Manual Computational Methods in Biometric Authentication: Statistical Methods for Performance Evaluation

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  1. Computational Methods in Biometric Authentication
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Computational Methods in Biometric Authentication

Lawrence University. Articles Cited by. International Journal of Image and Graphics 3 03 , , Biometric Technology for Human Identification , , Canadian journal of zoology 84 12 , , Journal of Quantitative Analysis in Sports 7 2 , Articles 1—20 Show more. Help Privacy Terms. Evaluation is based on selecting an interesting topic, completing appropriate research on the state of the art in that area, communicating your objectives in writing and in presentations, accurately estimating what resources will be required to complete your chosen task, coding necessary functionality, and executing your plan.

Learn more about the NETS program here. Networked Life looks at how our world is connected -- socially, economically, strategically and technologically -- and why it matters. Want to understand the sociological and algorithmic aspects of friend recommendation?

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Want to know how Google decides what 10 answers to return, out of the 10 million matching results? Want to understand how search engines have revolutionized advertising? Then this is the course for you! NETS provides an overview of the issues, theoretical foundations, and existing techniques in networks social, information, communication and markets on the Internet. Subsequent NETS courses are available for students wishing to explore any of these topics in greater detail. What is the "cloud"? How do we build software systems and components that scale to millions of users and petabytes of data, and are "always available"?

Services must scale across thousands of machines, tolerate failures, and support thousands of concurrent requests. Increasingly, the major providers including Amazon, Google, Microsoft, HP, and IBM are looking at "hosting" third-party applications in their data centers - forming so-called "cloud computing" services. This course, aimed at a sophomore with exposure to basic programming within the context of a single machine, focuses on the issues and programming models related to such cloud and distributed data processing technologies: how to think about dividing both data and work across large clusters of machines, both within and across data centers, how to design algorithms that do this parallel computation, and how to implement the algorithms in new frameworks such as Spark and MapReduce.

Want to understand how memes spread across the Internet? How organisms exhibit flocking behavior? How the structure of a network can help predict behavior among the nodes? This course is a rigorous study of the structure and function of complex networks. From World Wide Web to networks of banks and lenders that form the financial sector, to friendship networks that influence our opinion and everyday decision-making, networks have become an integral part of our daily lives. How should an auction for scarce goods be structured if the sellers wish to maximize their revenue?

How badly will traffic be snarled if drivers each selfishly try to minimize their commute time, compared to if a benevolent dictator directed traffic? How can couples be paired so that no two couples wish to swap partners in hindsight? How can you be as successful as the best horse-racing expert at betting on horse races, without knowing anything about horse racing?

In this course, we will take an algorithmic perspective on problems in game theory, to solve problems such as the ones listed above. Game theory has applications in a wide variety of settings in which multiple participants with different incentives are placed in the same environment, must interact, and each "player"'s actions affect the others.

CIS - Software Foundations Prerequisite s : CIS , , and or equivalents , plus substantial mathematical maturity at least two additional undergraduate courses in math or theoretical CS. Undergraduate-level coursework in programming languages, compilers, functional programming, or logic is helpful but not required. This course introduces basic concepts and techniques in the foundational study of programming languages. The central theme is the view of programs and programming languages as mathematical objects for which precise claims may be made and proved. Particular topics include operational techniques for formal definition of language features, type systems and type safety properties, polymorphism, constructive logic, and the Coq proof assistant.

Performance Evaluation: Methods and their qualities

This course is appropriate as an upper-level undergraduate CIS elective. Programming experience CIT or equivalent is helpful but not necessary. An investigation of paradigms for design and analysis of algorithms. The course will include dynamic programming, flows and combinatorial optimization algorithms, linear programming, randomization and a brief introduction to intractability and approximation algorithms. The course will include other advanced topics, time permitting. This course provides an introduction to fundamental concepts of distributed systems, and the design principles for building large scale computational systems.

Topics covered include communication, concurrency, programming paradigms, naming, managing shared state, caching, synchronization, reaching agreement, fault tolerance, security, middleware, and distributed applications.

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The course is about mathematical and algorithmic techniques used for geometric modeling and geometric design, using curves and surfaces. There are many applications in computer graphics as well as in robotics, vision, and computational geometry. Such techniques are used in 2D and 3D drawing and plot, object silhouettes, animating positions, product design cars, planes, buildings , topographic data, medical imagery, active surfaces of proteins, attribute maps color, texture, roughness , weather data, art, etc.

