Development and implementation of a virtual laboratory for training process improvement in the mechanics of continuous media

The project arises from the need to develop improved teaching methodologies in field of the mechanics of continuous media. The objective is to offer the student a learning process to acquire the necessary theoretical knowledge, cognitive skills and the responsibility and autonomy to professional development in this area. Traditionally the teaching of the concepts of these subjects was performed through lectures and laboratory practice. During these lessons the students attitude was usually passive, and therefore their effectiveness was poor. The proposed methodology has already been successfully employed in universities like University Bochum, Germany, University the South Australia and aims to improve the effectiveness of knowledge acquisition through use by the student of a virtual laboratory. This laboratory allows to adapt the curricula and learning techniques to the European Higher Education and improve current learning processes in the University School of Public Works Engineers -EUITOP- of the Technical University of Madrid -UPM-, due there are not laboratories in this specialization. The virtual space is created using a software platform built on OpenSim, manages 3D virtual worlds, and, language LSL -Linden Scripting Language-, which imprints specific powers to objects. The student or user can access this virtual world through their avatar -your character in the virtual world- and can perform practices within the space created for the purpose, at any time, just with computer with internet access and viewfinder. The virtual laboratory has three partitions. The virtual meeting rooms, where the avatar can interact with peers, solve problems and exchange existing documentation in the virtual library. The interactive game room, where the avatar is has to resolve a number of issues in time. And the video room where students can watch instructional videos and receive group lessons. Each audiovisual interactive element is accompanied by explanations framing it within the area of knowledge and enables students to begin to acquire a vocabulary and practice of the profession for which they are being formed. Plane elasticity concepts are introduced from the tension and compression testing of test pieces of steel and concrete. The behavior of reticulated and articulated structures is reinforced by some interactive games and concepts of tension, compression, local and global buckling will by tests to break articulated structures. Pure bending concepts, simple and composite torsion will be studied by observing a flexible specimen. Earthquake resistant design of buildings will be checked by a laboratory test video.

An Approach for a more Objective Evaluation of Practical Projects, Used in the Training Process

Well–prepared, adaptive and sustainably developing specialists are an important competitive advantage, but also one of the main challenges for businesses. One option of the education system for creation and development of staff adequate to the needs is the development of pro jects with topics from real economy ("Practical Projects"). The objective assessment is an essential driver and motivator, and is based on a system of well-chosen, well-defined and specific criteria and indicators. An approach to a more objective evaluation of practical projects is finding more objective weights of the criteria. A natural and reasonable approach is the accumulation of opinions of proven experts and subsequent bringing out the weights from the accumulated data. The preparation and conduction of a survey among recognized experts in the field of project-based learning in mathematics, informatics and information technologies is described. The processing of the data accumulated by applying AHP, allowed us to objectively determine weights of evaluation criteria and hence to achieve the desired objectiveness. ACM Computing Classification System (1998): K.3.2.

Development and interrater reliability testing of a telephone interview training programme for Australian nurse interviewers

Summary Background The final phase of a three phase study analysing the implementation and impact of the nurse practitioner role in Australia (the Australian Nurse Practitioner Project or AUSPRAC) was undertaken in 2009, requiring nurse telephone interviewers to gather information about health outcomes directly from patients and their treating nurse practitioners. A team of several registered nurses was recruited and trained as telephone interviewers. The aim of this paper is to report on development and evaluation of the training process for telephone interviewers. Methods The training process involved planning the content and methods to be used in the training session; delivering the session; testing skills and understanding of interviewers post-training; collecting and analysing data to determine the degree to which the training process was successful in meeting objectives and post-training follow-up. All aspects of the training process were informed by established educational principles. Results Interrater reliability between interviewers was high for well-validated sections of the survey instrument resulting in 100% agreement between interviewers. Other sections with unvalidated questions showed lower agreement (between 75% and 90%). Overall the agreement between interviewers was 92%. Each interviewer was also measured against a specifically developed master script or gold standard and for this each interviewer achieved a percentage of correct answers of 94.7% or better. This equated to a Kappa value of 0.92 or better. Conclusion The telephone interviewer training process was very effective and achieved high interrater reliability. We argue that the high reliability was due to the use of well validated instruments and the carefully planned programme based on established educational principles. There is limited published literature on how to successfully operationalise educational principles and tailor them for specific research studies; this report addresses this knowledge gap.

