Computer automation of land evaluation procedures

This study was concerned with the computer automation of land evaluation. This is a broad subject with many issues to be resolved, so the study concentrated on three key problems: knowledge based programming; the integration of spatial information from remote sensing and other sources; and the inclusion of socio-economic information into the land evaluation analysis. Land evaluation and land use planning were considered in the context of overseas projects in the developing world. Knowledge based systems were found to provide significant advantages over conventional programming techniques for some aspects of the land evaluation process. Declarative languages, in particular Prolog, were ideally suited to integration of social information which changes with every situation. Rule-based expert system shells were also found to be suitable for this role, including knowledge acquisition at the interview stage. All the expert system shells examined suffered from very limited constraints to problem size, but new products now overcome this. Inductive expert system shells were useful as a guide to knowledge gaps and possible relationships, but the number of examples required was unrealistic for typical land use planning situations. The accuracy of classified satellite imagery was significantly enhanced by integrating spatial information on soil distribution for Thailand data. Estimates of the rice producing area were substantially improved (30% change in area) by the addition of soil information. Image processing work on Mozambique showed that satellite remote sensing was a useful tool in stratifying vegetation cover at provincial level to identify key development areas, but its full utility could not be realised on typical planning projects, without treatment as part of a complete spatial information system.

Elaboration and testing of new methodologies for automatic abstracting

The primary objective of this research was to understand what kinds of knowledge and skills people use in `extracting' relevant information from text and to assess the extent to which expert systems techniques could be applied to automate the process of abstracting. The approach adopted in this thesis is based on research in cognitive science, information science, psycholinguistics and textlinguistics. The study addressed the significance of domain knowledge and heuristic rules by developing an information extraction system, called INFORMEX. This system, which was implemented partly in SPITBOL, and partly in PROLOG, used a set of heuristic rules to analyse five scientific papers of expository type, to interpret the content in relation to the key abstract elements and to extract a set of sentences recognised as relevant for abstracting purposes. The analysis of these extracts revealed that an adequate abstract could be generated. Furthermore, INFORMEX showed that a rule based system was a suitable computational model to represent experts' knowledge and strategies. This computational technique provided the basis for a new approach to the modelling of cognition. It showed how experts tackle the task of abstracting by integrating formal knowledge as well as experiential learning. This thesis demonstrated that empirical and theoretical knowledge can be effectively combined in expert systems technology to provide a valuable starting approach to automatic abstracting.

The development of an expert system for the identification anodic coating process defects as a contribution to the dissemination of anodizing technology

Initially this thesis examines the various mechanisms by which technology is acquired within anodizing plants. In so doing the history of the evolution of anodizing technology is recorded, with particular reference to the growth of major markets and to the contribution of the marketing efforts of the aluminium industry. The business economics of various types of anodizing plants are analyzed. Consideration is also given to the impact of developments in anodizing technology on production economics and market growth. The economic costs associated with work rejected for process defects are considered. Recent changes in the industry have created conditions whereby information technology has a potentially important role to play in retaining existing knowledge. One such contribution is exemplified by the expert system which has been developed for the identification of anodizing process defects. Instead of using a "rule-based" expert system, a commercial neural networks program has been adapted for the task. The advantages of neural networks over 'rule-based' systems is that they are better suited to production problems, since the actual conditions prevailing when the defect was produced are often not known with certainty. In using the expert system, the user first identifies the process stage at which the defect probably occurred and is then directed to a file enabling the actual defects to be identified. After making this identification, the user can consult a database which gives a more detailed description of the defect, advises on remedial action and provides a bibliography of papers relating to the defect. The database uses a proprietary hypertext program, which also provides rapid cross-referencing to similar types of defect. Additionally, a graphics file can be accessed which (where appropriate) will display a graphic of the defect on screen. A total of 117 defects are included, together with 221 literature references, supplemented by 48 cross-reference hyperlinks. The main text of the thesis contains 179 literature references. (DX186565)

Networks of Evolutionary Processors (NEP) as Decision Support Systems

This paper presents the application of Networks of Evolutionary Processors to Decision Support Systems, precisely Knowledge-Driven DSS. Symbolic information and rule-based behavior in Networks of Evolutionary Processors turn out to be a great tool to obtain decisions based on objects present in the network. The non-deterministic and massive parallel way of operation results in NP-problem solving in linear time. A working NEP example is shown.

