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International Conference on Computer Supported Cooperative Work in Design

作者： Jizhe Yu Yu Liu Hongkui Wei Kaiping Xu DUT School of Software Technology & DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Despite the significant progress in tiny object tracking algorithms based on convolutional neural networks, the performance falls short of the ideal level due to dataset challenges like limited scale, limited match backdrop, low match popularity, and low-resolution(720p) images. The lack of specialized large-scale tiny object tracking datasets leads data-hungry tracking algorithms to rely on small and saturated datasets from TrackNet. This paper propels the development of tiny object tracking and contributes the first large-scale dataset, Badminton100K, for tiny ball tracking. We provide over 100K annotations covering over 200 videos from 12 professional match events. Our dataset covers high-resolution(1080p) match videos in broad and diverse contexts. Moreover, considering the fast-moving tiny balls in videos often leads to challenges such as blur, afterimage, overlap, and disappearance. As another contribution, we also propose a novel Multi-stage and Multi-scale Fusion Network (MMFNet), which can extract local high-resolution details in the shallow stages and capture global semantic information in the deep stages, addressing these challenging tasks through multiple stages multi-scale feature fusion, enabling accurate identification and localization of tiny balls. Extensive experiments show that our method achieves unprecedented state-of-the-art tracking performance on Badminton100K. The dataset and the algorithm code are available at https://***/Gi-gigi/MMFNet.

关键词： Location awareness Accuracy Image resolution Semantics Transfer learning Propulsion Feature extraction

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Detection of Deepfakes: Protecting Images and Vedios Against Deepfake

Detection of Deepfakes: Protecting Images and Vedios Against...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Ying Tian Wang Zhou Amin Ul Haq Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer and Software Engineering Xihua University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

In recent years, with the rapid development of deep learning and computer vision technology, the forgery technology of images and videos has become increasingly mature, posing new challenges to information security and social stability. Behind the re-evolution of deepfake lies the rampant proliferation of fake content, which is used for election tampering, identity fraud, fraud, spreading fake news, and so on. To address these challenges, researchers are constantly exploring and developing image-based deepfake detection techniques, which aim to effectively identify and prevent deepfake content in images and videos. This article will introduce the current development status of deepfake detection technology, present its principles and methods in detail, and look ahead to its future development directions.

关键词： Deep learning Deepfakes Computer vision Voting Neural networks Information security Information processing Forgery Fraud Computer security

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CMIX: Causal Value Decomposition for Cooperative Multi-Agent Reinforcement Learning

CMIX: Causal Value Decomposition for Cooperative Multi-Agent...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Dunqi Yao Chuxiong Sun Kai Li Kaijie Zhou Hanyu Li Rui Wang Lixiang Liu University of Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Intelligent Game Beijing China Research Institute of Intelligent Decision Engineering China Aerospace Science and Industry Corporation Limited Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Value decomposition plays a pivotal role in ensuring effective credit assignment within Multi-Agent Reinforcement Learning (MARL), particularly in cooperative multi-agent tasks where agents are limited to accessing team rewards only. However, existing methods treat the mixing network as a black box, implicitly assuming that neural networks can autonomously extract important information and achieve rational credit assignment during policy learning. This approach not only lacks interpretability but may also prove inefficient in complex scenarios. To enhance the interpretability and rationality of value decomposition, we propose an innovative approach called “Causal Value Decomposition”(CMIX). CMIX employs causal inference-based models, introducing a set of metrics beyond environmental rewards to enhance robustness and model interpretability. Specifically, CMIX establishes intricate relational structures among agents in complex environments and leverages causal relationships between agents and their surroundings to address the credit assignment challenge in MARL. By employing do-calculus, CMIX accurately measures the impact of each agent's actions on environmental states, precisely determining their contribution to the collective reward. This approach not only enhances the interpretability of existing black-box models but also improves the accuracy of credit assignment in multi-agent systems. Moreover, CMIX exhibits high scalability and complements existing value decomposition techniques. Its effectiveness and scalability have been rigorously tested across various settings, including MPE, LBF, and SMAC environments.

关键词： Measurement Scalability Neural networks Closed box Cause effect analysis Reinforcement learning Robustness Data mining Cybernetics Multi-agent systems

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ALFLAT: Chinese NER Using ALBERT, Flat-Lattice Transformer, Word Segmentation and Entity Dictionary 2nd

ALFLAT: Chinese NER Using ALBERT, Flat-Lattice Transformer, ...

