Robust visual tracking based on online learning sparse representation

Handling appearance variations is a very challenging problem for visual tracking. Existing methods usually solve this problem by relying on an effective appearance model with two features: (1) being capable of discriminating the tracked target from its background, (2) being robust to the target's appearance variations during tracking. Instead of integrating the two requirements into the appearance model, in this paper, we propose a tracking method that deals with these problems separately based on sparse representation in a particle filter framework. Each target candidate defined by a particle is linearly represented by the target and background templates with an additive representation error. Discriminating the target from its background is achieved by activating the target templates or the background templates in the linear system in a competitive manner. The target's appearance variations are directly modeled as the representation error. An online algorithm is used to learn the basis functions that sparsely span the representation error. The linear system is solved via ℓ1 minimization. The candidate with the smallest reconstruction error using the target templates is selected as the tracking result. We test the proposed approach using four sequences with heavy occlusions, large pose variations, drastic illumination changes and low foreground-background contrast. The proposed approach shows excellent performance in comparison with two latest state-of-the-art trackers.

A systematic review protocol on the use of online learning and blended learning for teaching clinical skills to undergraduate health professional students

Aim: This paper is a review protocol that will be used to identify, critically appraise and synthesize the best current evidence relating to the use of online learning and blended learning approaches in teaching clinical skills in undergraduate nursing.

Background: Although previous systematic reviews on online learning versus face to face learning have been undertaken (Cavanaugh et al. 2010, Cook et al. 2010), a systematic review on the impact of online learning and blended learning for teaching clinical skills has yet to be considered in undergraduate nursing. By reviewing nursing students’ online learning experiences, systems can potentially be designed to ensure all students’ are supported appropriately to meet their learning needs.

Methods/Design: The key objectives of the review are to evaluate how online-learning teaching strategies assist nursing students learn; to evaluate the students satisfaction with this form of teaching; to explore the variety of online-learning strategies used; to determine what online-learning strategies are more effective and to determine if supplementary face to face instruction enhances learning. A search of the following databases will be made MEDLINE, CINAHL, BREI, ERIC and AUEI. This review will follow the Joanna Briggs Institute guidance for systematic reviews of quantitative and qualitative research.

Conclusion: This review intends to report on a combination of student experience and learning outcomes therefore increasing its utility for educators and curriculum developers involved in healthcare education.

Is shyness context specific? Relation between shyness and online self-disclosure with and without a live webcam in young adults

The Internet provides a new tool to investigate old questions in experimental social psychology regarding Person x Context interaction. We examined the interaction of self-reported shyness and context on computer-mediated communication measures. Sixty female undergraduates unfamiliar were paired in dyads and engaged in a 10 min free chat conversation on the Internet with and without a live webcam. Free chat conversations were archived, transcripts were objectively coded for communication variables, and a linear mixed model used for data analysis of dyadic interaction was performed on each communication measure. As predicted, increases in self-reported shyness were significantly related to decreases in the number of prompted self-disclosures (after controlling for the number of opportunities to self-disclose) only in the webcam condition. Self-reported shyness was not related to the number of prompted self-disclosures in the no webcam condition, suggesting that shyness was context dependent. The present study appears to be the first to objectively code measures of Internet behaviour in relation to the study of personality in general and shyness in particular. Theoretical and clinical implications for understanding the contextual nature of shyness are discussed. (C) 2006 Elsevier Inc. All rights reserved.

Online Multiperson Tracking With Occlusion Reasoning and Unsupervised Track Motion Model

We address the problem of multi-target tracking in realistic crowded conditions by introducing a novel dual-stage online tracking algorithm. The problem of data-association between tracks and detections, based on appearance, is often complicated by partial occlusion. In the first stage, we address the issue of occlusion with a novel method of robust data-association, that can be used to compute the appearance similarity between tracks and detections without the need for explicit knowledge of the occluded regions. In the second stage, broken tracks are linked based on motion and appearance, using an online-learned linking model. The online-learned motion-model for track linking uses the confident tracks from the first stage tracker as training examples. The new approach has been tested on the town centre dataset and has performance comparable with the present state-of-the-art

Semi batch learning with store management using enhanced conjugate gradient

This paper explores the performance of sliding-window based training, termed as semi batch, using multilayer perceptron (MLP) neural network in the presence of correlated data. The sliding window training is a form of higher order instantaneous learning strategy without the need of covariance matrix, usually employed for modeling and tracking purposes. Sliding-window framework is implemented to combine the robustness of offline learning algorithms with the ability to track online the underlying process of a function. This paper adopted sliding window training with recent advances in conjugate gradient direction with application of data store management e.g. simple distance measure, angle evaluation and the novel prediction error test. The simulation results show the best convergence performance is gained by using store management techniques. © 2012 Springer-Verlag.

Methodological issues for qualitative research with learning disabled children

This paper discusses key methodological issues for qualitative research with learning disabled children, based on the author's experience of involving learning disabled children in her doctoral study. The study was founded on the social model of disability and a sociological understanding of childhood that recognizes the abilities of disabled children as competent research participants. Issues that arose throughout the research process, from the early stages of gaining access to children, to communication challenges for interviewing learning disabled children, and the analysis and dissemination of data, are discussed. Within this context, this paper explores key methodological issues for researchers with regard to interviewing learning disabled children and actively involving them in qualitative research.

A systematic review evaluating the impact of online or blended learning vs. face-to-face learning of clinical skills in undergraduate nurse education.

Aim: To determine whether the use of an online or blended learning paradigm has the potential to enhance the teaching of clinical skills in undergraduate nursing.

Background: The need to adequately support and develop students in clinical skills is now arguably more important than previously considered due to reductions in practice opportunities. Online and blended teaching methods are being developed to try and meet this requirement, but knowledge about their effectiveness in teaching clinical skills is limited.

