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Your search keyword '"Bao, Yubin"' showing total 29 results
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29 results on '"Bao, Yubin"'

1. Probery: A Probability-based Incomplete Query Optimization for Big Data

Author

Song, Jie, Zhang, Yichuan, Bao, Yubin, and Yu, Ge

Subjects
Computer Science - Databases, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Nowadays, query optimization has been highly concerned in big data management, especially in NoSQL databases. Approximate queries boost query performance by loss of accuracy, for example, sampling approaches trade off query completeness for efficiency. Different from them, we propose an uncertainty of query completeness, called Probability of query Completeness (PC for short). PC refers to the possibility that query results contain all satisfied records. For example PC=0.95, it guarantees that there are no more than 5 incomplete queries among 100 ones, but not guarantees how incomplete they are. We trade off PC for query performance, and experiments show that a small loss of PC doubles query performance. The proposed Probery (PROBability-based data quERY) adopts the uncertainty of query completeness to accelerate OLTP queries. This paper illustrates the data and probability models, the probability based data placement and query processing, and the Apache Drill-based implementation of Probery. In experiments, we first prove that the percentage of complete queries is larger than the given PC confidence for various cases, namely that the PC guarantee is validate. Then Probery is compared with Drill, Impala and Hive in terms of query performance. The results indicate that Drill-based Probery performs as fast as Drill with complete query, while averagely 1.8x, 1.3x and 1.6x faster than Drill, Impala and Hive with possible complete query, respectively., Comment: 15 pages under the review

Published
2019

4. An Optimized Object Detection Algorithm for Marine Remote Sensing Images.

Author

Ren, Yougui, Li, Jialu, Bao, Yubin, Zhao, Zhibin, and Yu, Ge

Subjects
OBJECT recognition (Computer vision), REMOTE sensing, DATA augmentation, FEATURE extraction, DEEP learning
Abstract

In order to address the challenge of the small-scale, small-target, and complex scenes often encountered in offshore remote sensing image datasets, this paper employs an interpolation method to achieve super-resolution-assisted target detection. This approach aligns with the logic of popular GANs and generative diffusion networks in terms of super-resolution but is more lightweight. Additionally, the image count is expanded fivefold by supplementing the dataset with DOTA and data augmentation techniques. Framework-wise, based on the Faster R-CNN model, the combination of a residual backbone network and pyramid balancing structure enables our model to adapt to the characteristics of small-target scenarios. Moreover, the attention mechanism, random anchor re-selection strategy, and the strategy of replacing quantization operations with bilinear interpolation further enhance the model's detection capability at a low cost. Ablation experiments and comparative experiments show that, with a simple backbone, the algorithm in this paper achieves a mAP of 71.2% on the dataset, an improvement in accuracy of about 10% compared to the Faster R-CNN algorithm. [ABSTRACT FROM AUTHOR]

Published
2024
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5. A Semi-Supervised Active Learning Method for Structured Data Enhancement with Small Samples.

Author

Leng, Fangling, Li, Fan, Lv, Wei, Bao, Yubin, Liu, Xiaofeng, Zhang, Tiancheng, and Yu, Ge

Subjects
GENERATIVE adversarial networks, DATA augmentation, SAMPLING methods, PROBLEM solving
Abstract

In order to solve the problems of the small capacity of structured data and uneven distribution among classes in machine learning tasks, a supervised generation method for structured data called WAGAN and a cyclic sampling method named SACS (Semi-supervised and Active-learning Cyclic Sampling), based on semi-supervised active learning, are proposed. The loss function and neural network structure are optimized, and the quantity and quality of the small sample set are enhanced. To enhance the reliability of generating pseudo-labels, a Semi-supervised Active learning Framework (SAF) is designed. This framework redistributes class labels to samples, which not only enhances the reliability of generated samples but also reduces the influence of noise and uncertainty on the generation of false labels. To mine the diversity information of generated samples, an uncertain sampling strategy based on spatial overlap is designed. This strategy incorporates the idea of spatial overlap and uses global and local sampling methods to calculate the information content of generated samples. Experimental results show that the proposed method performs better than other data enhancement methods on three different datasets. Compared to the original data, the average F 1 m a c r o value of the classification model is improved by 11.5%, 16.1%, and 19.6% relative to compared methods. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Named Entity Recognition in Aircraft Design Field Based on Deep Learning

Author

Bao, Yubin, An, Yuanming, Cheng, Zhu, Jiao, Rimeng, Zhu, Chao, Leng, Fangling, Wang, Shuai, Wu, Ping, Yu, Ge, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Guojun, editor, Lin, Xuemin, editor, Hendler, James, editor, Song, Wei, editor, Xu, Zhuoming, editor, and Liu, Genggeng, editor

Published
2020
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9. Determining the Topic Hashtags for Chinese Microblogs Based on 5W Model

Author

Zhao, Zhibin, Sun, Jiahong, Mao, Zhenyu, Feng, Shi, Bao, Yubin, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wang, Yu, editor, Yu, Ge, editor, Zhang, Yanyong, editor, Han, Zhu, editor, and Wang, Guoren, editor

Published
2016
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18. HGraph: I/O-Efficient Distributed and Iterative Graph Computing by Hybrid Pushing/Pulling.

