Methods for Answer Extraction in Textual Question Answering

In this thesis we present and evaluate two pattern matching based methods for answer extraction in textual question answering systems. A textual question answering system is a system that seeks answers to natural language questions from unstructured text. Textual question answering systems are an important research problem because as the amount of natural language text in digital format grows all the time, the need for novel methods for pinpointing important knowledge from the vast textual databases becomes more and more urgent. We concentrate on developing methods for the automatic creation of answer extraction patterns. A new type of extraction pattern is developed also. The pattern matching based approach chosen is interesting because of its language and application independence. The answer extraction methods are developed in the framework of our own question answering system. Publicly available datasets in English are used as training and evaluation data for the methods. The techniques developed are based on the well known methods of sequence alignment and hierarchical clustering. The similarity metric used is based on edit distance. The main conclusions of the research are that answer extraction patterns consisting of the most important words of the question and of the following information extracted from the answer context: plain words, part-of-speech tags, punctuation marks and capitalization patterns, can be used in the answer extraction module of a question answering system. This type of patterns and the two new methods for generating answer extraction patterns provide average results when compared to those produced by other systems using the same dataset. However, most answer extraction methods in the question answering systems tested with the same dataset are both hand crafted and based on a system-specific and fine-grained question classification. The the new methods developed in this thesis require no manual creation of answer extraction patterns. As a source of knowledge, they require a dataset of sample questions and answers, as well as a set of text documents that contain answers to most of the questions. The question classification used in the training data is a standard one and provided already in the publicly available data.

Sleep quantity and quality during heat-based training and the effects of cold-water immersion recovery

- Introduction Heat-based training (HT) is becoming increasingly popular as a means of inducing acclimation before athletic competition in hot conditions and/or to augment the training impulse beyond that achieved in thermo-neutral conditions. Importantly, current understanding of the effects of HT on regenerative processes such as sleep and the interactions with common recovery interventions remain unknown. This study aimed to examine sleep characteristics during five consecutive days of training in the heat with the inclusion of cold-water immersion (CWI) compared to baseline sleep patterns. - Methods Thirty recreationally-trained males completed HT in 32 ± 1 °C and 60% rh for five consecutive days. Conditions included: 1) 90 min cycling at 40 % power at VO2max (Pmax) (90CONT; n = 10); 90 min cycling at 40 % Pmax with a 20 min CWI (14 ± 1 °C; 90CWI; n = 10); and 30 min cycling alternating between 40 and 70 % Pmax every 3 min, with no recovery intervention (30HIT; n = 10). Sleep quality and quantity was assessed during HT and four nights of 'baseline' sleep (BASE). Actigraphy provided measures of time in and out of bed, sleep latency, efficiency, total time in bed and total time asleep, wake after sleep onset, number of awakenings, and wakening duration. Subjective ratings of sleep were also recorded using a 1-5 Likert scale. Repeated measures analysis of variance (ANOVA) was completed to determine effect of time and condition on sleep quality and quantity. Cohen's d effect sizes were also applied to determine magnitude and trends in the data. - Results Sleep latency, efficiency, total time in bed and number of awakenings were not significantly different between BASE and HT (P > 0.05). However, total time asleep was significantly reduced (P = 0.01; d = 1.46) and the duration periods of wakefulness after sleep onset was significantly greater during HT compared with BASE (P = 0.001; d = 1.14). Comparison between training groups showed latency was significantly higher for the 30HIT group compared to 90CONT (P = 0.02; d = 1.33). Nevertheless, there were no differences between training groups for sleep efficiency, total time in bed or asleep, wake after sleep onset, number of awakenings or awake duration (P > 0.05). Further, cold-water immersion recovery had no significant effect on sleep characteristics (P > 0.05). - Discussion Sleep plays an important role in athletic recovery and has previously been demonstrated to be influenced by both exercise training and thermal strain. Present data highlight the effect of HT on reduced sleep quality, specifically reducing total time asleep due to longer duration awake during awakenings after sleep onset. Importantly, although cold water recovery accelerates the removal of thermal load, this intervention did not blunt the negative effects of HT on sleep characteristics. - Conclusion Training in hot conditions may reduce both sleep quantity and quality and should be taken into consideration when administering this training intervention in the field.

