767 resultados para Collaborative filtering
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Objectives: To develop a decision support system (DSS), myGRaCE, that integrates service user (SU) and practitioner expertise about mental health and associated risks of suicide, self-harm, harm to others, self-neglect, and vulnerability. The intention is to help SUs assess and manage their own mental health collaboratively with practitioners. Methods: An iterative process involving interviews, focus groups, and agile software development with 115 SUs, to elicit and implement myGRaCE requirements. Results: Findings highlight shared understanding of mental health risk between SUs and practitioners that can be integrated within a single model. However, important differences were revealed in SUs' preferred process of assessing risks and safety, which are reflected in the distinctive interface, navigation, tool functionality and language developed for myGRaCE. A challenge was how to provide flexible access without overwhelming and confusing users. Conclusion: The methods show that practitioner expertise can be reformulated in a format that simultaneously captures SU expertise, to provide a tool highly valued by SUs. A stepped process adds necessary structure to the assessment, each step with its own feedback and guidance. Practice Implications: The GRiST web-based DSS (www.egrist.org) links and integrates myGRaCE self-assessments with GRiST practitioner assessments for supporting collaborative and self-managed healthcare.
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In this paper we propose an adaptive power and message rate control method for safety applications at road intersections. The design objectives are to firstly provide guaranteed QoS support to both high priority emergency safety applications and low priority routine safety applications and secondly maximize channel utilization. We use an offline simulation based approach to find out the best possible configurations of transmit power and message rate for given numbers of vehicles in the network with certain safety QoS requirements. The identified configurations are then used online by roadside access points (AP) adaptively according to estimated number of vehicles. Simulation results show that this adaptive method could provide required QoS support to safety applications and it significantly outperforms a fixed control method. © 2013 International Information Institute.
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Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2016
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This study explores the ongoing pedagogical development of a number of undergraduate design and engineering programmes in the United Kingdom. Observations and data have been collected over several cohorts to bring a valuable perspective to the approaches piloted across two similar university departments while trialling a number of innovative learning strategies. In addition to the concurrent institutional studies the work explores curriculum design that applies the principles of Co-Design, multidisciplinary and trans disciplinary learning, with both engineering and product design students working alongside each other through a practical problem solving learning approach known as the CDIO learning initiative (Conceive, Design Implement and Operate) [1]. The study builds on previous work presented at the 2010 EPDE conference: The Effect of Personality on the Design Team: Lessons from Industry for Design Education [2]. The subsequent work presented in this paper applies the findings to mixed design and engineering team based learning, building on the insight gained through a number of industrial process case studies carried out in current design practice. Developments in delivery also aligning the CDIO principles of learning through doing into a practice based, collaborative learning experience and include elements of the TRIZ creative problem solving technique [3]. The paper will outline case studies involving a number of mixed engineering and design student projects that highlight the CDIO principles, combined with an external industrial design brief. It will compare and contrast the learning experience with that of a KTP derived student project, to examine an industry based model for student projects. In addition key areas of best practice will be presented, and student work from each mode will be discussed at the conference.
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Student engagement is vital in enhancing the student experience and encouraging deeper learning. Involving students in the design of assessment criteria is one way in which to increase student engagement. In 2011, a marking matrix was used at Aston University (UK) for logbook assessment (Group One) in a project-based learning module. The next cohort of students in 2012 (Group Two) were asked to collaboratively redesign the matrix and were given a questionnaire about the exercise. Group Two initially scored a lower average logbook mark than Group One. However, Group Two showed the greatest improvement between assessments, and the quality of, and commitment to, logbooks was noticeably improved. Student input resulted in a more defined, tougher mark scheme. However, this provided an improved feedback system that gave more scope for self-improvement. The majority of students found the exercise incorporated their ideas, enhanced their understanding, and was useful in itself.
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We show both numerically and experimentally that dispersion management can be realized by manipulating the dispersion of a filter in a passively mode-locked fibre laser. A programmable filter the dispersion of which can be software configured is employed in the laser. Solitons, stretched-pulses, and dissipative solitons can be targeted reliably by controlling the filter transmission function only, while the length of fibres is fixed in the laser. This technique shows remarkable advantages in controlling operation regimes in ultrafast fibre lasers, in contrast to the traditional technique in which dispersion management is achieved by optimizing the relative length of fibres with opposite-sign dispersion. Our versatile ultrafast fibre laser will be attractive for applications requiring different pulse profiles such as in optical signal processing and optical communications.
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This paper describes a process to enhance the quality of higher education. At the heart of the process is a cross-sparring collaborative model, whereby institutions are critical friends. This is based on a prior self-evaluation, where the institution / programme identifies quality criteria it wants to improve. Part of the process is to ensure the documentation of best practices so that they can be shared with others in a so called market place. Linking the best practices to a criterion makes them searchable on a large scale. Optimal pairings of institutions can then take place for the cross-sparring activities.
