A generic approach for developing process-level hydrologic modeling components

Component software architectures offer an alternative approach for building large, complex hydrologic modeling systems. In contrast to more traditional software paradigms (i.e. procedural or object-oriented approaches), using component-based approaches allows individuals to construct autonomous modeling units that can be linked together through shared boundary conditions during a simulation run. One of the challenges in component-based modeling is designing a simple yet robust means for authoring model components. We address this challenge by presenting an approach for efficiently creating standards-based, process-level hydrologic modeling components. Using this approach, a hydrologic process is implemented as a modeling component by (1) authoring a configuration file that defines the properties of the component and (2) creating a class with three methods that define the pre-run, runtime, and post-run behavior of the modeling component. We present the design and implementation of this approach, which we call the Simple Model Wrapper (SMW), and demonstrate how it can be applied to create an Open Modeling Interface (OpenMI)-compliant modeling component for a basic hydrologic process.     

Mining Web navigation patterns with a path traversal graph

Understanding the navigational behaviour of website visitors is a significant factor of success in the emerging business models of electronic commerce and even mobile commerce. However, Web traversal patterns obtained by traditional Web usage mining approaches are ineffective for the content management of websites. They do not provide the big picture of the intentions of the visitors. The Web navigation patterns, termed throughout-surfing patterns (TSPs) as defined in this paper, are a superset of Web traversal patterns that effectively display the trends toward the next visited Web pages in a browsing session. TSPs are more expressive for understanding the purposes of website visitors. In this paper, we first introduce the concept of throughout-surfing patterns and then present an efficient method for mining the patterns. We propose a compact graph structure, termed a path traversal graph, to record information about the navigation paths of website visitors. The graph contains the frequent surfing paths that are required for mining TSPs. In addition, we devised a graph traverse algorithm based on the proposed graph structure to discover the TSPs. The experimental results show the proposed mining method is highly efficient to discover TSPs.     

Video game experience predicts virtual, but not real navigation performance

In recent years, studies have shown that video game experience is related to improvements across a variety of cognitive and visuospatial tasks. This study investigated the relationship between prior video game experience and spatial performance in virtual and real environments. Across two experiments, gaming experience was related to performance in desktop virtual environments; those with more video game experience were more accurate in pointing to nonvisible targets. In contrast, gaming experience was unrelated to three different real environment tasks, suggesting that video games may primarily influence perceptual and cognitive abilities in the visual domain over abilities that also involve kinesthetic or vestibular input. Contrary to expectations, gaming experience was also related to performance in immersive virtual environments, which may be related to the use of a joystick interface during immersive travel. Video game experience was also positively related to performance in a dynamic spatial task and to verbal SAT and math SAT scores. Sex differences in desktop virtual navigation and dynamic spatial ability were eliminated when game experience was included as a covariate.     

Time-extended multi-robot coordination for domains with intra-path constraints

Many applications require teams of robots to cooperatively execute tasks. Among these domains are those in which successful coordination must respect intra-path constraints, which are constraints that occur on the paths of agents and affect route planning. This work focuses on multi-agent coordination for disaster response with intra-path precedence constraints, a compelling application that is not well addressed by current coordination methods. In this domain a group of fire truck agents attempt to address fires spread throughout a city in the wake of a large-scale disaster. The disaster has also caused many city roads to be blocked by impassable debris, which can be cleared by bulldozer robots. A high-quality coordination solution must determine not only a task allocation but also what routes the fire trucks should take given the intra-path precedence constraints and which bulldozers should be assigned to clear debris along those routes.     

Mining closed patterns in multi-sequence time-series databases

In this paper, we propose an efficient algorithm, called CMP-Miner, to mine closed patterns in a time-series database where each record in the database, also called a transaction, contains multiple time-series sequences. Our proposed algorithm consists of three phases. First, we transform each time-series sequence in a transaction into a symbolic sequence. Second, we scan the transformed database to find frequent patterns of length one. Third, for each frequent pattern found in the second phase, we recursively enumerate frequent patterns by a frequent pattern tree in a depth-first search manner. During the process of enumeration, we apply several efficient pruning strategies to remove frequent but non-closed patterns. Thus, the CMP-Miner algorithm can efficiently mine the closed patterns from a time-series database. The experimental results show that our proposed algorithm outperforms the modified Apriori and BIDE algorithms.     

