Application and systems software in Ada: Development experiences

In its most basic sense software development involves describing the tasks to be solved, including the given objects and the operations to be performed on those objects. Unfortunately, the way people describe objects and operations usually bears little resemblance to source code in most contemporary computer languages. There are two ways around this problem. One is to allow users to describe what they want the computer to do in everyday, typically imprecise English. The PRODOC methodology and software development environment is based on a second more flexible and possibly even easier to use approach. Rather than hiding program structure, PRODOC represents such structure graphically using visual programming techniques. In addition, the program terminology used in PRODOC may be customized so as to match the way human experts in any given application area naturally describe the relevant data and operations. The PRODOC methodology is described in detail

BiofilmQuant: A Computer-Assisted Tool for Dental Biofilm Quantification

Dental biofilm is the deposition of microbial material over a tooth substratum. Several methods have recently been reported in the literature for biofilm quantification; however, at best they provide a barely automated solution requiring significant input needed from the human expert. On the contrary, state-of-the-art automatic biofilm methods fail to make their way into clinical practice because of the lack of effective mechanism to incorporate human input to handle praxis or misclassified regions. Manual delineation, the current gold standard, is time consuming and subject to expert bias. In this paper, we introduce a new semi-automated software tool, BiofilmQuant, for dental biofilm quantification in quantitative light-induced fluorescence (QLF) images. The software uses a robust statistical modeling approach to automatically segment the QLF image into three classes (background, biofilm, and tooth substratum) based on the training data. This initial segmentation has shown a high degree of consistency and precision on more than 200 test QLF dental scans. Further, the proposed software provides the clinicians full control to fix any misclassified areas using a single click. In addition, BiofilmQuant also provides a complete solution for the longitudinal quantitative analysis of biofilm of the full set of teeth, providing greater ease of usability.Comment: 4 pages, 4 figures, 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014

The parser generator as a general purpose tool

The parser generator has proven to be an extremely useful, general purpose tool. It can be used effectively by programmers having only a knowledge of grammars and no training at all in the theory of formal parsing. Some of the application areas for which a table-driven parser can be used include interactive, query languages, menu systems, translators, and programming support tools. Each of these is illustrated by an example grammar

The nature and evaluation of commercial expert system building tools, revision 1

This memorandum reviews the factors that constitute an Expert System Building Tool (ESBT) and evaluates current tools in terms of these factors. Evaluation of these tools is based on their structure and their alternative forms of knowledge representation, inference mechanisms and developer end-user interfaces. Next, functional capabilities, such as diagnosis and design, are related to alternative forms of mechanization. The characteristics and capabilities of existing commercial tools are then reviewed in terms of these criteria

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

A High-Performance Domain-Specific Language and Code Generator for General N-body Problems

