Methods for defining user groups and user-adjusted information structures

A common problem in the design of information systems is how to structure the information in a way that is most useful to different groups of users. This paper describes some statistical methods for revealing the structure inherent in empirical data elicited from users. It is illustrated by the application of these methods to the design of some web pages giving information about the Universitat de Valencia. Three potential user groups were identified, administrative staff, teaching staff and students. The first analysis demonstrated that users within these three groups assign relatively homogeneous structures, but that the structures assigned by the three groups are not the same, and also, teaching and administrative staff were shown to be relatively similar and different from students. Second, the ideal information structures for each group were identified and validated against the original data. The methods described can be applied to any design situation where there is an existing user population that can be called on to provide data.     

Two Access Methods Using Compact Binary Trees

It is shown how a highly compact representation of binary trees can be used as the basis of two access methods for dynamic files, called BDS-trees and S-trees, respectively. Both these methods preserve key-order and offer easy and efficient sequential access. They are different in the way the compact binary trees are used for searching. With a BDS-tree the search is a digital search using binary digits. Although the S-tree search is performed on a bit-by-bit basis as well, it will appear to be slightly different. Actually, with S-trees the compact binary trees are used to represent separators at low storage costs. As a result, the fan-out, and thus performance, of a B-tree can be improved by using within each index page an S-tree for representing separators efficiently.     

Some performance measures useful in the design of controllers for two dimensional dynamical systems subject to uncertain inputs

Classical control theory has always been concerned with uncertain inputs. However, this concern has generally been implicit rather than explicit. Modern control methods tend to be more explicit. In particular, methods based on game theory and methods based on a Lyapunov type of analysis have been proposed. One question which seems to be largely ignored is: how do these various methods compare when applied to a typical control problem? Indeed what performance measures can we use to compare various control designs dealing with uncertain inputs? This paper addresses both of these questions. We define performance measures called the v-reachable set and the v-attractive set. The v-reachable set measures the extent that the uncertain input can drive the system away from the target set and the v-attractive set measures the extent that the uncertainty diminishes the size of the controllable set to the target. An ideal control law would be one in which the v-reachable set is the target set and the v-attractive set is the controllable set. Five different controllers based on five different design philosophies are applied to two different control systems. We find that there can be marked differences in the above performance measures. However using these performance measures as guide to choosing feedback parameters, the differences between the various design methods can be minimized. It is found that for these problems a modified game theoretic approach and the Lyapunov type of approach tend to provide the best designs.     

A framework for ontology based decision support system for e-learning modules, business modeling and manufacturing systems

This paper identifies how ontology models can be vigorously used to define semantics and relationships in representing objects/modules for e-learning, business modeling support and manufacturing processing details. Further extraction of these relations by intelligent decision-support systems using data mining as a tool is discussed. The paper envisages the possibility of establishing a common solution platform for product development and customization leading to increased profitability and better resource utilization. It showcases ways to link these different ontological models leading to cross platform compatibility. It also tries to explore manufacturer-customer relationship and using them to provide quality analysis methods for further improvement in the product processing model.     

Participatory methods effective for ergonomic workplace improvement

Recent experiences in using participatory methods for ergonomic workplace improvement are reviewed to know how these methods can be effective in different settings. The review covered participatory programmes for managers and workers in small enterprises, home workers, construction workers and farmers in Asian countries. To meet diversifying ergonomic needs, participatory steps reviewed are found to usually follow a good-practice approach easily adjustable according to local needs. These steps are found to usually focus on low-cost improvements. They can thus lead to concrete results particularly by addressing multiple technical areas together. Typical areas include materials handling, workstation design, physical environment and work organization. Further, the review confirms that the participatory methods are always modified according to each local situation. This is done by developing a group-work toolkit comprising action checklists and illustrated manuals and by building a support network of trained trainers. It is suggested that participatory methods taking a good-practice approach by multi-area low-cost improvements through the group use of locally adjusted toolkits are effective for improving small-scale workplaces including those in developing countries.     

A design framework for metaheuristics

This paper is concerned with taking an engineering approach towards the application of metaheuristic problem solving methods, i.e., heuristics that aim to solve a wide variety of problems. How can a practitioner solve a problem using metaheuristic methods? What choices do they have, and how are these choices influenced by the problem at hand? Are there sensible universal choices which can be made, or are these choices always problem-dependent? The aim of this paper is to address questions such as these in the context of a (soft) engineering design framework for the application of metaheuristics. The aim of this framework is to make explicit the choices which a practitioner needs to make in applying these techniques, and to give some guidelines for how metaheuristics might be tuned to problems by considering different problem- and solution-types.     