Blended learning in medical imaging pre-clinical skills training – does it work?

Undergraduate Medical Imaging (MI)students at QUT attend their first clinical placement towards the end of semester two. Students undertake two (pre)clinical skills development units – one theory and one practical. Students gain good contextual and theoretical knowledge during these units via a blended learning model with multiple learning methods employed. Students attend theory lectures, practical sessions, tutorial sessions in both a simulated and virtual environment and also attend pre-clinical scenario based tutorial sessions. The aim of this project is to evaluate the use of blended learning in the context of 1st year Medical Imaging Radiographic Technique and its effectiveness in preparing students for their first clinical experience. It is hoped that the multiple teaching methods employed within the pre-clinical training unit at QUT builds students clinical skills prior to the real situation. A quantitative approach will be taken, evaluating via pre and post clinical placement surveys. This data will be correlated with data gained in the previous year on the effectiveness of this training approach prior to clinical placement. In 2014 59 students were surveyed prior to their clinical placement demonstrated positive benefits of using a variety of learning tools to enhance their learning. 98.31%(n=58)of students agreed or strongly agreed that the theory lectures were a useful tool to enhance their learning. This was followed closely by 97% (n=57) of the students realising the value of performing role-play simulation prior to clinical placement. Tutorial engagement was considered useful for 93.22% (n=55) whilst 88.14% (n=52) reasoned that the x-raying of phantoms in the simulated radiographic laboratory was beneficial. Self-directed learning yielded 86.44% (n=51). The virtual reality simulation software was valuable for 72.41% (n=42) of the students. Of the 4 students that disagreed or strongly disagreed with the usefulness of any tool they strongly agreed to the usefulness of a minimum of one other learning tool. The impact of the blended learning model to meet diverse student needs continues to be positive with students engaging in most offerings. Students largely prefer pre -clinical scenario based practical and tutorial sessions where 'real-world’ situations are discussed.

Identification methods of key contributing factors in crashes with high numbers of fatalities and injuries in China

Objectives In China, “serious road traffic crashes” (SRTCs) are those in which there are 10-30 fatalities, 50-100 serious injuries or a total cost of 50-100 million RMB ($US8-16m), and “particularly serious road traffic crashes” (PSRTCs) are those which are more severe or costly. Due to the large number of fatalities and injuries as well as the negative public reaction they elicit, SRTCs and PSRTCs have become great concerns to China during recent years. The aim of this study is to identify the main factors contributing to these road traffic crashes and to propose preventive measures to reduce their number. Methods 49 contributing factors of the SRTCs and PSRTCs that occurred from 2007 to 2013 were collected from the database “In-depth Investigation and Analysis System for Major Road traffic crashes” (IIASMRTC) and were analyzed through the integrated use of principal component analysis and hierarchical clustering to determine the primary and secondary groups of contributing factors. Results Speeding and overloading of passengers were the primary contributing factors, featuring in up to 66.3% and 32.6% of accidents respectively. Two secondary contributing factors were road-related: lack of or nonstandard roadside safety infrastructure, and slippery roads due to rain, snow or ice. Conclusions The current approach to SRTCs and PSRTCs is focused on the attribution of responsibility and the enforcement of regulations considered relevant to particular SRTCs and PSRTCs. It would be more effective to investigate contributing factors and characteristics of SRTCs and PSRTCs as a whole, to provide adequate information for safety interventions in regions where SRTCs and PSRTCs are more common. In addition to mandating of a driver training program and publicisation of the hazards associated with traffic violations, implementation of speed cameras, speed signs, markings and vehicle-mounted GPS are suggested to reduce speeding of passenger vehicles, while increasing regular checks by traffic police and passenger station staff, and improving transportation management to increase income of contractors and drivers are feasible measures to prevent overloading of people. Other promising measures include regular inspection of roadside safety infrastructure, and improving skid resistance on dangerous road sections in mountainous areas.

Digital competence and professional development of vocational education and training teachers in Queensland

Teaching with digital technologies is essential to the development of 21st century students’ graduate capabilities. However, relatively little is known about the extent to which Queensland VET teachers engage with digitally-enhanced teaching, or have the capacity to do so. Using a mixed methods approach, this thesis investigated the current digital teaching capacities of VET teachers and how current professional development opportunities are helping to address their learning needs.

