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Use Of Exploratory Data Analysis And Visualization In Identification Of Commingled Human Remains, Vivek Bhat Hosmat 2019 University of Nebraska at Omaha

Use Of Exploratory Data Analysis And Visualization In Identification Of Commingled Human Remains, Vivek Bhat Hosmat

Student Research and Creative Activity Fair

The field of Exploratory data analysis and Visualization is revolutionizing the way we perceive things. At times, Visualization is seen as a subset of Exploratory data analysis and then there are branches in Visualization, types, technologies, tools which makes one feel that Visualization is a standalone pillar. Either way, they both work well together to extract meaningful information from almost any kind of data. These fields can very well be the stepping stone for in-depth or conclusive research. They can direct research based on facts and observations rather than one’s intuition or even a brute force technique. This project ...


A Theoretical Model Of Underground Dipole Antennas For Communications In Internet Of Underground Things, Abdul Salam, Mehmet C. Vuran, Xin Dong, Christos Argyropoulos, Suat Irmak 2019 Purdue University

A Theoretical Model Of Underground Dipole Antennas For Communications In Internet Of Underground Things, Abdul Salam, Mehmet C. Vuran, Xin Dong, Christos Argyropoulos, Suat Irmak

Faculty Publications

The realization of Internet of Underground Things (IOUT) relies on the establishment of reliable communication links, where the antenna becomes a major design component due to the significant impacts of soil. In this paper, a theoretical model is developed to capture the impacts of change of soil moisture on the return loss, resonant frequency, and bandwidth of a buried dipole antenna. Experiments are conducted in silty clay loam, sandy, and silt loam soil, to characterize the effects of soil, in an indoor testbed and field testbeds. It is shown that at subsurface burial depths (0.1-0.4m), change in soil ...


Smart Parking Systems Design And Integration Into Iot, Charles M. Menne 2019 University of Minnesota - Morris

Smart Parking Systems Design And Integration Into Iot, Charles M. Menne

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

This paper looks at two smart parking reservation algorithms, and examines the ongoing efforts to connect smart systems of different domains in a city's infrastructure. The reservation algorithms are designed to improve the performance of smart parking systems. The first algorithm considers the distance between parking areas and the number of free parking spaces in determining a parking space. The second algorithm uses distance between parking areas and driver destination, parking price, and the number of unoccupied spaces for each parking area. Neither of these smart parking systems cover how they could fit into a larger scale smart system ...


Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater 2019 Southern Methodist University

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide ...


A Comparative Evaluation Of Recommender Systems For Hotel Reviews, Ryan Khaleghi, Kevin Cannon, Raghuram Srinivas 2019 Southern Methodist University

A Comparative Evaluation Of Recommender Systems For Hotel Reviews, Ryan Khaleghi, Kevin Cannon, Raghuram Srinivas

SMU Data Science Review

There has been increasing growth in deployment of recommender systems across Internet sites, with various models being used. These systems have been particularly valuable for review sites, as they seek to add value to the user experience to gain market share and to create new revenue streams through deals. Hotels are a prime target for this effort, as there is a large number for most destinations and a lot of differentiation between them. In this paper, we present an evaluation of two of the most popular methods for hotel review recommender systems: collaborative filtering and matrix factorization. The accuracy of ...


Automatic Program Rewriting In Non-Ground Answer Set Programs, Nicholas Hippen, Yuliya Lierler 2018 University of Nebraska at Omaha

Automatic Program Rewriting In Non-Ground Answer Set Programs, Nicholas Hippen, Yuliya Lierler

Yuliya Lierler

Answer set programming is a popular constraint programming paradigm that has seen wide use across various industry applications. However, logic programs under answer set semantics often require careful design and nontrivial expertise from a programmer to obtain satisfactory solving times. In order to reduce this burden on a software engineer we propose an automated rewriting technique for non-ground logic programs that we implement in a system Projector. We conduct rigorous experimental analysis, which shows that applying system Projector to a logic program can improve its performance, even after significant human-performed optimizations.


Strong Equivalence And Program's Structure In Arguing Essential Equivalence Between First-Order Logic Programs, Yuliya Lierler 2018 Department of Compter Science

Strong Equivalence And Program's Structure In Arguing Essential Equivalence Between First-Order Logic Programs, Yuliya Lierler

Yuliya Lierler

Answer set programming  is a prominent declarative programming paradigm used in formulating combinatorial search problems and implementing distinct knowledge representation formalisms. It is common that several related and yet substantially different answer set programs exist for a given problem. Sometimes these encodings may display significantly different performance. Uncovering precise formal links between these programs is often important and yet far from trivial. This paper claims the correctness   of a number of interesting program rewritings. Notably, they  assume  programs with variables and  such important language features as choice, disjunction, and aggregates. We showcase the utility of some considered rewritings  by using ...


