Automatic programming methodologies for electronic hardware fault monitoring

This paper presents three variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the Stressor - susceptibility interaction model. A circuit or a system is considered to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after pre-processing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm and classification and regression trees. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.This research was supported by the International Joint Research Grant of the IITA (Institute of Information Technology Assessment) foreign professor invitation program of the MIC (Ministry of Information and Communication), Korea

Deciding subset relationship of co-inductively defined set constants

Static analysis of different non-strict functional programming languages makes use of set constants like Top, Inf, and Bot denoting all expressions, all lists without a last Nil as tail, and all non-terminating programs, respectively. We use a set language that permits union, constructors and recursive definition of set constants with a greatest fixpoint semantics. This paper proves decidability, in particular EXPTIMEcompleteness, of subset relationship of co-inductively defined sets by using algorithms and results from tree automata. This shows decidability of the test for set inclusion, which is required by certain strictness analysis algorithms in lazy functional programming languages

Formal Verification of Input-Output Mappings of Tree Ensembles

Recent advances in machine learning and artificial intelligence are now being considered in safety-critical autonomous systems where software defects may cause severe harm to humans and the environment. Design organizations in these domains are currently unable to provide convincing arguments that their systems are safe to operate when machine learning algorithms are used to implement their software. In this paper, we present an efficient method to extract equivalence classes from decision trees and tree ensembles, and to formally verify that their input-output mappings comply with requirements. The idea is that, given that safety requirements can be traced to desirable properties on system input-output patterns, we can use positive verification outcomes in safety arguments. This paper presents the implementation of the method in the tool VoTE (Verifier of Tree Ensembles), and evaluates its scalability on two case studies presented in current literature. We demonstrate that our method is practical for tree ensembles trained on low-dimensional data with up to 25 decision trees and tree depths of up to 20. Our work also studies the limitations of the method with high-dimensional data and preliminarily investigates the trade-off between large number of trees and time taken for verification

Safety arguments for next generation location aware computing

Concerns over the accuracy, availability, integrity and continuity of Global Navigation Satellite Systems (GNSS) have limited the integration of GPS and GLONASS for safety-critical applications. More recent augmentation systems, such as the European Geostationary Navigation Overlay Service (EGNOS) and the North American Wide Area Augmentation System (WAAS) have begun to address these concerns. Augmentation architectures build on the existing GPS/GLONASS infrastructures to support locationbased services in Safety of Life (SoL) applications. Much of the technical development has been directed by air traffic management requirements, in anticipation of the more extensive support to be offered by GPS III and Galileo. WAAS has already been approved to provide vertical guidance against ICAO safety performance criteria for aviation applications. During the next twelve months, we will see the full certification of EGNOS for SoL applications. This paper identifies strong similarities between the safety assessment techniques used in Europe and North America. Both have relied on hazard analysis techniques to derive estimates of the Probability of Hazardously Misleading Information (PHMI). Later sections identify significant differences between the approaches adopted in application development. Integrated fault trees have been developed by regulatory and commercial organisations to consider both infrastructure hazards and their impact on non-precision RNAV/VNAV approaches using WAAS. In contrast, EUROCONTROL and the European Space Agency have developed a more modular approach to safety-case development for EGNOS. It remains to be seen whether the European or North American strategy offers the greatest support as satellite based augmentation systems are used within a growing range of SoL applications from railway signalling through to Unmanned Airborne Systems. The key contribution of this paper is to focus attention on the safety arguments that might support this wider class of location based services

Using very large corpora to detect raising and control verbs

The distinction between raising and subject-control verbs, although crucial for the construction of semantics, is not easy to make given access to only the local syntactic configuration of the sentence. In most contexts raising verbs and control verbs display identical superficial syntactic structure. Linguists apply grammaticality tests to distinguish these verb classes. Our idea is to learn to predict the raising-control distinction by simulating such grammaticality judgments by means of pattern searches. Experiments with regression tree models show that using pattern counts from large unannotated corpora can be used to assess how likely a verb form is to appear in raising vs. control constructions. For this task it is beneficial to use the much larger but also noisier Web corpus rather than the smaller and cleaner Gigaword corpus. A similar methodology can be useful for detecting other lexical semantic distinctions: it could be used whenever a test employed to make linguistically interesting distinctions can be reduced to a pattern search in an unannotated corpus