Computer Sciences Commons^™

Why Triangular And Trapezoid Membership Functions Are Efficient In Design Applications, Afshin Gholamy, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many design problems, it is important to take into account expert knowledge. Expert often describe their knowledge by using imprecise ("fuzzy") natural-language words like "small". To describe this imprecise knowledge in computer-understandable terms, Zadeh came up with special fuzzy methodology -- techniques that have been successful in many applications. This methodology starts with eliciting, from the expert, a membership function corresponding to each imprecise term -- a function that assigns, to each possible value of the corresponding quantity, a degree to which this value satisfies the relevant property (e.g., a degree to which, in the expert's opinion, this value …

Formalizing Schoenberg’S Fundamentals Of Musical Composition Through Petri Nets, A. Baratè, G. Haus, L. A. Ludovico, Davide Andrea Mauro

Computer Sciences and Electrical Engineering Faculty Research

The formalization of musical composition rules is a topic that has been studied for a long time. It can lead to a better understanding of the underlying processes, and provide a useful tool for musicologist to aid and speed up the analysis process. In our attempt we introduce Schoenberg’s rules from Fundamentals of Musical Composition using a specialized version of Petri nets, called Music Petri nets. Petri nets are a formal tool for studying systems that are concurrent, asynchronous, distributed, parallel, nondeterministic, and/or stochastic. We present some examples highlighting how multiple approaches to the analysis task can find counterparts in …

A Building Permit System For Smart Cities: A Cloud-Based Framework, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur, Adwait Kaley, Arpit Patel, Akshar Joshi, Dhvani Shah

Faculty Publications

In this paper we propose a novel, cloud-based framework to support citizens and city officials in the building permit process. The proposed framework is efficient, user-friendly, and transparent with a quick turn-around time for homeowners. Compared to existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of both the end user experience and the permitting and urban planning processes. This is enabled through a data mining-powered permit recommendation engine as well as a data analytics process that allow a gleaning of key …

Lp Algorithms For Portfolio Optimization: The Portfoliooptim Package, Andrzej Palczewski

The R Journal

The paper describes two algorithms for financial portfolio optimization with the following risk measures: CVaR, MAD, LSAD and dispersion CVaR. These algorithms can be applied to discrete distributions of asset returns since then the optimization problems can be reduced to linear programs. The first algorithm solves a simple recourse problem as described by Haneveld using Benders de composition method. The second algorithm finds an optimal portfolio with the smallest distance to a given benchmark portfolio and is an adaptation of the least norm solution (called also normal solution) of linear programs due to Zhao and Li. The algorithms are implemented …

The Automated Design Of Probabilistic Selection Methods For Evolutionary Algorithms, Samuel N. Richter, Daniel R. Tauritz

Computer Science Faculty Research & Creative Works

Selection functions enable Evolutionary Algorithms (EAs) to apply selection pressure to a population of individuals, by regulating the probability that an individual's genes survive, typically based on fitness. Various conventional fitness based selection methods exist, each providing a unique relationship between the fitnesses of individuals in a population and their chances of selection. However, the full space of selection algorithms is only limited by max algorithm size, and each possible selection algorithm is optimal for some EA configuration applied to a particular problem class. Therefore, improved performance may be expected by tuning an EA's selection algorithm to the problem at …

Evolution Of Network Enumeration Strategies In Emulated Computer Networks, Sean Harris, Eric Michalak, Kevin Schoonover, Adam Gausmann, Hannah Reinbolt, Joshua Herman, Daniel R. Tauritz, Chris Rawlings, Aaron Scott Pope

Computer Science Faculty Research & Creative Works

Successful attacks on computer networks today do not often owe their victory to directly overcoming strong security measures set up by the defender. Rather, most attacks succeed because the number of possible vulnerabilities are too large for humans to fully protect without making a mistake. Regardless of the security elsewhere, a skilled attacker can exploit a single vulnerability in a defensive system and negate the benefits of those security measures. This paper presents an evolutionary framework for evolving attacker agents in a real, emulated network environment using genetic programming, as a foundation for coevolutionary systems which can automatically discover and …

Creating Real-Time Dynamic Knowledge Graphs, Swati Padhee, Sarasi Lalithsena, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Performance Comparison Of Support Vector Machine, Random Forest, And Extreme Learning Machine For Intrusion Detection, Iftikhar Ahmad, Muhammad Javed Iqbal, Mohammad Basheri, Aneel Rahim

