Loop closure detection on a suburban road network using a continuous appearance-based trajectory

This paper presents a novel technique for performing SLAM along a continuous trajectory of appearance. Derived from components of FastSLAM and FAB-MAP, the new system dubbed Continuous Appearance-based Trajectory SLAM (CAT-SLAM) augments appearancebased place recognition with particle-filter based ‘pose filtering’ within a probabilistic framework, without calculating global feature geometry or performing 3D map construction. For loop closure detection CAT-SLAM updates in constant time regardless of map size. We evaluate the effectiveness of CAT-SLAM on a 16km outdoor road network and determine its loop closure performance relative to FAB-MAP. CAT-SLAM recognizes 3 times the number of loop closures for the case where no false positives occur, demonstrating its potential use for robust loop closure detection in large environments.

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

A rebirth of physical activity in parents : applying a theory-based approach

Physical inactivity is a serious concern both nationally and internationally. Despite the numerous benefits of performing regular physical activity, many individuals lead sedentary lifestyles. Of concern, though, is research showing that some population sub-groups are less likely to be active, such as parents of young children. Although there is a vast amount of research dedicated to understanding people.s physical activity-related behaviours, there is a paucity of research examining those factors that influence parental physical activity. More importantly, research applying theoretical models to understand physical activity decision-making among this at-risk population is limited. Given the current obesity epidemic, the decline in physical activity with parenthood, and the many social and health benefits associated with regular physical activity, it is important that adults with young children are sufficiently active. In light of the dearth of research examining parental physical activity and the scant research applying a theory-based approach to gain this understanding, the overarching aim of the current program of research was to adopt a mixed methods approach as well as use sound theoretical frameworks to understand the regular physical activity behaviour of mothers and fathers with young children. This program of research comprised of three distinct stages: a qualitative stage exploring individual, social, and psychological factors that influence parental regular physical activity (Stage 1); a quantitative stage identifying the important predictors of parental regular physical activity intentions and behaviour using sound theoretical frameworks and testing a single-item measure for assessing parental physical activity behaviour (Stage 2); and a qualitative stage exploring strategies for an intervention program aimed at increasing parental regular physical activity (Stage 3). As a thesis by publication, eight papers report the findings of this program of research; these papers are presented according to the distinct stages of investigation that guided this program of research. Stage One of the research program comprised a qualitative investigation using a focus group/interview methodology with parents of children younger than 5 years of age (N = 40; n = 21 mothers, n = 19 fathers) (Papers 1, 2, and 3). Drawing broadly on a social constructionist approach (Paper 1), thematic analytic methods revealed parents. understandings of physical activity (e.g., requires effort), patterns of physical activity-related behaviours (e.g., grab it when you can, declining physical activity habits), and how constructions of social role expectations might influence parents. physical activity decision making (e.g., creating an active family culture, guilt and selfishness). Drawing on the belief-based framework of the TPB (Paper 2), thematic content analytic methods revealed parents. commonly held beliefs about the advantages (e.g., improves parenting practices), disadvantages (e.g., interferes with commitments), barriers (e.g., time), and facilitators (e.g., social support) to performing regular physical activity. Parents. normative beliefs about social approval from important others or groups (e.g., spouse/partner) were also identified. Guided by theories of social support, Paper Three identified parents. perceptions about the specific social support dimensions that influence their physical activity decision making. Thematic content analysis identified instrumental (e.g., providing childcare, taking over chores), emotional (e.g., encouragement, companionship), and informational support (e.g., ideas and advice) as being important to the decision-making of parents in relation to their regular physical activity behaviour. The results revealed also that having support for being active is not straightforward (e.g., guilt-related issues inhibited the facilitative nature of social support for physical activity). Stage Two of the research program comprised a quantitative examination of parents. physical activity intentions and behaviour (Papers 4, 5, 6, and 7). Parents completed an extended TPB questionnaire at Time 1 (N = 580; n = 288 mothers, n = 292 fathers) and self-reported their physical activity at Time 2, 1 week later (N = 458; n = 252 mothers, n = 206 fathers). Paper Four revealed key behavioural (e.g., improving parenting practices), normative (e.g., people I exercise with), and control (e.g., lack of time) beliefs as significant independent predictors of parental physical activity. A test of the TPB augmented to include the constructs of self-determined motivation and planning was assessed in Paper Five. The findings revealed that the effect of self-determined motivation on intention was fully mediated by the TPB variables and the impact of intention on behaviour was partially mediated by the planning variables. Slight differences in the model.s motivational sequence between the sexes were also noted. Paper Six investigated, within a TPB framework, a range of social influences on parents. intentions to be active. For both sexes, attitude, perceived behavioural control, group norms, friend general support, and an active parent identity predicted intentions, with subjective norms and family support further predicting mothers. intentions and descriptive norms further predicting fathers. intentions. Finally, the measurement of parental physical activity was investigated in Paper Seven of Stage Two. The results showed that parents are at risk of low levels of physical activity, with the findings also revealing validation support for a brief single-item physical activity measure. Stage Three of the research program comprised a qualitative examination of parents. (N = 12; n = 6 mothers, n = 6 fathers) ideas for strategies that may be useful for developing and delivering an intervention program aimed at increasing parental physical activity (Paper 8). Parents revealed a range of strategies for what to include in a physical activity intervention designed for parents of young children. For example, parents identified persuasion and information type messages, problem-solving strategies that engage parents in generating a priority list of their lifestyle commitments, and behavioural modification techniques such as goal setting and incentives. Social intervention strategies (e.g., social comparison, counselling) and environmental approaches (e.g., community-based integrative parent/child programs) were also identified as was a skill-based strategy in helping parents generate a flexible life/family plan. Additionally, a range of strategies for how to best deliver a parental physical activity intervention was discussed. Taken as a whole, Paper Eight found that adopting a multifaceted approach in both the design and implementation of a resultant physical activity intervention may be useful in helping to increase parental physical activity. Overall, this program of research found support for parents as a unique group who hold both similar and distinctive perceptions about regular physical activity to the general adult population. Thus, these findings highlight the importance of targeting intervention strategies for parents of young children. Additionally, the findings suggest that it might also be useful to tailor some messages specifically to each sex. Effective promotion of physical activity in parents of young children is essential given the low rate of activity in this population. Results from this program of research highlight parents as an at-risk group for inactivity and provide an important first step in identifying the factors that influence both mothers. and fathers. physical activity decision making. These findings, in turn, provide a foundation on which to build effective intervention programs aimed at increasing parents. regular physical activity which is essential for ensuring the health and well-being of parents with young children.

