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A novel semantic segmentation approach based on U-Net,WU-Net,and U-Net++deep learning for predicting areas sensitive to pluvial flood at tropical area

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International Journal of Digital Earth 2023年第1期16卷 3661-3679页

作者： Laura Melgar-García Francisco Martínez-álvarez Dieu Tien Bui Alicia Troncoso Data Science&Big Data Lab Pablo de Olavide UniversitySevilleSpain GIS Group Department of Business and ITUniversity of South-Eastern NorwayGullbringvegen 36B?i Telemark3800Norway

Floods remain one of the most devastating weather-induced disastersworldwide, resulting in numerous fatalities each year and severelyimpacting socio-economic development and the ***, the ability to predict flood-prone areas in advance is crucialfor effective risk management. The objective of this research is to assessand compare three convolutional neural networks, U-Net, WU-Net, andU-Net++, for spatial prediction of pluvial flood with a case study at atropical area in the north of Vietnam. They are relative new convolutionalgorithms developed based on U-shaped architectures. For this task, ageospatial database with 796 historical flood locations and 12 floodindicators was prepared. For training the models, the binary crossentropywas employed as the loss function, while the Adaptive momentestimation (ADAM) algorithm was used for the optimization of themodel parameters, whereas, F1-score and classification accuracy (Acc)were used to assess the performance of the models. The results unequivocally highlight the high performance of the three models,achieving an impressive accuracy rate of 96.01%. The flood susceptibility maps derived from this research possess considerable utility for local authorities, providing valuable insights and informationto enhance decision-making processes and facilitate the implementation of effective risk management strategies.

关键词： Flash-flood assessment convolutional networks climate change GIS

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Federated learning-outcome prediction with multi-layer privacy protection

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Frontiers of Computer science 2024年第6期18卷 205-214页

作者： Yupei ZHANG Yuxin LI Yifei WANG Shuangshuang WEI Yunan XU Xuequn SHANG School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China MIIT Lab of Big Data Storage and Management Xi’an 710129China

Learning-outcome prediction(LOP)is a long-standing and critical problem in educational *** studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection *** this end,this study proposes a distributed grade prediction model,dubbed FecMap,by exploiting the federated learning(FL)framework that preserves the private data of local clients and communicates with others through a global generalized *** considers local subspace learning(LSL),which explicitly learns the local features against the global features,and multi-layer privacy protection(MPP),which hierarchically protects the private features,including model-shareable features and not-allowably shared features,to achieve client-specific classifiers of high performance on LOP per *** is then achieved in an iteration manner with all datasets distributed on clients by training a local neural network composed of a global part,a local part,and a classification head in clients and averaging the global parts from clients on the *** evaluate the FecMap model,we collected three higher-educational datasets of student academic records from engineering *** results manifest that FecMap benefits from the proposed LSL and MPP and achieves steady performance on the task of LOP,compared with the state-of-the-art *** study makes a fresh attempt at the use of federated learning in the learning-analytical task,potentially paving the way to facilitating personalized education with privacy protection.

关键词： federated learning local subspace learning hierarchical privacy protection learning outcome prediction privacy-protected representation learning

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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science China(Information sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU 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 and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting

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Neural Computing and Applications 2025年第5期37卷 3245-3264页

作者： Chacón-Maldonado, A.M. Melgar-García, L. Asencio-Cortés, G. Troncoso, A. Data Science and Big Data Lab Pablo de Olavide University SevilleES-41013 Spain Department of Artificial Intelligence Polytechnic University of Madrid MadridES-28660 Spain

Predicting the occurrence of crop pests is becoming a crucial task in modern agriculture to facilitate farmers’ decision-making. One of the most significant pests is the olive fruit fly, a public concern because it causes damage that compromises oil quality, increasing acidity and altering its flavor. This paper proposes a hybrid deep learning model to predict the presence of olive flies in crops. This model is based on an autoencoder and an automated deep feed-forward neural network. First, the autoencoder neural network learns a representation of the data and then the automated deep feed-forward neural network automatically determines the best values for the hyperparameters in order to obtain the prediction of the number of flies caught in traps from the dataset generated by the autoencoder. On the other hand, farmers to trust the proposed deep learning models need these models to be explainable. Thus, explainable artificial intelligence techniques are applied to the produced models to interpret the results. Results using a dataset from different sources such as satellite image band data, vegetation indices, and meteorological variables are reported. The performance of the proposed model has been compared with classical benchmark algorithms and a deep learning model recently published in the literature. In addition, the comparison includes the automated deep feed-forward neural network individually to show how the autoencoder network improves the accuracy of predictions. © The Author(s) 2024.