BOWL:augmenting the semantic web with beliefs

As the Semantic Web is an open, complex and constantly evolving medium, it is the norm, but not exception that information at different sites is incomplete or inconsistent. This poses challenges for the engineering and development of agent systems on the Semantic Web, since autonomous software agents need to understand, process and aggregate this information. Ontology language OWL provides core language constructs to semantically markup resources on the Semantic Web, on which software agents interact and cooperate to accomplish complex tasks. However, as OWL was designed on top of (a subset of) classic predicate logic, it lacks the ability to reason about inconsistent or incomplete information. Belief-augmented Frames (BAF) is a frame-based logic system that associates with each frame a supporting and a refuting belief value. In this paper, we propose a new ontology language Belief-augmented OWL (BOWL) by integrating OWL DL and BAF to incorporate the notion of confidence. BOWL is paraconsistent, hence it can perform useful reasoning services in the presence of inconsistencies and incompleteness. We define the abstract syntax and semantics of BOWL by extending those of OWL. We have proposed reasoning algorithms for various reasoning tasks in the BOWL framework and we have implemented the algorithms using the constraint logic programming framework. One example in the sensor fusion domain is presented to demonstrate the application of BOWL.

Query Enrichment for Image Collections by Reuse of Classification Rules

User queries over image collections, based on semantic similarity, can be processed in several ways. In this paper, we propose to reuse the rules produced by rule-based classifiers in their recognition models as query pattern definitions for searching image collections.

Making sense of microposts (#MSM2013) concept extraction challenge

Microposts are small fragments of social media content that have been published using a lightweight paradigm (e.g. Tweets, Facebook likes, foursquare check-ins). Microposts have been used for a variety of applications (e.g., sentiment analysis, opinion mining, trend analysis), by gleaning useful information, often using third-party concept extraction tools. There has been very large uptake of such tools in the last few years, along with the creation and adoption of new methods for concept extraction. However, the evaluation of such efforts has been largely consigned to document corpora (e.g. news articles), questioning the suitability of concept extraction tools and methods for Micropost data. This report describes the Making Sense of Microposts Workshop (#MSM2013) Concept Extraction Challenge, hosted in conjunction with the 2013 World Wide Web conference (WWW'13). The Challenge dataset comprised a manually annotated training corpus of Microposts and an unlabelled test corpus. Participants were set the task of engineering a concept extraction system for a defined set of concepts. Out of a total of 22 complete submissions 13 were accepted for presentation at the workshop; the submissions covered methods ranging from sequence mining algorithms for attribute extraction to part-of-speech tagging for Micropost cleaning and rule-based and discriminative models for token classification. In this report we describe the evaluation process and explain the performance of different approaches in different contexts.

Development of prediction models for freeway incident durations using data mining techniques

The nation's freeway systems are becoming increasingly congested. A major contribution to traffic congestion on freeways is due to traffic incidents. Traffic incidents are non-recurring events such as accidents or stranded vehicles that cause a temporary roadway capacity reduction, and they can account for as much as 60 percent of all traffic congestion on freeways. One major freeway incident management strategy involves diverting traffic to avoid incident locations by relaying timely information through Intelligent Transportation Systems (ITS) devices such as dynamic message signs or real-time traveler information systems. The decision to divert traffic depends foremost on the expected duration of an incident, which is difficult to predict. In addition, the duration of an incident is affected by many contributing factors. Determining and understanding these factors can help the process of identifying and developing better strategies to reduce incident durations and alleviate traffic congestion. A number of research studies have attempted to develop models to predict incident durations, yet with limited success. ^ This dissertation research attempts to improve on this previous effort by applying data mining techniques to a comprehensive incident database maintained by the District 4 ITS Office of the Florida Department of Transportation (FDOT). Two categories of incident duration prediction models were developed: "offline" models designed for use in the performance evaluation of incident management programs, and "online" models for real-time prediction of incident duration to aid in the decision making of traffic diversion in the event of an ongoing incident. Multiple data mining analysis techniques were applied and evaluated in the research. The multiple linear regression analysis and decision tree based method were applied to develop the offline models, and the rule-based method and a tree algorithm called M5P were used to develop the online models. ^ The results show that the models in general can achieve high prediction accuracy within acceptable time intervals of the actual durations. The research also identifies some new contributing factors that have not been examined in past studies. As part of the research effort, software code was developed to implement the models in the existing software system of District 4 FDOT for actual applications. ^

