检索结果-内蒙古大学图书馆

5th International Conference on Control, Robotics, and Intelligent System, CCRIS 2024

作者： You, Qian Lyu, Xin Li, Chunhua Li, Xin Chen, Shangjing Fang, Yiwei Jiang, Wenxuan College of Computer Science and Software Engineering Hohai University Nanjing211100 China Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing211100 China

ISBN: (数字)9781510685871

ISBN: (纸本)9781510685864

Improving the accuracy of semantic segmentation for remote sensing images (RSIs) is crucial for the geoscientific research and applications. However, existing models tend to focus too much on the subject information for semantic objects, but less on the global-local boundary information of images. To tackle this issue, we introduce a novel approach named the Boundary Enhanced Network (BENet), which applies synchronized attention to both global and local aspects of images. Specifically, the proposed method utilizes the Global-Local Boundary Fusion module (GLBF) to aggregate the boundary information of the objects from a high-dimensional feature perspective. To adapt to the multi-scale characteristics of RSIs, GLBF is embed hierarchically to maintain boundary details across different stages. Besides, a Feature Enhancement Module (FEM) is devised with sequentially DEConv and Squeeze-and-Excitation (SE) blocks to refine the features from decoder. The experiments on the ISPRS Vaihingen and Potsdam datasets showed that BENet outperforms mainstream methodology, OA, mIoU and mF1 metrics were all significantly elevated. © 2024 SPIE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

引用

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 58-73页

作者： Qingsong Zhao Lei Shu Kailiang Li Mohamed Amine Ferrag Ximeng Liu Yanbin Li the College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210031 the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023 the State Key Laboratory of Cryptology Beijing 100878China IEEE the School of Engineering University of LincolnLincoln LN67TSUK the Artificial Intelligence and Digital Science Research Center Technology Innovation InstituteMasdar City 9639UAE the College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security *** issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

关键词： Challenges Internet of Things(IoT) privacy and security security requirements solar insecticidal lamps(SIL)

来源：评论

学校读者我要写书评

暂无评论

Locating What You Need: Towards Adapting Diffusion Models to OOD Concepts In-the-Wild 38

Locating What You Need: Towards Adapting Diffusion Models to...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Jianan Gao, Chenchao Xiao, Zhiqing Zhao, Junbo Wu, Sai Chen, Gang Wang, Haobo College of Computer Science and Technology Zhejiang University China Institute of Blockchain and Data Security China School of Software Technology Zhejiang University China International School of Information Science and Engineering Dalian University of Technology China

The recent large-scale text-to-image generative models have attained unprecedented performance, while people established adaptor modules like LoRA and DreamBooth to extend this performance to even more unseen concept tokens. However, we empirically find that this workflow often fails to accurately depict the out-of-distribution concepts. This failure is highly related to the low quality of training data. To resolve this, we present a framework called Controllable Adaptor Towards Out-of-Distribution Concepts (CATOD). Our framework follows the active learning paradigm which includes high-quality data accumulation and adaptor training, enabling a finer-grained enhancement of generative results. The aesthetics score and concept-matching score are two major factors that impact the quality of synthetic results. One key component of CATOD is the weighted scoring system that automatically balances between these two scores and we also offer comprehensive theoretical analysis for this point. Then, it determines how to select data and schedule the adaptor training based on this scoring system. The extensive results show that CATOD significantly outperforms the prior approaches with an 11.10 boost on the CLIP score and a 33.08% decrease on the CMMD metric. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The value of the FTO gene in breast cancer treatment and prevention: a systematic review with Meta-analysis 24

The value of the FTO gene in breast cancer treatment and pre...

引用

3rd International Conference on Public Health and data science, ICPHDS 2024

作者： Li, Zheng Gu, Fengming Zhang, Chengan School of Public Health Chengdu University of Traditional Chinese Medicine Sichuan Chengdu China College of Light Industry Science and Engineering Sichuan University Sichuan Chengdu China Chongqing University College of Big Data and Software Chongqing China

