Baijian (Justin) Yang

Books

1. Zhou, Z., Zhu, R., Zheng, P., and Yang, B, (2011) Windows Phone 7 Programming for Android and iOS Developers WROX.
2. Yang, B., Zheng, P. and Ni, L.M., (2007) Professional Microsoft Smartphone Programming Wiley.

Refereed Articles

1. Tang, Z., Liu, X., Li, Z., Zhang, T., Yang, B., Su, J., & Song, Q. (in press). SpaRx: Elucidate single-cell spatial heterogeneity of drug responses for personalized treatment. To appear Briefings in Bioinformatics. 2023.
2. Huang, H., Tang, Z., Zhang, T., and Yang, B. (2023), "Improved Clustering Using Nice Initialization", to appear in 2023 IEEE Globecom conference proceedings.
3. Jin, N., Shah, D., Terven, J., Lozada, D., Bennet, Z., Chen, L., and Yang, B. (2023). Making Autonomous Stores Smarter (MASS): A Practical Solution to Improve Product Detection Performance Using Synthetic Dataset at Scale, to appear in 2023 IEEE SmartCloud.
4. Huang, H., Tang, Z., Zhang, T., Yang, B., Song, Q., and Su, J. (2023). Feature  Selection for Unsupervised Machine Learning. to appear in IEEE SmartCloud 2023
5. Jin, N., Li, Z., Kettler, C., Yang, B., Tu, W., & Su, J. (2023). ARDaC Common Data Model Facilitates Data Dissemination and Enables Data Commons for Modern Clinical Studies. In Medinfo 2023. IOS Press.
6. Tang, Z., Zhang, T., Yang, B., Su, J., & Song, Q. (2023). PINet: Privileged Information Improve the Interpretablity and generalization of structural MRI in Alzheimer’s Disease, ACM-BCB 2023, 47, pp1-9. https://doi.org/10.1145/3584371.3613000
7. Tang, Z., Li, Z., Hou, T., Zhang, T., Yang, B., Su, J., & Song, Q. (2023). SiGra: 
   Single-cell spatial elucidation through image-augmented graph transformer. Nature Communications. 14, 5618 (2023). https://doi.org/10.1038/s41467-023-41437-w
8. Lu, Y., Zhang, J., Sun, S., Guo, Q., Cao, Z., Fei, S.,Yang, B.& Chen, Y. (2023). 
   Label-Efficient Video Object Segmentation with Motion Clues. IEEE Transactions on Circuits and Systems for Video Technology. DOI: 10.1109/TCSVT.2023.3298853
9. Mulkey, M., Huang, H., Albanese, T., Sunghan, K., & Yang, B. (2023). Supervised
   Deep Learning with Vision Transformer Predicts Delirium Using Limited Lead EEG, Scientific report, (2023)13:7890 https://doi.org/10.1038/s41598-023-35004-y
10. Tang, Z., Zhang, T., Yang, B., Su, J., & Song, Q. (2023). spaCI: deciphering spatial cellular communications through adaptive graph model. Briefings in Bioinformatics, 24(1). https://doi.org/10.1093/bib/bbac563
11. Mulkey, M., Albanese, T., Kim, S., Huang, H., & Yang, B. (2022). Delirium detection using GAMMA wave and machine learning: A pilot study. Research in Nursing &  Health ( RINAH ), 45(6). https://doi.org/https://doi.org/10.1002/nur.22268
12. Kocsis, J., Sabounchi, M., Yang, B., & Zhang, T. (2022). Cybersecurity Education in the  Age of Artificial Intelligence: A Novel Proactive and Collaborative Learning Paradigm. In 2022 IEEE Frontiers in Education Conference (FIE) (pp. 1–5). https://doi.org/10.1109/FIE56618.2022.9962643
13. Li, S., Tang, Z., Jin, N., Yang, Q., Gang Liu, Liu, T., … Ma, L. (2022). Uncovering Brain Differences in Preschoolers and Young Adolescents with Autism Spectrum Disorder Using Deep Learning. International Journal of Neural Systems, 32(9), 11. https://doi.org/https://doi.org/10.1142/S0129065722500447
14. J. Liang, Y. Wang, Y. Chen, B. Yang and D. Liu, "A Triangulation-Based Visual Localization for Field Robots," in IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 6, pp. 1083-1086, June 2022, doi: 10.1109/JAS.2022.105632.
