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Multi-focus image fusion based on fully convolutional networks

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Frontiers of Information Technology & Electronic engineering 2020年第7期21卷 1019-1033页

作者： Rui GUO Xuan-jing SHEN Xiao-yu DONG Xiao-li ZHANG Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China

We propose a multi-focus image fusion method, in which a fully convolutional network for focus detection(FD-FCN) is constructed. To obtain more precise focus detection maps, we propose to add skip layers in the network to make both detailed and abstract visual information available when using FD-FCN to generate maps. A new training dataset for the proposed network is constructed based on dataset CIFAR-10. The image fusion algorithm using FD-FCN contains three steps: focus maps are obtained using FD-FCN, decision map generation occurs by applying a morphological process on the focus maps, and image fusion occurs using a decision map. We carry out several sets of experiments, and both subjective and objective assessments demonstrate the superiority of the proposed fusion method to state-of-the-art algorithms.

关键词： Multi-focus image fusion Fully convolutional networks Skip layer Performance evaluation

Joint Optimization of UAV-Carried IRS for Urban Low Altitude mmWave Communications with Deep Reinforcement Learning

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arXiv 2025年

作者： Xie, Wenwen Sun, Geng Liu, Bei Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Kim, Dong In The College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Emerging technologies in sixth generation (6G) of wireless communications, such as terahertz communication and ultra-massive multiple-input multiple-output, present promising prospects. Despite the high data rate potential of millimeter wave communications, millimeter wave (mmWave) communications in urban low altitude economy (LAE) environments are constrained by challenges such as signal attenuation and multipath interference. Specially, in urban environments, mmWave communication experiences significant attenuation due to buildings, owing to its short wavelength, which necessitates developing innovative approaches to improve the robustness of such communications in LAE networking. In this paper, we explore the use of an unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to support low altitude mmWave communication. Specifically, we consider a typical urban low altitude communication scenario where a UAV-carried IRS establishes a line-of-sight (LoS) channel between the mobile users and a source user (SU) despite the presence of obstacles. Subsequently, we formulate an optimization problem aimed at maximizing the transmission rates and minimizing the energy consumption of the UAV by jointly optimizing phase shifts of the IRS and UAV trajectory. Given the non-convex nature of the problem and its high dynamics, we propose a deep reinforcement learning-based approach incorporating neural episodic control, long short-term memory, and an IRS phase shift control method to enhance the stability and accelerate the convergence. Simulation results show that the proposed algorithm effectively resolves the problem and surpasses other benchmark algorithms in various performances. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

Automatic Controversy Detection Based on Heterogeneous Signed Attributed Network and Deep Dual-Layer Self-Supervised Community Analysis

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SSRN

SSRN 2023年

作者： Li, Ying Zhang, Xiao Liang, Yu Li, Qianqian Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130012 China Institutes of Science and Development Chinese Academy of Sciences ZhongGuanCun BeiYiTiao Beijing100190 China School of Public Policy and Management University of Chinese Academy of Sciences ShiJingShan YuQuanLu Beijing100049 China

Controversial analysis entails identifying and scrutinizing contentious events or issues within a specific domain. Detecting controversy, particularly on social media, poses challenges due to the need to consider numerous contents and user interactions. Unlike previous work, rather than studying controversy on limited knowledge of the topic discussion, we introduce multidimensional and heterogeneous information to conduct a comprehensive evaluation. Our method to quantify controversy is based on heterogeneous signed attributed network. This framework constructs a comprehensive heterogeneous signed attributed network to integrate diverse semantic and structural information on social media, encompassing user, retweet, comment, summary, topic, sentiment information, and user attributes. We introduce a deep dual-layer self-supervised algorithm for community detection and controversial analysis within this network. We gauge controversy levels in hot events via community distinctions, betweenness centrality, and user representations. Extensive comparisons of state-of-the-art baseline approaches are performed. We find that existing methods shows instability on different datasets. Whereas our approach integrates the multidimensional features of topics and also takes into account various computational results, our method consistently yields stable results. A user-friendly web server has been developed for accessible analysis of other hot events (http://***/contro/). © 2023, The Authors. All rights reserved.

