Publications | Machine Learning and Data Intensive Computing

2024

Dingrong Wang, Hitesh Sapkota, Qi Yu: Adaptive Important Region Selection with Reinforced Hierarchical Search for Dense Object Detection, NeurIPS 2024.
Dayou Yu, Minghao Li, Weishi Shi, Qi Yu: Evidential Mixture Machines: Deciphering Multi-Label Correlations for Active Learning Sensitivity, NeurIPS 2024.
Krishna Prasad Neupane, Ervine Zheng, Qi Yu: Evidential Stochastic Differential Equations for Time-Aware Sequential Recommendation, NeurIPS 2024.
Deep Shankar Pandey, Spandan Pyakurel, Qi Yu: Be Confident in What You Know: Bayesian Parameter Efficient Fine-Tuning of Foundation Models, NeurIPS 2024.
Dingrong Wang, Hitesh Sapkota, Zhiqiang Tao, Qi Yu: Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness, KDD 2024.
Xiaofan Que and Qi Yu: Optimal Transport of Diverse Unsupervised Tasks for Robust Learning from Noisy Few-Shot Data, ECCV 2024.
Spandan Pyakurel and Qi Yu: Hierarchical Novelty Detection via Fine-Grained Evidence Allocation, ICML 2024.
Abhinab Acharya, Dayou Yu, Qi Yu, Xumin Liu: Balancing Feature Similarity and Label Variability for Optimal Size-Aware Subset Selection, ICML 2024.
Hitesh Sapkota, Krishna Prasad Neupane, Qi Yu: Meta Evidential Transformer for Few-Shot Open-Set Recognition, ICML 2024.
Xiaofan Que and Qi Yu: Dual-Level Curriculum Meta-Learning for Noisy Few-Shot Learning Tasks. AAAI 2024.
Weishi Shi, Heather Moses, Qi Yu, Samuel A. Malachowsky, Daniel E. Krutz: ALL: Supporting Experiential Accessibility Education and Inclusive Software Development. ACM Trans. Softw. Eng. Methodol. 33(2): 39:1-39:30 (2024).
J Hinz, Dayou Yu, Deep Shankar Pandey, Hitesh Sapkota, Qi Yu, DI Mihaylov, VV Karasiev, SX Hu: The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning. APL Machine Learning. 2(2) 2024.
Dayou Yu, Deep Shankar Pandey, Joshua Hinz, Deyan I. Mihaylov, Valentin V. Karasiev, S. X. Hu, Qi Yu:Deep energy-pressure regression for a thermodynamically consistent EOS model. Mach. Learn. Sci. Technol. 5(1): 15031 (2024)

2023

Dayou Yu, Weishi Shi, Qi Yu: Actively Testing Your Model While It Learns: Realizing Label-Efficient Learning in Practice. NeurIPS 2023.
Hitesh Sapkota, Dingrong Wang, Zhiqiang Tao, Qi Yu: Distributionally Robust Ensemble of Lottery Tickets Towards Calibrated Sparse Network Training. NeurIPS 2023.
Dayou Yu, Weishi Shi, Qi Yu: Discover-Then-Rank Unlabeled Support Vectors in the Dual Space for Multi-Class Active Learning. ICML 2023.
Ervine Zheng, Qi Yu: Evidential Interactive Learning for Medical Image Captioning. ICML 2023.
Dingrong Wang, Deep Shankar Pandey, Krishna Prasad Neupane, Zhiwei Yu, Ervine Zheng, Zhi Zheng, Qi Yu: Deep Temporal Sets with Evidential Reinforced Attentions for Unique Behavioral Pattern Discovery. ICML 2023.
Deep Shankar Pandey, Qi Yu: Learn to Accumulate Evidence from All Training Samples: Theory and Practice. ICML 2023.
Hitesh Sapkota, Qi Yu: Adaptive Robust Evidential Optimization For Open Set Detection from Imbalanced Data. ICLR 2023.
Jintang Li, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu, Changhua Meng, "Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks" AAAI 2023 (oral).
Ervine Zheng, Qi Yu, and Zhi Zheng: Sparse Maximum Margin Learning From Multimodal Human Behavioral Patterns, AAAI 2023 (oral).
Dayou Yu, Weishi Shi, and Qi Yu: STARS: Spatial-Temporal Active Re-Sampling for Label-Efficient Learning from Noisy Annotations. AAAI 2023.
Deep Pandey and Qi Yu: Evidential Conditional Neural Processes. AAAI 2023 (oral).
Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake: Knowledge Acquisition for Human-In-The-Loop Image Captioning. AISTATS 2023

