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Houshang Darabi
University of Illinois at Chicago
Machine learningPetri netComputer scienceReal-time computingSupervisory control
78Publications
12H-index
3,259Citations
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Publications 83
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Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples for these models. These adversarial samples can become a security concern. In this paper, we propose utilizing an adversarial transformation network (ATN) on a distilled model to attack various time series classification models. The proposed attack on the classification model utilizes a distilled model as a surrogate th...
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#1Samuel HarfordH-Index: 3
#2Fazle KarimH-Index: 5
Last. Houshang Darabi (UIC: University of Illinois at Chicago)H-Index: 12
view all 3 authors...
Classification models for the multivariate time series have gained significant importance in the research community, but not much research has been done on generating adversarial samples for these models. Such samples of adversaries could become a security concern. In this paper, we propose transforming the existing adversarial transformation network (ATN) on a distilled model to attack various multivariate time series classification models. The proposed attack on the classification model utiliz...
#1Julian TheisH-Index: 1
#2Houshang Darabi (UIC: University of Illinois at Chicago)H-Index: 12
In process mining, process models are extracted from event logs using process discovery algorithms and are commonly assessed using multiple quality dimensions. While the metrics that measure the relationship of an extracted process model to its event log are well-studied, quantifying the level by which a process model can describe the unobserved behavior of its underlying system falls short in the literature. In this paper, a novel deep learning-based methodology called Adversarial System Varian...
#1Houshang Darabi (UIC: University of Illinois at Chicago)H-Index: 12
#2Georgiana Ifrim (UCD: University College Dublin)H-Index: 11
Last. Diego Furtado Silva (UFSCar: Federal University of São Carlos)H-Index: 2
view all 4 authors...
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#1Renata A. Revelo (UIC: University of Illinois at Chicago)H-Index: 2
#2Janet Aderemi Omitoyin (UIC: University of Illinois at Chicago)
Last. Houshang Darabi (UIC: University of Illinois at Chicago)H-Index: 12
view all 5 authors...
This work-in-progress research paper explores the way in which low-socioeconomic status (SES), first-year undergraduate engineering students develop their engineering identity. Identification with the field of engineering, or engineering identity development, is an ongoing process for students. While scholars have used retrospective studies to understand the developmental aspect of this process, a longitudinal study that follows students’ engineering identity development could provide an advanta...
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#1Ashkan Sharabiani (UIC: University of Illinois at Chicago)H-Index: 3
#2Adam P. Bress (UofU: University of Utah)H-Index: 16
Last. Houshang Darabi (UIC: University of Illinois at Chicago)H-Index: 12
view all 5 authors...
Determining the optimal initial dose for warfarin is a critically important task. Several factors have an impact on the therapeutic dose for individual patients, such as patients’ physical attributes (Age, Height, etc medication profile, co-morbidities, and metabolic genotypes (CYP2C9 and VKORCI). These wide range factors influencing therapeutic dose, create a complex environment for clinicians to determine the optimal initial dose. Using a sample of 4,237 patients, we have proposed a companion ...
1 CitationsSource
#1Fazle Karim (UIC: University of Illinois at Chicago)H-Index: 5
#2Houshang Darabi (UIC: University of Illinois at Chicago)H-Index: 12
Last. Anooshiravan Sharabiani (UIC: University of Illinois at Chicago)H-Index: 3
view all 5 authors...
Time series classification problems are solved using a variety of algorithms. The success of each technique is earmarked by measures of efficiency and accuracy. In order to achieve efficiency and accuracy, existing methods detect the relevant data in segments within a time series. Within this trend, we propose a framework for Detecting Optimal Partial Observation (DOPO) in time series classification. The framework developed is applicable to any time series database. It isolates the most relevant...
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#1Fazle Karim (UIC: University of Illinois at Chicago)H-Index: 5
#2Somshubra Majumdar (UIC: University of Illinois at Chicago)H-Index: 5
Last. Samuel Harford (UIC: University of Illinois at Chicago)H-Index: 3
view all 4 authors...
Abstract Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time series classification model by augmenting the fully convolutional block with a squeeze-and-excitation block to further improve accuracy. Our proposed models outperform most state-of-the-art...
16 CitationsSource
#1Julian Theis (UIC: University of Illinois at Chicago)H-Index: 1
#2Houshang Darabi (UIC: University of Illinois at Chicago)H-Index: 12
Process Mining is a famous technique which is frequently applied to Software Development Processes, while being neglected in Human-Computer Interaction (HCI) recommendation applications. Organizations usually train employees to interact with required IT systems. Often, employees, or users in general, develop their own strategies for solving repetitive tasks and processes. However, organizations find it hard to detect whether employees interact efficiently with IT systems or not. Hence, we have d...
Source
#1Fazle Karim (UIC: University of Illinois at Chicago)H-Index: 5
#2Somshubra Majumdar (UIC: University of Illinois at Chicago)H-Index: 5
Last. Houshang Darabi (UIC: University of Illinois at Chicago)H-Index: 12
view all 3 authors...
Long short-term memory fully convolutional neural networks (LSTM-FCNs) and Attention LSTM-FCN (ALSTM-FCN) have shown to achieve the state-of-the-art performance on the task of classifying time series signals on the old University of California-Riverside (UCR) time series repository. However, there has been no study on why LSTM-FCN and ALSTM-FCN perform well. In this paper, we perform a series of ablation tests (3627 experiments) on the LSTM-FCN and ALSTM-FCN to provide a better understanding of ...
3 CitationsSource
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