关键词： Agriculture Deep learning Explainable artificial intelligence

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Human mobility and the infectious disease transmission:a systematic review

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地球空间信息科学学报（英文版） 2024年第6期27卷中插6,1824-1851页

作者： M.Naser Lessani Zhenlong Li Fengrui Jing Shan Qiao Jiajia Zhang Bankole Olatosi Xiaoming Li Geoinformation and Big Data Research Lab Department of GeographyUniversity of South CarolinaColumbiaSCUSA Big Data Health Science Center University of South CarolinaColumbiaSCUSA Big Data Health Science Center University of South CarolinaColumbiaSCUSA Department of Health Promotion Educationand BehaviorUniversity of South CarolinaColumbiaSCUSA Big Data Health Science Center University of South CarolinaColumbiaSCUSA Department of Epidemiology and Biostatistics Arnold School of Public HealthUniversity of South CarolinaColumbiaSCUSA Big Data Health Science Center University of South CarolinaColumbiaSCUSA Department of Health Services Policy and Management Arnold School of Public HealthUniversity of South CarolinaColumbiaSCUSA

Recent decades have witnessed several infectious disease outbreaks,including the coronavirus disease(COVID-19)pandemic,which had catastrophic impacts on societies around the *** the same time,the twenty-first century has experienced an unprecedented era of techno-logical development and demographic changes:exploding population growth,increased air-line flights,and increased rural-to-urban migration,with an estimated 281 million international migrants worldwide in 2020,despite COVID-19 movement *** this review,we synthesized 195 research articles that examined the association between human movement and infectious disease outbreaks to understand the extent to which human mobility has increased the risk of infectious disease *** article covers eight infectious diseases,ranging from respiratory illnesses to sexually transmitted and vector-borne *** review revealed a strong association between human mobility and infectious disease spread,particularly strong for respiratory illnesses like COVID-19 and *** significant research into the relationship between infectious diseases and human mobility,four knowl-edge gaps were identified based on reviewed literature in this study:1)although some studies have used big data in investigating infectious diseases,the efforts are limited(with the exception of COVID-19 disease),2)while some research has explored the use of multiple data sources,there has been limited focus on fully integrating these data into comprehensive analyses,3)limited research on the global impact of mobility on the spread of infectious disease with most studies focusing on local or regional outbreaks,and 4)lack of standardiza-tion in the methodology for measuring the impacts of human mobility on infectious disease *** tackling the recognized knowledge gaps and adopting holistic,interdisciplinary methods,forthcoming research has the potential to substantially enhance our comprehension of the intricate inter

关键词： Human mobility geography infectious diseases sexually transmitted diseases systematic review

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Precise control of page cache for containers

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Frontiers of Computer science 2024年第2期18卷 17-30页

作者： Kun WANG Song WU Shengbang LI Zhuo HUANG Hao FAN Chen YU Hai JIN 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 TechnologyWuhan 430074China

Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.

关键词： page cache memory cgroup container isolation cloud computing

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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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Olive Oil Fly Population Pest Forecasting Using Explainable Deep Learning 1

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Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2023

作者： Chacón-Maldonado, A.M. Troncoso-García, A.R. Martínez-Álvarez, F. Asencio-Cortés, G. Troncoso, A. Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain

ISBN: (数字)9783031425363

ISBN: (纸本)9783031425356

This paper proposes an application of the Automated Deep Learning model to predict the presence of olive flies in crops. Compared to baseline algorithms such as Random Forest or K-Nearest Neighbor, our Automated Deep Learning model demonstrates superior performance. Explainable Artificial Intelligence techniques such as Local Interpretable Model-Agnostic Explanations and Shapley Additive explanations are applied to interpret the results, revealing solar radiation as a key predictor for the presence of olive fly. This study enhances deep learning for agriculture, showcasing Automated Deep Learning superiority, and providing interpretable insights for effective pest management. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Olive oil

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Resource investment for DDoS attack resistant SDN: a practical assessment

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science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

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Frontiers of Computer science 2024年第4期18卷 1-23页

作者： Hongru GAO Xiaofei LIAO Zhiyuan SHAO Kexin LI Jiajie CHEN Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in *** most real-world scenarios,entities and their relationships are subject to constant *** that record such changes are called dynamic *** recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics *** the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing *** the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing ***-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph *** comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph *** addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.

关键词： dynamic graphs graph processing graph algorithms GPUs

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