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A Semantic-Aware and Multi-Guided Network for Infrared-Visible Image Fusion

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arXiv 2024年

作者： Zhang, Xiaoli Wang, Liying Zhao, Libo Li, Xiongfei Ma, Siwei The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing100871 China

Multi-modality image fusion aims at fusing modality-specific (complementarity) and modality-shared (correlation) information from multiple source images. To tackle the problem of the neglect of inter-modal information, high-frequency information loss and limited attention to downstream tasks, this paper focuses on how to model correlation-driven decomposing features and reason high-level graph representation by efficiently extracting complementary features and aggregating multi-guided features. We propose a three-branch encoder-decoder architecture along with corresponding fusion layers as the fusion strategy. The transformer with Multi-Dconv Head Transposed Attention and Local-enhanced Feed Forward network is used to extract shallow features after the depthwise convolution. In the three parallel branches of the encoder, Cross Attention and Invertible Block (CAI) enables to extract local features and preserve high-frequency texture details. Base Feature Extraction Module (BFE) with residual connections can capture long-range dependencies and enhance modality-shared information. Graph Reasoning Module (GR) is introduced to reason high-level cross-modality relations and simultaneously extract low-level detail features as CAI’s modality-specific complementary information. Experiments demonstrate that our method obtains competitive results compared with state-of-the-art methods in visible/infrared image fusion and medical image fusion tasks. Moreover, we surpass other fusion methods in terms of subsequent tasks, averagely scoring 8.27% mAP@0.5 higher in object detection and 5.85% mIoU higher in semantic segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

Meta-path-based outlier detection in heterogeneous information network

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Frontiers of computer science 2020年第2期14卷 388-403页

作者： Lu LIU Shang WANG Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012China College of Software Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China College of Communication Engineering Jilin UniversityChangchun 130012China Department of Computer Science New Jersey Institute of TechnologyUniversity HeightsNewark NJ 07102USA

Mining outliers in heterogeneous networks is crucial to many applications,but challenges *** this paper,we focus on identifying meta-path-based outliers in heterogeneous information network(HIN),and calculate the similarity between different types of *** propose a meta-path-based outlier detection method(MPOutliers)in heterogeneous information network to deal with problems in one go under a unified *** calculates the heterogeneous reachable probability by combining different types of objects and their *** discovers the semantic information among nodes in heterogeneous networks,instead of only considering the network *** also computes the closeness degree between nodes with the same type,which extends the whole heterogeneous ***,each node is assigned with a reliable weighting to measure its authority *** experiments on two real datasets(AMiner and Movies dataset)show that our proposed method is very effective and efficient for outlier detection.

关键词： data mining heterogeneous information network outlier detection short text similarity

A Digital Twin-based Intelligent Network architecture for Underwater Acoustic Sensor Networks

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arXiv 2024年

作者： Song, Shanshan Huangfu, Bingwen Guo, Jiani Liu, Jun Cui, Junhong Shen, Xuemin The College of Computer Science and Technology Jilin University Changchun130012 China The School of Electronic and Information Engineering Beihang University Beijing100191 China Shenzhen Institute for Advanced Study UESTC Shenzhen China The College of Computer Science and Technology Jilin University Changchun China The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Underwater acoustic sensor networks (UASNs) drive toward strong environmental adaptability, intelligence, and multifunctionality. However, due to unique UASN characteristics, such as long propagation delay, dynamic channel quality, and high attenuation, existing studies present untimeliness, inefficiency, and inflexibility in real practice. Digital twin (DT) technology is promising for UASNs to break the above bottlenecks by providing high-fidelity status prediction and exploring optimal schemes. In this article, we propose a Digital Twin-based Network architecture (DTNA), enhancing UASNs’ environmental adaptability, intelligence, and multifunctionality. By extracting real UASN information from local (node) and global (network) levels, we first design a layered architecture to improve the DT replica fidelity and UASN control flexibility. In local DT, we develop a resource allocation paradigm (RAPD), which rapidly perceives performance variations and iteratively optimizes allocation schemes to improve real-time environmental adaptability of resource allocation algorithms. In global DT, we aggregate decentralized local DT data and propose a collaborative Multi-agent reinforcement learning framework (CMFD) and a task-oriented network slicing (TNSD). CMFD patches scarce real data and provides extensive DT data to accelerate AI model training. TNSD unifies heterogeneous tasks’ demand extraction and efficiently provides comprehensive network status, improving the flexibility of multi-task scheduling algorithms. Finally, practical and simulation experiments verify the high fidelity of DT. Compared with the original UASN architecture, experiment results demonstrate that DTNA can: (i) improve the timeliness and robustness of resource allocation;(ii) greatly reduce the training time of AI algorithms;(iii) more rapidly obtain network status for multi-task scheduling at a low cost. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning

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arXiv 2025年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas. However, extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks. This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle (UAV) to ensure data security at the physical layer. Since such a system requires trade-off policies that balance the secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor critic (TransSAC) algorithm, which is a generative artificial intelligence-enable deep reinforcement learning approach to solve the reformulated problem, so that capturing global dependencies and diversely exploring weights. Simulation results demonstrate that the TransSAC outperforms various baselines, and achieves an optimal secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results find more suitable constraint values for the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

Relating brain structure images to personality characteristics using 3D convolution neural network

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CAAI Transactions on Intelligence Technology 2021年第3期6卷 338-346页

作者： Lixian Cao Yanchun Liang Wei Lv Kaechang Park Yasuhiro Miura Yuki Shinomiya Shinichi Yoshida Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin UniversityZhuhaiChina Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and TechnologyJilin UniversityChangchunChina School of Information&Research Institute Kochi University of TechnologyKochiJapan

The Keras deep learning framework is employed to study MRI brain data in a preliminary analysis of brain structure using a convolutional neural *** results obtained are matched with the content of personality *** Big Five personality traits provide easy differentiation for dividing personalities into different *** now,the highest accuracy obtained from the results of personality prediction from the analysis of brain structure is about 70%.Although there is still no effective evidence to prove a clear relationship between brain structure and personality,the obtained results could prove helpful in understanding the basic relationship between brain structure and personality characteristics.

