103 resultados para specialized corpora
em Queensland University of Technology - ePrints Archive
Resumo:
Background and Aims Successful cryopreservation of bryophytes is linked to intrinsic desiccation tolerance and survival can be enhanced by pre-treatment with abscisic acid (ABA) and sucrose. The pioneer moss Ditrichum plumbicola is naturally subjected to desiccation in the field but showed unexpectedly low survival of cryopreservation, as well as a poor response to pre-treatment. The effects of the cryopreservation protocol on protonemata of D. plumbicola were investigated in order to explore possible relationships between the production in vitro of cryopreservation-tolerant asexual propagules and the reproductive biology of D. plumbicola in nature. Methods Protonemata were prepared for cryopreservation using a four-step protocol involving encapsulation in sodium alginate, pre-treatment for 2 weeks with ABA and sucrose, desiccation for 6 h and rapid freezing in liquid nitrogen. After each stage, protonemata were prepared for light and electron microscopy and growth on standard medium was monitored. Further samples were prepared for light and electron microscopy at intervals over a 24-h period following removal from liquid nitrogen and re-hydration. Key Results Pre-treatment with ABA and sucrose caused dramatic changes to the protonemata. Growth was arrested and propagules induced with pronounced morphological and cytological changes. Most cells died, but those that survived were characterized by thick, deeply pigmented walls, numerous small vacuoles and lipid droplets in their cytoplasm. Desiccation and cryopreservation elicited no dramatic cytological changes. Cells returned to their pre-dehydration and cryopreservation state within 2 h of re-hydration and/or removal from liquid nitrogen. Regeneration was normal once the ABA/sucrose stimulus was removed. Conclusions The ABA/sucrose pre-treatment induced the formation of highly desiccation- and cryopreservation-tolerant propagules from the protonemata of D. plumbicola. This parallels behaviour in the wild, where highly desiccation-tolerant rhizoids function as perennating organs allowing the moss to endure extreme environmental conditions. An involvement of endogenous ABA in the desiccation tolerance of D. plumbicola is suggested.
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This article presents and evaluates a model to automatically derive word association networks from text corpora. Two aspects were evaluated: To what degree can corpus-based word association networks (CANs) approximate human word association networks with respect to (1) their ability to quantitatively predict word associations and (2) their structural network characteristics. Word association networks are the basis of the human mental lexicon. However, extracting such networks from human subjects is laborious, time consuming and thus necessarily limited in relation to the breadth of human vocabulary. Automatic derivation of word associations from text corpora would address these limitations. In both evaluations corpus-based processing provided vector representations for words. These representations were then employed to derive CANs using two measures: (1) the well known cosine metric, which is a symmetric measure, and (2) a new asymmetric measure computed from orthogonal vector projections. For both evaluations, the full set of 4068 free association networks (FANs) from the University of South Florida word association norms were used as baseline human data. Two corpus based models were benchmarked for comparison: a latent topic model and latent semantic analysis (LSA). We observed that CANs constructed using the asymmetric measure were slightly less effective than the topic model in quantitatively predicting free associates, and slightly better than LSA. The structural networks analysis revealed that CANs do approximate the FANs to an encouraging degree.
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In a range test, one party holds a ciphertext and needs to test whether the message encrypted in the ciphertext is within a certain interval range. In this paper, a range test protocol is proposed, where the party holding the ciphertext asks another party holding the private key of the encryption algorithm to help him. These two parties run the protocol to implement the test. The test returns TRUE if and only if the encrypted message is within the certain interval range. If the two parties do not conspire, no information about the encrypted message is revealed from the test except what can be deduced from the test result. Advantages of the new protocol over the existing related techniques are that it achieves correctness, soundness, °exibility, high e±ciency and privacy simultaneously.
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Faces are complex patterns that often differ in only subtle ways. Face recognition algorithms have difficulty in coping with differences in lighting, cameras, pose, expression, etc. We propose a novel approach for facial recognition based on a new feature extraction method called fractal image-set encoding. This feature extraction method is a specialized fractal image coding technique that makes fractal codes more suitable for object and face recognition. A fractal code of a gray-scale image can be divided in two parts – geometrical parameters and luminance parameters. We show that fractal codes for an image are not unique and that we can change the set of fractal parameters without significant change in the quality of the reconstructed image. Fractal image-set coding keeps geometrical parameters the same for all images in the database. Differences between images are captured in the non-geometrical or luminance parameters – which are faster to compute. Results on a subset of the XM2VTS database are presented.
