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Integration of Soil Information System and Interactive Self-Organizing Data for Agricultural Developing Zones in Red Soil Region 认领 引用 被引量：3
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作者 SHI ZHOU WANG RENCHAO and M. AL-ABED 《Pedosphere》 SCIE CAS 1999年第1期61-68,共8页
Integration of soil information system (SIS) and interactive self-organizing data (ISODATA) was studied to establish proper agricultural developing zones in red soil region of southern China which are of crucial impor... Integration of soil information system (SIS) and interactive self-organizing data (ISODATA) was studied to establish proper agricultural developing zones in red soil region of southern China which are of crucial importance to farmers, researchers, and decision makers while utilizing and managing red soil resources. SIS created by using ARC/INPO was used to provide data acquisition, systematic model parameter assignment, and visual display of analytic results. Topography, temperature, soil component (e.g., organic matter and pH) and condition of agricultural production were selected as parameters of ISODATA model. Taking Longyou County, Zhejiang Province as the case study area, the effect of the integration and recommendations are discussed for future research. 展开更多
关键词 agricultural developing zonest interactive self-organizing data red soil resources soil information system
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Interactive early warning technique based on SVDD 认领 引用 被引量：6
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作者 Lin Jian Peng Minjing 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第3期527-533,共7页
After reviewing current researches on early warning,it is found that“bad”data of some systems is not easy to obtain,which makes methods proposed by these researches unsuitable for monitored systems.An interactive ea... After reviewing current researches on early warning,it is found that“bad”data of some systems is not easy to obtain,which makes methods proposed by these researches unsuitable for monitored systems.An interactive early warning technique based on SVDD(support vector data description)is proposed to adopt“good”data as samples to overcome the difficulty in obtaining the“bad”data.The process consists of two parts:(1)A hypersphere is fitted on“good”data using SVDD.If the data object are outside the hypersphere,it would be taken as“suspicious”;(2)A group of experts would decide whether the suspicious data is“bad”or“good”,early warning messages would be issued according to the decisions.And the detailed process of implementation is proposed.At last,an experiment based on data of a macroeconomic system is conducted to verify the proposed technique. 展开更多
关键词 interactive data mining early warning support vector data description group decision making.
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Blockchain and MEC-Assisted Reliable Billing Data Transmission over Electric Vehicular Network:An Actor–Critic RL Approach 认领 引用 被引量：5
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作者 Xinyu Ye Meng Li +3 位作者 Pengbo Si Ruizhe Yang Enchang Sun Yanhua Zhang 《China Communications》 SCIE EI CSCD 2021年第8期279-296,共18页
Recently,electric vehicles(EVs)have been widely used under the call of green travel and environmental protection,and diverse requirements for charging are also increasing gradually.In order to ensure the authenticity ... Recently,electric vehicles(EVs)have been widely used under the call of green travel and environmental protection,and diverse requirements for charging are also increasing gradually.In order to ensure the authenticity and privacy of charging information interaction,blockchain technology is proposed and applied in charging station billing systems.However,there are some issues in blockchain itself,including lower computing efficiency of the nodes and higher energy consumption in the consensus process.To handle the above issues,in this paper,combining blockchain and mobile edge computing(MEC),we develop a reliable billing data transmission scheme to improve the computing capacity of nodes and reduce the energy consumption of the consensus process.By jointly optimizing the primary and replica nodes offloading decisions,block size and block interval,the transaction throughput of the blockchain system is maximized,as well as the latency and energy consumption of the system are minimized.Moreover,we formulate the joint optimization problem as a Markov decision process(MDP).To tackle the dynamic and continuity of the system state,the reinforcement learning(RL)is introduced to solve the MDP problem.Finally,simulation results demonstrate that the performance improvement of the proposed scheme through comparison with other existing schemes. 展开更多
关键词 electric vehicles billing data interaction blockchain mobile edge computing reinforcement learning
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Banian: A Cross-Platform Interactive Query System for Structured Big Data 认领 引用 被引量：2
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作者 Tao Xu Dongsheng Wang Guodong Liu 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2015年第1期62-71,共10页