2022

Hitesh Sapkota, Qi Yu: Balancing Bias and Variance for Active Weakly Supervised Learning. KDD 2022.
Zhu, Zulun, Jiaying Peng, Jintang Li, Liang Chen, Qi Yu, and Siqiang Luo. "Spiking Graph Convolutional Networks." IJCAI 2022 (long oral).
Krishna Neupane, Ervine Zheng, Yu Kong, and Qi Yu: A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations, AAAI 2022.
Wentao Bao, Qi Yu, Yu Kong: OpenTAL: Towards Open Set Temporal Action Localization. CVPR 2022 (oral).
Deep Pandey and Qi Yu: Multidimensional Belief Quantification for Label-Efficient Meta-Learning. CVPR 2022.
Hitesh Sapkota and Qi Yu: Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection. CVPR 2022
Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake: Dual-Level Adaptive Information Filtering for Interactive Image Segmentation. AISTATS 2022
Yuansheng Zhu, Wentao Bao, Qi Yu: Towards Open Set Video Anomaly Detection. ECCV 2022
Moayad Alshangiti, Weishi Shi, Eduardo Lima, Xumin Liu, Qi Yu: Hierarchical Bayesian multi-kernel learning for integrated classification and summarization of app reviews. FSE 2022.
Niranjana Deshpande, Naveen Sharma, Qi Yu, Daniel E. Krutz: Online Learning Using Incomplete Execution Data for Self-Adaptive Service-Oriented Systems. ICWS 2022.

2021

Weishi Shi, Dayou Yu, and Qi Yu: A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning, NeurIPS 2021.
Dingrong Wang, Hitesh Sapkota, Xumin Liu, and Qi Yu: Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval, ICDM 2021 (full paper).
Krishna Neupane, Ervine Zheng, and Qi Yu:MetaEDL: Meta Evidential Learning For Uncertainty-Aware Cold-Start Recommendations, ICDM 2021 (short paper).
Wentao Bao, Qi Yu, Yu Kong: Evidential Deep Learning for Open Set Action Recognition. ICCV 2021 (oral).
Wentao Bao, Qi Yu, Yu Kong: DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation. ICCV 2021.
Niranjana Deshpande, Naveen Sharma, Qi Yu and Daniel Krutz: R-CASS: Using Algorithm Selection for Self-Adaptive Service Oriented Systems, ICWS 2021 (best paper award)
Weishi Shi, Qi Yu: Active Learning with Maximum Margin Sparse Gaussian Processes. AISTATS 2021: 406-414
Hitesh Sapkota, Yiming Ying, Feng Chen, Qi Yu: Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning. AISTATS 2021: 2188-2196
Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake: A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation. AAAI 2021
Rui Liu, Chao Peng, Yunbo Zhang, Hannah Husarek, Qi Yu: A survey of immersive technologies and applications for industrial product development. Computers & Graphics 2021.

2020

Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake: Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains. NeurIPS 2020
Weishi Shi, Xujiang Zhao, Feng Chen, Qi Yu: Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning. NeurIPS 2020
Peng-Nien Yin, KC Kishan, Shishi Wei, Qi Yu, Rui Li, Anne R Haake, Hiroshi Miyamoto, Feng Cui: Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches, BMC medical informatics and decision making 2020.
Wentao Bao, Qi Yu, Yu Kong: Uncertainty-based traffic accident anticipation with spatio-temporal relational learning, Proceedings of the 28th ACM International Conference on Multimedia (ACM MM) 2020.
Wentao Bao, Qi Yu, Yu Kong: Object-Aware Centroid Voting for Monocular 3D Object Detection, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.
Yasmine El-Glaly, Weishi Shi, Samuel Malachowsky, Qi Yu, Daniel E Krutz: Presenting and evaluating the impact of experiential learning in computing accessibility education, 2020 IEEE/ACM 42nd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET), 2020.
Moayad Alshangiti, Weishi Shi, Xumin Liu, Qi Yu: A Bayesian learning model for design-phase service mashup popularity prediction, Expert Systems with Applications (ESWA), 2020.
Hongbing Wang, Jiajie Li, Qi Yu, Tianjing Hong, Jia Yan, Wei Zhao: Integrating recurrent neural networks and reinforcement learning for dynamic service composition, Future Generation Computer Systems 2020.
Hongbing Wang, Xingguo Hu, Qi Yu, Mingzhu Gu, Wei Zhao, Jia Yan, Tianjing Hong: Integrating reinforcement learning and skyline computing for adaptive service composition, Information Sciences 2020.

