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3rd International Conference on Artificial Intelligence and Advanced Manufacture, AIAM 2021

作者： Lei, Wenli Ren, xincheng Li, Bin Liu, Zhouzhou School of Electronics and Information Northwestern Polytechnical University Xi'an710072 China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an716000 China Xi'an Aeronautical Polytechnic Institute Xi'an710089 China

ISBN: (纸本)9781450385046

Aiming at the problem of node location in forest fire monitoring, an improved Low Power Wide Area Network node location algorithm for forest fire monitoring system is proposed this paper. This location algorithm makes full use of the location information of known anchor nodes to modify the location information of unknown nodes without adding other hard nodes in the network. At the same time, the positioning accuracy is further improved. The test results show that the algorithm has better positioning effect than other non-ranging algorithms. © 2021 ACM.

关键词： Fires

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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Multi-View Fusion Networks for Multimodal Fake News Detection

Multi-View Fusion Networks for Multimodal Fake News Detectio...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Wei Cui Pu Li Xuerui Zhang College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

The proliferation of multimedia fake news misleads social opinion, damages social harmony, and seriously challenges the authority of news media. Current multimodal fake news detection methods focus on extracting better unimodal features using pre-trained models and developing complex networks for visual and textual feature fusion, mostly ignoring social contextual information. In the text, we present a novel Multi-View Fusion Network (MVFN) for fake news detection, which attempts to achieve fusion learning of heterogeneous multimodal features. The model not only explores cross-modal graph interactions between visual regions and sentence words, but also investigates the effect of social context on feature learning. In addition, we consider the alignment relationship of features in both perspectives for better feature fusion. Experimental results on Chinese and English datasets show that the proposed model outperforms current multimodal detection approaches.

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big-MOE: BYPASS ISOLATED GATING MOE FOR GENERALIZED MULTIMODAL FACE ANTI-SPOOFING

arXiv

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arxiv 2024年

作者： Ma, Yingjie Yu, Zitong Lin, Xun xie, Weicheng Shen, Linlin Great Bay University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address these issues effectively. We identified three limitations in traditional MoE approaches to multimodal FAS: (1) Coarse-grained experts’ inability to capture nuanced spoofing indicators;(2) Gated networks’ susceptibility to input noise affecting decision-making;(3) MoE’s sensitivity to prompt tokens leading to overfitting with conventional learning methods. To mitigate these, we propose the Bypass Isolated Gating MoE (big-MoE) framework, featuring: (1) Fine-grained experts for enhanced detection of subtle spoofing cues;(2) An isolation gating mechanism to counteract input noise;(3) A novel differential convolutional prompt bypass enriching the gating network with critical local features, thereby improving perceptual capabilities. Extensive experiments on four benchmark datasets demonstrate significant generalization performance improvement in multimodal FAS task. The code is released at https://***/murInJ/bigMoE. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

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Efficient Heuristic Algorithm for Precedence-Constrained Multi-Robot Task Assignment

Efficient Heuristic Algorithm for Precedence-Constrained Mul...

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第43届中国控制会议

作者： xiaoshan Bai Baode Li Fuqiang Liu Li Qiu Guangzhong Cao College of Mechanical and Control Engineering Shenzhen University Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University College of Mechanical and Vehicle Engineering Chongqing University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Due to the widespread application of multi-robot systems, the efficient task assignment for a team of robots has become particularly critical for performing complex tasks. This paper studies the task assignment problem for multiple robots to visit a group of target locations with precedence constraints, which determine the order/sequence in which certain target locations need to be visited before others. A marginal-cost-based heuristic algorithm is developed to minimize the time for the robots to visit the last target location. The algorithm first uses a timestamp strategy to update the anticipated visiting time of each assigned target and the corresponding earliest time that each successor target can be visited under the precedence constraints. Then, it utilizes the topological sorting technique and the marginal-cost-based mechanism to insert the feasible target that induces the minimum marginal cost into the robots' current routes. Numerical simulations demonstrate the effectiveness of the proposed algorithm compared with the popular greedy algorithm and the existing simple iterative auction algorithm.

