检索结果-内蒙古大学图书馆

Congress on Evolutionary Computation

作者： Qi-Te Yang Liu-Yue Luo Chun-Hua Chen Jian-Yu Li Jing-Hui Zhong Jun Zhang Zhi-Hui Zhan School of Computer Science and Engineering South China University of Technology Guangzhou China School of Software Engineering South China University of Technology Guangzhou China College of Artificial Intelligence Nankai University Tianjin China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Feature selection (FS), which aims to minimize the classification error and the number of selected features, can essentially be modeled as a multiobjective optimization problem. To deal with such multiobjective FS (MOFS) problems, many multiobjective evolutionary algorithms (MOEAs) have been proposed. These MOEAs can find multiple optimal solutions, whereas substantial computational resources are required for fitness evaluations (FEs). More seriously, due to the high dimensionality and sparsity of FS problems, many FEs will be spent on unpromising solutions, resulting in a meaningless loss of computational resources. In this paper, two innovations are made so as to improve the performance of MOEAs for MOFS. First, we propose a surrogate-assisted flip (SF) strategy for MOEAs to reduce the FE waste on potentially unpromising solutions and improve search efficiency. This SF strategy is free of FE consumption and theoretically can be embedded in any MOEA to deal with MOFS problems. The experimental results show that this SF can improve the performance of different MOEAs, especially in reducing the number of selected features. Second, based on SF, we propose a more efficient SF -assisted MOEA for dealing with high-dimensional MOFS problems. The proposed algorithm divides the whole search space into different subspaces based on redundant feature subsets clustering to achieve parallel search, so as to reduce the search difficulty. The experimental results show that this algorithm is even more competitive than other SF -assisted MOEAs.

关键词： Technological innovation Computational modeling Clustering algorithms Evolutionary computation Feature extraction Search problems Probabilistic logic

来源：评论

学校读者我要写书评

暂无评论

TGL_sta: Low-resource Textual Graph Learning with Semantic and Topological Awareness via LLMs 39

TGL<sub>sta</sub>: Low-resource Textual Graph Learning with ...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Qin Li, Xiaowei Liu, Ziqi Fan, Xiaochen Chen, Xiaojun Pan, Shirui College of Computer Science and Software Engineering Shenzhen University China Institute for Electronics and Information Technology in Tianjin Tsinghua University China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)157735897X

Textual Graphs (TGs) present a graph-based representation of textual data and find wide applications in real-world scenarios, such as citation networks, knowledge graphs, and social networks. While the traditional “pre-train, fine-tune” framework effectively addresses tasks requiring abundant labeled data, it falls short in scenarios with limited resource or zero-shot learning capabilities, particularly in low-resource textual graph node classification. Additionally, prevalent approaches that convert text nodes into shallow or manually engineered features fail to capture the rich semantic nuances within the text. The conventional methods often neglect the fusion of semantic and topological information, resulting in suboptimal model learning. To overcome these challenges, we proposed a novel method of low-resource textual graph node classification based on large language models, i.e., Textual graph learning with semantic and topological awareness (TGLsta), which comprehensively explores the semantic information, near neighborhood information, and the topology information in textual graphs, where these components are the most important information source contained in textual graphs. Graph prompt tuning for both zero- and few-shot textual graph node classification is further introduced. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

A Multi-Population Genetic Algorithm for Multiobjective Recommendation System

A Multi-Population Genetic Algorithm for Multiobjective Reco...

引用

2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Hong, Jun Shi, Lin Du, Ke-Jing Chen, Chun-Hua Wang, Hua Zhang, Jun Zhan, Zhi-Hui School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Software Engineering South China University of Technology Guangzhou510006 China College of Artificial Intelligence Nankai University Tianjin300350 China Institute for Sustainable Industries and Liveable Cities Victoria University MelbourneVIC8001 Australia Hanyang University Ansan15588 Korea Republic of

ISBN: (纸本)9781665430654

Nowadays, recommendation systems (RSs) have been widely used in many real-world applications. However, traditional recommendation techniques mainly aim at improving recommendation accuracy, while other metrics to measure the performance of the RSs are not considered. In this paper, a multiobjective recommendation model that considers different metrics, including accuracy, diversity, and novelty of recommendations is established. Compared with recommendation models that only consider accuracy, this model can recommend more different items with higher diversity and more fresh items with higher novelty to enhance the long-term performance of RSs. Moreover, to efficiently solve this multiobjective recommendation model, a multi-population genetic algorithm (MPGA), which follows the multiple populations for multiple objectives (MPMO) framework, is proposed. As far as we know, it is the first time that the advanced MPMO framework is used in RSs. We conduct comparison experiments on three real-world datasets with three state-of-the-art multiobjective recommendation algorithms and two traditional multiobjective evolutionary algorithms. The experimental results indicate that the performance of MPGA is better than all the compared methods. © 2023 IEEE.

