Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
The Future Of Cryptography Under Quantum Computers, Marco A. Barreno
The Future Of Cryptography Under Quantum Computers, Marco A. Barreno
Dartmouth College Undergraduate Theses
Cryptography is an ancient art that has passed through many paradigms, from simple letter substitutions to polyalphabetic substitutions to rotor machines to digital encryption to public-key cryptosystems. With the possible advent of quantum computers and the strange behaviors they exhibit, a new paradigm shift in cryptography may be on the horizon. Quantum computers could hold the potential to render most modern encryption useless against a quantum-enabled adversary. The aim of this thesis is to characterize this convergence of cryptography and quantum computation. We provide definitions for cryptographic primitives that frame them in general terms with respect to complexity. We explore …
Performance And Interoperability In Solar, A Abram White
Performance And Interoperability In Solar, A Abram White
Dartmouth College Undergraduate Theses
Ubiquitous computing promises to integrate computers into our physical environment, surrounding us with applications that are able to adapt to our dynamics. Solar is a software infrastructure designed to deliver contextual information to these applications. To serve the large number and wide variety of context-aware devices envisioned by ubiquitous computing, Solar must exhibit both high performance and the ability to interoperate with many computing platforms. We created a testing framework to measure the performance of distributed systems such as Solar, as well as a pluggable data-transfer mechanism to support the dissemination of information to heterogeneous applications. This paper explores the …
Role Definition Language (Rdl): A Language To Describe Context-Aware Roles, Christopher P. Masone
Role Definition Language (Rdl): A Language To Describe Context-Aware Roles, Christopher P. Masone
Dartmouth College Undergraduate Theses
As wireless networks become more prevalent, a widening array of computational resources becomes available to the mobile user. Since not all users should have unrestricted access to these resources, a method of access control must be devised. In a context-aware environment, context information can be used to supplement more conventional password-based access control systems. We believe the best way to achieve this is through the use of Context-Aware Role-Based Access Control, a model in which permissions are assigned to entities called roles, each principal is a member of one or more roles, and a role's membership is determined using context …
Analysis Of Protein Sequences Using Time Frequency And Kolmogorov-Smirnov Methods, Kobby Essien
Analysis Of Protein Sequences Using Time Frequency And Kolmogorov-Smirnov Methods, Kobby Essien
Dartmouth College Undergraduate Theses
The plethora of genomic data currently available has resulted in a search for new algorithms and analysis techniques to interpret genomic data. In this two-fold study we explore techniques for locating critical amino acid residues in protein sequences and for estimating the similarity between proteins. We demonstrate the use of the Short-Time Fourier Transform and the Continuous Wavelet Transform together with amino acid hydrophobicity in locating important amino acid domains in proteins and also show that the Kolmogorov-Smirnov statistic can be used as a metric of protein similarity.
Information-Theoretic Bounds On The Training And Testing Error Of Boosting, Sebastien M. Lahaie
Information-Theoretic Bounds On The Training And Testing Error Of Boosting, Sebastien M. Lahaie
Dartmouth College Undergraduate Theses
Boosting is a means of using weak learners as subroutines to produce a strong learner with markedly better accuracy. Recent results showing the connection between logistic regression and boosting provide the foundation for an information-theoretic analysis of boosting. We describe the analogy between boosting and gambling, which allows us to derive a new upper bound on training error. This upper bound explicitly describes the effect of noisy data on training error. We also use information-theoretic techniques to derive an alternative upper-bound on testing error which is independent of the size of the weak-learner space.