关键词： Multi-robot task assignment Precedence constraints Heuristic algorithm Marginal cost

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Multi-View Disease-Symptom Association Prediction Based on Disease Knowledge Graph Embedding

Multi-View Disease-Symptom Association Prediction Based on D...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Zongqing Yang Hanyue Liu Hong Yu Feng Hu Chongqing Key Laboratory of Computational Intelligence (Chongqing University of Posts and Telecommunications) Chongqing China Chongqing Key Laboratory of Computational Intelligence (Chongqing University of Posts and Telecommunications) Key Laboratory of Big Data Intelligent Computing (Chongqing University of Posts and Telecommunications) Chongqing China

Disease symptoms are significant in disease treatment and diagnosis, as well as in developing a hierarchical model from disease phenotypes to genes to improve our understanding of complex disease causes. While previous studies have focused on genetics and proteomics, this study proposes a novel compu-tational model, MGAEMF, based on disease knowledge graph embedding for disease-symptom association (DSA) prediction (i.e., disease nodes have connections with multiple other node types). First, the disease knowledge graph is decomposed into multiple disease-related bipartite networks, form which multiple disease similarity networks and symptom similarity networks can be extracted, and the similarity networks about diseases and symptoms are separately integrated into a single similarity network. Then, the integrated disease similarity network and symptom similarity network are combined with bipartite networks built of known disease-symptom connections to generate nonlinear feature representations of diseases and symptoms using GAE. Meanwhile, the disease-symptom association matrix is calculated using a non-negative matrix factorization to provide linear feature representations of diseases and symptoms. Finally, a fully connected neural network incorporating the linear and nonlinear representations of diseases and symptoms calculates the final prediction scores for all disease-symptom pairs. During the 10-fold cross-validation experiment, the MGAEMF model outperforms other comparable methods with a superior average AUC value of 0.8372.

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Diversity-Aware Top-N Recommendation: A Deep Reinforcement Learning Way 1

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8th China Computer Federation International Conference on big data, CCF bigdata 2020

作者： Wang, Tao Shi, xiaoyu Shang, Mingsheng Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9789811607059

ISBN: (纸本)9789811607042

The increasing popularity of the recommender system deeply influences our decisions on the Internet, which is a typical continuous interaction process between the system and its users. Most previous recommender systems heavily focus on optimizing recommendation accuracy while neglecting the other important aspects of recommendation quality, such as diversity of recommendation list. In this study, we propose a novel recommendation framework to optimize the recommendation list for the Top-N task, named Collaborative Filtering-based Deep Reinforcement Learning (CFDRL), which promotes the diversity of recommendation results without sacrificing the recommendation accuracy. More specifically, to effectively capture the continuous user-item interaction for recommendations, we adopt the deep reinforcement learning (DRL) to update the recommendation strategy dynamically according to the user’s real-time feedback. Meanwhile, to generate diverse and complementary items for recommendation, we design a diversity-aware reward function that can lead to maximizing reward with the trade-off between diversity and accuracy. Besides, to alleviate the disadvantage of DQN that directly picking the recommendations with the highest Q-values from the unselected items, we define a modified Ε -greedy explore policy with jointly CF model. It firstly utilizes CF model to sort the items and divide them into two part according to the item similarity, then with a probability the agent selects from them and generates an action list with the modified Ε -greedy explore policy. The experimental results conducted on two real-world e-commerce datasets demonstrate the effectiveness of the proposed model. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Location and Lightpath Selection in Multi-Domain Optical data Center Networks

Location and Lightpath Selection in Multi-Domain Optical Dat...