Event mining for system and service management

Modern IT infrastructures are constructed by large scale computing systems and administered by IT service providers. Manually maintaining such large computing systems is costly and inefficient. Service providers often seek automatic or semi-automatic methodologies of detecting and resolving system issues to improve their service quality and efficiency. This dissertation investigates several data-driven approaches for assisting service providers in achieving this goal. The detailed problems studied by these approaches can be categorized into the three aspects in the service workflow: 1) preprocessing raw textual system logs to structural events; 2) refining monitoring configurations for eliminating false positives and false negatives; 3) improving the efficiency of system diagnosis on detected alerts. Solving these problems usually requires a huge amount of domain knowledge about the particular computing systems. The approaches investigated by this dissertation are developed based on event mining algorithms, which are able to automatically derive part of that knowledge from the historical system logs, events and tickets. ^ In particular, two textual clustering algorithms are developed for converting raw textual logs into system events. For refining the monitoring configuration, a rule based alert prediction algorithm is proposed for eliminating false alerts (false positives) without losing any real alert and a textual classification method is applied to identify the missing alerts (false negatives) from manual incident tickets. For system diagnosis, this dissertation presents an efficient algorithm for discovering the temporal dependencies between system events with corresponding time lags, which can help the administrators to determine the redundancies of deployed monitoring situations and dependencies of system components. To improve the efficiency of incident ticket resolving, several KNN-based algorithms that recommend relevant historical tickets with resolutions for incoming tickets are investigated. Finally, this dissertation offers a novel algorithm for searching similar textual event segments over large system logs that assists administrators to locate similar system behaviors in the logs. Extensive empirical evaluation on system logs, events and tickets from real IT infrastructures demonstrates the effectiveness and efficiency of the proposed approaches.^

O Ensino de ligações químicas por meio do conceito de energia: uma proposta didática para o ensino médio

This work presents discussions on the teaching of Chemical Bonds in high school and some implications of this approach in learning chemistry by students. In general, understanding how the chemicals combine to form substances and compounds, it is a key point for understanding the properties of substances and their structure. In this sense, the chemical bonds represent an extremely important issue, and their knowledge is essential for a better understanding of the changes occurring in our world. Despite these findings, it is observed that the way in which this concept is discussed in chemistry class has contributed, paradoxically, to the emergence of several alternative designs, making the understanding of the subject by students. It is believed that one of the explanations for these observations is the exclusive use of the "octet rule" as an explanatory model for the Chemical Bonds. The use of such a model over time eventually replace chemical principles that gave rise to it, transforming knowledge into a series of uninteresting rituals and even confusing for students. Based on these findings, it is deemed necessary a reformulation in the way to approach this content in the classroom, taking into account especially the fact that the explanations of the formation of substances should be based on the energy concept, which is fundamental to understanding how atoms combine. Thus, the main question of the survey and described here of the following question: Can the development of an explanatory model for the Chemical Bonds in high school based on the concept of energy and without the need to use the "octet rule"? Based on the concepts and methodologies of modeling activity, we sought the development of a teaching model was made through Teaching Units designed to give subsidies to high school teachers to address the chemical bonds through the concept of energy. Through this work it is intended to make the process of teaching and learning of Chemical Bonds content becomes more meaningful to students, developing models that contribute to the learning of this and hence other basic fundamentals of chemistry.

O Ensino de ligações químicas por meio do conceito de energia: uma proposta didática para o ensino médio

This work presents discussions on the teaching of Chemical Bonds in high school and some implications of this approach in learning chemistry by students. In general, understanding how the chemicals combine to form substances and compounds, it is a key point for understanding the properties of substances and their structure. In this sense, the chemical bonds represent an extremely important issue, and their knowledge is essential for a better understanding of the changes occurring in our world. Despite these findings, it is observed that the way in which this concept is discussed in chemistry class has contributed, paradoxically, to the emergence of several alternative designs, making the understanding of the subject by students. It is believed that one of the explanations for these observations is the exclusive use of the "octet rule" as an explanatory model for the Chemical Bonds. The use of such a model over time eventually replace chemical principles that gave rise to it, transforming knowledge into a series of uninteresting rituals and even confusing for students. Based on these findings, it is deemed necessary a reformulation in the way to approach this content in the classroom, taking into account especially the fact that the explanations of the formation of substances should be based on the energy concept, which is fundamental to understanding how atoms combine. Thus, the main question of the survey and described here of the following question: Can the development of an explanatory model for the Chemical Bonds in high school based on the concept of energy and without the need to use the "octet rule"? Based on the concepts and methodologies of modeling activity, we sought the development of a teaching model was made through Teaching Units designed to give subsidies to high school teachers to address the chemical bonds through the concept of energy. Through this work it is intended to make the process of teaching and learning of Chemical Bonds content becomes more meaningful to students, developing models that contribute to the learning of this and hence other basic fundamentals of chemistry.