The rapid growth of structured data has presented new technological challenges in the research fields of big data and relational database. In this paper, we present an efficient system for managing and analyzing PB le... The rapid growth of structured data has presented new technological challenges in the research fields of big data and relational database. In this paper, we present an efficient system for managing and analyzing PB level structured data called Banian. Banian overcomes the storage structure limitation of relational database and effectively integrates interactive query with large-scale storage management. It provides a uniform query interface for cross-platform datasets and thus shows favorable compatibility and scalability. Banian＇s system architecture mainly includes three layers：（1） a storage layer using HDFS for the distributed storage of massive data;（2） a scheduling and execution layer employing the splitting and scheduling technology of parallel database; and（3）an application layer providing a cross-platform query interface and supporting standard SQL. We evaluate Banian using PB level Internet data and the TPC-H benchmark. The results show that when compared with Hive, Banian improves the query performance to a maximum of 30 times and achieves better scalability and concurrency. 展开更多
关键词 big data interactive query relational database HDFS cross platform
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Towards Sensor-free Academic Emotion Prediction in Programming Environment 认领 引用
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作者 Tao Lin Zhiming Wu +2 位作者 Juan Zheng Shenggen Ju Yu Fu 《计算机教育》 2020年第12期77-84,共8页
he transition from traditional learning to practice-oriented programming learning will bring learners discomfort.The discomfort quickly breeds negative emotions when encountering programming difficulties,which leads t... he transition from traditional learning to practice-oriented programming learning will bring learners discomfort.The discomfort quickly breeds negative emotions when encountering programming difficulties,which leads the learner to lose interest in programming or even give up.Emotion plays a crucial role in learning.Educational psychology research shows that positive emotion can promote learning performance,increase learning interest and cultivate creative thinking.Accurate recognition and interpretation of programming learners’emotions can give them feedback in time,and adjust teaching strategies accurately and individually,which is of considerable significance to improve effects of programming learning and education.The existing methods of sensor-free emotion prediction include emotion prediction based on keyboard dynamic,mouse interaction data and interaction logs,respectively.However,none of the three studies considered the temporal characteristics of emotion,resulting in low recognition accuracy.For the first time,this paper proposes an emotion prediction model based on time series and context information.Then,we establish a Bi-recurrent neural network,obtain the time sequence characteristics of data automatically,and explore the application of deep learning in the field of Academic Emotion prediction.The results show that the classification ability of this model is much better than that of the original LSTM(Long-Short Term Memory),GRU(Gate Recurrent Unit)and RNN(Re-current Neural Network),and this model has better generalization ability. 展开更多
关键词 emotion prediction emotional state programming behavior data Bi-directional Recurrent Neural Network interaction sequence data
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Interaction dataset of autonomous vehicles with traffic lights and signs 认领 引用
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作者 Zheng Li Zhipeng Bao +4 位作者 Haoming Meng Haotian Shi Qianwen Li Handong Yao Xiaopeng Li 《Communications in Transportation Research》 SCIE EI CSCD 2025年第1期613-628,共16页
This study presents the development of a comprehensive dataset capturing interactions between autonomous vehicles(AVs)and traffic control devices,specifically traffic lights and stop signs.Derived from the Waymo Motio... This study presents the development of a comprehensive dataset capturing interactions between autonomous vehicles(AVs)and traffic control devices,specifically traffic lights and stop signs.Derived from the Waymo Motion dataset,our work addresses a critical gap in the existing literature by providing real-world trajectory data on how AVs navigate these traffic control devices.We propose a methodology for identifying and extracting relevant interaction trajectory data from the Waymo Motion dataset,incorporating over 37,000 instances with traffic lights and 44,000 with stop signs.Our methodology includes defining rules to identify various interaction types,extracting trajectory data,and applying a wavelet-based denoising method to smooth the acceleration and speed profiles and eliminate anomalous values,thereby enhancing the trajectory quality.Quality assessment metrics indicate that trajectories obtained in this study have anomaly proportions in acceleration and jerk profiles reduced to near-zero levels across all interaction categories.By making this dataset publicly available,we aim to address the current gap in datasets containing AV interaction behaviors with traffic lights and signs.Based on the organized and published dataset,we can gain a more in-depth understanding of AVs’behavior when interacting with traffic lights and signs.This will facilitate research on AV integration into existing transportation infrastructures and networks,supporting the development of more accurate behavioral models and simulation tools. 展开更多
关键词 Autonomous vehicles(AVs) Traffic lights Stop signs Interaction data Waymo motion dataset
BPMC:blockchain-pedersen multi-aggregation communication framework for data privacy-preserving interaction 认领 引用
7