15. Tang, W., Zhang, T. and Yang, B, (2022) "SemiCon: A Semi-supervised Learning for Industrial Image Inspection", In 2022 IEEE CSCloud/EdgeCom. (pp. 12–17). https://doi.org/10.1109/CSCloud-EdgeCom54986.2022.00012
16. Tang, W., Vian, C.M., Tang, Z. & Yang, B. (2021). Anomaly detection of core failures in die casting X-ray inspection images using a convolutional autoencoder. Machine Vision and Applications 32, 102,  doi: 10.1007/s00138-021-01226-1
17. Liu, D., Cui, Y., Yan, L., Mousas, C., Yang, B., & Chen, Y. (2021). DenseNet: Weakly Supervised Visual Localization Using Multi-scale Feature Aggregation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 7, pp. 6101-6109).
18. Mendez Mena, D., & Yang, B. (2021). Decentralized Actionable Cyber Threat Intelligence for Networks and the Internet of Things. IoT, 2(1), 1-16.
19. Liu, D., Cui, Y., Guo, X., Ding, W., Yang, B., & Chen, Y. (2021, January). Visual localization for autonomous driving: Mapping the accurate location in the city maze. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 3170-3177). IEEE.
20. Zhang, T., & Yang, B. (2021). Online multiple learning with working sufficient statistics for generalized linear models in big data. Statistics and Its Interface, 14(4), 403-416
21. Zhang, T., & Yang, B. (2021). Accounting for Factor Variables in Big Data Regression. Statistica Sinica, Vo 31. pp1-28, doi:10.5705/ss.202018.0309
22. Liu X.,  Huang, H., Tang, W., Zhang, T., and Yang, B (2020). Low-Rank Sparse Tensor Approximations for Large High-Resolution Videos, to appear in proceedings of 2020 IEEE ICMLA, Miami, FL, Dec 2020, IEEE.
23. Tang, Z., Liu, X., and Yang, B (2020) PENet: Object Detection using Points Estimationin High Definition Aerial Images, to appear in proceedings of 2020 IEEE ICMLA, Miami, FL, Dec 2020, IEEE.
24. Huang, H, Liu, X., Zhang, T., & Yang, B. (accepted). Regression PCA for Moving Objects Separation. To appear in proceedings of 2020 IEEE Globecom, Taipei, Taiwan, Dec. 2020, IEEE.
25. Kalyanam, R, Willis, C., Kirkpatrick, C., & Yang, B. (accepted). CHEESE: Cyber Human Ecosystem of Engaged Security Education. To appear in proceedings of 2020 IEEE Frontiers in Education, Uppsala, Sweden, Oct. 2020, IEEE.
26. Tang Z, Liu X, Chen H, Hupy J, Yang B. Deep Learning Based Wildfire Event Object Detection from 4K Aerial Images Acquired by UAS. AI. 2020 April; 1(2):166-179. doi: 10.3390/ai1020010.
27. Liu, X., Huang, H., Tang, Z., Zhang, T., and Yang, B.. (2019, December). Sparse Block Regression (SBR) for Big Data with Categorical Variables. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 221-227). IEEE., ISBN: 978-1-7281-0857-5
28. Shakdher, A.G, Agrawal, S.G, & Yang, B. (2019). Security Vulnerabilities in Consumer IoT Applications. IEEE BigDataSecurity 2019.
29. Liu, X.G, Tang, Z.G, & Yang, B. (2019). Predicting Network Attacks with CNN by Constructing Images from NetFlow Data. IEEE BigDataSecurity.
30. Hansen, R. A., Seigfried-Spellar, K. C., Lee, S., Chowdhury, S., Abraham, N., Springer, J. A., Yang, B., & Rogers, M. K. (2018). File Toolkit for Selective Analysis \& Reconstruction (FileTSAR) for Large-Scale Networks. 2018 IEEE International Conference on Big Data (Big Data) (pp. 3059--3065). IEEE. doi: 10.1109/bigdata.2018.8621914
31. Chiang, W.G, Liu, X.G, Zhang, T., & Yang, B. (2018). A study of Exact Ridge Regression for Big Data (pp. 3821-3830). Seattle, WA: IEEE Big Data 2018.
32. Yang, B., Wang, M.G, Xu, Z.G, & Zhang, T. (2018). Streaming Algorithm for Big Data Logistic Regression (pp. 2940-2950). Seattle, WA: IEEE Big Data 2018
33. Albabtain, Y.G, Yang, B., Dietz, J. E., Min, B.-C., & Gusev, D. A. (2018). Survey of GPU Vulnerabilities and Forensic Science. Technology Interface International Journal (TIIJ).