2019

E Lima, W Shi, X Liu, and Q. Yu Multi-Level Tag Recommendation for Question Answering, ACM Transactions on Internet Technology (TOIT), Volume 19 Issue 3, May 2019, Article No. 34.
H Wang, Y Tao, Q. Yu, H Tianjing, C Xin, W Qin, Personalized service selection using Conditional Preference Networks, Knowledge-Based Systems, 164, 292-308 2019.
H Wang, S Peng, Q. Yu, A parallel refined probabilistic approach for QoS-aware service composition, Journal of Systems and Software, 151, 180-193, 2019.
H. Wang, H. Fei, Q. Yu, W Zhao, J Yan, T Hong, A motifs-based Maximum Entropy Markov Model for realtime reliability prediction in System of Systems, Future Generation Computer Systems, 98, 609-626, 2019.
W. Shi and Q. Yu, Integrating Generative and Discriminative Sparse Kernel Machines for Multi-class Active Learning, Thirty-third Conference on Neural Information Processing Systems (NeurIPS), 2282-2291, 2019.
W. Shi and Q. Yu, Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning, Thirty-sixth International Conference on Machine Learning (ICML), 2019: 5769-5778.
M. Alshangiti, H. Sapkota, P. Murukannaiah, X. Liu, and Q. Yu, Why is Developing Machine Learning Applications Challenging? A Study on Stack Overflow Posts, ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2019, Accepted.

2018

X. Liu, M. Alshangiti, C. Ding, Q. Yu, Log sequence clustering for workflow mining in multi-workflow systems, Data & Knowledge Engineering, 117, 1-17, 2018.
W. Shi and Q. Yu, An Efficient Many-Class Active Learning Framework for Knowledge-Rich Domains, IEEE International Conference on Data Minining (ICDM), 2018: 1230-1235.

2017

X. Liu, W. Shi, A. Kale, C. Ding, and Q. Yu, Statistical Learning of Domain-Specific Quality-of-Service Features from User Reviews, ACM Trans. Internet Techn. (TOIT), 17(2): 22:1-22:24, 2017.
H. Wang, L. Wang, Q. Yu, Z. Zheng, A. Bouguettaya, and M. Lyu Online Reliability Prediction via Motifs-based Dynamic Bayesian Networks for Service-Oriented Systems, IEEE Transactions on Software Engineering (TSE), 43(6): 556-579, 2017.
Bouguettaya, A., Singh, M., Huhns, M., Sheng, Q.Z., Dong, H., Yu, Q., Neiat, A.G., Mistry, S., Benatallah, B., Medjahed, B., Ouzzani, M., Casati, F., Liu, X., Wang, H., Georgakopoulos, D., Chen, L., Nepal, S., Malik, Z., Erradi, A., Wang, Y., Blake, B., Dustdar, S., Leymann, F., Papazoglou, M., A Service Computing Manifesto: The Next Ten Years, Communications of the ACM (CACM), 60(4): 64-72, 2017 (PDF).
H. Wang, P. Ma, Q. Yu, D. Yang, J. Li, and Huanhuan Fei Combining quantitative constraints with qualitative preferences for effective non-functional properties-aware service composition , J. Parallel Distrib. Comput., 100: 71-84 (2017).
H. Wang, L. Wang, X. Chen, Q. Wu, Q. Yu, X. Hu, Z. Zheng, and A. Bouguettaya Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition , ACM Transactions on Autonomous and Adaptive Systems (TAAS), 2(2): 8:1-8:42 (2017).
X. Guo, R. Li, Q. Yu, and A. Haake Modeling Physicians' Utterances to Explore Diagnostic Decision-making , International Joint Conference on Artificial Intelligence (IJCAI), 2017: 3700-3706.
W. Shi, X. Liu, and Q. Yu, Correlation-Aware Multi-Label Active Learning for Web Service Tag Recommendation , IEEE International Conference on Web Services (ICWS), Honolulu, HI, 2017 (Research Track: 21%): 229-236.
H. Wang, Z. Yang, and Q. Yu, Online Reliability Prediction via Long Short Term Memory for Service-Oriented Systems , IEEE International Conference on Web Services (ICWS), Honolulu, HI, 2017 (Research Track: 21%): 81-88.
S. Peng, H. Wang, and Q. Yu, Estimation of Distribution with Restricted Boltzmann Machine for Adaptive Service Composition , IEEE International Conference on Web Services (ICWS), Honolulu, HI, 2017 (Research Track: 21%): 114-121.