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.

Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions

This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. The approach followed has been a combined use of ANNs and ray-tracing, the latter allowing the identification and parameterization of the so-called dominant path. A complete description of the process for creating and training an ANN-based model is presented with special emphasis on the training process. More specifically, we will be discussing various techniques to arrive at valid predictions focusing on an optimum selection of the training set. A quantitative analysis based on results from two narrowband measurement campaigns, one outdoors and the other indoors, is also presented.

Fast training of self organizing maps for the visual exploration of molecular compounds

Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions.

Managing environmental training in organizations: Theoretical review and proposal of a model

Purpose: The aim of this work is to address the issue of environmental training in organizations, presenting a theoretical review on the subject and proposing a model that highlights the importance of this type of training for organizations. Design/methodology/approach: The paper presents a thorough, updated literature review, discusses typology and the best practices of environmental training, and presents a framework integrating environmental training and organizational results. Findings: A careful consideration allows identifying a significant theoretical gap related to the lack of theoretical references, best practices, and an alignment between environmental training and organizational results. To overcome this gap, a model was proposed that helps to manage the environmental training process in organizations. Research limitations/implications: The paper needs to be complemented with empirical research on the topic. Originality/value: Environmental training is considered to be an essential element for organizations seeking to mitigate their environmental impacts. ISO 14001 states that environmental management is a duty of certified organizations. However, there have been few published articles that suggest models and insights to improve the environmental training in organizations. © Emerald Group Publishing Limited.

Training in intensive care medicine. A challenge within reach.

The medical training model is currently immersed in a process of change. The new paradigm is intended to be more effective, more integrated within the healthcare system, and strongly oriented towards the direct application of knowledge to clinical practice. Compared with the established training system based on certification of the completion of a series or rotations and stays in certain healthcare units, the new model proposes a more structured training process based on the gradual acquisition of specific competences, in which residents must play an active role in designing their own training program. Training based on competences guarantees more transparent, updated and homogeneous learning of objective quality, and which can be homologated internationally. The tutors play a key role as the main directors of the process, and institutional commitment to their work is crucial. In this context, tutors should receive time and specific formation to allow the evaluation of training as the cornerstone of the new model. New forms of objective summative and training evaluation should be introduced to guarantee that the predefined competences and skills are effectively acquired. The free movement of specialists within Europe is very desirable and implies that training quality must be high and amenable to homologation among the different countries. The Competency Based training in Intensive Care Medicine in Europe program is our main reference for achieving this goal. Scientific societies in turn must impulse and facilitate all those initiatives destined to improve healthcare quality and therefore specialist training. They have the mission of designing strategies and processes that favor training, accreditation and advisory activities with the government authorities.

Use of Tablet Pcs in Higher Education: A new Strategy for Training Engineers in European Bachelors and Masters Programmes

The use of technology in classrooms in Spanish universities has been following an upward path, and in many cases technological devices are substituting other materials that until now have been used, such as books, notebooks and so on. Step by step in higher education, more of these latest generationdevices are being used, and are providing significant improvements in training. Nowadays, there are Spanish universities that use tablets, a device with multiple applications for teaching as well as for students to study differently. They are definitely a notable innovation that will gradually become incorporated into university life. Tablet PCs make teaching more dynamic and available to students through the use of up to date digital materials, something which is key in training engineers. This paper presents their different functions employed in three Spanish universities to support teachingin engineering degrees and masters using the tablet PC, and their impact on the training process. Possible uses in specific programs like the Erasmus Masters Programmes are also assessed. The main objective of using tabletsis to improve the academic performance of students through the use of technology.

