检索结果-内蒙古大学图书馆

arXiv 2023年

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

The federated recommendation system is an emerging AI service architecture that provides recommendation services in a privacy-preserving manner. Using user-relation graphs to enhance federated recommendations is a promising topic. However, it is still an open challenge to construct the user-relation graph while preserving data locality-based privacy protection in federated settings. Inspired by a simple motivation, similar users share a similar vision (embeddings) to the same item set, this paper proposes a novel Graph-guided Personalization for Federated Recommendation (GPFedRec). The proposed method constructs a user-relation graph from user-specific personalized item embeddings at the server without accessing the users' interaction records. The personalized item embedding is locally fine-tuned on each device, and then a user-relation graph will be constructed by measuring the similarity among client-specific item embeddings. Without accessing users' historical interactions, we embody the data locality-based privacy protection of vanilla federated learning. Furthermore, a graph-guided aggregation mechanism is designed to leverage the user-relation graph and federated optimization framework simultaneously. Extensive experiments on five benchmark datasets demonstrate GPFedRec's superior performance. The in-depth study validates that GPFedRec can generally improve existing federated recommendation methods as a plugin while keeping user privacy safe. Code is available to ease reproducibility. Copyright © 2023, The Authors. All rights reserved.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Evolutionary generative adversarial networks with crossover based knowledge distillation

arXiv

引用

arXiv 2021年

作者： Li, Junjie Zhang, Junwei Gong, Xiaoyu Lü, Shuai Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China

Generative Adversarial Networks (GAN) is an adversarial model, and it has been demonstrated to be effective for various generative tasks. However, GAN and its variants also suffer from many training problems, such as mode collapse and gradient vanish. In this paper, we firstly propose a general crossover operator, which can be widely applied to GANs using evolutionary strategies. Then we design an evolutionary GAN framework named C-GAN based on it. And we combine the crossover operator with evolutionary generative adversarial networks (E-GAN) to implement the evolutionary generative adversarial networks with crossover (CE-GAN). Under the premise that a variety of loss functions are used as mutation operators to generate mutation individuals, we evaluate the generated samples and allow the mutation individuals to learn experiences from the output in a knowledge distillation manner, imitating the best output outcome, resulting in better offspring. Then, we greedily select the best offspring as parents for subsequent training using discriminator as an evaluator. Experiments on real datasets demonstrate the effectiveness of CE-GAN and show that our method is competitive in terms of generated images quality and time efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

A Novel Drug-Drug Interaction Prediction Model Based on Line Subgraph Generation Strategy

A Novel Drug-Drug Interaction Prediction Model Based on Line...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tian Bai Chu Li Xinyue Peng Haotian Guan Zefan Zhang Guishen Wang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China College of Software Jilin University Changchun China School of Nursing Jilin University Changchun China School of Life Sciences Jilin University Changchun China College of Computer Science and Engineering Changchun University of Technology Changchun China

Drug-Drug Interaction (DDI) prediction task is helpful for better-understanding drugs. In this paper, we propose a novel drug-drug interaction prediction model based on line subgraph generation strategy, named DDI-LSG model. Our DDI-LSG model consists of three main parts which include drug relation graph construction, line subgraph generation strategy, and graph-level classification. To consider more relationships among drugs, we propose a node feature-enhancing method to encode drug features in drug relation graph construction process. To consider drugs and DDI as equivalent factors of our DDI-LSG model, we introduce line graph transformation to integrate DDI with drug feature enhancing vector. Combining Jaccard similarity with cosine similarity, we propose a line subgraph generation strategy to evaluate node relation and extract key structures around target DDI in the line graph. Then, we reformulate the DDI prediction task into a graph-level classification task for the line subgraph of the target DDI. Therefore, in the final part of our DDI-LSG model, we use a graph-level classifier to classify the line subgraphs. Our DDI-LSG model outperforms better experiment results than baselines. Ablation results have validated the node feature enhancing method and line subgraph generation strategy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improved fluid search optimization-based real-time weed mapping

