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12th EAI International Conference on Simulation Tools and Techniques, SIMUtools 2020

作者： Xu, Dayou Lin, Yanping Li, Chenggang Tong, Tiantian School of Business Management Guizhou University of Finance and Economics Guiyang550025 China School of Big Data Application and Economics Guizhou University of Finance and Economics Guiyang550025 China Guizhou Key Laboratory of Big Data Statistics Analysis Guizhou University of Finance and Economics Guiyang Guizhou China

ISBN: (纸本)9783030727918

The brands associated with characteristic agricultural products reflect the quality, advantages, and culture of regional products;these brands affirm the regional characteristics and advantages of specific agricultural products. Exploring the path to building the brands of characteristic agricultural products helps provide a reference for transforming the advantages of existing resources into competitive advantages for the southwest region. This can further enhance the level of brand development of characteristic agricultural products. This study investigated the factors influencing characteristic agricultural product brands, constructed a theoretical model for brand building for characteristic agricultural products, and conducted an empirical study on the mechanism forming of the brands of characteristic agricultural products in southwestern China using a structural equation model (SEM). The empirical results show that in descending order of importance, the factors influencing the regional brand reputation are: geographical resource endowment, management, regional culture, and government support. The geographical resource endowment and regional culture factors influence regional brand reputation through management. The intermediary effect of management is significant in the corresponding path, and serves as the core driving force to advance the reputation of the regional brand. The intermediary effect of government support is not significant in the corresponding path. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021.

关键词： Agricultural products

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EFFECTS OF IONOSPHERE ON LOWER-FREQUENCY SPACEBORNE SAR IMAGING

EFFECTS OF IONOSPHERE ON LOWER-FREQUENCY SPACEBORNE SAR IMAG...

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2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

作者： Wang, Kuan Chen, Bingxu Li, Ning Guo, Zhengwei Fu, Zewen College of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China

The propagation of spaceborne synthetic aperture radar (SAR) signal is affected by the ionosphere, which will lead to the degradation of the imaging quality, especially in the conditions of low-frequency and wide-bandwidth. This paper analyzed the influence of ionosphere on SAR imaging under different carrier frequency, different bandwidth, different ionosphere total electron content (TEC), different azimuth resolution and different scintillation strength by time-domain simulation. The results show that the ionospheric dispersion effect leads to SAR image defocusing, which is closely related to the radar carrier frequency, signal bandwidth, azimuth resolution and TEC value on the propagation path. At the same time, due to the influence of time delay, the dispersion effect also leads to the range displacement of SAR image. The ionospheric scintillation effect reduces the azimuth resolution significantly and in extreme cases imaging is not possible. © 2021 IEEE

关键词： Synthetic aperture radar

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Understanding the mechanism of social tie in the propagation process of social network with communication channel

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Frontiers of Computer Science 2019年第6期13卷 1296-1308页

作者： Kai LI Guangyi LV Zhefeng WANG Qi LIU Enhong CHEN Lisheng QIAO Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China

The propagation of information in online social networks plays a critical role in modern life,and thus has been studied *** have proposed a series of propagation models,generally,which use a single transition probability or consider factors such as content and time to describe the way how a user activates her/his ***,the research on the mechanism how social ties between users play roles in propagation process is still ***,comprehensive summary of factors which affect user’s decision whether to share neighbor’s content was lacked in existing works,so that the existing models failed to clearly describe the process a user be activated by a *** this end,in this paper,we analyze the close correspondence between social tie in propagation process and communication channel,thus we propose to exploit the communication channel to describe the information propagation process between users,and design a social tie channel(STC)*** model can naturally incorporate many factors affecting the information propagation through edges such as content topic and user preference,and thus can effectively capture the user behavior and relationship characteristics which indicate the property of a social *** experiments conducted on two real-world datasets demonstrate the effectiveness of our model on content sharing prediction between users.

关键词： information propagation social networks mechanism of social tie communication channel

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Modeling Offline Knowledge Evolution Effect for Online Knowledge Tracing

Modeling Offline Knowledge Evolution Effect for Online Knowl...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xiaonan Zeng Shuanghong Shen Zhenya Huang Enhong Chen Yu Su Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence Hefei China Hefei Comprehensive National Science Center School of Computer Science and Technology Hefei Normal University & Institute of Artificial Intelligence Hefei China State Key Laboratory of Cognitive Intelligence & iFLYTEK AI Research (Central China) iFLYTEK Co. Ltd Hefei China

Knowledge Tracing (KT) focuses on tracing stu-dents' evolving knowledge state in the process of question-answering, which has been widely applied to online learning systems. Existing KT methods leverage students' online question-answering logs to infer their knowledge state and predict their subsequent performance. However, these methods only focus on students' online behaviors without considering the fact that students' knowledge also evolves offline. Specifically, students have plenty of offline learning actions that also make changes in their knowledge state, which is called the offline knowledge evolution effect in this paper. However, these offline actions are not recorded by online learning systems, therefore leading to unreliable knowledge tracing results. To alleviate this problem, we propose a novel model named offline-aware knowledge tracing (OKT) that considers students' offline knowledge evolution for KT. In O KT, we introduce a novel concept named offline trait to represent student's offline learning action preference. Based on the preference, the model evaluates offline knowledge evolution, and apply the evolution to generate a new knowledge state for students' future performance prediction. To the end, we conduct experiments on three real-world benchmark datasets and demonstrate that OKT achieves state-of-the-art performance. Besides, the experimental results indicate that considering offline actions can help interpret the KT results.

