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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Shen, Zhijie Liu, Bowen Xu, Xiaolong Qi, Lianyong Dai, Fei Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Shenzhen China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Qingdao China School of Computer Science Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350319934

As a standardized and systematic management method, business process management plays an important role in the industrial Internet of Things (IIoT) scenario. With the rapid development of mobile Internet and the increasing amount of data generated by the Internet of Things devices, the traditional storage scheme can no longer meet the requirements of managing the IIoT mobile business data. The cloud-edge collaborative data storage scheme provides mass storage capability by the cloud data center, and meanwhile takes access delay into account by storing data in the edge servers near to user, making it an effective solution to manage mobile business data in the IIoT scenario. However, the storage capacity of edge servers is relatively limited. How to design a reasonable data placement strategy to maximize the speed of data reading and tasks execution in IIoT business process has become an urgent problem to be solved. In order to shorten the overall running time, reduce data transmission costs and enhance users experience, we propose a data placement strategy based on Bayesian optimization algorithm, which can reasonably allocate storage capacity in the edge side, and further determine which edge server the data should be placed on. The results of the experiment show that the strategy based on our proposed algorithm performs better in a variety of different scenarios than the ones that based on other kind of optimization algorithms which have been adopted in similar studies. © 2022 IEEE.

关键词： Edge computing

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Syntax-enhanced pre-trained model 59

Syntax-enhanced pre-trained model

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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Xu, Zenan Guo, Daya Tang, Duyu Su, Qinliang Shou, Linjun Gong, Ming Zhong, Wanjun Quan, Xiaojun Jiang, Daxin Duan, Nan School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Microsoft Research Asia Beijing China Microsoft Search Technology Center Asia Beijing China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781954085527

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens. © 2021 Association for Computational Linguistics

关键词： Syntactics

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Integrity maintenance of continually changed OWL ontology

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Journal of Computational Information Systems 2012年第7期8卷 2931-2939页

作者： Cui, Xianji Ouyang, Dantong Ye, Yuxin Wang, Haiyan College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Computation and Knowledge Engineering Ministry of Education Changchun 130012 China National Engineering Research Center of Geophysics Exploration Instruments Changchun 130061 China

OWL Ontologies may change continually to meet user's dynamic request and it may damage the integrity of the ontology. Thus, it is in urgent to propose an effective strategy to maintain the integrity of the continually changed ontology. In this paper, we propose a framework of integrity maintenance of continually changed OWL ontology. At the basis of the connectedness between ontology data and integrity constraints, we define the maximal relevant subontology of the continually changed ontology and perform the integrity checking in its maximal relevant subontology instead of the entire ontology. In further, for the different change operations, the integrity checking is realized by finding the corresponding maximal relevant subontologies. Finally, for the unintegrity ontology data, obtain the integrity ontology for the continually changed ontology which satisfies all the integrity constraints by modifying the ontology axioms. Thus, maintain the integrity of the continually changed ontology. © 2012 Binary Information Press.

关键词： Ontology

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Discovering attractive segments in the user generated video streams 3rd

Discovering attractive segments in the user generated video ...

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3rd APWeb and WAIM Joint Conference on Web and Big data, APWeb-WAIM 2019

作者： Zhou, Jie Ai, Jiangbo Wang, Zheng Chen, Si Wei, Qin Guizhou Provincial Key Laboratory of Public Big Data GuiZhou University GuiyangGuizhou55002 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Information Science Academy China Electronics Technology Group Corporation Chengdu China

ISBN: (纸本)9783030260743

With the rapid development of digital equipment and the continuous upgrading of online media, a growing number of people are willing to post videos on the web to share their daily lives [1, 2]. Generally, not all video segments are popular with audiences, some of which may be boring. In recent years, crowd-sourced time-sync video comments have emerged worldwide, supporting further research on temporal video labelling. In this paper, we propose a novel framework to achieve the following goal: Predicting which segment in a newly generated video stream will be popular among the audiences. At last, experimental results on real-world data demonstrate the effectiveness of the proposed framework and justify the idea of predicting the popularities of segments in a video exploiting crowd-sourced time-sync comments as a bridge to analyse videos. © 2019, Springer Nature Switzerland AG.

关键词： Forecasting

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Investigation of the Salinity Distribution of the Soil along West Main Canal in Ningxia, China

Investigation of the Salinity Distribution of the Soil along...

