Computer Sciences Commons^™

Why Stable Teams Are More Efficient In Education, Francisco Zapata, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

It is known that study groups speed up learning. Recent studies have shown that stable study groups are more efficient than shifting-membership groups. In this paper, we provide a theoretical explanation for this empirical observation.

Why Are Fgm Copulas Successful: A Simple Explanation, Songsak Sriboonchitta, Vladik Kreinovich

Departmental Technical Reports (CS)

One of the most computationally convenient non-redundant ways to describe the dependence between two variables is by describing the corresponding copula. In many application, a special class of copulas -- known as FGM copulas -- turned out to be most successful in describing the dependence between quantities. The main result of this paper is that these copulas are the fastest-to-compute, and this explains their empirical success.

As an auxiliary result, we also show that a similar explanation can be given in terms of fuzzy logic.

Why Convex Optimization Is Ubiquitous And Why Pessimism Is Widely Spread, Angel F. Garcia Contreras, Martine Ceberio, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical applications, the objective function is convex. The use of convex objective functions makes optimization easier, but ubiquity of such objective function is a mystery: many practical optimization problems are not easy to solve, so it is not clear why the objective function -- whose main goal is to describe our needs -- would always describe easier-to-achieve goals. In this paper, we explain this ubiquity based on the fundamental ideas about human decision making. This explanation also helps us explain why in decision making under uncertainty, people often make pessimistic decisions, i.e.., decisions based on the worst-case scenarios.

In Fuzzy Decision Making, General Fuzzy Sets Can Be Replaced By Fuzzy Numbers, Christian Servin, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many real decision situations, for each of the alternatives, we only have fuzzy information about the consequences of each action. This fuzzy information can be described by a fuzzy number, i.e., by a membership function with a single local maximum, or it can be described by a more complex fuzzy set, with several local maxima. We show that, from the viewpoint of decision making, it is sufficient to consider only fuzzy numbers. To be more precise, the decisions will be the same if we replace each original fuzzy set with the smallest fuzzy number of all fuzzy numbers of …

Can We Detect Crisp Sets Based Only On The Subsethood Ordering Of Fuzzy Sets? Fuzzy Sets And/Or Crisp Sets Based On Subsethood Of Interval-Valued Fuzzy Sets?, Christian Servin, Gerardo Muela, Vladik Kreinovich

Departmental Technical Reports (CS)

Fuzzy sets are naturally ordered by the subsethood relation. If we only know which set which fuzzy set is a subset of which -- and have no access to the actual values of the corresponding membership functions -- can we detect which fuzzy sets are crisp? In this paper, we show that this is indeed possible. We also show that if we start with interval-valued fuzzy sets, then we can similarly detect type-1 fuzzy sets and crisp sets.

How To Deal With Uncertainties In Computing: From Probabilistic And Interval Uncertainty To Combination Of Different Approaches, With Applications To Engineering And Bioinformatics, Vladik Kreinovich

Departmental Technical Reports (CS)

Most data processing techniques traditionally used in scientific and engineering practice are statistical. These techniques are based on the assumption that we know the probability distributions of measurement errors etc.

In practice, often, we do not know the distributions, we only know the bound D on the measurement accuracy -- hence, after the get the measurement result X, the only information that we have about the actual (unknown) value x of the measured quantity is that $x$ belongs to the interval [X − D, X + D]. Techniques for data processing under such interval uncertainty are called interval computations; these …

Towards Predictive Statistics: A Pedagogical Explanation, Vladik Kreinovich

Departmental Technical Reports (CS)

In statistics application area, lately, several publications appeared that warn about the dangers of the inappropriate application of statistics and remind the users of the recall that prediction is the ultimate objective of the statistical analysis. This trend is known as predictive statistics. However, while the intended message is aimed at the very general audience of practitioners and researchers who apply statistics, many of these papers are not easy to read since they are either too technical and/or too philosophical for the general reader. In this short paper, we describe the main ideas and recommendation of predictive statistics in …

Plans Are Worthless But Planning Is Everything: A Theoretical Explanation Of Eisenhower's Observation, Angel F. Garcia Contreras, Martine Ceberio, Vladik Kreinovich

Departmental Technical Reports (CS)

The 1953-1961 US President Dwight D. Eisenhower emphasized that his experience as the Supreme Commander of the Allied Expeditionary Forces in Europe during the Second World War taught him that "plans are worthless, but planning is everything". This sound contradictory: if plans are worthless, why bother with planning at all? In this paper, we show that Eisenhower's observation has a meaning: while directly following the original plan in constantly changing circumstances is often not a good idea, the existence of a pre-computed original plan enables us to produce an almost-optimal strategy -- a strategy that would have been computationally difficult …