Three broad classes of problems will be considered: Approximating curved shapes, using smooth curves or surfaces; Interpolating curved shapes, using smooth curves or surfaces; Rendering smooth curves or surfaces. Review of regular and context-free languages and machine models. Advanced topics as time permits: Circuit complexity and parallel computation, randomized complexity, approximability, interaction and cryptography.

Offered Spring Course Website. This course provides firm foundations in linear algebra and basic optimization techniques. Emphasis is placed on teaching methods and tools that are widely used in various areas of computer science. Both theoretical and algorithmic aspects will be discussed. Offered: Fall Course Website.

The course will examine the expressive power of various logical languages over the class of finite structures. The course begins with an exposition of some of the fundamental theorems about the behavior of first--order logic in the context of finite structures, in particular, the Ehrenfeucht--Fraisse Theorem and the Trahktenbrot Theorem. The first of these results is used to show limitations on the expressive power of first--order logic over finite structures while the second result demonstrates that the problem of reasoning about finite structures using first--order logic is surprisingly complex.

The course then proceeds to consider various extensions of first--order logic including fixed--point operators, generalized quantifiers, infinitary languages, and higher--order languages. The expressive power of these extensions will be studied in detail and will be connected to various problems in the theory of computational complexity.

This last motif, namely the relation between descriptive and computational complexity, will be one of the main themes of the course. Basic programming experience. This course covers the foundations of statistical machine learning. The focus is on probabilistic and statistical methods for prediction and clustering in high dimensions. Deep learning techniques now touch on data systems of all varieties. Sometimes, deep learning is a product; sometimes, deep learning optimizes a pipeline; sometimes, deep learning provides critical insights; sometimes, deep learning sheds light on neuroscience or vice versa.

The purpose of this course is to deconstruct the hype by teaching deep learning theories, models, skills, and applications that are useful for applications. Google translate can instantly translate between any pair of over fifty human languages for instance, from French to English. How does it do that? Why does it make the errors that it does? And how can you build something better? Modern translation systems like Google Translate and Bing Translator learn how to translate by reading millions of words of already translated text, and this course will show you how they work.

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The course covers a diverse set of fundamental building blocks from linguistics, machine learning, algorithms, data structures, and formal language theory, along with their application to a real and difficult problem in artificial intelligence. Computational approaches to the problem of understanding and producing natural language text and speech, including speech processing, syntactic parsing, semantic interpretation, discourse meaning, and the role of pragmatics and world knowledge. The course will examine both rule-based and corpus-based techniques.

Offered: Spring Course Website. Prerequistites: This course will assume a solid knowledge of modern biology. The course covers methods used in computational biology, including the statistical models and algorithms used and the biological problems which they address. The course will focus on sequence analysis problems such as exon, motif, and gene finding, and on comparative methods but will also cover gene expression and proteomics.

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TBD Course Website. While traditional image processing techniques will be discussed to provide context, the emphasis will be on cutting edge aspects of all areas of image analysis including registration, segmentation, and high-dimensional statistical analysis.

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Significant coverage of state-of-the-art biomedical research and clinical applications will be incorporated to reinforce the theoretical basis of the analysis methods. It is suitable for students who have an undergraduate degree in computer science, or computer engineering, or electrical engineering.

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This course is focused on principles underlying design and analysis of computational elements that interact with the physical environment. Increasingly, such embedded computers are everywhere, from smart cameras to medical devices to automobiles. While the classical theory of computation focuses on the function that a program computes, to understand embedded computation, we need to focus on the reactive nature of the interaction of a component with its environment via inputs and outputs, the continuous dynamics of the physical world, different ways of communication among components, and requirements concerning safety, timeliness, stability, and performance.

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Developing tools for approaching design, analysis, and implementation of embedded systems in a principled manner is an active research area. This course will attempt to give students a coherent introduction to this emerging area. Undergraduates who have satisfied the prerequisites are welcome to enroll. No permission from the instructor is needed. This course is focused on cyber physical systems with emphasis on real-time issues. Cyber phsyical systems are integrations of computation and communication with physical processes. Embedded computers monitor and control physical processes in real-time.

As these embedded computers are increasingly networked, it is believed that there will be a revolutionary transformation. Just as personal computers have transformed from word processors to global communications devices for information gathering and sharing, embedded computers will change from small self-contained systems to cyber-physical systems by sensing, monitoring, controlling our physical environment.