Browsing Computer Science and Automation (CSA) by Title
Now showing items 340-359 of 394
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Security of Post-Quantum Multivariate Blind Signature Scheme: Revisited and Improved
Current cryptosystems face an imminent threat from quantum algorithms like Shor's and Grover's, leading us to post-quantum cryptography. Multivariate signatures are prominent in post-quantum cryptography due to their fast, ... -
Semantic Analysis of Web Pages for Task-based Personal Web Interactions
(2017-11-27)Mobile widgets now form a new paradigm of simplified web. Probably, the best experience of the Web is when a user has a widget for every frequently executed task, and can execute it anytime, anywhere on any device. However, ... -
Semi-Supervised Classification Using Gaussian Processes
(2010-03-26)Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised classification tasks. In this thesis, we propose new algorithms for solving ... -
Sentiment-Driven Topic Analysis Of Song Lyrics
(2015-08-17)Sentiment Analysis is an area of Computer Science that deals with the impact a document makes on a user. The very field is further sub-divided into Opinion Mining and Emotion Analysis, the latter of which is the basis for ... -
Signcryption in a Quantum World
With recent advancements and research on quantum computers, it is conjectured that in the foreseeable future, sufficiently large quantum computers will be built to break essentially all public key cryptosystems currently ... -
Simulation Based Algorithms For Markov Decision Process And Stochastic Optimization
(2010-08-06)In Chapter 2, we propose several two-timescale simulation-based actor-critic algorithms for solution of infinite horizon Markov Decision Processes (MDPs) with finite state-space under the average cost criterion. On the ... -
Single and Multi-Agent Finite Horizon Reinforcement Learning Algorithms for Smart Grids
In this thesis, we study sequential decision-making under uncertainty in the context of smart grids using reinforcement learning. The underlying mathematical model for reinforcement learning algorithms are Markov Decision ... -
Sparse Multiclass And Multi-Label Classifier Design For Faster Inference
(2013-06-20)Many real-world problems like hand-written digit recognition or semantic scene classification are treated as multiclass or multi-label classification prob-lems. Solutions to these problems using support vector machines (SVMs) ... -
Specification Synthesis with Constrained Horn Clauses
Many practical problems in software development, verification and testing rely on specifications. The problem of specification synthesis is to automatically find relational constraints for undefined functions, called ... -
Spill Code Minimization And Buffer And Code Size Aware Instruction Scheduling Techniques
(2009-05-19)Instruction scheduling and Software pipelining are important compilation techniques which reorder instructions in a program to exploit instruction level parallelism. They are essential for enhancing instruction level ... -
A Static Slicing Tool for Sequential Java Programs
(2018-07-28)A program slice consists of a subset of the statements of a program that can potentially affect values computed at some point of interest. Such a point of interest along with a set of variables is called a slicing criterion. ... -
Statistical Network Analysis: Community Structure, Fairness Constraints, and Emergent Behavior
Networks or graphs provide mathematical tools for describing and analyzing relational data. They are used in biology to model interactions between proteins, in economics to identify trade alliances among countries, in ... -
Stochastic Approximation Algorithms with Set-valued Dynamics : Theory and Applications
(2018-07-05)Stochastic approximation algorithms encompass a class of iterative schemes that converge to a sought value through a series of successive approximations. Such algorithms converge even when the observations are erroneous. ... -
Stochastic Approximation with Markov Noise: Analysis and applications in reinforcement learning
Stochastic approximation algorithms are sequential non-parametric methods for finding a zero or minimum of a function in the situation where only the noisy observations of the function values are available. Two time-scale ... -
Stochastic approximation with set-valued maps and Markov noise: Theoretical foundations and applications
Stochastic approximation algorithms produce estimates of a desired solution using noisy real world data. Introduced by Robbins and Monro, in 1951, stochastic approximation techniques have been instrumental in the asymptotic ... -
Stochastic Newton Methods With Enhanced Hessian Estimation
(2018-05-22)Optimization problems involving uncertainties are common in a variety of engineering disciplines such as transportation systems, manufacturing, communication networks, healthcare and finance. The large number of input ... -
Stochastic Optimization And Its Application In Reinforcement Learning
Numerous engineering fields, such as transportation systems, manufacturing, communication networks, healthcare, and finance, frequently encounter problems requiring optimization in the presence of uncertainty. Simulation-based ... -
Structured Regularization Through Convex Relaxations Of Discrete Penalties
Motivation. Empirical risk minimization(ERM) is a popular framework for learning predictive models from data, which has been used in various domains such as computer vision, text processing, bioinformatics, neuro-biology, ...