关键词： Social networking (online)

When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions

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arXiv 2023年

作者： Zhang, Chunxu Zhang, Zijian Long, Guodong Yan, Peng Zhou, Tianyi Yang, Bo College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China College of Computer Science and Technology Jilin University City University of Hong Kong China Australian Artificial Intelligence Institute FEIT University of Technology Sydney Sydney Australia Computer Science and UMIACS University of Maryland Maryland United States

Federated recommendation systems usually trains a global model on the server without direct access to users’ private data on their own devices. However, this separation of the recommendation model and users’ private data poses a challenge in providing quality service, particularly when it comes to new items, namely cold-start recommendations in federated settings. This paper introduces a novel method called Item-aligned Federated Aggregation (IFedRec) to address this challenge. It is the first research work in federated recommendation to specifically study the cold-start scenario. The proposed method learns two sets of item representations by leveraging item attributes and interaction records simultaneously. Additionally, an item representation alignment mechanism is designed to align two item representations and learn the meta attribute network at the server within a federated learning framework. Experiments on four benchmark datasets demonstrate IFedRec’s superior performance for cold-start scenarios. Furthermore, we also verify IFedRec owns good robustness when the system faces limited client participation and noise injection, which brings promising practical application potential in privacy-protection enhanced federated recommendation systems. The implementation code is available1 Copyright © 2023, The Authors. All rights reserved.

关键词： Recommender systems

Aerial Secure Collaborative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach

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arXiv 2025年

作者： Li, Jiahui Sun, Geng Wu, Qingqing Liang, Shuang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Information Science and Technology Northeast Normal University Changchun130117 China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism ach

关键词： Multiobjective optimization

PepLand: A large-scale pre-trained peptide representation model for a comprehensive landscape of both canonical and non-canonical amino acids

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arXiv 2023年

作者： Zhang, Ruochi Wu, Haoran Xiu, Yuting Li, Kewei Chen, Ningning Wang, Yu Wang, Yan Gao, Xin Zhou, Fengfeng Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun130012 China School of Artificial Intelligence Jilin University Changchun130012 China Syneron Technology Guangzhou510700 China College of Computer Science and Technology Jilin University Jilin Changchun130012 China Thuwal23955 Saudi Arabia Thuwal23955 Saudi Arabia

In recent years, the scientific community has become increasingly interested on peptides with non-canonical amino acids due to their superior stability and resistance to proteolytic degradation. These peptides present promising modifications to biological, pharmacological, and physiochemical attributes in both endogenous and engineered peptides. Notwithstanding their considerable advantages, the scientific community exhibits a conspicuous absence of an effective pre-trained model adept at distilling feature representations from such complex peptide sequences. We herein propose PepLand, a novel pre-training architecture for representation and property analysis of peptides spanning both canonical and non-canonical amino acids. In essence, PepLand leverages a comprehensive multi-view heterogeneous graph neural network tailored to unveil the subtle structural representations of peptides. Empirical validations underscore PepLand's effectiveness across an array of peptide property predictions, encompassing protein-protein interactions, permeability, solubility, and synthesizability. The rigorous evaluation confirms PepLand's unparalleled capability in capturing salient synthetic peptide features, thereby laying a robust foundation for transformative advances in peptide-centric research domains. We have made all the source code utilized in this study publicly accessible via GitHub at https://***/zhangruochi/pepland. © 2023, CC BY-NC-ND.

