306 resultados para electronic text
Resumo:
Reliability of the performance of biometric identity verification systems remains a significant challenge. Individual biometric samples of the same person (identity class) are not identical at each presentation and performance degradation arises from intra-class variability and inter-class similarity. These limitations lead to false accepts and false rejects that are dependent. It is therefore difficult to reduce the rate of one type of error without increasing the other. The focus of this dissertation is to investigate a method based on classifier fusion techniques to better control the trade-off between the verification errors using text-dependent speaker verification as the test platform. A sequential classifier fusion architecture that integrates multi-instance and multisample fusion schemes is proposed. This fusion method enables a controlled trade-off between false alarms and false rejects. For statistically independent classifier decisions, analytical expressions for each type of verification error are derived using base classifier performances. As this assumption may not be always valid, these expressions are modified to incorporate the correlation between statistically dependent decisions from clients and impostors. The architecture is empirically evaluated by applying the proposed architecture for text dependent speaker verification using the Hidden Markov Model based digit dependent speaker models in each stage with multiple attempts for each digit utterance. The trade-off between the verification errors is controlled using the parameters, number of decision stages (instances) and the number of attempts at each decision stage (samples), fine-tuned on evaluation/tune set. The statistical validation of the derived expressions for error estimates is evaluated on test data. The performance of the sequential method is further demonstrated to depend on the order of the combination of digits (instances) and the nature of repetitive attempts (samples). The false rejection and false acceptance rates for proposed fusion are estimated using the base classifier performances, the variance in correlation between classifier decisions and the sequence of classifiers with favourable dependence selected using the 'Sequential Error Ratio' criteria. The error rates are better estimated by incorporating user-dependent (such as speaker-dependent thresholds and speaker-specific digit combinations) and class-dependent (such as clientimpostor dependent favourable combinations and class-error based threshold estimation) information. The proposed architecture is desirable in most of the speaker verification applications such as remote authentication, telephone and internet shopping applications. The tuning of parameters - the number of instances and samples - serve both the security and user convenience requirements of speaker-specific verification. The architecture investigated here is applicable to verification using other biometric modalities such as handwriting, fingerprints and key strokes.
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Managing large cohorts of undergraduate student nurses during off-campus clinical placement is complex and challenging. Clinical facilitators are required to support and assess nursing students during clinical placement. Therefore clear communication between university academic coordinators and clinical facilitators is essential for consistency and prompt management of emerging issues. Increasing work demands require both coordinators and facilitators to have an efficient and effective mode of communication. The aim of this study was to explore the use of Short Message Service (SMS) texts, sent between mobile phones, for communication between university Unit Coordinators and off-campus Clinical Facilitators. This study used an after-only design. During a two week clinical placement 46 clinical facilitators working with first and second year Bachelor of Nursing students from a large metropolitan Australian university were regularly sent SMS texts of relevant updates and reminders from the university coordinator. A 15 item questionnaire comprising x of 5 point likert scale and 3 open-ended questions was then used to survey the clinical facilitators. The response rate was 47.8% (n=22). Correlations were found between the approachability of the coordinator and facilitator perception of a) that the coordinator understood issues on clinical placement (r=0.785, p<0.001,), and b) being part of the teaching team (r=0.768, p<0.001). Analysis of responses to qualitative questions revealed three themes: connection, approachability and collaboration. Results indicate that SMS communication is convenient and appropriate in this setting. This quasi-experimental after-test study found regular SMS communication improves a sense of connection, approachability and collaboration.
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This thesis investigates the experiences of teachers who trialled an electronic curriculum and assessment tool in the wider context of text-mediated ruling relations organising their work. Problematised as policy and text, this tool is interrogated as a 'solution' to problems perceived in teachers' work in an era of increased accountability. It provides evidence that teachers' work is shaped by forces operating outside their control and mediated by the policy discourses and subjectivities available to them.
