Random forests based monitoring of human larynx using questionnaire data

This paper is concerned with soft computing techniques-based noninvasive monitoring of human larynx using subject’s questionnaire data. By applying random forests (RF), questionnaire data are categorized into a healthy class and several classes of disorders including: cancerous, noncancerous, diffuse, nodular, paralysis, and an overall pathological class. The most important questionnaire statements are determined using RF variable importance evaluations. To explore data represented by variables used by RF, the t-distributed stochastic neighbor embedding (t-SNE) and the multidimensional scaling (MDS) are applied to the RF data proximity matrix. When testing the developed tools on a set of data collected from 109 subjects, the 100% classification accuracy was obtained on unseen data in binary classification into the healthy and pathological classes. The accuracy of 80.7% was achieved when classifying the data into the healthy, cancerous, noncancerous classes. The t-SNE and MDS mapping techniques applied allow obtaining two-dimensional maps of data and facilitate data exploration aimed at identifying subjects belonging to a “risk group”. It is expected that the developed tools will be of great help in preventive health care in laryngology.     

Unified problem modeling language for knowledge engineering of complex systems

The 90s has seen the emergence of hybrid configurations of four most commonly used intelligent methodologies, namely, symbolic knowledge based systems (e.g. expert systems), artificial neural networks, fuzzy systems and genetic algorithms. These hybrid configurations are used for different problem solving tasks/situations. In this paper we describe unified problem modeling language at two different levels, the task structure level for knowledge engineering of complex data intensive domains, and the computational level of the task level hybrid architecture. Among other aspects, the unified problem modeling language considers various intelligent methodologies and their hybrid configurations as technological primitives used to accomplish various tasks defined at the task structure level. The unified problem modeling language is defined in the form of five problem solving adapters. The problem solving adapters outline the goals, tasks, percepts/inputs, and hard and soft computing methods for modeling complex problems. The task structure level has been applied in modeling several applications in e-commerce, image processing, diagnosis, and other complex, time critical, and data intensive domains. We also define a layered intelligent multi-agent, operating system processes, intelligent technologies with the task structure level associative hybrid architecture. The layered architecture also facilitates component based software modeling process.Work Supported by VPAC grant no EPPNLA002.2001     

Unified problem modeling language for knowledge engineering of complex systems-part II

Real time applications to control industrial, medical, scientific, consumer, environmental and other processes is rapidly growing. Today such systems can be found in nuclear power stations, computer-controlled chemical plants, flight control, etc. This growth, however, has also brought to the forefront some of problems with the existing technologies. In domains like real-time alarm processing in a power system control centre existing technologies like expert systems cannot efficiently cope with. These problems have pushed for research into new techniques which could be used for solving these problems. The problems range from among other aspects, the enormous size of the power system and the fast response time constraints in emergency situations. In this paper we describe the application of the Intelligent Multi-Agent Hybrid Distributed Architecture for real-time alarm processing in a power system control centre. We show how the IMAHDA architecture is able to model the complexity and size of the power system as well as meet the desired response time constraints. Implementation of a large scale real time system like alarm processing involves realization of various objectives. These include methodology related objectives, domain related objectives, and management related objectives. This paper also describes the realization of these objectives.     

Solving the problem of incomplete data in medical diagnosis via interval modeling

This paper presents an approach to making accurate and high-quality decisions under incomplete information. Our comprehensive approach includes interval modeling of incomplete data, uncertaintification of classical models and aggregation of incomplete results. We conducted a thorough evaluation of our approach using medical data for ovarian tumor diagnosis, where the problem of missing data is commonly encountered. The results confirmed that methods based on interval modeling and aggregation make it possible to reduce the negative impact of lack of data and lead to meaningful and accurate decisions. A diagnostic model developed in this way proved better than classical diagnostic models for ovarian tumor. Additionally, a framework in R that implements our method was created and is available for reproduction of our results. The proposed approach has been incorporated into a real-life diagnosis support system – OvaExpert.     

基于UML与CCSP的产品配置方法

为了有效开发易维护可重用的产品配置模型以及实现配置问题的快速求解,提出了结合面向对象建模技术与条件约束满足问题理论的产品配置方法。给出了条件约束满足问题理论模型;提出了基于统一建模语言和条件约束满足问题的产品配置建模与求解方法;通过定义统一建模语言表示的产品配置概念模型与条件约束满足问题之间的映射规则集,建立了基于条件约束满足问题的产品配置模型。以某可配置医用监测器为应用实例,阐述了所提方法应用于配置模型构建与求解的可行性和有效性。     

Random field modeling with insufficient field data for probability analysis and design