Influences on the use of observational methods by practitioners when identifying risk factors in physical work

Most observational methods for musculoskeletal disorder risk assessment have been developed by researchers to be applied in specific situations, and practitioners could find difficulties in their use in real-work conditions. The main objective of this study was to identify the factors which have an influence on how useful the observational techniques are perceived to be by practitioners and to what extent these factors influence their perception. A survey was conducted on practitioners regarding the problems normally encountered when implementing these methods, as well as the perceived overall utility of these techniques. The results show that practitioners place particular importance on the support the methods provide in making decisions regarding changes in work systems and how applicable they are to different types of jobs. The results of this study can serve as guide to researchers for the development of new assessment techniques that are more useful and applicable in real-work situations.     

THE USE OF BAYESIAN BELIEF NETS IN SAFETY ASSESSMENT OF SOFTWARE BASED SYSTEMS

The paper describes an investigation of methods to perform a reliability and safety assessment of the software in programmable safety relevant systems. It emphasises in particular how disparate information sources and different quantitative and qualitative methods should be combined in such an assessment. It starts with a general discussion of rule based, probabilistic and expert judgement methods and their applicability on software reliability. Then a method for combining different evidences in a reliability and safety assessment is pinpointed, viz. the Bayesian Belief Net (BBN) methodology. It is also illustrated how this method may be applied for safety assessment of software.     

蒙特卡洛方法在仿真中的应用

本文介绍了蒙特卡洛方法,一种利用随机数(或伪随机数)来解决许多类型计算问题的通用算法。首先描述了蒙特卡洛方法的基本原理,并且通过两个典型应用案例,讨论了蒙特卡洛方法的适用范围和使用条件,展示了该方法的优点,体现了该方法在解决高自由度问题方面的优势。     

Using Predicate/Transition-Nets to Model and Analyze Distributed Database Systems

In this paper, a net model for decentralized control of user accesses to a distributed database is proposed. It is developed in detail for the restricted case of updating distributed copies of a single database. Predicate/transition-nets, a first-order extension of Petri nets, are shown to provide suitable means for concise representation of complex decentralized systems and for their rigorous formal analysis. It will be demonstrated in the present paper how these net models can be constructed and interpreted in a quite natural manner and how they can be analyzed by linear algebraic methods. By this, it will be shown that the modeled distributed database system is deadlock-free and guarantees a consistent database as well as a fair and effective service to the users.     

Rigid body constraints realized in massively-parallel molecular dynamics on graphics processing units

Molecular dynamics (MD) methods compute the trajectory of a system of point particles in response to a potential function by numerically integrating Newton?s equations of motion. Extending these basic methods with rigid body constraints enables composite particles with complex shapes such as anisotropic nanoparticles, grains, molecules, and rigid proteins to be modeled. Rigid body constraints are added to the GPU-accelerated MD package, HOOMD-blue, version 0.10.0. The software can now simulate systems of particles, rigid bodies, or mixed systems in microcanonical (NVE), canonical (NVT), and isothermal-isobaric (NPT) ensembles. It can also apply the FIRE energy minimization technique to these systems. In this paper, we detail the massively parallel scheme that implements these algorithms and discuss how our design is tuned for the maximum possible performance. Two different case studies are included to demonstrate the performance attained, patchy spheres and tethered nanorods. In typical cases, HOOMD-blue on a single GTX 480 executes 2.5–3.6 times faster than LAMMPS executing the same simulation on any number of CPU cores in parallel. Simulations with rigid bodies may now be run with larger systems and for longer time scales on a single workstation than was previously even possible on large clusters.     

On sampling regional data

The region quadtree is a very popular hierarchical data structure for the representation of binary images (regional data) and it is heavily used at the physical level of many spatial databases. Random sampling algorithms obtain approximate answers of aggregate queries on these databases efficiently. In the present report, we examine how four different sampling methods are applied to specific quadtree implementations (to the most widely used linear implementations). In addition, we examine how two probabilistic models (a parametric model of random images and a model of random trees) can be used for analysing the cost of these methods.     