Complexity Results For Fourier-Motzkin Elimination, Delaram Talaashrafi 2018 The University of Western Ontario

Complexity Results For Fourier-Motzkin Elimination, Delaram Talaashrafi

Electronic Thesis and Dissertation Repository

In this thesis, we propose a new method for removing all the redundant inequalities generated by Fourier-Motzkin elimination. This method is based on Kohler’s work and an improved version of Balas’ work. Moreover, this method only uses arithmetic operations on matrices. Algebraic complexity estimates and experimental results show that our method outperforms alternative approaches based on linear programming.


Mobility-Driven Ble Transmit-Power Adaptation For Participatory Data Muling, Chung-Kyun HAN, Archan MISRA, Shih-Fen CHENG 2018 Singapore Management University

Mobility-Driven Ble Transmit-Power Adaptation For Participatory Data Muling, Chung-Kyun Han, Archan Misra, Shih-Fen Cheng

Research Collection School Of Information Systems

This paper analyzes a human-centric framework, called SmartABLE, for easy retrieval of the sensor values from pervasively deployed smart objects in a campus-like environment. In this framework, smartphones carried by campus occupants act as data mules, opportunistically retrieving data from nearby BLE (Bluetooth Low Energy) equipped smart object sensors and relaying them to a backend repository. We focus specifically on dynamically varying the transmission power of the deployed BLE beacons, so as to extend their operational lifetime without sacrificing the frequency of sensor data retrieval. We propose a memetic algorithm-based power adaptation strategy that can handle deployments of thousands of ...


Determinants Of Interval Matrices, Jaroslav Horáček, Milan Hladík, Josef Matějka 2018 Charles University, Prague, Czech Republic

Determinants Of Interval Matrices, Jaroslav Horáček, Milan Hladík, Josef Matějka

Electronic Journal of Linear Algebra

In this paper we shed more light on determinants of real interval matrices. Computing the exact bounds on a determinant of an interval matrix is an NP-hard problem. Therefore, attention is first paid to approximations. NP-hardness of both relative and absolute approximation is proved. Next, methods computing verified enclosures of interval determinants and their possible combination with preconditioning are discussed. A new method based on Cramer's rule was designed. It returns similar results to the state-of-the-art method, however, it is less consuming regarding computational time. Other methods transferable from real matrices (e.g., the Gerschgorin circles, Hadamard's inequality ...


Constrained K-Means Clustering Validation Study, Nicholas McDaniel, Stephen Burgess, Jeremy Evert 2018 Southwestern Oklahoma State University

Constrained K-Means Clustering Validation Study, Nicholas Mcdaniel, Stephen Burgess, Jeremy Evert

Student Research

Machine Learning (ML) is a growing topic within Computer Science with applications in many fields. One open problem in ML is data separation, or data clustering. Our project is a validation study of, “Constrained K-means Clustering with Background Knowledge" by Wagstaff et. al. Our data validates the finding by Wagstaff et. al., which shows that a modified k-means clustering approach can outperform more general unsupervised learning algorithms when some domain information about the problem is available. Our data suggests that k-means clustering augmented with domain information can be a time efficient means for segmenting data sets. Our validation study focused ...


Sampling Complexity Of Bosonic Random Walkers On A One-Dimensional Lattice, Gopikrishnan Muraleedharan, Akimasa Miyake, Ivan Deutsch 2018 University of New Mexico - Main Campus

Sampling Complexity Of Bosonic Random Walkers On A One-Dimensional Lattice, Gopikrishnan Muraleedharan, Akimasa Miyake, Ivan Deutsch

Shared Knowledge Conference

Computers based quantum logic are believed to solve problems faster and more efficiently than computers based on classical boolean logic. However, a large-scale universal quantum computer with error correction may not be realized in near future. But we can ask the question: can we devise a specific problem that a quantum device can solve faster than current state of the art super computers? One such problem is the so called "Boson Sampling" problem introduced by Aaronson and Arkhipov. The problem is to generate random numbers according to same distribution as the output number configurations of photons in linear optics. It ...


Genetic Algorithm Design Of Photonic Crystals For Energy-Efficient Ultrafast Laser Transmitters, Troy A. Hutchins-Delgado 2018 University of New Mexico

Genetic Algorithm Design Of Photonic Crystals For Energy-Efficient Ultrafast Laser Transmitters, Troy A. Hutchins-Delgado

Shared Knowledge Conference

Photonic crystals allow light to be controlled and manipulated such that novel photonic devices can be created. We are interested in using photonic crystals to increase the energy efficiency of our semiconductor whistle-geometry ring lasers. A photonic crystal will enable us to reduce the ring size, while maintaining confinement, thereby reducing its operating power. Photonic crystals can also exhibit slow light that will increase the interaction with the material. This will increase the gain, and therefore, lower the threshold for lasing to occur. Designing a photonic crystal for a particular application can be a challenge due to its number of ...


