Efficient indexing algorithms for one-dimensional discretely-scaled strings

The discretely-scaled string indexing problem asks one to preprocess a given text string T, so that for a queried pattern P, the matched positions in T that P appears with some discrete scale can be reported efficiently. For solving this problem, Wang et al. first show that with an O(|T|log|T|)-time preprocessing on T, all matched positions can be reported in O(|P|+Ud) time, where |T|, |P|, and Ud denote the length of T, the length of P, and the number of matched positions for discretely-scaled P in T, respectively. In this paper, for fixed alphabets we propose the first-known optimal indexing algorithm that takes O(|T|) and O(|P|+Ud) time in its preprocessing and query phases, respectively. For integer and unbounded alphabets, our new algorithm can also be extended to obtain the best-known results.     

The pragmatic quality of Resources‐ Events‐Agents diagrams: an experimental evaluation

The Resources‐Events‐Agents (REA) model is a semantic data model for the development of enterprise information systems. Although this model has been proposed as a benchmark for enterprise information modelling, only few studies have attempted to empirically validate the claimed benefits of REA modelling. Moreover, these studies focused on the evaluation of REA‐based system implementations rather than directly assessing the REA‐modelled conceptual schemas that these systems are based on. This paper presents a laboratory experiment that measured the user understanding of diagrammatic conceptual schemas developed using the REA model. The theoretical foundation for the hypotheses are cognitive theories that explain pattern recognition phenomena and the resulting reduction in cognitive effort for understanding conceptual schemas. The results of the experiment indicate a more accurate understanding of the business processes and policies modelled when users recognize the REA model’s core pattern of enterprise information in the diagram. The implication for modelling practice is that the use of the REA model improves the requirements engineering process by facilitating the user validation of conceptual schemas produced by analysts, and thus helps ensuring the quality of the enterprise information system that is developed or implemented.     

Automatic detection of breast cancers in mammograms using structured support vector machines

Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.     

Fuzzy clustering of time series in the frequency domain

Traditional and fuzzy cluster analyses are applicable to variables whose values are uncorrelated. Hence, in order to cluster time series data which are usually serially correlated, one needs to extract features from the time series, the values of which are uncorrelated. The periodogram which is an estimator of the spectral density function of a time series is a feature that can be used in the cluster analysis of time series because its ordinates are uncorrelated. Additionally, the normalized periodogram and the logarithm of the normalized periodogram are also features that can be used. In this paper, we consider a fuzzy clustering approach for time series based on the estimated cepstrum. The cepstrum is the spectrum of the logarithm of the spectral density function. We show in our simulation studies for the typical generating processes that have been considered, fuzzy clustering based on the cepstral coefficients performs very well compared to when it is based on other features.     

DynaMICs: Comprehensive Support for Run-Time Monitoring

Software engineering strives to enable the economic construction of software systems that behave reliably, predictably, and safely. In other engineering disciplines, safety is assured in part by detailed monitoring of processes. In software, we may achieve some level of confidence in the operation of programs by monitoring their execution. DynaMICs is a software tool that facilitates the collection and use of constraints for software systems. In addition, it supports traceability by mapping constraints to system artifacts. Constraint specifications are stored separately from code; constraint-monitoring code is automatically generated from the specifications and inserted into the program at appropriate places; and constraints are verified at execution time. These constraint checks are triggered by changes made to variable values. We describe the architecture of DynaMICs, discuss alternative verification techniques, and outline research directions for the DynaMICs project.     

New driving scheme for intelligent power‐efficient high‐voltage display drivers

Abstract— A new bistable‐display driver is presented. The innovation in the developed driver is the addition of a new logical block that calculates the most energy‐efficient driving waveforms. In this paper, the algorithms being applied to the row and column waveforms in order to reduce the power consumption are discussed. Some theoretical as well as experimental results are shown, proving a reduction in the power consumption by about 50%. The proposed algorithms are especially important for battery‐powered applications.     

Kinematics of heelstrike during walking and carrying: implications for slip resistance testing

Slip resistance measurements of shoes and floors are used to evaluate the potential for slip and fall injuries. These measurements are believed to have increased validity when they more closely reflect actual heelstrike biomechanics during locomotion. The purpose of this study was to describe heelstrike kinematics during load carrying to provide data towards improved slip resistance testing. Foot kinematics during load carrying (unladen and carrying from 0 to 13.5 kg) at various cadences (70, 90, 100 steps/min) was recorded. Measures before, during and after heelstrike were analysed. Cadence was an important predictor for all variables measured, associated with changes from 13% to over 100%. The load carried was an important predictor for only the heel slip distance after heelstrike, but this effect needs to be investigated further. These results can be used to improve the fidelity of slip resistance measurements, which is critical to reduce slip and fall injuries in the workplace or during activities of daily living.     

Intelligent interaction design: the role of human-computer interaction research in the design of intelligent systems

As more intelligent systems are introduced into the marketplace, it is becoming increasingly urgent to consider usability for such systems. Historically, the two fields of artificial intelligence (AI) and human- computer interaction (HCI) have had little in common. In this paper, we consider how established HCI techniques can usefully be applied to the design and evaluation of intelligent systems, and where there is an urgent need for new approaches. Some techniques - notably those for requirements acquisition and empirical evaluation - can usefully be adopted, and indeed are, within many projects. However, many of the tools and techniques developed within HCI to support design and theory-based evaluation cannot be applied in their present forms to intelligent systems because they are based on inappropriate assumptions; there is consequently a need for new approaches. Conversely, there are approaches that have been developed within AI - e.g. in research on dialogue and on ontologies - that could usefully be adapted and encapsulated to respond to this need. These should form the core of a future research agenda for intelligent interaction design.     

Reinforcement Learning Rules in a Repeated Game

This paper examines the performance of simple reinforcement learningalgorithms in a stationary environment and in a repeated game where theenvironment evolves endogenously based on the actions of other agents. Sometypes of reinforcement learning rules can be extremely sensitive to smallchanges in the initial conditions, consequently, events early in a simulationcan affect the performance of the rule over a relatively long time horizon.However, when multiple adaptive agents interact, algorithms that performedpoorly in a stationary environment often converge rapidly to a stableaggregate behaviors despite the slow and erratic behavior of individuallearners. Algorithms that are robust in stationary environments can exhibitslow convergence in an evolving environment.     

A comment on the influence of substrates on the superfluid density of thin helium-four films atT=0

The influence of substrate potentials on the superfluid density in thin films of ⁴ He atT=0 is discussed. Estimates indicate that an observabl large reduction in _s is to be expected.