Browsing Computer Science and Automation (CSA) by Title
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Scalable Low Power Issue Queue And Store Queue Design For Superscalar Processors
(20090309)A Large instruction window is a key requirement to exploit greater Instruction Level Parallelism in outoforder superscalar processors. Along with the instruction window size, the size of various other structures including ... 
Scalable Sprase Bayesian Nonparametric and Matrix Trifactorization Models for Text Mining Applications
(20180523)Hierarchical Bayesian Models and Matrix factorization methods provide an unsupervised way to learn latent components of data from the grouped or sequence data. For example, in document data, latent component cornresponds ... 
Scaling Blockchains Using Coding Theory and Verifiable Computing
The issue of scalability has been restricting blockchain from its widespread adoption. The current transaction rate of bitcoin is around seven transactions/second while its size has crossed the 300 GB mark. Although many ... 
Scaling ContextSensitive PointsTo Analysis
(20140506)Pointer analysis is one of the key static analyses during compilation. The efficiency of several compiler optimizations and transformations depends directly on the scalability and precision of the underlying pointer analysis. ... 
Security of PostQuantum 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 postquantum cryptography. Multivariate signatures are prominent in postquantum cryptography due to their fast, ... 
Semantic Analysis of Web Pages for Taskbased Personal Web Interactions
(20171127)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, ... 
SemiSupervised Classification Using Gaussian Processes
(20100326)Gaussian Processes (GPs) are promising Bayesian methods for classiﬁcation and regression problems. They have also been used for semisupervised classiﬁcation tasks. In this thesis, we propose new algorithms for solving ... 
SentimentDriven Topic Analysis Of Song Lyrics
(20150817)Sentiment Analysis is an area of Computer Science that deals with the impact a document makes on a user. The very field is further subdivided 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
(20100806)In Chapter 2, we propose several twotimescale simulationbased actorcritic algorithms for solution of infinite horizon Markov Decision Processes (MDPs) with finite statespace under the average cost criterion. On the ... 
Sparse Multiclass And MultiLabel Classifier Design For Faster Inference
(20130620)Many realworld problems like handwritten digit recognition or semantic scene classiﬁcation are treated as multiclass or multilabel classiﬁcation problems. Solutions to these problems using support vector machines (SVMs) ... 
Spill Code Minimization And Buffer And Code Size Aware Instruction Scheduling Techniques
(20090519)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
(20180728)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 Setvalued Dynamics : Theory and Applications
(20180705)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 nonparametric 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 timescale ... 
Stochastic approximation with setvalued 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
(20180522)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 ...