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Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification. However, the effectiveness of removing stopwords in the context of Twitter sentiment classification has been debated in the last few years. In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods. To this end, we apply six different stopword identification methods to Twitter data from six different datasets and observe how removing stopwords affects two well-known supervised sentiment classification methods. We assess the impact of removing stopwords by observing fluctuations on the level of data sparsity, the size of the classifier's feature space and its classification performance. Our results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches. On the other hand, the dynamic generation of stopword lists, by removing those infrequent terms appearing only once in the corpus, appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and substantially shrinking the feature space
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Purpose – The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution. Design/methodology/approach – The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments. Findings – Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes. Practical implications – The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept. Originality/value – The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.
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This report presents findings from a research project which investigates NEST schemes, how they operate and the experiences of the native English speaker teachers (NESTs) and local English teachers (LETs) taking part. The paper provides useful recommendations for how NESTs and LETs can be best supported to make the most out of their collaborations.
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Starting from the Schumpeterian producer-driven understanding of innovation, followed by user-generated solutions and understanding of collaborative forms of co-creation, scholars investigated the drivers and the nature of interactions underpinning success in various ways. Innovation literature has gone a long way, where open innovation has attracted researchers to investigate problems like compatibilities of external resources, networks of innovation, or open source collaboration. Openness itself has gained various shades in the different strands of literature. In this paper the author provides with an overview and a draft evaluation of the different models of open innovation, illustrated with some empirical findings from various fields drawn from the literature. She points to the relevance of transaction costs affecting viable forms of (open) innovation strategies of firms, and the importance to define the locus of innovation for further analyses of different firm and interaction level formations.
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The implementation of collaborative planning and teaching models in ten flexibly scheduled elementary and middle school library media centers was studied to determine which factors facilitated the collaborative planning process and to learn what occurs when library media specialists (LMSs) and classroom teachers (CTs) plan together. In this qualitative study, 61 principals, CTs, and LMSs were interviewed on a range of topics including the principal's role, school climate, the value of team planning, the importance of information literacy instruction, and the ideal learning environment. Other data sources were observations, videotapes of planning sessions, and documents. This three-year school reform effort was funded by the Library Power Project to improve library programs, to encourage collaborative planning, and to increase curricular integration of information literacy skills instruction. ^ The findings included a description of typical planning sessions and the identification of several major factors which impacted the success of collaborative planning: the individuals involved, school climate, time for planning, the organization of the school, the facility and collection, and training. Of these factors, the characteristics and actions of the people involved were most critical to the implementation of the innovation. The LMS was the pivotal player and, in the views of CTs, principals, and LMSs themselves, must be knowledgeable about curriculum, the library collection, and instructional design and delivery; must be open and welcoming to CTs and use good interpersonal skills; and must be committed to information literacy instruction and willing to act as a change agent. The support of the principal was vital; in schools with successful programs, the principal served as an advocate for collaborative planning and information literacy instruction, provided financial support for the library program including clerical staff, and arranged for LMSs and CTs to have time during the school day to plan together. ^ CTs involved in positive planning partnerships with LMSs were flexible, were open to change, used a variety of instructional materials, expected students to be actively involved in their own learning, and were willing to team teach with LMSs. Most CTs planning with LMSs made lesson plans in advance and preferred to plan with others. Also, most CTs in this study planned with grade level or departmental groups, which expedited the delivery of information literacy instruction and the effective use of planning time. ^ Implications of the findings of this research project were discussed for individual schools, for school districts, and for colleges and universities training LMSs, CTs, and administrators. Suggestions for additional research were also included. ^
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The objective of this study was to investigate the relationship of organizational culture and organizational climate on participant perceptions of collaborative capacity for planning, within the context of the Florida School Readiness Coalitions (FSRCs). Three hypotheses were proposed for study: First, that organizational culture would be correlated to organizational climate; second, that organizational culture would be correlated to collaborative capacity for planning; and the third that organizational climate would be correlated to collaborative capacity for planning. ^ A cross-sectional survey research design was used to obtain data from participants in 25 Florida School Readiness Coalitions. Pearson product-moment correlations were used to examine the association between the dependent variable, collaborative capacity for planning, and the independent variables, organizational culture and climate. Bivariate analyses revealed a significant level of association for five culture indicators to collaborative capacity for planning: motivation, interpersonal, service, supportive and individualistic indicators, and four climate indicators: cooperation, job satisfaction, organizational commitment, and role clarity. Findings suggest (a) a constructive culture and positive climate were present within the FSRCs during the period of study and (b) participants perceived that the collaborative capacity for planning existed. Hierarchical multiple regression, controlling for effects of participant demographics, were used to examine the degree to which organizational culture and climate predict collaborative capacity. The culture indicators, supportive and individualistic, and the climate indicator job satisfaction accounted for 46% of the variance in collaborative capacity for planning. No other indicators of the independent variables demonstrated significance. The findings suggests that (a) culture and climate should be studied together, (b) culture and climate are two constructs that may provide knowledge about the way community groups work together, and (c) the collaborative capacity of groups planning services such as the FSRCs may benefit through consideration of how culture and climate affect service planners' relationships, communication, and ability to achieve a mission or goal. Culture and climate may offer social workers new information about internal factors affecting the collaborative process. Further investigation of these constructs with other types of groups is warranted. ^
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We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.