Long-term modifications of coastal defences enhance marine biodiversity

Realization that hard coastal infrastructures support lower biodiversity than natural habitats has prompted a wealth of research seeking to identify design enhancements offering ecological benefits. Some studies showed that artificial structures could be modified to increase levels of diversity. Most studies, however, only considered the short-term ecological effects of such modifications, even though reliance on results from short-term studies may lead to serious misjudgements in conservation. In this study, a seven-year experiment examined how the addition of small pits to otherwise featureless seawalls may enhance the stocks of a highly-exploited limpet. Modified areas of the seawall supported enhanced stocks of limpets seven years after the addition of pits. Modified areas of the seawall also supported a community that differed in the abundance of littorinids, barnacles and macroalgae compared to the controls. Responses to different treatments (numbers and size of pits) were species-specific and, while some species responded directly to differences among treatments, others might have responded indirectly via changes in the distribution of competing species. This type of habitat enhancement can have positive long-lasting effects on the ecology of urban seascapes. Understanding of species interactions could be used to develop a rule-based approach to enhance biodiversity.

Long-term modifications of coastal defences enhance marine biodiversity

Realization that hard coastal infrastructures support lower biodiversity than natural habitats has prompted a wealth of research seeking to identify design enhancements offering ecological benefits. Some studies showed that artificial structures could be modified to increase levels of diversity. Most studies, however, only considered the short-term ecological effects of such modifications, even though reliance on results from short-term studies may lead to serious misjudgements in conservation. In this study, a seven-year experiment examined how the addition of small pits to otherwise featureless seawalls may enhance the stocks of a highly-exploited limpet. Modified areas of the seawall supported enhanced stocks of limpets seven years after the addition of pits. Modified areas of the seawall also supported a community that differed in the abundance of littorinids, barnacles and macroalgae compared to the controls. Responses to different treatments (numbers and size of pits) were species-specific and, while some species responded directly to differences among treatments, others might have responded indirectly via changes in the distribution of competing species. This type of habitat enhancement can have positive long-lasting effects on the ecology of urban seascapes. Understanding of species interactions could be used to develop a rule-based approach to enhance biodiversity.

Combined Machine Learning Techniques for Decision Making Support in Medicine

Computational intelligent support for decision making is becoming increasingly popular and essential among medical professionals. Also, with the modern medical devices being capable to communicate with ICT, created models can easily find practical translation into software. Machine learning solutions for medicine range from the robust but opaque paradigms of support vector machines and neural networks to the also performant, yet more comprehensible, decision trees and rule-based models. So how can such different techniques be combined such that the professional obtains the whole spectrum of their particular advantages? The presented approaches have been conceived for various medical problems, while permanently bearing in mind the balance between good accuracy and understandable interpretation of the decision in order to truly establish a trustworthy ‘artificial’ second opinion for the medical expert.

I. Quantal effects in biochemical cooperativity and a proposed mechanism for the differentiation of calcium signaling in synaptic plasticity. II. Evolutionary algorithms for the optimization of methods in computational chemistry

In Part 1 of this thesis, we propose that biochemical cooperativity is a fundamentally non-ideal process. We show quantal effects underlying biochemical cooperativity and highlight apparent ergodic breaking at small volumes. The apparent ergodic breaking manifests itself in a divergence of deterministic and stochastic models. We further predict that this divergence of deterministic and stochastic results is a failure of the deterministic methods rather than an issue of stochastic simulations.

Ergodic breaking at small volumes may allow these molecular complexes to function as switches to a greater degree than has previously been shown. We propose that this ergodic breaking is a phenomenon that the synapse might exploit to differentiate Ca$^{2+}$ signaling that would lead to either the strengthening or weakening of a synapse. Techniques such as lattice-based statistics and rule-based modeling are tools that allow us to directly confront this non-ideality. A natural next step to understanding the chemical physics that underlies these processes is to consider \textit{in silico} specifically atomistic simulation methods that might augment our modeling efforts.

In the second part of this thesis, we use evolutionary algorithms to optimize \textit{in silico} methods that might be used to describe biochemical processes at the subcellular and molecular levels. While we have applied evolutionary algorithms to several methods, this thesis will focus on the optimization of charge equilibration methods. Accurate charges are essential to understanding the electrostatic interactions that are involved in ligand binding, as frequently discussed in the first part of this thesis.