In-training assessment - Its potential in enhancing clinical teaching

Context In-training assessment (ITA) has established its place alongside formative and summative assessment at both the undergraduate and postgraduate level. In this paper the authors aimed to identify those characteristics of ITA that could enhance clinical teaching. Methods A literature review and discussions by an expert working group at the Ninth Cambridge Conference identified the aspects of ITA that could enhance clinical teaching. Results The features of ITA identified included defining the specific benefits to the learner, teacher and institution, and highlighting the patient as the context for ITA and clinical teaching. The ‘mapping’ of a learner’s progress towards the clinical teaching objectives by using multiple assessments over time, by multiple observers in both a systematic and opportunistic way correlates with the incremental nature of reaching clinical competence. Conclusions The importance of ITA based on both direct and indirect evidence of what the learner actually does in the real clinical setting is emphasized. Particular attention is given to addressing concerns in the more controversial areas of assessor training, ratings and documentation for ITA. Areas for future research are also identified.

Comparison of Multiclass SVM Classification Methods to Use in a Supportive System for Distance Relay Coordination

This paper aims at evaluating the methods of multiclass support vector machines (SVMs) for effective use in distance relay coordination. Also, it describes a strategy of supportive systems to aid the conventional protection philosophy in combating situations where protection systems have maloperated and/or information is missing and provide selective and secure coordinations. SVMs have considerable potential as zone classifiers of distance relay coordination. This typically requires a multiclass SVM classifier to effectively analyze/build the underlying concept between reach of different zones and the apparent impedance trajectory during fault. Several methods have been proposed for multiclass classification where typically several binary SVM classifiers are combined together. Some authors have extended binary SVM classification to one-step single optimization operation considering all classes at once. In this paper, one-step multiclass classification, one-against-all, and one-against-one multiclass methods are compared for their performance with respect to accuracy, number of iterations, number of support vectors, training, and testing time. The performance analysis of these three methods is presented on three data sets belonging to training and testing patterns of three supportive systems for a region and part of a network, which is an equivalent 526-bus system of the practical Indian Western grid.

Subject Teacher Training and Teaching in English

The purpose of this Master s thesis is on one hand to find out how CLIL (Content and Language Integrated Learning) teachers and English teachers perceive English and its use in teaching, and on the other hand, what they consider important in subject teacher education in English that is being planned and piloted in STEP Project at the University of Helsinki Department of Teacher Education. One research question is also what kind of language requirements teachers think CLIL teachers should have. The research results are viewed in light of previous research and literature on CLIL education. Six teachers participate in this study. Two of them are English teachers in the comprehensive school, two are class teachers in bilingual elementary education, and two are subject teachers in bilingual education, one of whom teaches in a lower secondary school and the other in an upper secondary school. One English teacher and one bilingual class teacher have graduated from a pilot class teacher program in English that started at the University of Helsinki in the middle of the 1990 s. The bilingual subject teachers are not trained in English but they have learned English elsewhere, which is a particular focus of interest in this study because it is expected that a great number of CLIL teachers in Finland do not have actual studies in English philology. The research method is interview and this is a qualitative case study. The interviews are recorded and transcribed for the ease of analysis. The English teachers do not always use English in their lessons and they would not feel confident in teaching another subject completely in English. All of the CLIL teachers trust their English skills in teaching, but the bilingual class teachers also use Finnish during lessons either because some teaching material is in Finnish, or they feel that rules and instructions are understood better in mother tongue or students English skills are not strong enough. One of the bilingual subject teachers is the only one who consciously uses only English in teaching and in discussions with students. Although teachers good English skills are generally considered important, only the teachers who have graduated from the class teacher education in English consider it important that CLIL teachers would have studies in English philology. Regarding the subject teacher education program in English, the respondents hope that its teachers will have strong enough English skills and that it will deliver what it promises. Having student teachers of different subjects studying together is considered beneficial. The results of the study show that acquiring teaching material in English continues to be the teachers own responsibility and a huge burden for the teachers, and there has, in fact, not been much progress in the matter since the beginning of CLIL education. The bilingual subject teachers think, however, that using one s own material can give new inspiration to teaching and enable the use of various pedagogical methods. Although it is questionable if the language competence requirements set for CLIL teachers by the Finnish Ministry of Education are not adhered to, it becomes apparent in the study that studies in English philology do not necessarily guarantee strong enough language skills for CLIL teaching, but teachers own personality and self-confidence have significance. Keywords: CLIL, bilingual education, English, subject teacher training, subject teacher education in English, STEP

