检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Tian, Zhen Bai, Ting Zhang, Zibin Xu, Zhiyuan Lin, Kangyi Wen, Ji-Rong Zhao, Wayne Xin Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing University of Posts and Telecommunications Beijing China Weixin Open Platform Tencent Guangzhou China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia China Beijing Key Laboratory of Big Data Management and Analysis Methods China

With the growth of high-dimensional sparse data in web-scale recommender systems, the computational cost to learn high-order feature interaction in CTR prediction task largely increases, which limits the use of high-order interaction models in real industrial applications. Some recent knowledge distillation based methods transfer knowledge from complex teacher models to shallow student models for accelerating the online model inference. However, they suffer from the degradation of model accuracy in knowledge distillation process. It is challenging to balance the efficiency and effectiveness of the shallow student models. To address this problem, we propose a Directed Acyclic Graph Factorization Machine (KD-DAGFM) to learn the high-order feature interactions from existing complex interaction models for CTR prediction via Knowledge Distillation. The proposed lightweight student model DAGFM can learn arbitrary explicit feature interactions from teacher networks, which achieves approximately lossless performance and is proved by a dynamic programming algorithm. Besides, an improved general model KD-DAGFM+ is shown to be effective in distilling both explicit and implicit feature interactions from any complex teacher model. Extensive experiments are conducted on four real-world datasets, including a large-scale industrial dataset from WeChat platform with billions of feature dimensions. KD-DAGFM achieves the best performance with less than 21.5% FLOPs of the state-ofthe-art method on both online and offline experiments, showing the superiority of DAGFM to deal with the industrial scale data in CTR prediction task1 © 2022, CC BY.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Research On Tourist Destination Passenger Flow Prediction Based On Decomposition Clustering Technology For data Continuity Problem

Research On Tourist Destination Passenger Flow Prediction Ba...

引用

Culture-Oriented Science and Technology (CoST), International Conference on

作者： Lianze Feng Lin Mu Yi Yang Caihong Li School of information Science and Engineering Lanzhou University Lanzhou China Key Laboratory of Tourism Information Fusion Processing and Data Ownership Protection Ministry of Culture and Tourism Lanzhou China Tourism quality supervision and management institute of China Beijing China

ISBN: (数字)9798350380347

ISBN: (纸本)9798350380354

Tourist flow prediction faces challenges in achieving high accuracy due to the complex and dynamic nature of tourism data. This study addresses these challenges by proposing a multi-feature combined prediction model, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Incremental Entropy-K-means clustering-Time Convolutional Network-Coati Optimization Algorithm (CEEMDAN-IEK-TT-COA). The model integrates signal decomposition, clustering reconstruction, component prediction, and optimization algorithms. key features are selected through feature engineering, followed by data decomposition and reconstruction using CEEMDAN, Incremental Entropy, and K-means clustering. A model pool of Time Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (Bi-GRU), Long Short-Term Memory network (LSTM), and Transformer filters optimal sub-models for combined prediction. The Coati Optimization Algorithm (COA) refines the final prediction. Experimental results on Jiuzhaigou and Siguniangshan datasets demonstrate significant RMSE improvements of $21.81 \%$ and $17.84 \%$, validating the model’s effectiveness.

关键词： Adaptation models Clustering algorithms Predictive models Prediction algorithms Feature extraction Transformers data models