Author

Wang, Zhigang, Gu, Yu, Bao, Yubin, Yu, Ge, Yu, Jeffrey Xu, and Wei, Zhiqiang

Subjects
GRAPH algorithms, SCALABILITY, FAULT tolerance (Engineering), TASK analysis, BIG data
Abstract

In the big data era, distributed computation is becoming a preferred solution for iterative graph analysis. However, graphs are rapidly growing in size and more importantly, there exist a lot of messages across iterations. For better scalability, many distributed systems keep graph data and message data on disk. Now these systems solely employ either pushing or pulling mode to manage data, but neither can always work well during the entire computation. This is mainly because I/O access patterns are dynamic and complex. This article proposes a hybrid solution. It achieves the optimal performance in different scenarios by dynamically and adaptively switching modes between pushing and pulling. Specifically, we first devise a new block-centric pulling technique. It pulls messages much more I/O-efficiently than the existing vertex-centric pulling mode. We then combine pushing and pulling. For general-purpose, we categorize graph algorithms and accordingly present two seamless switching frameworks. We also design performance prediction components specialized to the two frameworks, to decide how and when we can switch modes. Some optimization strategies are also given to further enhance performance, such as priority scheduling and lightweight fault-tolerance. Extensive experiments against state-of-the-art solutions confirm the effectiveness of our proposals. [ABSTRACT FROM AUTHOR]

Published
2021
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20. MSSUTop-k: Determining the Minimum Scan Scope for UTop-kQuery over Uncertain Data

Author

Zhibin Zhao, Lan Yao, Ge Yu, Zhengbing Ma, and Bao Yubin

Subjects
Uncertain data, Scope (project management), Computer Networks and Communications, Computer science, Semantics (computer science), General Engineering, Data mining, Tuple, computer.software_genre, computer, Time complexity
Abstract

The semantics of UTop-kquery is based on the possible world model, and the greatest challenge in processing UTop-kqueries is the explosion of possible world space. In this direction, several optimized algorithms have been developed. However, uncertain databases are different in data distributions under different scoring functions, which has significant influence on the performance of the existing optimizing algorithms. In this paper, we propose two novel algorithms, MSSUTop-kand quick MSSUTop-k, for determining the minimum scan scope for UTop-kquery processing. This work is important because before UTop-kquery processing is started, users hope to know in advance how many and which tuples will be involved in UTop-kquery processing. Then, they can make a balance between result precision and processing cost. So, it should be the prerequisite for answering UTop-kqueries. MSSUTop-kcan achieve accurate results but is relatively more costly in time complexity. Oppositely, quick MSSUTop-kcan only achieve approximate results but performs better in time cost. We conduct comprehensive experiments to evaluate the performance of our proposed algorithms and analyze the relationship between the data distribution and the minimum scan scope of UTop-kqueries.

Published
2015

22. FSP

Author

Wang, Zhigang, primary, Gao, Lixin, additional, Gu, Yu, additional, Bao, Yubin, additional, and Yu, Ge, additional

Published
2017
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23. Research on the Semantic of Entity-Oriented U-Topk Query and Its Processing

Author

Zhao Zhibin, Yu Ge, Yao Lan, Ma Zhengbing, and Bao Yubin

Subjects
Information retrieval, Computer science, View, InformationSystems_DATABASEMANAGEMENT, Online aggregation, Query language, computer.software_genre, Query optimization, Query expansion, Web query classification, Sargable, Query by Example, Data mining, computer, computer.programming_language
Abstract

The result of U-Topk query simply consist of k tuples, which is not satisfactory in many cases mainly for the following two reasons: firstly, the probability of result is so small that it is hard for users to accept it, secondly, it abandons the relations between the tuples and the corresponding entities, accordingly it can't completely reflect the real state of monitored entities. Aiming at shortage of tuple-oriented semantic of U-Topk query, this paper proposes an entity-oriented U-Topk query named as EoU-Topk as well as query processing algorithm. The basic idea of the algorithm is converting tuple-oriented probabilistic database into entity-oriented probabilistic database. In this process, some exclusive tuples that meet the pre-defined rules will be merged. The algorithm of EoU-Topk query has two advantage: firstly, it can greatly reduce the size of probabilistic database, secondly, it can truly reflect whole state of entities, and avoid the one sidedness of the tuple-oriented U-Topk query. Finally, the efficiency and quality of the EoU-Topk query proposed in the paper are verified by experiments using real data.

