Aryya Gangopadhyay

University of Maryland Baltimore County, Dept of Information Systems, Faculty Member

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Design and Deployment of a Flash Flood Monitoring IoT: Challenges and Opportunities

2020 IEEE International Conference on Smart Computing (SMARTCOMP)

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Flood Detection Framework Fusing The Physical Sensing & Social Sensing

2020 IEEE International Conference on Smart Computing (SMARTCOMP), 2020

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Evaluating Disaster Time-Line from Social Media with Wavelet Analysis

2018 IEEE International Conference on Smart Computing (SMARTCOMP), 2018

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Image segmentation for dust detection using unsupervised machine learning CyberTraining 2020: Big Data + High-Performance Computing + Atmospheric Sciences

This work is supported by the grant “CyberTraining: DSE: Cross-Training of Researchers in Computi... more This work is supported by the grant “CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Re- sources” from the National Science Foundation (grant no. OAC–1730250). The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF). The facility is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS–0821258, CNS– 1228778, and OAC–1726023) and the SCREMS program (grant no. DMS– 0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources.

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A Methodology for Conceptual Database Design from Natural Language Specifications

In this paper we describe a structured method for developing a conceptual data model by starting ... more In this paper we describe a structured method for developing a conceptual data model by starting from a functional model expressed in a natural language. We have used the Conceptual Dependency theory for mapping natural language descriptions to conceptual dependency diagrams. We have developed algorithms to convert these conceptual dependency diagrams into unit conceptual dependency tables, which are then merged to represent the whole context of the application. We also show how transactional requirements can be incorporated into the unit conceptual dependency table, and subsequently convert the unit conceptual dependency table into a corresponding conceptual model.

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Analyzing attack strategies against rule-based intrusion detection systems

Proceedings of the Workshop Program of the 19th International Conference on Distributed Computing and Networking, 2018

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STAR: A Scalable Self-taught Learning Framework for Older Adults’ Activity Recognition

2021 IEEE International Conference on Smart Computing (SMARTCOMP), 2021

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A Flash Flood Categorization System Using Scene-Text Recognition

2018 IEEE International Conference on Smart Computing (SMARTCOMP), 2018

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Analyzing the Sentiment of Crowd for Improving the Emergency Response Services

2018 IEEE International Conference on Smart Computing (SMARTCOMP), 2018

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Using Randomness to Improve Robustness of Tree-Based Models Against Evasion Attacks

IEEE Transactions on Knowledge and Data Engineering, 2020

Machine learning models have been widely used in security applications. However, it is well-known... more Machine learning models have been widely used in security applications. However, it is well-known that adversaries can adapt their attacks to evade detection. There has been some work on making machine learning models more robust to such attacks. However, one simple but promising approach called randomization is under-explored. In addition, most existing works focus on models with differentiable error functions while tree-based models do not have such error functions but are quite popular because they are easy to interpret. This paper proposes a novel randomization-based approach to improve robustness of tree-based models against evasion attacks. The proposed approach incorporates randomization into both model training time and model application time (meaning when the model is used to detect attacks). We also apply this approach to random forest, an existing ML method which already has incorporated randomness at training time but still often fails to generate robust models. We proposed a novel weighted-random-forest method to generate more robust models and a clustering method to add randomness at model application time. We also proposed a theoretical framework to provide a lower bound for adversaries’ effort. Experiments on intrusion detection and spam filtering data show that our approach further improves robustness of random-forest method.

Machine Learning Based Algorithms for Global Dust Aerosol Detection From Satellite Images: Inter-Comparisons and Evaluation

Identifying dust aerosols from passive satellite images is of great interest for many application... more Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed 5 different machine-learning (ML) and deep-learning (DL) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML and DL algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track. The 16 VIIRS M-band observations with the center wavelength ranging from deep blue to thermal infrared, together with solar-viewing geometries and pixel time and locations, are used as the predictor variables. Four different sets of training input data are const...

