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Unsupervised Classification for Circulating Tumor Cells

IEEE Access 2025年 13卷 85669-85681页

作者： An, Ling Hu, Haibo Song, Mengke Cheng, Lin Ba, Shuo Liu, Zhaocong Yu, Zhuohang Zhang, Zhenyu Liu, Yi Zhou, Chichun Dali University School of Engineering Yunnan Dali671003 China Yunnan Provincial Department of Education Air-Space-Ground Integrated Intelligence and Big Data Application Engineering Research Center Yunnan Dali671003 China

Circulating tumor cell (CTC) detection is crucial for reducing cancer mortality and disease burden. Traditional methods rely on the physical or biological properties of CTCs and involve complex enrichment and separation processes. These methods are often inefficient and limited by cellular heterogeneity and detection accuracy, making them unsuitable for rapid screening. In contrast, intelligent cell recognition and classification using low-resolution microscopy images allows faster detection. Supervised learning has shown excellent performance in CTC detection. However, it heavily depends on large quantities of labeled immunofluorescence image data, which not only consumes significant expert time and resources but also increases costs, limiting its widespread applications. Furthermore, the scarcity of public datasets and the high costs of proprietary datasets further hinder the effectiveness of the detection. Therefore, developing personalized and cost-effective datasets is essential for practical application. Unsupervised methods, which perform classification without the need for manual labeling, offer promising solutions to overcome these challenges. This study adopts an unsupervised classification method that performs excellently on fungal datasets, integrating two-step dimensionality reduction and hybrid clustering voting for automated CTC classification. The main advantage of this method is its domain independence: it does not rely on domain-specific features and is robust to imaging differences, making it applicable to various image classification tasks in specialized domains, which enables the method to be successfully transferred to the field of CTC detection. This method does not require manual labeling, achieves an accuracy of 98.2%, and has a discard rate of 25.23%, effectively distinguishing CTCs from white blood cells (WBCs). This method reduces the reliance on manually labeled data, lowering the cost and complexity of creating personalized CTC datasets

关键词： Support vector machines

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Grassland Mouse Hole Recognition Model Based on UAV Remote Sensing and Improved YOLOv7

Grassland Mouse Hole Recognition Model Based on UAV Remote S...

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

作者： Han, Yu Bai, Xiangyu Gao, Xiaochen Cheng, Haoran Zhou, Kexin College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China School of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (纸本)9781665410205

As the most widely distributed vegetation type on Earth, grasslands are the second largest ecosystem after forests, and are known as the 'skin of the earth'. However, the frequency of rodent infestation has been increasing year by year in recent years due to climate change, loose soil and other factors. As the current traditional monitoring methods are time-consuming and laborious, while the deep learning method has the advantages of low cost, high efficiency and high accuracy. Therefore, this paper proposes a combination of UAV remote sensing and deep learning methods to monitor grassland mouse hole. First, because there is no publicly available grassland mouse hole dataset, this study used a UAV to capture images to create a grassland mouse hole dataset. Second, an improved YOLOv7 model, YOLOAPM, is proposed with the goal of improving the accuracy of small target recognition. Through ablation and comparison experiments, the validity of the improved model as well as the method of this paper are verified to be improved compared to other models. Finally, in order to facilitate the monitoring task of the researchers, a monitoring system is established and the mouse hole identification of this paper is embedded into the system. © 2024 IEEE.

关键词： Abiotic

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SAND: semi-automated adaptive network defense via programmable rule generation and deployment

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Science China(Information Sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense adaptive defense automated defense programmable defense security services software-defined networking security management

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Improved U-Net network model for solar PV panel detection based on attention mechanism and residual module 4

Improved U-Net network model for solar PV panel detection ba...

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4th International Conference on Geology, Mapping, and Remote Sensing, ICGMRS 2023

作者： Fei, Jianan Song, Weidong Transportation Spatio-Temporal Big Data Research Center Liaoning University of Engineering and Technology Fuxin123000 China

ISBN: (纸本)9781510672741

The detection accuracy is low in the existing deep learning detection to extract solar PV panels sticky, incomplete and under the interference of factors such as roofs and substations in four different scenarios: water, saline, low-density grass and fields. In this study, U-Net neural network is improved by adding attention mechanism and residual network. A deep learning network model for solar PV panels detection that incorporates attention mechanism and residual structure: UNet Pro, is established. This model, based on U-Net, is able to pay more attention to the detection of solar PV panels by incorporating up-sampling and down-sampling in the process of attention mechanism. It is able to pay more attention to the effective detailed information and exclude the interference factors. Meanwhile, the residual module is embedded after the Relu function in each layer of the U-Net neural network. It effectively avoids the problems of gradient explosion, feature loss and network degradation. For solar PV datasets in different scenarios, the effects of adding the attention mechanism and the residual structure have been improved to different degrees. Compared with U-Net, mF1 value can be improved up to 94.67% and at most 4.46%. mIoU can be improved up to 93.75% and at most 4.74%. © 2024 COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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Generative Artificial Intelligence in Elementary School Science Teaching and Learning 4

Generative Artificial Intelligence in Elementary School Scie...

