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Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

作者： Yiming Wang Linkai Luo Qifeng Zhou Junsong Lin Zixuan Wu Department of Automation Xiamen University Xiamen P.R. China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Xiamen P.R. China Department of Automation Xiamen University Xiamen P.R. China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Xiamen P.R. China Department of Automation Xiamen University Xiamen P.R. China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Xiamen P.R. China Xiamen Airlines Xiamen P.R.China

ISBN: (纸本)9781450387835

It is critical for airlines to develop an effective fuel-saving strategy because the fuel cost accounts for a large proportion of the operating costs in air transport industry. This paper aims to optimize the flight extra fuel predicted by dispatchers via data mining techniques on the consideration of both economy and safety. We first establish a random forest (RF) model from history data to predict the consumption of trip fuel. Then to ensure safety index, we add a margin to the output of the model as the modified prediction value. Finally, the experimental results on real flight data show that the proposed method can make a more accurate prediction for the extra fuel adding amount under the condition of ensuring safety. It indicates that the proposed method is beneficial for airlines in economic cost saving.

关键词： Fuel consumption prediction extra fuel optimization random forest

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Highly sensitive ethylene glycol gas sensor based on SnO 2 /ZnO heterojunction composites

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Vacuum 2025年 240卷

作者： JunTang Dong Te Duan TingTing Shao NingJuan Li JiaKe Guo Fuchun Zhang School of Physics and Electronic Information Yan'an University Yan'an 716000 China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Yan'an 716000 China

Ethylene glycol (EG), widely used in industry, poses severe health and environmental risks even at low concentrations, necessitating the development of highly sensitive gas sensors. Although ZnO and SnO 2 are promising semiconductor metal oxides for gas detection, their single-component limitations hinder optimal performance. This work focuses on SnO 2 -decorated ZnO nanosheets, where a heterojunction structure is constructed to overcome individual material drawbacks. The aim is to enhance EG detection performance through compositional tuning and interface engineering. SnO 2 /ZnO composites with varying SnO 2 contents were synthesized via a one-step hydrothermal method and systematically evaluated. The SnO 2 /ZnO-1 sample (1:1 M ratio) exhibited the best performance, achieving a response of 169.89 to 100 ppm EG at 240 °C, a detection limit as low as 158 ppb, high linearity (R 2 = 0.99593), and strong repeatability and selectivity. Structural characterizations confirmed the successful formation of an n–n heterojunction and increased surface area and mesoporosity, enhancing gas adsorption and electron transfer. XPS revealed a higher proportion of adsorbed oxygen (30.21 %) in the composite versus pure ZnO (27.28 %), contributing to better reactivity. This research demonstrates that tailoring heterostructure interfaces and material morphology significantly improves gas sensing performance, offering a promising strategy for reliable and real-time EG detection in environmental and safety monitoring.

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Community Detection Based on DeepWalk in Large Scale Networks 1st

Community Detection Based on DeepWalk in Large Scale Network...

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1st International Conference on big data and Security, ICBDS 2019

作者： Chen, Yunfang Wang, Li Qi, Dehao Zhang, Wei Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China

ISBN: (纸本)9789811575297

The large scale and complex structure of real networks bring enormous challenges to traditional community detection methods. In order to detect community structure in large scale networks more accurately and efficiently, we propose a community detection algorithm based on network embedding representation method. This algorithm first selects the DeepWalk network embedding method to represent the vertices in the network graph as low-dimensional vector representations which reflect the topology in the network. Then this algorithm treats each vertex as a sample, and each dimension of the vector representation as a feature. Finally, the sample data are input into the variational Gaussian mixture clustering model to obtain the clusters in the network. Experimental results on the DBLP dataset show that the model method of this paper can more effectively discover the communities in large scale networks. Through further analysis of the excavated community structure, the organizational characteristics within the community are better revealed. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Network embeddings

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How to train your agent to read and write

arXiv

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arXiv 2021年

作者： Liu, Li He, Mengge Xu, Guanghui Tan, Mingkui Wu, Qi School of Software Engineering South China University of Technology China Pazhou Laboratory China University of Adelaide Australia Key Laboratory of Big Data and Intelligent Robot Ministry of Education United Kingdom

