A closer look at declarative interpretations

AbstractThree semantics have been proposed as the most promising candidates for a declarative interpretation for logic programs and pure Prolog programs: the least Herbrand model, the least term model, i.e., the C-semantics, and the I-semantics. Previous results show that a strictly increasing information ordering between these semantics exists for the class of all programs. In particular, the I-semantics allows us to model the computed answer substitutions, which is not the case for the other two.We study here the relationship between these three semantics for specific classes of programs. We show that for a large class of programs (which is Turing complete), these three semantics are isomorphic. As a consequence, given a query, we can extract from the least Herbrand model of a program in this class all computed answer substitutions. However, for specific programs the least Herbrand model is tedious to construct and reason about because it contains “ill-typed” facts. Therefore, we propose a fourth semantics that associates with a “correctly typed” program the “well-typed” subset of its least Herbrand model. This semantics is used to reason about partial correctness and absence of failures of correctly typed programs. The results are extended to programs with arithmetic

Polychotomiser for case-based reasoning beyond the traditional Bayesian classification approach

This work implements an enhanced Bayesian classifier with better performance as compared to the ordinary naïve Bayes classifier when used with domains and datasets of varying characteristics. Text classification is an active and on-going research field of Artificial Intelligence (AI). Text classification is defined as the task of learning methods for categorising collections of electronic text documents into their annotated classes, based on its contents. An increasing number of statistical approaches have been developed for text classification, including k-nearest neighbor classification, naïve Bayes classification, decision tree, rules induction, and the algorithm implementing the structural risk minimisation theory called the support vector machine. Among the approaches used in these applications, naïve Bayes classifiers have been widely used because of its simplicity. However this generative method has been reported to be less accurate than the discriminative methods such as SVM. Some researches have proven that the naïve Bayes classifier performs surprisingly well in many other domains with certain specialised characteristics. The main aim of this work is to quantify the weakness of traditional naïve Bayes classification and introduce an enhance Bayesian classification approach with additional innovative techniques to perform better than the traditional naïve Bayes classifier. Our research goal is to develop an enhanced Bayesian probabilistic classifier by introducing different tournament structures ranking algorithms along with a high relevance keywords extraction facility and an accurately calculated weighting factors facility. These were done to improve the performance of the classification tasks for specific datasets with different characteristics. Other researches have used general datasets, such as Reuters-21578 and 20_newsgroups to validate the performance of their classifiers. Our approach is easily adapted to datasets with different characteristics in terms of the degree of similarity between classes, multi-categorised documents, and different dataset organisations. As previously mentioned we introduce several techniques such as tournament structures ranking algorithms, higher relevance keyword extraction, and automatically computed document dependent (ACDD) weighting factors. Each technique has unique response while been implemented in datasets with different characteristics but has shown to give outstanding performance in most cases. We have successfully optimised our techniques for individual datasets with different characteristics based on our experimental results

Text document pre-processing using the Bayes formula for classification based on the vector space model

This work utilizes the Bayes formula to vectorize a document according to a probability distribution based on keywords reflecting the probable categories that the document may belong to. The Bayes formula gives a range of probabilities to which the document can be assigned according to a pre determined set of topics (categories). Using this probability distribution as the vectors to represent the document, the text classification algorithms based on the vector space model, such as the Support Vector Machine (SVM) and Self-Organizing Map (SOM) can then be used to classify the documents on a multi-dimensional level, thus improving on the results obtained using only the highest probability to classify the document, such as that achieved by implementing the naïve Bayes classifier by itself. The effects of an inadvertent dimensionality reduction can be overcome using these algorithms. We compare the performance of these classifiers for high dimensional data

Text document pre-processing using the Bayes formula for classification based on the vector space model

This work utilizes the Bayes formula to vectorize a document according to a probability distribution based on keywords reflecting the probable categories that the document may belong to. The Bayes formula gives a range of probabilities to which the document can be assigned according to a pre determined set of topics (categories). Using this probability distribution as the vectors to represent the document, the text classification algorithms based on the vector space model, such as the Support Vector Machine (SVM) and Self-Organizing Map (SOM) can then be used to classify the documents on a multi-dimensional level, thus improving on the results obtained using only the highest probability to classify the document, such as that achieved by implementing the naïve Bayes classifier by itself. The effects of an inadvertent dimensionality reduction can be overcome using these algorithms. We compare the performance of these classifiers for high dimensional data

Paper Session III-C - Advanced Mechanisms For Space Applications

The Air Force Research Laboratory (AFRL) is currently engaged in developing and demonstrating several advanced spacecraft and launch vehicle mechanism technologies. A variety of mechanisms are required to accomplish spacecraft and launch vehicle functions such as deployment, articulation, positioning, and isolation. Current off-the-shelf mechanisms such as pyrotechnics, gimbals, paraffin actuators, and electro-mechanical devices may not be able to meet future satellite requirements. For this reason, advanced technologies are needed that will increase mechanism efficiency in terms of cost, weight, reliability/survivability, and power consumption. In addition to developing these technologies, it is necessary to prove them in flight demonstrations in order to make technology transition feasible. This paper summarizes the status of several space-related programs being conducted by AFRL for developing and demonstrating new technology to support future DoD space requirements. One of these flight programs will fly the first whole-spacecraft isolation system on a Taurus launch vehicle in January 1998 and another will demonstrate the first solar array with overall array specific power greater than 150W/Kg in the fall of 2002. This solar array is being developed for flight on the third New Millennium Program technology demonstration flight

Blood immunoglobulins, complement and TNF receptor following minimally invasive surgery in patients undergoing pulmonary lobectomy

The reasons for improved survival following minimally invasive surgery remain elusive. Circulating mediators link surgical trauma, vascular and tissue homeostasis. Acute phase reactants, leukocytes and leukocyte Reactive Oxygen Species (ROS) are affected differentially by minimally invasive video-assisted thoracic surgery (VATS). Also, immunoglobulins, complement, TNF receptor and P-selectin changes have been observed, but the influence of minimally invasive surgery on these opsonins is less well defined. In this prospective randomised trial, 41 patients were randomly assigned to minimally invasive or open thoracic surgery, and immunoglobulins and vascular endothelial damage biomarkers were analysed. Humoral mediators (blood IgG, IgM, IgA; complement fragments C3, C4, and complement haemolytic index of activation CH50; TNF receptors I, II and P-selectin) were analysed before and 2, 5 and 7 days after surgery. Post-surgical changes in individual patients were determined. Substantial immunoglobulin decreases followed minimally invasive and open surgery. Decreased IgG, IgM and IgE were detected 2 days after surgery, and IgG and IgM after 7 days. These changes were greater than haemodilution, reaching greater significance in open surgery patients. Immunoglobulin decreases followed lymphocyte decreases. In contrast, increased complement and inflammatory endothelial cell signals (C3 and C4, soluble TNFR-II) were detected 7 days after surgery. In both groups, increased C3 and TNFR-II followed early acute phase reactants CRP, IL-6 and ROS. Acute phase reactants and CD4/CD8 lymphocytes were factors most attenuated in patients undergoing minimally invasive thoracic surgery (VATS). This study suggests local trauma mediators are better biomarkers than circulating opsonins in defining the response to minimally invasive surgery, and a systems approach, comparing individual metabolic responses, is effective in small patient groups