(Hypothetical) Negative Probabilities Can Speed Up Uncertainty Propagation Algorithms, Andrzej Pownuk, Vladik Kreinovich

Departmental Technical Reports (CS)

One of the main features of quantum physics is that, as basic objects describing uncertainty, instead of (non-negative) probabilities and probability density functions, we have complex-valued probability amplitudes and wave functions. In particular, in quantum computing, negative amplitudes are actively used. In the current quantum theories, the actual probabilities are always non-negative. However, there have been some speculations about the possibility of actually negative probabilities. In this paper, we show that such hypothetical negative probabilities can lead to a drastic speed up of uncertainty propagation algorithms.

A Consultation System For Cold - Heat Diagnosis According To Vietnamese Traditional Medicine Combining Positive And Negative Knowledge, Tran Thi Hue, Nguyen Hoang Phuong, Vladik Kreinovich

Departmental Technical Reports (CS)

The aim of the paper is to show that in practice, the Cold - Heat diagnosis of Vietnamese Traditional Medicine is better to combining positive and negative knowledge. Based on text book and experiences of Traditional medicine practitioner we build the knowledge base combining positive knowledge and negative knowledge for Cold - Heat diagnosis. Then we use the FuzzRESS - A Fuzzy Rule-based Expert System Shell for Medical Consultation combining Positive and Negative Knowledge to such called A consultation system for Cold - Heat diagnosis according to Vietnamese Traditional medicine combining positive and negative knowledge. Finally, the developed system is …

Fuzzy Systems Are Universal Approximators For Random Dependencies: A Simplified Proof, Mahdokhat Afravi, Vladik Kreinovich

Departmental Technical Reports (CS)

In many real-life situations, we do not know the actual dependence y = f(x₁, ..., x_n) between the physical quantities x_i and y, we only know expert rules describing this dependence. These rules are often described by using imprecise ("fuzzy") words from natural language. Fuzzy techniques have been invented with the purpose to translate these rules into a precise dependence y = f(x₁, ..., x_n). For deterministic dependencies y = f(x₁, ..., x_n), there are universal approximation results according to which for each continuous function on a …

Why Mixture Of Probability Distributions, Andrzej Pownuk, Vladik Kreinovich

Departmental Technical Reports (CS)

If we have two random variables ξ₁ and ξ₁, then we can form their mixture if we take ξ₁ with some probability w and ξ₂ with the remaining probability 1 − w. The probability density function (pdf) ρ(x) of the mixture is a convex combination of the pdfs of the original variables: ρ(x) = w * ρ₁(x) +( 1 − w) * ρ₂(x). A natural question is: can we use other functions f(ρ₁, ρ₂) to combine the pdfs, i.e., to produce a new pdf ρ(x) =f(ρ_{1 …}

Experimentally Observed Dark Matter Confinement Clarifies A Discrepancy In Estimating The Universe's Expansion Speed, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

It is well known the our Universe is expanding. In principle, we can estimate the expansion speed either directly, by observing the current state of the Universe, or indirectly, by analyzing the cosmic background radiation. Surprisingly, these two estimates lead to somewhat different expansion speeds. This discrepancy is a important challenge for cosmologists. Another challenge comes from recent experiments that show that, contrary to the original idea that dark matter and regular (baryonic) matter practically do not interact, dark matter actually "shadows" the normal matter.

In this paper, we show that this "dark matter confinement" can explain the discrepancy between …

Which Value X Best Represents A Sample X1, ..., Xn: Utility-Based Approach Under Interval Uncertainty, Andrzej Pownuk, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we have several estimates x₁, ..., x_n of the same quantity x. In such situations, it is desirable to combine this information into a single estimate x. Often, the estimates come with interval uncertainty, i.e., instead of the exact values x_i, we only know the intervals [x_i] containing these values. In this paper, we formalize the problem of finding the combined estimate x as the problem of maximizing the corresponding utility, and we provide an efficient (quadratic-time) algorithm for computing the resulting estimate.