Articles

Intrusion detection is a fundamental part of security tools, such as adaptive security appliances, intrusion detection systems, intrusion prevention systems, and firewalls. Various intrusion detection techniques are used, but their performance is an issue. Intrusion detection performance depends on accuracy, which needs to improve to decrease false alarms and to increase the detection rate. To resolve concerns on performance, multilayer perceptron, support vector machine (SVM), and other techniques have been used in recent work. Such techniques indicate limitations and are not efficient for use in large data sets, such as system and network data. The intrusion detection system is used …

Searching For Relevant Lessons Learned Using Hybrid Information Retrieval Classifiers: A Case Study In Software Engineering, Tamer Mohamed Abdellatif Mohamed, Luiz Fernando Capretz, Danny Ho

Electrical and Computer Engineering Publications

The lessons learned (LL) repository is one of the most valuable sources of knowledge for a software organization. It can provide distinctive guidance regarding previous working solutions for historical software management problems, or former success stories to be followed. However, the unstructured format of the LL repository makes it difficult to search using general queries, which are manually inputted by project managers (PMs). For this reason, this repository may often be overlooked despite the valuable information it provides. Since the LL repository targets PMs, the search method should be domain specific rather than generic as in the case of general …

Optimization Under Fuzzy Constraints: From A Heuristic Algorithm To An Algorithm That Always Converges, Vladik Kreinovich, Juan Carlos Figueroa-Garcia

Departmental Technical Reports (CS)

An efficient iterative heuristic algorithm has been used to implement Bellman-Zadeh solution to the problem of optimization under fuzzy constraints. In this paper, we analyze this algorithm, explain why it works, show that there are cases when this algorithm does not converge, and propose a modification that always converges.

A Turing Machine Is Just A Finite Automaton With Two Stacks: A Comment On Teaching Theory Of Computation, Vladik Kreinovich, Olga Kosheleva

Departmental Technical Reports (CS)

Traditionally, when we teach Theory of Computation, we start with finite automata, we show that they are not sufficient, then we switch to pushdown automata (i.e., automata-with-stacks). Automata-with-stacks are also not sufficient, so we introduce Turing machines. The problem is that while the transition from finite automata to automata-with-stacks is reasonably natural, Turing machine are drastically different, and as a result, transition to Turing machines is difficult for some students. In this paper, we propose to solve this pedagogical problem by emphasizing that a Turing machine is, in effect, nothing else but a finite automaton with two stacks. This representation …

A Cyber-Physical System Framework For Early Detection Of Paroxysmal Diseases, Zuxing Gu, Yu Jiang, Min Zhou, Xiaoyu Song, Lui Sha

Computer Science Faculty Publications and Presentations

Paroxysmal diseases of inpatients are globally recognized as one of the top challenges in medicine. Poor clinical outcomes are primarily caused by delayed recognition, especially due to diverse clinical diagnostic criteria with complex manifestations, irregular episodes, and already overloaded clinical activities. With the proliferation of measuring devices and increased computational capabilities, cyber-physical characterization plays an increasingly important role in many domains to provide enabling technologies. This paper presents a cyber-physical system (CPS) framework to assist physicians in making earlier diagnoses of paroxysmal sympathetic hyperactivity based on existing medical knowledge. We propose a configurable diagnostic knowledge model to characterize clinical criteria …

Enhancing Received Signal Strength-Based Localization Through Coverage Hole Detection And Recovery, Shuangjiao Zhai, Zhanyong Tang, Dajin Wang, Qingpei Li, Zhanglei Li, Xiaojiang Chen, Dingyi Fang, Feng Chen, Zheng Wang

Department of Computer Science Faculty Scholarship and Creative Works

In wireless sensor networks (WSNs), Radio Signal Strength Indicator (RSSI)-based localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of WSNs. Insufficient coverage will significantly reduce the effectiveness of the applications. However, most existing studies on coverage assume that the sensing range of a sensor node is a disk, and the disk coverage model is too simplistic for many localization techniques. Moreover, there are some localization techniques of WSNs whose coverage model is non-disk, such as RSSI-based localization techniques. In …

Changes In R, R Core Team

The R Journal

CHANGES IN R 3.5.0 patched

CHANGES IN R 3.5.0

Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis

The R Journal

In the past 7 months, 1178 new packages were added to the CRAN package repository. 18 packages were unarchived, 493 archived and none removed. The following shows the growth of the number of active packages in the CRAN package repository:

R Day Report, Fernando P. Mayer, Walmes M. Zeviani, Wagner H. Bonat, Elias T. Krainski, Paulo J. Ribeiro Jr.