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2nd EAI International Conference on Applied Cryptography in Computer and Communications, AC3 2022

作者： Lv, Haifeng Ding, Yong School of Data Science and Software Engineering Wuzhou University Wuzhou China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

ISBN: (纸本)9783031170805

Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information. However, one hand, since the lattice structure is dynamic and complex, although some existing lattice-based models are effectively utilize the parallel computation of GPUs, they do not fully utilize word segmentation boundary tags that as features are helpful for NER task. On the other hand, the character-word vector needs to be trained, and the user-defined entity dictionary cannot be effectively used. In this paper, we propose ALFLAT: based on a flat-lattice Transformer to incorporate ALBERT pre-trained model, word segmentation information and user-defined entity dictionary for Chinese NER. ALFLAT converts the lattice structure into a flat structure consisting of spans, integrate word segmentation embedding with the output of flat-lattice Transformer model, then modifies the emission scores according to the user-defined entity dictionary, finally utilize Viterbi decoding of the CRF layer to obtain the correct entity results. Each span corresponds to a character or latent word and its position in the original lattice. With the power of ALBERT pre-trained model, Transformer and position encoding, ALFLAT can fully leverage the lattice, word segmentation and user-defined entity dictionary information. Experiments on MSRA dataset show ALFLAT outperforms other lexicon-based models in performance and efficiency. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Computational linguistics

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Byzantine-Robust Federated Learning on Non-IID Data via Inversing Artificial Gradients

Byzantine-Robust Federated Learning on Non-IID Data via Inve...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Minyang Li Xiangyun Tang Tao Zhang Guangyuan Liu Yu Weng Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Minzu University of China China School of Software Engineering Beijing Jiaotong University China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Federated Learning (FL) is a distributed machine learning framework that enhances privacy by enabling multiple participants to train a global model without sharing their raw data. However, FL still faces the threat posed by data and Byzantine attacks (e.g., data poisoning and model poisoning attacks) from malicious clients aimed to decrease the FL global training accuracy. Previous studies have proposed solutions to improve the robustness of FL systems. However, these robust approaches have not effectively defended against Byzantine attacks from malicious clients in non-Independent and Identically Distributed (non-IID) environments, decreasing overall training accuracy. In this work to address this issues, we propose a new defence framework that aims to enhance model accuracy against Byzantine attacks from malicious clients in non-IID environments. In our approach, the central server performs the Inverse Deep Learning Gradient attack using the gradient data submitted by users in each round, obtaining an inversed artificial gradient. We then assess the squared L2 norm difference between this inversed gradient and the actual gradients to detect malicious clients. Simultaneously, we assign weights to each client involved in the aggregation based on differences in the squared L2 norm, aiming to minimize the impact of malicious actors on the overall model. We have experimentally evaluated our method and demonstrated its ability to maintain training accuracy under attack in non-IID environments using standard datasets.

关键词： Training Accuracy Federated learning Distributed databases Robustness Data models Servers Global communication Standards Faces

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Multi-scale convolutional fusion network embedded with improved attention mechanism for polarimetric synthetic aperture radar image classification

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Li, Jiewen Zhu, Yanan Ai, Jiaqiu Zhao, Zhicheng Shi, Jun Hefei University of Technology School of Computer Science and Information Engineering Hefei China Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei China Hefei University of Technology School of Artificial Intelligence Hefei China Hefei University of Technology School of Software Hefei China

Convolutional neural networks (CNNs) have been widely applied in the field of polarimetric synthetic aperture radar (PolSAR) image classification and have achieved good classification results. However, due to the complexity of polarimetric information, traditional CNN structures are difficult to fully extract and integrate information from multiple polarimetric channels, resulting in insufficient feature diversity, which limits further improvement in classification accuracy. To this problem, this article proposes a multi-scale convolutional fusion network (MCF-Net). MCF-Net employs convolutional kernels of various sizes to perform parallel convolutions, extracting multi-scale features and optimally fusing them, thereby greatly enhancing the diversity and completeness of feature representation. In addition, to enhance the network's focus on important features and suppress irrelevant information, this article proposes an improved squeeze-and-excitation (SE) module, guided by mean, entropy, and Z-score (MEZ-SE), to weigh the importance of feature maps. By embedding the MEZ-SE module into MCF-Net, MEZ-MCF-Net significantly improves the completeness of feature representation and enhances the ability to distinguish different land cover categories. On the Flevoland, San Francisco, and Oberfaffenhofen PolSAR datasets, the overall accuracy of MEZ-MCF-Net exceeds that of the second-best method by 3.04%, 2.09%, and 1.99%, respectively. There is no doubt that MEZ-MCF-Net is an effective method for PolSAR image classification. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Convolutional neural networks

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Relation-Based Multi-Teacher Knowledge Distillation

Relation-Based Multi-Teacher Knowledge Distillation

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International Joint Conference on Neural Networks (IJCNN)

作者： Yutian Wang Yi Xu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Traditional knowledge distillation adopts one teacher model to instruct the training of a lightweight student model. To improve the performance of knowledge distillation, multi-teacher knowledge distillation utilizing multi-party knowledge to guide model learning was proposed. However, existing methods mainly use the summation-result of teacher models of a single sample to integrate multi-party knowledge directly, ignoring the relation knowledge among samples. In this paper, we propose a novel multi-teacher knowledge distillation method, which utilizes the data relation knowledge to allocate weights for teachers adaptively. To obtain the data relation knowledge, we design output-based strategy and feature-based strategy, which would help to allocate more weight for the teacher who has better learned the data relation knowledge. Extensive experiments have demonstrated the performance and efficiency of our proposed multi-teacher knowledge distillation method.

