70,749 research outputs found

    Semantic user profiling techniques for personalised multimedia recommendation

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    Due to the explosion of news materials available through broadcast and other channels, there is an increasing need for personalised news video retrieval. In this work, we introduce a semantic-based user modelling technique to capture users’ evolving information needs. Our approach exploits implicit user interaction to capture long-term user interests in a profile. The organised interests are used to retrieve and recommend news stories to the users. In this paper, we exploit the Linked Open Data Cloud to identify similar news stories that match the users’ interest. We evaluate various recommendation parameters by introducing a simulation-based evaluation scheme

    Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.

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    This report gives an overview of the most relevant organisational and\ud behavioural aspects regarding user profiling. It discusses not only the\ud most important aims of user profiling from both an organisation’s as\ud well as a user’s perspective, it will also discuss organisational motives\ud and barriers for user profiling and the most important conditions for\ud the success of user profiling. Finally recommendations are made and\ud suggestions for further research are given

    Getting Real: Time to Re-Invest in the Public Employment Service

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    In this paper, NELP advocates for renewed focus on our nation's public reemployment services. First, we recommend significantly increasing the amount of federal funding for the Employment Service that is a part of the nationwide system of One-Stop Career Centers. Doing so would provide more workers with improved job placement services, in-person job search assistance, and pre-training counseling. Second, we recommend placing programmatic priority on unemployment insurance recipients who are most likely to have trouble finding a new job

    Triumph of hope over experience: learning from interventions to reduce avoidable hospital admissions identified through an Academic Health and Social Care Network.

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    BACKGROUND: Internationally health services are facing increasing demands due to new and more expensive health technologies and treatments, coupled with the needs of an ageing population. Reducing avoidable use of expensive secondary care services, especially high cost admissions where no procedure is carried out, has become a focus for the commissioners of healthcare. METHOD: We set out to identify, evaluate and share learning about interventions to reduce avoidable hospital admission across a regional Academic Health and Social Care Network (AHSN). We conducted a service evaluation identifying initiatives that had taken place across the AHSN. This comprised a literature review, case studies, and two workshops. RESULTS: We identified three types of intervention: pre-hospital; within the emergency department (ED); and post-admission evaluation of appropriateness. Pre-hospital interventions included the use of predictive modelling tools (PARR - Patients at risk of readmission and ACG - Adjusted Clinical Groups) sometimes supported by community matrons or virtual wards. GP-advisers and outreach nurses were employed within the ED. The principal post-hoc interventions were the audit of records in primary care or the application of the Appropriateness Evaluation Protocol (AEP) within the admission ward. Overall there was a shortage of independent evaluation and limited evidence that each intervention had an impact on rates of admission. CONCLUSIONS: Despite the frequency and cost of emergency admission there has been little independent evaluation of interventions to reduce avoidable admission. Commissioners of healthcare should consider interventions at all stages of the admission pathway, including regular audit, to ensure admission thresholds don't change

    Squaring the circle: a new alternative to alternative-assessment

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    Many quality assurance systems rely on high-stakes assessment for course certification. Such methods are not as objective as they might appear; they can have detrimental effects on student motivation and may lack relevance to the needs of degree courses increasingly oriented to vocational utility. Alternative assessment methods can show greater formative and motivational value for students but are not well suited to the demands of course certification. The widespread use of virtual learning environments and electronic portfolios generates substantial learner activity data to enable new ways of monitoring and assessing students through Learning Analytics. These emerging practices have the potential to square the circle by generating objective, summative reports for course certification while at the same time providing formative assessment to personalise the student experience. This paper introduces conceptual models of assessment to explore how traditional reliance on numbers and grades might be displaced by new forms of evidence-intensive student profiling and engagement

    Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation

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    The growing expanse of e-commerce and the widespread availability of online databases raise many fears regarding loss of privacy and many statistical challenges. Even with encryption and other nominal forms of protection for individual databases, we still need to protect against the violation of privacy through linkages across multiple databases. These issues parallel those that have arisen and received some attention in the context of homeland security. Following the events of September 11, 2001, there has been heightened attention in the United States and elsewhere to the use of multiple government and private databases for the identification of possible perpetrators of future attacks, as well as an unprecedented expansion of federal government data mining activities, many involving databases containing personal information. We present an overview of some proposals that have surfaced for the search of multiple databases which supposedly do not compromise possible pledges of confidentiality to the individuals whose data are included. We also explore their link to the related literature on privacy-preserving data mining. In particular, we focus on the matching problem across databases and the concept of ``selective revelation'' and their confidentiality implications.Comment: Published at http://dx.doi.org/10.1214/088342306000000240 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

    The Governor’s Task Force on African-Americans in Prison, 2001

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    Report produced by the Department of Correction
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