Published
2015

24. An Algorithm for LBS-based Schedule Recommendation with Time Constraint

Author

Yao Lan, Zhao Zhibin, Bao Yubin, and Cai Yuxiang

Subjects
Schedule, Point of interest, Computer science, business.industry, media_common.quotation_subject, Sorting, Path (graph theory), Genetic algorithm, Global Positioning System, Time constraint, business, Function (engineering), Algorithm, media_common
Abstract

Recently, Point of Interest Recommendation is widely used in LBS navigation systems. It makes use of the real-time GPS locations of users as well as their preferences to recommend POIs that mostly match these preferences and the paths leading to the POIs. Previous studies are focused on the following two issues: (1) Similarity measurement between POIs and the user preferences, and (2) Optimum path selection from the user location to the recommended POIs. However in most scenarios, users need not only some isolated POIs, but a combination of several POIs that covers the user preferences as well as the paths connecting them. Essentially, it is the schedule planning problem. Schedule planning is usually with strict time limits, and equivalent to Generalized Traveling Sales Man Problem (GTSP) which was proved a NP-hard problem. This imposes great challenge to its solution. In this paper, we formalize the problem of Schedule Planning with strict Time Constraint (SPwTC). Especially, we wrap the static paths between POIs into route activities, thus a globally unified model of user activity can be defined. Based on Genetic Algorithm, we propose the schedule recommendation algorithm to generate candidate route plans. Subsequently, we propose the recommendation function for sorting the recommended schedule plans so as to make the recommended result more in line with user expectation. At the end of this paper, we verify the efficiency of the algorithm as well as its rationality of the recommended result with real road network data.

Published
2015

25. MSSUTop-k: Determining the Minimum Scan Scope for UTop-k Query over Uncertain Data

Author

Zhao, Zhibin, Yao, Lan, Yu, Ge, Bao, Yubin, and Ma, Zhengbing

Subjects
Article Subject
Abstract

The semantics of UTop-k query is based on the possible world model, and the greatest challenge in processing UTop-k queries is the explosion of possible world space. In this direction, several optimized algorithms have been developed. However, uncertain databases are different in data distributions under different scoring functions, which has significant influence on the performance of the existing optimizing algorithms. In this paper, we propose two novel algorithms, MSSUTop-k and quick MSSUTop-k, for determining the minimum scan scope for UTop-k query processing. This work is important because before UTop-k query processing is started, users hope to know in advance how many and which tuples will be involved in UTop-k query processing. Then, they can make a balance between result precision and processing cost. So, it should be the prerequisite for answering UTop-k queries. MSSUTop-k can achieve accurate results but is relatively more costly in time complexity. Oppositely, quick MSSUTop-k can only achieve approximate results but performs better in time cost. We conduct comprehensive experiments to evaluate the performance of our proposed algorithms and analyze the relationship between the data distribution and the minimum scan scope of UTop-k queries.

Published
2015
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29. NMR used to study the side-reactions between peroxides and antioxidants during the reactive extrusion process of the impact polypropylene

Author

Li, Chunfa, Huang, Ting, Bao, Yubin, Chen, Zhe, and Lin, Guangxin

Abstract

Side reaction pathways are observed to occur between antioxidants and peroxide during the processing of multiple resin grades mediated by controlled peroxide-induced degradation of impact polypropylene. In the present work, the reaction mechanism and main by-products between antioxidants and peroxides were investigated. The results demonstrate that peroxides greatly accelerate the decomposition reactions, and the free radicals formed from peroxide decomposition react with, for example, the antioxidant AO1010 to produce dehydrogenation of phenyl propionic unit(s) at the αposition (cinnamic acid ester moiety), which generates a conjugated system leading to the increased color of the product. It is the first time to confirm the cinnamic acid moiety's existence and report its NMR data. Further, this work confirms the dehydrogenation mechanism by comparison with different sterically hindered phenolic antioxidants. It also systematically summarizes the oxidation and degradation mechanism of AO168 and AO1010 under air and peroxide environments, respectively. Based on present study, clear guidelines are obtained to improve the quality of polymeric products, especially the appearance and stability, during product development.

Published
2021
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