检索结果-内蒙古大学图书馆

12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Fengshan, Huang Shaozi, Li Weiqiang, Yang Xiehua, Yu University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence MinNan Science and Technology University Quanzhou China Xiamen University School of Informatics Xiamen China

ISBN: (纸本)9798350310153

The proportion of house fires in society is increasing day by day. Smoke alarm is of practical significance to prevent house fires and reduce losses caused by fire. Through careful study of the traditional smoke alarms, a new type of smoke alarm with safety, convenience and efficiency has been innovated. The traditional smoke alarms have many problems, such as complicated routing, high maintenance cost in the later stage, single notification object, inability to monitor in real time, and late warning or failure to warn. This paper puts forward a technical scheme that combines STM32 single-chip microcomputer with the Internet of Things NB-IOT and an APP. STM32 single chip microcomputer with the Internet of Things NB-IOT can solve the problems of tedious routing and single notification object. MQ-2 smoke sensor is adopted, which features high sensitivity. The A9500 wireless communication module refreshes data at high frequency and connects it to the cloud platform, thus greatly reducing the phenomenon of late warning and failure to warn. An APP application software, which can realize real-time monitoring and timely feedback of information at home. Users can put out the fire at the best time. This design starts from the reality of life and serves the needs of people so as to improve and innovate a new type of smoke alarm. © 2022 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Sustainable Self-evolution Adversarial Training 24

Sustainable Self-evolution Adversarial Training

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Wenxuan Wang, Chenglei Qi, Huihui Ye, Menghao Qian, Xuelin Wang, Peng Zhang, Yanning School of Computer Science Northwestern Polytechnical University China Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi'an China School of Automation Northwestern Polytechnical University The BRain and Artificial INtelligence Lab Shaanxi Xi'an China

ISBN: (纸本)9798400706868

With the wide application of deep neural network models in various computer vision tasks, there has been a proliferation of adversarial example generation strategies aimed at deeply exploring model security. However, existing adversarial training defense models, which rely on single or limited types of attacks under a one-time learning process, struggle to adapt to the dynamic and evolving nature of attack methods. Therefore, to achieve defense performance improvements for models in long-term applications, we propose a novel Sustainable Self-Evolution Adversarial Training (SSEAT) framework. Specifically, we introduce a continual adversarial defense pipeline to realize learning from various kinds of adversarial examples across multiple stages. Additionally, to address the issue of model catastrophic forgetting caused by continual learning from ongoing novel attacks, we propose an adversarial data replay module to better select more diverse and key relearning data. Furthermore, we design a consistency regularization strategy to encourage current defense models to learn more from previously trained ones, guiding them to retain more past knowledge and maintain accuracy on clean samples. Extensive experiments have been conducted to verify the efficacy of the proposed SSEAT defense method, which demonstrates superior defense performance and classification accuracy compared to competitors. © 2024 ACM.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Brain-inspired artificial intelligence research: A review

引用

Science China(Technological Sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

来源：评论

学校读者我要写书评

暂无评论

Enhancing Point Cloud Sampling Quality with Dual-Branch Fusion Networks

Enhancing Point Cloud Sampling Quality with Dual-Branch Fusi...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Jin, Yi Wang, Xu Hu, Mengxia Yu, Hui Li, Yidong Wang, Tao Feng, Songhe Lang, Congyan State Key Laboratory of Intelligent Vehicle Safety Technology China School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing China China Automotive Engineering Research Institute Co. Ltd. China School of Psychology & Neuroscience University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9781665410205

Task-oriented point cloud sampling methods have attracted considerable attention for their ability to adaptively select important point sets based on downstream tasks, achieving an excellent balance between data simplification and task performance. However, existing task-oriented sampling models, primarily based on single-branch designs, struggle to fully extract features from input point clouds that comprehensively reflect multi-dimensional key information, thus limiting their sampling performance. In this paper, we introduce a dual-branch sampling network, named DBS-NET, which conducts crucial point sampling from both the global and local importance perspectives separately before merging them, thereby preserving multi-dimensional key information of the input data during the sampling process. Qualitative and quantitative experimental results demonstrate the competitive performance of DBS-NET on the classification benchmark task. © 2024 IEEE.

关键词： Importance sampling

来源：评论

学校读者我要写书评

暂无评论

Design of Graphic Interactive Experimental Platform Based on MATLAB 12

Design of Graphic Interactive Experimental Platform Based on...