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Information privacy is a critical success/failure factor in information technology supported healthcare (eHealth). eHealth systems utilise electronic health records (EHR) as the main source of information, thus, implementing appropriate privacy preserving methods for EHRs is vital for the proliferation of eHealth. Whilst information privacy may be a fundamental requirement for eHealth consumers, healthcare professionals demand non-restricted access to patient information for improved healthcare delivery, thus, creating an environment where stakeholder requirements are contradictory. Therefore, there is a need to achieve an appropriate balance of requirements in order to build successful eHealth systems. Towards achieving this balance, a new genre of eHealth systems called Accountable-eHealth (AeH) systems has been proposed. In this paper, an access control model for EHRs is presented that can be utilised by AeH systems to create information usage policies that fulfil both stakeholders’ requirements. These policies are used to accomplish the aforementioned balance of requirements creating a satisfactory eHealth environment for all stakeholders. The access control model is validated using a Web based prototype as a proof of concept.
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Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that more than 80% of users preferred personalized search results. As a result, many studies paid a great deal of efforts (referred to as col- laborative filtering) investigating on personalized notions for enhancing retrieval performance. One of the fundamental yet most challenging steps is to capture precise user information needs. Most Web users are inexperienced or lack the capability to express their needs properly, whereas the existent retrieval systems are highly sensitive to vocabulary. Researchers have increasingly proposed the utilization of ontology-based tech- niques to improve current mining approaches. The related techniques are not only able to refine search intentions among specific generic domains, but also to access new knowledge by tracking semantic relations. In recent years, some researchers have attempted to build ontological user profiles according to discovered user background knowledge. The knowledge is considered to be both global and lo- cal analyses, which aim to produce tailored ontologies by a group of concepts. However, a key problem here that has not been addressed is: how to accurately match diverse local information to universal global knowledge. This research conducts a theoretical study on the use of personalized ontolo- gies to enhance text mining performance. The objective is to understand user information needs by a \bag-of-concepts" rather than \words". The concepts are gathered from a general world knowledge base named the Library of Congress Subject Headings. To return desirable search results, a novel ontology-based mining approach is introduced to discover accurate search intentions and learn personalized ontologies as user profiles. The approach can not only pinpoint users' individual intentions in a rough hierarchical structure, but can also in- terpret their needs by a set of acknowledged concepts. Along with global and local analyses, another solid concept matching approach is carried out to address about the mismatch between local information and world knowledge. Relevance features produced by the Relevance Feature Discovery model, are determined as representatives of local information. These features have been proven as the best alternative for user queries to avoid ambiguity and consistently outperform the features extracted by other filtering models. The two attempt-to-proposed ap- proaches are both evaluated by a scientific evaluation with the standard Reuters Corpus Volume 1 testing set. A comprehensive comparison is made with a num- ber of the state-of-the art baseline models, including TF-IDF, Rocchio, Okapi BM25, the deploying Pattern Taxonomy Model, and an ontology-based model. The gathered results indicate that the top precision can be improved remarkably with the proposed ontology mining approach, where the matching approach is successful and achieves significant improvements in most information filtering measurements. This research contributes to the fields of ontological filtering, user profiling, and knowledge representation. The related outputs are critical when systems are expected to return proper mining results and provide personalized services. The scientific findings have the potential to facilitate the design of advanced preference mining models, where impact on people's daily lives.