来源：评论

学校读者我要写书评

暂无评论

A joint call for actions to advance taxonomy in China

引用

Zoological Systematics 2022年第3期47卷 188-197页

作者： Chaodong Zhu Arong Luo Ming Bai Michael COrr Zhonge Hou Siqin Ge Jun Chen Yibo Hu Xuming Zhou Gexia Qiao Hongzhi Kong Limin Lu Xiaohua Jin Lei Cai Xinli Wei Ruilin Zhao Wei Miao Qingfeng Wang Zhongli Sha Qiang Lin Meng Qu Jianping Jiang Jiatang Li Jing Che Xuelong Jiang Xiaoyong Chen Lianming Gao Zongxin Ren Chunlei Xiang Shixiao Luo Donghui Wu Dong Liu Yanqiong Peng Tao Su Chenyang Cai Tianqi Zhu Wanzhi Cai Xingyue Liu Hu Li Huaijun Xue Zhen Ye Xuexin Chen Pu Tang Shujun Wei Hong Pang Qiang Xie Feng Zhang Feng Zhang Xianjin Peng Aibing Zhang Taiping Gao Changfa Zhou Chen Shao Libin Ma Zhaoming Wei Yunxia Luan Ziwei Yin Wu Dai Cong Wei Xiaolei Huang Jingxian Liu Xiangsheng Chen Tianci Yi Zhisheng Zhang Zhulidezi Aishan Qin Li Hongying Hu CAS Key Laboratory of Zoological Systematics and Evolution Institute of ZoologyChinese Academy of SciencesBeijing 100101China CAS Key Laboratory of Animal Ecology and Conservation Biology Institute of ZoologyChinese Academy of SciencesBeijing 100101China State Key Laboratory of Systematic and Evolutionary Botany Institute of BotanyChinese Academy of SciencesBeijing 100093China State Key Laboratory of Mycology(SKLM) Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China CAS Key Laboratory of Aquatic Biodiversity and Conservation Institute of HydrobiologyChinese Academy of SciencesWuhan 430072China Wuhan Botanical Garden Chinese Academy of SciencesWuhan 430074China Department of Marine Organism Taxonomy&Phylogeny Institute of OceanologyChinese Academy of SciencesQingdao 266071China CAS Key Laboratory of Tropical Marine Bio-resources and Ecology South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization&Ecological Restoration and Biodiversity Conservation Key Laboratory of Sichuan Province Chengdu Institute of BiologyChinese Academy of SciencesChengdu 610041China State Key Laboratory of Genetic Resources and Evolution&Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain Kunming Institute of ZoologyChinese Academy of SciencesKunming 650221China CAS Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of SciencesKunming 650201China CAS Key Laboratory of Plant Resource Conservation and Sustainable Utilization South China Botanical GardenChinese Academy of SciencesGuangzhou 510650China CAS Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China CAS Key Laboratory of Tropical Forest Ecology Xishuangbanna Tropical Botanical GardenChinese Academy of SciencesMenglunMenglaYunnan 66630

Taxonomy plays an important role in understanding the origin, evolution, and ecological functionality of biodiversity. There are large number of unknown species yet to be described by taxonomists, which together with their ecosystem services cannot be effectively protected prior to description. Despite this, taxonomy has been increasingly underrated insufficient funds and permanent positions to retain young talents. Further, the impact factordriven evaluation systems in China exacerbate this downward trend, so alternative evaluation metrics are urgently necessary. When the current generation of outstanding taxonomists retires,there will be too few remaining taxonomists left to train the next generation. In light of these challenges, all co-authors worked together on this paper to analyze the current situation of taxonomy and put out a joint call for immediate actions to advance taxonomy in China.

关键词： Endangered taxonomists morphology-based taxonomy integrative taxonomy capacity building systematics

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserved Neural Graph Similarity Learning

arXiv

引用

arXiv 2022年

作者： Hou, Yupeng Zhao, Wayne Xin Li, Yaliang Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China China Alibaba Group China Beijing Key Laboratory of Big Data Management and Analysis Methods China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education

To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years. Although GSL models are frequently deployed in privacy-sensitive scenarios, the user privacy protection of neural GSL models has not drawn much attention. To comprehensively understand the privacy protection issues, we first introduce the concept of attackable representation to systematically characterize the privacy attacks that each model can face. Inspired by the qualitative results, we propose a novel Privacy-Preserving neural Graph Matching network model, named PPGM, for graph similarity learning. To prevent reconstruction attacks, the proposed model does not communicate node-level representations between devices. Instead, we learn multi-perspective graph representations based on learnable context vectors. To alleviate the attacks to graph properties, the obfuscated features that contain information from both graphs are communicated. In this way, the private properties of each graph can be difficult to infer. Based on the node-graph matching techniques while calculating the obfuscated features, PPGM can also be effective in similarity measuring. To quantitatively evaluate the privacy-preserving ability of neural GSL models, we further propose an evaluation protocol via training supervised black-box attack models. Extensive experiments on widely-used benchmarks show the effectiveness and strong privacy-protection ability of the proposed model PPGM. The code is available at: https://***/RUCAIBox/PPGM. © 2022, CC BY.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction

arXiv

引用

arXiv 2021年

作者： Wang, Yikai Sun, Fuchun Huang, Wenbing He, Fengxiang Tao, Dacheng State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China JD Explore Academy *** Inc China

