Compact Reconfigurable Binary-Decision-Diagram Logic Circuit on a GaAs Nanowire Network

We describe a reconfigurable binary-decision-diagram logic circuit based on Shannon's expansion of Boolean logic function and its graphical representation on a semiconductor nanowire network. The circuit is reconfigured by using programmable switches that electrically connect and disconnect a small number of branches. This circuit has a compact structure with a small number of devices compared with the conventional look-up table architecture. A variable Boolean logic circuit was fabricated on an etched GaAs nanowire network having hexagonal topology with Schottky wrap gates and SiN-based programmable switches, and its correct logic operation together with dynamic reconfiguration was demonstrated.

An automata theoretic decision procedure for the propositional mu-calculus

The propositional mu-calculus is a propositional logic of programs which incorporates a least fixpoint operator and subsumes the propositional dynamic logic of Fischer and Ladner, the infinite looping construct of Streett, and the game logic of Parikh. We give an elementary time decision procedure, using a reduction to the emptiness problem for automata on infinite trees. A small model theorem is obtained as a corollary.

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.

An application of the analytic hierarchy process and fuzzy logic inference in a decision support system for forage selection

Forage selection plays a prominent role in the process of returning cultivated lands back into grasslands. The conventional method of selecting forage species can only provide attempts for problem-solving without considering the relationships among the decision factors globally. Therefore, this study is dedicated to developing a decision support system to help farmers correctly select suitable forage species for the target sites. After collecting data through a field study, we developed this decision support system. It consists of three steps: (1) the analytic hierarchy process (AHP), (2) weights determination, and (3) decision making. In the first step, six factors influencing forage growth were selected by reviewing the related references and by interviewing experts. Then a fuzzy matrix was devised to determine the weight of each factor in the second step. Finally, a gradual alternative decision support system was created to help farmers choose suitable forage species for their lands in the third step. The results showed that the AHP and fuzzy logic are useful for forage selection decision making, and the proposed system can provide accurate results in a certain area (Gansu Province) of China.

A novel Information sharing model using binary decision diagrams for redundancy, shadowing, generalisation and correlation.

This paper outlines a novel information sharing method using Binary Decision Diagrams (BBDs). It is inspired by the work of Al-Shaer and Hamed, who applied BDDs into the modelling of network firewalls. This is applied into an information sharing policy system which optimizes the search of redundancy, shadowing, generalisation and correlation within information sharing rules.

Evaluation of binary decision diagrams for redundancy, shadowing, generalisation and correlation in an Information sharing model.

This paper defines a structured methodology which is based on the foundational work of Al-Shaer et al. in [1] and that of Hamed and Al-Shaer in [2]. It defines a methodology for the declaration of policy field elements, through to the syntax, ontology and functional verification stages. In their works of [1] and [2] the authors concentrated on developing formal definitions of possible anomalies between rules in a network firewall rule set. Their work is considered as the foundation for further works on anomaly detection, including those of Fitzgerald et al. [3], Chen et al. [4], Hu et al. [5], among others. This paper extends this work by applying the methods to information sharing policies, and outlines the evaluation related to these.

Understanding nurses’ decision-making when managing weaning from mechanical ventilation: a study of novice and experienced critical care nurses in Scotland and Greece.

Aim and objectives To examine how nurses collect and use cues from respiratory assessment to inform their decisions as they wean patients from ventilatory support. Background Prompt and accurate identification of the patient's ability to sustain reduction of ventilatory support has the potential to increase the likelihood of successful weaning. Nurses' information processing during the weaning from mechanical ventilation has not been well-described. Design A descriptive ethnographic study exploring critical care nurses' decision-making processes when weaning mechanically ventilated patients from ventilatory support in the real setting. Methods Novice and expert Scottish and Greek nurses from two tertiary intensive care units were observed in real practice of weaning mechanical ventilation and were invited to participate in reflective interviews near the end of their shift. Data were analysed thematically using concept maps based on information processing theory. Ethics approval and informed consent were obtained. Results Scottish and Greek critical care nurses acquired patient-centred objective physiological and subjective information from respiratory assessment and previous knowledge of the patient, which they clustered around seven concepts descriptive of the patient's ability to wean. Less experienced nurses required more encounters of cues to attain the concepts with certainty. Subjective criteria were intuitively derived from previous knowledge of patients' responses to changes of ventilatory support. All nurses used focusing decision-making strategies to select and group cues in order to categorise information with certainty and reduce the mental strain of the decision task. Conclusions Nurses used patient-centred information to make a judgment about the patients' ability to wean. Decision-making strategies that involve categorisation of patient-centred information can be taught in bespoke educational programmes for mechanical ventilation and weaning. Relevance to clinical practice Advanced clinical reasoning skills and accurate detection of cues in respiratory assessment by critical care nurses will ensure optimum patient management in weaning mechanical ventilation

Exploring the relationship between impulsivity and decision-making on mobile devices.

Mobile devices offer a common platform for both leisure and work-related tasks but this has resulted in a blurred boundary between home and work. In this paper we explore the security implications of this blurred boundary, both for the worker and the employer. Mobile workers may not always make optimum security-related choices when ‘on the go’ and more impulsive individuals may be particularly affected as they are considered more vulnerable to distraction. In this study we used a task scenario, in which 104 users were asked to choose a wireless network when responding to work demands while out of the office. Eye-tracking data was obtained from a subsample of 40 of these participants in order to explore the effects of impulsivity on attention. Our results suggest that impulsive people are more frequent users of public devices and networks in their day-to-day interactions and are more likely to access their social networks on a regular basis. However they are also likely to make risky decisions when working on-the-go, processing fewer features before making those decisions. These results suggest that those with high impulsivity may make more use of the mobile Internet options for both work and private purposes but they also show attentional behavior patterns that suggest they make less considered security-sensitive decisions. The findings are discussed in terms of designs that might support enhanced deliberation, both in the moment and also in relation to longer term behaviors that would contribute to a better work-life balance.

A scenario-driven decision support system for serious crime investigation

Q. Shen, J. Keppens, C. Aitken, B. Schafer, and M. Lee. A scenario driven decision support system for serious crime investigation. Law, Probability and Risk, 5(2):87-117, 2006. Sponsorship: UK Engineering and Physical Sciences Research Council grant GR/S63267; partially supported by grant GR/S98603

Compositional Bayesian modelling and its application to decision support in crime investigation

J. Keppens, Q. Shen and M. Lee. Compositional Bayesian modelling and its application to decision support in crime investigation. Proceedings of the 19th International Workshop on Qualitative Reasoning, pages 138-148.

Fuzzy-Rough Feature Significance for Fuzzy Decision Trees

R. Jensen and Q. Shen, 'Fuzzy-Rough Feature Significance for Fuzzy Decision Trees,' in Proceedings of the 2005 UK Workshop on Computational Intelligence, pp. 89-96, 2005.