Computer Sciences Commons^™

How Better Are Predictive Models: Analysis On The Practically Important Example Of Robust Interval Uncertainty, Vladik Kreinovich, Hung T. Nguyen, Songsak Sriboonchitta, Olga Kosheleva

Departmental Technical Reports (CS)

One of the main applications of science and engineering is to predict future value of different quantities of interest. In the traditional statistical approach, we first use observations to estimate the parameters of an appropriate model, and then use the resulting estimates to make predictions. Recently, a relatively new predictive approach has been actively promoted, the approach where we make predictions directly from observations. It is known that in general, while the predictive approach requires more computations, it leads to more accurate predictions. In this paper, on the practically important example of robust interval uncertainty, we analyze how more accurate …

How To Gauge Accuracy Of Processing Big Data: Teaching Machine Learning Techniques To Gauge Their Own Accuracy, Vladik Kreinovich, Thongchai Dumrongpokaphan, Hung T. Nguyen, Olga Kosheleva

Departmental Technical Reports (CS)

When the amount of data is reasonably small, we can usually fit this data to a simple model and use the traditional statistical methods both to estimate the parameters of this model and to gauge this model's accuracy. For big data, it is often no longer possible to fit them by a simple model. Thus, we need to use generic machine learning techniques to find the corresponding model. The current machine learning techniques estimate the values of the corresponding parameters, but they usually do not gauge the accuracy of the corresponding general non-linear model. In this paper, we show how …

Kuznets Curve: A Simple Dynamical System-Based Explanation, Thongchai Dumrongpokaphan, Vladik Kreinovich

Departmental Technical Reports (CS)

In the 1950s, a future Nobelist Simon Kuznets discovered the following phenomenon: as a country's economy improves, inequality first grows but then decreases. In this paper, we provide a simple dynamical system-based explanation for this empirical phenomenon.

Taking Into Account Interval (And Fuzzy) Uncertainty Can Lead To More Adequate Statistical Estimates, Ligang Sun, Hani Dbouk, Steffen Schön, Vladik Kreinovich

Departmental Technical Reports (CS)

Traditional statistical data processing techniques (such as Least Squares) assume that we know the probability distributions of measurement errors. Often, we do not have full information about these distributions. In some cases, all we know is the bound of the measurement error; in such cases, we can use known interval data processing techniques. Sometimes, this bound is fuzzy; in such cases, we can use known fuzzy data processing techniques.

However, in many practical situations, we know the probability distribution of the random component of the measurement error and we know the upper bound -- numerical or fuzzy -- on the …

Entropy As A Measure Of Average Loss Of Privacy, Luc Longpre, Vladik Kreinovich, Thongchai Dumrongpokaphan

Departmental Technical Reports (CS)

Privacy means that not everything about a person is known, that we need to ask additional questions to get the full information about the person. It therefore seems to reasonable to gauge the degree of privacy in each situation by the average number of binary ("yes"-"no") questions that we need to ask to determine the full information -- which is exactly Shannon's entropy. The problem with this idea is that it is possible, by asking two binary questions -- and thus, strictly speaking, getting only two bits of information -- to sometimes learn a large amount of information. In this …

Maximum Entropy As A Feasible Way To Describe Joint Distributions In Expert Systems, Thongchai Dumrongpokaphan, Vladik Kreinovich, Hung T. Nguyen

Departmental Technical Reports (CS)

In expert systems, we elicit the probabilities of different statements from the experts. However, to adequately use the expert system, we also need to know the probabilities of different propositional combinations of the experts' statements -- i.e., we need to know the corresponding joint distribution. The problem is that there are exponentially many such combinations, and it is not practically possible to elicit all their probabilities from the experts. So, we need to estimate this joint distribution based on the available information. For this purpose, many practitioners use heuristic approaches -- e.g., the t-norm approach of fuzzy logic. However, this …

Why Student Distributions? Why Matern's Covariance Model? A Symmetry-Based Explanation, Steffen Schön, Gaël Kermarrec, Boris Kargoll, Ingo Neumann, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In this paper, we show that empirical successes of Student distribution and of Matern's covariance models can be indirectly explained by a natural requirement of scale invariance -- that fundamental laws should not depend on the choice of physical units. Namely, while neither the Student distributions nor Matern's covariance models are themselves scale-invariant, they are the only one which can be obtained by applying a scale-invariant combination function to scale-invariant functions.

Markowitz Portfolio Theory Helps Decrease Medicines' Side Effect And Speed Up Machine Learning, Thongchai Dumrongpokaphan, Vladik Kreinovich

Departmental Technical Reports (CS)

In this paper, we show that, similarly to the fact that distributing the investment between several independent financial instruments decreases the investment risk, using a combination of several medicines can decrease the medicines' side effects. Moreover, the formulas for optimal combinations of medicine are the same as the formulas for the optimal portfolio, formulas first derived by the Nobel-prize winning economist H. M. Markowitz. A similar application to machine learning explains a recent success of a modified neural network in which the input neurons are also directly connected to the output ones.