作者 Zhang Jiazheng Han Haolun Li Shouwei 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2025年第6期84-96,106,共13页
In contemporary data interactions,it is common for multiple parties to engage in transactions,with each party'being granted access to specific information while maintaining the confidentiality of other parties dat... In contemporary data interactions,it is common for multiple parties to engage in transactions,with each party'being granted access to specific information while maintaining the confidentiality of other parties data.Nevertheless,these interactions often fall short of meeting the data privacy requirements stipulated by the cooperative participants.This paper proposes the blockchain-pedersen multi-aggregation communication(BPMC)framework,which integrates pedersen secret sharing,multi-party aggregation signatures,and blockchain technologies to address these challenges.The integration of these technologies ensures the secure distribution of'encrypted private information,thereby facilitating the verification of participants legitimacy and the validity of their data.Multi-party aggregation signatures,in conjunction with blockchain and smart contracts,ensure the transparency and immutability of the verification process by enabling the generation of individual signatures using private keys,which are then aggregated into a single signature.Experimental results demonstrate that the BPMC framework significantly improves multi-party signature efficiency by 97.4%compared to the standard Boneh-LynnShacham(BLS)algorithm in three-party scenarios,and also enhances verification efficiency by 93.4%and 68%compared to BLS multi-party signatures(BLSMultiSig)and the Shamir algorithms,respectively,while further optimizing data privacy protection. 展开更多
关键词 blockchain pedersen verified secret sharing multi-party aggregation computing data interaction access control
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Beyond the horizon:immersive developments for animal ecology research 认领 引用 被引量：1
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作者 Ying Zhang Karsten Klein +1 位作者 Falk Schreiber Kamran Safi 《Visual Computing for Industry,Biomedicine,and Art》 EI 2023年第1期139-152,共14页
More diverse data on animal ecology are now available.This“data deluge”presents challenges for both biologists and computer scientists;however,it also creates opportunities to improve analysis and answer more holist... More diverse data on animal ecology are now available.This“data deluge”presents challenges for both biologists and computer scientists;however,it also creates opportunities to improve analysis and answer more holistic research questions.We aim to increase awareness of the current opportunity for interdisciplinary research between animal ecology researchers and computer scientists.Immersive analytics(IA)is an emerging research field in which investigations are performed into how immersive technologies,such as large display walls and virtual reality and augmented reality devices,can be used to improve data analysis,outcomes,and communication.These investigations have the potential to reduce the analysis effort and widen the range of questions that can be addressed.We propose that biologists and computer scientists combine their efforts to lay the foundation for IA in animal ecology research.We discuss the potential and the challenges and outline a path toward a structured approach.We imagine that a joint effort would combine the strengths and expertise of both communities,leading to a well-defined research agenda and design space,practical guidelines,robust and reusable software frameworks,reduced analysis effort,and better comparability of results. 展开更多
关键词 Immersive analytics Animal ecology Collaboration Interactive data visualization
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Tracking a maneuvering target in clutter with out-of-sequence measurements for airborne radar 认领 引用 被引量：3
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作者 Weihua Wu Jing Jiang Yang Wan 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2015年第4期746-753,共8页
There are many proposed optimal or suboptimal al- gorithms to update out-of-sequence measurement（s） （OoSM（s）） for linear-Gaussian systems, but few algorithms are dedicated to track a maneuvering target in clutte... There are many proposed optimal or suboptimal al- gorithms to update out-of-sequence measurement（s） （OoSM（s）） for linear-Gaussian systems, but few algorithms are dedicated to track a maneuvering target in clutter by using OoSMs. In order to address the nonlinear OoSMs obtained by the airborne radar located on a moving platform from a maneuvering target in clut- ter, an interacting multiple model probabilistic data association （IMMPDA） algorithm with the OoSM is developed. To be practical, the algorithm is based on the Earth-centered Earth-fixed （ECEF） coordinate system where it considers the effect of the platform＇s attitude and the curvature of the Earth. The proposed method is validated through the Monte Carlo test compared with the perfor- mance of the standard IMMPDA algorithm ignoring the OoSM, and the conclusions show that using the OoSM can improve the track- ing performance, and the shorter the lag step is, the greater degree the performance is improved, but when the lag step is large, the performance is not improved any more by using the OoSM, which can provide some references for engineering application. 展开更多
关键词 out-of-sequence measurement（s） （OoSM（s）） Earth-centered Earth-fixed （ECEF） interacting multiple model （IMM）,probabilistic data association （PDA） attitude.