关键词： multiobjective evolutionary algorithms (MOEAs) multiple populations for multiple objectives recommendation system

来源：评论

学校读者我要写书评

暂无评论

Less Redundancy: Deduplicating Cad Datasets for Efffcient Deep Learning

SSRN

引用

SSRN 2024年

作者： Zhang, Zijian Fan, Rubin He, Fazhi Yan, Xiaohu School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University Wuhan430072 China School of Undergraduate Education Shenzhen Polytechnic University Shenzhen518055 China

A 2D/3D shape can be represented with its geometric formats, but such formats can not covers the design process and the designer's intention. Thus, for engineering design, the CAD datasets with operation sequences have been rolled out in recent years and attract increasing attention for deep learning. However, existing CAD datasets can not balance well the size of data and the quality of data, which means that larger datasets are accompanied by more simple and duplicate data while high quality datasets usually have a limit of size. Since deep learning is widely used in CAD generation and reconstruction and data cleaning is necessary for deep learning, this paper provides a brand new method that take process redundancy and semantic redundancy into account to deduplicate CAD datasets with operation sequences for higher quality. At present, the proposed method mainly focuses on 2D sketch datasets, but also has the ability to extend to 3D CAD datasets. Various experimental results are given to show the effectiveness and generalization of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Redundancy

来源：评论

学校读者我要写书评

暂无评论

SIBW: A Swarm Intelligence-Based Network Flow Watermarking Approach for Privacy Leakage Detection in Digital Healthcare Systems

引用

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Qiao, Sibo Guo, Qiang Shi, Fengdong Wang, Min Zhu, Haohao Khan, Fazlullah Rodrigues, Joel J.P.C. Lyu, Zhihan School of Software Tiangong University Tianjin300387 China School of Artificial Intelligence Tiangong University Tianjin300387 China National Demonstration Center for Experimental Engineering Training Education Tiangong University Tianjin300387 China School of Life Sciences Tiangong University Tianjin300387 China School of Control Sciences and Engineering Tiangong University Tianjin300387 China School of Computer Science Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315104 China Higher School of Technology Amazonas State University Amazonas Manaus69050-010 Brazil Department of Game Design Faculty of Arts Uppsala University Uppsala Sweden

The exponential growth of sensitive patient information and diagnostic records in digital healthcare systems has increased the complexity of data protection, while frequent medical data breaches severely compromise system security and reliability. Existing privacy protection techniques often lack robustness and real-time capabilities in high-noise, high-packet-loss, and dynamic network environments, limiting their effectiveness in detecting healthcare data leaks. To address these challenges, we propose a Swarm Intelligence-Based Network Watermarking (SIBW) method for real-time privacy data leakage detection in digital healthcare systems. SIBW integrates fountain codes with outer error correction codes and employs a Multi-Phase Synergistic Swarm Optimization Algorithm (MPSSOA) to dynamically optimize encoding parameters, significantly enhancing the robustness and interference resistance of watermark detection. Additionally, a reliable synchronization sequence and lightweight embedding mechanism are designed to ensure adaptability to complex, dynamic networks. Experimental results demonstrate that SIBW achieves over 90% detection accuracy under high latency jitter and packet loss conditions, surpassing existing methods in both robustness and efficiency. With a compact design of only 3.7 MB, SIBW is particularly suited for rapid deployment in resource-constrained digital healthcare systems. © 2013 IEEE All rights reserved.

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

Detached and Interactive Multimodal Learning

arXiv

引用

arXiv 2024年

作者： Fan, Yunfeng Xu, Wenchao Wang, Haozhao Liu, Junhong Guo, Song Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Hubei China School of Software Beihang University Beijing China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Recently, Multimodal Learning (MML) has gained significant interest as it compensates for single-modality limitations through comprehensive complementary information within multimodal data. However, traditional MML methods generally use the joint learning framework with a uniform learning objective that can lead to the modality competition issue, where feedback predominantly comes from certain modalities, limiting the full potential of others. In response to this challenge, this paper introduces DI-MML, a novel detached MML framework designed to learn complementary information across modalities under the premise of avoiding modality competition. Specifically, DI-MML addresses competition by separately training each modality encoder with isolated learning objectives. It further encourages cross-modal interaction via a shared classifier that defines a common feature space and employing a dimension-decoupled unidirectional contrastive (DUC) loss to facilitate modality-level knowledge transfer. Additionally, to account for varying reliability in sample pairs, we devise a certainty-aware logit weighting strategy to effectively leverage complementary information at the instance level during inference. Extensive experiments conducted on audio-visual, flow-image, and front-rear view datasets show the superior performance of our proposed method. The code is released at https://***/fanyunfeng-bit/DI-MML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

CherryPicker: A Parallel Solving and State Sharing Hybrid Fuzzing System

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Qingyu Lin, Jiayi Sun, Chenxin Qian, Chenxiong Luo, Xiapu School of Information and Software Engineering University of Electronic Science and Technology of China China Beijing University of Posts and Telecommunications China University of Hong Kong Hong Kong Computer Science Department HKU Hong Kong Hong Kong Polytechnic University Hong Kong