Sat-Based Explicit Ltlf Satisfiability Checking, Jianwen Li, Kristin Y. Rozier, Geguang Pu, Yueling Zhang, Moshe Y. Vardi 2018 Iowa State University

Sat-Based Explicit Ltlf Satisfiability Checking, Jianwen Li, Kristin Y. Rozier, Geguang Pu, Yueling Zhang, Moshe Y. Vardi

Aerospace Engineering Publications

We present here a SAT-based framework for LTLf (Linear Temporal Logic on Finite Traces) satisfiability checking. We use propositional SAT-solving techniques to construct a transition system for the input LTLf formula; satisfiability checking is then reduced to a path-search problem over this transition system. Furthermore, we introduce CDLSC (Conflict-Driven LTLf Satisfiability Checking), a novel algorithm that leverages information produced by propositional SAT solvers from both satisfiability and unsatisfiability results. Experimental evaluations show that CDLSC outperforms all other existing approaches for LTLf satisfiability checking, by demonstrating an approximate four-fold speedup compared to the second-best solver.


A Mathematical Framework On Machine Learning: Theory And Application, Bin Shi 2018 Florida International University

A Mathematical Framework On Machine Learning: Theory And Application, Bin Shi

FIU Electronic Theses and Dissertations

The dissertation addresses the research topics of machine learning outlined below. We developed the theory about traditional first-order algorithms from convex opti- mization and provide new insights in nonconvex objective functions from machine learning. Based on the theory analysis, we designed and developed new algorithms to overcome the difficulty of nonconvex objective and to accelerate the speed to obtain the desired result. In this thesis, we answer the two questions: (1) How to design a step size for gradient descent with random initialization? (2) Can we accelerate the current convex optimization algorithms and improve them into nonconvex objective? For application ...


Unsupervised User Identity Linkage Via Factoid Embedding, XIE, Ka Wei, Roy LEE, Roy Ka-Wei LEE, Feida ZHU, Ee-peng LIM 2018 Singapore Management University

Unsupervised User Identity Linkage Via Factoid Embedding, Xie, Ka Wei, Roy Lee, Roy Ka-Wei Lee, Feida Zhu, Ee-Peng Lim

Research Collection School Of Information Systems

User identity linkage (UIL), the problem of matching user account across multiple online social networks (OSNs), is widely studied and important to many real-world applications. Most existing UIL solutions adopt a supervised or semisupervised approach which generally suffer from scarcity of labeled data. In this paper, we propose Factoid Embedding, a novel framework that adopts an unsupervised approach. It is designed to cope with different profile attributes, content types and network links of different OSNs. The key idea is that each piece of information about a user identity describes the real identity owner, and thus distinguishes the owner from other ...


Using Finite-State Models For Log Differencing, Hen AMAR, Lingfeng BAO, Nimrod BUSANY, David LO, Shahar MAOZ 2018 Singapore Management University

Using Finite-State Models For Log Differencing, Hen Amar, Lingfeng Bao, Nimrod Busany, David Lo, Shahar Maoz

Research Collection School Of Information Systems

Much work has been published on extracting various kinds ofmodels from logs that document the execution of running systems.In many cases, however, for example in the context of evolution,testing, or malware analysis, engineers are interested not only in asingle log but in a set of several logs, each of which originated froma different set of runs of the system at hand. Then, the differencebetween the logs is the main target of interest.In this work we investigate the use of finite-state models forlog differencing. Rather than comparing the logs directly, we generate concise models to describe and highlight ...


Exploring The Effect Of Different Numbers Of Convolutional Filters And Training Loops On The Performance Of Alphazero, Jared Prince 2018 Western Kentucky University

Exploring The Effect Of Different Numbers Of Convolutional Filters And Training Loops On The Performance Of Alphazero, Jared Prince

Masters Theses & Specialist Projects

In this work, the algorithm used by AlphaZero is adapted for dots and boxes, a two-player game. This algorithm is explored using different numbers of convolutional filters and training loops, in order to better understand the effect these parameters have on the learning of the player. Different board sizes are also tested to compare these parameters in relation to game complexity. AlphaZero originated as a Go player using an algorithm which combines Monte Carlo tree search and convolutional neural networks. This novel approach, integrating a reinforcement learning method previously applied to Go (MCTS) with a supervised learning method (neural networks ...


Influence Maximization On Social Graphs: A Survey, Yuchen LI, Ju FAN, Yanhao WANG, Kian-Lee TAN 2018 Singapore Management University

Influence Maximization On Social Graphs: A Survey, Yuchen Li, Ju Fan, Yanhao Wang, Kian-Lee Tan

Research Collection School Of Information Systems

Influence Maximization (IM), which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technical challenges, IM has been extensively studied in the past decade. In this paper, we survey and synthesize a wide spectrum of existing studies on IM from an algorithmic perspective, with a special focus on the following key aspects (1) a review of well-accepted diffusion models that capture information diffusion process and build the foundation ...


Smt-Based Constraint Answer Set Solver Ezsmt+ For Non-Tight Programs, Da Shen, Yuliya Lierler 2018 University of Nebraska at Omaha

Smt-Based Constraint Answer Set Solver Ezsmt+ For Non-Tight Programs, Da Shen, Yuliya Lierler

Yuliya Lierler

Constraint answer set programming integrates answer set programming with constraint processing. System Ezsmt+ is a constraint answer set programming tool that utilizes satisfiability modulo theory solvers for search. The truly unique feature of ezsmt+ is its capability to process linear as well as nonlinear constraints simultaneously containing integer and real variables.


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