Training-free probability models for whole-image based place recognition

Whole-image descriptors such as GIST have been used successfully for persistent place recognition when combined with temporal filtering or sequential filtering techniques. However, whole-image descriptor localization systems often apply a heuristic rather than a probabilistic approach to place recognition, requiring substantial environmental-specific tuning prior to deployment. In this paper we present a novel online solution that uses statistical approaches to calculate place recognition likelihoods for whole-image descriptors, without requiring either environmental tuning or pre-training. Using a real world benchmark dataset, we show that this method creates distributions appropriate to a specific environment in an online manner. Our method performs comparably to FAB-MAP in raw place recognition performance, and integrates into a state of the art probabilistic mapping system to provide superior performance to whole-image methods that are not based on true probability distributions. The method provides a principled means for combining the powerful change-invariant properties of whole-image descriptors with probabilistic back-end mapping systems without the need for prior training or system tuning.

Overview of complex systems in sport

The complex systems approach offers an opportunity to replace the extant pre-dominant mechanistic view on sport-related phenomena. The emphasis on the environment-system relationship, the applications of complexity principles, and the use of nonlinear dynamics mathematical tools propose a deep change in sport science. Coordination dynamics, ecological dynamics, and network approaches have been successfully applied to the study of different sport-related behaviors, from movement patterns that emerge at different scales constrained by specific sport contexts to game dynamics. Sport benefit from the use of such approaches in the understanding of technical, tactical, or physical conditioning aspects which change their meaning and dilute their frontiers. The creation of new learning and training strategies for teams and individual athletes is a main practical consequence. Some challenges for the future are investigating the influence of key control parameters in the nonlinear behavior of athlete-environment systems and the possible relatedness of the dynamics and constraints acting at different spatio-temporal scales in team sports. Modelling sport-related phenomena can make useful contributions to a better understanding of complex systems and vice-versa.

Towards training-free appearance-based localization : probabilistic models for whole-image descriptors

Whole image descriptors have recently been shown to be remarkably robust to perceptual change especially compared to local features. However, whole-image-based localization systems typically rely on heuristic methods for determining appropriate matching thresholds in a particular environment. These environment-specific tuning requirements and the lack of a meaningful interpretation of these arbitrary thresholds limits the general applicability of these systems. In this paper we present a Bayesian model of probability for whole-image descriptors that can be seamlessly integrated into localization systems designed for probabilistic visual input. We demonstrate this method using CAT-Graph, an appearance-based visual localization system originally designed for a FAB-MAP-style probabilistic input. We show that using whole-image descriptors as visual input extends CAT-Graph’s functionality to environments that experience a greater amount of perceptual change. We also present a method of estimating whole-image probability models in an online manner, removing the need for a prior training phase. We show that this online, automated training method can perform comparably to pre-trained, manually tuned local descriptor methods.

Doctoral research training : encouraging timely completion – case study reflections of one university on how it supports a vibrant research training culture

This is a case study of a young university striving to generate and sustain a vibrant Research Training culture. The university’s research training framework is informed by a belief in a project management approach to achieving successful research candidature. This has led to the definition and reporting of key milestones during candidature. In turn, these milestones have generated a range of training programs to support Higher Degree Research (HDR) students to meet these milestones in a timely fashion. Each milestone focuses on a specific set of skills blended with supporting the development of different parts of the doctoral thesis. Data on student progress and completion has provided evidence in highlighting the role that the milestones and training are playing in supporting timely completion. A university-wide reporting cycle generated data on the range of workshops and training provided to Higher Degree Research students and supervisors. The report provided details of thesis topic and format, as well as participation in research training events and participant evaluation of those events. Analysis of the data led to recommendations and comments on the strengths and weaknesses of the current research training program. Discussion considered strategies and drivers for enhancements into the future. In particular, the paper reflects on the significant potential role of centrally curated knowledge systems to support HDR student and supervisor access, and engagement and success. The research training program was developed using blended learning as a model. It covered face-to-face workshops as well as online modules. These were supplemented by web portals that offered a range of services to inform and educate students and supervisors and included opportunities for students to interact with each other. Topics ranged from the research life cycle, writing and publication, ethics, managing research data, managing copyright, and project management to use of software and the University’s Code of Conduct for Research. The challenges discussed included: How to reach off campus students and those studying in external modes? How best to promote events to potential participants? How long and what format is best for face-to-face sessions? What online resources best supplement face-to-face offerings? Is there a place for peer-based learning and what form should this take? These questions are raised by a relatively young university seeking to build and sustain a vibrant research culture. The rapid growth in enrolments in recent years has challenged previous one-to-one models of support. This review of research training is timely in seeking strategies to address changing research training support capacity and student needs. Part of the discussion will focus on supervisory training, noting that good supervision is the one remaining place where one-to-one support is provided. Ensuring that supervisors are appropriately equipped to address student expectations is considered in the context of the research training provisions. The paper concludes with reflection on the challenges faced, and recommended ways forward as the number of research students grows into the future.

Fuzzy logic to evaluate driving maneuvers: An integrated approach to improve training

Driver training is one of the interventions aimed at mitigating the number of crashes that involve novice drivers. Our failure to understand what is really important for learners, in terms of risky driving, is one of the many drawbacks restraining us to build better training programs. Currently, there is a need to develop and evaluate Advanced Driving Assistance Systems that could comprehensively assess driving competencies. The aim of this paper is to present a novel Intelligent Driver Training System (IDTS) that analyses crash risks for a given driving situation, providing avenues for improvement and personalisation of driver training programs. The analysis takes into account numerous variables acquired synchronously from the Driver, the Vehicle and the Environment (DVE). The system then segments out the manoeuvres within a drive. This paper further presents the usage of fuzzy set theory to develop the safety inference rules for each manoeuvre executed during the drive. This paper presents a framework and its associated prototype that can be used to comprehensively view and assess complex driving manoeuvres and then provide a comprehensive analysis of the drive used to give feedback to novice drivers.

Centroid training to achieve effective text classification

Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification) aims to build an uncertainty boundary to absorb as many indeterminate objects as possible so as to elevate the certainty of the relevant and irrelevant groups through the centroid clustering and training process. The clustering starts from the two training subsets labelled as relevant or irrelevant respectively to create two principal centroid vectors by which all the training samples are further separated into three groups: POS, NEG and BND, with all the indeterminate objects absorbed into the uncertain decision boundary BND. Two pairs of centroid vectors are proposed to be trained and optimized through the subsequent iterative multi-learning process, all of which are proposed to collaboratively help predict the polarities of the incoming objects thereafter. For the assessment of the proposed model, F1 and Accuracy have been chosen as the key evaluation measures. We stress the F1 measure because it can display the overall performance improvement of the final classifier better than Accuracy. A large number of experiments have been completed using the proposed model on the Reuters Corpus Volume 1 (RCV1) which is important standard dataset in the field. The experiment results show that the proposed model has significantly improved the binary text classification performance in both F1 and Accuracy compared with three other influential baseline models.

Electronic medical record systems in Saudi Arabia: Knowledge and preferences of healthcare professionals

Background The use of Electronic Medical Record (EMR) systems is increasing internationally, though developing countries, such as Saudi Arabia, have tended to lag behind in the adoption and implementation of EMR systems due to several barriers. The literature shows that the main barriers to EMR in Saudi Arabia are lack of knowledge or experience using EMR systems and staff resistance to using the implemented EMR system. Methods A quantitative methodology was used to examine health personnel knowledge and acceptance of and preference for EMR systems in seven Saudi public hospitals in Jeddah, Makkah and Taif cities. Results Both English literacy and education levels were significantly correlated with computer literacy and EMR literacy. Participants whose first language was not Arabic were more likely to prefer using an EMR system compared to those whose first language was Arabic. Conclusion This study suggests that as computer literacy levels increase, so too do staff preferences for using EMR systems. Thus, it would be beneficial for hospitals to assess English language proficiency and computer literacy levels of staff prior to implementing an EMR system. It is recommended that hospitals need to offer training and targeted educational programs to the potential users of the EMR system. This would help to increase English language proficiency and computer literacy levels of staff as well as staff acceptance of the system.

Place recognition with ConvNet landmarks: Viewpoint-robust, condition-robust, training-free

Place recognition has long been an incompletely solved problem in that all approaches involve significant compromises. Current methods address many but never all of the critical challenges of place recognition – viewpoint-invariance, condition-invariance and minimizing training requirements. Here we present an approach that adapts state-of-the-art object proposal techniques to identify potential landmarks within an image for place recognition. We use the astonishing power of convolutional neural network features to identify matching landmark proposals between images to perform place recognition over extreme appearance and viewpoint variations. Our system does not require any form of training, all components are generic enough to be used off-the-shelf. We present a range of challenging experiments in varied viewpoint and environmental conditions. We demonstrate superior performance to current state-of-the- art techniques. Furthermore, by building on existing and widely used recognition frameworks, this approach provides a highly compatible place recognition system with the potential for easy integration of other techniques such as object detection and semantic scene interpretation.

Reviewing in-vehicle systems to improve fuel efficiency and road safety

Road transport plays a significant role in various industries and mobility services around the globe and has a vital impact on our daily lives. However it also has serious impacts on both public health and the environment. In-vehicle feedback systems are a relatively new approach to encouraging driver behavior change for improving fuel efficiency and safety in automotive environments. While many studies claim that the adoption of eco-driving practices, such as eco-driving training programs and in-vehicle feedback to drivers, has the potential to improve fuel efficiency, limited research has integrated safety and eco-driving. Therefore, it is crucial to understand the human factors related theories and practices which will inform the design of an in-vehicle Human Machine Interface (HMI) that could provide real-time driver feedback and consequently improve both fuel efficiency and safety. This paper provides a comprehensive review of the current state of published literature on in-vehicle systems to identify and evaluate the impact of eco-driving and safety feedback systems. This paper also discusses how these factors may conflict with one another and have a negative effect on road safety, while also exploring possible eco-driving practices that could encourage more sustainable, environmentally-conscious and safe driving behavior. The review revealed a lack of comprehensive theoretical research integrating eco-driving and safe driving, and no current available HMI covering both aspects simultaneously. Furthermore, the review identified that some eco-driving in-vehicle systems may enhance fuel efficiency without compromising safety. The review has identified a range of concepts which can be developed to influence driver acceptance of safety and eco-driving systems within the area of HMI. This can promote new research aimed at enhancing our understanding of the relationship between eco-driving and safety from the human factors viewpoint. This provides a foundation for developing innovative, persuasive and acceptable in-vehicle HMI systems to improve fuel efficiency and road safety.

Leveraging coherent distributed space-time codes for noncoherent communication in relay networks via training

For point to point multiple input multiple output systems, Dayal-Brehler-Varanasi have proved that training codes achieve the same diversity order as that of the underlying coherent space time block code (STBC) if a simple minimum mean squared error estimate of the channel formed using the training part is employed for coherent detection of the underlying STBC. In this letter, a similar strategy involving a combination of training, channel estimation and detection in conjunction with existing coherent distributed STBCs is proposed for noncoherent communication in Amplify-and-Forward (AF) relay networks. Simulation results show that the proposed simple strategy outperforms distributed differential space-time coding for AF relay networks. Finally, the proposed strategy is extended to asynchronous relay networks using orthogonal frequency division multiplexing.

Drivers for uptake of environmental management systems by pastoralists in western Queensland

Pastoralists from 37 beef cattle and sheep properties in western Queensland developed and implemented an environmental management system (EMS) over 18 months. The EMS implemented by them was customised for the pastoral industry as part of a national EMS pilot project, and staff from this project encouraged and assisted pastoralists during this trial. The 31 pastoralists surveyed at the end of the pilot project identified few benefits of EMS implementation, and these were largely associated with environmental management and sustainability. In terms of the reasons for uptake of an EMS, these pastoralists identified drivers similar to those reported in other primary industry sectors. These included improving property and environmental management, financial incentives, a range of market benefits, assistance with red tape issues, access to other training opportunities and assistance and support with the development of their EMS. However, these drivers are weak, and are not motivating pastoralists to adopt an EMS. In contrast, barriers to adoption such as the time involved in developing and implementing EMS are tangible and immediate. Given a lack of effective drivers and that pastoralists are under considerable pressure from ongoing rural adjustment processes, it is not surprising that an EMS is a low priority. It is concluded that widespread uptake and on-going use of an EMS in the pastoral industry will not occur unless pastoralists are required or rewarded for this by markets, governments, financiers, and regional natural resource management bodies.

Transitions to K–12 Education Systems: Experiences from Five Case Countries

Currently more than 140 countries offer, or are in transition to, what has become the international norm for pre-tertiary education, namely a kindergarten through grade 12 (K–12) school education system—kindergarten because of the preponderance of research asserting the long-term learning and social benefits of school readiness programs; and 12 years of primary and secondary schooling due to the time needed to acquire the knowledge and skills sets necessary for 21st century university education, postsecondary training, or decent1 work. This desk study2 conveys the experiences of four countries and one province in preparing and implementing a transition to a K–12 school education system: Mongolia, Ontario (Canada), the Philippines, Poland, and Turkey. Looking at K–12 transition in countries and systems that vary as broadly as this set enables common threads to stand out and divergent options to be noted.

Improving cost-effectiveness of supplementation systems for breeder herds in northern Australia

Low level strategic supplements constitute one of the few options for northern beef producers to increase breeder productivity and profitability. Objectives of the project were to improve the cost-effectiveness of using such supplements and to improve supplement delivery systems. Urea-based supplements fed during the dry season can substantially reduce breeder liveweight loss and increase fertility during severe dry seasons. Also when fed during the late wet season these supplements increased breeder body liveweight and increased fertility of breeders in low body condition. Intake of dry lick supplements fed free choice is apparently determined primarily by the palatability of supplements relative to pasture, and training of cattle appears to be of limited importance. Siting of supplementation points has some effect on supplement intake, but little effect on grazing behaviour. Economic analysis of supplementation (urea, phosphorus or molasses) and weaning strategies was based on the relative efficacy of these strategies to maintain breeder body condition late in the dry season. Adequate body condition of breeders at this time of the year is needed to avoid mortality from under-nutrition and achieve satisfactory fertility of breeders during the following wet season. Supplements were highly cost-effective when they reduced mortality, but economic returns were generally low if the only benefit was increased fertility.

High-Rate Space-Time Coded Large-MIMO Systems: Low-Complexity Detection and Channel Estimation

In this paper, we present a low-complexity algorithm for detection in high-rate, non-orthogonal space-time block coded (STBC) large-multiple-input multiple-output (MIMO) systems that achieve high spectral efficiencies of the order of tens of bps/Hz. We also present a training-based iterative detection/channel estimation scheme for such large STBC MIMO systems. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed multistage likelihood ascent search (M-LAS) detector in conjunction with the proposed iterative detection/channel estimation scheme at low complexities. The fact that we could show such good results for large STBCs like 16 X 16 and 32 X 32 STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot based training for channel estimation and turbo coding) establishes the effectiveness of the proposed detector and channel estimator. We decode perfect codes of large dimensions using the proposed detector. With the feasibility of such a low-complexity detection/channel estimation scheme, large-MIMO systems with tens of antennas operating at several tens of bps/Hz spectral efficiencies can become practical, enabling interesting high data rate wireless applications.

Use of Document Management Systems - a Case Study of the Finnish Construction Industry

Triggered by the very quick proliferation of Internet connectivity, electronic document management (EDM) systems are now rapidly being adopted for managing the documentation that is produced and exchanged in construction projects. Nevertheless there are still substantial barriers to the efficient use of such systems, mainly of a psychological nature and related to insufficient training. This paper presents the results of empirical studies carried out during 2002 concerning the current usage of EDM systems in the Finnish construction industry. The studies employed three different methods in order to provide a multifaceted view of the problem area, both on the industry and individual project level. In order to provide an accurate measurement of overall usage volume in the industry as a whole telephone interviews with key personnel from 100 randomly chosen construction projects were conducted. The interviews showed that while around 1/3 of big projects already have adopted the use of EDM, very few small projects have adopted this technology. The barriers to introduction were investigated through interviews with representatives for half a dozen of providers of systems and ASP-services. These interviews shed a lot of light on the dynamics of the market for this type of services and illustrated the diversity of business strategies adopted by vendors. In the final study log files from a project which had used an EDM system were analysed in order to determine usage patterns. The results illustrated that use is yet incomplete in coverage and that only a part of the individuals involved in the project used the system efficiently, either as information producers or consumers. The study also provided feedback on the usefulness of the log files.