Hybrid testing, combining fuzz testing and concolic execution, has emerged as an effective technique for bug discovery. However, concolic execution becomes the performance bottleneck when applied to real-world software. Despite numerous approaches to optimize seed scheduling, symbolic simulation, and constraint solving, concolic execution remains inefficient and ineffective due to two limitations. First, the concolic executor and fuzzer do not synchronize the testing state in real time, leading to the generation of numerous duplicate inputs in both concolic execution and the fuzzer. Second, the concolic executor overlooks the independence of constraint solving and solves constraints sequentially, which introduces significant slowdown. In this paper, we first conduct a study to identify these limitations in existing hybrid testing systems. We then propose a novel design for hybrid fuzzing, CherryPicker, where the fuzzer and concolic executor share testing states, and concolic execution runs in parallel mode. Finally, we evaluate our system using the LAVA-M benchmark and real-world software and compare it to state-of-the-art systems. The results demonstrate that CherryPicker outperforms current systems in terms of efficiency and effectiveness, delivering improved runtime performance, generating more intriguing inputs, and activating more code. Notably, CherryPicker exclusively uncovers six previously unknown bugs during the evaluation, which have been reported to developers, all of which have been confirmed with three CVEs assigned. © 2004-2012 IEEE.

关键词： software testing

来源：评论

学校读者我要写书评

暂无评论

Self-temperature compensation approach for fiber specklegram magnetic field sensor based on polarization specklegram analysis 27

Self-temperature compensation approach for fiber specklegram...

引用

27th International Conference on Optical Fiber Sensors, OFS 2022

作者： Hu, Shicheng Liu, Haifeng Liu, Bo Lin, Wei Zhang, Hao Song, Binbin Wu, Jixuan Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology Institute of Modern Optics Nankai University Tianjin300350 China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Institute of Modern Optics Nankai University Tianjin300350 China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519000 China Key Laboratory of Computer Vision and Systems School of Computer Science and Engineering Tianjin University of Technology Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems College of Electronic and Information Engineering Tianjin Polytechnic University Tianjin300387 China

We proposed a self-temperature-compensation approach for fiber specklegram sensor (FSS) based on polarization specklegram analysis, and designed a fiber specklegram magnetic field sensor with high stability and good r... 详细信息

ISBN: (纸本)9781957171142

关键词： Polarization

来源：评论

学校读者我要写书评

暂无评论

A flower recognition system based on MobileNet for smart agriculture 3

A flower recognition system based on MobileNet for smart agr...

引用

3rd IEEE International Conference on Frontiers Technology of Information and computer, ICFTIC 2021

作者： Sun, Yiwen Zhang, Jingyao Han, Chaoyue Tianjin University of Technology School of Computer Science and Engineering Tianjin China University of Electronic Science Technology of China School of Software Engineering Beijing China Northwest A F University College of Mechanical and Electronic Engineering Xian China

ISBN: (纸本)9781665406055

Flower identification is the basic research in many fields, such as botany research, so the study of flower identification has far-reaching significance. The traditional flower recognition has the characteristics of weak generalization ability, high cost, and low accuracy. Because of the above problems, this paper proposes to use a lightweight neural network MobileNet model based on CNN to recognize flowers. The depth-separable convolution model reduces the number of parameters by dividing the convolution into Depthwise Convolution and Pointwise Convolution. That is to say, compared with the traditional convolutional neural network, the number of layers of the separable convolutional neural network can be deeper in the case of the same number of parameters. In this experiment, the CNN and MobileNet neural network models are trained by using public data sets, and the final experimental results show that the correct rate of the MobileNet model reaches 88. 37%, which is 22. 3% higher than that of the traditional CNN network model. © 2021 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Self-temperature compensation approach for fiber specklegram magnetic field sensor based on polarization specklegram analysis 8

Self-temperature compensation approach for fiber specklegram...

引用

8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Hu, Shicheng Liu, Haifeng Liu, Bo Lin, Wei Zhang, Hao Song, Binbin Wu, Jixuan Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology Institute of Modern Optics Nankai University Tianjin300350 China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Institute of Modern Optics Nankai University Tianjin300350 China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519000 China Key Laboratory of Computer Vision and Systems Ministry of Education School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems College of Electronic and Information Engineering Tianjin Polytechnic University Tianjin300387 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

In this letter, we proposed a self-temperature-compensation approach for fiber specklegram sensor (FSS) based on polarization specklegram analysis. The temperature compensation is achieved by comparing the variation difference of the two specklegrams with vertical linear polarization state. We designed a fiber specklegram magnetic field sensor employing this method and the magneto-birefringence effect of magnetic fluid. The measurement of magnetic field with high stability and good repeatability has been successfully achieved, which demonstrated the effectiveness of the proposed method. Due to the merits of low cost, ease of implement and system simplicity, the proposed method can promote the practical use of the FSS in long term monitoring. © 2022 SPIE

关键词： Polarization

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：