关键词： Training Adaptation models Neural networks

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Multi-Sensor Fusion-Driven Surface Vessel Identification and Tracking Using Unmanned Aerial Vehicles for Maritime Surveillance

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhao, Chenjie Bao, Mengwei Liang, Jinhao Liang, Maohan Liu, Ryan Wen Pang, Guangyao School of Navigation Wuhan University of Technology Wuhan430063 China Hubei Key Laboratory of Inland Shipping Technology Wuhan430063 China Department of Civil and Environmental Engineering National University of Singapore Singapore119077 Singapore Guangxi Colleges Universities Key Laboratory of Intelligent Software Wuzhou University Wuzhou543002 China

—The rapidly-developing artificial intelligence and edge computing technologies have been actively promoting the evolution of autonomous vehicles. The flexible and powerful unmanned aerial vehicles (UAVs) have become suitable application platforms for maritime surveillance. In the maritime Internet of Things (IoT), both airborne cameras and automatic identification system (AIS), which, respectively, provide visual and positioning data, have become the frequently-used and cost-effective sensors. It becomes necessary to fuse the AIS and visual data to detect the visual appearances of surface vessels with obtaining the abundant information on position, movements, and identity, etc. In this work, we propose to develop a multi-sensor fusion-driven computational method (termed MSF-VIT) for highly-reliable surface vessel identification and tracking under different conditions. In particular, it mainly consists of two components, i.e., multi-sensor data fusion, and vessel identification and tracking. In particular, the multi-sensor data fusion method is, firstly, presented to process multi-source heterogeneous data and match the visual and AIS targets. We then propose to improve the traditional SORT algorithm to achieve more accurate vessel tracking. Extensive experiments have been implemented on the edge computing device to evaluate the feasibility and robustness of MSF-VIT. Moreover, it is also proven that our method is capable of promoting real-time maritime surveillance and supporting the privacy-preserving of vessel information. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Node Confidence Determination Based Moving Target Defense Strategy for SDN

A Node Confidence Determination Based Moving Target Defense ...

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Shimin Sun Guowei Xu Li Han Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong Tianjin China School of Computer Science and Engineering Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

With the rapid development of Internet technology, network assets have become an indispensable part of modern society. Especially in the face of high-value network assets, the attacker is faced with paying a greater cost to study the network structure. Due to the unknown attacker and attack means, the attacker has an advantage in the attack and defense situation. The moving target defense technique can flexibly change the static characteristics of the network attack surface and increase the difficulty for the attacker to detect the network, thus achieving the purpose of enhancing network security. Based on this technique, this paper proposes a defense strategy based on node confidence. It puts forward stricter constraints on the differential selection of address/port hopping paths, dynamically selects the hopping objects according to the node’s confidence, and implements the defense strategy of differential complexity. This further increases the network’s uncertainty and the difficulty for the attacker to detect the network structure. The experimental results show that in the path differentiation comparison, the maximum improvement is about 18% compared to the Dynamic Random Route Hopping strategy. In the CPU load experiment, the CPU load rate is reduced by about 4.625% on average compared to the Double Address Hopping strategy. Moreover, for larger hopping period, the proposed strategy also performs well in terms of CPU load rate. The proposed strategy also exhibits good performance in terms of CPU load ratio for large hopping cycles.

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Real-Time Estimation for the Swimming Direction of Robotic Fish Based on IMU Sensors*

Real-Time Estimation for the Swimming Direction of Robotic F...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Shikun Li Yufan Zhai Chen Wang Guangming Xie State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Institute of Ocean Research Peking University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

An increasing number of underwater robots inspired by Carangidae are developed, which is characterized by high efficiency and flexibility. However, estimating the swimming direction of these robotic fish is challenging due to the constant swinging of the head during movement, which complicates precise control. In this study, we installed two low-cost inertial measurement unit (IMU) sensors separately on the head and tail parts of a double-joint robotic fish and presented a method for accurately and timely estimating the swimming direction. Firstly, we effectively compensated for the yaw angle drift of the IMU sensors through a fused Kalman Filter. Furthermore, we propose the Anti-Shake Estimation (ASE) algorithm to calculate the real-time swimming direction using filtered yaw angles at a high updating rate of 100Hz. Finally, we applied the method to swimming direction feedback control for evaluation and comparison. The results show that our ASE method performs better than other existing methods in straight-line swimming experiments. The experiment of S-curve swimming also demonstrates the effectiveness of our method in complex missions.

关键词： Measurement units Estimation Tail Sensor phenomena and characterization Sensor fusion Robot sensing systems Fish

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