Software reliability optimization by redundancy and software quality management

This study investigates both the trade-offs among system reliability improvement, resource consumption, and other relevant constraints, and the application of statistical control methods to monitor variations. A process for reliability-related quality programming is developed to fill existing gaps in software design and development so that a quality programming plan can be achieved. A software reliability-to-cost relation is developed both from a software reliability-related cost model and software redundancy models with common-cause failures. The software reliability optimization problem will be formulated into a mixed-integer programming problem and solved by a branch-and-bound technique;A procedure will be developed to identify, define, develop, and demonstrate a quality performance measure to improve system operation that is based on statistical control methods. Despite the most painful effort to control product quality, variation in product quality is unavoidable. Though the use of process control techniques, such as statistical control chart, unusual variations in the software development process can be controlled and reduced

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.

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Establishing the boundaries: the hippocampal contribution to imagining scenes

When we visualize scenes, either from our own past or invented, we impose a viewpoint for our “mind's eye” and we experience the resulting image as spatially coherent from that viewpoint. The hippocampus has been implicated in this process, but its precise contribution is unknown. We tested a specific hypothesis based on the spatial firing properties of neurons in the hippocampal formation of rats, that this region supports the construction of spatially coherent mental images by representing the locations of the environmental boundaries surrounding our viewpoint. Using functional magnetic resonance imaging, we show that hippocampal activation increases parametrically with the number of enclosing boundaries in the imagined scene. In contrast, hippocampal activity is not modulated by a nonspatial manipulation of scene complexity nor to increasing difficulty of imagining the scenes in general. Our findings identify a specific computational role for the hippocampus in mental imagery and episodic recollection

Measurements, Models, Systems and Design

531 s.

Computational characterization and prediction of metal-organic framework properties

In this introductory review, we give an overview of the computational chemistry methods commonly used in the field of metal-organic frameworks (MOFs), to describe or predict the structures themselves and characterize their various properties, either at the quantum chemical level or through classical molecular simulation. We discuss the methods for the prediction of crystal structures, geometrical properties and large-scale screening of hypothetical MOFs, as well as their thermal and mechanical properties. A separate section deals with the simulation of adsorption of fluids and fluid mixtures in MOFs