关键词： convolution structure dividing

An empirical examination of balancing strategy for counterfactual estimation on time series 24

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An empirical examination of balancing strategy for counterfa...

Proceedings of the 41st International Conference on Machine Learning

作者： Qiang Huang Chuizheng Meng Defu Cao Biwei Huang Yi Chang Yan Liu School of Artificial Intelligence and International Center of Future Science Jilin University Changchun Jilin China Department of Computer Science University of Southern California California Los Angeles Halicioğlu Data Science Institute University of California San Diego San Diego California School of Artificial Intelligence and International Center of Future Science Jilin University Changchun Jilin China and Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Changchun Jilin China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings.

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Mentored learning: improving generalization and convergence of student learner via teaching feedback

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2024年第1期25卷 15891-15935页

作者： Xiaofeng Cao Yaming Guo Heng Tao Shen Ivor W. Tsang James T. Kwok School of Artificial Intelligence Jilin University Changchun China and Australian Artifcial Intelligence Institute (AAII) University of Technology Sydney (UTS) NSW Australia School of Artificial Intelligence Jilin University Changchun China School of Computer Science and Technology Tongji University Shanghai China and School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Centre for Frontier AI Research and Institute of High Performance Computing Agency for Science Technology and Research(A*STAR) Singapore Singapore and College of Computing and Data Science Nanyang Technological University Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong SAR China

Student learners typically engage in an iterative process of actively updating its hypotheses, like active learning. While this behavior can be advantageous, there is an inherent risk of introducing mistakes through incremental updates including weak initialization, inaccurate or insignificant history states, resulting in expensive convergence cost. In this work, rather than solely monitoring the update of the learner's status, we propose monitoring the disagreement w.r.t. $\mathcal{F}^\mathcal{T}(\cdot)$ between the learner and teacher, and call this new paradigm "Mentored Learning", which consists of 'how to teach' and 'how to learn'. By actively incorporating feedback that deviates from the learner's current hypotheses, convergence will be much easier to analyze without strict assumptions on learner's historical status, then deriving tighter generalization bounds on error and label complexity. Formally, we introduce an approximately optimal teaching hypothesis, hT, incorporating a tighter slack term $\left(1+\mathcal{F}^{\mathcal{T}}(\widehat{h}_t)\right)\Delta_t$ to replace the typical 2Δt used in hypothesis pruning. Theoretically, we demonstrate that, guided by this teaching hypothesis, the learner can converge to tighter generalization bounds on error and label complexity compared to non-educated learners who lack guidance from a teacher: 1) the generalization error upper bound can be reduced from R(h*)+4ΔT-1 to approximately R(hT )+2ΔT-1, and 2) the label complexity upper bound can be decreased from 4θ(TR(h*) + 2O(√T)) to approximately 2θ(2TR(hT) + 3O(√T)). To adhere strictly to our assumption, self-improvement of teaching is proposed when hT loosely approximates h*. In the context of learning, we further consider two teaching scenarios: instructing a white-box and black-box learner. Experiments validate this teaching concept and demonstrate superior generalization performance compared to fundamental active learning strategies, such as IWAL (Beygelzimer et al

关键词： machine teaching hypothesis pruning active learning error disagreement convergence generalization error label complexity

An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series

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arXiv 2024年

作者： Huang, Qiang Meng, Chuizheng Cao, Defu Huang, Biwei Chang, Yi Liu, Yan School of Artificial Intelligence Jilin University Jilin Changchun China International Center of Future Science Jilin University Jilin Changchun China Department of Computer Science University of Southern California California Los Angeles United States Halicioğlu Data Science Institute University of California San Diego San DiegoCA United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Jilin Changchun China

关键词： Time series analysis

An Efficient PCA-Based Target Pose Estimation Algorithm for Low-Precision Point Clouds 11

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An Efficient PCA-Based Target Pose Estimation Algorithm for ...

11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Jia, Zhizhou Lai, Zhengchao Li, Yuetao Yang, Zhuo Rao, Qun Zhang, Shaohui School of Optics and Photonics Beijing Key Laboratory for Precision Optoelectronic Measurement Instrument and Technology Beijing Institute of Technology Beijing100081 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China School of Opto-Electronic Engineering Changchun University of Science and Technology Jilin Changchun130022 China National University of Defense Technology Laboratory of Science and Technology on Integrated Logistics Support Changsha410073 China

ISBN: (纸本)9798350333664

This paper introduces a grasping pose estimation algorithm tailored for two-fingered parallel grippers and consumer-grade depth cameras that can only capture low-precision point cloud information. Distinguishing itself from deep learning-based methods, this algorithm does not necessitate prior training on extensive datasets. Instead, it only requires acquiring a single frame of point cloud data from the target object's surface. After projection and subsequent two-dimensional PCA analysis, the algorithm adaptively generates grasping poses suitable for the current task, while considering the mechanical structure of the robotic arm. The experimental results demonstrate that the algorithm proposed in this paper achieves a high success rate in grasping common everyday objects. Furthermore, this algorithm can readily adapt to various combinations of robotic arm configurations and grippers. © 2023 IEEE.

关键词： grasp pose estimation low-precision point cloud targets Principal Component Analysis