USER TRAINING IN THE PUBLIC LIBRARY OF COSTA RICA

This document is aimed to serve as a tool for officials (as) public library in the training process and educational users who make use of the services offered, and in turn, fulfill the aims and purposes these cultural centers should pursue as a social entity.

Improving automatic speaker verification using SVM techniques

Automatic recognition of people is an active field of research with important forensic and security applications. In these applications, it is not always possible for the subject to be in close proximity to the system. Voice represents a human behavioural trait which can be used to recognise people in such situations. Automatic Speaker Verification (ASV) is the process of verifying a persons identity through the analysis of their speech and enables recognition of a subject at a distance over a telephone channel { wired or wireless. A significant amount of research has focussed on the application of Gaussian mixture model (GMM) techniques to speaker verification systems providing state-of-the-art performance. GMM's are a type of generative classifier trained to model the probability distribution of the features used to represent a speaker. Recently introduced to the field of ASV research is the support vector machine (SVM). An SVM is a discriminative classifier requiring examples from both positive and negative classes to train a speaker model. The SVM is based on margin maximisation whereby a hyperplane attempts to separate classes in a high dimensional space. SVMs applied to the task of speaker verification have shown high potential, particularly when used to complement current GMM-based techniques in hybrid systems. This work aims to improve the performance of ASV systems using novel and innovative SVM-based techniques. Research was divided into three main themes: session variability compensation for SVMs; unsupervised model adaptation; and impostor dataset selection. The first theme investigated the differences between the GMM and SVM domains for the modelling of session variability | an aspect crucial for robust speaker verification. Techniques developed to improve the robustness of GMMbased classification were shown to bring about similar benefits to discriminative SVM classification through their integration in the hybrid GMM mean supervector SVM classifier. Further, the domains for the modelling of session variation were contrasted to find a number of common factors, however, the SVM-domain consistently provided marginally better session variation compensation. Minimal complementary information was found between the techniques due to the similarities in how they achieved their objectives. The second theme saw the proposal of a novel model for the purpose of session variation compensation in ASV systems. Continuous progressive model adaptation attempts to improve speaker models by retraining them after exploiting all encountered test utterances during normal use of the system. The introduction of the weight-based factor analysis model provided significant performance improvements of over 60% in an unsupervised scenario. SVM-based classification was then integrated into the progressive system providing further benefits in performance over the GMM counterpart. Analysis demonstrated that SVMs also hold several beneficial characteristics to the task of unsupervised model adaptation prompting further research in the area. In pursuing the final theme, an innovative background dataset selection technique was developed. This technique selects the most appropriate subset of examples from a large and diverse set of candidate impostor observations for use as the SVM background by exploiting the SVM training process. This selection was performed on a per-observation basis so as to overcome the shortcoming of the traditional heuristic-based approach to dataset selection. Results demonstrate the approach to provide performance improvements over both the use of the complete candidate dataset and the best heuristically-selected dataset whilst being only a fraction of the size. The refined dataset was also shown to generalise well to unseen corpora and be highly applicable to the selection of impostor cohorts required in alternate techniques for speaker verification.