ISBN: (纸本)9798400711671

Background: The FTO gene has been shown to be significantly associated with obesity and body fat content, and Recent studies have suggested that it may be strongly associated with a variety of cancers, including breast cancer, by affecting obesity. The aim of this study was to systematically evaluate the role of the FTO gene in the prevention and treatment of breast cancer and to determine the association between the two by meta-analysis. Method: Relevant literature published between 2019 and January 20, 2024 in five online databases including pubmed and others were systematically searched in this study. Estimates of the association between FTO rs9939609 and breast cancer were determined by Meta-analysis and expressed using Odds ratio (OR) and its 95% confidence interval (95% CI). Results: A total of 16 STUDIES, INCLUDING 6, 039 subjects, were included in this study. Seven FTO-related SNPs were involved in the association between FTO and BC. The overall study quality was moderately low. Meta-analysis showed that FTO rs9939609 all comparison groups (including heterozygous, pure, dominant and recessive) were in significantly associated with BC. Conclusion: The FTO gene can be used as a screening indicator for breast cancer risk, and its use as a target for breast cancer treatment is feasible. More research is needed to support its application to the treatment of breast cancer as well as its prevention in practice. © 2024 Copyright held by the owner/author(s).

关键词： breast cancer

来源：评论

学校读者我要写书评

暂无评论

Multimodal Hybrid Approach for Fine-Grained Classification of Diverse Dermatological Conditions 2

Multimodal Hybrid Approach for Fine-Grained Classification o...

引用

2nd International Conference on Intelligent Perception and Computer Vision, CIPCV 2024

作者： Kang, Kai Xu, Jianyu An, Shilong Chen, Jilin Wang, Renfang Wang, Han Ocean University of China Faculty of Information Science and Engineering Qingdao China Zhejiang Wanli University College of Big Data and Software Engineering Ningbo China Qingdao Municipal Center For Disease Control Prevention Qingdao China

ISBN: (纸本)9798350360097

Among the spectrum of dermatological conditions, melanoma stands as the most lethal, emanating from melanocytes-cells responsible for producing melanin, the skin's pigment. Distinctively more aggressive than its counterparts, melanoma possesses a higher propensity to metastasize, rendering it one of the most formidable types of skin cancer. Given its rapid invasion and propensity for widespread metastasis, prompt and accurate diagnosis, followed by immediate intervention, is imperative. Fine-grained classification tasks are particularly pertinent in this context, aiming to discern between closely resembling subclasses, a challenge akin to differentiating subtle visual nuances among similar-appearing skin pathologies. Consequently, fine-grained classification emerges as a critical tool in the nuanced diagnosis, early detection, and ongoing surveillance of skin diseases. In response, we have developed a innovative network, termed Multimodal Hybrid Approach for Fine-Grained Classification of Diverse Dermatological Conditions (MultiFG), adept at handling a variety of skin conditions. This model proficiently extracts and processes metadata while concurrently capturing visual details through a Convolutional Neural Network (CNN). This is followed by the integration of visual and metadata insights within a Transformer architecture, facilitating enhanced fusion and refined fine-grained analysis. Our proposed methodology's efficacy is substantiated through rigorous testing on two distinct datasets, where it has demonstrated promising results. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Prediction and Analysis of Stroke Risk Based on Ensemble Learning 15th

Prediction and Analysis of Stroke Risk Based on Ensemble Lea...

引用

15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Zuo, Xiuji Guo, Xin Yin, Zilong Tseng, Shih-Pang School of Information Science and Technology Sanda University Shanghai201209 China Department of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China School of Software and Big Data Changzhou College of Information Technology Changzhou213164 China

ISBN: (纸本)9789819994113

With the development of science and technology, the application of data mining in medical field is becoming more and more popular. Machine learning methods also plays an important role in disease prediction. Stroke is characterized by high incidence rate, high disability rate, high mortality rate and high recurrence rate, and it is also likely to cause other kinds of complications. In this paper, each feature in the stroke dataset was analyzed in order to find out the factors affecting stroke and conducts classification and prediction research on whether there is a disease risk. Specifically, the PCA (Principal Component Analysis) algorithm is used to extract the main feature components of data, the SMOTE (Synthetic Minority Oversampling Technique) algorithm is used to adjust imbalanced feature categories. Traditional machine learning classification algorithms, such as decision tree, SVM(support vector machines), and various ensemble learning algorithms are used for the prediction of stroke risk, so as to study the relationship between stroke disease and each feature, and the classification prediction model, so that we can prevent strokes in time and reduce the risk of stroke. Among all the models, Bagging (Bootstrap aggregating) has the best performance with an ROC value of 0.97. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

