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Joint Feature Representation and Classifier Learning Based Unsupervised Domain Adaption ELM

Chinese Journal of Electronics 2021年第1期30卷 109-118页

作者： WANG Xuesong ZHAO Jijuan CHENG Yuhu YU Qiang School of Information and Control Engineering China University of Mining and Technology Xuzhou Key Laboratory of Artificial Intelligence and Big Data

In the problem of unsupervised domain adaption Extreme learning machine(ELM), the output layer parameters need to have both classification and domain adaptation functions, which often cannot be simultaneously fully utilized. In addition, traditional matching method based on data probability distribution cannot find the common subspace of source and target domains under large difference between domains. In order to alleviate the pressure of double functions of classifier parameters, the entire ELM learning process is mainly divided into two stages: feature representation and adaptive classifier learning, thus a joint feature representation and classifier learning based unsupervised domain adaption ELM model is proposed. In the feature representation stage, the source and target domain data are projected to their respective subspace while minimizing the difference in probability distribution between the two domains. In the adaptive classifier learning stage, the smooth manifold regularization term of target domain is used to improve the parameter adaptive *** on six different types of datasets show that the proposed model has higher cross-domain classification accuracy.

关键词： entire ELM learning process unsupervised domain adaption ELM model output layer parameters image representation double functions parameter adaptive ability common subspace classifier parameters joint feature representation unsupervised domain adaption Extreme learning machine data probability distribution learning (artificial intelligence) higher cross-domain classification accuracy target domain probability pattern classification adaptive classifier learning stage traditional matching method target domains unsupervised learning feature representation stage domain adaptation functions

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SS-MAF: Semi-Supervised Echocardiographic Segmentation Using Multi-Atlas Fusion 8

SS-MAF: Semi-Supervised Echocardiographic Segmentation Using...

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8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Yang, Shuaihua Zheng, Caixia College of Information Science and Technology Northeast Normal University Changchun130117 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

ISBN: (纸本)9798350305838

Left ventricle (LV) segmentation in echocardiography is of paramount significance in cardiac function analysis. Recently, semi-supervised segmentation has garnered considerable attention due to its potential mitigation of labeling costs. Despite this, most semi-supervised methodologies tend to overlook the specialized anatomical priors specific to echocardiography, often yielding suboptimal outcomes. Simultaneously, atlas priors have been validated on other organs and imaging modalities. However, their direct application in echocardiography is fraught with challenges, primarily attributed to the indistinct boundaries of the LV and the presence of fan-shaped image edges, which impede atlas image registration. This paper presents a new method of semi-supervised segmentation using multi-atlas fusion, named SS- MAF, which is designed to incorporate atlas priors tailored for echocardiography while circumventing the intricate image registration process in traditional atlas-based methods. The SS-MAF comprises two components: a Multi-Task Learning (MTL) module and a Multi-Atlas Fusion (MAF) module. The MTL module serves precise landmark localization and segmentation estimation. The MAF module is primarily focused on the generation of probabilistic atlases, utilizing annotated atlas data with the guidance of key landmarks to facilitate the automatic annotation of unlabeled data, avoiding the traditional image-level registration, thus enhancing the performance of the MTL module. Our SS- MAF is rigorously evaluated on the publicly available CAMUS dataset, with results demonstrating its superiority over other state-of-the-art semi-supervised segmentation techniques in echocardiography. © 2023 IEEE.

关键词： Image registration

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ReconBoost: Boosting Can Achieve Modality Reconcilement 41

ReconBoost: Boosting Can Achieve Modality Reconcilement

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41st International Conference on Machine Learning, ICML 2024

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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AuRoRA: A One-for-all Platform for Augmented Reasoning and Refining with Task-Adaptive Chain-of-Thought Prompting 30

AuRoRA: A One-for-all Platform for Augmented Reasoning and R...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zou, Anni Zhang, Zhuosheng Zhao, Hai Li, Xiaoshan Bian, Minjie Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Technology Innovation Department Shanghai Data Group Co. Ltd. China Shanghai Data Group Co. Ltd. China

ISBN: (纸本)9782493814104

Large language models (LLMs) empowered by chain-of-thought (CoT) prompting have yielded remarkable prowess in reasoning tasks. Nevertheless, current methods predominantly lean on handcrafted or task-specific demonstrations, lack reliable knowledge basis and thus struggle for trustworthy responses in an automated pattern. While recent works endeavor to improve upon one certain aspect, they ignore the importance and necessity of establishing an integrated and interpretable reasoning system. To address these drawbacks and provide a universal solution, we propose AuRoRA: a one-for-all platform for augmented reasoning and refining based on CoT prompting that excels in adaptability, reliability, integrity, and interpretability. The system exhibits superior performances across six reasoning tasks and offers real-time visual analysis, which has pivotal academic and application value in the era of LLMs. The AuRoRA platform is available at https://***/spaces/Anni123/AuRoRA. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Refining