Multimodal fusion and multitask learning are two vital topics in machine learning. Despite the fruitful progress, existing methods for both problems are still brittle to the same challenge—it remains dilemmatic to integrate the common information across modalities (resp. tasks) meanwhile preserving the specific patterns of each modality (resp. task). Besides, while they are actually closely related to each other, multimodal fusion and multitask learning are rarely explored within the same methodological framework before. In this paper, we propose Channel-Exchanging-Network (CEN) which is self-adaptive, parameter-free, and more importantly, applicable for multimodal and multitask dense image prediction. At its core, CEN adaptively exchanges channels between subnetworks of different modalities. Specifically, the channel exchanging process is self-guided by individual channel importance that is measured by the magnitude of Batch-Normalization (BN) scaling factor during training. For the application of dense image prediction, the validity of CEN is tested by four different scenarios: multimodal fusion, cycle multimodal fusion, multitask learning, and multimodal multitask learning. Extensive experiments on semantic segmentation via RGB-D data and image translation through multi-domain input verify the effectiveness of CEN compared to state-of-the-art methods. Detailed ablation studies have also been carried out, which demonstrate the advantage of each component we propose. Our code is available at https://***/yikaiw/CEN. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective

arXiv

引用

arXiv 2022年

作者： Zhao, Yunrui Xu, Qianqian Jiang, Yangbangyan Wen, Peisong Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones. Compared with ordinary semi-supervised learning, this task is much more challenging due to the absence of any known negative labels. While existing cost-sensitive-based methods have achieved state-of-the-art performances, they explicitly minimize the risk of classifying unlabeled data as negative samples, which might result in a negative-prediction preference of the classifier. To alleviate this issue, we resort to a label distribution perspective for PU learning in this paper. Noticing that the label distribution of unlabeled data is fixed when the class prior is known, it can be naturally used as learning supervision for the model. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions, which is formulated by aligning their expectations. Moreover, we further adopt the entropy minimization and Mixup regularization to avoid the trivial solution of the label distribution consistency on unlabeled data and mitigate the consequent confirmation bias. Experiments on three benchmark datasets validate the effectiveness of the proposed method. Code available at: https://***/Ray-rui/Dist-PU-Positive-UnlabeledLearning-from-a-Label-Distribution-Perspective. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

来源：评论

学校读者我要写书评

暂无评论

Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection 41

Size-Invariance Matters: Rethinking Metrics and Losses for I...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming 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 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 Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright 2024 by the author(s)

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Incentive Announcement System for Internet of Vehicles

Blockchain-Based Incentive Announcement System for Internet ...

引用

IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Yang Yang Jialiang Chen Xianghan Zheng Ximeng Liu Wenzhong Guo Hairong Lv College of Mathematics and Computer Science Fuzhou University Fuzhou China Guangxi Key Laboratory of Cryptography and Information Security Guangnxi China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Fuzhou China Ministry of Education Key Laboratory of Bioinformatics Bioinformatics Division and Center for Synthetic and Systems Biology Beijing National Research Center for Information Science and TechnologyDepartment of Automation Tsinghua University Beijing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

With the rapid development of computation and communication technologies, the traditional vehicle ad hoc networks (VANETs) are changing to Internet of vehicle (IoV). Vehicular announcement networks in IoV have been widely used in the communication of vehicles. Generally, we need to solve two problems while establishing a vehicular announcement system. First, we need to protect user's privacy when broadcasting the message. Second, participants usually lack the enthusiasm to reply to the announcement. To solve these two problems, we propose a novel blockchain-based incentive announcement system that not only allows participants to anonymously announce their message on the blockchain in a non-trusted environment, but also motivates witnesses to respond to the request of the traffic information with incentive mechanism. Meanwhile, traffic messages and signatures in our system are tamper-resistant, which are recorded on the blockchain. According to the security and performance analysis, it shows that our system is privacy-preserving and efficient in computation cost.