34. Mendez Mena, D. M.G, & Yang, B. (2018). Blockchain-Based Whitelisting for Consumer IoT Devices and Home Networks (pp. 7-12). Proceedings of the 19th Annual Conference of ACM SIGITE.
35. Ryu, S., & Yang, B. (2018). A Comparative Study of Machine Learning Algorithms and Their Ensembles for Botnet Detection. Journal of Computer and Communications, 6(05), 119.
36. Mendez Mena, D., Papapanagiotou, I., & Yang, B. (2018). Internet of things: Survey on security. Information Security Journal: A Global Perspective, 27(3), 162--182.
37. Zhang, T., & Yang (2018), B. Dimension reduction for big data. Statistics and Its Interface, 11(2), 295-306.
38. Liu, F.G*, Wang, S.G, Liu, X.G, Zhang, T., Yang, B., Han, Q., Yang, D.G, & Vian, C. (2018). Al-Driven Smart Manufacturing of Diecastings (vol. 2018). Chicago: The North American Die Casting Association.
39. Zhao, J.G, Liu, X.G, Guo, C., Guan, R., Zhang, J., Yang, B., Qian, Z. Y., & Chen, Y. V. (2018). Phoenix Map: Spatio-Temporal Distribution Analysis with Deep Learning Classifications. Berlin,: IEEE VIS 2018.
40. Zhao, J., Liu, X., Kuang, Y., Chen, Y., & Yang, B. (2018). Deep CNN-Based Methods to Evaluate Neighborhood-Scale Urban Valuation Through Street Scenes Perception. 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC).
41. AlBabtain, Y., & Yang, B. (2018). The Process of Reverse Engineering GPU Malware and Provide Protection To GPUS. IEEE Trustcom 2018.
42. AlBabtain, Y.G, & Yang, B. (2018). Live GPU Forensics: the Process of Recovering  Video Frames from NVIDIA GPU. Annual ADFSL Conference on Digital Forensics, Security and Law.,
43. Albabtain, Y.G, & Yang, B. (2017). GPU Forensics: Recovering Artifacts From The Gpus Global Memory Using Opencl. The Third International Conference on Information Security and Digital Forensics (ISDF2017) (pp. 12-20).,
44. Ryu, S.-H.G, & Yang, B. Comparison of Machine Learning Algorithms and Their Ensembles for Botnet Detection. Dekalb, IL: International Conference of Information and Computer Technology, 2018.
45. Lee, P.-T.G, & Yang, B. (2017). Indexing Architecture for File Extraction from Network Traffic (pp. 17-21). Rochester, NY: RIIT '17. doi: 10.1145/3125649.3125655
46. Beckman, J.G, Bari, S.G, Chen, Y., Dark, M. J., & Yang, B. (2017). The Impacts of Representational Fluency on Cognitive Processing of Cryptography Concepts (pp. 59-67). USENIX.
47. Kalyanam, R., & Yang, B. (2017). Try-CybSI: An Extensible Cybersecurity Learning and Demonstration Platform (pp. 41-46). ACM: Proceedings of the 18th Annual Conference on Information Technology Education 2017.
48. Beckman, J.G, Dark, M. J., P.G, Bari, S.G, Wagstaff, S. S., Chen, Y., & Yang, B. (2017). Cognitive Processing of Cryptography Concepts: An fMRI Study. Columbus, Ohio: ASEE 2017.
49. Gao, Y., Zhang, T., & Yang, B. (2017). Finding the best box-cox transformation in Big Data with Meta-model learning: A Case Study on QCT Developer Cloud (pp. 31-34). New York City: IEEE CSCloud 2017.
50. Li, P.G, Fang, H.G, Liu, X.G, & Yang, B. (2017). A countermeasure against relay attack in NFC payment. Cambridge, United Kingdom: Proceeding ICC '17 Proceedings of the Second International Conference on Internet of things and Cloud Computing. doi: http://dx.doi.org/10.1145/3018896.3025144
51. Siddharth, G.G, Kodam, S.G, & Yang, B. (2017). Multichannel Key Exchange (pp. 5). Cambridge, United Kingdom: ICC '17. doi: http://doi.acm.org/10.1145/3018896.3025145
52. Visan, B.G, Lee, J.G, Yang, B., Smith, A. H., & Matson, E. T. (2017). Vulnerabilities in hub architecture IoT devices. (vol. 2017, pp. 83-88). 2017 14th IEEE Consumer Communications & Networking Conference (CCNC).