Taxonomy and phylogeny of white-rot polypores : case studies in Hymenochaetales and Polyporales (Basidiomycota)

Tutkimus käsittelee kääpien sukulaisuussuhteita. Käävät ovat kantasienten (Basidiomycota) muotoryhmä, joiden itiöemien alapinta muodostuu yhteensulautuneista pilleistä. Muotoryhmänä kääpiä voi verrata vaikka puihin siinä mielessä, että käävät kuten puutkaan eivät ole samankaltaisuudestaan huolimatta kaikki sukua toisilleen. DNA:n käyttö sukulaisuussuhteiden selvittämisessä on aloittanut mullistuksen kääpien luokittelussa. Aiemmin käytetty, itiöemien ominaisuuksiin perustunut luokittelu on osoittautunut keinotekoiseksi sukulaisuussuhteiden kannalta. Tutkimuksessani syvennyttiin useamman kääpäsuvun polveutumishistoriaan hyödyntäen DNA:ta ja perinteisiä menetelmiä. Tutkimuksen keskeisimmät tulokset liittyvät sitkokääpien sukuun (Antrodiella). Tämä noin 70 lajia sisältävä suku osoittautui rikkonaiseksi - sitkokääpiin luetut lajit kuuluvat kahteen sienilahkoon ja oikesti vähintään 13 sukuun. Tutkimuksessa löytyi kaksi Suomelle uutta sitkokääpää, leppikääpä (A. ichnusana) ja nipukkakääpä (A. leucoxantha). Uudet suvut kuvattiin Suomessa esiintyville sirppikääville (Sidera) ja talikääville (Obba). Uusi kääpäsuku ja -laji kuvattiin myös Indonesiasta (Sebipora aquosa). Valtaosa sitkokääpiin luetuista lajeista kuuluu orakarakoiden heimoon (Steccherinaceae), joka rajattiin tässä tutkimuksessa uudelleen. Heimoon kuuluvat mm. karakäävät (Junghuhnia) ja orakasmaiset orakarakat (Steccherinum). Sen sisällä selvitettiin kääpien ja orakkaiden sukulaisuussuhteita. Perinteisesti käävät ja orakkaat on viety eri sukuihin riippumatta niiden mikroskooppisesta samankaltaisuudesta. Tulosten valossa orakarakoiden heimossa käävät ja orakkaat pysyvät pääosin erillisissä suvuissa, mutta tästä on myös poikkeuksia (Antrodiella, Metuloidea ja Steccherinum). Lähes kaikki DNA:n perusteella määriteltävissä olevat suvut ovat tunnistettavissa itiöemien ominaisuuksiensa perusteella. Tulokset antavat eväitä kääpien luokitteluun laajemminkin osoittamalla, mitkä ominaisuudet ovat luokittelun kannalta merkityksellisiä. Tarkentunut tieto lajimäärästä ja lajien sukulaisuussuhteista hyödyttää ekologista tutkimusta sekä arvioita lajien uhanalaisuudesta. Tutkimuksen aikana luotua DNA-kirjastoa käytetään lajien tunnistamiseen. Tuloksia voidaan hyödyntää myös etsittäessä bioteknologisia sovelluksia käävistä, sillä sovellusten kannalta kiinnostavat ominaisuudet seuraavat usein sienten sukupuuta.

Training and Voids in Receive Antenna Subset Selection in Time-Varying Channels

Receive antenna selection (AS) provides many benefits of multiple-antenna systems at drastically reduced hardware costs. In it, the receiver connects a dynamically selected subset of N available antennas to the L available RF chains. Due to the nature of AS, the channel estimates at different antennas, which are required to determine the best subset for data reception, are obtained from different transmissions of the pilot sequence. Consequently, they are outdated by different amounts in a time-varying channel. We show that a linear weighting of the estimates is necessary and optimum for the subset selection process, where the weights are related to the temporal correlation of the channel variations. When L is not an integer divisor of N , we highlight a new issue of ``training voids'', in which the last pilot transmission is not fully exploited by the receiver. We then present new ``void-filling'' methods that exploit these voids and greatly improve the performance of AS. The optimal subset selection rules with void-filling, in which different antennas turn out to have different numbers of estimates, are also explicitly characterized. Closed-form equations for the symbol error probability with and without void-filling are also developed.

