729 resultados para Distributed learning environments
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Report published in the Proceedings of the National Conference on "Education in the Information Society", Plovdiv, May, 2013
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Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015
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In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.
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With the recent explosion in the complexity and amount of digital multimedia data, there has been a huge impact on the operations of various organizations in distinct areas, such as government services, education, medical care, business, entertainment, etc. To satisfy the growing demand of multimedia data management systems, an integrated framework called DIMUSE is proposed and deployed for distributed multimedia applications to offer a full scope of multimedia related tools and provide appealing experiences for the users. This research mainly focuses on video database modeling and retrieval by addressing a set of core challenges. First, a comprehensive multimedia database modeling mechanism called Hierarchical Markov Model Mediator (HMMM) is proposed to model high dimensional media data including video objects, low-level visual/audio features, as well as historical access patterns and frequencies. The associated retrieval and ranking algorithms are designed to support not only the general queries, but also the complicated temporal event pattern queries. Second, system training and learning methodologies are incorporated such that user interests are mined efficiently to improve the retrieval performance. Third, video clustering techniques are proposed to continuously increase the searching speed and accuracy by architecting a more efficient multimedia database structure. A distributed video management and retrieval system is designed and implemented to demonstrate the overall performance. The proposed approach is further customized for a mobile-based video retrieval system to solve the perception subjectivity issue by considering individual user's profile. Moreover, to deal with security and privacy issues and concerns in distributed multimedia applications, DIMUSE also incorporates a practical framework called SMARXO, which supports multilevel multimedia security control. SMARXO efficiently combines role-based access control (RBAC), XML and object-relational database management system (ORDBMS) to achieve the target of proficient security control. A distributed multimedia management system named DMMManager (Distributed MultiMedia Manager) is developed with the proposed framework DEMUR; to support multimedia capturing, analysis, retrieval, authoring and presentation in one single framework.
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Black students, in general, are underserved academically (Darling-Hammond, 2000; Townsend, 2002) and overrepresented in special education (Donovan & Cross, 2002). Black students with disabilities are further overrepresented in more restrictive educational environments (Skiba, Poloni-Staudinger, Gallini, Simmons & Feggins-Azziz, 2006). Although the National Longitudinal Transition Study 2 (NLTS2) revealed that the academic performance of students with learning disabilities is positively related to the percentage of courses taken in the general education setting (Newman, 2006), the research specifically on placement of Black students with disabilities, particularly at the secondary level, as it relates to academic achievement is lacking. While previous studies have sought to determine which placement is better for students with disabilities, no study was found that specifically examined the impact of placement specific to Black students with specific learning disabilities (SLD) in urban settings (Fore, III, Hagan-Burke, Burke, Boon & Smith, 2008; Rea, McLaughlin & Walther-Thomas, 2002). This study examined educational placement, instructional best practices, and achievement gains of Black students with SLD in urban secondary settings using an ex post facto research design. Achievement, placement, and demographic data were collected and analyzed on approximately 314 Black eighth grade students with SLD. The Teacher Instructional Practices Survey was developed and used to collect and analyze data from the teachers of 78 of these students as it relates to instructional best practices. Results indicate no significant difference in reading but a significant difference in math gains of students served in inclusive settings as compared to resource settings with a small effect size. Also, no significant relationship was found between achievement gains and the reported use of instructional best practices. However, there was a relationship between educational placement and the use of instructional best practices. The results implied that there is a need for training with both general and special education teachers on instructional best practices for SWD and that there should be certain IEP team considerations when making placement decisions for this population of students with disabilities. It is recommended that future research in this area include classroom observations and factors other than test scores to measure growth in achievement.
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The current study examined the impact of an early summer literacy program and the mediating effects of the home literacy environment on the language and literacy outcomes of a group of children at-risk for long-term developmental and academic delays. Participating children (n=54) were exposed to an intensive book-reading intervention each summer (June through mid August) over a 3-year period. The current study implemented an ex post facto, quasi-experimental design. This nonequivalent group design involved a pretest and posttest over three time points for a non-randomized treatment group and a matched non-treatment comparison group. Results indicated that literacy scores did improve for the children over the 3-year period; however, language scores did not experience the same rate of change over time. Receptive language was significantly impacted by attendance, and race/ethnicity. Expressive language was impacted significantly by gestational age and attendance. Results also indicated that language outcomes for young children who are exposed to a literacy program were higher than those who did not participate; however, only receptive language yielded significance at the p<.05 level. These study results also found that activities in the home that support literacy and learning do indeed impact language and literacy outcomes for these children, specifically, the age at which a child is read to, the number of books in the home, a child’s enjoyment of reading, and whether a child looks at books on his or her own impact language scores. This study concluded that at-risk young children do benefit from center-based literacy intervention. This literacy experience, however, is also driven by the children's home environment, their attendance to the program, whether they were premature or not and the type of caregiver.