2016

X. Guo, Q. Yu, R. Li, C. Alm, C. Calvelli, P. Shi, and A. Haake, Intelligent Medical Image Grouping through Interactive Learning , International Journal of Data Science and Analytics (JDSA), 2(3-4): 95-105 (2016).
H. Wang, C. Yu, L. Wang, and Q. Yu, Effective BigData-Space Service Selection over Trust and Heterogeneous QoS Preferences, IEEE Transactions on Services Computing (TSC), Accepted, 2016 (PDF).
H. Wang, X. Wang, X. Zhang, Q. Yu, and X. Hu, Effective service composition using multi-agent reinforcement learning, Knowledge-Based Systems, 92: 151-168 (2016).
X. Liu, S. Argwal, C. Ding, and Q. Yu, A LDA-SVM Active Learning Framework for Web Service Classification , IEEE International Conference on Web Services (ICWS), San Francisco, 2016 (Research Track: 13%)(PDF).
H. Wang, G. Huang, and Q. Yu, Automatic Hierarchical Reinforcement Learning for Efficient Large-scale Service Composition , IEEE International Conference on Web Services (ICWS), San Francisco, 2016 (Research Track: 13%)(PDF).
H. Wang, X. Zhang, and Q. Yu, Integrating POMDP and SARSA(λ) for Service Composition with Incomplete Information, 13th International Conference on Service Oriented Computing (ICSOC 2016), Alberta, Canada, 2016 (Short reserach paper)(PDF).
Y. Wan, L. Chen, Q. Yu, T. Liang, and J. Wu, Incorporating Heterogeneous Information for Mashup Discovery with Consistent Regularization , 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Auckland, New Zealand, 2016.
X. Guo, Q. Yu, R. Li, C. Alm, C. Calvelli, P. Shi, and A. Haake, An Expert-in-the-loop Paradigm for Learning Medical Image Grouping , 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Auckland, New Zealand, 2016.

2015

Q. Yu, CloudRec: A Framework for Personalized Service Recommendation in the Cloud, Knowledge and Information Systems (KAIS), vol. 43, no. 2, pp. 417-443, 2015, (PDF).
A. Bouguettaya, Q. Yu, X. Liu, X. Zhou, and A. Song, Efficient agglomerative hierarchical clustering, Expert Syst. Appl., vol. 42, no. 5, pp. 2785-2797, 2015, (PDF).
J. Wu, L. Chen, Q. Yu, P. Han, and Z. Wu, Trust-aware Media Recommendation in Heterogeneous Social Networks, World Wide Web Journal (WWWJ), vol. 18, no. 1, pp. 139-157, 2015.(PDF).
A. Jain, X. Liu, and Q. Yu, Aggregating Functionality, Use History, and Popularity of APIs to Recommend Mashup Creation, 12th International Conference on Service Oriented Computing (ICSOC 2015), Goa India, 2015 (Full reserach paper: 16%)(PDF).
H. Wang, Q. Wu, and X. Chen, and Q. Yu, Integrating Gaussian Process with Reinforcement Learning for Adaptive Service Composition, 12th International Conference on Service Oriented Computing (ICSOC 2015), Goa India, 2015 (Full reserach paper: 16%)(PDF).
Q. Yu, H. Wang, and L. Chen, Learning Sparse Functional Factors for Large-scale Service Clustering, IEEE International Conference on Web Services (ICWS), New York, 2015 (Research Track: 17.4%)(PDF).
L. Chen, Q. Yu, and J. Wu, WS-HFS: A Heterogenous Feature Selection Framework for Web Services Mining, IEEE International Conference on Web Services (ICWS), New York, 2015 (Research Track: 17.4%)(PDF).
X. Liu, A. Kale, J. Wasani, C. Ding, and Q. Yu, Extracting, Ranking, and Evaluating Quality Features of Web Services through User Review Sentiment Analysis, IEEE International Conference on Web Services (ICWS), New York, 2015 (Research Track: 17.4%)(PDF).
W. Yao, L. Chen, J. Wu, and Q. Yu, Time-aware API Popularity Prediction via Heterogeneous Features, IEEE International Conference on Web Services (ICWS), New York, 2015 (Application Track: 21.9%)(PDF).
H. Wang, S. Zhou, and Q. Yu, Requirement Decomposition Through Service Discovery, IEEE International Conference on Web Services (ICWS), New York, 2015 (Short Paper Track)(PDF).

2014

X. Guo, Q. Yu, C. Alm, C. Calvelli, J. Pelz, P. Shi, and A. Haake, From spoken narratives to domain knowledge: Mining linguistic data for medical image understanding , International Journal of Artificial Intelligence in Medicine (AIIM), vol. 66, no. 2, pp. 79-90, 2014.
X. Chen, Z. Zheng, Q. Yu, and Michael R. Lyu, Web Service Recommendation via Exploiting Location and QoS Information, IEEE Transactions on Parallel and Distributed Systems (TPDS), vol. 25, no. 7, pp. 1913-1924, 2014. (PDF).
Q. Yu, Qos-aware Service Selection via Collaborative QoS Evaluation, World Wide Web Journal (WWWJ), vol. 14, no. 1, pp. 33-57, 2014 (PDF).
H. Wang, X. Chen, Q. Wu, Q. Yu, Z. Zheng, and A. Bouguettaya, Integrating on-policy reinforcement learning with multi-agent techniques for adaptive service composition, 12th International Conference on Service Oriented Computing (ICSOC 2014), Paris, France, 2014 (Acceptance rate: 15%) (PDF).
J. Bullard, C. Alm, Q. Yu, P. Shi, and A. Haake, Towards multimodal modeling of physicians’ diagnostic confidence and self-awareness using medical narrative, 25th International Conference on Computational Linguistics (COLING), Dublin, Ireland 2014 (PDF).
H. Wang, L. Wang, Q. Yu, Z. Zheng, A Novel Online Reliability Prediction Approach for Service-Oriented Systems, IEEE International Conference on Web Services (ICWS), Alaska, 2014 (PDF).
H. Wang, Q. Wu, X. Chen, Q. Yu, Z. Zheng, Adaptive and Dynamic Service Composition via Multi-agent reinforcement learning, IEEE International Conference on Web Services (ICWS), Alaska, 2014 (PDF).

2013

Q. Yu, Efficient Large-scale Service Clustering via Sparse Functional Representation and Accelerated Optimization, International Journal of Cooperative Information Systems (IJCIS), vol. 22, no. 4, 2013 (PDF).
J. Wu, L. Chen, Q. Yu, L. Kuang, Y. Wang, and Z. Wu, Selecting Skyline Services for QoS-aware Composition by Upgrading MapReduce Paradigm, Cluster Computing, vol. 16, no. 4, pp 693-706, 2013 (PDF).
J. P. Andhale, M. Rege, and Q. Yu, Discovering Heterogeneous Evolving Web Service Communities Using Semi-Supervised Non Negative Matrix Factorization, International Journal of Machine Learning and Computing (IJMLC), vol. 3, no. 2, 2013 (PDF).
Q. Yu and A. Bouguettaya, Efficient Service Skyline Computation for Composite Service Selection, IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 25, no. 4, pp. 776-789, 2013 (PDF).
Q. Yu, Z. Zheng, and H. Wang, Trace Norm Regularized Matrix Factorization for Service Recommendation, IEEE International Conference on Web Services (ICWS), Santa Clara CA, 2013 (Acceptance rate: 19%) (PDF).
L. Chen, Y. Wang, Q. Yu, Z. Zheng, and J. Wu, WT-LDA: User Tagging Augmented LDA for Web Service Clustering, 11th International Conference on Service Oriented Computing (ICSOC 2013), Berlin, Germany, 2013 (Acceptance rate: 13%) (PDF).
L. Wang, H. Wang, Q. Yu, H. Sun, and A. Bouguettaya, Online Reliability Time Series Prediction for Service-Oriented System of Systems, 11th International Conference on Service Oriented Computing (ICSOC 2013), Short paper, Berlin, Germany, 2013, (Acceptance rate 22%) (PDF).
H. Wang, H. Sun, and Q. Yu, Reliable Service Composition via Automatic QoS Prediction, 10th International Conference on Services Computing (SCC 2013), Santa Clara CA, 2013 (PDF).
H. Wang, X. Wang, and Q. Yu, Optimal Self-Healing of Service-Oriented Systems with Incomplete Information, IEEE Big Data Congress, Santa Clara CA, 2013 (PDF).
S. Naik and Q. Yu, Evolutionary User Selection to Maximize the Influence of Viral Marketing, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Poster, Niagara Falls, Ontario, Canada, 2013 (PDF).
Q. Yu, On bootstrapping web service recommendation. Handbook of Web Services, Springer, 2013.