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4th International Conference on Educational Technology, ICET 2024

作者： Zhang, Qian Wang, Yali Ding, Yuxin Yuan, Pan Dai, Zhicheng Peng, Xian Central China Normal University National Engineering Research Center for Educational Big Data Wu Han China

ISBN: (纸本)9798350376944

With the State attaching great importance to the cultivation of innovative talents, elementary school science education, as a key link in the cultivation of future innovative talents, has received unprecedented attention. However, elementary school science teaching still faces challenges in practice, such as insufficient teaching resources, single teaching method and difficulty in stimulating students' interest. Generative artificial intelligence, with its data generation, learning and innovation capabilities, has shown great potential in the field of education, providing new ideas to address these challenges. In this paper, we first sort out the specific challenges facing elementary school science teaching, and then explore its potential applications in elementary school science teaching. Finally, we analyze the feasibility and specific strategies for its integration into the elementary science classroom, aiming to provide new perspectives and strategies for the progress of elementary science education. © 2024 IEEE.

关键词： artificial intelligence in education elementary science education generative artificial intelligence

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COMET ■: “cone of experience” enhanced large multimodal model for mathematical problem generation

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Science China(Information Sciences) 2024年第12期67卷 97-98页

作者： Sannyuya LIU Jintian FENG Zongkai YANG Yawei LUO Qian WAN Xiaoxuan SHEN Jianwen SUN National Engineering Research Center of Educational Big Data Central China Normal University Faculty of Artificial Intelligence in Education Central China Normal University School of Software Technology Zhejiang University

The impact of generative artificial intelligence on education is unprecedented [1]. researchers have been exploring possibilities of combining the large multimodal model(LMM)with the teaching process. Specifically, Luo and Yang [2]have explored large model collaborative domain models to support smart education, fostering personalized and adaptive educational experiences. However, existing studies still lack in-depth research on generating educational resources,especially in mathematical problem generation.

关键词： Personnel training

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Answering reachability queries with ordered label constraints over labeled graphs

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Frontiers of Computer Science 2024年第1期18卷 105-117页

作者： Daoliang HE Pingpeng YUAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science&TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Reachability query plays a vital role in many graph analysis *** researches proposed many methods to efficiently answer reachability queries between vertex *** many real graphs are labeled graph,it highly demands Label-Constrained Reachability(LCR)query inwhich constraint includes a set of labels besides vertex *** researches proposed several methods for answering some LCR queries which require appearance of some labels specified in constraints in the *** that constraint may be a label set,query constraint may be ordered labels,namely OLCR(Ordered-Label-Constrained Reachability)queries which retrieve paths matching a sequence of ***,no solutions are available for ***,we propose DHL,a novel bloom filter based indexing technique for answering OLCR *** can be used to check reachability between vertex *** the answers are not no,then constrained DFS is ***,we employ DHL followed by performing constrained DFS to answer OLCR *** show that DHL has a bounded false positive rate,and it's powerful in saving indexing time and *** experiments on 10 real-life graphs and 12 synthetic graphs demonstrate that DHL achieves about 4.8-22.5 times smaller index space and 4.6-114 times less index construction time than two state-of-art techniques for LCR queries,while achieving comparable query response *** results also show that our algorithm can answer OLCR queries effectively.

关键词： graph ordered-label-constrained reachability partial index bloom filter query processing

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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of Computer Science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalability,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalability brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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A Sentinel-2-based classification algorithm of algal blooms and aquatic vegetation 5

A Sentinel-2-based classification algorithm of algal blooms ...

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5th International Conference on Geology, Mapping, and Remote Sensing, ICGMRS 2024

作者： Wang, Kang Huang, Linsheng Ni, Guigao Ruan, Chao National Engineering Research Center for Agro-Ecological Big Data Analysis& Application Anhui University Hefei China

ISBN: (纸本)9781510681712

Floating/emergent aquatic vegetation (FEAV) and algal blooms (AB) play different roles in regulating lake ecosystems. High spatiotemporal information is crucial for assessing lake status. However, distinguishing between AB and FEAV using satellite remote sensing poses challenges due to their similar spectral characteristics. To address this, we propose an automatic classification algorithm (BF algorithm), incorporating multiple spectral features, based on Sentinel-2 imagery. Validation in Lake Taihu yielded an average overall classification accuracy of 93.00% for the BF algorithm. Subsequently, the algorithm was successfully applied for long-term monitoring (2019-2022) of AB and FEAV in Lake Taihu. This algorithm holds prospects for long-term monitoring in small to medium-sized eutrophic lakes. © 2024 SPIE.

关键词： Remote sensing

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Anti-Interference High-Speed Modulation Decoder for Quantum Key Distribution

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Chinese Physics Letters 2025年第1期42卷 34-39页

作者： Hua-Xing Xu Shao-Hua Wang Chang-Lei Wang Ping Zhang National Engineering Research Center for Public Safety Risk Perception and Control by Big Data China Academic of Electronics and Information TechnologyBeijing 100041China CETC Academy of Electronics and Information Technology Group Co. Ltd.Beijing 100041China

Quantum key distribution is increasingly transitioning toward network applications,necessitating advancements in system performance,including photonic integration for compact designs,enhanced stability against environmental disturbances,higher key rates,and improved *** this letter,we propose an orthogonal polarization exchange reflector Michelson interferometer model to address quantum channel disturbances caused by environmental *** on this model,we designed a Sagnac reflector-Michelson interferometer decoder and verified its performance through an interference *** interference fringe visibility exceeded 98%across all four coding phases at 625 *** results indicate that the decoder effectively mitigates environmental interference while supporting high-speed modulation *** addition,the proposed anti-interference decoder,which does not rely on magneto-optical devices,is well-suited for photonic integration,aligning with the development trajectory for next-generation quantum communication devices.

关键词： decoder interferometer polarization

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