引用

12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Meilong, Chen Xiehua, Yu Shaozi, Li Fengfei, Kuang MinNan Science and Technology University Key Laboratory of Big Data and Artificial Intelligence University of Fujian Province Quanzhou China Xiamen University School of Informatics Xiamen China

ISBN: (纸本)9798350310153

Aiming at consolidating theoretical knowledge and improving actual ability, the paper directs at solving the unreadability of digital signal processing courses, such as intricate abstract concepts, various mathematical formulas, disable learning difficulties and so on. The paper puts forward to designing a platform based on MATLAB digital processing. Easy-operating, direct and perceptual platform improves the teaching quality and effects of the courses to a certain degree. It can also be extended as an experimental platform for other courses. © 2022 IEEE.

关键词： Digital signal processing

来源：评论

学校读者我要写书评

暂无评论

A Novel Distributed K-Means Clustering Algorithm for big Text data 23

A Novel Distributed K-Means Clustering Algorithm for Big Tex...

引用

7th International Conference on High Performance Compilation, Computing and Communications, HP3C 2023

作者： Li, Min Li, Meijing Cheng, Yonglong Ryu, Keun Ho College of Information Engineering Shanghai Maritime University Shanghai201306 China Big Data and Artificial Intelligence Laboratory Industrial and Commercial Bank of China Software Development Center Shanghai201306 China Data Science Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9781450399883

With the advancement of diverse industries, text clustering has garnered significant attention in fields such as data mining, document retrieval, and pattern classification. The K-Means algorithm, a simple, efficient, and widely adopted clustering approach, has been extensively studied for its applicability in text data analysis. However, the exponential growth of information presents a formidable challenge when clustering large-scale data. The traditional K-Means algorithm proves ineffective in handling high-dimensional, massive text data, and the initial centroid selection significantly impacts the clustering outcomes. To address these limitations, this research introduces a clustering algorithm based on the virtual centroid and nearest centroid strategy. Furthermore, we incorporate the MapReduce parallelization framework to tackle the clustering problem associated with vast text data. Experimental results demonstrate the algorithm's commendable scalability and effective handling of large datasets. © 2023 ACM.

关键词： MapReduce

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence for Edge Service Optimization in Internet of Vehicles:A Survey

引用

Tsinghua Science and technology 2022年第2期27卷 270-287页

作者： Xiaolong Xu Haoyuan Li Weijie Xu Zhongjian Liu Liang Yao Fei Dai the School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and Technology and EngineeringNanjing 210044China the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China the School of Big Data and Intelligence Engineering Southwest Forestry UniversityKunming 650233China

The Internet of Vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time ***,collected information is transmitted to a centralized resourceintensive cloud platform for service *** Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular ***,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time *** intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources *** extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite *** address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in ***,we establish the general condition and relative concepts about IoV,EC,and ***,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service ***,we discuss a number of open issues in optimizing edge services with AI.

关键词： edge service internet of vehicles artificial intel igence

来源：评论

学校读者我要写书评

暂无评论

Diagnosis model for mouse lung cancer based on terahertz spectroscopy and a transformer network

引用

Optics Express 2025年第11期33卷 23178-23193页

作者： Li, Jianwei Zhao, Shisheng Ma, Xuxu Li, Ziyu Li, Xichuan Hebei Key Laboratory of Big Data Computing School of Artificial Intelligence Hebei University of Technology Tianjin300401 China School of Life Sciences Tianjin Normal University Tianjin300387 China

Terahertz time-domain spectroscopy technology offers a non-destructive and noninvasive approach to elucidating microstructure and chemical properties of cancer tissues, providing significant advantages for early diagnosis and monitoring. However, challenges such as the presence of outliers, sample class imbalance, and limited diagnostic accuracy persist. To enhance dataset quality, the boxplots were applied to identify and remove abnormal data, and the Savitzky-Golay filter was employed for spectral denoising in this paper. A high-quality dataset comprising 1,028 spectra from normal mouse lung tissues and 1,547 spectra from mouse lung cancer tissues was obtained. To address the shortage of normal lung tissue samples, a Wasserstein generative adversarial network with gradient penalty was utilized. Furthermore, to improve diagnostic accuracy, we developed a novel diagnostic model, MSFPT-Net. This model integrated seven distinct terahertz spectral features and employed a feature pyramid network to extract multi-scale representations, alongside a Transformer network to capture long-range dependencies within spectral sequences. The processed features were subsequently input into a fully connected network to achieve precise lung cancer diagnosis. Experimental results indicate that MSFPT-Net achieves outstanding performance across six evaluation metrics and opens new possibilities for lung cancer diagnosis and treatment. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Terahertz spectroscopy