Reading and writing research papers is one of the most privileged abilities that a qualified researcher should master. However, it is difficult for new researchers (e.g., students) to fully grasp this ability. It would be fascinating if we could train an intelligent agent to help people read and summarize papers, and perhaps even discover and exploit the potential knowledge clues to write novel papers. Although there have been existing works focusing on summarizing (i.e., reading) the knowledge in a given text or generating (i.e., writing) a text based on the given knowledge, the ability of simultaneously reading and writing is still under development. Typically, this requires an agent to fully understand the knowledge from the given text materials and generate correct and fluent novel paragraphs, which is very challenging in practice. In this paper, we propose a Deep ReAder-Writer (DRAW) network, which consists of a Reader that can extract knowledge graphs (KGs) from input paragraphs and discover potential knowledge, a graph-to-text Writer that generates a novel paragraph, and a Reviewer that reviews the generated paragraph from three different aspects. Extensive experiments show that our DRAW network outperforms considered baselines and several state-of-the-art methods on AGENDA and M-AGENDA datasets. Our code and supplementary are released at https://***/menggehe/DRAW. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

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Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels

arXiv

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arXiv 2022年

作者： Ma, Zhongchen Li, Lisha Mao, Qirong Chen, Songcan The School of Computer Science and Communications Engineering Jiangsu University Zhenjiang212013 China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China Nanjing211106 China

Contrastive learning (CL) has shown impressive advances in image representation learning in whichever supervised multi-class classification or unsupervised learning. However, these CL methods fail to be directly adapted to multi-label image classification due to the difficulty in defining the positive and negative instances to contrast a given anchor image in multi-label scenario, let the label missing one alone, implying that borrowing a commonly-used way from contrastive multi-class learning to define them will incur a lot of false negative instances unfavorable for learning. In this paper, with the introduction of a label correction mechanism to identify missing labels, we first elegantly generate positives and negatives for individual semantic labels of an anchor image, then define a unique contrastive loss for multilabel image classification with missing labels (CLML), the loss is able to accurately bring images close to their true positive images and false negative images, far away from their true negative images. Different from existing multi-label CL losses, CLML also preserves low-rank global and local label dependencies in the latent representation space where such dependencies have been shown to be helpful in dealing with missing labels. To the best of our knowledge, this is the first general multi-label CL loss in the missing-label scenario and thus can seamlessly be paired with those losses of any existing multi-label learning methods just via a single hyperparameter. The proposed strategy has been shown to improve the classification performance of the Resnet101 model by margins of 1.2%, 1.6%, and 1.3% respectively on three standard datasets, MSCOCO, VOC, and NUS-WIDE. Code is available at https://***/chuangua/ContrastiveLossMLML. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning systems

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A Novel Gradient Descent Least Squares (GDLS) Algorithm for Efficient SMV Gridless Line Spectrum Estimation with Applications in Tomographic SAR Imaging

arXiv

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arXiv 2022年

作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Jiangsu Suzhou215123 China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing100190 China

This paper presents a novel efficient method for gridless line spectrum estimation problem with single snapshot, namely the gradient descent least squares (GDLS) method. Conventional single snapshot (a.k.a. single measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the array aperture, or adopt the sparsity constraint and utilize compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least squares (LS) estimation problem and solves the corresponding objective function via gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, as well as performance analysis are discussed in this paper. Numerical simulations and real data experiments show that the proposed GDLS algorithm outperforms the state-of-the-art methods e.g., CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experiment data. Results show great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectrum analysis

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An Offensive Language Identification Based on Deep Semantic Feature Fusion

An Offensive Language Identification Based on Deep Semantic ...

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International Conference on Computer and Communications (ICCC)

作者： Xiang Li Zhi Zeng Mingmin Wu Zhongqiang Huang Ying Sha Lei Shi College of Informatics Huazhong Agricultural University Wuhan China Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education

Various forms of social interactions are often char-acterized by toxic or offensive words that can be collectively referred to as offensive languages, which has become a unique linguistic phenomenon in social media platforms. How to detect and identify these offensive languages in social media platforms has become one of the important research in the field of natural language processing. Existing methods utilize machine learning algorithms or text representation models based on deep learning to learn the features of offensive languages and identify them, which have achieved good performances. However, traditional machine learning-based methods mainly rely on keyword identi-fication and blocking, deep learning-based methods do not ade-quately explore the fused deep semantic features of the content by combining word-level embeddings and sentence-level deep semantic feature representations of sentences, which cannot ef-fectively identify offensive languages that do not contain common offensive words but indicate offensive meanings. In this research, we propose a novel offensive language identification model based on deep semantic feature fusion, which uses the pre-trained model Bert to obtain word-level embedding representations of offensive languages, and then integrates the RCNN that combines with the attention mechanism to extract the fused deep semantic feature representations of offensive languages, and label encoder and offensive predictor to improve the identification accuracy and generalization ability of the model so that the performances of the model do not rely on the offensive language lexicon entirely and can identify offensive languages that do not contain common offensive words but indicate offensive meanings. Experimental results on Wikipedia and Twitter comment datasets show that our proposed model can better understand the context and discover potential offensive meanings, and outperforms existing methods.