A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K-nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue-type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright (C) 2011 John Wiley & Sons, Ltd.

Prototype learning methods for online handwriting recognition

In this paper, we study different methods for prototype selection for recognizing handwritten characters of Tamil script. In the first method, cumulative pairwise- distances of the training samples of a given class are used to select prototypes. In the second method, cumulative distance to allographs of different orientation is used as a criterion to decide if the sample is representative of the group. The latter method is presumed to offset the possible orientation effect. This method still uses fixed number of prototypes for each of the classes. Finally, a prototype set growing algorithm is proposed, with a view to better model the differences in complexity of different character classes. The proposed algorithms are tested and compared for both writer independent and writer adaptation scenarios.

Scalable sequential alternating proximal methods for sparse structural SVMs and CRFs

Structural Support Vector Machines (SSVMs) and Conditional Random Fields (CRFs) are popular discriminative methods used for classifying structured and complex objects like parse trees, image segments and part-of-speech tags. The datasets involved are very large dimensional, and the models designed using typical training algorithms for SSVMs and CRFs are non-sparse. This non-sparse nature of models results in slow inference. Thus, there is a need to devise new algorithms for sparse SSVM and CRF classifier design. Use of elastic net and L1-regularizer has already been explored for solving primal CRF and SSVM problems, respectively, to design sparse classifiers. In this work, we focus on dual elastic net regularized SSVM and CRF. By exploiting the weakly coupled structure of these convex programming problems, we propose a new sequential alternating proximal (SAP) algorithm to solve these dual problems. This algorithm works by sequentially visiting each training set example and solving a simple subproblem restricted to a small subset of variables associated with that example. Numerical experiments on various benchmark sequence labeling datasets demonstrate that the proposed algorithm scales well. Further, the classifiers designed are sparser than those designed by solving the respective primal problems and demonstrate comparable generalization performance. Thus, the proposed SAP algorithm is a useful alternative for sparse SSVM and CRF classifier design.

Novel Transmit Precoding Methods for Rayleigh Fading Multiuser TDD-MIMO Systems With CSIT and No CSIR

In this paper, we present novel precoding methods for multiuser Rayleigh fading multiple-input-multiple-output (MIMO) systems when channel state information (CSI) is available at the transmitter (CSIT) but not at the receiver (CSIR). Such a scenario is relevant, for example, in time-division duplex (TDD) MIMO communications, where, due to channel reciprocity, CSIT can be directly acquired by sending a training sequence from the receiver to the transmitter(s). We propose three transmit precoding schemes that convert the fading MIMO channel into a fixed-gain additive white Gaussian noise (AWGN) channel while satisfying an average power constraint. We also extend one of the precoding schemes to the multiuser Rayleigh fading multiple-access channel (MAC), broadcast channel (BC), and interference channel (IC). The proposed schemes convert the fading MIMO channel into fixed-gain parallel AWGN channels in all three cases. Hence, they achieve an infinite diversity order, which is in sharp contrast to schemes based on perfect CSIR and no CSIT, which, at best, achieve a finite diversity order. Further, we show that a polynomial diversity order is retained, even in the presence of channel estimation errors at the transmitter. Monte Carlo simulations illustrate the bit error rate (BER) performance obtainable from the proposed precoding scheme compared with existing transmit precoding schemes.

Broadland Council Training Services – Winning your e-Learning Spurs with Xerte/Maxos

Broadland Council Training Services have reined in their reliance on traditional learning methods by introducing Xerte/Maxos to their equine-based students. Learners who were once deluged by stacks of paper and unable to utilise an internet connection in a horse yard are now able to access interactive learning exercises using Maxos: Xerte on a memory stick. Students are now more engaged and focused on their studies, teaching methods are much more diverse, and success rates have improved.