Virtual Machine for SpartanGold

The field of blockchain and cryptocurrencies can be both difficult to grasp and improve upon, which makes aids that can assist in these tasks very useful. SpartanGold is a simplified blockchain-based cryptocurrency created at San Jose State University as a learning aid for blockchain and cryptocurrencies. In its current state, it closely resembles Bitcoin, and it is also easily expandable to implement other features. This project extends SpartanGold with a virtual machine resembling the Ethereum Virtual Machine. Implementing this feature results in SpartanGold having Ethereum- related features, which would allow the cryptocurrency to both be a helpful learning aid for Ethereum and be able to solve interesting blockchain problems associated with virtual machines and smart contracts. Using my virtual machine implementation, I was able to produce a simplified token that resembles Ethereum tokens and works with SpartanGold. This token demonstrates the SpartanGold Virtual Machine’s usefulness in simulating smart contracts of real world interest. Going forward, developers can experiment with the SpartanGold Virtual Machine to test out new ideas without dealing with the full complexity of the Ethereum Virtual Machine

Geometric Methods in Machine Learning and Data Mining

In machine learning, the standard goal of is to find an appropriate statistical model from a model space based on the training data from a data space; while in data mining, the goal is to find interesting patterns in the data from a data space. In both fields, these spaces carry geometric structures that can be exploited using methods that make use of these geometric structures (we shall call them geometric methods), or the problems themselves can be formulated in a way that naturally appeal to these methods. In such cases, studying these geometric structures and then using appropriate geometric methods not only gives insight into existing algorithms, but also helps build new and better algorithms. In my research, I develop methods that exploit geometric structure of problems for a variety of machine learning and data mining problems, and provide strong theoretical and empirical evidence in favor of using them. My dissertation is divided into two parts. In the first part, I develop algorithms to solve a well known problem in data mining i.e. distance embedding problem. In particular, I use tools from computational geometry to build a unified framework for solving a distance embedding problem known as multidimensional scaling (MDS). This geometry-inspired framework results in algorithms that can solve different variants of MDS better than previous state-of-the-art methods. In addition, these algorithms come with many other attractive properties: they are simple, intuitive, easily parallelizable, scalable, and can handle missing data. Furthermore, I extend my unified MDS framework to build scalable algorithms for dimensionality reduction, and also to solve a sensor network localization problem for mobile sensors. Experimental results show the effectiveness of this framework across all problems. In the second part of my dissertation, I turn to problems in machine learning, in particular, use geometry to reason about conjugate priors, develop a model that hybridizes between discriminative and generative frameworks, and build a new set of generative-process-driven kernels. More specifically, this part of my dissertation is devoted to the study of the geometry of the space of probabilistic models associated with statistical generative processes. This study --- based on the theory well grounded in information geometry --- allows me to reason about the appropriateness of conjugate priors from a geometric perspective, and hence gain insight into the large number of existing models that rely on these priors. Furthermore, I use this study to build hybrid models more naturally i.e., by combining discriminative and generative methods using the geometry underlying them, and also to build a family of kernels called generative kernels that can be used as off-the-shelf tool in any kernel learning method such as support vector machines. My experiments of generative kernels demonstrate their effectiveness providing further evidence in favor of using geometric methods

ANALYSIS OF CLASSIFICATION ALGORITHMS ON DIFFERENT DATASETS

Purpose. Data mining is the forthcoming research area to solve different problems and classification is one of main problem in the field of data mining. In this paper, we use two classification algorithms J48 and Sequential Minimal Optimization alias SMO of the Weka interface. Methodology. It can be used for testing several datasets. The performance of J48 and Sequential Minimal Optimization has been analyzed to choose the better algorithm based on the conditions of the datasets. The datasets have been chosen from UCI Machine Learning Repository. Findings. Algorithm J48 is based on C4.5 decision-based learning and algorithm Sequential Minimal Optimization uses the Support Vector Machine approach for classification of datasets. When comparing the performance of both algorithms we found Sequential Minimal Optimization is better algorithm in most of the cases. Originality. This is the first implemented research work up to my knowledge, data sets classification problem handled in data mining using SMO with Weka interface

Lifelong Reinforcement Learning On Mobile Robots

Machine learning has shown tremendous growth in the past decades, unlocking new capabilities in a variety of fields including computer vision, natural language processing, and robotic control. While the sophistication of individual problems a learning system can handle has greatly advanced, the ability of a system to extend beyond an individual problem to adapt and solve new problems has progressed more slowly. This thesis explores the problem of progressive learning. The goal is to develop methodologies that accumulate, transfer, and adapt knowledge in applied settings where the system is faced with the ambiguity and resource limitations of operating in the physical world. There are undoubtedly many challenges to designing such a system, my thesis looks at the component of this problem related to how knowledge from previous tasks can be a benefit in the domain of reinforcement learning where the agent receives rewards for positive actions. Reinforcement learning is particularly difficult when training on physical systems, like mobile robots, where repeated trials can damage the system and unrestricted exploration is often associated with safety risks. I investigate how knowledge can be efficiently accumulated and applied to future reinforcement learning problems on mobile robots in order to reduce sample complexity and enable systems to adapt to novel settings. Doing this involves mathematical models which can combine knowledge from multiple tasks, methods for restructuring optimizations and data collection to handle sequential updates, and data selection strategies that can be used to address resource limitations

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

Why Philosophers Should Care About Computational Complexity

One might think that, once we know something is computable, how efficiently it can be computed is a practical question with little further philosophical importance. In this essay, I offer a detailed case that one would be wrong. In particular, I argue that computational complexity theory---the field that studies the resources (such as time, space, and randomness) needed to solve computational problems---leads to new perspectives on the nature of mathematical knowledge, the strong AI debate, computationalism, the problem of logical omniscience, Hume's problem of induction, Goodman's grue riddle, the foundations of quantum mechanics, economic rationality, closed timelike curves, and several other topics of philosophical interest. I end by discussing aspects of complexity theory itself that could benefit from philosophical analysis.Comment: 58 pages, to appear in "Computability: G\"odel, Turing, Church, and beyond," MIT Press, 2012. Some minor clarifications and corrections; new references adde

Big Data in the Oil and Gas Industry: A Promising Courtship

The energy industry remains one of the highest money-producing and investment industries in the world. The United States’ own economic stability depends greatly on the stability of oil and gas prices. Various factors affect the amount of money that will continue to be invested in producing oil. A main disadvantage to the oil and gas industry is its lack of technological adaptation. This weakens the industry because the surest measures are not currently being taken to produce oil in optimally efficient, safe, and cost-effective ways. Big data has gained global recognition as an opportunity to gather large volumes of information in real-time and translate data sets into actionable insights. In a low commodity price environment, saving time, reducing costs, and improving safety are crucial outcomes that can be realized using machine learning in oil and gas operations. Big data provides the opportunity to use unsupervised learning. For example, with this approach, engineers can predict oil wells’ optimal barrels of production given the completion data in a specific area. However, a caveat to utilizing big data in the oil and gas industry is that there simply is neither enough physical data nor data velocity in the industry to be properly referred to as “big data.” Big data, as it develops, will nonetheless significantly change the energy business in the future, as it already has in various other industries.Petroleum and Geosystems Engineerin