关键词： Learning systems Predictive models Benchmark testing Behavioral sciences

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Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification

arXiv

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arXiv 2022年

作者： Zhang, Kai Zhang, Kun Liu, Qi Huang, Zhenya Zhang, Mengdi Wu, Wei Cheng, Mingyue Chen, Enhong Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China State Key Laboratory of Cognitive Intelligence Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Meituan Beijing China Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China Hefei China

Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models. © 2022, CC BY.

关键词： Sentiment analysis

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dataset of Urban Plant Color Evaluation and Residents'Anxiety in Shanghai(2017)

全球变化数据仓储（中英文）

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全球变化数据仓储（中英文） 2023年第3期 14-15页

作者： WU Jiayu WANG Shiyi LI Hong TA Na Institute of Landscape Architecture Zhejiang UniversityHangzhou 310058China Key Laboratory of Geographic Information Science Ministry of EducationEast China Normal UniversityShanghai 200241China School of Geographic Sciences East China Normal UniversityShanghai 200241China Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural ResourcesShanghai 200241China

Based on the questionnaire survey of residents'activity logs conducted in Shanghai from March to July in 2017,buffer zones were established with the center of the buffer zone in the residential area and workplace respectively,and the radius of the buffer zone was chosen to be 500 m and 1000 *** green view index(GVI),as well as the foliage color and flower color diversity were evaluated for each buffer *** Anxiety was assessed using the Depression-Anxiety-Stress Scale(DASS-21).The dataset includes:(1)results of individual anxiety level evaluation and economic and social characteristics;(2)results of plant color evaluation(centered on residential areas);(3)results of plant color evaluation(centered on workplace);(4)descriptive analysis of samples;(5)average plant color evaluation and comparison between groups;(6)Results of ordinal logistic regression models on plant color and anxiety;(7)the counts for plant foliage color in concentrated construction area of Shanghai;(8)the counts for plant flower colors;(9)the average green view *** dataset is archived *** *** formats,and consists of 4 data files with data size of 450 KB(compressed into one single file with 159 KB).The analysis paper based on the dataset will be published in coordination with Acta Geographica Sinica,Vol.78,No.4,2023.

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Backdoor Defense via Deconfounded Representation Learning

arXiv

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arXiv 2023年

作者： Zhang, Zaixi Liu, Qi Wang, Zhicai Lu, Zepu Hu, Qingyong Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Hong Kong University of Science and Technology Hong Kong University of Science and Technology of China China

Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by injecting a few poisoned examples into the training dataset. While extensive efforts have been made to detect and remove backdoors from backdoored DNNs, it is still not clear whether a backdoor-free clean model can be directly obtained from poisoned datasets. In this paper, we first construct a causal graph to model the generation process of poisoned data and find that the backdoor attack acts as the confounder, which brings spurious associations between the input images and target labels, making the model predictions less reliable. Inspired by the causal understanding, we propose the Causality-inspired Backdoor Defense (CBD), to learn deconfounded representations for reliable classification. Specifically, a backdoored model is intentionally trained to capture the confounding effects. The other clean model dedicates to capturing the desired causal effects by minimizing the mutual information with the confounding representations from the backdoored model and employing a sample-wise re-weighting scheme. Extensive experiments on multiple benchmark datasets against 6 state-of-the-art attacks verify that our proposed defense method is effective in reducing backdoor threats while maintaining high accuracy in predicting benign samples. Further analysis shows that CBD can also resist potential adaptive attacks. The code is available at https://***/zaixizhang/CBD. © 2023, CC BY-NC-SA.

