Linking temporal first-order logic with Bayesian networks for the simulation of pervasive computing systems

The authors’ previous work discussed a scalable abstract knowledge representation and reasoning scheme for Pervasive Computing Systems, where both low-level and abstract knowledge is maintained in the form of temporal first-order logic (TFOL) predicates. Furthermore, we introduced a novel concept of a generalised event, an abstract event, which we define as a change in the truth value of an abstract TFOL predicate. Abstract events represent real-time knowledge about the system and they are defined with the help of well-formed TFOL expressions whose leaf nodes are concrete, low-level events using our AESL language.In this paper, we propose to simulate pervasive systems by providing estimated knowledge about its entities and situations that involve them. To achieve this goal, we enhance AESL with higher-order function predicates that denote approximate knowledge about the likelihood of a predicate instance having the value True with respect to a time reference. We define a mapping function between a TFOL predicate and a Bayesian network that calculates likelihood estimates for that predicate as well as a confidence level, i.e., a metric of how reliable the likelihood estimation is for that predicate.Higher-order likelihood predicates are implemented by a novel middleware component, the Likelihood Estimation Service (LES). LES implements the above mapping; first, for each abstract predicate, it learns a Bayesian network that corresponds to that predicate from the knowledge stored in the sensor-driven system. Once trained and validated, the Bayesian networks generate a likelihood estimate and a confidence level. This new knowledge is maintained in the middleware as approximate knowledge therefore providing a simulation of the pervasive system, in the absence of real-time data. Last but not least, we describe an experimental evaluation of our system using the Active BAT location system.     

ODTLES: A multi-scale model for 3D turbulent flow based on one-dimensional turbulence modeling

A novel multi-scale approach for extending the one-dimensional turbulence (ODT) model of [A.R. Kerstein. One-dimensional turbulence: model formulation and application to homogeneous turbulence, shear flows, and buoyant stratified flows, J. Fluid Mech. 392 (1999) 277] to treat turbulent flow in three-dimensional (3D) domains is described. In this model, here called ODTLES, 3D aspects of the flow are captured by embedding three, mutually orthogonal, one-dimensional ODT domain arrays within a coarser 3D mesh. The ODTLES model is obtained by developing a consistent approach for dynamically coupling the different ODT line sets to each other and to the large scale processes that are resolved on the 3D mesh. The model is implemented computationally and its performance is tested by performing simulations of decaying isotropic turbulence at two different Reynolds numbers and comparing to the experimental data of [H. Kang, S. Chester, C. Meneveau. Decaying turbulence in an active-grid-generated flow and comparisons with large-eddy simulations, J. Fluid Mech. 480 (2003) 129; G. Comte-Bellot, S. Corrsin, Simple Eulerian correlation of full-and narrow band velocity signals in grid-generated ’isotropic’ turbulence, J. Fluid Mech. 48 (1971) 273].     

Filtering Data Streams for Entity-Based Continuous Queries

The idea of allowing query users to relax their correctness requirements in order to improve performance of a data stream management system (e.g., location-based services and sensor networks) has been recently studied. By exploiting the maximum error (or tolerance) allowed in query answers, algorithms for reducing the use of system resources have been developed. In most of these works, however, query tolerance is expressed as a numerical value, which may be difficult to specify. We observe that in many situations, users may not be concerned with the actual value of an answer, but rather which object satisfies a query (e.g., "who is my nearest neighbor?”). In particular, an entity-based query returns only the names of objects that satisfy the query. For these queries, it is possible to specify a tolerance that is "nonvalue-based.” In this paper, we study fraction-based tolerance, a type of nonvalue-based tolerance, where a user specifies the maximum fractions of a query answer that can be false positives and false negatives. We develop fraction-based tolerance for two major classes of entity-based queries: 1) nonrank-based query (e.g., range queries) and 2) rank-based query (e.g., k-nearest-neighbor queries). These definitions provide users with an alternative to specify the maximum tolerance allowed in their answers. We further investigate how these definitions can be exploited in a distributed stream environment. We design adaptive filter algorithms that allow updates be dropped conditionally at the data stream sources without affecting the overall query correctness. Extensive experimental results show that our protocols reduce the use of network and energy resources significantly.     

Plans, Actions and Dialogues Using Linear Logic

We describe how Intuitionistic Linear Logic can be used to provide a unified logical account for agents to find and execute plans. This account supports the modelling of agent interaction, including dialogue; allows agents to be robust to unexpected events and failures; and supports significant reuse of agent specifications. The framework has been implemented and several case studies have been considered. Further applications include human–computer interfaces as well as agent interaction in the semantic web.     

Multiagent System Simulations of Signal Averaging in English Auctions with Two-Dimensional Value Signals

Computational experiments with a multiagent system show that bidders use signal averaging to avoid the winner’s curse in English auctions. The results vary with the percent of common value in a two-dimensional value signal, information levels, uncertainty, and the number of bidders. The complexity introduced by the combinations of these factors affects the bidding strategies and auction outcomes in interesting ways—usually nonlinearly and sometimes non-monotonically. Of main concern to a seller is the effect of these factors on revenue. I find that revenue increases with the percent of common value in the two-dimensional value signal, decreases with uncertainty, and increases with the number of bidders. There is very little impact of information level on revenue when values are pure private and pure common. However, for the intermediate cases of two-dimensional value signals, revenue decreases with increased information.      

Discovery of sound sources by an autonomous mobile robot

In this work, we describe an autonomous mobile robotic system for finding, investigating, and modeling ambient noise sources in the environment. The system has been fully implemented in two different environments, using two different robotic platforms and a variety of sound source types. Making use of a two-step approach to autonomous exploration of the auditory scene, the robot first quickly moves through the environment to find and roughly localize unknown sound sources using the auditory evidence grid algorithm. Then, using the knowledge gained from the initial exploration, the robot investigates each source in more depth, improving upon the initial localization accuracy, identifying volume and directivity, and, finally, building a classification vector useful for detecting the sound source in the future.     

Applications of ontologies in requirements engineering: a systematic review of the literature

There is an increase use of ontology-driven approaches to support requirements engineering (RE) activities, such as elicitation, analysis, specification, validation and management of requirements. However, the RE community still lacks a comprehensive understanding of how ontologies are used in RE process. Thus, the main objective of this work is to investigate and better understand how ontologies support RE as well as identify to what extent they have been applied to this field. In order to meet our goal, we conducted a systematic literature review (SLR) to identify the primary studies on the use of ontologies in RE, following a predefined review protocol. We then identified the main RE phases addressed, the requirements modelling styles that have been used in conjunction with ontologies, the types of requirements that have been supported by the use of ontologies and the ontology languages that have been adopted. We also examined the types of contributions reported and looked for evidences of the benefits of ontology-driven RE. In summary, the main findings of this work are: (1) there are empirical evidences of the benefits of using ontologies in RE activities both in industry and academy, specially for reducing ambiguity, inconsistency and incompleteness of requirements; (2) the majority of studies only partially address the RE process; (3) there is a great diversity of RE modelling styles supported by ontologies; (4) most studies addressed only functional requirements; (5) several studies describe the use/development of tools to support different types of ontology-driven RE approaches; (6) about half of the studies followed W3C recommendations on ontology-related languages; and (7) a great variety of RE ontologies were identified; nevertheless, none of them has been broadly adopted by the community. Finally, we conclude this work by showing several promising research opportunities that are quite important and interesting but underexplored in current research and practice.     

A SAS(®) macro implementation of a multiple comparison post hoc test for a Kruskal-Wallis analysis

The Kruskal-Wallis (KW) nonparametric analysis of variance is often used instead of a standard one-way ANOVA when data are from a suspected non-normal population. The KW omnibus procedure tests for some differences between groups, but provides no specific post hoc pair wise comparisons. This paper provides a SAS^® macro implementation of a multiple comparison test based on significant Kruskal-Wallis results from the SAS NPAR1WAY procedure. The implementation is designed for up to 20 groups at a user-specified alpha significance level. A Monte-Carlo simulation compared this nonparametric procedure to commonly used parametric multiple comparison tests.     

The BIOPHILE Sample Process Management System - Automated Sample Access at Ultra Low Temperatures

The genomics, proteomics, clinical, and drug discovery laboratories have a growing need to maintain valuable samples at ultra-low (−80°C) temperatures in a validated, secure environment. Automated sample processing systems have until now required manual (off-line) storage of samples at −80°C, reducing system reliability and speed. Both of these important needs are addressed by the Sample Process Management System being introduced by BIOPHILE Inc. Conventional sample management processes, such as storage, retrieval, and cataloging, are increasingly strained by the growing sample populations. There are variable sample types, access requirements and storage requirements. Security and inventory procedures are implemented manually. The evolving technologies present in the laboratory cannot interface with conventional manual storage techniques. Addressing these limitations, the primary benefits of BIOPHILE's solutions are:

• Fully validated sample management process that coordinates the life-cycles of samples and their related data.

• Robotic technology to securely store and retrieve samples, improving their accessibility and stability. Thermal shock is reduced, improving sample longevity and quality. The robotic technology allows integration with larger automation systems.

• A process program to develop a Sample Management Strategy. This strategy is developed by analyzing long-term research goals, current baseline processes, and identification of current sample life cycles. A full validation documentation package can be generated, providing a high level of quality assurance.

• Improved sample visibility and quality assurance - automated sample population cataloging; controlled sample management access and security.