Data Predictive Control Using Regression Trees And Ensemble Learning, 2017 University of Pennsylvania

#### Data Predictive Control Using Regression Trees And Ensemble Learning, Achin Jain, Francesco Smarra, Rahul Mangharam

*Real-Time and Embedded Systems Lab (mLAB)*

Decisions on how to best operate large complex plants such as natural gas processing, oil refineries, and energy efficient buildings are becoming ever so complex that model-based predictive control (MPC) algorithms must play an important role. However, a key factor prohibiting the widespread adoption of MPC, is the cost, time, and effort associated with learning first-principles dynamical models of the underlying physical system. An alternative approach is to employ learning algorithms to build black-box models which rely only on real-time data from the sensors. Machine learning is widely used for regression and classification, but thus far data-driven models have not ...

Approximation Algorithms For Effective Team Formation, 2017 City University of New York (CUNY)

#### Approximation Algorithms For Effective Team Formation, George Rabanca

*All Graduate Works by Year: Dissertations, Theses, and Capstone Projects*

This dissertation investigates the problem of creating multiple disjoint teams of maximum efficacy from a fixed set of workers. We identify three parameters which directly correlate to the team effectiveness — team expertise, team cohesion and team size — and propose efficient algorithms for optimizing each in various settings. We show that under standard assumptions the problems we explore are not optimally solvable in polynomial time, and thus we focus on developing efficient algorithms with guaranteed worst case approximation bounds. First, we investigate maximizing team expertise in a setting where each worker has different expertise for each job and each job may ...

Morphogenesis And Growth Driven By Selection Of Dynamical Properties, 2017 The Graduate Center, City University of New York

#### Morphogenesis And Growth Driven By Selection Of Dynamical Properties, Yuri Cantor

*All Graduate Works by Year: Dissertations, Theses, and Capstone Projects*

Organisms are understood to be complex adaptive systems that evolved to thrive in hostile environments. Though widely studied, the phenomena of organism development and growth, and their relationship to organism dynamics is not well understood. Indeed, the large number of components, their interconnectivity, and complex system interactions all obscure our ability to see, describe, and understand the functioning of biological organisms.

Here we take a synthetic and computational approach to the problem, abstracting the organism as a cellular automaton. Such systems are discrete digital models of real-world environments, making them more accessible and easier to study then their physical world ...

Parallelization Of Molecular Docking Algorithms Using Cuda For Use In Drug Discovery, 2017 Purdue University

#### Parallelization Of Molecular Docking Algorithms Using Cuda For Use In Drug Discovery, Brandon Stewart, Jonathan Fine, Gaurav Chopra Phd

*The Summer Undergraduate Research Fellowship (SURF) Symposium*

Traditional drug discovery methodology uses a multitude of software packages to design and evaluate new drug-like compounds. While software packages implement a wide variety of methods, the serial (i.e. single core) implementation for many of these algorithms, prohibit large scale docking, such as proteome-wide docking (i.e. thousands of compounds with thousands of proteins). Several docking algorithms can be parallelized, significantly reducing the runtime of the calculations, thus enabling large-scale docking. Implementing algorithms that take advantage of the distributed nature of graphical processing units (GPUs) via the Compute Unified Device Architecture (CUDA) enables us to efficiently implement massively parallel ...

Vertex Weighted Spectral Clustering, 2017 East Tennessee State University

#### Vertex Weighted Spectral Clustering, Mohammad Masum

*Electronic Theses and Dissertations*

Spectral clustering is often used to partition a data set into a specified number of clusters. Both the unweighted and the vertex-weighted approaches use eigenvectors of the Laplacian matrix of a graph. Our focus is on using vertex-weighted methods to refine clustering of observations. An eigenvector corresponding with the second smallest eigenvalue of the Laplacian matrix of a graph is called a Fiedler vector. Coefficients of a Fiedler vector are used to partition vertices of a given graph into two clusters. A vertex of a graph is classified as unassociated if the Fiedler coefficient of the vertex is close to ...

Classification With Large Sparse Datasets: Convergence Analysis And Scalable Algorithms, 2017 The University of Western Ontario

#### Classification With Large Sparse Datasets: Convergence Analysis And Scalable Algorithms, Xiang Li

*Electronic Thesis and Dissertation Repository*

Large and sparse datasets, such as user ratings over a large collection of items, are common in the big data era. Many applications need to classify the users or items based on the high-dimensional and sparse data vectors, e.g., to predict the profitability of a product or the age group of a user, etc. Linear classifiers are popular choices for classifying such datasets because of their efficiency. In order to classify the large sparse data more effectively, the following important questions need to be answered.