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2021 International Conference on Agriculture Science and Water Resource, ASWR 2021

作者： Wu, Juntao Jing, Hefang Wang, Weihong Guo, Xiaoxiao Guo, Xinxia Research Institute of Numerical Computation and Engineering Applications North Minzu University Yinchuan China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province North Minzu University Yinchuan China School of Mathematics and Information Science North Minzu University Yinchuan China

As one of the main irrigation canals in Ningxia, West Main Canal plays a very important role in the irrigation of the land nearby the canal, such as farms, meadows, and tree farms. Because of the long-term use of the Yellow River water for irrigation, the soil salinization in this area become more serious. In this paper, the problem of soil salinization along the West Main Canal in Ningxia is taken as the research object. 82 sample points in the studied area are chosen and a total number of 164 soil samples are measured for the eight main salt ions and pH value. Correlation analysis and principal component analysis are employed to study the distribution of the soil salinity ions and pH value. In addition, two dimensional interpolation is applied to obtain the spatial distribution of the total saltiness. It can be found that Cl-, SO42-, Ca2+, Na+ and HCO3- are the main salt ions and the main soluble salts are NaCl, KCl, CaSO4, Na2SO4 and CaCl2 in the soil of the studied area. The spatial distribution of the total soil salt is mainly in the patterns of north high and south low, far high and near low, up high and down low. © Published under licence by IOP Publishing Ltd.

关键词： Sodium chloride

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Swarm Intelligence Algorithm Inspired by Route Choice Behavior

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Journal of Bionic engineering 2016年第4期13卷 669-678页

作者： Daxin Tian Junjie Hu Zhengguo Sheng Yunpeng Wang Jianming Ma Jian Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China School of Transportation Science and Engineering Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beihang University Beijing 1 O0191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Nanjing 210096 China Department of Engineering and Design University of Sussex Brighton BN1 9RH UK The Texas Department of Transportation Austin TX78750 USA College of Computer Science and Technology Jilin University Changchun 130012 China

Travelers＇ route choice behavior, a dynamical learning process based on their own experience, traffic information, and influence of others, is a type of cooperation optimization and a constant day-to-day evolutionary process. Travelers adjust their route choices to choose the best route, minimizing travel time and distance, or maximizing expressway use. Because route choice behavior is based on human beings, the most intelligent animals in the world, this swarm behavior is expected to in- corporate more intelligence. Unlike existing research in route choice behavior, the influence of other travelers is considered for updating route choices on account of the reality, which makes the route choice behavior from individual to swarm. Anew swarm intelligence algorithm inspired by travelers＇ route choice behavior for solving mathematical optimization problems is introduced in this paper. A comparison of the results of experiments with those of the classical global Particle Swarm Optimization （PSO） algorithm demonstrates the efficacy of the Route Choice Behavior Algorithm （RCBA）. The novel algorithm provides a new approach to solving complex problems and new avenues for the study of route choice behavior.

关键词： swarm intelligence route choice behavior particle swarm optimization mathematical optimization

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ViT-based Classification of Oracle Bones for Periodization of the Shang Dynasty

ViT-based Classification of Oracle Bones for Periodization o...

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2023 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Li, Bang Ding, Zengmao Zheng, Rongxin Han, Shengwei Zhang, Han Zhang, Zhan Guo, An Wang, Nan Gao, Feng Liu, Yongge School of Computer & Information Engineering Anyang Normal University Henan Anyang455000 China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Henan Anyang455000 China International Joint Research Laboratory for Perception Data Intelligent Processing of Henan Anyang Normal University Henan Anyang455000 China

ISBN: (纸本)9781510671881

Oracle bone inscriptions are the oldest systematically written characters in China, and their periodization and chronology are essential tasks in historical research. However, current deep-learning models face significant challenges when they are used to recognize oracle bone images directly. These models can only achieve an accuracy of no more than 45%. As the research on the periodization of oracle bones mainly relies on the characters inscribed on the bones, a domain knowledge-based oracle bone classification model is proposed that improves the recognition accuracy of Vision Transformer (ViT) by 5.28%. The proposed approach incorporates results obtained from oracle bone character detection to provide a more effective recognition object. Moreover, a novel model with a style network and category embedding is proposed in this paper that focuses on characters, resulting in a notable improvement in recognition accuracy of 16.01%. Our study reveals the importance of domain knowledge in the classification of historical documents, and it presents new insights into using artificial intelligence for assisting in historical document organization tasks. © 2023 SPIE. All rights reserved.

关键词： Domain knowledge

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Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation 26

Geo-teaser: Geo-temporal sequential embedding rank for point...