2012

Q. Yu and J. Kang, Integrating User Invocation Data and Extended Semantics for Service Community Discovery, International Journal of Next-Generation Computing (IJNGC), vol. 3, no. 2, 2012 (PDF).
Q. Yu and A. Bouguettaya, Multi-Attribute Optimization in Service Selection, World Wide Web Journal (WWWJ), vol. 15, no. 1, pp. 1-31, 2012 (PDF). [Invited for a book publication in Springer as one of the mostly cited papers in the World Wide Web Journal]
Q. Yu, Sparse Functional Representation for Large-scale Service Clustering, 10th International Conference on Service Oriented Computing (ICSOC 2012), Shanghai, China, 2012 (Acceptance rate 17%) (PDF). [Invited to International Journal of Cooperative Information Systems 'Special Issue on Best Papers' of ICSOC'12]
Q. Yu, Decision Tree Learning from Incomplete QoS to Bootstrap Service Recommendation, IEEE International Conference on Web Services (ICWS), Honolulu, Hawaii, 2012 (Acceptance rate 17%) (PDF).

2011

A. Bouguettaya, Q. Yu, Xumin Liu, and Z. Malik, Service-centric Framework for a Digital Government Application, IEEE Transactions on Services Computing (TSC), vol. 4, no. 1, pp. 3-16, 2011 (PDF).
K. Xu, Q. Yu, Q. Liu, J. Zhang, and A. Bouguettaya, Web Service management system for bioinformatics research: a case study, Service Oriented Computing and Applications (SOCA), Springer, vol 5, no. 1, pp.1-15, 2011 (PDF).
X. Liu, A. Bouguettaya, Q. Yu, and Z. Malik, Efficient change management in long-term composed services, Service Oriented Computing and Applications (SOCA), Springer, vol 5, no. 2, pp.87-103, 2011 (PDF).
Q. Yu, Place Semantics into Context: Service Community Discovery from the WSDL Corpus, 9th International Conference on Service Oriented Computing (ICSOC 2011), Paphos, Cyprus, 2011 (Acceptance rate 16%) (PDF).
A. Knight, Q. Yu and M. Rege, Efficient Range Query Processing on Uncertain Data, IEEE International Conference on Information Reuse and Integration (IRI), Las Vegas NV, 2011 (PDF). [Invited to "Information Reuse and Integration in Academia and Industry", Springer]

2010

Q. Yu, M. Rege, A. Bouguettaya, B. Medjahed and M. Ouzzani, A Two-phase Framework for Quality-aware Web Service Selection, Service Oriented Computing and Applications (SOCA), Springer, vol 4, no. 2, pp.63-79, 2010 (PDF).
Q. Yu and A. Bouguettaya, Computing Service Skyline from Uncertain QoWS, IEEE Transactions on Services Computing (TSC), vol. 3, no.1, pp. 17-29, 2010 (PDF).
Q. Yu and A. Bouguettaya, Guest Editorial: Special Section on Query Models and Efficient Selection of Web Services, IEEE Transactions on Services Computing (TSC), vol. 3, no.3, pp. 161-162, 2010 (PDF).
B. Desai, P. Andhale, M. Rege, and Q. Yu, Biclustering and Feature Selection Techniques in Bioinformatics, Workshop on Data Mining with Graphs and Matrices (WDGM'10), in conjunction with International Conference on Data Engineering and Management (ICDEM), TN, India, 2010 (PDF).
Q. Yu and M. Rege, On Service Community Learning: A Co-Clustering Approach, IEEE International Conference on Web Services (ICWS), Miami FL, 2010 (Acceptance rate 17%) (PDF).
Q. Yu and A. Bouguettaya, Computing Service Skylines over Sets of Services, IEEE International Conference on Web Services (ICWS), Miami FL, 2010 (Acceptance rate 17%) (PDF).