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.

A child led participatory approach for technology-based intervention

There is significant interest in Human-computer interaction methods that assist in the design of applications for use by children. Many of these approaches draw upon standard HCI methods,such as personas, scenarios, and probes. However, often these techniques require communication and kinds of thinking skills that are designer centred,which prevents children with Autism Spectrum Disorders or other learning and communication disabilities from being able to participate. This study investigates methods that might be used with children with ASD or other learning and communication disabilities to inspire the design of technology based intervention approaches to support their speech and language development. Similar to Iversen and Brodersen, we argue that children with ASD should not be treated as being in some way “cognitively incomplete”. Rather they are experts in their everyday lives and we cannot design future IT without involving them. However, how do we involve them Instead of beginning with HCI methods, we draw upon easy to use technologies and methods used in the therapy professions for child engagement, particularly utilizing the approaches of Hanen (2011) and Greenspan (1998). These approaches emphasize following the child’s lead and ensuring that the child always has a legitimate turn at a detailed level of interaction. In a pilot project, we have studied a child’s interactions with their parents about activities over which they have control – photos that they have taken at school on an iPad. The iPad was simple enough for this child with ASD to use and they enjoyed taking and reviewing photos. We use this small case study as an example of a child-led approach for a child with ASD. We examine interactions from this study in order to assess the possibilities and limitations of the child-led approach for supporting the design of technology based interventions to support speech and language development.

A nonlinear PI and backstepping based controller for tractor-steerable trailer influenced by slip

Autonomous guidance of agricultural vehiclesis vital as mechanized farming production becomes more prevalent. It is crucial that tractor-trailers are guided with accuracy in both lateral and longitudinal directions, whilst being affected by large disturbance forces, or slips, owing to uncertain and undulating terrain. Successful research has been concentrated on trajectory control which can provide longitudinal and lateral accuracy if the vehicle moves without sliding, and the trailer is passive. In this paper, the problem of robust trajectory tracking along straight and circular paths of a tractor-steerable trailer is addressed. By utilizing a robust combination of backstepping and nonlinear PI control, a robust, nonlinear controller is proposed. For vehicles subjected to sliding, the proposed controller makes the lateral deviations and the orientation errors of the tractor and trailer converge to a neighborhood near the origin. Simulation results are presented to illustrate that the suggested controller ensures precise trajectory tracking in the presence of slip.

Integrating collaborative filtering and search-based techniques for personalized online product recommendation

The existing Collaborative Filtering (CF) technique that has been widely applied by e-commerce sites requires a large amount of ratings data to make meaningful recommendations. It is not directly applicable for recommending products that are not frequently purchased by users, such as cars and houses, as it is difficult to collect rating data for such products from the users. Many of the e-commerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended to the user. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their online navigation behaviour. This paper proposes to integrate collaborative filtering and search-based techniques to provide personalized recommendations for infrequently purchased products. Two different techniques are proposed, namely CFRRobin and CFAg Query. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAg Query technique uses the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAg Query perform better than the standard Collaborative Filtering (CF) and the Basic Search (BS) approaches, which are widely applied by the current e-commerce applications. The CFRRobin and CFAg Query approaches also outperform the e- isting query expansion (QE) technique that was proposed for recommending infrequently purchased products.

