke, Guanzhou Xu, Jie School of Computer Science and Engineering Southeast University Nanjing210096 China College of Computer Science Sichuan University China School of Computer and Control Engineering Yantai University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications [Southeast University Ministry of Education China College of Computer National University of Defense Technology China College of Computer Science and Technology Zhejiang University China Department of Control Science and Intelligence Engineering Nanjing University China School of Economics and Management Beijing Jiaotong University China School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)157735897X

Incomplete multi-view clustering (IMVC) has garnered increasing attention in recent years due to the common issue of missing data in multi-view datasets. The primary approach to address this challenge involves recovering the missing views before applying conventional multi-view clustering methods. Although imputation-based IMVC methods have achieved significant improvements, they still encounter notable limitations: 1) heavy reliance on paired data for training the data recovery module, which is impractical in real scenarios with high missing data rates;2) the generated data often lacks diversity and discriminability, resulting in suboptimal clustering results. To address these shortcomings, we propose a novel IMVC method called Diffusion Contrastive Generation (DCG). Motivated by the consistency between the diffusion and clustering processes, DCG learns the distribution characteristics to enhance clustering by applying forward diffusion and reverse denoising processes to intra-view data. By performing contrastive learning on a limited set of paired multi-view samples, DCG can align the generated views with the real views, facilitating accurate recovery of views across arbitrary missing view scenarios. Additionally, DCG integrates instance-level and category-level interactive learning to exploit the consistent and complementary information available in multi-view data, achieving robust and end-to-end clustering. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches. Copyright © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Contrastive Learning

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A Hybrid Medical Images Watermarking Technique Using DWT and DCT 7th

A Hybrid Medical Images Watermarking Technique Using DWT and...

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7th International Conference on artificial intelligence in Renewable Energetic Systems, IC-AIRES 2023

作者： Hamami, Rania Zermi, Narima Boubchir, Larbi Khaldi, Amine LASA Laboratory Faculty of Technology Badji Mokhtar Annaba University P.O. BOX 12 Annaba23000 Algeria LIASD Laboratory Department of Computer Science University of Paris 8 Saint-Denis93526 France Computer Science Department Faculty of Sciences and Technology Artificial Intelligence and Information University of Kasdi Merbah Ouargla Algeria

ISBN: (纸本)9783031606311

Medical images, such as X-rays, MRI scans, and CT scans, play an important role in diagnosing and treating various diseases. However, the sensitive nature of these images requires protection against unauthorized use and distribution. This article presents a novel approach for embedding hospital logos, patient information, and hospital/doctor details into medical images using a combination of wavelet transform and discrete cosine transform, To ensure enhanced security and robustness, we hash the watermark data using the MD5 algorithm before the embedding. Experimental results demonstrate the exceptional performance of the proposed technique, with a peak signal-to-noise ratio (PSNR) exceeding 50 dB, and very good robustness against attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Discrete cosine transforms

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