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Background The prevalence of type 2 diabetes is rising internationally. Patients with diabetes have a higher risk of cardiovascular events accounting for substantial premature morbidity and mortality, and health care expenditure. Given healthcare workforce limitations, there is a need to improve interventions that promote positive self-management behaviours that enable patients to manage their chronic conditions effectively, across different cultural contexts. Previous studies have evaluated the feasibility of including telephone and Short Message Service (SMS) follow up in chronic disease self-management programs, but only for single diseases or in one specific population. Therefore, the aim of this study is to evaluate the feasibility and short-term efficacy of incorporating telephone and text messaging to support the care of patients with diabetes and cardiac disease, in Australia and in Taiwan. Methods/design A randomised controlled trial design will be used to evaluate a self-management program for people with diabetes and cardiac disease that incorporates the use of simple remote-access communication technologies. A sample size of 180 participants from Australia and Taiwan will be recruited and randomised in a one-to-one ratio to receive either the intervention in addition to usual care (intervention) or usual care alone (control). The intervention will consist of in-hospital education as well as follow up utilising personal telephone calls and SMS reminders. Primary short term outcomes of interest include self-care behaviours and self-efficacy assessed at baseline and four weeks. Discussion If the results of this investigation substantiate the feasibility and efficacy of the telephone and SMS intervention for promoting self management among patients with diabetes and cardiac disease in Australia and Taiwan, it will support the external validity of the intervention. It is anticipated that empirical data from this investigation will provide valuable information to inform future international collaborations, while providing a platform for further enhancements of the program, which has potential to benefit patients internationally.
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This article examines manual textual categorisation by human coders with the hypothesis that the law of total probability may be violated for difficult categories. An empirical evaluation was conducted to compare a one step categorisation task with a two step categorisation task using crowdsourcing. It was found that the law of total probability was violated. Both a quantum and classical probabilistic interpretations for this violation are presented. Further studies are required to resolve whether quantum models are more appropriate for this task.
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Voltammetric techniques have been introduced to monitor the formation of gold nanoparticles produced via the reaction of the amino acid glycyl-L-tyrosine with Au(III) (bromoaurate) in 0.05 M KOH conditions. The alkaline conditions facilitate amino acid binding to Au(III), inhibit the rate of reduction to Au(0), and provide an excellent supporting electrolyte for voltammetric studies. Data obtained revealed that a range of time-dependent gold solution species are involved in gold nanoparticle formation and that the order in which reagents are mixed is critical to the outcome. Concomitantly with voltammetric measurements, the properties of gold nanoparticles formed are probed by examination of electronic spectra in order to understand how the solution environment present during nanoparticle growth affects the final distribution of the nanoparticles. Images obtained by the ex situ transmission electron microscopy (TEM) technique enable the physical properties of the nanoparticles isolated in the solid state to be assessed. Use of this combination of in situ and ex situ techniques provides a versatile framework for elucidating the details of nanoparticle formation.
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Gambling activities and the revenues derived have been seen as a way to increase economic development in deprived areas. There are also, however, concerns about the effects of gambling in general and electronic gaming machines (EGMs) in particular, on the resources available to the localities in which they are situated. This paper focuses on the factors that determine the extent and spending of community benefit-related EGM-generated resources within Victoria, Australia, focusing in particular on the relationships between EGM activity and socio-economic and social capital indicators, and how this relates to the community benefit resources generated by gaming.
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Purpose Contrast adaptation has been speculated to be an error signal for emmetropization. Myopic children exhibit higher contrast adaptation than emmetropic children. This study aimed to determine whether contrast adaptation varies with the type of text viewed by emmetropic and myopic young adults. Methods Baseline contrast sensitivity was determined in 25 emmetropic and 25 spectacle-corrected myopic young adults for 0.5, 1.2, 2.7, 4.4, and 6.2 cycles per degree (cpd) horizontal sine wave gratings. The adults spent periods looking at a 6.2 cpd high-contrast horizontal grating and reading lines of English and Chinese text (these texts comprised 1.2 cpd row and 6 cpd stroke frequencies). The effects of these near tasks on contrast sensitivity were determined, with decreases in sensitivity indicating contrast adaptation. Results Contrast adaptation was affected by the near task (F2,672 = 43.0; P < 0.001). Adaptation was greater for the grating task (0.13 ± 0.17 log unit, averaged across all frequencies) than reading tasks, but there was no significant difference between the two reading tasks (English 0.05 ± 0.13 log unit versus Chinese 0.04 ± 0.13 log unit). The myopic group showed significantly greater adaptation (by 0.04, 0.04, and 0.05 log units for English, Chinese, and grating tasks, respectively) than the emmetropic group (F1,48 = 5.0; P = 0.03). Conclusions In young adults, reading Chinese text induced similar contrast adaptation as reading English text. Myopes exhibited greater contrast adaptation than emmetropes. Contrast adaptation, independent of text type, might be associated with myopia development.