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Observation evidence for the entropy switch model of substorm onset 认领 引用
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作者 YunXiang Song ChuXin Chen 《Earth and Planetary Physics》 EI CAS CSCD 2022年第2期161-176,共16页
The cause of substorm onset is not yet understood. Chen CX(2016) proposed an entropy switch model, in which substorm onset results from the development of interchange instability. In this study, we sought observationa... The cause of substorm onset is not yet understood. Chen CX(2016) proposed an entropy switch model, in which substorm onset results from the development of interchange instability. In this study, we sought observational evidence for this model by using Time History of Events and Macroscale Interactions during Substorms(THEMIS) data. We examined two events, one with and the other without a streamer before substorm onset. In contrast to the stable magnetosphere, where the total magnetic field strength is a decreasing function and entropy is an increasing function of the downtail distance, in both events the total magnetic field strength and entropy were reversed before substorm onset. After onset, the total magnetic field strength, entropy, and other plasma quantities fluctuated. In addition, a statistical study was performed. By confining the events with THEMIS satellites located in the downtail region between ~8 and ~12 Earth radii, and 3 hours before and after midnight, we found the occurrence rate of the total magnetic field strength reversal to be 69% and the occurrence rate of entropy reversal to be 77% of the total 205 events. 展开更多
关键词 substorm onset entropy switch model interchange or ballooning instability Time History of Events and Macroscale Interactions during Substorms(THEMIS)data
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Towards efficient and effective unlearning of large language models for recommendation 认领 引用
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作者 Hangyu WANG Jianghao LIN +4 位作者 Bo CHEN Yang YANG Ruiming TANG Weinan ZHANG Yong YU 《Frontiers of Computer Science》 SCIE EI CSCD 2025年第3期119-121,共3页
1 Introduction Large Language Models(LLMs)possess massive parameters and are trained on vast datasets,demonstrating exceptional proficiency in various tasks.The remarkable advancements in LLMs also inspire the explora... 1 Introduction Large Language Models(LLMs)possess massive parameters and are trained on vast datasets,demonstrating exceptional proficiency in various tasks.The remarkable advancements in LLMs also inspire the exploration of leveraging LLMs as recommenders(LLMRec),whose effectiveness stems from extensive open-world knowledge and reasoning ability in LLMs[1].LLMRec obtains the recommendation ability through instruction tuning on the user interaction data.But in many cases,it is also crucial for LLMRec to forget specific user data,which is referred to as recommendation unlearning[2],as shown in Fig.1. 展开更多
关键词 large language models llms possess user interaction data large language models instruction tuning recommendation unlearning
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A digital twin model of urban utility tunnels and its application 认领 引用
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作者 Wu Jiansong Fan chen +4 位作者 Hu Yanzhu Fu Ming Cai Jitao Zou Xiaofu Wang Xin 《Digital Twin》 2024年第3期96-114,共19页
Background Multiple pipelines in utility tunnels may lead to various accidents and serious social impact.In the era of digitalization,how to better model the operation of a utility tunnel,dynamically predict the accid... Background Multiple pipelines in utility tunnels may lead to various accidents and serious social impact.In the era of digitalization,how to better model the operation of a utility tunnel,dynamically predict the accident evolutions,and support corresponding decision-makings are essential issues.Methods In this study,a CFD-based digital twin framework for accidents in utility tunnels is proposed.First,Kalman filtering is applied to correct the parameter drift of sensors used for long-term monitoring.A data interaction system is then developed based on Internet of Things(IOT)and OPC Unified Architecture(OPC UA)to comprehensively manage data transmission within the utility tunnel.Subsequently,a natural gas leakage prediction model is developed to enable the efficient prediction of the spatial and temporal distribution in the case of leakage.Finally,these components are integrated for visualization in a digital twin platform for natural gas leakage in utility tunnels.Additionally,numerical simulations are employed to validate of the proposed method.Results The utility tunnel data transmission system based on IoT and OPC UA proposed in this paper is case-validated.By comparing the simulation results at 10 s,20 s,30 s,and 40 s,the model accurately predicts the methane concentration at the leak position after 10 seconds and maintains acceptable accuracy thereafter.The simulation results of different cases are introduced to verify the reliability of the risk indicator proposed in this paper,which increases with the leakage rate.Finally,A process for visualizing numerical simulation is proposed into a digital twin.Conclusions The proposed predictive digital twin technology facilitates the rapid risk assessment of and emergency management of natural gas accidents in utility tunnels.Based on the results of predictive model,a risk indicator is introduced to evaluate the natural gas accidents. 展开更多
关键词 Utility tunnels natural gas leakage Digital twin predictive model data interaction
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