Design: Mixed methods systematic review, which follows the Joanna Briggs Institute User guide version 5.

Data Sources: Computerized searches of five databases were undertaken for the period 1995-August 2013.

Review Methods: Critical appraisal and data extraction were undertaken using Joanna Briggs Institute tools for experimental/observational studies and interpretative and critical research. A narrative synthesis was used to report results.

Results: Nineteen published papers were identified. Seventeen papers reported on online approaches and only two papers reported on a blended approach. The synthesis of findings focused on the following four areas: performance/clinical skill, knowledge, self-efficacy/clinical confidence and user experience/satisfaction. The e-learning interventions used varied throughout all the studies.

Conclusion: The available evidence suggests that online learning for teaching clinical skills is no less effective than traditional means. Highlighted by this review is the lack of available evidence on the implementation of a blended learning approach to teaching clinical skills in undergraduate nurse education. Further research is required to assess the effectiveness of this teaching methodology.

Adaptive NormalHedge for robust visual tracking

In this paper, we propose a novel visual tracking framework, based on a decision-theoretic online learning algorithm namely NormalHedge. To make NormalHedge more robust against noise, we propose an adaptive NormalHedge algorithm, which exploits the historic information of each expert to perform more accurate prediction than the standard NormalHedge. Technically, we use a set of weighted experts to predict the state of the target to be tracked over time. The weight of each expert is online learned by pushing the cumulative regret of the learner towards that of the expert. Our simulation experiments demonstrate the effectiveness of the proposed adaptive NormalHedge, compared to the standard NormalHedge method. Furthermore, the experimental results of several challenging video sequences show that the proposed tracking method outperforms several state-of-the-art methods.

Methodologies for the Development of the Management of Cough: CHEST Guideline and Expert Panel Report

BACKGROUND: This series of guidance documents on cough, which will be published over time, is a hybrid of two processes: (1) evidence-based guidelines and (2) trustworthy consensus statements based on a robust and transparent process.

METHODS: The CHEST Guidelines Oversight Committee selected a nonconflicted Panel Chair and jointly assembled an international panel of experts in each clinical area with few, if any, conflicts of interest. PICO (population, intervention, comparator, outcome)-based key questions and parameters of eligibility were developed for each clinical topic to inform the comprehensive literature search. Existing guidelines, systematic reviews, and primary studies were assessed for relevance and quality. Data elements were extracted into evidence tables and synthesized to provide summary statistics. These, in turn, are presented to support the evidence-based graded recommendations. A highly structured consensus-based Delphi approach was used to provide expert advice on all guidance statements. Transparency of process was documented.

RESULTS: Evidence-based guideline recommendations and consensus-based suggestions were carefully crafted to provide direction to health-care providers and investigators who treat and/or study patients with cough. Manuscripts and tables summarize the evidence in each clinical area supporting the recommendations and suggestions.

CONCLUSIONS: The resulting guidance statements are based on a rigorous methodology and transparency of process. Unless otherwise stated, the recommendations and suggestions meet the guidelines for trustworthiness developed by the Institute of Medicine and can be applied with confidence by physicians, nurses, other health-care providers, investigators, and patients.

English Language Education at University: Trends and Challenges in Teaching and Learning Academic Discourse in the UK

This chapter discusses English Language Education at university and highlights a number of trends and their associated challenges in teaching and learning academic discourse. Academic discourse refers to the ways in which language is used by participants in academia. It encompasses written discourse, from article and book publishing, PhD theses to course assignments; spoken discourse, from study groups, tutorials, conference presentations to inaugural lectures; and more recently, computer-mediated discourse, from asynchronous text-based conferencing to academic blogs. The role of English language educators in preparing students and academics for successful participation in these academic events, or the academy, in English is not to be underestimated. Academic communication is not only vital to an individual’s success at university, but to the maintenance and creation of academic communities and to scientific progress itself (Hyland, 2009). This chapter presents an overview of academic discourse and discusses recent issues which have an impact on teaching and learning English at university and discusses their associated challenges: first, the increasing internationalisation of universities. Second, the emergence of a mobile academe in its broadest sense, in which students and academics move across traditional geopolitical, institutional and disciplinary boundaries, is discussed. Third, the growth of UK transnational higher education is examined as a trend which sees academics and students vicariously or otherwise involved in English language teaching and learning. Fourth, the chapter delves into the rapid and ongoing development in technology assisted and online learning. While responding to trends can be difficult, they can also inspire ingenuity. Furthermore, such trends and challenges will not emerge in the same manner in different contexts. The discussion in this chapter is illustrated with examples from a UK context but the implications of the trends and challenges are such that they reach beyond borders.

Second-Order Training of Adaptive Critics for Online Process Control

A new algorithm for training of nonlinear optimal neuro-controllers (in the form of the model-free, action-dependent, adaptive critic paradigm). Overcomes problems with existing stochastic backpropagation training: need for data storage, parameter shadowing and poor convergence, offering significant benefits for online applications.

Is there a place for e-learning in clinical skills? A survey of undergradute medical students experiences and attitudes

e-learning is established in many medical schools. However the effectiveness of e-learning has been difficult to quantify and there have been concerns that such educational activities may be driven more by novelty, than pedagogical evidence. Where some domains may lend themselves well to e-learning, clinical skills has been considered a challenging area for online learning.

Uncertainty, learning and growth

The paper extends Blackburn and Galindev's (Economics Letters, Vol. 79 (2003), pp. 417-421) stochastic growth model in which productivity growth entails both external and internal learning behaviour with a constant relative risk aversion utility function and productivity shocks. Consequently, the relationship between long-term growth and short-term volatility depends not only on the relative importance of each learning mechanism but also on a parameter measuring individuals' attitude towards risk.