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26th International World Wide Web Conference, WWW 2017 Companion

作者： Zhao, Shenglin Zhao, Tong King, Irwin Lyu, Michael R. Shenzhen Key Laboratory of Rich Media Big Data Analytics Application Shenzhen Research Institute Chinese University of Hong Kong Nanshan District Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong

ISBN: (纸本)9781450349147

Point-of-interest (POI) recommendation is an important application for location-based social networks (LBSNs), which learns the user preference and mobility pattern from check-in sequences to recommend POIs. Previous studies show that modeling the sequential pattern of user check-ins is necessary for POI recommendation. Markov chain model, recurrent neural network, and the word2vec framework are used to model check-in sequences in previous work. However, all previous sequential models ignore the fact that check-in sequences on different days naturally exhibit the various temporal characteristics, for instance, "work" on weekday and "entertainment" on weekend. In this paper, we take this challenge and propose a Geo-Temporal sequential embedding rank (Geo-Teaser) model for POI recommendation. Inspired by the success of the word2vec framework to model the sequential contexts, we propose a temporal POI embedding model to learn POI representations under some particular temporal state. The temporal POI embedding model captures the contextual check-in information in sequences and the various temporal characteristics on different days as well. Furthermore, We propose a new way to incorporate the geographical influence into the pairwise preference ranking method through discriminating the unvisited POIs according to geographical information. Then we develop a geographically hierarchical pairwise preference ranking model. Finally, we propose a unified framework to recommend POIs combining these two models. To verify the effectiveness of our proposed method, we conduct experiments on two real-life datasets. Experimental results show that the Geo-Teaser model outperforms state-of-the-art models. Compared with the best baseline competitor, the Geo-Teaser model improves at least 20% on both datasets for all metrics. © 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.

关键词： Location based services

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Simulation, comparison and analysis of Wireless Sensor Networks protocols: LEACH, LEACH-C, LEACH-1R, and HEED

Simulation, comparison and analysis of Wireless Sensor Netwo...

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International Conference on Electrical engineering (ICEE)

作者： Mohammed Omari Soumia Laroui Department of Computer Science and Engineering Shahid Beheshti University Tehran Iran Knowledge and Data Engineering Group Trinity College Dublin LDDI Laboratory Computer Science Department University of Colonel Ahmed Draya Adrar 01000 Algeria

Wireless Sensor Networks (WSNs) are made up of tiny sensor nodes which sense the data and communicate to the base station via other nodes. These sensor nodes are inexpensive portable devices with limited processing power and energy resources which make them in need of smart clustering protocols. Many clustering and routing protocols were proposed in the literature to serve large networks of such tiny devices. In this paper, we have implemented and analyzed different clustering protocols, namely LEACH, LEACH-C, LEACH-1R, and HEED using MATLAB environment. These clustering protocols are compared in different terms such as residual energy, data delivery to the base station, maximum number of rounds and the number of live nodes. Experimental results showed a better performance of the LEACH protocols when compared to the different versions of HEED. Moreover, LEACH-1R proved to be efficient in terms of network lifetime.

关键词： Wireless sensor networks Base stations Mathematical model Energy consumption Batteries Routing protocols

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Bursaphelenchus xylophilus disaster monitoring based on deep learning and Remotely sensed images 2

Bursaphelenchus xylophilus disaster monitoring based on deep...

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2nd International Conference on Computer Science, Electronic Information engineering and Intelligent Control Technology, CEI 2022

作者： Li, Bingzheng Huang, Fenghua Digital China Research Institute Fuzhou350108 China Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing Yango University Fuzhou350015 China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University Fuzhou350015 China

ISBN: (数字)9781665476164

ISBN: (纸本)9781665476164

With the increasingly severe epidemic of forest diseases and insect disasters in China, the monitoring of forest diseases and insect pests has attracted more and more attention from the academic community. This paper first introduces the changes in the spectral characteristics and identification methods of Bursaphelenchus xylophilus disease. Secondly, the research status, research hotspots, and development trends of Bursaphelenchus xylophilus disease monitoring based on hyperspectral remote sensing are elaborated. It then discusses the development of deep learning and its research status in pest identification in combination with hyperspectral remote sensing and introduces three of the most popular methods of current Bursaphelenchus xylophilus disaster monitoring: You Only Look Once (YOLO), Faster Region-CNN (Faster R-CNN), and Single Shot MultiBox Detector (SSD). Finally, the main work and problems of the existing literature research are summarized to guide future work in this field. © 2022 IEEE.

关键词： Disasters

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