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In truck manufacturing, the exhaust and air inlet pipes are specialized equipment that requires highly skilled, heavy machinery and small batch production methods. This paper describes a project to develop the computer numerically controlled (CNC) pipe bending process for a truck component manufacturer. The company supplies a huge range of heavy duty truck parts to the domestic market and is a significant supplier in Australia. The company has been using traditional methods of machine assisted manual pipe bending techniques. In a drive of continuous improvement, the company has acquired a pre-owned CNC bending machine capable of bending pipes automatically up to 25 bends. However, due to process mismatch, this machine is only used for single bending operation. The researchers studied the bending system and changed the manufacturing process. Using an example exhaust pipe as the benchmark, a significant drop of manufacturing lead time from 70 minutes to 40 minutes for each pipe was demonstrated. There was also a decrease of material cost due to the multiple bends part in one piece without cutting excessive materials for each single bend like it used to be.
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Peer to peer systems have been widely used in the internet. However, most of the peer to peer information systems are still missing some of the important features, for example cross-language IR (Information Retrieval) and collection selection / fusion features. Cross-language IR is the state-of-art research area in IR research community. It has not been used in any real world IR systems yet. Cross-language IR has the ability to issue a query in one language and receive documents in other languages. In typical peer to peer environment, users are from multiple countries. Their collections are definitely in multiple languages. Cross-language IR can help users to find documents more easily. E.g. many Chinese researchers will search research papers in both Chinese and English. With Cross-language IR, they can do one query in Chinese and get documents in two languages. The Out Of Vocabulary (OOV) problem is one of the key research areas in crosslanguage information retrieval. In recent years, web mining was shown to be one of the effective approaches to solving this problem. However, how to extract Multiword Lexical Units (MLUs) from the web content and how to select the correct translations from the extracted candidate MLUs are still two difficult problems in web mining based automated translation approaches. Discovering resource descriptions and merging results obtained from remote search engines are two key issues in distributed information retrieval studies. In uncooperative environments, query-based sampling and normalized-score based merging strategies are well-known approaches to solve such problems. However, such approaches only consider the content of the remote database but do not consider the retrieval performance of the remote search engine. This thesis presents research on building a peer to peer IR system with crosslanguage IR and advance collection profiling technique for fusion features. Particularly, this thesis first presents a new Chinese term measurement and new Chinese MLU extraction process that works well on small corpora. An approach to selection of MLUs in a more accurate manner is also presented. After that, this thesis proposes a collection profiling strategy which can discover not only collection content but also retrieval performance of the remote search engine. Based on collection profiling, a web-based query classification method and two collection fusion approaches are developed and presented in this thesis. Our experiments show that the proposed strategies are effective in merging results in uncooperative peer to peer environments. Here, an uncooperative environment is defined as each peer in the system is autonomous. Peer like to share documents but they do not share collection statistics. This environment is a typical peer to peer IR environment. Finally, all those approaches are grouped together to build up a secure peer to peer multilingual IR system that cooperates through X.509 and email system.
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Many contemporary currents in applied linguistics have favored discourse studies within assessment; there have been calls for cross-fertilization with other areas within applied linguistics, critiques of the positivist tradition within language testing research, and the growing impact of Conversation Analysis (CA) and sociocultural theory. This chapter focuses on the resulting increase in discourse-based studies of oral proficiency assessment techniques. These studies initially focused on the traditional oral proficiency interview but have since been extended to new test formats, including paired and group interaction. We discuss the research carried out on a number of factors in the assessment setting, including the role of the interlocutor, candidate, and rater, and the impact of tasks, task performance conditions, and rating criteria. Recent research has also concentrated more specifically on the assessment of pragmatic competence and on the applications of technology within the assessment of spoken language, including the comparability of semidirect and direct methods for such assessment and the use of computer corpora.
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This paper presents Scatter Difference Nuisance Attribute Projection (SD-NAP) as an enhancement to NAP for SVM-based speaker verification. While standard NAP may inadvertently remove desirable speaker variability, SD-NAP explicitly de-emphasises this variability by incorporating a weighted version of the between-class scatter into the NAP optimisation criterion. Experimental evaluation of SD-NAP with a variety of SVM systems on the 2006 and 2008 NIST SRE corpora demonstrate that SD-NAP provides improved verification performance over standard NAP in most cases, particularly at the EER operating point.