Towards semantic search and inference in electronic medical records : an approach using concept-based information retrieval

For more than a decade research in the field of context aware computing has aimed to find ways to exploit situational information that can be detected by mobile computing and sensor technologies. The goal is to provide people with new and improved applications, enhanced functionality and better use experience (Dey, 2001). Early applications focused on representing or computing on physical parameters, such as showing your location and the location of people or things around you. Such applications might show where the next bus is, which of your friends is in the vicinity and so on. With the advent of social networking software and microblogging sites such as Facebook and Twitter, recommender systems and so on context-aware computing is moving towards mining the social web in order to provide better representations and understanding of context, including social context. In this paper we begin by recapping different theoretical framings of context. We then discuss the problem of context- aware computing from a design perspective.

Rule-based approach for identifying assertions in clinical free-text data

A rule-based approach for classifying previously identified medical concepts in the clinical free text into an assertion category is presented. There are six different categories of assertions for the task: Present, Absent, Possible, Conditional, Hypothetical and Not associated with the patient. The assertion classification algorithms were largely based on extending the popular NegEx and Context algorithms. In addition, a health based clinical terminology called SNOMED CT and other publicly available dictionaries were used to classify assertions, which did not fit the NegEx/Context model. The data for this task includes discharge summaries from Partners HealthCare and from Beth Israel Deaconess Medical Centre, as well as discharge summaries and progress notes from University of Pittsburgh Medical Centre. The set consists of 349 discharge reports, each with pairs of ground truth concept and assertion files for system development, and 477 reports for evaluation. The system’s performance on the evaluation data set was 0.83, 0.83 and 0.83 for recall, precision and F1-measure, respectively. Although the rule-based system shows promise, further improvements can be made by incorporating machine learning approaches.

Project-based organization as adaptation to risk and opportunity in small firms

Recent years have seen a rapid increase in SMEs working collaboratively in inter-organizational projects. But what drives the emergence of such projects, and what types of industries breed them the most? To address these questions, this paper extends the long running literature on the firm and industry antecedents of new venturing and alliance formation to the domain of project-based organization by SMEs. Based on survey data collected among 1,725 small and medium sized organizations and longitudinal industry data, we find an overall pattern that indicates that IOPV participation is primarily determined by a focal SME’s scope of innovative activities, and the munificence, dynamism and complexity of its environment. Unexpectedly, these variables have different effects on whether SMEs are likely to engage in IOPVs, compared to with how many there are in their portfolio at a time. Implications for theory development are discussed.

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).

Performance and scalability assessment for non-certificate-based public key management in VANETS

Current research in secure messaging for Vehicular Ad hoc Networks (VANETs) appears to focus on employing a digital certificate-based Public Key Cryptosystem (PKC) to support security. The security overhead of such a scheme, however, creates a transmission delay and introduces a time-consuming verification process to VANET communications. This paper proposes a non-certificate-based public key management for VANETs. A comprehensive evaluation of performance and scalability of the proposed public key management regime is presented, which is compared to a certificate-based PKC by employing a number of quantified analyses and simulations. Not only does this paper demonstrate that the proposal can maintain security, but it also asserts that it can improve overall performance and scalability at a lower cost, compared to the certificate-based PKC. It is believed that the proposed scheme will add a new dimension to the key management and verification services for VANETs.

New perspectives on context-based chemistry education : using a dialectical sociocultural approach to view teaching and learning

Context-based chemistry education aims to improve student interest and motivation in chemistry by connecting canonical chemistry concepts with real-world contexts. Implementation of context-based chemistry programmes began 20 years ago in an attempt to make the learning of chemistry meaningful for students. This paper reviews such programmes through empirical studies on six international courses, ChemCom (USA), Salters (UK), Industrial Science (Israel), Chemie im Kontext (Germany), Chemistry in Practice (The Netherlands) and PLON (The Netherlands). These studies are categorised through emergent characteristics of: relevance, interest/attitudes motivation and deeper understanding. These characteristics can be found to an extent in a number of other curricular initiatives, such as science-technology-society approaches and problem-based learning or project based science, the latter of which often incorporates an inquiry-based approach to science education. These initiatives in science education are also considered with a focus on the characteristics of these approaches that are emphasised in context-based education. While such curricular studies provide a starting point for discussing context-based approaches in chemistry, to advance our understanding of how students connect canonical science concepts with the real-world context, a new theoretical framework is required. A dialectical sociocultural framework originating in the work of Vygotsky is used as a referent for analysing the complex human interactions that occur in context-based classrooms, providing teachers with recent information about the pedagogical structures and resources that afford students the agency to learn.

An aspect query language model based on query decomposition and high-order contextual term associations

In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model

The internal validity of web-based studies

Honing and Ladinig (2008) make the assertion that while the internal validity of web-based studies may be reduced, this is offset by an increase in external validity possible when experimenters can sample a wider range of participants and experimental settings. In this paper, the issue of internal validity is more closely examined, and it is agued that there is no necessary reason why internal validity of a web-based study should be worse than that of a lab-based one. Errors of measurement or inconsistencies of manipulation will typically balance across conditions of the experiment, and thus need not necessarily threaten the validity of a study’s findings.