关键词： Peptides

Document-Level Relation Extraction with Entity Type Constraints

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SSRN

SSRN 2024年

作者： Han, Ridong Peng, Tao Zhu, BeiBei Bi, Haijia Han, Jiayu Liu, Lu College of Computer Science and Technology Jilin University Jilin Changchun130012 China College of Software Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China College of Computer and Artificial Intelligence Liaoning Normal University Liaoning Dalian116081 China Department of Linguistics University of Washington SeattleWA98195 United States

Long-tail problem and multi-label problem are two commonly encountered challenges in document-level relation extraction task. Current efforts are concerned with enhancing the representations of entity pairs through Transformer or document graphs, which cannot tackle the above challenges well. Relation correlations are a potential solution, which allows head relations to assist in the training of tail ones by transferring correlation knowledge between them, and can measure the semantic distance between relations to assist the classifier in assigning multiple semantically similar relations to multi-label instances. This paper proposes to learn relation correlations from both global and local views using entity type constraints, which means that the subject-object entity types limit the scope of possible relations. Globally, we statistically construct the Type-constrained Graph between entity types and relations, which formulates all possible subject/object types for each relation. Different relations are connected by common entity types, reflecting the desired correlations. Locally, given an entity pair, the classification probability of relations matching its entity types should be greater than those unmatched. Therefore, the Type-constrained Loss is proposed to make the matched relations have greater probabilities. Detailed experiments are conducted on DocRED and DWIE datasets, and our model significantly outperforms baselines under long-tail and multi-label setups. © 2024, The Authors. All rights reserved.

关键词： Extraction

FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification

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arXiv 2022年

作者： Xia, Tingyu Wang, Yue Tian, Yuan Chang, Yi School of Artificial Intelligence Jilin University China School of Information and Library Science University of North Carolina Chapel Hill United States Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University China International Center of Future Science Jilin University China

Weakly-supervised text classification aims to train a classifier using only class descriptions and unlabeled data. Recent research shows that keyword-driven methods can achieve state-of-the-art performance on various tasks. However, these methods not only rely on carefully-crafted class descriptions to obtain class-specific keywords but also require substantial amount of unlabeled data and takes a long time to train. This paper proposes FastClass, an efficient weakly-supervised classification approach. It uses dense text representation to retrieve class-relevant documents from external unlabeled corpus and selects an optimal subset to train a classifier. Compared to keyword-driven methods, our approach is less reliant on initial class descriptions as it no longer needs to expand each class description into a set of class-specific keywords. Experiments on a wide range of classification tasks show that the proposed approach frequently outperforms keyword-driven models in terms of classification accuracy and often enjoys orders-of-magnitude faster training speed. Copyright © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

Improving Generalization of Neural Vehicle Routing Problem Solvers Through the Lens of Model Architecture

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arXiv 2024年

作者： Xiao, Yubin Wang, Di Wu, Xuan Wu, Yuesong Li, Boyang Du, Wei Wang, Liupu Zhou, You Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639956 Singapore WeBank-NTU Joint Research Institute on Fintech Nanyang Technological University 639956 Singapore College of Computing and Data Science Nanyang Technological University 639956 Singapore

Neural models produce promising results when solving Vehicle Routing Problems (VRPs), but may often fall short in generalization. Recent attempts to enhance model generalization often incur unnecessarily large training cost or cannot be directly applied to other models solving different VRP variants. To address these issues, we take a novel perspective on model architecture in this study. Specifically, we propose a plug-and-play Entropy-based Scaling Factor (ESF) and a Distribution-Specific (DS) decoder to enhance the size and distribution generalization, respectively. ESF adjusts the attention weight pattern of the model towards familiar ones discovered during training when solving VRPs of varying sizes. The DS decoder explicitly models VRPs of multiple training distribution patterns through multiple auxiliary light decoders, expanding the model representation space to encompass a broader range of distributional scenarios. We conduct extensive experiments on both synthetic and widely recognized real-world benchmarking datasets and compare the performance with seven baseline models. The results demonstrate the effectiveness of using ESF and DS decoder to obtain a more generalizable model and showcase their applicability to solve different VRP variants, i.e., travelling salesman problem and capacitated VRP. Notably, our proposed generic components require minimal computational resources, and can be effortlessly integrated into conventional generalization strategies to further elevate model generalization. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization