Improving recall in appearance-based visual SLAM using visual expectation

In this paper, we present a new algorithm for boosting visual template recall performance through a process of visual expectation. Visual expectation dynamically modifies the recognition thresholds of learnt visual templates based on recently matched templates, improving the recall of sequences of familiar places while keeping precision high, without any feedback from a mapping backend. We demonstrate the performance benefits of visual expectation using two 17 kilometer datasets gathered in an outdoor environment at two times separated by three weeks. The visual expectation algorithm provides up to a 100% improvement in recall. We also combine the visual expectation algorithm with the RatSLAM SLAM system and show how the algorithm enables successful mapping

Tracking the defining moments of crisis process and practice

Crisis holds the potential for profound change in organizations and industries. The past 50 years of crisis management highlight key shifts in crisis practice, creating opportunities for multiple theories and research tracks. Defining crises such as Tylenol, Exxon Valdez, and September 11 terrorist attacks have influenced or challenged the principles of best practice of crisis communication in public relations. This study traces the development of crisis process and practice by identifying shifts in crisis research and models and mapping these against key management theories and practices. The findings define three crisis domains: crisis planning, building and testing predictive models, and mapping and measuring external environmental influences. These crisis domains mirror but lag the evolution of management theory, suggesting challenges for researchers to reshape the research agenda to close the gap and lead the next stage of development in the field of crisis communication for effective organizational outcomes.

An ontology-based mining approach for user search intent discovery

Discovering proper search intents is a vi- tal process to return desired results. It is constantly a hot research topic regarding information retrieval in recent years. Existing methods are mainly limited by utilizing context-based mining, query expansion, and user profiling techniques, which are still suffering from the issue of ambiguity in search queries. In this pa- per, we introduce a novel ontology-based approach in terms of a world knowledge base in order to construct personalized ontologies for identifying adequate con- cept levels for matching user search intents. An iter- ative mining algorithm is designed for evaluating po- tential intents level by level until meeting the best re- sult. The propose-to-attempt approach is evaluated in a large volume RCV1 data set, and experimental results indicate a distinct improvement on top precision after compared with baseline models.

Introduction to the First International Workshop on Empirical Research in Business Process Management (ER-BPM 2009)

Providing effective IT support for business processes has become crucial for enterprises to stay competitive. In response to this need numerous process support paradigms (e.g., workflow management, service flow management, case handling), process specification standards (e.g., WS-BPEL, BPML, BPMN), process tools (e.g., ARIS Toolset, Tibco Staffware, FLOWer), and supporting methods have emerged in recent years. Summarized under the term “Business Process Management” (BPM), these paradigms, standards, tools, and methods have become a success-critical instrument for improving process performance.

Managing large collections of business process models - Current techniques and challenges

Nowadays, business process management is an important approach for managing organizations from an operational perspective. As a consequence, it is common to see organizations develop collections of hundreds or even thousands of business process models. Such large collections of process models bring new challenges and provide new opportunities, as the knowledge that they encapsulate requires to be properly managed. Therefore, a variety of techniques for managing large collections of business process models is being developed. The goal of this paper is to provide an overview of the management techniques that currently exist, as well as the open research challenges that they pose.

Enriching XML documents clustering by using concise structure and content

With the growing number of XML documents on theWeb it becomes essential to effectively organise these XML documents in order to retrieve useful information from them. A possible solution is to apply clustering on the XML documents to discover knowledge that promotes effective data management, information retrieval and query processing. However, many issues arise in discovering knowledge from these types of semi-structured documents due to their heterogeneity and structural irregularity. Most of the existing research on clustering techniques focuses only on one feature of the XML documents, this being either their structure or their content due to scalability and complexity problems. The knowledge gained in the form of clusters based on the structure or the content is not suitable for reallife datasets. It therefore becomes essential to include both the structure and content of XML documents in order to improve the accuracy and meaning of the clustering solution. However, the inclusion of both these kinds of information in the clustering process results in a huge overhead for the underlying clustering algorithm because of the high dimensionality of the data. The overall objective of this thesis is to address these issues by: (1) proposing methods to utilise frequent pattern mining techniques to reduce the dimension; (2) developing models to effectively combine the structure and content of XML documents; and (3) utilising the proposed models in clustering. This research first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. A clustering framework with two types of models, implicit and explicit, is developed. The implicit model uses a Vector Space Model (VSM) to combine the structure and the content information. The explicit model uses a higher order model, namely a 3- order Tensor Space Model (TSM), to explicitly combine the structure and the content information. This thesis also proposes a novel incremental technique to decompose largesized tensor models to utilise the decomposed solution for clustering the XML documents. The proposed framework and its components were extensively evaluated on several real-life datasets exhibiting extreme characteristics to understand the usefulness of the proposed framework in real-life situations. Additionally, this research evaluates the outcome of the clustering process on the collection selection problem in the information retrieval on the Wikipedia dataset. The experimental results demonstrate that the proposed frequent pattern mining and clustering methods outperform the related state-of-the-art approaches. In particular, the proposed framework of utilising frequent structures for constraining the content shows an improvement in accuracy over content-only and structure-only clustering results. The scalability evaluation experiments conducted on large scaled datasets clearly show the strengths of the proposed methods over state-of-the-art methods. In particular, this thesis work contributes to effectively combining the structure and the content of XML documents for clustering, in order to improve the accuracy of the clustering solution. In addition, it also contributes by addressing the research gaps in frequent pattern mining to generate efficient and concise frequent subtrees with various node relationships that could be used in clustering.

Thermal profile of a near-adiabatic compression process in a cylindrical tube and establishment of critical control elements for repeatable process control

The compressed gas industry and government agencies worldwide utilize "adiabatic compression" testing for qualifying high-pressure valves, regulators, and other related flow control equipment for gaseous oxygen service. This test methodology is known by various terms including adiabatic compression testing, gaseous fluid impact testing, pneumatic impact testing, and BAM testing as the most common terms. The test methodology will be described in greater detail throughout this document but in summary it consists of pressurizing a test article (valve, regulator, etc.) with gaseous oxygen within 15 to 20 milliseconds (ms). Because the driven gas1 and the driving gas2 are rapidly compressed to the final test pressure at the inlet of the test article, they are rapidly heated by the sudden increase in pressure to sufficient temperatures (thermal energies) to sometimes result in ignition of the nonmetallic materials (seals and seats) used within the test article. In general, the more rapid the compression process the more "adiabatic" the pressure surge is presumed to be and the more like an isentropic process the pressure surge has been argued to simulate. Generally speaking, adiabatic compression is widely considered the most efficient ignition mechanism for directly kindling a nonmetallic material in gaseous oxygen and has been implicated in many fire investigations. Because of the ease of ignition of many nonmetallic materials by this heating mechanism, many industry standards prescribe this testing. However, the results between various laboratories conducting the testing have not always been consistent. Research into the test method indicated that the thermal profile achieved (i.e., temperature/time history of the gas) during adiabatic compression testing as required by the prevailing industry standards has not been fully modeled or empirically verified, although attempts have been made. This research evaluated the following questions: 1) Can the rapid compression process required by the industry standards be thermodynamically and fluid dynamically modeled so that predictions of the thermal profiles be made, 2) Can the thermal profiles produced by the rapid compression process be measured in order to validate the thermodynamic and fluid dynamic models; and, estimate the severity of the test, and, 3) Can controlling parameters be recommended so that new guidelines may be established for the industry standards to resolve inconsistencies between various test laboratories conducting tests according to the present standards?

Gaussian process models for sensor-centric robot localisation

This paper presents an approach to building an observation likelihood function from a set of sparse, noisy training observations taken from known locations by a sensor with no obvious geometric model. The basic approach is to fit an interpolant to the training data, representing the expected observation, and to assume additive sensor noise. This paper takes a Bayesian view of the problem, maintaining a posterior over interpolants rather than simply the maximum-likelihood interpolant, giving a measure of uncertainty in the map at any point. This is done using a Gaussian process framework. To validate the approach experimentally, a model of an environment is built using observations from an omni-directional camera. After a model has been built from the training data, a particle filter is used to localise while traversing this environment

The development of a conceptual framework for a district 4-Year Pavement Management Plan, implementation of network-level project screening process

The Texas Department of Transportation (TxDOT) is concerned about the widening gap between preservation needs and available funding. Funding levels are not adequate to meet the preservation needs of the roadway network; therefore projects listed in the 4-Year Pavement Management Plan must be ranked to determine which projects should be funded now and which can be postponed until a later year. Currently, each district uses locally developed methods to prioritize projects. These ranking methods have relied on less formal qualitative assessments based on engineers’ subjective judgment. It is important for TxDOT to have a 4-Year Pavement Management Plan that uses a transparent, rational project ranking process. The objective of this study is to develop a conceptual framework that describes the development of the 4-Year Pavement Management Plan. It can be largely divided into three Steps; 1) Network-Level project screening process, 2) Project-Level project ranking process, and 3) Economic Analysis. A rational pavement management procedure and a project ranking method accepted by districts and the TxDOT administration will maximize efficiency in budget allocations and will potentially help improve pavement condition. As a part of the implementation of the 4-Year Pavement Management Plan, the Network-Level Project Screening (NLPS) tool including the candidate project identification algorithm and the preliminary project ranking matrix was developed. The NLPS has been used by the Austin District Pavement Engineer (DPE) to evaluate PMIS (Pavement Management Information System) data and to prepare a preliminary list of candidate projects for further evaluation.

A capabilities-based approach to obtaining a deeper understanding of information technology governance effectiveness : evidence from IT steering committees

Given the substantial investment in information technology (IT), and the significant impact IT has on organizational success, organizations consume considerable resources to manage acquisition and use of their IT resources. While various arguments proposed suggest which IT governance arrangements may work best, our understanding of the effectiveness of such initiatives is limited. We examine the relationship between the effectiveness of IT steering committee driven IT governance initiatives and firm's IT management and IT infrastructure related capabilities. We further propose that firm's ITrelated capabilities generated through IT governance initiatives should improve its business processes and firm-level performance. We test these relationships empirically by a field survey. Results suggest that firms' effectiveness of IT steering committee driven IT governance initiatives positively relates to the level of their IT-related capabilities. We also found positive relationships between IT-related capabilities and internal process-level performance. Our results also support that improvement in internal process-level performance positively relates to improvement in customer service and firm-level performance.

A process evaluation of a comprehensive school based injury prevention program

A process evaluation enables understanding of critical issues that can inform the improved, ongoing implementation of an intervention program. This study describes the process evaluation of a comprehensive, multi-level injury prevention program for adolescents. The program targets change in injury associated with violence, transport and alcohol risks and incorporates two primary elements: an 8-week, teacher delivered attitude and behaviour change curriculum for Grade 8 students; and a professional development program for teachers on school level methods of protection, focusing on strategies to increase students’ connectedness to school.

A sequence analytic model of entrepreneurial discovery and exploitation process

This study examined the effect that temporal order within the entrepreneurial discovery exploitation process has on the outcomes of venture creation. Consistent with sequential theories of discovery-exploitation, the general flow of venture creation was found to be directed from discovery toward exploitation in a random sample of nascent ventures. However, venture creation attempts which specifically follow this sequence derive poor outcomes. Moreover, simultaneous discovery-exploitation was the most prevalent temporal order observed, and venture attempts that proceed in this manner more likely become operational. These findings suggest that venture creation is a multi-scale phenomenon that is at once directional in time, and simultaneously driven by symbiotically coupled discovery and exploitation.

Do balanced skills help nascent entrepreneurs to make progress in the venture creation process?

We apply Lazear’s jack-of-all-trades theory to investigate the effect of nascent entrepreneurs´ balanced skill set across various functional areas on the performance of nascent projects. Analyzing longitudinal data on innovative nascent projects, we find that nascent entrepreneurs with a more balanced skill set are more successful in that they progress faster in the venture creation process.

Measuring bitrate and quality trade-off in a fast region-of-interest based video coding

Prevailing video adaptation solutions change the quality of the video uniformly throughout the whole frame in the bitrate adjustment process; while region-of-interest (ROI)-based solutions selectively retains the quality in the areas of the frame where the viewers are more likely to pay more attention to. ROI-based coding can improve perceptual quality and viewer satisfaction while trading off some bandwidth. However, there has been no comprehensive study to measure the bitrate vs. perceptual quality trade-off so far. The paper proposes an ROI detection scheme for videos, which is characterized with low computational complexity and robustness, and measures the bitrate vs. quality trade-off for ROI-based encoding using a state-of-the-art H.264/AVC encoder to justify the viability of this type of encoding method. The results from the subjective quality test reveal that ROI-based encoding achieves a significant perceptual quality improvement over the encoding with uniform quality at the cost of slightly more bits. Based on the bitrate measurements and subjective quality assessments, the bitrate and the perceptual quality estimation models for non-scalable ROI-based video coding (AVC) are developed, which are found to be similar to the models for scalable video coding (SVC).

Context-aware methods for process modeling

Recent studies have started to explore context-awareness as a driver in the design of adaptable business processes. The emerging challenge of identifying and considering contextual drivers in the environment of a business process are well understood, however, typical methods used in business process modeling do not yet consider this additional contextual information in their process designs. In this chapter, we describe our research towards innovative and advanced process modeling methods that include mechanisms to incorporate relevant contextual drivers and their impacts on business processes in process design models. We report on our ongoing work with an Australian insurance provider and describe the design science we employed to develop these innovative and useful artifacts as part of a context-aware method framework. We discuss the utility of these artifacts in an application in the claims handling process at the case organization.