53. B. Yang and T. Zhang, “A Scalable Feature Selection and Model Updating Approach for Big Data Machine Learning”, to appear IEEE SmartCloud 2016, NYC, USA, 2016
54. T. Zhang and B. Yang, “Big Data Dimension Reduction using PCA”, to appear IEEE SmartCloud 2016, NYC, USA, 2016
55. S. Nanda, F. Zafari, C. DeCusatisy, E. Wedaaz and B. Yang , “Predicting Network Attack Patterns in SDN using Machine Learning Approach”, IEEE NFV-SDN 2016, Palo Alto, CA, USA
56. B. Ravandi, I. Papapanagiotou, B. Yang, "A Black-Box Self-Learning Scheduler for Cloud Block Storage Systems", IEEE CLOUD 2016, San Francisco, USA
57. Serrano Aazco, M., Magana DeLeon, A. J., Yang, B. (2016). Employing Model-Eliciting Activities in Cybersecurity Education (pp. 9). ASEE.
58. Zhang, T., Yang, B. (2016). Box-Cox Transformation in Big Data. Technometrics. www.tandfonline.com/loi/utch20
59. Lerums, J., Yang, B., Dietz, J. E. (2016). Checking, Increasing, and Confirming a Smart Home’s IoT Security. ITERA 2016.
60. Wang, T., Yang, B., Hansen, R. A. (2016). Re-recognition of Http Strict Transport Security (HSTS). ITERA 2016.
61. Flory. T., Yang, B., Hansen, R. A. (2016). Torʼs Effect on User Experiences and Computer Performance While Browsing. ITERA 2016.
62. Yang, B., Zhang, T. (2016). A Scalable Meta-Model for Big Data Security Analyses. IEEE BigDataSecurity 2016.
63. Seaton, B., Yang, B. (2015). Developing a BYOD Deployment Strategy in an Enterprise Environment (pp. 244-259). Pittsburgh, PA: ATMAE 2015.
64. Brooks, M., Yang, B. (2015). A Man-in-the-Middle attack against OpenDayLight SDN controller (pp. 45-49). Chicago, IL: ACM RIIT 2015.
65. Hands, N., Yang, B., Hansen, R. A. (2015). A Study on Botnets Utilizing DNS (pp. 23-28). Chicago, IL: ACM RIIT 2015.
66. Ansari, F.+, Yang, B., (June. 2015), A Pilot Study on VM Template Authentication, Proceedings of ASEE, Seattle, WA
67. Wang, W.+, Yang, B., and Chen, Y., (May. 2015), A Visual Analytics based approach on detecting Server Redirections and Data Exfiltration, Proceedings of IEEE ISI 2015, Baltimore, MD
68. Flory, C., Yang, B., (March 2015), SIM Cards: A Threat to Network Security?, Proceedings of ITERA 2015, Arlington, VA
69. Tian, C*., Yang, B*., Zhong, J., and Liu, X. (Nov 2014). Trust-based incentive mechanism to motivate cooperation in hybrid P2P networks. Computer Networks, Vol(73)., pp. 244-255.
70. Wang, W.+, Yang, B., and Chen, Y., (Oct. 2014), Detecting subtle port scans through characteristics based on interactive visualization, Proceedings of ACM RIIT 2014., pp 33-38, Atlanta, GA
71. Misata, K.+, Hansen, R., and Yang, B. (Oct. 2014), A taxonomy of privacy-protecting tools to browse the world wide web, Proceedings of ACM RIIT 2014., pp 33-38, Atlanta, GA
72. Tian, C*., Yang, B*. (July 2012). A D-S evidence theory based fuzzy trust model in file-sharing P2P networks. Peer-to-Peer Networking and Applications.
    Kambic, J., Smith, A., and Yang, B..(April 2013) An Introduction to SCADA/ICS Systems and the Security Surrounding Them. ITERA 2013, Cincinnati, OH
73. X. Wang, S. Li, M. Li and B. Yang (2012). Cable-Based Moving Reachability with Mobile Sensors. Ad hoc & Sensor Wireless Networks Old City Publishing., Vol 14, Number 3-4, 227-250.
74. C. Tian and B. Yang (2011). R2 Trust, a Reputation and Risk based Trust management Framework for Large-scale, Fully Decentralized Overlay Networks. Future Generation Computer Systems, 27(8), 1135-1141.
75. Mirzoev, T., Yang, B., Davis, M., Williams, T. (2011). A Case Study on Virtual and Physical I/O Throughputs. Journal of Industrial Technology
76. B. Yang and D. Hua (2011). Storing and Maintaining Virtual Machines in Computer Technology Labs. 2011 ATMAE Conference Proceedings Papers (pp.92-101).