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Adults participate in communities of practice (COP) in diverse environments. As the number of US citizens 55 years or older increases, so might the number residing in adult living environments. COP research would be valuable in such settings.
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Marine phytoplankton can evolve rapidly when confronted with aspects of climate change because of their large population sizes and fast generation times. Despite this, the importance of environment fluctuations, a key feature of climate change, has received little attention-selection experiments with marine phytoplankton are usually carried out in stable environments and use single or few representatives of a species, genus or functional group. Here we investigate whether and by how much environmental fluctuations contribute to changes in ecologically important phytoplankton traits such as C:N ratios and cell size, and test the variability of changes in these traits within the globally distributed species Ostreococcus. We have evolved 16 physiologically distinct lineages of Ostreococcus at stable high CO2 (1031±87?µatm CO2, SH) and fluctuating high CO2 (1012±244?µatm CO2, FH) for 400 generations. We find that although both fluctuation and high CO2 drive evolution, FH-evolved lineages are smaller, have reduced C:N ratios and respond more strongly to further increases in CO2 than do SH-evolved lineages. This indicates that environmental fluctuations are an important factor to consider when predicting how the characteristics of future phytoplankton populations will have an impact on biogeochemical cycles and higher trophic levels in marine food webs.
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How experience alters neuronal ensemble dynamics and how locus coeruleus-mediated norepinephrine release facilitates memory formation in the brain are the topics of this thesis. Here we employed a visualization technique, cellular compartment analysis of temporal activity by fluorescence in situ hybridization (catFISH), to assess activation patterns of neuronal ensembles in the olfactory bulb (OB) and anterior piriform cortex (aPC) to repeated odor inputs. Two associative learning models were used, early odor preference learning in rat pups and adult rat go-no-go odor discrimination learning. With catFISH of an immediate early gene, Arc, we showed that odor representation in the OB and aPC was sparse (~5-10%) and widely distributed. Odor associative learning enhanced the stability of the rewarded odor representation in the OB and aPC. The stable component, indexed by the overlap between the two ensembles activated by the rewarded odor at two time points, increased from ~25% to ~50% (p = 0.004-1.43E⁻4; Chapter 3 and 4). Adult odor discrimination learning promoted pattern separation between rewarded and unrewarded odor representations in the aPC. The overlap between rewarded and unrewarded odor representations reduced from ~25% to ~14% (p = 2.28E⁻⁵). However, learning an odor mixture as a rewarded odor increased the overlap of the component odor representations in the aPC from ~23% to ~44% (p = 0.010; Chapter 4). Blocking both α- and β-adrenoreceptors in the aPC prevented highly similar odor discrimination learning in adult rats, and reduced OB mitral and granule ensemble stability to the rewarded odor. Similar treatment in the OB only slowed odor discrimination learning. However, OB adrenoceptor blockade disrupted pattern separation and ensemble stability in the aPC when the rats demonstrated deficiency in discrimination (Chapter 5). In another project, the role of α₂-adrenoreceptors in the OB during early odor preference learning was studied. OB α2-adrenoceptor activation was necessary for odor learning in rat pups. α₂-adrenoceptor activation was additive with β-adrenoceptor mediated signalling to promote learning (Chapter 2). Together, these experiments suggest that odor representations are highly adaptive at the early stages of odor processing. The OB and aPC work in concert to support odor learning and top-down adrenergic input exerts a powerful modulation on both learning and odor representation.
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Postprint
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Constant technology advances have caused data explosion in recent years. Accord- ingly modern statistical and machine learning methods must be adapted to deal with complex and heterogeneous data types. This phenomenon is particularly true for an- alyzing biological data. For example DNA sequence data can be viewed as categorical variables with each nucleotide taking four different categories. The gene expression data, depending on the quantitative technology, could be continuous numbers or counts. With the advancement of high-throughput technology, the abundance of such data becomes unprecedentedly rich. Therefore efficient statistical approaches are crucial in this big data era.
Previous statistical methods for big data often aim to find low dimensional struc- tures in the observed data. For example in a factor analysis model a latent Gaussian distributed multivariate vector is assumed. With this assumption a factor model produces a low rank estimation of the covariance of the observed variables. Another example is the latent Dirichlet allocation model for documents. The mixture pro- portions of topics, represented by a Dirichlet distributed variable, is assumed. This dissertation proposes several novel extensions to the previous statistical methods that are developed to address challenges in big data. Those novel methods are applied in multiple real world applications including construction of condition specific gene co-expression networks, estimating shared topics among newsgroups, analysis of pro- moter sequences, analysis of political-economics risk data and estimating population structure from genotype data.