关键词： Blockchain Privacy Smart contracts Authentication Internet Remuneration Hash functions

来源：评论

学校读者我要写书评

暂无评论

Study on the Relationships between Utilization Ratios and Conversion Rates of Nitrogen,Phosphorus and Potassium for Wheat

引用

Meteorological and Environmental Research 2020年第5期11卷 46-54页

作者： Zhiping HUANG Xianda HOU Shutian LIU Shuojin WANG Hongyan ZHENG Jian DING Changhong MI Ling LU Yanlin HOU Agro-Environmental Protection Institute Ministry of Agriculture and Rural AffairsTianjin 300191China Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology Nanning Normal UniversityNanning 530001China Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation Nanning Normal UniversityNanning 530001China Key Laboratory of Environment Change and Resources Use in Beibu Gulf(Nanning Normal University) Nanning 530001China

This paper studied the relationships between utilization ratios and conversion rates of nitrogen,phosphorus and potassium for wheat by data mining method based on data of"3414 fertilizer field trials"of wheat by published papers from national soil testing and recommendation since *** results and conclusions were as follows:①the conceptual model of fertilizer utilization ratio of wheat for plot was that,Y N≈a+b×optimum nitrogen application amount+c×optimum fertilizer amount of K+d×rainfall in growth season,Y P≈a+b×optimum fertilizer amount of K+c×rainfall in growth season,Y K≈a+b×temperature in growth season+c×rainfall in growth season-d×optimum yield-e×optimum nitrogen application amount,respectively.②Fertilizer utilization ratio and nutrient conversion rate had 4 kinds of *** significant relationships:13,of which 8 were soil nutrients,the results showed that soil nutrients had little effect on nutrient conversion rate and fertilizer utilization ratio;consistent significant relationships:4,they were all nitrogen and phosphorus,explaining the importance of nitrogen and phosphorus;contrary significant relationships:6,they were all nitrogen and phosphorus,illustrating that phosphorus and nitrogen had inhibitory effect;complementary significant relationships:31,the cause of the complementary phenomenon was that the fertilizer utilization ratio was the measuring index of fertilizer efficiency in one season,while the nutrient conversion rate was the measuring index of fertilizer efficiency in more *** showed that the fertilizer utilization rate was significantly correlated,or the nutrient conversion rate was significantly correlated.

关键词： Optimum yield Optimum fertilization amount Nutrient conversion rate Fertilizer utilization ratio Relative abundance-deficiency value of soil nutrient

来源：评论

学校读者我要写书评

暂无评论

Specific Index System of Ecological Balanced Fertilization for Wheat

引用

Meteorological and Environmental Research 2020年第5期11卷 38-45页

作者： Zhiping HUANG Ling LU Shutian LIU Shuojin WANG Xianda HOU Hongyan ZHENG Jian DING Changhong MI Yanlin HOU Agro-Environmental Protection Institute Ministry of Agriculture and Rural AffairsTianjin 300191China Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology Nanning Normal UniversityNanning 530001China Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation Nanning Normal UniversityNanning 530001China Key Laboratory of Environment Change and Resources Use in Beibu Gulf(Nanning Normal University) Nanning 530001China

This paper studied the index system of ecological balanced fertilization for wheat by data mining method based on data of"3414 fertilizer field trials"of wheat by published papers from national soil testing and recommendation since *** results and conclusions were as follows.①Soil nutrient content could not be used as indicator of fertilizer application of wheat.②The conceptual model of N utilization ratio of wheat for plot was Y N≈a+b×optimum production-c×optimum fertilizer amount of N-d×temperature in growing season-e×rainfall in growing season,and the conceptual model of P conversion rate was Y P≈a+b×optimum production+c×temperature in growing season+d×precipitation in growing season-e×optimum fertilizer amount of P-f×optimum fertilizer amount of K,and the conceptual model of K conversion rate of wheat plots was that,Y K≈a-b×optimum fertilizer amount of K.③The conceptual model of the optimum fertilizer amount of N of wheat for plot was Y N≈a+b×optimum production+c×optimum fertilizer amount of P-d×temperature in growing season-e×rainfall in growing season,and the conceptual model of the optimum fertilizer amount of P was Y P≈a+b×optimum production+c×optimum fertilizer amount of N+d×optimum fertilizer amount of K,and the conceptual model of the optimum fertilizer amount of K was Y K≈a+b×optimum fertilizer amount of P.④The conceptual model of relative abundance-deficiency value of soil N of wheat for plot was that Y N≈a-b×temperature in growing season-c×precipitation in growing season,and the conceptual model of relative abundance-deficiency value of soil P was Y P≈a+b×optimum yield+c×optimum fertilizer amount of N+d×precipitation in growing season-e×optimum fertilizer amount of K,and the conceptual model of relative abundance-deficiency value of soil K was Y K≈a+b×optimum yield+c×optimum fertilizer amount of K.

关键词： Optimum yield Optimum fertilization amount Nutrient conversion rate Fertilizer utilization ratio Relative abundance-deficiency value of soil nutrient

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：