Application of the Strain Index in multiple task jobs

Six different methods to calculate the Strain Index (SI) scores for jobs with multiple forces/tasks were developed. Exposure data of 733 subjects from 12 different worksites were used to calculate these SI scores. Results show that using different SI computation methods could result in different SI scores, hence different risk level classifications. However, some simpler methods generated SI scores were comparable to the more complicated composite SI method. Despite differences in the scores between the six different SI computation methods, Spearman rank-order correlation coefficients of 0.61-0.97 were found between the methods. With some confidence, ergonomic practitioners may use simpler methods, depending on their specificity requirement in job evaluations and available resources. Some SI computation methods may tend to over-estimate job risk levels, while others may tend to under-estimate job risk levels, due to different ways used in obtaining the various SI parameters and computations.     

An interdisciplinary heuristic evaluation method for universal building design

This study highlights how heuristic evaluation as a usability evaluation method can feed into current building design practice to conform to universal design principles. It provides a definition of universal usability that is applicable to an architectural design context. It takes the seven universal design principles as a set of heuristics and applies an iterative sequence of heuristic evaluation in a shopping mall, aiming to achieve a cost-effective evaluation process. The evaluation was composed of three consecutive sessions. First, five evaluators from different professions were interviewed regarding the construction drawings in terms of universal design principles. Then, each evaluator was asked to perform the predefined task scenarios. In subsequent interviews, the evaluators were asked to re-analyze the construction drawings. The results showed that heuristic evaluation could successfully integrate universal usability into current building design practice in two ways: (i) it promoted an iterative evaluation process combined with multi-sessions rather than relying on one evaluator and on one evaluation session to find the maximum number of usability problems, and (ii) it highlighted the necessity of an interdisciplinary ad hoc committee regarding the heuristic abilities of each profession. A multi-session and interdisciplinary heuristic evaluation method can save both the project budget and the required time, while ensuring a reduced error rate for the universal usage of the built environments.     

Kalman filtering over a packet-delaying network: A probabilistic approach

In this paper, we consider Kalman filtering over a packet-delaying network. Given the probability distribution of the delay, we can characterize the filter performance via a probabilistic approach. We assume that the estimator maintains a buffer of length D so that at each time k, the estimator is able to retrieve all available data packets up to time k−D+1. Both the cases of sensor with and without necessary computation capability for filter updates are considered. When the sensor has no computation capability, for a given D, we give lower and upper bounds on the probability for which the estimation error covariance is within a prescribed bound. When the sensor has computation capability, we show that the previously derived lower and upper bounds are equal to each other. An approach for determining the minimum buffer length for a required performance in probability is given and an evaluation on the number of expected filter updates is provided. Examples are provided to demonstrate the theory developed in the paper.     

Worst-case optimal algorithm for XPath evaluation over XML streams

We consider the XPath evaluation problem: Evaluate an XPath query Q on a streaming XML document D; i.e., determine the set Q(D) of document elements selected by Q. We mainly consider Conjunctive XPath queries that involve only the child and descendant axes. Previously known in-memory algorithms for this problem use O(|D|) space and O(|Q||D|) time. Several previously known algorithms for the streaming version use Ω(dn) space and Ω(dn|D|) time in the worst case; d denotes the depth of D, and n denotes the number of location steps in Q. Their exponential space requirement could well exceed the O(|D|) space used by the in-memory algorithms. We present an efficient algorithm that uses O(d|Q|+nc) space and O((|Q|+dn)|D|) time in the worst case; c denotes the maximum number of elements of D that can be candidates for output, at any one instant. For some worst case Q and D, the memory space used by our algorithm matches our lower bound proved in a different paper; so, our algorithm uses optimal memory space in the worst case.     

Performance analysis of security aspects by weaving scenarios extracted from UML models

Aspect-oriented modeling (AOM) allows software designers to describe features that address pervasive concerns separately as aspects, and to systematically incorporate the features into a design model using model composition techniques. The goal of this paper is to analyze the performance effects of different security features that may be represented as aspect models. This is part of a larger research effort to integrate methodologies and tools for the analysis of security and performance properties early in the software development process. In this paper, we describe an extension to the AOM approach that provides support for performance analysis. We use the performance analysis techniques developed previously in the PUMA project, which take as input UML models annotated with the standard UML Profile for Schedulability, Performance and Time (SPT), and transform them first into Core Scenario Model (CSM), and then into different performance models. The composition of the aspects with the primary (base) model is performed at the CSM level. A new formal definition of CSM properties and operations is described as a foundation for scenario-based weaving. The proposed approach is illustrated with an example that utilizes two standards, TPC-W and SSL.     

Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms

In recent research, we proposed a general framework of quantum-inspired multi-objective evolutionary algorithms (QMOEA) and gave one of its sufficient convergence conditions to the Pareto optimal set. In this paper, two Q-gate operators, H gate and R&N gate, are experimentally validated as two Q-gate paradigms meeting the convergence condition. The former is a modified rotation gate, and the latter is a combination of rotation gate and NOT gate with the specified probability. To investigate their effectiveness and applicability, several experiments on the multi-objective 0/1 knapsack problems are carried out. Compared to two typical evolutionary algorithms and the QMOEA only with rotation gate, the QMOEA with H gate and R&N gate have more powerful convergence ability in high complex instances. Moreover, the QMOEA with R&N gate has the best convergence in almost all of the experimental problems. Furthermore, the appropriate ε value regions for two Q-gates are verified.     

Benchmarking Image Segmentation Algorithms

We present a thorough quantitative evaluation of four image segmentation algorithms on images from the Berkeley Segmentation Database. The algorithms are evaluated using an efficient algorithm for computing precision and recall with regard to human ground-truth boundaries. We test each segmentation method over a representative set of input parameters, and present tuning curves that fully characterize algorithm performance over the complete image database. We complement the evaluation on the BSD with segmentation results on synthetic images. The results reported here provide a useful benchmark for current and future research efforts in image segmentation.     

Improving the generalization performance of RBF neural networks using a linear regression technique

In this paper we present a method for improving the generalization performance of a radial basis function (RBF) neural network. The method uses a statistical linear regression technique which is based on the orthogonal least squares (OLS) algorithm. We first discuss a modified way to determine the center and width of the hidden layer neurons. Then, substituting a QR algorithm for the traditional Gram–Schmidt algorithm, we find the connected weight of the hidden layer neurons. Cross-validation is utilized to determine the stop training criterion. The generalization performance of the network is further improved using a bootstrap technique. Finally, the solution method is used to solve a simulation and a real problem. The results demonstrate the improved generalization performance of our algorithm over the existing methods.     

Quantitative analysis in a heuristic evaluation experiment

In usability context of interactive systems the heuristic evaluation method is widespread. In most applications the results tend to be qualitative, describing such aspects that require some improvement for the benefit of usability. However, these qualitative results do not allow us to determine how usable it is or how it becomes an interactive system. Hence the need for quantitative results may also be very necessary in order to determine the effort that would be needed to get a sufficiently usable system.This article describes, following the idea of the UsabAIPO Project, a new experiment to obtain quantitative results after a heuristic evaluation. This new experimentation has required some variation on the original idea, working with a set of different heuristic categories, while considering the use of the score depending on severity and frequency parameters.