Often engineered systems entail randomness as a function of spatial (or temporal) variables. The random field can be found in the form of geometry, material property, and/or loading in engineering products and processes. In some applications, consideration of the random field is a key to accurately predict variability in system performances. However, existing methods for random field modeling are limited for practical use because they require sufficient field data. This paper thus proposes a new random field modeling method using a Bayesian Copula that facilitates the random field modeling with insufficient field data and applies this method for engineering probability analysis and robust design optimization. The proposed method is composed of three key ideas: (i) determining the marginal distribution of random field realizations at each measurement location, (ii) determining optimal Copulas to model statistical dependence of the field realizations at different measurement locations, and (iii) modeling a joint probability density function of the random field. A mathematical problem was first employed for the purpose of demonstrating the accuracy of the random field modeling with insufficient field data. The second case study deals with the assembly process of a two-door refrigerator that challenges predicting the door assembly tolerance and minimizing the tolerance by designing the random field and parameter variables in the assembly process with insufficient random field data. It is concluded that the proposed random field modeling can be used to successfully conduct the probability analysis and robust design optimization with insufficient random field data.     

Random Boolean nets and features of language

Describes an attempt to cast several abstract properties of natural languages in the framework of Kauffman's (1993, 1995) random Boolean nets (RBN). The properties are complexity, interconnectedness, stability, diversity, and underdeterminedness. A language is modeled as a Boolean net attractor. (Groups of) net nodes are linguistic principles or parameters as posited by Chomskyan theory, according to which the language learner sets parameters to appropriate values on the basis of very limited experience of the language. The setting of one parameter can have a complex effect on the settings of others. A RBN is generated to find an attractor. A state from this attractor is degraded, which represents the degenerate input of language to the learner, and this state is then input to a net with the same connectivity and activation functions as the original net to see whether it converges on the same attractor. Many nets degenerate into attractors representing complete uncertainty. Others settle at intermediate levels of uncertainty, and some manage to overcome the incompleteness of input and converge on attractors identical to that from which the original inputs were (de)generated. Finally, an attempt was made to select a population of such successful nets, using a genetic algorithm where fitness was correlated with an ability to acquire several different languages faithfully. This has so far proved impossible, supporting the Chomskyan suggestion that the human language acquisition capacity is not the outcome of natural selection     

Multiparadigm modeling by the SODAR-2 language

The SODAR-2 modeling language designed for specifying user commands to automate the integrity control of multi-language program projects is described. Using a model of the program project of the “double black box” type that is abstracted from the specific content of computing processes, we demonstrate the capabilities and benefits of interpretation metaprogramming in small projects utilizing program codes that are freely accessible in the Internet. The fragments of programs generated in the run of the processes of such metaprograms are presented.     

Investigation and modeling of the structure of texting language

Language usage over computer mediated discourses, such as chats, emails and SMS texts, significantly differs from the standard form of the language and is referred to as texting language (TL). The presence of intentional misspellings significantly decrease the accuracy of existing spell checking techniques for TL words. In this work, we formally investigate the nature and type of compressions used in SMS texts, and develop a Hidden Markov Model based word-model for TL. The model parameters have been estimated through standard machine learning techniques from a word-aligned SMS and standard English parallel corpus. The accuracy of the model in correcting TL words is 57.7%, which is almost a threefold improvement over the performance of Aspell. The use of simple bigram language model results in a 35% reduction of the relative word level error rates.     

A bit of progress in language modeling

In the past several years, a number of different language modeling improvements over simple trigram models have been found, including caching, higher-order n -grams, skipping, interpolated Kneser–Ney smoothing, and clustering. We present explorations of variations on, or of the limits of, each of these techniques, including showing that sentence mixture models may have more potential. While all of these techniques have been studied separately, they have rarely been studied in combination. We compare a combination of all techniques together to a Katz smoothed trigram model with no count cutoffs. We achieve perplexity reductions between 38 and 50% (1 bit of entropy), depending on training data size, as well as a word error rate reduction of 8.9%. Our perplexity reductions are perhaps the highest reported compared to a fair baseline.     

Random forests to evaluate biotic interactions in fish distribution models

Previous research indicated that high predictive performance in species distribution modelling can be obtained by combining both biotic and abiotic habitat variables. However, models developed for fish often only address physical habitat characteristics, thus omitting potentially important biotic factors. Therefore, we assessed the impact of biotic variables on fish habitat preferences in four selected stretches of the upper Cabriel River (E Spain). The occurrence of Squalius pyrenaicus and Luciobarbus guiraonis was related to environmental variables describing biotic interactions (inferred by relationships among fish abundances) and channel hydro-morphological characteristics. Random Forests (RF) models were trained and then validated using independent datasets. To build RF models, the conditional variable importance was used together with the model improvement ratio technique. The procedure showed effectiveness in identifying a parsimonious set of not correlated variables, which minimize noise and improve model performance in both training and validation phases. Water depth, channel width, fine substrate and water-surface gradient were selected as most important habitat variables for both fish. Results showed clear habitat overlapping between fish species and suggest that competition is not a strong factor in the study area.     