Beyond Traditional Applications Of Fuzzy Techniques: Main Idea And Case Studies, Vladik Kreinovich, Olga Kosheleva, Thongchai Dumrongpokaphan

Departmental Technical Reports (CS)

Fuzzy logic techniques were originally designed to translate expert knowledge -- which is often formulated by using imprecise ("fuzzy") from natural language (like "small") -- into precise computer-understandable models and control strategies. Such a translation is still the main use of fuzzy techniques. Lately, it turned out that fuzzy methods can help in another class of applied problems: namely, in situations when there are semi-heuristic techniques for solving the corresponding problems, i.e., techniques for which there is no convincing theoretical justification. Because of the lack of a theoretical justification, users are reluctant to use these techniques, since their previous empirical …

Why Decimal System And Binary System Are The Most Widely Used: A Possible Explanation, Gerardo Muela

Departmental Technical Reports (CS)

What is so special about numbers 10 and 2 that decimal and binary systems are the most widely used? One interesting fact about 10 is that when we start with a unit interval and we want to construct an interval of half width, then this width is exactly 5/10; when we want to find a square of half area, its sides are almost exactly 7/10, and when we want to construct a cube of half volume its sides are almost exactly 8/10. In this paper, we show that 2, 4, and 10 are the only numbers with this property -- …

F-Transform As A First Step Towards A General Approach To Data Processing And Data Fusion, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In data fusion, we have several approximations to the desired objects, and we need to fuse them into a single -- more accurate -- approximation. In the traditional approach to data fusion, we usually assume that all the given approximations were obtained by minimizing the same distance function -- most frequently, the Euclidean (L₂) distance. In practice, however, we sometimes need to use approximations corresponding to different distance functions. To handle such situations, a new more general approach to data processing and data fusion is needed. In this paper, we show that the simplest cases of such new …

Why Unexpectedly Positive Experiences Make Decision Makers More Optimistic: An Explanation, Andrzej Pownuk, Vladik Kreinovich

Departmental Technical Reports (CS)

Experiments show that unexpectedly positive experiences make decision makers more optimistic. However, there seems to be no convincing explanation for this experimental fact. In this paper, we show that this experimental phenomenon can be naturally explained within the traditional utility-based decision theory.

Probabilistic And More General Uncertainty-Based (E.G., Fuzzy) Approaches To Crisp Clustering Explain The Empirical Success Of The K-Sets Algorithm, Vladik Kreinovich, Olga Kosheleva, Shahnaz Shahbazova, Songsak Sriboonchitta

Departmental Technical Reports (CS)

Recently, a new empirically successful algorithm was proposed for crisp clustering: the K-sets algorithm. In this paper, we show that a natural uncertainty-based formalization of what is clustering automatically leads to the mathematical ideas and definitions behind this algorithm. Thus, we provide an explanation for this algorithm's empirical success.

Computational Methods For Prediction And Classification Of G Protein-Coupled Receptors, Khodeza Begum

Open Access Theses & Dissertations

G protein-coupled receptors (GPCRs) constitute the largest group of membrane receptor proteins in eukaryotes. Due to their significant roles in many physiological processes such as vision, smell, and inflammation, GPCRs are the targets of many prescribed drugs. However, the functional and structural diversity of GPCRs has kept their prediction and classification based on amino acid sequence data as a challenging bioinformatics problem. As existing computational methods to predict and classify GPCRs are focused on mammalian (mostly human) data, the ultimate goal of our project is to establish an ensemble approach and implement a web-based software that can be used reliably …

Classification Of Radar Jammer Fm Signals Using A Neural Network Approach, Ariadna Estefania Mendoza

Open Access Theses & Dissertations

A Neural Network (NN) used to classify radar signals is proposed for the purpose of military survivability and lethality analysis. The goal of the NN is to correctly differentiate Frequency-Modulated (FM) signals from Additive White Gaussian Noise (AWGN) using limited signal pre-processing. The FM signals used to test the NN approach are the linear or chirp FM and the power-law FM. Preliminary simulations using the moments of the signals in the time and frequency domain yielded better results in the frequency domain, suggesting that time domain training would not be as effective frequency domain training. To test this hypoThesis, we …

Essays On Sofwtare Development Projects: Impact Of Social And Technological Factors On Project Performance And Co-Diffusion Of Software Sourcing Arrangements, Niharika Dayyala

Open Access Theses & Dissertations

Software development is a complicated process which is accomplished by the combined effort of the key elements such as people, processes, and technology. Software development firms are on a constant quest to identify the best practices for managing the key elements to improve their software development project outcomes and to deliver successful software projects. This Dissertation aims to study key elements that can influence the software development project performance through three distinctive studies. The first essay of this Dissertation investigates the impact of people factors on software project outcomes. Specifically, it examines the impact of team characteristics on the software …

Analysis Of Gesture Frequency And Amplitude As A Function Of Personality In Virtual Agents, Alex Michael Rayon

Open Access Theses & Dissertations

Embodied conversational agents are changing the way humans interact with technology. Agents are expected to behave like people, both verbally and non-verbally. Researchers within the Human-Computer Interaction community have found many attributes of an agentâ??s gesture contribute to an agentâ??s perceived personality and believability.

