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


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When teaching students with visual impairments educators generally rely on tactile tools to depict visual mathematical topics. Tactile media, such as embossed paper and simple manipulable materials, are typically used to convey graphical information. Although these tools are easy to use and relatively inexpensive, they are solely tactile and are not modifiable. Dynamic and interactive technologies such as pin matrices and haptic pens are also commercially available, but tend to be more expensive and less intuitive. This study aims to bridge the gap between easy-to-use tactile tools and dynamic, interactive technologies in order to facilitate the haptic learning of mathematical concepts. We developed an haptic assistive device using a Tanvas electrostatic touchscreen that provides the user with multimodal (haptic, auditory, and visual) output. Three methodological steps comprise this research: 1) a systematic literature review of the state of the art in the design and testing of tactile and haptic assistive devices, 2) a user-centered system design, and 3) testing of the system’s effectiveness via a usability study. The electrostatic touchscreen exhibits promise as an assistive device for displaying visual mathematical elements via the haptic modality.

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Authentication plays an important role in how we interact with computers, mobile devices, the web, etc. The idea of authentication is to uniquely identify a user before granting access to system privileges. For example, in recent years more corporate information and applications have been accessible via the Internet and Intranet. Many employees are working from remote locations and need access to secure corporate files. During this time, it is possible for malicious or unauthorized users to gain access to the system. For this reason, it is logical to have some mechanism in place to detect whether the logged-in user is the same user in control of the user's session. Therefore, highly secure authentication methods must be used. We posit that each of us is unique in our use of computer systems. It is this uniqueness that is leveraged to "continuously authenticate users" while they use web software. To monitor user behavior, n-gram models are used to capture user interactions with web-based software. This statistical language model essentially captures sequences and sub-sequences of user actions, their orderings, and temporal relationships that make them unique by providing a model of how each user typically behaves. Users are then continuously monitored during software operations. Large deviations from "normal behavior" can possibly indicate malicious or unintended behavior. This approach is implemented in a system called Intruder Detector (ID) that models user actions as embodied in web logs generated in response to a user's actions. User identification through web logs is cost-effective and non-intrusive. We perform experiments on a large fielded system with web logs of approximately 4000 users. For these experiments, we use two classification techniques; binary and multi-class classification. We evaluate model-specific differences of user behavior based on coarse-grain (i.e., role) and fine-grain (i.e., individual) analysis. A specific set of metrics are used to provide valuable insight into how each model performs. Intruder Detector achieves accurate results when identifying legitimate users and user types. This tool is also able to detect outliers in role-based user behavior with optimal performance. In addition to web applications, this continuous monitoring technique can be used with other user-based systems such as mobile devices and the analysis of network traffic.

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At present, in large precast concrete enterprises, the management over precast concrete component has been chaotic. Most enterprises take labor-intensive manual input method, which is time consuming and laborious, and error-prone. Some other slightly better enterprises choose to manage through bar-code or printing serial number manually. However, on one hand, this is also labor-intensive, on the other hand, this method is limited by external environment, making the serial number blur or even lost, and also causes a big problem on production traceability and quality accountability. Therefore, to realize the enterprise’s own rapid development and cater to the needs of the time, to achieve the automated production management has been a big problem for a modern enterprise. In order to solve the problem, inefficiency in production and traceability of the products, this thesis try to introduce RFID technology into the production of PHC tubular pile. By designing a production management system of precast concrete components, the enterprise will achieve the control of the entire production process, and realize the informatization of enterprise production management. RFID technology has been widely used in many fields like entrance control, charge management, logistics and so on. RFID technology will adopt passive RFID tag, which is waterproof, shockproof, anti-interference, so it’s suitable for the actual working environment. The tag will be bound to the precast component steel cage (the structure of the PHC tubular pile before the concrete placement), which means each PHC tubular pile will have a unique ID number. Then according to the production procedure, the precast component will be performed with a series of actions, put the steel cage into the mold, mold clamping, pouring concrete (feed), stretching, centrifugalizing, maintenance, mold removing, welding splice. In every session of the procedure, the information of the precast components can be read through a RFID reader. Using a portable smart device connected to the database, the user can check, inquire and management the production information conveniently. Also, the system can trace the production parameter and the person in charge, realize the traceability of the information. This system can overcome the disadvantages in precast components manufacturers, like inefficiency, error-prone, time consuming, labor intensity, low information relevance and so on. This system can help to improve the production management efficiency, and can produce a good economic and social benefits, so, this system has a certain practical value.