The R Journal

R Day¹- National Meeting of R Users, took place on May, 22, 2018 at Federal University of Paraná (UFPR), Curitiba, Brazil. It was the first event in Brazil endorsed by The R Foundation.

Setmethods: An Add-On R Package For Advanced Qca, Ioana-Elena Oana, Carsten Q. Scheider

The R Journal

This article presents the functionalities of the R package SetMethods, aimed at performing advanced set-theoretic analyses. This includes functions for performing set-theoretic multi-method research, set-theoretic theory evaluation, Enhanced Standard Analysis, diagnosing the impact of temporal, spatial, or substantive clusterings of the data on the results obtained via Qualitative Comparative Analysis (QCA), indirect calibration, and visualising QCA results via XY plots or radar charts. Each functionality is presented in turn, the conceptual idea and the logic behind the procedure being first summarized, and afterwards illustrated with data from Schneider et al. (2010).

Simple Features For R: Standardized Support For Spatial Vector Data, Edzer Pebesma

The R Journal

Simple features are a standardized way of encoding spatial vector data (points, lines, polygons) in computers. The sf package implements simple features in R, and has roughly the same capacity for spatial vector data as packages sp, rgeos, and rgdal. We describe the need for this package, its place in the R package ecosystem, and its potential to connect R to other computer systems. We illustrate this with examples of its use.

Epistemic Game Theory: Putting Algorithms To Work, Bilge BaşEr, Nalan Cinemre

The R Journal

The aim of this study is to construct an epistemic model in which each rational choice under common belief in rationality is supplemented by a type which expresses such a belief. In practice, the finding of type depends on manual solution approach with some mathematical operations in scope of the theory. This approach becomes less convenient with the growth of the size of the game. To solve this difficulty, a linear programming model is constructed for two-player, static and non-cooperative games to find the type that is supporting that player’s rational choice is optimal under common belief in rationality and …

Grpstring: An R Package For Analysis Of Groups Of Strings, Hui Tang, Elizabeth L. Day, Molly B. Atkinson, Norbert J. Pienta

The R Journal

The R package GrpString was developed as a comprehensive toolkit for quantitatively analyzing and comparing groups of strings. It offers functions for researchers and data analysts to prepare strings from event sequences, extract common patterns from strings, and compare patterns be tween string vectors. The package also finds transition matrices and complexity of strings, determines clusters in a string vector, and examines the statistical difference between two groups of strings.

Lba: An R Package For Latent Budget Analysis, Enio G. Jelihovschi, Ivan Bezerra Allaman

The R Journal

The latent budget model is a mixture model for compositional data sets in which the entries, a contingency table, may be either realizations from a product multinomial distribution or distribution free. Based on this model, the latent budget analysis considers the interactions of two variables; the explanatory (row) and the response (column) variables. The package lba uses expectation-maximization and active constraints method (ACM) to carry out, respectively, the maximum likelihood and the least squares estimation of the model parameters. It contains three main functions, lba which performs the analysis, goodnessfit for model selection and goodness of fit and the plotting …

Icsoutlier: Unsupervised Outlier Detection For Low-Dimensional Contamination Authors: Structure, Aurore Archimbaud, Klaus Nordhausen, Anne Ruiz-Gazen

The R Journal

Detecting outliers in a multivariate and unsupervised context is an important and ongoing problem notably for quality control. Many statistical methods are already implemented in R and are briefly surveyed in the present paper. But only a few lead to the accurate identification of potential outliers in the case of a small level of contamination. In this particular context, the Invariant Coordinate Selection (ICS) method shows remarkable properties for identifying outliers that lie on a low-dimensional subspace in its first invariant components. It is implemented in the ICSOutlier package. The main function of the package, ics.outlier, offers the possibility of …

Onewaytests: An R Package For One-Way Tests In Independent Groups Designs, Osman Dag, Anil Dolgun, Naime Meric Konar

The R Journal

One-way tests in independent groups designs are the most commonly utilized statistical methods with applications on the experiments in medical sciences, pharmaceutical research, agriculture, biology, engineering, social sciences and so on. In this paper, we present the one-way tests package to investigate treatment effects on the dependent variable. The package offers the one-way tests in independent groups designs, which include ANOVA, Welch’s heteroscedastic F test, Welch’s heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander Govern test, James second order test and Kruskal-Wallis test. The package also provides pairwise comparisons, graphical approaches, and assesses variance homogeneity and …

Bayesian Testing, Variable Selection And Model Averaging In Linear Models Using R With Bayesvarsel, Gonzalo Garcia-Donato, Anabel Forte

The R Journal

In this paper, objective Bayesian methods for hypothesis testing and variable selection in linear models are considered. The focus is on BayesVarSel, an R package that computes posterior probabilities of hypotheses/models and provides a suite of tools to properly summarize the results. We introduce the usage of specific functions to compute several types of model averaging estimations and predictions weighted by posterior probabilities. BayesVarSel contains exact algorithms to perform fast computations in problems of small to moderate size and heuristic sampling methods to solve large problems. We illustrate the functionalities of the package with several data examples.