2009

K. C. Tan, E. J. Teoh, Q. Yu and K. C. Goh, A hybrid evolutionary algorithm for attribute selection in data mining, Expert Systems With Applications: An International Journal, Elsevier, vol. 36, no. 4, 2009 (PDF).
Q. Yu and M. Rege, A Relational Approach for Efficient Service Selection, IEEE International Conference on Web Services (ICWS), Los Angeles CA, 2009 (Acceptance rate 18%) (PDF).
Q. Yu and A. Bouguettaya, Foundations for Efficient Web Service Selection, Springer-Verlag, ISBN: 978-1-4419-0313-6, 2009.

2008

M. Rege and Q. Yu, Efficient Mining of Heterogeneous Star-structured Data, International Journal of Software and Informatics, Chinese Academy of Sciences, vol. 2, no. 2, pp. 141-161, 2008 (PDF).
Q. Yu and A. Bouguettaya, Framework for Web Service Query Algebra and Optimization, ACM Transactions on the Web (TWEB), ACM Press, vol. 2, no. 1, pp. 1-35, 2008.
Q. Yu, X. Liu, A. Bouguettaya, and B. Medjahed, Deploying and Managing Web Services: Issues, Solutions, and Directions, VLDB Journal, Springer, vol. 17, no. 3, pp. 537-572, 2008.
A. Bouguettaya and Q. Yu, SLINK and UPGMA Clustering of High Dimensional Data. Encyclopedia of Data Warehousing and Mining-2nd Edition, 2008.

2007

A. Bouguettaya, D. Gracanin, Q. Yu, X. Zhang, X. Liu, and Z. Malik, WebSenior: A Digital Government Infrastructure for Senior Citizens. International Workshop on the Management of Business Processes in Government, co-located with 5th International Conference on Business Process Management (BPM 2007), Brisbane, Australia, September 2007

2006

K. C. Tan, Q. Yu, and J. H. Ang, A dual-objective evolutionary algorithm for rules extraction in data mining, Computational Optimization and Applications, Springer, vol. 34, pp. 273-294, 2006.
K. C. Tan, Q. Yu, J. H. Ang, and T. H. Lee, A coevolutionary algorithm for rules discovery in data mining, International Journal of Systems Science, Taylor & Francis, vol. 37, no. 12, pp. 835-864, 2006.
A. Bouguettaya, D. Gracanin, Q. Yu, X. Zhang, X. Liu, and Z. Malik, Ubiquitous Web Services for E-Government Social Services, AAAI Spring Symposium The Semantic Web meets eGovernment, Stanford University, California, USA, March 27-29, 2006.

2005

K. C. Tan, Q. Yu, and T. H. Lee, A distributed coevolutionary classifier for knowledge discovery in data mining, IEEE Transactions on Systems, Man and Cybernetics: Part C (Applications and Reviews), IEEE, vol. 35, no. 2, pp. 131-142, 2005.
Q. Yu, K. C. Tan, and T. H. Lee, An evolutionary algorithm for rules discovery in data mining, Evolutionary Computation in Data Mining, A. Ghosh and L. C. Jain (Eds.), Physica-Verlag, Germany, pp. 101-123, 2005.

2004

A. Bouguettaya, B. Medjahed, A. Rezgui, M. Ouzzani, X. Liu, and Q. Yu, WebDG - A platform for E-Government Web services. ER (Workshops) 2004: 553-565, November 2004.

2003

K. C. Tan, Q. Yu, C. M. Heng, and T. H. Lee, Evolutionary Computing for Knowledge Discovery in Medical Diagnosis. Artificial Intelligence in Medicine, vol. 27, no. 2, pp. 129-154, 2003.