Computer Science and Automation (CSA): Recent submissions
Now showing items 281-300 of 542
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Efficient Schemes for Partitioning Based Scheduling of Real-Time Tasks in Multicore Architecture
The correctness of hard real-time systems depends not only on its logical correctness but also, on its ability to meet all its deadline. Existing real-time systems use either a pure real-time scheduler or a real-time ... -
P3 : An Effective Technique for Partitioned Path Profiling
Acyclic path profile is an abstraction of dynamic control flow paths of procedures and has been found to be useful in a wide spectrum of activities. Unfortunately, the runtime overhead of obtaining such a profile can be ... -
Constant-rate Non-malleable Codes and their Applications
Non-malleable codes(NMC) introduced by Dziembowski, Pietrzak and Wichs in ITCS 2010, provide powerful security guarantees where error-correcting codes can not provide any guarantee: a decoding of tampered codeword is ... -
Model Extraction Defense using Modified Variational Autoencoder
Machine Learning as a Service (MLaaS) exposes machine learning (ML) models that are trained on confidential datasets to users in the form of an Application Programming Interface (API). Since the MLaaS models are deployed ... -
FA RCU: Fault Aware Read-Copy-Update
Deferred freeing is the fundamental technique used in Read-Copy-Update (RCU) synchronization technique where reclamation of resources is deferred until the completion of all active RCU read-side critical sections. We observe ... -
Novel Neural Architecture for Multi-Hop Question Answering
Natural language understanding has been one of the key drivers responsible for advancing the eld of AI. To this end, automated Question Answering (QA) has served as an effective way of measuring the language understanding ... -
Model Extraction and Active Learning
Machine learning models are increasingly being offered as a service by big companies such as Google, Microsoft and Amazon. They use Machine Learning as a Service (MLaaS) to expose these machine learning models to the ... -
Modeling and Adaptive Scheduling Strategies for Distributed Graph Algorithms
Graph processing at scales of millions-billions of vertices and edges has become common to solve real-world problems in domains like social networks, smart cities and genomics. Distributed "Big Data" platforms for graph ... -
Model Checking Temporal Properties of Presburger Counter Systems
Counter systems are a well-known and powerful modeling notation for specifying infnite state systems. In this thesis we target the problem of checking temporal properties of counter systems. We address three predominant ... -
Deep Learning for Bug Localization and Program Repair
In this thesis, we focus on the problem of program debugging and present novel deep learning based techniques for bug-localization and program repair. Deep learning techniques have been successfully applied to a variety ... -
Deep Learning with Minimal Supervision
Abstract In recent years, deep neural networks have achieved extraordinary performance on supervised learning tasks. Convolutional neural networks (CNN) have vastly improved the state of the art for most computer vision ... -
Geometric and Topological Methods for Biomolecular Visualization
Biomolecules like proteins are the basic building blocks of living systems. It has been observed that the structure of a biomolecule plays an important role in defining its function. In this thesis, we describe novel ... -
An Improved Lower Bound for Multi-r-ic Depth Four Circuits as a Function of the Number of Input Variables
In this work we study the multi-r-ic formula model introduced by [KS15c] and improve upon the lower bound for multi-r-ic depth four circuits given in [KST16b], when viewed as a function of the number of input variables ... -
Approximation Algorithms for Geometric Packing and Covering Problems
We study a host of geometric optimization problems that are NP-hard and design polynomial time approximation algorithms for them. More precisely, we are given a family of geometric objects and a point set, mostly in the ... -
Deep Learning Models for Few-shot and Metric Learning
Deep neural network-based models have achieved unprecedented performance levels over many tasks in the traditional supervised setting and scale well with large quantities of data. On the other hand, improving performance ... -
Utilizing Worker Groups And Task Dependencies in Crowdsourcing
Crowdsourcing has emerged as a convenient mechanism to collect human judgments on a variety of tasks, ranging from document and image classification to scientific experimentation. However, in recent times crowdsourcing has ... -
Heterogeneity Aware Shared DRAM Cache for Integrated Heterogeneous Architectures
Integrated Heterogeneous System (IHS) processors pack throughput-oriented GPGPUs along-side latency-oriented CPUs on the same die sharing certain resources, e.g., shared last level cache, network-on-chip (NoC), and the ... -
Fully Resilient Non-Interactive ID-Based Hierarchical Key Agreement
Non-Interactive Key Agreement (NIKA) is a cryptographic primitive which allows two parties to agree on a shared secret key without any interaction. Identity-based Non-Interactive Key Agreement (ID-NIKA) allows each party ... -
An Exploratory Framework for Cyclone Identification and Tracking
Analyzing depressions plays an important role in meteorology, especially in the study of cyclones. In particular, the study of the temporal evolution of cyclones requires a robust depression tracking framework. To cope ... -
Computing Contour Tress for 2D Piecewise Polynomial Functions
Contour trees are extensively used in scalar field analysis. The contour tree is a data structure that tracks the evolution of level set topology in a scalar field. Scalar fields are typically available as samples at ...