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Discovery of temporal neighborhoods through discretization methods

Intelligent Data Analysis, 2014

ABSTRACT Neighborhood discovery is a precursor to knowledge discovery in complex and large datase... more ABSTRACT Neighborhood discovery is a precursor to knowledge discovery in complex and large datasets such as Temporal data, which is a sequence of data tuples measured at successive time instances. Hence instead of mining the entire data, we are interested in dividing the huge data into several smaller intervals of interest which we call as temporal neighborhoods. In this paper we propose a class of algorithms to generate temporal neighborhoods through unequal depth discretization. We describe four novel algorithms (a) Similarity based Merging (SMerg), (b) Stationary distribution based Merging (StMerg), (c) Greedy Merge (GMerg) and, (d) Optimal Merging (OptMerg). The SMerg and STMerg algorithms are based on the robust framework of Markov models and the Markov Stationary distribution respectively. GMerg is a greedy approach and OptMerg algorithm is geared towards discovering optimal binning strategies for the most effective partitioning of the data into temporal neighborhoods. Both these algorithms do not use Markov models. We identify temporal neighborhoods with distinct demarcations based on unequal depth discretization of the data. We discuss detailed experimental results in both synthetic and real world data. Specifically, we show (i) the efficacy of our algorithms through precision and recall of labeled bins, (ii) the ground truth validation in real world traffic monitoring datasets and, (iii) Knowledge discovery in the temporal neighborhoods such as global anomalies. Our results indicate that we are able to identify valuable knowledge based on our ground truth validation from real world traffic data.

Using Crowd Sourcing to Analyze Consumers' Response to Privacy Policies of Online Social Network and Financial Institutions at Micro Level

International Journal of Information Security and Privacy, 2016

As it becomes easy and inexpensive to store huge amount of data, concerns about privacy are incre... more As it becomes easy and inexpensive to store huge amount of data, concerns about privacy are increasing as well. Although service providers have privacy policies, research shows that users rarely read privacy policies. As a result, there has been little work done on how consumers respond to individual segments of privacy policies, which is important for organizations when designing privacy policies. In this study, the authors break down privacy policies of two well-known social network companies (Facebook, Twitter) and financial institution (Bank of America) into simple segments. They then use crowd sourcing to analyze consumers' response to these policy segments. The authors ask questions on users' awareness, expectations, familiarity, and privacy concerns of these policy segments. The relationships between various factors such as demographic factors, data type, data flow and consumers' privacy concerns were also investigated. The authors conclude with guidelines and sug...

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A smart segmentation technique towards improved infrequent non-speech gestural activity recognition model

Pervasive and Mobile Computing, 2017

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Target-Based, Privacy Preserving, and Incremental Association Rule Mining

IEEE Transactions on Services Computing, 2017

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Social Media Analytics for Behavioral Health

International Journal of Emergency Mental Health and Human Resilience, 2015

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FFTM: A Fuzzy Feature Transformation Method for Medical Documents

Proceedings of BioNLP 2014, 2014

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A Multi-agent System for Multimedia Information Retrieval

The purpose of this paper is to describe a multi-agent system for collaborative multimedia inform... more The purpose of this paper is to describe a multi-agent system for collaborative multimedia information retrieval in an electronic retailing application. We use content-based information (CBIR) retrieval for multimedia data including full-texts and images. Content-based image retrieval deals with retrieving data based on automatic content analysis as opposed to manual annotations, such as metadata specifications. While both collaborative agent technology and content-based information retrieval have been developed in previous research, few papers have attempted to combine both technologies in the same problem area. In this paper we describe a collaborative multi-agent system for multimedia information retrieval in an electronic commerce application.

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Temporal neighborhood discovery through unequal depth binning

Temporal Neighborhood Discovery Using Markov Models

2009 Ninth IEEE International Conference on Data Mining, 2009

... Sandipan Dey , Vandana P. Janeja , Aryya Gangopadhyay Department of Computer Science &... more ... Sandipan Dey , Vandana P. Janeja , Aryya Gangopadhyay Department of Computer Science & Electrical Engineering Department of Information Systems University of ... k = 1, this re-duces to, d(Bi,Bj)=(µi �?� µj) 2 2 �?2 i +�?2 j . Definition 6 (Bhattacharyya Distance Measure ...