77. T. Mirzoev and B. Yang (2010). Securing Virtualized Datacenters. International Journal of Engineering Research & Innovation (IJERI), Spring 2010, 23-29.
78. Y. He, H. Ren, Y. Liu, and B. Yang (2009). On the reliability of large-scale distributed systems – A topological view. Computer Networks, Vol. 53 Issue 12, 2140 - 2152.
79. Xu, L., Chen, G., Yin, X., Yang, P., and Yang, B (2009). LORP: A Load-balancing Based Optimal Routing Protocol for Sensor Networks with Bottlenecks. Proceedings of the 2009 IEEE Conference on Wireless Communications & Networking Conference (pp.2438-2443). Budapest, Hungary.
80. Yang, Z., Xu, B., Ye, S., and Yang, B (2009). tk-converage: Time-based K-coverage for energy efficient monitoring. The 15th International Conference on Parallel Processing and Distributed Systems (ICPADS’09)
81. Yang, B, and Gao, T (2008). Enhancing Network Availability and Security Via Multi-homed Virtual Private Networks. International Journal of Modern Engineering
82. Yang, Z, Xu, B, Yang, B, Dai, J, and Gu, T (2008). PAS: Prediction-based Adaptive Sleeping for Diffusion Stimulus Monitoring Sensor Networks. Ad Hoc & Sensor Wireless Networks, Vol 15, 235-246.
83. Yang, B. (2007). Project teaches students to diagnose an ailing Windows OS. Techdirections (pp.23-26).
84. Dai, J, Yang, B. and Wong, C (2007). Design a Building Intelligence Management System with SOA and RFID Technology. International Conference on Software Engineering Research and Practice (SERP’07)
85. Zhang, Y., Huai, J., Liu, Y. Lin, L. and Yang, B. (2006). A framework to Provide Trust and Incentive in CROWN Grid for Dynamic Resource Management. The 15th International Conference on Computer Communications and Networks (ICCCN) (pp.390-395). Arlington, VA.
86. Muravsky*, A. and Yang, B (2006). Server-features Performance Evaluation of Node in P2P Networks. The 10th World Multi-Conference on Systemics, Cybernetics, and Informatics, Vol 3,263-266.
87. Li, M and Yang, B (2006). A Survey on Topology Issues in Wireless Sensor Network. Proceedings of the 2006 International Conference on Wireless Networks (pp.467-473).
88. Cheng, D., Han, J., Li, M. and Yang, B (2006). Achieving Anonymous Communication in Ad Hoc Netowrks. The Proceedings of International Conference on Communications, Circuits, and Systems, Vol 3, 1547-1551.
89. Yang, B., and Hua, D. (2006). OS and Application Management in a Dynamic Classroom Environment. 2006 NAIT Selected Papers Cleveland, OH.
90. Yang, B. and Gao, T (2006). Building a Secure and Reliable Network via Multi-homed VPNs. International Conference on Engineering & Technology (IJME) Union, NJ.
91. Liu, Y., Xiao, L., Ni, L.M., and Yang, B. (2004). Efficient Gnutella-like P2P Overlay Construction. IFIP International Conference on Network and Parallel Computing Wuhan, China.
92. Yang, B. and Mohapatra P (Aug 2004). DifferServ-Aware Multicasting. Journal of High-Speed Networks, Vol 13, No. 1, 37-57.
93. Yang, B. and Mohapatra P (Feb 2004). Multicasting in MPLS domains. Journal of Computer Communications, Vol 27, Issue 2, 162-170.
94. Yang, B., Esfahanian, A. H., Ni, L.M. and Mohapatra P (2003). A Tree Building Technique for Overlay Multicasting in DiffServ Domain. International Conference on Internet Computing(pp.893-899).
95. Yang, B. and Mohapatra, P. (2002). Multicasting in Differentiated Service Domain. Proceedings of Global Telecommunications Conference (pp.2074-2078).taiwan.
96. Yang, B. and Mohapatra, P. (2002). Edge Router Multicasting with MPLS Traffic Engineering. Proceedings of 10th IEEE International Conference On Networks (pp.43-48).Singapore.
97. Cao, H., Yang, B. Luo, Y., Yang, S. and Peng, Y. (1997). A Practical approach for job-shop scheduling problems using genetic algorithm. Proceedings of IEEE International Conference On Intelligent Processing Systems Beijing, China., Vol. 1, 543-547.