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Classification of Table Cells Based on LLM Prompts

Classification of Table Cells Based on LLM Prompts

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IEEE International Conference on Systems, Man and Cybernetics

作者： Mengjie Liu Chenyang Bu Shengxing Bai Bingbing Dong Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Tables, as an important means of data storage, are widely used in spreadsheets, web tables, and PDFs. By integrating information from table data with knowledge re-trieved from an external knowledge base, and examining the correspondences between cell values in the table and instances in the knowledge base, we can extract knowledge from the table to augment and enrich the knowledge base. To achieve this goal, we first need to classify table cells based on their functions in the layout. Due to the diverse structures arising from the arrangements of rows and columns, as well as the complexity of content resulting from concise data storage, current automation techniques heavily rely on stylistic features of table cells, such as font or color. Moreover, these methods are rarely experimented with or validated on tables without style features. Recent literature indicates that large language models (LLMs) demonstrate an ability to understand the structure and content of tables in tasks such as table judgment reasoning. Even without extensive feature inputs or pre-training, LLMs still show comparable results to machine learning and deep learning in these tasks. Therefore, this paper attempts to apply LLMs to table cell classification without using other stylistic features. We have designed a 4-component prompt paradigm (Classification Definition, Instruction, Table, Com-pletion), representing respectively the classification definition, task instructions, table data, and result output. We conduct experiments on three datasets CIUS, SAUS, and DEEX for table cell classification with one-shot learning. Our experimental results show that with the assistance of LLMs, better results can be achieved without utilizing stylistic features.

关键词： Deep learning Image color analysis Large language models knowledge based systems Layout Memory One shot learning Benchmark testing Probability density function data mining

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Identification of Plant Nutrient Deficiency Based on Improved MobileNetV3-Large Model

Identification of Plant Nutrient Deficiency Based on Improve...

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Chinese Intelligent Automation Conference, CIAC 2023

作者： Yan, Qian Chen, Yifei Wu, Caicong China Agriculture University Beijing100080 China China Agriculture University College of Information and Electrical Engineering Key Laboratory of Agricultural Machinery Monitoring and Big Data Beijing100080 China

ISBN: (纸本)9789819961863

The nutrient element content of plants is one of the important factors affecting the growth and yield of crops. The use of the convolutional neural network (CNN) can quickly and accurately detect the degree of nutrient deficiency in plants, thereby liberating manpower and improving crop yields. This study uses MobileNetV3-Large as the backbone, combines a convolutional block attention module (CBAM) to obtain MobileNetV3-CBAM, and introduces gated linear units (GLU). During the training process, fine-tuning of transfer learning improves the speed and accuracy of model training. The identification of elements deficient in plants is studied using an open-source dataset, and compared with the cutting-edge CNN, the proposed lightweight MobileNetV3-CBAM with GLU has better comprehensive performance: I. For the imbalanced dataset, the proposed MobileNetV3-CBAM model achieves outstanding results in classifying plant nutrient deficiencies, with a remarkable test accuracy of 96.54%. II. The proposed model occupies a small memory (10.5M), and the training speed is remarkable, which can realize the identification of plant nutrient deficiency in a low-computing environment. III. After 10-fold cross-validation, the robustness of the model is good. This study can provide a theoretical basis and technical support for the real-time detection of nutrient deficiency in plant leaves. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Convolutional neural networks

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On the Security of Smart Home Systems:A Survey

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Journal of Computer Science & Technology 2023年第2期38卷 228-247页

作者：袁斌万俊吴宇晗邹德清金海 School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Distributed System Security Huazhong University of Science and Technology Wuhan 430074China Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan 430074China National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Lab Huazhong University of Science and TechnologyWuhan 430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518057China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Among the plethora of IoT(Internet of Things)applications,the smart home is one of the ***,the rapid development of the smart home has also made smart home systems a target for ***,researchers have made many efforts to investigate and enhance the security of smart home *** a more secure smart home ecosystem,we present a detailed literature review on the security of smart home ***,we categorize smart home systems’security issues into the platform,device,and communication *** exploring the research and specific issues in each of these security areas,we summarize the root causes of the security flaws in today's smart home systems,which include the heterogeneity of internal components of the systems,vendors'customization,the lack of clear responsibility boundaries and the absence of standard security ***,to better understand the security of smart home systems and potentially provide better protection for smart home systems,we propose research directions,including automated vulnerability mining,vigorous security checking,and data-driven security analysis.

关键词： IoT(Internet of Things) smart home IoT security smart home security

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Assisted diagnosis of breast cancer based on federated learning and improved DenseNet 5

Assisted diagnosis of breast cancer based on federated learn...

