Indiana University-Purdue University Indianapolis (IUPUI) / Learning in Partially Observable Markov Decision process (POMDP) is motivated by the essential need to address a number of realistic problems. A number of methods exist for learning in POMDPs, but learning with limited amount of information about the model of POMDP remains a highly anticipated feature. Learning with minimal information is desirable in complex systems as methods requiring complete information among decision makers are impractical in complex systems due to increase of problem dimensionality. In this thesis we address the problem of decentralized control of POMDPs with unknown transition probabilities and reward. We suggest learning in POMDP using a tree based approach. States of the POMDP are guessed using this tree. Each node in the tree has an automaton in it and acts as a decentralized decision maker for the POMDP. The start state of POMDP is known as the landmark state. Each automaton in the tree uses a simple learning scheme to update its action choice and requires minimal information. The principal result derived is that, without proper knowledge of transition probabilities and rewards, the automata tree of decision makers will converge to a set of actions that maximizes the long term expected reward per unit time obtained by the system. The analysis is based on learning in sequential stochastic games and properties of ergodic Markov chains. Simulation results are presented to compare the long term rewards of the system under different decision control algorithms.

Indiana University-Purdue University Indianapolis (IUPUI) / A Laboratory Information Management Systems (LIMS) is designed to manage laboratory processes and data. It has the ability to extend the core functionality of the LIMS through configuration tools and add-on modules to support the implementation of complex laboratory workflows. The purpose of this project is to demonstrate how laboratory data and processes from a complex workflow can be implemented using a LIMS. Genomic samples have become an important part of the drug development process due to advances in molecular testing technology. This technology evaluates genomic material for disease markers and provides efficient, cost-effective, and accurate results for a growing number of clinical indications. The preparation of the genomic samples for evaluation requires a complex laboratory process called the precision aliquotting workflow. The precision aliquotting workflow processes genomic samples into precisely created aliquots for analysis. The workflow is defined by a set of aliquotting scheme attributes that are executed based on scheme specific rules logic. The aliquotting scheme defines the attributes of each aliquot based on the achieved sample recovery of the genomic sample. The scheme rules logic executes the creation of the aliquots based on the scheme definitions. LabWare LIMS is a Windows® based open architecture system that manages laboratory data and workflow processes. A LabWare LIMS model was developed to implement the precision aliquotting workflow using a combination of core functionality and configured code.

Knowledge has always been a decisive factor of humankind's social evolutions. Collecting the world's knowledge is one of the greatest challenges of our civilization. Knowledge involves the use of information but information is not knowledge. It is a way of acquiring and understanding information. Improving the visibility and the accessibility of information requires to organize it efficiently. This thesis focuses on this general purpose.<p><p>A fundamental objective of computer science is to store and retrieve information efficiently. This is known as the dictionary problem. A dictionary asks for a data structure which allows essentially the search operation. In general, information that is important and popular at a given time has to be accessed faster than less relevant information. This can be achieved by dynamically managing the data structure periodically such that relevant information is located closer from the search starting point. The second part of this thesis is devoted to the development and the understanding of self-adjusting dictionaries in various models of computation. In particular, we focus our attention on dictionaries which do not have any knowledge of the future accesses. Those dictionaries have to auto-adapt themselves to be competitive with dictionaries specifically tuned for a given access sequence. <p><p>This approach, which transforms the information structure, is not always feasible. Reasons can be that the structure is based on the semantic of the information such as categorization. In this context, the search procedure is linked to the structure itself and modifying the structure will affect how a search is performed. A solution developed to improve search in static structure is the hotlink assignment. It is a way to enhance a structure without altering its original design. This approach speeds up the search by creating shortcuts in the structure. The first part of this thesis is devoted to this approach. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Indiana University-Purdue University Indianapolis (IUPUI) / Blömmer, Otto, and Seifert presented a fault attack on elliptic curve scalar multiplication called the Sign Change Attack, which causes a fault that changes the sign of the accumulation point. As the use of a sign bit for an extended integer is highly unlikely, this appears to be a highly selective manipulation of the key stream. In this thesis we describe two plausible fault attacks on a smart card implementation of elliptic curve cryptography. King and Wang designed a new attack called counter fault attack by attacking the scalar multiple of discrete-log cryptosystem. They then successfully generalize this approach to a family of attacks. By implementing King and Wang's scheme on RSA, we successfully attacked RSA keys for a variety of sizes. Further, we generalized the attack model to an attack on any implementation that uses NAF and wNAF key.