Working with actors in open disclosure simulations

A key strategy in facilitating learning in Open Disclosure training is the use of hypothetical, interactive scenarios called ‘simulations’. According to Clapper (2010), the ‘advantages of using simulation are numerous and include the ability to help learners make meaning of complex tasks, while also developing critical thinking and cultural skills’. Simulation, in turn, functions largely through improvisation and role-play, in which participants ‘act out’ particular roles and characters according to a given scenario, without recourse to a script. To maximise efficacy in the Open Disclosure training context, role-play requires the specialist skills of professionally trained actors. Core capacities that professional actors bring to the training process include (among others) believability, an observable and teachable skill which underpins the western traditions of actor training; and flexibility, which pertains to the actor’s ability to vary performance strategies according to the changing dynamics of the learning situation. The Patient Safety and Quality Improvement Service of Queensland Health utilises professional actors as a key component of their Open Disclosure Training Program. In engaging actors in this work, it is essential that Facilitators of Open Disclosure training have a solid understanding of the acting process: what acting is; how actors work to a brief; how they improvise; and how they sustainably manage a wide range of emotional states. In the simulation context, the highly skilled actor can optimise learning outcomes by adopting or enacting – in collaboration with the Facilitator - a pedagogical function.

Robust image hash functions using higher order spectra

Robust hashing is an emerging field that can be used to hash certain data types in applications unsuitable for traditional cryptographic hashing methods. Traditional hashing functions have been used extensively for data/message integrity, data/message authentication, efficient file identification and password verification. These applications are possible because the hashing process is compressive, allowing for efficient comparisons in the hash domain but non-invertible meaning hashes can be used without revealing the original data. These techniques were developed with deterministic (non-changing) inputs such as files and passwords. For such data types a 1-bit or one character change can be significant, as a result the hashing process is sensitive to any change in the input. Unfortunately, there are certain applications where input data are not perfectly deterministic and minor changes cannot be avoided. Digital images and biometric features are two types of data where such changes exist but do not alter the meaning or appearance of the input. For such data types cryptographic hash functions cannot be usefully applied. In light of this, robust hashing has been developed as an alternative to cryptographic hashing and is designed to be robust to minor changes in the input. Although similar in name, robust hashing is fundamentally different from cryptographic hashing. Current robust hashing techniques are not based on cryptographic methods, but instead on pattern recognition techniques. Modern robust hashing algorithms consist of feature extraction followed by a randomization stage that introduces non-invertibility and compression, followed by quantization and binary encoding to produce a binary hash output. In order to preserve robustness of the extracted features, most randomization methods are linear and this is detrimental to the security aspects required of hash functions. Furthermore, the quantization and encoding stages used to binarize real-valued features requires the learning of appropriate quantization thresholds. How these thresholds are learnt has an important effect on hashing accuracy and the mere presence of such thresholds are a source of information leakage that can reduce hashing security. This dissertation outlines a systematic investigation of the quantization and encoding stages of robust hash functions. While existing literature has focused on the importance of quantization scheme, this research is the first to emphasise the importance of the quantizer training on both hashing accuracy and hashing security. The quantizer training process is presented in a statistical framework which allows a theoretical analysis of the effects of quantizer training on hashing performance. This is experimentally verified using a number of baseline robust image hashing algorithms over a large database of real world images. This dissertation also proposes a new randomization method for robust image hashing based on Higher Order Spectra (HOS) and Radon projections. The method is non-linear and this is an essential requirement for non-invertibility. The method is also designed to produce features more suited for quantization and encoding. The system can operate without the need for quantizer training, is more easily encoded and displays improved hashing performance when compared to existing robust image hashing algorithms. The dissertation also shows how the HOS method can be adapted to work with biometric features obtained from 2D and 3D face images.

Speeding up AdaBoost Classifier with Random Projection

The development of techniques for scaling up classifiers so that they can be applied to problems with large datasets of training examples is one of the objectives of data mining. Recently, AdaBoost has become popular among machine learning community thanks to its promising results across a variety of applications. However, training AdaBoost on large datasets is a major problem, especially when the dimensionality of the data is very high. This paper discusses the effect of high dimensionality on the training process of AdaBoost. Two preprocessing options to reduce dimensionality, namely the principal component analysis and random projection are briefly examined. Random projection subject to a probabilistic length preserving transformation is explored further as a computationally light preprocessing step. The experimental results obtained demonstrate the effectiveness of the proposed training process for handling high dimensional large datasets.