Unary finite automata vs. arithmetic progressions

We point out a subtle error in the proof of Chrobak's theorem that every unary NFA can be represented as a union of arithmetic progressions that is at most quadratically large. We propose a correction for this and show how Martinez's polynomial time algorithm, which realizes Chrobak's theorem, can be made correct accordingly. We also show that Martinez's algorithm cannot be improved to have logarithmic space, unless L = NL.     

On the implementation of bytecode compression for interpreted languages

This paper describes a new method for code space optimization for interpreted languages called LZW‐CC . The method is based on a well‐known and widely used compression algorithm, LZW , which has been adapted to compress executable program code represented as bytecode. Frequently occurring sequences of bytecode instructions are replaced by shorter encodings for newly generated bytecode instructions. The interpreter for the compressed code is modified to recognize and execute those new instructions. When applied to systems where a copy of the interpreter is supplied with each user program, space is saved not only by compressing the program code but also by automatically removing the unused implementation code from the interpreter. The method's implementation within two compiler systems for the programming languages Haskell and Java is described and implementation issues of interest are presented, notably the recalculations of target jumps and the automated tailoring of the interpreter to program code. Applying LZW‐CC to nhc98 Haskell results in bytecode size reduction by up to 15.23% and executable size reduction by up to 11.9%. Java bytecode is reduced by up to 52%. The impact of compression on execution speed is also discussed; the typical speed penalty for Java programs is between 1.8 and 6.6%, while most compressed Haskell executables run faster than the original. Copyright © 2008 John Wiley & Sons, Ltd.     

Contact type dependency of texture classification in a whiskered mobile robot

Actuated artificial whiskers modeled on rat macrovibrissae can provide effective tactile sensor systems for autonomous robots. This article focuses on texture classification using artificial whiskers and addresses a limitation of previous studies, namely, their use of whisker deflection signals obtained under relatively constrained experimental conditions. Here we consider the classification of signals obtained from a whiskered robot required to explore different surface textures from a range of orientations and distances. This procedure resulted in a variety of deflection signals for any given texture. Using a standard Gaussian classifier we show, using both hand-picked features and ones derived from studies of rat vibrissal processing, that a robust rough-smooth discrimination is achievable without any knowledge of how the whisker interacts with the investigated object. On the other hand, finer discriminations appear to require knowledge of the target’s relative position and/or of the manner in which the whisker contact its surface. Electronic Supplementary Material  The online version of this article () contains supplementary material, which is available to authorized users.

What levels of moral reasoning and values explain adherence to information security rules? An empirical study

It is widely agreed that employee non-adherence to information security policies poses a major problem for organizations. Previous research has pointed to the potential of theories of moral reasoning to better understand this problem. However, we find no empirical studies that examine the influence of moral reasoning on compliance with information security policies. We address this research gap by proposing a theoretical model that explains non-compliance in terms of moral reasoning and values. The model integrates two well-known psychological theories: the Theory of Cognitive Moral Development by Kohlberg and the Theory of Motivational Types of Values by Schwartz. Our empirical findings largely support the proposed model and suggest implications for practice and research on how to improve information security policy compliance.     

How to trust a few among many

The presence of numerous and disparate information sources available to support decision-making calls for efficient methods of harnessing their potential. Information sources may be unreliable, and misleading reports can affect decisions. Existing trust and reputation mechanisms typically rely on reports from as many sources as possible to mitigate the influence of misleading reports on decisions. In the real world, however, it is often the case that querying information sources can be costly in terms of energy, bandwidth, delay overheads, and other constraints. We present a model of source selection and fusion in resource-constrained environments, where there is uncertainty regarding the trustworthiness of sources. We exploit diversity among sources to stratify them into homogeneous subgroups to both minimise redundant sampling and mitigate the effect of certain biases. Through controlled experiments, we demonstrate that a diversity-based approach is robust to biases introduced due to dependencies among source reports, performs significantly better than existing approaches when sampling budget is limited and equally as good with an unlimited budget.