Recommending Personalized Schedules In Urban Environments, Cen Chen

Dissertations and Theses Collection

In this thesis, we are broadly interested in solving real world problems that involve decision support for coordinating agent movements in dynamic urban environments, where people are agents exhibiting different human behavior patterns and preferences. The rapid development of mobile technologies makes it easier to capture agent behavioral and preference information. Such rich agent specific information, coupled with the explosive growth of computational power, opens many opportunities that we could potentially leverage, to better guide/influence the agents in urban environments. The purpose of this thesis is to investigate how we can effectively and efficiently guide and coordinate the agents with …

A Direct D-Bar Method For Partial Boundary Data Electrical Impedance Tomography With A Priori Information, Melody Alsaker, Sarah J. Hamilton, Andreas Hauptmann

Mathematics, Statistics and Computer Science Faculty Research and Publications

Electrical Impedance Tomography (EIT) is a non-invasive imaging modality that uses surface electrical measurements to determine the internal conductivity of a body. The mathematical formulation of the EIT problem is a nonlinear and severely ill-posed inverse problem for which direct D-bar methods have proved useful in providing noise-robust conductivity reconstructions. Recent advances in D-bar methods allow for conductivity reconstructions using EIT measurement data from only part of the domain (e.g., a patient lying on their back could be imaged using only data gathered on the accessible part of the body). However, D-bar reconstructions suffer from a loss of sharp edges …

Selecting Link Resolver And Knowledge Base Software: Implications Of Interoperability, Cyndy Chisare, Jody C. Fagan, David J. Gaines, Michael Trocchia

Libraries

Link resolver software and their associated knowledge bases are essential technologies for modern academic libraries. However, because of the increasing number of possible integrations involving link resolver software and knowledge bases, a library’s vendor relationships, product choices, and consortial arrangements may have the most dramatic effects on the user experience and back-end maintenance workloads. A project team at a large comprehensive university recently investigated link resolver products in an attempt to increase efficiency of back-end workflows while maintaining or improving the patron experience. The methodology used for product comparison may be useful for other libraries.

Levity Polymorphism, Richard A. Eisenberg, Simon Peyton Jones

Computer Science Faculty Research and Scholarship

Parametric polymorphism is one of the linchpins of modern typed programming, but it comes with a real performance penalty. We describe this penalty; offer a principled way to reason about it (kinds as calling conventions); and propose levity polymorphism. This new form of polymorphism allows abstractions over calling conventions; we detail and verify restrictions that are necessary in order to compile levity-polymorphic functions. Levity polymorphism has created new opportunities in Haskell, including the ability to generalize nearly half of the type classes in GHC's standard library.

Chinese Font Style Transfer With Neural Network, Xue Hanyu

Dartmouth College Master’s Theses

Font design is an important area in digital art. However, designers have to design character one by one manually. At the same time, Chinese contains more than 20,000 characters. Chinese offical dataset GB 18030-2000 has 27,533 characters. ZhongHuaZiHai, an official Chinese dictionary, contains 85,568 characters. And JinXiWenZiJing, an dataset published by AINet company, includes about 160,000 chinese characters. Thus Chinese font design is a hard task. In the paper, we introduce a method to help designers finish the process faster. With the method, designers only need to design a small set of Chinese characters. Other characters will be generated automatically. …

Comparing Grounded Theory And Topic Modeling: Extreme Divergence Or Unlikely Convergence?, Eric P.S. Baumer, David Mimno, Shion Guha, Emily Quan, Geri K. Gay

Mathematics, Statistics and Computer Science Faculty Research and Publications

Researchers in information science and related areas have developed various methods for analyzing textual data, such as survey responses. This article describes the application of analysis methods from two distinct fields, one method from interpretive social science and one method from statistical machine learning, to the same survey data. The results show that the two analyses produce some similar and some complementary insights about the phenomenon of interest, in this case, nonuse of social media. We compare both the processes of conducting these analyses and the results they produce to derive insights about each method's unique advantages and drawbacks, as …

Introduction (2017), Association Of Christians In The Mathematical Sciences

ACMS Conference Proceedings 2017

No abstract provided.

Paper Abstracts (2017), Association Of Christians In The Mathematical Sciences

ACMS Conference Proceedings 2017

No abstract provided.

Exponential Chain Dual To Ratio Cum Dual To Product Estimator For Finite Population Mean In Double Sampling Scheme, Yater Tato, B. K. Singh

Applications and Applied Mathematics: An International Journal (AAM)

This paper considers an exponential chain dual to ratio cum dual to product estimator for estimating finite population mean using two auxiliary variables in double sampling scheme when the information on another additional auxiliary variable is available along with the main auxiliary variable. The expressions for bias and mean square error of the asymptotically optimum estimator are identified in two different cases. The optimum value of the first phase and second phase sample size has been obtained for the fixed cost of survey. To illustrate the results, theoretical and empirical studies have also been carried out to judge the merits …

An Engagement Strategy For Teaching Computing Concepts, El Sayed Mahmoud

Publications and Scholarship

The research work in this paper investigates a new teaching strategy that uses active learning through play to increase students’ uptake of learning computing concepts. The strategy promotes student engagement through playing a customized Jenga game. The game consists of a set of blocks, one side of each block is covered with a piece of dry-erase tape to allow erasing and writing on the blocks. This allows instructors to reuse this editable Jenga for developing their own game-based learning activities. The editable Jenga can be used without writing if needed. Three sample activities with writing have been developed and conducted …

The Introduction Of Informal Cooperative Learning Into Our Programming Laboratories, Guity Ravai, Ludmila Nunes, Ronald Erdei

IMPACT Presentations

Presented at the Women in Engineering ProActive Network (WEPAN) Change Leader Forum: Creating a Mindset for Action in Westminster, CO, USA

Tackling The Interleaving Problem In Activity Discovery, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

Activity discovery (AD) is the unsupervised process of discovering activities in data produced from streaming sensor networks that are recording the actions of human subjects. One major challenge for AD systems is interleaving, the tendency for people to carry out multiple activities at a time a parallel. Following on from our previous work, we continue to investigate AD in interleaved datasets, with a view towards progressing the state-of-the-art for AD.