引用

Information Processing in Agriculture 2020年第3期7卷 403-417页

作者： Chengcheng Chen Shengsheng Wang Xianchang Wang Helong Yu Ruyi Dong College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun 130012China College of Information Technology Jilin Agricultural UniversityChangchun 130118China Chengdu Kestrel Artificial Intelligence Institute Chengdu 611730China

In the field of agriculture,variable-rate herbicide spraying(VRHS)technology has been used to solve the low efficiency of pesticides and crop chemical *** key of VRHS is the quick and precise identification of weeds from field images,which forms a weed *** search optimization(FSO)was able to simplify the threshold optimization process to create a weed map,which simulated the fluid flowing from high pressure to low pressure,but it is time consuming and often converges ***,an explosion mechanism and a twophase optimization were introduced to improve the FSO-based segmentation *** of segmentation weeds from a corn field at seedling growth stage showed that the IFSO algorithm obtained the best accuracy of 93.3%and the least running time of 0.019 s,compared with the standard PSO,GA,and FSO algorithms.

关键词： Variable-rate herbicide spraying Weed map Fluid search optimization algorithm Otsu

来源：评论

学校读者我要写书评

暂无评论

Wide aspect ratio matching for robust face detection

arXiv

引用

arXiv 2021年

作者： Luo, Shi Li, Xiongfei Zhang, Xiaoli Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China

Recently, anchor-based methods have achieved great progress in face detection. Once anchor design and anchor matching strategy determined, plenty of positive anchors will be sampled. However, faces with extreme aspect ratio always fail to be sampled according to standard anchor matching strategy. In fact, the max IoUs between anchors and extreme aspect ratio faces are still lower than fixed sampling threshold. In this paper, we firstly explore the factors that affect the max IoU of each face in theory. Then, anchor matching simulation is performed to evaluate the sampling range of face aspect ratio. Besides, we propose a Wide Aspect Ratio Matching (WARM) strategy to collect more representative positive anchors from ground-truth faces across a wide range of aspect ratio. Finally, we present a novel feature enhancement module, named Receptive Field Diversity (RFD) module, to provide diverse receptive field corresponding to different aspect ratios. Extensive experiments show that our method can help detectors better capture extreme aspect ratio faces and achieve promising detection performance on challenging face detection benchmarks, including WIDER FACE and FDDB datasets. © 2021, CC BY.

关键词： Aspect ratio

来源：评论

学校读者我要写书评

暂无评论

Dataset-Level Color Augmentation and Multi-Scale Exploration Methods for Polyp Segmentation

SSRN

引用

SSRN 2024年

作者： Chen, Haipeng Ju, Honghong Qin, Jun Song, Jincai Lyu, Yingda Liu, Xianzhu 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 Changchun University of Science and Technology Changchun130022 China Public Computer Education and Research Center Jilin University Changchun130012 China National and Local Joint Engineering Research Center of Space Optoelectronics Technology Changchun University of Science and Technology Changchun130022 China College of Opto-Electronic Engineering Changchun University of Science and Technology Changchun130022 China

Automatic segmentation of polyps from colonoscopy images plays a critical role in early screening and treatment of colorectal cancer. Although deep learning methods have made significant progress, precise polyp segmentation faces two challenges: (1) the imbalance of color appearances in the limited training dataset hinders the generalization of the model, and (2) polyps have the diverse scales, locations and shapes with blurred boundary. To address the issues, Dataset-Level Color Augmentation (DLCA) and Convolutional Multi-scale Attention Module (CMAM) are proposed. DLCA employs the dataset-level color knowledge and generate new color appearances, to avoid the model learning false associations with colors. Meanwhile, CMAM simultaneously explores the region and boundary clues and model multi-scale context, which improves the accuracy of polyp localization and fine-grained segmentation. We conduct comprehensive experiments and compare our network with state-of-the-art methods. The proposed model is superior on multiple polyp datasets, especially on ETIS, where mDice and mIoU reach 0.839 and 0.766. Furthermore, it's validated that DLCA can be widely applied to most polyp segmentation methods, and CMAM is practical and plug-and-play. Finally, the model demonstrates promising generalization to breast ultrasound and skin lesion segmentation. © 2024, The Authors. All rights reserved.

