Mukesh Singhal

University of California, Merced

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Ajay D. Kshemkalyani

University of Illinois Chicago

Akhil Kapoor

Homi Bhabha National Institute

Venkata C. Giruka

University of Kentucky

Saikat Chakrabarti

Indian Institute of Technology Kanpur

Santosh Chandrasekhar

Shri Sathya Sai Medical College and Research Institute

Surender Beniwal

Sardar Patel Medical College

Satya Narayan

University of Florida

Harvindra Singh Kumar

Sardar Patel Medical College

Mehdi Bahrami

Fasa University of Medical Sciences

Pooya Tavallali

University of California, Merced

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Sardar Patel Medical College

The Ohio State University

University of California, Merced

University of Kentucky

Indian Institute of Technology Roorkee

University of Illinois Urbana-Champaign

All India Institute of Medical Sciences

University of Missouri

Homi Bhabha National Institute

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Distributed Computing

Ajay D. Kshemkalyani Mukesh Singhal

Designing distributed computing systems is a complex process requiring a solid understanding of the design problems and the theoretical and practical aspects of their solutions. This comprehensive textbook covers the fundamental principles and models underlying the theory, algorithms and systems aspects of distributed computing. Broad and detailed coverage of the theory is balanced with practical systems-related issues such as mutual exclusion, deadlock detection, authentication, and failure recovery. Algorithms are carefully selected, lucidly presented, and described without complex proofs. Simple explanations and illustrations are used to elucidate the algorithms. Important emerging topics such as peer-to-peer networks and network security are also considered. With vital algorithms, numerous illustrations, examples and homework problems, this textbook is suitable for advanced undergraduate and graduate students of electrical and computer engineering and computer science. Practitioners in data networking and sensor networks will also find this a valuable resource. Additional resources are available online at www.cambridge.org/9780521876346.

Science and engineering

10.1017/cbo9780511805318

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Citations (329)

Adaptive Online Runtime Prediction to Improve HPC Applications Latency in Cloud

Mina Naghshnejad Mukesh Singhal

We propose adaptive online application runtime prediction methods to improve application latency in clouds. Scheduling algorithms are highly sensitive to the value of application runtime. %Currently available schedulers rely on inaccurate user predictions as well as inaccurate over the shelf forecasting tools. Predicting the application runtime values in the cloud is challenging because of highly dynamic nature of application runtime values, the requirement of real-time prediction and scarcity of training data. To provide an accurate and fast online prediction model, we first analyze available HPC traces and design hybrid models from adaptive online generative models known as State Space Models. We show how State Space Models are generalizations of widely used methods for application runtime prediction in HPC clusters including exponential smoothing and recursive least square. To overcome high variance of application runtimes in the cloud, our proposed methods facilitate on-the-fly automated model selection and model switching. Our machine learning prediction results on HPC application traces show that our adaptive online prediction models predict the runtimes 33% to 80% more accurately than existing prediction approaches. Besides, our extensive trace-based scheduling simulations show that our predicted runtimes improve the performance (wait time) by 25%.

Performance Prediction

10.1109/cloud.2018.00104

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LOCA analysis for assessment of stagnation feeder break size for taps no. 3 and 4

Tahir Ali Khan Mukesh Singhal H. P. Rammohan Praveen K. Malhotra S.G. Ghadge

During certain breaks that occur in the inlet piping connected to the fuel channel (single channel event) of horizontal core of Pressurized Heavy Water Reactor (PHWR), fuel damage in affected channel can not be prevented. The affected channel experiences sustained very low flow condition during a narrow range of breaks in inlet piping/feeder known as stagnation feeder break. In this type of accident, flow through the affected channel gets stopped or reduces to very low value when reactor is operating at full power causing a gross mismatch of the heat generation and heat removal in the channel, thus leading to rapid temperature rise in the affected channel. A study has been carried out to examine this aspect for the range of break size at different locations for Tarapur Atomic Power Station 3 and 4 (540 MWe). This paper brings out the assessment carried out for this type of accident. (authors)

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Symmetric Rank-One Quasi-Newton Methods for Deep Learning Using Cubic Regularization

arXiv (Cornell University) (2025)

Aditya Ranganath Mukesh Singhal Roummel F. Marcia

Stochastic gradient descent and other first-order variants, such as Adam and AdaGrad, are commonly used in the field of deep learning due to their computational efficiency and low-storage memory requirements. However, these methods do not exploit curvature information. Consequently, iterates can converge to saddle points or poor local minima. On the other hand, Quasi-Newton methods compute Hessian approximations which exploit this information with a comparable computational budget. Quasi-Newton methods re-use previously computed iterates and gradients to compute a low-rank structured update. The most widely used quasi-Newton update is the L-BFGS, which guarantees a positive semi-definite Hessian approximation, making it suitable in a line search setting. However, the loss functions in DNNs are non-convex, where the Hessian is potentially non-positive definite. In this paper, we propose using a limited-memory symmetric rank-one quasi-Newton approach which allows for indefinite Hessian approximations, enabling directions of negative curvature to be exploited. Furthermore, we use a modified adaptive regularized cubics approach, which generates a sequence of cubic subproblems that have closed-form solutions with suitable regularization choices. We investigate the performance of our proposed method on autoencoders and feed-forward neural network models and compare our approach to state-of-the-art first-order adaptive stochastic methods as well as other quasi-Newton methods.x

Regularization

Rank (graph theory)

10.48550/arxiv.2502.12298

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Adversarial Poisoning Attacks and Defense for General Multi-Class Models Based On Synthetic Reduced Nearest Neighbors

arXiv (Cornell University) (2021)

Pooya Tavallali Vahid Behzadan Peyman Tavallali Mukesh Singhal

State-of-the-art machine learning models are vulnerable to data poisoning attacks whose purpose is to undermine the integrity of the model. However, the current literature on data poisoning attacks is mainly focused on ad hoc techniques that are only applicable to specific machine learning models. Additionally, the existing data poisoning attacks in the literature are limited to either binary classifiers or to gradient-based algorithms. To address these limitations, this paper first proposes a novel model-free label-flipping attack based on the multi-modality of the data, in which the adversary targets the clusters of classes while constrained by a label-flipping budget. The complexity of our proposed attack algorithm is linear in time over the size of the dataset. Also, the proposed attack can increase the error up to two times for the same attack budget. Second, a novel defense technique based on the Synthetic Reduced Nearest Neighbor (SRNN) model is proposed. The defense technique can detect and exclude flipped samples on the fly during the training procedure. Through extensive experimental analysis, we demonstrate that (i) the proposed attack technique can deteriorate the accuracy of several models drastically, and (ii) under the proposed attack, the proposed defense technique significantly outperforms other conventional machine learning models in recovering the accuracy of the targeted model.

Attack model

10.48550/arxiv.2102.05867

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New access index for fast execution of conjunctive queries over text data

Yuping Yang Mukesh Singhal

Complex queries with multiple conjunctive (AND) predicates have attracted increasing attention for their importance in OLAP systems. The signature index is ideal for the fast execution of queries involving multiple conjunctive predicates. However, previous signature-based methods did not separate the signatures from different attributes. We have developed a signature-augmented access index, called an MG-tree. Its main features are: (1) it is a lightweight search tree for indexing multiple attributes of text data, and (2) it eliminates the mix of signatures from different attributes, so the construction and maintenance of the index is easy and searching is efficient. Our analyses and experiments show that the MG-tree achieved a significant improvement in terms of access speed and storage space overhead over both tree-like indexes and previous multi-dimensional signature indexes, and is easier to build and maintain. The MG-tree can be employed in practical DBMS implementations.

Conjunctive query

Signature (topology)

Tree (set theory)

Access method

Online analytical processing

B-tree

10.1109/ideas.1999.787274

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A cloud-based web crawler architecture

Mehdi Bahrami Mukesh Singhal Zixuan Zhuang

Web crawlers work on the behalf of applications or services to find interesting and related information on the web. For example, search engines use web crawlers to index the Internet. Web crawlers have several challenges, such as complexity between links and highly intensive computation requirements when a web crawler wants to retrieve complex connected links. Another issue is the storage of a massive amount of indexed links or downloaded unstructured data, such as binary files, videos or images. As the volume of information on the Internet increases rapidly and requests may search data in a variety of formats including unstructured data, no cloud-based architecture exists in the literatures for web crawlers that could effectively address both highly intensive computing and storage issues. The cloud computing paradigm provides support for elastic resources and unstructured data, and provides pay-peruse features that allow individual businesses to run their own web crawlers for crawling the Internet or a limited web hosts. In this paper, we propose a cloud-based web crawler architecture that uses cloud computing features and the MapReduce programming technique. The proposed web crawler allows us to crawl the web by using distributed agents and each agent stores its own finding on a Cloud Azure Table (NoSQL database). The proposed web crawler also could store unstructured and massive amount of data on Azure Blob storage. We analyze the performance and scalability of the proposed web crawler and we describe the advantages of the proposed web crawler over traditional distributed web crawlers.

Web crawler

Focused crawler

Unstructured data

10.1109/icin.2015.7073834

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Citations (36)

Rehabilitation of an Orbital Defect in a Post-COVID Mucormycosis Patient - A Case Report

Journal of Research and Advancement in Dentistry (2023)

Rashmi Joshi Chandana Nair Nivedita Dixit Mukesh Singhal Zahida Nazir

Case ReportMucormycosis or black fungus is caused by mucormycetes and has been reported as a post-COVID complication in immunocompromised patients who are on prolonged use of steroids.This fungus is progressive and invasive in nature involving the nasal and paranasal sinuses and resulting in the surgical exenteration of the involved tissues.Rehabilitation of such defects is a challenge for prosthodontists in terms of esthetics, retention, and mental satisfaction of the patient.This article describes the fabrication of an orbital prosthesis for a 28-year-old patient who had an orbital exenteration of the left eye due to mucormycosis using heat cure acrylic resin.The retention was provided using anatomical undercuts and eye frames.This technique was an economical alternative for patients.

2019-20 coronavirus outbreak

10.53064/jrad.2022.14.4.362x

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Automated Accident Notification System Using Artificial Intelligence

International Journal For Multidisciplinary Research (2024)

Nitish Kumar Mukesh Singhal Nikhil Sunita Bishnoi

Road Accident continues to be a major global concern, so it’s critical to come up with innovative approaches to enhance emergency response and mitigate the severity of the effects. In view of the fact that very little research has been done in this field till now and no effective system is available as accident notification system, the proposed work introduces an Automated Accident Notification System (AANS) that employs Artificial Intelligence (AI) devices and applications for swift and accurate accident detection. The study focuses on designing an AI based algorithm, implementing it in a compact and efficient device, and developing a user friendly mobile application. The algorithm utilizes machine learning techniques for real time analysis of image and sensor data, improving accuracy and responsiveness. The AI device integrates the algorithm with various sensors to detect accidents promptly. The accompanying mobile application ensures seamless transmission of real time accident data, enabling automatic emergency call initiation and location sharing. Evaluation metrics include algorithm accuracy, response time improvements, and user satisfaction. This research may contribute to the advancement of intelligent systems, fostering enhanced public safety and a more efficient response to road accidents. Algorithm refinement, additional mobile application features, and collaboration with emergency services for broader implementation are future aspects of the proposed work.

Accident (philosophy)

10.36948/ijfmr.2024.v06i06.31312

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Distributed mutual exclusion algorithms

Cambridge University Press eBooks (2008)

Ajay D. Kshemkalyani Mukesh Singhal

Mutual exclusion is a fundamental problem in distributed computing systems. Mutual exclusion ensures that concurrent access of processes to a shared resource or data is serialized, that is, executed in a mutually exclusive manner. Mutual exclusion in a distributed system states that only one process is allowed to execute the critical section (CS) at any given time. In a distributed system, shared variables (semaphores) or a local kernel cannot be used to implement mutual exclusion. Message passing is the sole means for implementing distributed mutual exclusion. The decision as to which process is allowed access to the CS next is arrived at by message passing, in which each process learns about the state of all other processes in some consistent way. The design of distributed mutual exclusion algorithms is complex because these algorithms have to deal with unpredictable message delays and incomplete knowledge of the system state. There are three basic approaches for implementing distributed mutual exclusion:

10.1017/cbo9780511805318.010

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Citations (3)