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5th International Conference on Computer Vision and data Mining, ICCVDM 2024

作者： Huang, Shan Xiang, Hexia Li, Jingjie Sun, Yongkai Duan, Xiaodong College of Computer Science and Engineering Dalian Minzu University Dalian116600 China State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian116600 China

ISBN: (纸本)9781510682863

Breast cancer is one of the common cancers worldwide, and how to identify breast cancer as early as possible plays an important role in its later diagnosis and treatment. However, relying only on data from a single organisation can limit the ability of trained neural networks to meet the performance demands of medical diagnostic services, and centralised learning that collects data from decentralised organisations can lead to privacy leakage. To address the above challenges, this paper proposes an assisted diagnosis model for breast cancer combining federated learning and improved DenseNet. Specifically, in contrast to mainstream centralised learning, this paper introduces federated learning to integrate multiparty data and effectively prevent privacy leakage. Each client sends the local model to the central server for aggregation, and then the global model obtained from the aggregation is sent to each client for training, thus achieving collaborative training of neural network models. In addition, in order to enhance the ability to capture feature information, this paper incorporates a Dynamic Convolution module into the densely connected network to improve the diagnostic accuracy without significantly increasing the size of the network model. The experimental results show that the proposed method has better diagnostic performance in breast cancer diagnosis compared with other methods. © 2024 SPIE.

关键词： Information leakage

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Selective activation of peroxymonosulfate to singlet oxygen by engineering oxygen vacancy defects in Ti_(3)CNT_(x) MXene for effective removal of micropollutants in water

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Fundamental Research 2023年第5期3卷 770-776页

作者： Limin Jin Shijie You Nanqi Ren Yanbiao Liu College of Environmental Science and Engineering Textile Pollution Controlling Engineering Center of the Ministry of Ecology and EnvironmentDonghua University2999 North Renmin RoadShanghai 201620China State Key Laboratory of Urban Water Resource and Environment School of EnvironmentHarbin Institute of TechnologyHarbin 150090China

Defect engineering is an effective strategy to boost the catalytic activity of MXene towards heterogeneous peroxymonosulfate(PMS)activation for water ***,we developed a facile approach to fine-tune the generation of oxygen vacancies(OVs)on Ti_(3)CNT_(x)crystals by Ce-doping(Ce-Ti_(3)CNT_(x))with the aim of mediating PMS activation for the degradation of micropollutants in *** varying the dopant content,the OV concentrations of Ti_(3)CNT_(x)could be varied to enable the activation of PMS to almost 100%singlet oxygen(1O2),and hence the effective degradation of sulfamethoxazole(SMX,a model micropollutant).Various advanced characterization techniques were employed to obtain detailed information on the microstructure,morphology,and defect states of the *** experimental results showed that SMX removal was proportional to the OVs *** functional theory(DFT)models demonstrated that,in contrast to pristine Ti_(3)CNT_(x),the OVs on 10%CeTi_(3)CNT_(x)could adsorb the terminal O of PMS,which facilitated the formation of SO_(5)•−as well as the generation of *** further loaded the optimized catalysts onto a polytetrafluoroethylene microfiltration membrane and also demonstrated the efficient removal of SMX from water using a convection-enhanced mass transport flowthrough *** study provides new insights into the effective removal of micropollutants from water by integrating state-of-the-art defect engineering,advanced oxidation,and microfiltration techniques.

关键词： Oxygen vacancy Ce-doped MXene Peroxymonosulfate activation Singlet oxygen Water decontamination

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Towards Exploratory Query Optimization for Template-Based SQL Workloads 40

Towards Exploratory Query Optimization for Template-Based SQ...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Feng, Jieming Li, Zhanhuai Chen, Qun School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi'an China Key Laboratory of Big Data Storage and Management Ministry of Industry and Information Technology Northwestern Polytechnical University Shaanxi Xi'an China

ISBN: (纸本)9798350317152

SQL query optimization aims to choose an optimal Query Execution Plan (QEP) for a query. The existing optimizers usually choose the plan with the minimal execution cost. However, in some real scenarios (e.g., cloud OLAP), it is a business imperative to minimize the cost of an entire workload. Unfortunately, the greedy optimizers are prone to generating execution plans that are only suboptimal from the workload perspective. In this paper, we propose a novel QEP optimization approach for template-based SQL that aims to minimize the execution cost of an entire workload instead of individual queries. While choosing an execution plan for a query, the proposed approach considers a plan's impact on future query execution as well as its own execution cost. We first define an exploratory metric, Plan Exploration Value (PEV), to measure a plan's potential benefit to future query execution. Then, we model exploratory plan selection as an optimization problem with cost constraints, and present an efficient algorithm based on dynamic programming for its solution. Finally, we evaluate the performance of the proposed approach by a comparative study on benchmark workloads. Our extensive experiments have shown that it generates more efficient workload execution plans than the existing alternatives. © 2024 IEEE.

关键词： Dynamic programming

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