A requirements modeling language and its logic

This paper describes some aspects of a Requirements Modeling Language (RML) which can be used in the initial phases of software development. RML is based on the idea that a requirements specification should embody a conceptual world model and that the language for expressing it should provide facilities for organizing and abstracting details, yet at the same time have qualities such as precision, consistency and clarity.RML has a number of novel features including assertion classes, the treatment of time and various abbreviation techniques, all integrated into one uniform object-oriented framework. The precise semantics of these and other features are provided in this paper by relating RML to a logic involving time. This demonstrates that a language can offer highly structured and convenient mechanisms for requirements specifications while having solid mathematical underpinnings.     

Random and fuzzy sets in coarse data analysis

The theoretical aspects of statistical inference with imprecise data, with focus on random sets, are considered. On the setting of coarse data analysis imprecision and randomness in observed data are exhibited, and the relationship between probability and other types of uncertainty, such as belief functions and possibility measures, is analyzed. Coarsening schemes are viewed as models for perception-based information gathering processes in which random fuzzy sets appear naturally. As an implication, fuzzy statistics is statistics with fuzzy data. That is, fuzzy sets are a new type of data and as such, complementary to statistical analysis in the sense that they enlarge the domain of applications of statistical science.     

Toward formalizing domain modeling semantics in language syntax

Information systems are situated in and are representations of some business or organizational domain. Hence, understanding the application domain is critical to the success of information systems development. To support domain understanding, the application domain is represented in conceptual models. The correctness of conceptual models can affect the development outcome and prevent costly rework during later development stages. This paper proposes a method to restrict the syntax of a modeling language to ensure that only possible configurations of a domain can be modeled, thus increasing the likelihood of creating correct domain models. The proposed method, based on domain ontologies, captures relationships among domain elements via constraints on the language metamodel, thus restricting the set of statements about the domain that can be generated with the language. In effect, this method creates domain specific modeling languages from more generic ones. The method is demonstrated using the Unified Modeling Language (UML). Specifically, it is applied to the subset of UML dealing with object behavior and its applicability is demonstrated on a specific modeling example.     

基于空间问题建模概念过程的空间分析建模与实现

提出一种基于空间问题建模概念过程的空间分析建模方法,结合城镇土地定级模型和流程,构建基于空间问题建模概念过程的空间分析模型.运用该模型,采用ArcGIS Engine技术,开发独立于GIS平台的城镇土地定级信息系统.实践表明,将空间问题建模概念过程和城镇土地定级模型相结合进行空间分析建模的方法,可优化空间分析流程,有效保证空间分析模型的合理性和分析结果的准确性.     

Entity-life modeling: modeling a thread architecture on the problem environment

With Java threads and the wider availability of multiprocessors, more programmers are confronted with multithreading. Concurrent threads let you take advantage of multiprocessors to speed up execution. They are also useful on a single processor, where one thread can compute while others wait for external input. Entity-life modeling is an approach for designing multithread programs.     

Documenting electronic commerce systems and software using the unified modeling language

Electronic commerce (EC) systems are complex systems consisting of cooperating heterogeneous software, hardware and database subsystems that are distributed among processing nodes [1]. They are reactive, real-time and concurrent distributed systems. They are financially critical systems since they perform distributed business functions, the success of which is very critical for the business operation. The use of well-defined specification and documentation techniques is very essential for the effective development and maintenance of these systems. In this paper, we propose the use of the unified modeling language (UML) [2] as a technique for documenting and specifying EC systems at various levels of abstractions and from different views. We believe that the use of UML ensures a better reliability and reusability of these systems.     

Random matrix theory for modeling uncertainties in computational mechanics

This paper deals with data uncertainties and model uncertainties issues in computational mechanics. If data uncertainties can be modeled by parametric probabilistic methods, for a given mean model, a nonparametric probabilistic approach can be used for modeling model uncertainties. The first part is devoted to random matrix theory for which we summarize previous published results and for which two new ensembles of random matrices useful for the nonparametric models are introduced. In a second part, the nonparametric probabilistic approach of random uncertainties is presented for linear dynamical systems and for nonlinear dynamical systems constituted of a linear part with additional localized nonlinearities. In a third part, a new method is proposed for estimating the parameters of the nonparametric approach from experiments. Finally, examples with experimental comparisons are given.