关键词： Color

来源：评论

学校读者我要写书评

暂无评论

Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

引用

IEEE Transactions on Affective computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks

arXiv

引用

arXiv 2024年

作者： Li, Hongjuan Kang, Hui Sun, Geng Li, Jiahui Wang, Jiacheng Wang, Xue 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 Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Communication Engineering Jilin University Changchun130012 China 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

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Multi-objective Optimization for Multi-UAV-assisted Mobile Edge computing

arXiv

引用

arXiv 2024年

作者： Sun, Geng Wang, Yixian Sun, Zemin Wu, Qingqing Kang, Jiawen Niyato, Dusit Leung, Victor C.M. 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 Shanghai China School of Automation Guangdong University of Technology Guangzhou510006 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Recent developments in unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) have provided users with flexible and resilient computing services. However, meeting the computing-intensive and latency-sensitive demands of users poses a significant challenge due to the limited resources of UAVs. To address this challenge, we present a multi-objective optimization approach for multi-UAV-assisted MEC systems. First, we formulate a multi-objective optimization problem aiming at minimizing the total task completion delay, reducing the total UAV energy consumption, and maximizing the total amount of offloaded tasks by jointly optimizing task offloading, computation resource allocation, and UAV trajectory control. Since the problem is a mixed-integer non-linear programming (MINLP) and NP-hard problem which is challenging, we propose a joint task offloading, computation resource allocation, and UAV trajectory control (JTORATC) approach to solve the problem. However, since the decision variables of task offloading, computation resource allocation, and UAV trajectory control are coupled with each other, the original problem is split into three sub-problems, i.e., task offloading, computation resource allocation, and UAV trajectory control, which are solved individually to obtain the corresponding decisions. Moreover, the sub-problem of task offloading is solved by using distributed splitting and threshold rounding methods, the sub-problem of computation resource allocation is solved by adopting the Karush-Kuhn-Tucker (KKT) method, and the sub-problem of UAV trajectory control is solved by employing the successive convex approximation (SCA) method. Simulation results show that the proposed JTORATC has superior performance compared to the other benchmark methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Resource Scheduling for UAVs-aided D2D Networks: A Multi-objective Optimization Approach

arXiv

引用

arXiv 2023年

作者： Pan, Hongyang Liu, Yanheng Sun, Geng Wang, Pengfei Yuen, Chau College of Computer Science and Technology Jilin University Changchun130012 China Pillar Singapore University of Technology and Design Singapore487372 Singapore Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore

Unmanned aerial vehicles (UAVs)-aided device-to-device (D2D) networks have attracted great interests with the development of 5G/6G communications, while there are several challenges about resource scheduling in UAVs-aided D2D networks. In this work, we formulate a UAVs-aided D2D network resource scheduling optimization problem (NetResSOP) to comprehensively consider the number of deployed UAVs, UAV positions, UAV transmission powers, UAV flight velocities, communication channels, and UAV-device pair assignment so as to maximize the D2D network capacity, minimize the number of deployed UAVs, and minimize the average energy consumption over all UAVs simultaneously. The formulated NetResSOP is a mixed-integer programming problem (MIPP) and an NP-hard problem, which means that it is difficult to be solved in polynomial time. Moreover, there are trade-offs between the optimization objectives, and hence it is also difficult to find an optimal solution that can simultaneously make all objectives be optimal. Thus, we propose a non-dominated sorting genetic algorithm-III with a Flexible solution dimension mechanism, a Discrete part generation mechanism, and a UAV number adjustment mechanism (NSGA-III-FDU) for solving the problem comprehensively. Simulation results demonstrate the effectiveness and the stability of the proposed NSGA-III-FDU under different scales and settings of the D2D networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：