Tackling Uncertainties Of Species Distribution Model Projections With Package Mopa, M. Iturbide, J. Bedia, J.M. Gutiérrez

The R Journal

Species Distribution Models (SDMs) constitute an important tool to assist decision-making in environmental conservation and planning in the context of climate change. Nevertheless, SDM projections are affected by a wide range of uncertainty factors (related to training data, climate projections and SDM techniques), which limit their potential value and credibility. The new package mopa provides tools for designing comprehensive multi-factor SDM ensemble experiments, combining multiple sources of uncertainty (e.g. baseline climate, pseudo-absence realizations, SDM techniques, future projections) and allowing to assess their contribution to the overall spread of the ensemble projection. In addition, mopa is seamlessly integrated with the climate4R …

Panjen: An R Package For Ranking Transformations In A Linear Regression, Cathrine Ulla Jensen, Toke Emil Panduro

The R Journal

PanJen is an R-package for ranking transformations in linear regressions. It provides users with the ability to explore the relationship between a dependent variable and its independent variables. The package offers an easy and data-driven way to choose a functional form in multiple linear regression models by comparing a range of parametric transformations. The parametric functional forms are benchmarked against each other and a non-parametric transformation. The package allows users to generate plots that show the relation between a covariate and the dependent variable. Furthermore, PanJen will enable users to specify specific functional transformations, driven by a priori and theory-based …

Arco: An R Package To Estimate Artificial Counterfactuals, Yuri R. Fonseca, Ricardo P. Masini, Marcelo C. Medeiros, Gabriel F.R. Vasconcelos

The R Journal

In this paper we introduce the ArCo package for R which consists of a set of functions to implement the the Artificial Counterfactual (ArCo) methodology to estimate causal effects of an intervention (treatment) on aggregated data and when a control group is not necessarily available. The ArCo method is a two-step procedure, where in the first stage a counterfactual is estimated from a large panel of time series from a pool of untreated peers. In the second-stage, the average treatment effect over the post-intervention sample is computed. Standard inferential procedures are available. The package is illustrated with both simulated and …

Infotrad: An R Package For Estimating The Probability Of Informed Trading, Duygu Çelik, Murat Tiniç

The R Journal

The purpose of this paper is to introduce the R package InfoTrad for estimating the probability of informed trading (PIN) initially proposed by Easley et al. (1996). PIN is a popular information asymmetry measure that proxies the proportion of informed traders in the market. This study provides a short survey on alternative estimation techniques for the PIN. There are many problems documented in the existing literature in estimating PIN. InfoTrad package aims to address two problems. First, the sequential trading structure proposed by Easley et al. (1996) and later extended by Easley et al. (2002) is prone to sample selection …

Conference Report: Erum 2018, Gergely Daróczi

The R Journal

The European R Users Meeting (eRum) is an international conference that aims at bringing together users of the R language living in Europe– in the years when the useR! conference is hosted outside of the continent.

The first eRum conference was held in 2016 in Poznan, Poland with around 250 attendees and 20 sessions spanning over 3 days, including more than 80 speakers. Around that time, we also held a smaller conference in Budapest: the first satRday event happened with 25 speakers and almost 200 attendees from 19 countries in 2016.

The eRum 2018 conference is heritage of these two …

Realvams: An R Package For Fitting A Multivariate Value-Added Model (Vam), Jennifer Broatch, Jennifer Green, Andrew Karl

The R Journal

We present RealVAMS, an R package for fitting a generalized linear mixed model to multimembership data with partially crossed and partially nested random effects. RealVAMS utilizes a multivariate generalized linear mixed model with pseudo-likelihood approximation for fitting normally distributed continuous response(s) jointly with a binary outcome. In an educational context, the model is referred to as a multidimensional value-added model, which extends previous theory to estimate the relationships between potential teacher contributions toward different student outcomes and to allow the consideration of a binary, real-world outcome such as graduation. The simultaneous joint modeling of continuous and binary outcomes was not …