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In this study I examine the development of three inclusive music bands in Cork city. Derived from Jellison’s research on inclusive music education, inclusive music bands involve students with disabilities coming together with typically developing peers to make and learn music that is meaningful (Jellison, 2012). As part of this study, I established three inclusive music bands to address the lack of inclusive music making and learning experiences in Cork city. Each of these bands evolved and adapted in order to be socio-culturally relevant within formal and informal settings: Circles (community education band), Till 4 (secondary school band) and Mish Mash (third level and community band). I integrated Digital Musical Instruments into the three bands, in order to ensure access to music making and learning for band members with profound physical disabilities. Digital Musical Instruments are electronic music devices that facilitate active music making with minimal movement. This is the first study in Ireland to examine the experiences of inclusive music making and learning using Digital Musical Instruments. I propose that the integration of Digital Musical Instruments into inclusive music bands has the potential to further the equality and social justice agenda in music education in Ireland. In this study, I employed qualitative research methodology, incorporating participatory action research methodology and case study design. In this thesis I reveal the experiences of being involved in an inclusive music band in Cork city. I particularly focus on examining whether the use of this technology enhances meaningful music making and learning experiences for members with disabilities within inclusive environments. To both inform and understand the person centered and adaptable nature of these inclusive bands, I draw theoretical insights from Sen’s Capabilities Approach and Deleuze and Guatarri’s Rhizome Theory. Supported by descriptive narrative from research participants and an indepth examination of literature, I discover the optimum conditions and associated challenges of inclusive music practice in Cork city.
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Background: Healthcare worldwide needs translation of basic ideas from engineering into the clinic. Consequently, there is increasing demand for graduates equipped with the knowledge and skills to apply interdisciplinary medicine/engineering approaches to the development of novel solutions for healthcare. The literature provides little guidance regarding barriers to, and facilitators of, effective interdisciplinary learning for engineering and medical students in a team-based project context. Methods: A quantitative survey was distributed to engineering and medical students and staff in two universities, one in Ireland and one in Belgium, to chart knowledge and practice in interdisciplinary learning and teaching, and of the teaching of innovation. Results: We report important differences for staff and students between the disciplines regarding attitudes towards, and perceptions of, the relevance of interdisciplinary learning opportunities, and the role of creativity and innovation. There was agreement across groups concerning preferred learning, instructional styles, and module content. Medical students showed greater resistance to the use of structured creativity tools and interdisciplinary teams. Conclusions: The results of this international survey will help to define the optimal learning conditions under which undergraduate engineering and medicine students can learn to consider the diverse factors which determine the success or failure of a healthcare engineering solution.
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The lived environment is the arena where our cognitive skills, preferences, and attitudes come together to determine our ability to interact with the world. The mechanisms through which lived environments can benefit cognitive health in older age are yet to be fully understood. The existing literature suggests that environments which are perceived as stimulating, usable and aesthetically appealing can improve or facilitate cognitive performance both in young and older age. Importantly, optimal stimulation for cognition seems to depend on experiencing sufficiently stimulating environments while not too challenging. Environmental complexity is an important contributor to determining whether an environment provides such an optimal stimulation. The present paper reviews a selection of studies which have explored complexity in relation to perceptual load, environmental preference and perceived usability to propose a framework which explores direct and indirect environmental influences on cognition, and to understand these influences in relation to aging processes. We identify ways to define complexity at different environmental scales, going from micro low-level perceptual features of scenes, to design qualities of proximal environments (e.g., streets, neighborhoods), to broad geographical areas (i.e., natural vs. urban environments). We propose that studying complexity at these different scales will provide new insight into the design of cognitive-friendly environments.
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Research on the mechanisms and processes underlying navigation has traditionally been limited by the practical problems of setting up and controlling navigation in a real-world setting. Thanks to advances in technology, a growing number of researchers are making use of computer-based virtual environments to draw inferences about real-world navigation. However, little research has been done on factors affecting human–computer interactions in navigation tasks. In this study female students completed a virtual route learning task and filled out a battery of questionnaires, which determined levels of computer experience, wayfinding anxiety, neuroticism, extraversion, psychoticism and immersive tendencies as well as their preference for a route or survey strategy. Scores on personality traits and individual differences were then correlated with the time taken to complete the navigation task, the length of path travelled,the velocity of the virtual walk and the number of errors. Navigation performance was significantly influenced by wayfinding anxiety, psychoticism, involvement and overall immersive tendencies and was improved in those participants who adopted a survey strategy. In other words, navigation in virtual environments is effected not only by navigational strategy, but also an individual’s personality, and other factors such as their level of experience with computers. An understanding of these differences is crucial before performance in virtual environments can be generalised to real-world navigational performance.