General N-body problems are a set of problems in which an update to a single element in the system depends on every other element. N-body problems are ubiquitous, with applications in various domains ranging from scientific computing simulations in molecular dynamics, astrophysics, acoustics, and fluid dynamics all the way to computer vision, data mining and machine learning problems. Different N-body algorithms have been designed and implemented in these various fields. However, there is a big gap between the algorithm one designs on paper and the code that runs efficiently on a parallel system. It is time-consuming to write fast, parallel, and scalable code for these problems. On the other hand, the sheer scale and growth of modern scientific datasets necessitate exploiting the power of both parallel and approximation algorithms where there is a potential to trade-off accuracy for performance. The main problem that we are tackling in this thesis is how to automatically generate asymptotically optimal N-body algorithms from the high-level specification of the problem. We combine the body of work in performance optimizations, compilers and the domain of N-body problems to build a unified system where domain scientists can write programs at the high level while attaining performance of code written by an expert at the low level.In order to generate a high-performance, scalable code for this group of problems, we take the following steps in this thesis; first, we propose a unified algorithmic framework named PASCAL in order to address the challenge of designing a general algorithmic template to represent the class of N-body problems. PASCAL utilizes space-partitioning trees and user-controlled pruning/approximations to reduce the asymptotic runtime complexity from linear to logarithmic in the number of data points. In PASCAL, we design an algorithm that automatically generates conditions for pruning or approximation of an N-body problem considering the problem's definition. In order to evaluate PASCAL, we developed tree-based algorithms for six well-known problems: k-nearest neighbors, range search, minimum spanning tree, kernel density estimation, expectation maximization, and Hausdorff distance. We show that applying domain-specific optimizations and parallelization to the algorithms written in PASCAL achieves 10x to 230x speedup compared to state-of-the-art libraries on a dual-socket Intel Xeon processor with 16 cores on real-world datasets. Second, we extend the PASCAL framework to build PASCAL-X that adds support for NUMA-aware parallelization. PASCAL-X also presents insights on the influence of tuning parameters. Tuning parameters such as leaf size (influences the shape of the tree) and cut-off level (controls the granularity of tasks) of the space-partitioning trees result in performance improvement of up to 4.6x. A key goal is to generate scalable and high-performance code automatically without sacrificing productivity. That implies minimizing the effort the users have to put in to generate the desired high-performance code. Another critical factor is the adaptivity, which indicates the amount of effort that is required to extend the high-performance code generation to new N-body problems. Finally, we consider these factors and develop a domain-specific language and code generator named Portal, which is built on top of PASCAL-X. Portal's language design is inspired by the mathematical representation of N-body problems, resulting in an intuitive language for rapid implementation of a variety of problems. Portal's back-end is designed and implemented to generate optimized, parallel, and scalable implementations for multi-core systems. We demonstrate that the performance achieved by using Portal is comparable to that of expert hand-optimized code while providing productivity for domain scientists. For instance, using Portal for the k-nearest neighbors problem gains performance that is similar to the hand-optimized code, while reducing the lines of code by 68x. To the best of our knowledge, there are no known libraries or frameworks that implement parallel asymptotically optimal algorithms for the class of general N-body problems and this thesis primarily aims to fill this gap. Finally, we present a case study of Portal for the real-world problem of face clustering. In this case study, we show that Portal not only provides a fast solution for the face clustering problem with similar accuracy as the state-of-the-art algorithm, but also it provides productivity by implementing the face clustering algorithm in only 14 lines of Portal code

Learning Aerial Image Segmentation from Online Maps

This study deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic map generation. Recently, deep convolutional neural networks (CNNs) have shown impressive performance and have quickly become the de-facto standard for semantic segmentation, with the added benefit that task-specific feature design is no longer necessary. However, a major downside of deep learning methods is that they are extremely data-hungry, thus aggravating the perennial bottleneck of supervised classification, to obtain enough annotated training data. On the other hand, it has been observed that they are rather robust against noise in the training labels. This opens up the intriguing possibility to avoid annotating huge amounts of training data, and instead train the classifier from existing legacy data or crowd-sourced maps which can exhibit high levels of noise. The question addressed in this paper is: can training with large-scale, publicly available labels replace a substantial part of the manual labeling effort and still achieve sufficient performance? Such data will inevitably contain a significant portion of errors, but in return virtually unlimited quantities of it are available in larger parts of the world. We adapt a state-of-the-art CNN architecture for semantic segmentation of buildings and roads in aerial images, and compare its performance when using different training data sets, ranging from manually labeled, pixel-accurate ground truth of the same city to automatic training data derived from OpenStreetMap data from distant locations. We report our results that indicate that satisfying performance can be obtained with significantly less manual annotation effort, by exploiting noisy large-scale training data.Comment: Published in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSIN

Timetabling in constraint logic programming

In this paper we describe the timetabling problem and its solvability in a Constraint Logic Programming Language. A solution to the problem has been developed and implemented in ECLiPSe, since it deals with finite domains, it has well-defined interfaces between basic building blocks and supports good debugging facilities. The implemented timetable was based on the existing, currently used, timetables at the School of Informatics at out university. It integrates constraints concerning room and period availability