Principles of model-based fault detection

The paper outlines the principles of fault detection and isolation (PDI) in dynamic systems using a mathematical model of the system. Implemented on a digital computer, these model-based algorithms can efficiently be applied to signal validation, on-line detection of abrupt faults and early fault diagnosis in a long-term system supervision. The basic concept of the model-based approach to fault detection and isolation is described, and the different methods of residual generation developed in the past two decades are reviewed. Among the methods considered are the parameter identification approach, the different observer-based concepts, the parity space approach and the knowledge-based strategy. It is also shown how structured residuals for the localization of the faults can be generated with the aid of observer schemes. As an issue of great practical relevance the robustness with respect to modeling errors is taken into consideration. Here emphasis is placed upon the unknown input observer approach and its application to instrument, actuator and component fault detection.     

Application of the method of characteristics to crystallizer simulation and comparison with finite difference for controller performance evaluation

Finite difference methods are widely used to simulate crystallization processes, although they suffer from numerical dispersion. The method of characteristics can tackle this problem, but the traditional algorithm cannot simulate crystallization processes, if the growth rate depends on crystal size, or if discrete control actions are taken. This paper shows how the method of characteristics can be applied to simulate such processes. Furthermore, the impact of numerical dispersion is discussed by comparing simulation results obtained from these two algorithms. It is revealed that the optimal gains and the responses obtained with a finite difference method are clearly different from those with the method of characteristics.     

Monte Carlo methods for matrix computations on the grid

Many scientific and engineering applications involve inverting large matrices or solving systems of linear algebraic equations. Solving these problems with proven algorithms for direct methods can take very long to compute, as they depend on the size of the matrix. The computational complexity of the stochastic Monte Carlo methods depends only on the number of chains and the length of those chains. The computing power needed by inherently parallel Monte Carlo methods can be satisfied very efficiently by distributed computing technologies such as Grid computing. In this paper we show how a load balanced Monte Carlo method for computing the inverse of a dense matrix can be constructed, show how the method can be implemented on the Grid, and demonstrate how efficiently the method scales on multiple processors.     

Checking Finite Traces Using Alternating Automata

Alternating automata have been commonly used as a basis for static verification of reactive systems. In this paper we show how alternating automata can be used in runtime verification. We present three algorithms to check at runtime whether a reactive program satisfies a temporal specification, expressed by a linear-time temporal logic formula. The three methods start from the same alternating automaton but traverse the automaton in different ways: depth-first, breadth-first, and backwards, respectively. We then show how an extension of these algorithms, that collects statistical data while verifying the execution trace, can be used for a more detailed analysis of the runtime behavior. All three methods have been implemented and experimental results are presented.     

Using big data techniques for measuring productive friction in mass collaboration online environments

The advent of the social web brought with it challenges and opportunities for research on learning and knowledge construction. Using the online-encyclopedia Wikipedia as an example, we discuss several methods that can be applied to analyze the dynamic nature of knowledge-related processes in mass collaboration environments. These methods can help in the analysis of the interactions between the two levels that are relevant in computer-supported collaborative learning (CSCL) research: The individual level of learners and the collective level of the group or community. In line with constructivist theories of learning, we argue that the development of knowledge on both levels is triggered by productive friction, that is, the prolific resolution of socio-cognitive conflicts. By describing three prototypical methods that have been used in previous Wikipedia research, we review how these techniques can be used to examine the dynamics on both levels and analyze how these dynamics can be predicted by the amount of productive friction. We illustrate how these studies make use of text classifiers, social network analysis, and cluster analysis in order to operationalize the theoretical concepts. We conclude by discussing implications for the analysis of dynamic knowledge processes from a learning sciences perspective.     

The role of assumptions in knowledge engineering

Problem-solving methods are means of describing the inference process of knowledge-based systems. In recent years, a number of these problem-solving methods have been identified that can be reused for building new systems. However, problem-solving methods require specific types of domain knowledge and introduce specific restrictions on the tasks that can be solved by them. These requirements and restrictions are assumptions that play a key role in the reuse of problem-solving methods, in the acquisition of domain knowledge, and in the definition of the problem that can be tackled by knowledge-based systems. In this paper we discuss the different roles assumptions play in the development of knowledge-based systems and provide a survey of assumptions used in diagnostic problem solving. We show how such assumptions introduce targets and bias for goal-driven machine learning and knowledge discovery techniques. © 1998 John Wiley & Sons, Inc.