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358 results on '"Shah, Smit"'

5. A Clustering-Based Approach for Effective Prevention of SQL Injections

Author

Shah, Smit P., Krishna, V. Achyuta, Kartheek, V. L., Gururaj, R., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Rao, Udai Pratap, editor, Alazab, Mamoun, editor, Gohil, Bhavesh N., editor, and Chelliah, Pethuru Raj, editor

Published
2023
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7. Building an Automated and Self-Aware Anomaly Detection System

Author

Chakraborty, Sayan, Shah, Smit, Soltani, Kiumars, Swigart, Anna, Yang, Luyao, and Buckingham, Kyle

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Organizations rely heavily on time series metrics to measure and model key aspects of operational and business performance. The ability to reliably detect issues with these metrics is imperative to identifying early indicators of major problems before they become pervasive. It can be very challenging to proactively monitor a large number of diverse and constantly changing time series for anomalies, so there are often gaps in monitoring coverage, disabled or ignored monitors due to false positive alarms, and teams resorting to manual inspection of charts to catch problems. Traditionally, variations in the data generation processes and patterns have required strong modeling expertise to create models that accurately flag anomalies. In this paper, we describe an anomaly detection system that overcomes this common challenge by keeping track of its own performance and making changes as necessary to each model without requiring manual intervention. We demonstrate that this novel approach outperforms available alternatives on benchmark datasets in many scenarios., Comment: 11 pages, 5 figures, Accepted to 2020 IEEE International Conference on Big Data (IEEE BigData 2020)

Published
2020

8. Using Large Pretrained Language Models for Answering User Queries from Product Specifications

Author

Roy, Kalyani, Shah, Smit, Pai, Nithish, Ramtej, Jaidam, Nadkarn, Prajit Prashant, Banerjee, Jyotirmoy, Goyal, Pawan, and Kumar, Surender

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval
Abstract

While buying a product from the e-commerce websites, customers generally have a plethora of questions. From the perspective of both the e-commerce service provider as well as the customers, there must be an effective question answering system to provide immediate answers to the user queries. While certain questions can only be answered after using the product, there are many questions which can be answered from the product specification itself. Our work takes a first step in this direction by finding out the relevant product specifications, that can help answering the user questions. We propose an approach to automatically create a training dataset for this problem. We utilize recently proposed XLNet and BERT architectures for this problem and find that they provide much better performance than the Siamese model, previously applied for this problem. Our model gives a good performance even when trained on one vertical and tested across different verticals., Comment: 5 pages

Published
2020

9. Root Cause Detection Among Anomalous Time Series Using Temporal State Alignment

Author

Chakraborty, Sayan, Shah, Smit, Soltani, Kiumars, and Swigart, Anna

Subjects
Computer Science - Machine Learning, Statistics - Applications, Statistics - Machine Learning
Abstract

The recent increase in the scale and complexity of software systems has introduced new challenges to the time series monitoring and anomaly detection process. A major drawback of existing anomaly detection methods is that they lack contextual information to help stakeholders identify the cause of anomalies. This problem, known as root cause detection, is particularly challenging to undertake in today's complex distributed software systems since the metrics under consideration generally have multiple internal and external dependencies. Significant manual analysis and strong domain expertise is required to isolate the correct cause of the problem. In this paper, we propose a method that isolates the root cause of an anomaly by analyzing the patterns in time series fluctuations. Our method considers the time series as observations from an underlying process passing through a sequence of discretized hidden states. The idea is to track the propagation of the effect when a given problem causes unaligned but homogeneous shifts of the underlying states. We evaluate our approach by finding the root cause of anomalies in Zillows clickstream data by identifying causal patterns among a set of observed fluctuations., Comment: 6 pages, 7 figures, 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)

Published
2020

12. Frailty and outcomes in lacunar stroke

Author

Vazquez, Sima, Stadlan, Zehavya, Lapow, Justin M, Feldstein, Eric, Shah, Smit, Das, Ankita, Naftchi, Alexandria F, Spirollari, Eris, Thaker, Akash, Kazim, Syed Faraz, Dominguez, Jose F, Patel, Neisha, Kurian, Christeena, Chong, Ji, Mayer, Stephan A, Kaur, Gurmeen, Gandhi, Chirag D, Bowers, Christian A, and Al-Mufti, Fawaz

Published
2023
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14. Endovascular thrombectomy with and without preceding intravenous thrombolysis for treatment of large vessel anterior circulation stroke: A cross-sectional analysis of 50,000 patients

Author

Dicpinigaitis, Alis J., Gandhi, Chirag D., Shah, Smit P., Galea, Vincent P., Cooper, Jared B., Feldstein, Eric, Shapiro, Steven D., Kamal, Haris, Kurian, Christeena, Kaur, Gurmeen, Tyagi, Rachana, Biswas, Arundhati, Rosenberg, Jon, Bauerschmidt, Andrew, Bowers, Christian A., Mayer, Stephan A., and Al-Mufti, Fawaz

Published
2022
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17. Spinal Epidural Abscess Patients Have Higher Modified Frailty Indexes Than Back Pain Patients on Emergency Room Presentation: A Single-Center Retrospective Case-Control Study

Author

Dominguez, Jose F., Shah, Smit, Ampie, Leonel, Chen, Xintong, Li, Boyi, Ng, Christina, Feldstein, Eric, Wainwright, John V., Schmidt, Meic, Cole, Chad, Koo, Donna C., Chadha, Bhawneet, Lee, Joo, Yarrabothula, Akshitha, Rao, Naina, Adkoli, Anusha, Miller, Ivan, Gandhi, Chirag D., Al-Mufti, Fawaz, Santarelli, Justin, and Bowers, Christian

Published
2021
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19. N‐Glycosylation‐Induced Pathologic Protein Conformations as a Tool to Guide the Selection of Biologically Active Small Molecules.

Author

Magni, Andrea, Sciva, Cristiano, Castelli, Matteo, Digwal, Chander S., Rodina, Anna, Sharma, Sahil, Ochiana, Stefan, Patel, Hardik J., Shah, Smit, Chiosis, Gabriela, Moroni, Elisabetta, and Colombo, Giorgio

Subjects
LIGANDS (Biochemistry), SMALL molecules, QSAR models, MOLECULAR dynamics, POST-translational modification
Abstract

Post‐translational modifications such as protein N‐glycosylation, significantly influence cellular processes. Dysregulated N‐glycosylation, exemplified in Grp94, a member of the Hsp90 family, leads to structural changes and the formation of epichaperomes, contributing to pathologies. Targeting N‐glycosylation‐induced conformations offers opportunities for developing selective chemical tools and drugs for these pathologic forms of chaperones. We here demonstrate how a specific Grp94 conformation induced by N‐glycosylation, identified previously via molecular dynamics simulations, rationalizes the distinct behavior of similar ligands. Integrating dynamic ligand unbinding information with SAR development, we differentiate ligands productively engaging the pathologic Grp94 conformers from those that are not. Additionally, analyzing binding site stereoelectronic properties and QSAR models using cytotoxicity data unveils relationships between chemical, conformational properties, and biological activities. These findings facilitate the design of ligands targeting specific Grp94 conformations induced by abnormal glycosylation, selectively disrupting pathogenic protein networks while sparing normal mechanisms. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Chemical tools for epichaperome-mediated interactome dysfunctions of the central nervous system

Author

Bolaender, Alexander, Zatorska, Danuta, He, Huazhong, Joshi, Suhasini, Sharma, Sahil, Digwal, Chander S., Patel, Hardik J., Sun, Weilin, Imber, Brandon S., Ochiana, Stefan O., Patel, Maulik R., Shrestha, Liza, Shah, Smit. K., Wang, Shuo, Karimov, Rashad, Tao, Hui, Patel, Pallav D., Martin, Ananda Rodilla, Yan, Pengrong, Panchal, Palak, Almodovar, Justina, Corben, Adriana, Rimner, Andreas, Ginsberg, Stephen D., Lyashchenko, Serge, Burnazi, Eva, Ku, Anson, Kalidindi, Teja, Lee, Sang Gyu, Grkovski, Milan, Beattie, Bradley J., Zanzonico, Pat, Lewis, Jason S., Larson, Steve, Rodina, Anna, Pillarsetty, Nagavarakishore, Tabar, Viviane, Dunphy, Mark P., Taldone, Tony, Shimizu, Fumiko, and Chiosis, Gabriela

Published
2021
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35. Incorporating motif analysis into gene co-expression networks reveals novel modular expression pattern and new signaling pathways.

Author

Ma, Shisong, Shah, Smit, Bohnert, Hans J, Snyder, Michael, and Dinesh-Kumar, Savithramma P

Subjects
Arabidopsis, Transcription Factors, Cluster Analysis, Computational Biology, Signal Transduction, Gene Expression Regulation, Plant, Algorithms, Gene Regulatory Networks, Promoter Regions, Genetic, Nucleotide Motifs, Developmental Biology, Genetics
Abstract

Understanding of gene regulatory networks requires discovery of expression modules within gene co-expression networks and identification of promoter motifs and corresponding transcription factors that regulate their expression. A commonly used method for this purpose is a top-down approach based on clustering the network into a range of densely connected segments, treating these segments as expression modules, and extracting promoter motifs from these modules. Here, we describe a novel bottom-up approach to identify gene expression modules driven by known cis-regulatory motifs in the gene promoters. For a specific motif, genes in the co-expression network are ranked according to their probability of belonging to an expression module regulated by that motif. The ranking is conducted via motif enrichment or motif position bias analysis. Our results indicate that motif position bias analysis is an effective tool for genome-wide motif analysis. Sub-networks containing the top ranked genes are extracted and analyzed for inherent gene expression modules. This approach identified novel expression modules for the G-box, W-box, site II, and MYB motifs from an Arabidopsis thaliana gene co-expression network based on the graphical Gaussian model. The novel expression modules include those involved in house-keeping functions, primary and secondary metabolism, and abiotic and biotic stress responses. In addition to confirmation of previously described modules, we identified modules that include new signaling pathways. To associate transcription factors that regulate genes in these co-expression modules, we developed a novel reporter system. Using this approach, we evaluated MYB transcription factor-promoter interactions within MYB motif modules.

Published
2013

37. The epichaperome is a mediator of toxic hippocampal stress and leads to protein connectivity-based dysfunction

Author

Inda, Maria Carmen, Joshi, Suhasini, Wang, Tai, Bolaender, Alexander, Gandu, Srinivasa, Koren III, John, Che, Alicia Yue, Taldone, Tony, Yan, Pengrong, Sun, Weilin, Uddin, Mohammad, Panchal, Palak, Riolo, Matthew, Shah, Smit, Barlas, Afsar, Xu, Ke, Chan, Lon Yin L., Gruzinova, Alexandra, Kishinevsky, Sarah, Studer, Lorenz, Fossati, Valentina, Noggle, Scott A., White, Julie R., de Stanchina, Elisa, Sequeira, Sonia, Anthoney, Kyle H., Steele, John W., Manova-Todorova, Katia, Patil, Sujata, Dunphy, Mark P., Pillarsetty, NagaVaraKishore, Pereira, Ana C., Erdjument-Bromage, Hediye, Neubert, Thomas A., Rodina, Anna, Ginsberg, Stephen D., De Marco Garcia, Natalia, Luo, Wenjie, and Chiosis, Gabriela

Published
2020
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48. Data Structures for Storing Small Sets in the Bitprobe Model

Author

Radhakrishnan, Jaikumar, Shah, Smit, Shannigrahi, Saswata, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, de Berg, Mark, editor, and Meyer, Ulrich, editor

Published
2010
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