引用

Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks

引用

IEEE Transactions on Mobile Computing 2025年第7期24卷 5681-5698页

作者： Tang, Jian Li, Xiuhua Li, Hui Li, Penghua Wang, Xiaofei Leung, Victor C. M. Chongqing University School of Big Data & Software Engineering Chongqing400000 China Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

Federated Learning (FL) has significant potential to protect data privacy and mitigate network burden in mobile edge computing (MEC) networks. However, due to the system and data heterogeneity of mobile clients (MCs), client selection and bandwidth allocation is key for achieving cost-efficient FL in MEC networks with limited bandwidth. To address these challenges, we investigate the issue of joint client selection and bandwidth allocation for reducing the cost (i.e., latency and energy consumption) of FL training. We formulate the problem and decompose it into a holistic subproblem to reduce the number of rounds and a partial subproblem to reduce the costs of FL each round. We propose a joint class-balanced client selection and bandwidth allocation (CBCSBA) framework to address the whole problem. Specifically, for the holistic subproblem, CBCSBA combines MCs into groups, each having data distribution as close as possible to class-balanced distribution;For the partial subproblem, CBCSBA reduces costs by exploratively selecting a group and sequentially optimizing the latency and energy consumption of MCs within the group. Experimental results show that CBCSBA outperforms the baseline frameworks in reducing latency by 28.2% and energy consumption by 25.3% on average in the considered four datasets. © 2002-2012 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Forensic Investigation of Malicious Activities in Digital Environments 4

Forensic Investigation of Malicious Activities in Digital En...

引用

4th IEEE International Conference on data engineering and Communication Systems, ICDECS 2024

作者： Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. Ravikumar, K. Abdul Kareem, D. Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India Rrase College of Engineering Padappai Department of Computer Science & Engineering Chennai India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruttani India

ISBN: (纸本)9798350393354

Several digital dangers were investigated. Malware dominated analysis with 45 attacks. We found 30 phishing attacks. 22 data breaches, 15 cyber espionage, 18 identity theft. This indicates the kind and frequency of harassment digital forensic specialists endure. Digital evidence was crucial to the case. 65 pieces of evidence were collected on computers. 42 incidents showed smartphones and tablets' digital investigative potential. There were 55 examples of communication patterns requiring network logs. In 28 and 12, cloud services and removable media provided proof. A wide range of evidence sources stresses the need for extensive collecting. Digital forensics were essential to the inquiry. In 70% of cases, disk imaging software created forensic copies of metadata-and timestamp-containing storage media. Network packet analyzers examined 48 traffic logs and communication patterns. To analyze volatile memory, 35 cases used memory forensics. In 25 cases, malware analysis platforms decrypted malware. In 60 situations, file recovery software recovered lost or hidden files. Digital forensic specialists' diverse arsenal for thorough investigations is shown here. Legal and ethical considerations dominated the study. The law was important, and all inquiries followed norms. A strong chain of custody protects evidence integrity and admissibility. The data privacy rules were respected. Legal evidence is admissible in court. Ethics ensured objective investigations. This underlines digital forensic investigations' legal and ethical foundation. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Learned Video Compression With Refined Adaptive Flow Pyramid And Coordinate-Aware Attention

Learned Video Compression With Refined Adaptive Flow Pyramid...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Qian Liu, Wenting Li, Xin Wang, Yiming College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China

ISBN: (纸本)9798350368741

In video compression, motion estimation and motion compensation are critical for achieving efficient encoding. Although the commonly used SpyNet and bilinear interpolation have contributed in improving the compression efficiency, they still have limitations. SpyNet often loses details and fails to fully utilize the feature extraction capabilities of deep networks. Furthermore, bilinear interpolation inherently attenuates high-frequency information, leading to frame blurring and distortion. In this paper, we propose a novel video compression algorithm. To overcome the limitations of SpyNet, we propose a refined adaptive flow pyramid network. This network uses a multi-scale feature pyramid to capture more details. Moreover, we use an iterative cost volume refinement engine that improves the feature representation of the network and iteratively improve the accuracy of motion estimation. In addition, to overcome the limitation of bilinear interpolation, we propose a coordinate-aware attention module, which captures more high-frequency information to improve the accuracy of motion compensation. Experimental results show that our method outperforms VTM-13.2 (LDP) in terms of PSNR. © 2025 IEEE.

关键词： coordinate network cross attention flow pyramid learned video compression

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：