Publications

@article{belkhouja_out\textendashdistribution_2023,

title = {Out-of-distribution Detection in Time-series Domain: A Novel Seasonal Ratio Scoring Approach},

author = {Taha Belkhouja and Yan Yan and Janardhan Rao Doppa},

url = {https://dl.acm.org/doi/10.1145/3630633},

doi = {10.1145/3630633},

issn = {2157-6904},

year  = {2023},

date = {2023-12-01},

urldate = {2023-12-01},

journal = {ACM Trans. Intell. Syst. Technol.},

volume = {15},

number = {1},

pages = {8:1\textendash8:24},

abstract = {Safe deployment of time-series classifiers for real-world applications relies on the ability to detect the data that is not generated from the same distribution as training data. This task is referred to as out-of-distribution (OOD) detection. We consider the novel problem of OOD detection for the time-series domain. We discuss the unique challenges posed by time-series data and explain why prior methods from the image domain will perform poorly. Motivated by these challenges, this article proposes a novel Seasonal Ratio Scoring (SRS) approach. SRS consists of three key algorithmic steps. First, each input is decomposed into class-wise semantic component and remainder. Second, this decomposition is employed to estimate the class-wise conditional likelihoods of the input and remainder using deep generative models. The seasonal ratio score is computed from these estimates. Third, a threshold interval is identified from the in-distribution data to detect OOD examples. Experiments on diverse real-world benchmarks demonstrate that the SRS method is well-suited for time-series OOD detection when compared to baseline methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zhao_data-driven_2023,

title = {Data-Driven Inverse Kinematics Approximation of a Delta Robot with Stepper Motors},

author = {Anni Zhao and Arash Toudeshki and Reza Ehsani and Jian-Qiao Sun},

url = {https://www.mdpi.com/2218-6581/12/5/135},

doi = {10.3390/robotics12050135},

issn = {2218-6581},

year  = {2023},

date = {2023-10-01},

urldate = {2023-10-01},

journal = {Robotics},

volume = {12},

number = {5},

pages = {135},

abstract = {The Delta robot is a parallel robot that is over-actuated and has a highly nonlinear dynamic model, which poses a significant challenge to its control design. The inverse kinematics that maps the motor angles to the position of the end effector is highly nonlinear and extremely important for the control design of the Delta robot. It has been experimentally shown that geometry-based inverse kinematics is not accurate enough to capture the dynamics of the Delta robot due to manufacturing component errors, measurement errors, joint flexibility, backlash, friction, etc. To address this issue, we propose a neural network model to approximate the inverse kinematics of the Delta robot with stepper motors. The neural network model is trained with randomly sampled experimental data and implemented on the hardware in an open-loop control for trajectory tracking. Extensive experimental results show that the neural network model achieves excellent performance in terms of the trajectory tracking of the Delta robot under different operation conditions, and outperforms the geometry-based inverse kinematics model. A critical numerical observation indicates that neural networks trained with the specific trajectory data fall short of anticipated performance due to a lack of data. Conversely, neural networks trained on random experimental data capture the rich dynamics of the Delta robot and are quite robust to model uncertainties compared to geometry-based inverse kinematics.},

note = {Number: 5 

Publisher: Multidisciplinary Digital Publishing Institute},

keywords = {delta robot, inverse kinematics, neural networks, stepper motor},

pubstate = {published},

tppubtype = {article}

}

@article{noauthor_regular_nodate,

title = {“Regular” CS × Inclusive Design = Smarter Students and Greater Diversity textbar ACM Transactions on Computing Education},

author = {Garcia, Rosalinda; Patricia Morreale; Lara Letaw; Amreeta Chatterjee; Pakati Patel; Sarah Yang; Isaac Tijerina Escobar; Geraldine Jimena Noa; and Margaret Burnett},

url = {https://dl.acm.org/doi/10.1145/3603535},

year  = {2023},

date = {2023-07-22},

urldate = {2023-07-22},

journal = {ACM Transactions on Computing Education},

keywords = {Human-Computer Interaction},

pubstate = {published},

tppubtype = {article}

}

@article{ghosh_improving_2023,

title = {Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis},

author = {Ghosh, Subhankar and Belkhouja, Taha and Yan, Yan and Doppa, Janardhan Rao},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/25936},

doi = {10.1609/aaai.v37i6.25936},

issn = {2374-3468},

year  = {2023},

date = {2023-06-01},

urldate = {2023-06-01},

journal = {Proceedings of the AAAI Conference on Artificial Intelligence},

volume = {37},

number = {6},

pages = {7722\textendash7730},

abstract = {Safe deployment of deep neural networks in high-stake real-world applications require theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled framework for uncertainty quantification of deep models in the form of prediction set for classification tasks with a user-specified coverage (i.e., true class label is contained with high probability). This paper proposes a novel algorithm referred to as Neighborhood Conformal Prediction (NCP) to improve the efficiency of uncertainty quantification from CP for deep classifiers (i.e., reduce prediction set size). The key idea behind NCP is to use the learned representation of the neural network to identify k nearest-neighbor calibration examples for a given testing input and assign them importance weights proportional to their distance to create adaptive prediction sets. We theoretically show that if the learned data representation of the neural network satisfies some mild conditions, NCP will produce smaller prediction sets than traditional CP algorithms. Our comprehensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using diverse deep neural networks strongly demonstrate that NCP leads to significant reduction in prediction set size over prior CP methods.},

note = {Number: 6},

keywords = {AI, Water},

pubstate = {published},

tppubtype = {article}

}