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Mobile Internet, Cloud computing and Information Security (MICCIS), International Conference on

作者： Changle Xu Xu Zhang Chuan Feng xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

In the era of booming fifth-generation fixed networks (F5G), the service demands for high bandwidth continue to grow. Meanwhile, in multi-domain network scenarios, service requests are vulnerable to physical layer attacks initiated by malicious clients. In this paper, a risk-aware heuristic algorithm is proposed to address the problem of lightpath provisioning in multi-domain elastic optical data center networks (EODCNs). Simulation results show that the blocking probability of the proposed algorithm is lower than the comparison algorithm, 8.2% and 8.8% lower in NSFNET topology and US-Backbone topology, respectively.

关键词： Integrated optics Multiplexing data centers Network topology Optical design Simulation Optical fiber networks

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Advanced Detection and Signal Reception System Utilizing Rydberg Atomic Dynamics for Applications

Advanced Detection and Signal Reception System Utilizing Ryd...

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European Conference on Antennas and Propagation, EuCAP

作者： Wenjie Ding Guoda xie Yingsong Li Zhixiang Huang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Ministry of Education Center for Big Data and Population Health of IHM Anhui University Hefei China The Jianghuai Advance Technology Center Hefei China

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

In this paper, we introduce a novel Monte Carlo wave function method (MCWFM) for simulating the transient response of Rydberg atoms under rapidly varying electric fields, aimed at high-precision electric field measurements and wireless communication reception. This approach leverages the electromagnetically induced transparency (EIT) effect and Autler- Townes (AT) splitting in Rydberg atoms, offering enhanced efficiency compared to traditional density matrix methods by focusing solely on wave function evolution, thereby reducing computational complexity. Additionally, we account for Doppler effects in AT splitting through MCWFM, providing faster results than conventional Runge-Kutta methods. This work lays a solid foundation for advancing Rydberg atom technology in the development of high-capacity, high-fidelity wireless communication systems.

关键词： Wireless communication Atomic measurements Transient response Rydberg atoms Receivers Wave functions Optical fiber communication Doppler effect Electric fields Computational complexity

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multiPI-TransBTS: A multi-path learning framework for brain tumor image segmentation based on multi-physical information

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Computers in Biology and Medicine 2025年 191卷 110148-110148页

作者： Zhu, Hongjun Huang, Jiaohang Chen, Kuo Ying, Xuehui Qian, Ying School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Engineering Research Center of Software Quality Assurance Testing and Assessment Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

Brain Tumor Segmentation (BraTS) plays a critical role in clinical diagnosis, treatment planning, and monitoring the progression of brain tumors. However, due to the variability in tumor appearance, size, and intensity across different MRI modalities, automated segmentation remains a challenging task. In this study, we propose a novel Transformer-based framework, multiPI-TransBTS, which integrates multi-physical information to enhance segmentation accuracy. The model leverages spatial information, semantic information, and multi-modal imaging data, addressing the inherent heterogeneity in brain tumor characteristics. The multiPI-TransBTS framework consists of an encoder, an Adaptive Feature Fusion (AFF) module, and a multi-source, multi-scale feature decoder. The encoder incorporates a multi-branch architecture to separately extract modality-specific features from different MRI sequences. The AFF module fuses information from multiple sources using channel-wise and element-wise attention, ensuring effective feature recalibration. The decoder combines both common and task-specific features through a Task-Specific Feature Introduction (TSFI) strategy, producing accurate segmentation outputs for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET) regions. Comprehensive evaluations on the BraTS2019 and BraTS2020 datasets demonstrate the superiority of multiPI-TransBTS over the state-of-the-art methods. The model consistently achieves better Dice coefficients, Hausdorff distances, and Sensitivity scores, highlighting its effectiveness in addressing the BraTS challenges. Our results also indicate the need for further exploration of the balance between precision and recall in the ET segmentation task. The proposed framework represents a significant advancement in BraTS, with potential implications for improving clinical outcomes for brain tumor patients. © 2025

关键词： Decoding

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