Gesture amplitude and frequency are two of the attributes that contribute to the naturalness of a gesture. Currently, no previous studies have defined an empirical baseline for generating natural gestures for these two attributes. I seek to discover whether gesture amplitude and frequency affect how users perceive an agentâ??s naturalness, and which gestures would be preferred …

Deep Learning Method Vs. Hand-Crafted Features For Lung Cancer Diagnosis And Breast Cancer Risk Analysis, Wenqing Sun

Open Access Theses & Dissertations

Breast cancer and lung cancer are two major leading causes of cancer deaths, and researchers have been developing computer aided diagnosis (CAD) system to automatically diagnose them for decades. In recent studies, we found that the techniques in CAD system can also be used for breast cancer risk analysis, like feature design and machine learning. Also we noticed that with the development of deep learning methods, the performance of CAD system can be improved by using computer automatically generated features. To explore these possibilities, we conducted a series of studies: the first two studies focused on transferring the original CAD …

Contextual Representation Of Documents, Entities, And Faces Of People Using A News Corpus, Md Abdul Kader

Open Access Theses & Dissertations

A massive amount of unstructured data, in this information age, is composed of document collections. Examples include news articles, blog posts, scholarly publications, and reports generated by organizations as well as people. Many data mining and machine learning algorithms have been developed in the past decade to support text mining in many applications. Text mining applications cover a wide range of tasks spanning from personal information management --- to organizational decision making, to disease control through epidemic prediction, and to intelligence analysis for national security. One bottleneck has been dominating data mining and machine learning theories for all these text …

Integration Of Heterogeneous Traffic Data To Address Mobility Challenges In The City Of El Paso, Daniel Michael Mejia

Open Access Theses & Dissertations

Transportation performance measures are defined as quantitative and qualitative indicators that rely on data or information to explain mobility, traffic congestion, safety, environmental sustainability and other factors. Although performance measures have been used for freeways and other highways, not many have been specified and applied to the freight transportation system. Under the Fixing America's Surface Transportation Act (FAST Act), state transportation agencies and metropolitan planning organizations in the United States are implementing freight performance measurement systems for performance assessment. This research aims to expand the existing limited freight performance measures and organize them into a comprehensive framework that can be …

Analysing The Effects Of Data Augmentation And Free Parameters For Text Classification With Recurrent Convolutional Neural Networks, Jonathan Quijas

Open Access Theses & Dissertations

Convolutional neural networks have seen much success in computer vision and natural language processing tasks. When training convolutional neural networks for text classification tasks, a common technique is to transform an input sequence of words into a dense matrix of word embeddings, or words represented as dense vectors, using table lookup operations. This enables the inputs to be represented in a way that the well-known convolution/pooling operations can be applied to them in a manner similar to images. These word embeddings may be further incorporated into the neural network itself as a trainable layer to allow fine-tuning, usually leading to …

Describing Data And Workflow Provenance Using Design Patterns And Controlled Vocabularies, Smriti Rajkarnikar Tamrakar

Open Access Theses & Dissertations

In any scientific experiment, researchers are required to access, compute, and analyze data to produce useful information to the scientific community. In order to instill trust on such scientific research products, the product users need to understand the procedure applied and the assumptions incorporated. The reuse and replication of reliable scientific data need methods that help the users to understand data origin and the derivation process, i.e. provenance. Although several standards for representing provenance such as PROV model (a W3C recommendation) have been recommended, they have not been widely utilized by scientific communities due to difficulty in aligning such recommended …

Optimization Of Neural Network Architecture For Classification Of Radar Jamming Fm Signals, Alberto Soto

Open Access Theses & Dissertations

Radar jamming signal classification is valuable when situational awareness of radar systems is sought out for timely deployment of electronic support measures. Our Thesis shows that artificial neural networks can be utilized for effective and efficient signal classification. The goal is to optimize an artificial Neural Network (NN) approach capable of distinguishing between two common radar waveforms, namely bandlimited white Gaussian jamming noise (BWGN) and the ubiquitous linearly frequency modulated (LFM) signal. This is made possible by creating a theoretical framework for NN architecture testing that leads to a high probability of detection (PD) and a low probability of false …

Towards Decision Making Under Interval Uncertainty, Andrzej Pownuk, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we know the exact form of the objective function, and we know the optimal decision corresponding to each values of the corresponding parameters x_i. What should we do if we do not know the exact values of x_i, and instead, we only know each x_i with uncertainty -- e.g., with interval uncertainty? In this case, one of the most widely used approaches is to select, for each i, one value from the corresponding interval -- usually, a midpoint -- and to use the exact-case optimal decision corresponding to the selected values. …