**1. Sparse data and convergence behavior**. *How different properties of a dataset, such as ...*

An Analysis Of The Application Of Simplified Silhouette To The Evaluation Of K-Means Clustering Validity, 2017 Dublin Institute of Technology

#### An Analysis Of The Application Of Simplified Silhouette To The Evaluation Of K-Means Clustering Validity, Fei Wang, Hector-Hugo Franco-Penya, John D. Kelleher, John Pugh, Robert Ross

*Conference papers*

Silhouette is one of the most popular and effective internal measures for the evaluation of clustering validity. Simplified Silhouette is a computationally simplified version of Silhouette. However, to date Simplified Silhouette has not been systematically analysed in a specific clustering algorithm. This paper analyses the application of Simplified Silhouette to the evaluation of k-means clustering validity and compares it with the k-means Cost Function and the original Silhouette from both theoretical and empirical perspectives. The theoretical analysis shows that Simplified Silhouette has a mathematical relationship with both the k-means Cost Function and the original Silhouette, while empirically, we show that ...

Educational Magic Tricks Based On Error-Detection Schemes, 2017 Loyola University Chicago

#### Educational Magic Tricks Based On Error-Detection Schemes, Ronald I. Greenberg

*Computer Science: Faculty Publications and Other Works*

Magic tricks based on computer science concepts help grab student attention and can motivate them to delve more deeply. Error detection ideas long used by computer scientists provide a rich basis for working magic; probably the most well known trick of this type is one included in the CS Unplugged activities. This paper shows that much more powerful variations of the trick can be performed, some in an unplugged environment and some with computer assistance. Some of the tricks also show off additional concepts in computer science and discrete mathematics.

Game Specific Approaches To Monte Carlo Tree Search For Dots And Boxes, 2017 Western Kentucky University

#### Game Specific Approaches To Monte Carlo Tree Search For Dots And Boxes, Jared Prince

*Honors College Capstone Experience/Thesis Projects*

In this project, a Monte Carlo tree search player was designed and implemented for the child’s game dots and boxes, the computational burden of which has left traditional artificial intelligence approaches like minimax ineffective. Two potential improvements to this player were implemented using game-specific information about dots and boxes: the lack of information for decision-making provided by the net score and the inherent symmetry in many states. The results of these two approaches are presented, along with details about the design of the Monte Carlo tree search player. The first improvement, removing net score from the state information, was ...

Resource Bound Guarantees Via Programming Languages, 2017 The University of Western Ontario

#### Resource Bound Guarantees Via Programming Languages, Michael J. Burrell

*Electronic Thesis and Dissertation Repository*

We present a programming language in which every well-typed program halts in time polynomial with respect to its input and, more importantly, in which upper bounds on resource requirements can be inferred with certainty. Ensuring that software meets its resource constraints is important in a number of domains, most prominently in hard real-time systems and safety critical systems where failing to meet its time constraints can result in catastrophic failure. The use of test- ing in ensuring resource constraints is of limited use since the testing of every input or environment is impossible in general. Static analysis, whether via the ...

Quo Vadis-A Framework For Intelligent Routing In Large Communication Networks., 2017 Iowa State University

#### Quo Vadis-A Framework For Intelligent Routing In Large Communication Networks., Armin Mikler, Johnny S. Wong, Vasant Honavar

*Johnny Wong*

This paper presents Quo Vadis, an evolving framework for intelligent traffic management in very large communication networks. Quo Vadis is designed to exploit topological properties of large networks as well as their spatio-temporal dynamics to optimize multiple performance criteria through cooperation among nodes in the network. It employs a distributed representation of network state information using local load measurements supplemented by a less precise global summary. Routing decisions in Quo Vadis are based on parameterized heuristics designed to optimize various performance metrics in an anticipatory or pro-active as well as compensatory or reactive mode and to minimize the overhead associated ...

Tree-Based Algorithm To Find The K-Th Value In Distributed Systems, 2017 Iowa State University

#### Tree-Based Algorithm To Find The K-Th Value In Distributed Systems, Yoonsik Cheon, Johnny S. Wong

*Johnny Wong*

In this paper, we study distributed algorithms for finding the k-th value in the decentralized systems. First we consider the case of circular configuration of processors where no processor knows the total number of participants. Later a network of arbitrary configuration is examined and a tree-based algorithm is proposed. The proposed algorithm requires O(N) messages and O(log N) rounds of message passing, where N is the number of nodes in the network.

Utility-Theoretic Heuristics For Intelligent Adaptive Routing In Large Communcation Networks, 2017 Iowa State University

#### Utility-Theoretic Heuristics For Intelligent Adaptive Routing In Large Communcation Networks, Armin Mikler, Vasant Honavar, Johnny S. Wong

*Johnny Wong*

Utility theory offers an elegant and powerful theoretical framework for design and analysis of autonomous adaptive communication networks. Routing of messages in such networks presents a real-time instance of a multi-criterion quasi-optimization problem in a dynamic and uncertain environment. In this paper, we examine several heuristic decision functions that can be used to guide messages along a near-optimal (e.g., minimum delay) path in a large network. We present an analysis of properties of such heuristics under a set of simplifying assumptions about the network topology and load dynamics. In particular, we identify the conditions under which one such utility-theoretic ...