关键词： Deep neural networks

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Adam revisited:a weighted past gradients perspective

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Frontiers of Computer Science 2020年第5期14卷 61-76页

作者： Hui Zhong Zaiyi Chen Chuan Qin Zai Huang Vincent W.Zheng Tong Xu Enhong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei230027China Zhejiang Cainiao Supply Chain Management Co.Ltd Hangzhou311122China Advanced Digital Sciences Center Singapore138602Singapore

Adaptive learning rate methods have been successfully applied in many fields,especially in training deep neural *** results have shown that adaptive methods with exponential increasing weights on squared past gradients(i.e.,ADAM,RMSPROP)may fail to converge to the optimal *** many algorithms,such as AMSGRAD and ADAMNC,have been proposed to fix the non-convergence issues,achieving a data-dependent regret bound similar to or better than ADAGRAD is still a challenge to these *** this paper,we propose a novel adaptive method weighted adaptive algorithm(WADA)to tackle the non-convergence *** AMSGRAD and ADAMNC,we consider using a milder growing weighting strategy on squared past gradient,in which weights grow *** on this idea,we propose weighted adaptive gradient method framework(WAGMF)and implement WADA algorithm on this ***,we prove that WADA can achieve a weighted data-dependent regret bound,which could be better than the original regret bound of ADAGRAD when the gradients decrease *** bound may partially explain the good performance of ADAM in ***,extensive experiments demonstrate the effectiveness of WADA and its variants in comparison with several variants of ADAM on training convex problems and deep neural networks.

关键词： adaptive learning rate methods stochastic gradient descent online learning

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Large-Scale OD Matrix Estimation with A Deep Learning Method

arXiv

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arXiv 2023年

作者： Xiong, Zheli Lian, Defu Chen, Enhong Chen, Gang Cheng, Xiaomin The School of Data Science University of Science and Technology of China Anhui Hefei230000 China The Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230000 China The Yangtze River Delta Information Intelligence Innovation Research Institute China

The estimation of origin-destination (OD) matrices is a crucial aspect of Intelligent Transport Systems (ITS). It involves adjusting an initial OD matrix by regressing the current observations like traffic counts of road sections (e.g., using least squares). However, the OD estimation problem lacks sufficient constraints and is mathematically underdetermined. To alleviate this problem, some researchers incorporate a prior OD matrix as a target in the regression to provide more structural constraints. However, this approach is highly dependent on the existing prior matrix, which may be outdated. Others add structural constraints through sensor data, such as vehicle trajectory and speed, which can reflect more current structural constraints in real-time. Our proposed method integrates deep learning and numerical optimization algorithms to infer matrix structure and guide numerical optimization. This approach combines the advantages of both deep learning and numerical optimization algorithms. The neural network(NN) learns to infer structural constraints from probe traffic flows, eliminating dependence on prior information and providing real-time performance. Additionally, due to the generalization capability of NN, this method is economical in engineering. We conducted tests to demonstrate the good generalization performance of our method on a large-scale synthetic dataset. Subsequently, we verified the stability of our method on real traffic data. Our experiments provided confirmation of the benefits of combining NN and numerical *** Codes I.2.1 Copyright © 2023, The Authors. All rights reserved.

关键词： Statistical tests

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Boosting the Electrochemical Performance of Li-and Mn-Rich Cathodes by a Three-in-One Strategy

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Nano-Micro Letters 2021年第12期13卷 311-321页

作者： Wei He Fangjun Ye Jie Lin Qian Wang Qingshui Xie Fei Pei Chenying Zhang Pengfei Liu Xiuwan Li Laisen Wang Baihua Qu Dong-Liang Peng State Key Lab of Physical Chemistry of Solid Surface Collaborative Innovation Center of Chemistry for Energy MaterialsCollege of Materials and Pen-Tung Sah Institute of Micro-Nano Science and TechnologyXiamen UniversityXiamen 361005People’s Republic of China College of Chemistry and Chemical Engineering Xiamen UniversityXiamen 361005People’s Republic of China Zhengzhou Key Laboratory of Big Data Analysis and Application Henan Academy of Big DataZhengzhou UniversityZhengzhou 450002People’s Republic of China Fujian Provincial Key Laboratory of Light Propagation and Transformation College of Information Science and EngineeringHuaqiao UniversityXiamen 361021People’s Republic of China Shenzhen Research Institute of Xiamen University Shenzhen 518000People’s Republic of China

There are plenty of issues need to be solved before the practi-cal application of Li-and Mn-rich cathodes,including the detrimental voltage decay and mediocre rate capability,*** doping can e ectively solve the above problems,but cause the loss of *** introduction of appropriate defects can compensate the capacity loss;however,it will lead to structural mismatch and stress ***,a three-in-one method that combines cation–polyanion co-doping,defect construction,and stress engineering is *** co-doped Na^(+)/SO_(4)^(2-)can stabilize the layer framework and enhance the capacity and voltage *** induced defects would activate more reac-tion sites and promote the electrochemical ***,the unique alternately distributed defect bands and crystal bands structure can alleviate the stress accumulation caused by changes of cell parameters upon ***,the modified sample retains a capacity of 273 mAh g^(-1)with a high-capacity retention of 94.1%after 100 cycles at 0.2 C,and 152 mAh g^(-1)after 1000 cycles at 2 C,the corresponding voltage attenuation is less than 0.907 mV per cycle.

关键词： Li-and Mn-rich cathodes Cation–polyanion co-doping Defect and stress engineering Good structure stability Electrochemical performance

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