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This paper presents a vulnerability within the generic object oriented substation event (GOOSE) communication protocol. It describes an exploit of the vulnerability and proposes a number of attack variants. The attacks sends GOOSE frames containing higher status numbers to the receiving intelligent electronic device (IED). This prevents legitimate GOOSE frames from being processed and effectively causes a hijacking of the communication channel, which can be used to implement a denial–of–service (DoS) or manipulate the subscriber (unless a status number roll-over occurs). The authors refer to this attack as a poisoning of the subscriber. A number of GOOSE poisoning attacks are evaluated experimentally on a test bed and demonstrated to be successful.
Resumo:
Background Managing large student cohorts can be a challenge for university academics, coordinating these units. Bachelor of Nursing programmes have the added challenge of managing multiple groups of students and clinical facilitators whilst completing clinical placement. Clear, time efficient and effective communication between coordinating academics and clinical facilitators is needed to ensure consistency between student and teaching groups and prompt management of emerging issues. Methods This study used a descriptive survey to explore the use of text messaging via a mobile phone, sent from coordinating academics to off-campus clinical facilitators, as an approach to providing direction and support. Results The response rate was 47.8% (n = 22). Correlations were found between the approachability of the coordinating academic and clinical facilitator perception that, a) the coordinating academic understood issues on clinical placement (r = 0.785, p < 0.001), and b) being part of the teaching team (r = 0.768, p < 0.001). Analysis of responses to qualitative questions revealed three themes: connection, approachability and collaboration. Conclusions This study demonstrates that use of regular text messages improves communication between coordinating academics and clinical facilitators. Findings suggest improved connection, approachability and collaboration between the coordinating academic and clinical facilitation staff.
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Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.
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A significant minority of young job-seekers remain unemployed for many months, and are at risk of developing depression. Both empirical studies and theoretical models suggest that cognitive, behavioural and social isolation factors interact to increase this risk. Thus, interventions that reduce or prevent depression in young unemployed job-seekers by boosting their resilience are required. Mobile phones may be an effective medium to deliver resilience-boosting support to young unemployed people by using SMS messages to interrupt the feedback loop of depression and social isolation. Three focus groups were conducted to explore young unemployed job-seekers’ attitudes to receiving and requesting regular SMS messages that would help them to feel supported and motivated while job-seeking. Participants reacted favourably to this proposal, and thought that it would be useful to continue to receive and request SMS messages for a few months after commencing employment as well.
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Objective To evaluate the effects of Optical Character Recognition (OCR) on the automatic cancer classification of pathology reports. Method Scanned images of pathology reports were converted to electronic free-text using a commercial OCR system. A state-of-the-art cancer classification system, the Medical Text Extraction (MEDTEX) system, was used to automatically classify the OCR reports. Classifications produced by MEDTEX on the OCR versions of the reports were compared with the classification from a human amended version of the OCR reports. Results The employed OCR system was found to recognise scanned pathology reports with up to 99.12% character accuracy and up to 98.95% word accuracy. Errors in the OCR processing were found to minimally impact on the automatic classification of scanned pathology reports into notifiable groups. However, the impact of OCR errors is not negligible when considering the extraction of cancer notification items, such as primary site, histological type, etc. Conclusions The automatic cancer classification system used in this work, MEDTEX, has proven to be robust to errors produced by the acquisition of freetext pathology reports from scanned images through OCR software. However, issues emerge when considering the extraction of cancer notification items.