Encryption Backdoors: A Discussion Of Feasibility, Ethics, And The Future Of Cryptography, 2017 Seattle Pacific University

#### Encryption Backdoors: A Discussion Of Feasibility, Ethics, And The Future Of Cryptography, Jennifer A. Martin

*Honors Projects*

In the age of technological advancement and the digitization of information, privacy seems to be all but an illusion. Encryption is supposed to be the white knight that keeps our information and communications safe from unwanted eyes, but how secure are the encryption algorithms that we use? Do we put too much trust in those that are charged with implementing our everyday encryption systems? This paper addresses the concept of backdoors in encryption: ways that encryption systems can be implemented so that the security can be bypassed by those that know about its existence. Many governments around the world are ...

Neural Network Ai For Fightingice, 2017 California Polytechnic State University, San Luis Obispo

#### Neural Network Ai For Fightingice, Alan D. Robison

*Computer Engineering*

Game AI in the ﬁghting game genre, along the lines of Street Fighter, Mortal Kombat and Tekken, is traditionally script-based, with hard-coded reactions to various situations. Though this approach is often easy to understand and tweak, it requires substantial time and understanding of the game to implement in a way that is challenging and satisfying for the player due to the very large possibility space. This paper explores the use of neural networks as an alternative approach by implementing and training a network to select an action to take each frame based on the game state.

Cataloging Github Repositories, 2017 Singapore Management University

#### Cataloging Github Repositories, Abhishek Sharma, Ferdian Thung, Pavneet Singh Kochhar, Agus Sulistya, David Lo

*Research Collection School Of Information Systems*

GitHub is one of the largest and most popular repository hosting service today, having about 14 million users and more than 54 million repositories as of March 2017. This makes it an excellent platform to find projects that developers are interested in exploring. GitHub showcases its most popular projects by cataloging them manually into categories such as DevOps tools, web application frameworks, and game engines. We propose that such cataloging should not be limited only to popular projects. We explore the possibility of developing such cataloging system by automatically extracting functionality descriptive text segments from readme files of GitHub repositories ...

Community Detection In Social Networks, 2017 San Jose State University

#### Community Detection In Social Networks, Ketki Kulkarni

*Master's Projects*

The rise of the Internet has brought people closer. The number of interactions between people across the globe has gone substantially up due to social awareness, the advancements of the technology, and digital interaction. Social networking sites have built societies, communities virtually. Often these societies are displayed as a network of nodes depicting people and edges depicting relationships, links. This is a good and e cient way to store, model and represent systems which have a complex and rich information. Towards that goal we need to nd e ective, quick methods to analyze social networks. One of the possible solution ...

Influence Detection And Spread Estimation In Social Networks, 2017 San Jose State University

#### Influence Detection And Spread Estimation In Social Networks, Madhura Kaple

*Master's Projects*

A social network is an online platform, where people communicate and share information with each other. Popular social network features, which make them di erent from traditional communication platforms, are: following a user, re-tweeting a post, liking and commenting on a post etc. Many companies use various social networking platforms extensively as a medium for marketing their products. A xed amount of budget is alloted by the companies to maximize the positive in uence of their product. Every social network consists of a set of users (people) with connections between them. Each user has the potential to extend its in ...

An Improved Algorithm For Learning To Perform Exception-Tolerant Abduction, 2017 Washington University in St Louis

#### An Improved Algorithm For Learning To Perform Exception-Tolerant Abduction, Mengxue Zhang

*Engineering and Applied Science Theses & Dissertations*

Abstract

Inference from an observed or hypothesized condition to a plausible cause or explanation for this condition is known as abduction. For many tasks, the acquisition of the necessary knowledge by machine learning has been widely found to be highly effective. However, the semantics of learned knowledge are weaker than the usual classical semantics, and this necessitates new formulations of many tasks. We focus on a recently introduced formulation of the abductive inference task that is thus adapted to the semantics of machine learning. A key problem is that we cannot expect that our causes or explanations will be perfect ...

Algorithmic Factorization Of Polynomials Over Number Fields, 2017 Rose-Hulman Institute of Technology

#### Algorithmic Factorization Of Polynomials Over Number Fields, Christian Schulz

*Mathematical Sciences Technical Reports (MSTR)*

The problem of exact polynomial factorization, in other words expressing a polynomial as a product of irreducible polynomials over some field, has applications in algebraic number theory. Although some algorithms for factorization over algebraic number fields are known, few are taught such general algorithms, as their use is mainly as part of the code of various computer algebra systems. This thesis provides a summary of one such algorithm, which the author has also fully implemented at https://github.com/Whirligig231/number-field-factorization, along with an analysis of the runtime of this algorithm. Let k be the product of the degrees of ...