关键词： Learning systems Social networking (online) Computational modeling Semantics Blogs Encyclopedias Predictive models

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An Explainable Multi-view Semantic Fusion Model for Multimodal Fake News Detection

An Explainable Multi-view Semantic Fusion Model for Multimod...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhi Zeng Mingmin Wu Guodong Li Xiang Li Zhongqiang Huang Ying Sha College of Informatics Huazhong Agricultural University Wuhan China Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Urumqi China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Wuhan China

The existing models have been achieved great success in capturing and fusing miltimodal semantics of news. However, they paid more attention to the global information, ignoring the interactions of global and local semantics and the inconsistency between different modalities. Therefore, we propose an explainable multi-view semantic fusion model (EMSFM), where we aggregate the important inconsistent semantics from local and global views to compensate the global information. Inspired by various forms of artificial fake news and real news, we summarize four views of multimodal correlation: consistency and inconsistency in the local and global views. Integrating these four views, our EMSFM can interpretatively establish global and local fusion between consistent and inconsistent semantics in multimodal relations for fake news detection. The extensive experimental results show that the EMSFM can improve the performance of multimodal fake news detection and provide a novel paradigm for explainable multi-view semantic fusion.

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Discrimination-Aware Domain Adversarial Neural Network

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Journal of Computer Science & Technology 2020年第2期35卷 259-267页

作者： Yun-Yun Wang Jian-Min Gu Chao Wang Song-Can Chen Hui Xue College of Computer Science and Engineering Nanjing University of Posts and Telecommunications Nanjing 210046China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210046China College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 210023China Key Laboratory of Pattern Analysis and Machine Intelligence Ministry of Industry and Information Technology Nanjing 210023China School of Computer Science and Engineering Southeast UniversityNanjing 210096China

The domain adversarial neural network(DANN)methods have been successfully proposed and attracted much attention *** DANNs,a discriminator is trained to discriminate the domain labels of features generated by a generator,whereas the generator attempts to confuse it such that the distributions between domains are *** a result,it actually encourages the whole alignment or transfer between domains,while the inter-class discriminative information across domains is not *** this paper,we present a Discrimination-Aware Domain Adversarial Neural Network(DA2NN)method to introduce the discriminative information or the discrepancy of inter-class instances across domains into deep domain ***2NN considers both the alignment within the same class and the separation among different classes across domains in knowledge transfer via multiple *** results show that DA2NN can achieve better classification performance compared with the DANN methods.

关键词： adversarial learning inter-class separation deep neural network discrimination-aware domain adaptation

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Research on intelligent Scheduling Method of Multi-Machine Cooperative Operation Based on Nsga-Iii and Improved Ant Colony Algorithm

SSRN

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SSRN 2022年

作者： Zhang, Man Li, Shichao Cao, Ruyue Wang, Ning Zhang, Zhenqian Ji, Yuhan Li, Han Wang, Hao Key Lab of Smart Agriculture System Integration Ministry of Education China Agricultural University Beijing100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China National Research Center of Intelligent Equipment for Agriculture Beijing100097 China

Aiming at the problems of over-reliance on traditional manual harvest operation experience and lack of effective collaborative operation scheduling method in agricultural machinery cluster operation in agricultural harvest scenario. A multi-objective combined optimization intelligent scheduling model of harvesters and grain transport vehicles was established to save agricultural machinery resources, and improve the overall efficiency of harvesters and transport vehicles, to meet the constraints of plot location, task number, operation time window, and path planning. The model was solved by an intelligent search algorithm, and an intelligent scheduling method of command multi-machine cooperative operation based on NSGA-III and an improved ant colony algorithm was proposed. It can be seen from the simulation test that the scheduling model established, and the solution algorithm designed in this research can obtain many groups of good optimization results. Moreover, it can make the unproductive waiting time of harvester 0, and minimize the transfer distance and number of grain transporters. This method can be combined closely with the practical application of unmanned farms, which lays a theoretical foundation for improving the efficiency of multi-machine cooperative operations. © 2022, The Authors. All rights reserved.

关键词： Scheduling

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