Demo Data from VLDB 2026 / reviewers in the wild / expert
Taorong Qiu
dblp:55/6710
· DBLP profile ↗
11ranked-venue papers
1as first author
8since 2021 · last 2025
0000-0002-6376-1208ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 6 · 1 first-author · 4 since 2021Databases, data management, data science and information retrieval · 3 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 3 · 3 since 2021Systems, architecture and hardware · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems › data reduction
data deduplication |
0.7 | 1 | 2023 | LoopDelta: Embedding Locality-aware Opportunistic Delta Compression in Inline Deduplication for Highly Efficient Data Reduction · USENIX ATC 2023 |
Storage systems › data compression
delta compression |
0.7 | 1 | 2023 | LoopDelta: Embedding Locality-aware Opportunistic Delta Compression in Inline Deduplication for Highly Efficient Data Reduction · USENIX ATC 2023 |
Storage systems
file systems |
0.2 | 1 | 2023 | LoopDelta: Embedding Locality-aware Opportunistic Delta Compression in Inline Deduplication for Highly Efficient Data Reduction · USENIX ATC 2023 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | An improved residual learning model and its application to hardware image classification
Yuxi Cheng, Qingnan Huang, Taorong Qiu |
Vis. Comput. | 5 |
| 2024 | Feature fusion and decomposition: exploring a new way for Chinese calligraphy style classification
Li Liu 0010, Taorong Qiu, Yue Lu 0001, Ching Y. Suen |
Vis. Comput. | 4 |
| 2023 | Modeling Cross-layer Interaction for Chinese Calligraphy Style Classification
Li Liu 0010, Taorong Qiu, Yue Lu 0001, Ching Y. Suen |
ICDAR (4) | 3 |
| 2023 | LoopDelta: Embedding Locality-aware Opportunistic Delta Compression in Inline Deduplication for Highly Efficient Data Reduction
Hong Jiang 0001, Dan Feng 0001, Nan Jiang 0013, Taorong Qiu, Wei Huang 0013 |
USENIX ATC | 5 |
| 2022 | ScriptNet: A Two Stream CNN for Script Identification in Camera-Based Document Images
Minzhen Deng, Li Liu 0010, Taorong Qiu, Yue Lu 0001, Ching Y. Suen |
ICONIP (6) | 4 |
| 2022 | Automated detection and recognition of thyroid nodules in ultrasound images using Improve Cascade Mask R-CNN
Yinghao Zheng, Lina Qin, Taorong Qiu, Aiyun Zhou, Zhixin Xue |
Multim. Tools Appl. | 3 |
| 2021 | Multi-loss Siamese Convolutional Neural Network for Chinese Calligraphy Style Classification
Li Liu 0010, Wenyan Cheng, Taorong Qiu, Chengying Tao, Qiu Chen, Yue Lu 0001, Ching Y. Suen |
ICONIP (6) | 3 |
| 2021 | Document image classification: Progress over two decades
Li Liu 0010, Taorong Qiu, Qiu Chen, Yue Lu 0001, Ching Y. Suen |
Neurocomputing | 3 |
| 2020 | Combination of spatially enhanced bag-of-visual-words model and genuine difference subspace for fake coin detection
Li Liu 0010, Taorong Qiu, Yue Lu 0001, Qiu Chen, Ching Y. Suen |
Expert Syst. Appl. | 2 |
| 2017 | Fuzzy theoretic approach to signals and systems: Static systems
Mohit Kumar 0001, Yihua Mao, Yuhao Wang 0001, Taorong Qiu, Yang Chenggen, Weiping Zhang 0001 |
Inf. Sci. | 4 |
| 2010 | Granular computing approach to finding association rules in relational databaseabstractGranular computing is a new information-processing method. The main objective of this paper is to present a granular computing approach to finding association rules in relational databases. Elementary granules are generated by scanning relational database, and granule table structure for storing information granules is established. By keeping attributes in order and referring to granular computing, frequent k-item sets are gradually generated from frequent 1-item sets, frequent 2-item sets, and so on. Corresponding algorithms are proposed and illustrated with a real example. Experiments on real data show that the algorithms can reduce the number of candidate item sets and save the computing time. © 2009 Wiley Periodicals, Inc. Taorong Qiu, Xiaoqing Chen 0003, Qing Liu 0011, Houkuan Huang |
Int. J. Intell. Syst. | 1 |