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Managed execution frameworks, such as the.NET Common Language Runtime or the Java Virtual Machine, provide a rich environment for the creation of application programs. These execution environments are ideally suited for languages that depend on type-safety and the declarative control of feature access. Furthermore, such frameworks typically provide a rich collection of library primitives specialized for almost every domain of application programming. Thus, when a new language is implemented on one of these frameworks it becomes necessary to provide some kind of mapping from the new language to the libraries of the framework. The design of such mappings is challenging since the type-system of the new language may not span the domain exposed in the library application programming interfaces (APIs). The nature of these design considerations was clarified in the implementation of the Gardens Point Component Pascal (gpcp) compiler. In this paper we describe the issues, and the solutions that we settled on in this case. The problems that were solved have a wider applicability than just our example, since they arise whenever any similar language is hosted in such an environment.
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Problem-based learning (PBL) is a pedagogical methodology that presents the learner with a problem to be solved to stimulate and situate learning. This paper presents key characteristics of a problem-based learning environment that determines its suitability as a data source for workrelated research studies. To date, little has been written about the availability and validity of PBL environments as a data source and its suitability for work-related research. We describe problembased learning and use a research project case study to illustrate the challenges associated with industry work samples. We then describe the PBL course used in our research case study and use this example to illustrate the key attributes of problem-based learning environments and show how the chosen PBL environment met the work-related research requirements of the research case study. We propose that the more realistic the PBL work context and work group composition, the better the PBL environment as a data source for a work-related research. The work context is more realistic when relevant and complex project-based problems are tackled in industry-like work conditions over longer time frames. Work group composition is more realistic when participants with industry-level education and experience enact specialized roles in different disciplines within a professional community.
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Noise and vibration in complex ship structures are becoming a prominent issue for ship building industry and ship companies due to the constant demand of building faster ships of lighter weight, and the stringent noise and libration regulation of the industry. In order to retain the full benefit of building faster ships without compromising too much on ride comfort and safety, noise and vibration control needs to be implemented. Due to the complexity of ship structures, the coupling of different wave types and multiple wave propagation paths, active control of global hull modes is difficult to implement and very expensive. Traditional passive control such as adding damping materials is only effective in the high frequency range. However, most severe damage to ship structures is caused by large structural deformation of hull structures and high dynamic stress concentration at low frequencies. The most discomfort and fatigue of passengers and the crew onboard ships is also due to the low frequency noise and vibration. Innovative approaches are therefore, required to attenuate the noise and vibration at low frequencies. This book was developed from several specialized research topics on vibration and vibration control of ship structures, mostly from the author's own PhD work at the University of Western Australia. The book aims to provide a better understanding of vibration characteristics of ribbed plate structures, plate/plate coupled structures and the mechanism governing wave propagation and attenuation in periodic and irregular ribbed structures as well as in complex ship structures. The book is designed to be a reference book for ship builders, vibro-acoustic engineers and researchers. The author also hopes that the book can stimulate more exciting future work in this area of research. It is the author's humble desire that the book can be some use for those who purchase it. This book is divided into eight chapters. Each chapter focuses on providing solution to address a particular issue on vibration problems of ship structures. A brief summary of each chapter is given in the general introduction. All chapters are inter-dependent to each other to form an integration volume on the subject of vibration and vibration control of ship structures and alike. I am in debt to many people in completing this work. In particular, I would like to thank Professor J. Pan, Dr N.H. Farag, Dr K. Sum and many others from the University of Western Australia for useful advices and helps during my times at the University and beyond. I would also like to thank my wife, Miaoling Wang, my children, Anita, Sophia and Angela Lin, for their sacrifice and continuing supports to make this work possible. Financial supports from Australian Research Council, Australian Defense Science and Technology Organization and Strategic Marine Pty Ltd at Western Australia for this work is gratefully acknowledged.
Resumo:
Purpose of the Study: A framework aids choice of interventions to manage wandering and prevent elopement in consideration of associated risks and mobility needs of wanderers. ---------- Design and Methods: A literature review, together with research results, published wandering tools, clinical reports, author clinical experience, and consensus-based judgments was used to build a decision-making framework. Results: Referencing a published definition of wandering and originating a clinical description of problematic wandering, authors introduce a framework comprising (1) wandering and related behaviors; (2) goals of wandering-specific care, (3) interpersonally, technologically, and policy-mediated wandering interventions, and (4) estimates of relative frequencies of wandering behaviors, magnitudes of elopement risk, and restrictiveness of strategies. ---------- Implications: Safeguarding wanderers from elopement risk is rendered person-centered and humane when goals of care guide intervention choice. Despite limitations, a reasoned, systematized approach to wandering management provides a basis for tailoring a specialized program of care. The need for framework refinement and related research is emphasized.
Resumo:
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.