来源：评论

学校读者我要写书评

暂无评论

The Impact of Medical Resources and Oral Antiviral Drugs on SARS-CoV-2 Mortality—Hong Kong SAR,China,2022

引用

China CDC weekly 2024年第21期6卷 469-477,I0001-I0005页

作者： Zhiqi Zeng Jingyi Liang Zhijie Lin Wenda Guan Wei He Qianying Li Lixi Liang Weiqi Pan Zige Liu Guibin Lu Xiaoyan Deng Eric HY Lau Chitin Hon Zifeng Yang Guangzhou key laboratory for clinical rapid diagnosis and early warning of infectious diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao SARChina Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovative EngineeringMacao University of Science and TechnologyMacao SARChina State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory Healththe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina School of Public Health The University of Hong KongHong Kong SARChina Laboratory of Data Discovery for Health Hong Kong SARChina Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina

Introduction:The Omicron variant of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)demonstrates increased transmissibility compared to earlier strains,contributing to a significant number of fatalities in Hong Kong Special Administrative Region(HKSAR),*** medical resources and medications are essential in mitigating these *** study evaluates the effects of supplementary resources from the Chinese mainland during the fifth wave of the pandemic in ***:Vector autoregression(VAR)was employed to analyze data from the Oxford coronavirus disease 2019(COVID-19)Government Response Tracker to assess the effectiveness of control measures during five waves of the pandemic in ***,a transmission dynamics model was created to investigate the influence of supplementary medical resources from the Chinese mainland and oral medications on ***:In the initial four waves,workplace closures,restrictions on public events,international travel bans,and shielding the elderly significantly influenced pandemic ***,during the fifth wave,these measures showed no notable *** comparing a situation without extra medical resources or COVID-19 oral medication,there was a 17.7%decrease in COVID-19 fatalities with mainland medical resources and an additional 10.2%reduction with oral ***,they contributed to a 26.6%decline in ***:With the rapid spread of the virus,regional reallocation of medical resources may reduce mortality even when the local healthcare system is overstretched.

关键词： mainland Impact travel

来源：评论

学校读者我要写书评

暂无评论

AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking

arXiv

引用

arXiv 2023年

作者： Yu, Yang Liu, Qi Zhang, Kai Zhang, Yuren Song, Chao Hou, Min Yuan, Yuqing Ye, Zhihao Zhang, Zaixi Yu, Sanshi Lei Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China OPPO Research Institute China Hefei University of Technology China

User modeling, which aims to capture users' characteristics or interests, heavily relies on task-specific labeled data and suffers from the data sparsity issue. Several recent studies tackled this problem by pre-training the user model on massive user behavior sequences with a contrastive learning task. Generally, these methods assume different views of the same behavior sequence constructed via data augmentation are semantically consistent, i.e., reflecting similar characteristics or interests of the user, and thus maximizing their agreement in the feature space. However, due to the diverse interests and heavy noise in user behaviors, existing augmentation methods tend to lose certain characteristics of the user or introduce noisy behaviors. Thus, forcing the user model to directly maximize the similarity between the augmented views may result in a negative transfer. To this end, we propose to replace the contrastive learning task with a new pretext task: Augmentation-Adaptive Self-Supervised Ranking (AdaptSSR), which alleviates the requirement of semantic consistency between the augmented views while pre-training a discriminative user model. Specifically, we adopt a multiple pairwise ranking loss which trains the user model to capture the similarity orders between the implicitly augmented view, the explicitly augmented view, and views from other users. We further employ an in-batch hard negative sampling strategy to facilitate model training. Moreover, considering the distinct impacts of data augmentation on different behavior sequences, we design an augmentation-adaptive fusion mechanism to automatically adjust the similarity order constraint applied to each sample based on the estimated similarity between the augmented views. Extensive experiments on both public and industrial datasets with six downstream tasks verify the effectiveness of AdaptSSR. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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：