900 resultados para recommender system, user profiling, personalization, implicit feedbacks


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Shipping list no.: 90-577-P.

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Dynamic binary translation is the process of translating, modifying and rewriting executable (binary) code from one machine to another at run-time. This process of low-level re-engineering consists of a reverse engineering phase followed by a forward engineering phase. UQDBT, the University of Queensland Dynamic Binary Translator, is a machine-adaptable translator. Adaptability is provided through the specification of properties of machines and their instruction sets, allowing the support of different pairs of source and target machines. Most binary translators are closely bound to a pair of machines, making analyses and code hard to reuse. Like most virtual machines, UQDBT performs generic optimizations that apply to a variety of machines. Frequently executed code is translated to native code by the use of edge weight instrumentation, which makes UQDBT converge more quickly than systems based on instruction speculation. In this paper, we describe the architecture and run-time feedback optimizations performed by the UQDBT system, and provide results obtained in the x86 and SPARC® platforms.

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The data available during the drug discovery process is vast in amount and diverse in nature. To gain useful information from such data, an effective visualisation tool is required. To provide better visualisation facilities to the domain experts (screening scientist, biologist, chemist, etc.),we developed a software which is based on recently developed principled visualisation algorithms such as Generative Topographic Mapping (GTM) and Hierarchical Generative Topographic Mapping (HGTM). The software also supports conventional visualisation techniques such as Principal Component Analysis, NeuroScale, PhiVis, and Locally Linear Embedding (LLE). The software also provides global and local regression facilities . It supports regression algorithms such as Multilayer Perceptron (MLP), Radial Basis Functions network (RBF), Generalised Linear Models (GLM), Mixture of Experts (MoE), and newly developed Guided Mixture of Experts (GME). This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install & use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

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Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. miniDVMS v1.8 provides a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualisation domain. The advantage of this interface is that the user is directly involved in the data mining process. Principled projection methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), are integrated with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, and user interaction facilities, to provide this integrated visual data mining framework. The software also supports conventional visualisation techniques such as principal component analysis (PCA), Neuroscale, and PhiVis. This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install and use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

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We describe the results of in-vivo trials of a portable fiber Bragg grating based temperature profile monitoring system. The probe incorporates five Bragg gratings along a single fiber and prevents the gratings from being strained. Illumination is provided by a superluminescent diode, and a miniature CCD based spectrometer is used for demultiplexing. The CCD signal is read into a portable computer through a small A/D interface; the computer then calculates the positions of the center wavelengths of the Bragg gratings, providing a resolution of 0.2°C. Tests were carried out on rabbits undergoing hyperthermia treatment of the kidney and liver via inductive heating of metallic implants and comparison was made with a commercial Fluoroptic thermometry system.

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We describe the results of in-vivo trials of a portable fiber Bragg grating based temperature profile monitoring system. The probe incorporates five Bragg gratings along a single fiber and prevents the gratings from being strained. Illumination is provided by a superluminescent diode, and a miniature CCD based spectrometer is used for demultiplexing. The CCD signal is read into a portable computer through a small A/D interface; the computer then calculates the positions of the center wavelengths of the Bragg gratings, providing a resolution of 0.2 °C. Tests were carried out on rabbits undergoing hyperthermia treatment of the kidney and liver via inductive heating of metallic implants and comparison was made with a commercial Fluoroptic thermometry system.

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In recent years Web has become mainstream medium for communication and information dissemination. This paper presents approaches and methods for adaptive learning implementation, which are used in some contemporary web-interfaced Learning Management Systems (LMSs). The problem is not how to create electronic learning materials, but how to locate and utilize the available information in personalized way. Different attitudes to personalization are briefly described in section 1. The real personalization requires a user profile containing information about preferences, aims, and educational history to be stored and used by the system. These issues are considered in section 2. A method for development and design of adaptive learning content in terms of learning strategy system support is represented in section 3. Section 4 includes a set of innovative personalization services that are suggested by several very important research projects (SeLeNe project, ELENA project, etc.) dated from the last few years. This section also describes a model for role- and competency-based learning customization that uses Web Services approach. The last part presents how personalization techniques are implemented in Learning Grid-driven applications.

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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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The performance of a compact, wearable Conformal Strongly Coupled Magnetic Resonance (CSCMR) system is studied when the antenna is in the air and is worn on a user’s arm. The wireless powering system consists of the receiver and load elements designed on a printed circuit board that is attached to a polyester fabric band. The wearable antenna achieves high efficiency, has a small volume, and can be easily printed on substrates. Although the user effect on mobile terminal antennas has been studied in detail, absorption losses in wearable antennas have not been widely investigated. Our results show that efficiency of the antenna in free space is 70% and on a user’s arm is 50%. Human tissue in the close proximity of our wearable Conformal SCMR caused a decrease in radiated efficiency and total efficiency. This undesired degradation in antenna efficiency might be attributed to body loss and absorption losses. Our findings can be used as a reference for future studies on wearable devices and their applications, such as health and sports monitoring.

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Funded by United States-Israel Binational Science Foundation (BSF), Jerusalem, Israel Israel Science Foundation (ISF). Grant Number: 1349 Israel Science Foundation Israel Strategic Alternative Energy Foundation (I-SAEF) BBSRC. Grant Number: BB/L009951/1 Scottish Government Food, Land and People program Society for Applied Microbiology

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Funded by United States-Israel Binational Science Foundation (BSF), Jerusalem, Israel Israel Science Foundation (ISF). Grant Number: 1349 Israel Science Foundation Israel Strategic Alternative Energy Foundation (I-SAEF) BBSRC. Grant Number: BB/L009951/1 Scottish Government Food, Land and People program Society for Applied Microbiology

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In order to address the increasing compromise of user privacy on mobile devices, a Fuzzy Logic based implicit authentication scheme is proposed in this paper. The proposed scheme computes an aggregate score based on selected features and a threshold in real-time based on current and historic data depicting user routine. The tuned fuzzy system is then applied to the aggregated score and the threshold to determine the trust level of the current user. The proposed fuzzy-integrated implicit authentication scheme is designed to: operate adaptively and completely in the background, require minimal training period, enable high system accuracy while provide timely detection of abnormal activity. In this paper, we explore Fuzzy Logic based authentication in depth. Gaussian and triangle-based membership functions are investigated and compared using real data over several weeks from different Android phone users. The presented results show that our proposed Fuzzy Logic approach is a highly effective, and viable scheme for lightweight real-time implicit authentication on mobile devices.