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Abstract. We combine Artificial Immune Systems (AIS) technology with Collaborative Filtering (CF) and use it to build a movie recommendation system. We already know that Artificial Immune Systems work well as movie recommenders from previous work by Cayzer and Aickelin ([3], [4], [5]). Here our aim is to investigate the effect of different affinity measure algorithms for the AIS. Two different affinity measures, Kendall's Tau and Weighted Kappa, are used to calculate the correlation coefficients for the movie recommender. We compare the results with those published previously and show that that Weighted Kappa is more suitable than others for movie problems. We also show that AIS are generally robust movie recommenders and that, as long as a suitable affinity measure is chosen, results are good.

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Sound is potentially an effective way of analysing data and it is possible to simultaneously interpret layers of sounds and identify changes. Multiple attempts to use sound with scientific data have been made, with varying levels of success. On many occasions this was done without including the end user during the development. In this study a sonified model of the 8 planets of our solar system was built and tested using an end user approach. The sonification was created for the Esplora Planetarium, which is currently being constructed in Malta. The data requirements were gathered from a member of the planetarium staff, and 12 end users, as well as the planetarium representative tested the sonification. The results suggest that listeners were able to discern various planetary characteristics without requiring any additional information. Three out of eight sound design parameters did not represent characteristics successfully. These issues have been identified and further development will be conducted in order to improve the model.

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Somatic embryogenesis has been shown to be an efficient in vitro plant regeneration system for many crops such as the important ornamental plant Cyclamen persicum, for which this regeneration pathway of somatic embryogenesis is of interest for the vegetative propagation of parental lines as well as elite plants. However, somatic embryogenesis is not commercially used in many crops due to several unsolved problems, such as malformations, asynchronous development, deficiencies in maturation and germination of somatic embryos. In contrast, zygotic embryos in seeds develop and germinate without abnormalities in most cases. Instead of time-consuming and labor-intensive experiments involving tests of different in vitro culture conditions and plant growth regulator supplements, we follow a more directed approach. Zygotic embryos served as a reference and were compared to somatic embryos in metabolomic analyses allowing the future optimization of the in vitro system. The aims of this study were to detect differences in the metabolite profiles of torpedo stage somatic and zygotic embryos of C. persicum. Moreover, major metabolites in endosperm and testa were identified and quantified. Two sets of extracts of two to four biological replicates each were analyzed. In total 52 metabolites were identified and quantified in the different tissues. One of the most significant differences between somatic and zygotic embryos was that the proline concentration in the zygotic embryos was about 40 times higher than that found in somatic embryos. Epicatechin, a scavenger for reactive oxygen species, was found in highest abundance in the testa. Sucrose, the most abundant metabolite was detected in significantly higher concentrations in zygotic embryos. Also, a yet unknown trisaccharide, was significantly enriched in zygotic embryos.

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The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify unknown motifs which repeat within time series data. The power of the algorithm is derived from its use of a small number of parameters with minimal assumptions. The algorithm searches from a completely neutral perspective that is independent of the data being analysed and the underlying motifs. In this paper the motif tracking algorithm is applied to the search for patterns within sequences of low level system calls between the Linux kernel and the operating system’s user space. The MTA is able to compress data found in large system call data sets to a limited number of motifs which summarise that data. The motifs provide a resource from which a profile of executed processes can be built. The potential for these profiles and new implications for security research are highlighted. A higher level system call language for measuring similarity between patterns of such calls is also suggested.

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Research on the criminological side of system trespassing (i.e. unlawfully gaining access to a computer system) is relatively rare and has yet to examine the effect of the presence of other users on the system during the trespassing event (i.e. the time of communication between a trespasser’s system and the infiltrated system). This thesis seeks to analyze this relationship drawing on principles of Situational Crime Prevention, Routine Activities Theory, and restrictive deterrence. Data were collected from a randomized control trial of target computers deployed on the Internet network of a large U.S. university. This study examined whether the number (one or multiple) and type (administrative or non-administrative) of computer users present on a system reduced the seriousness and frequency of trespassing. Results indicated that the type of user (administrative) produced a restrictive deterrent effect and significantly reduced the frequency and duration of trespassing events.

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The air-sea flux of greenhouse gases (e.g. carbon dioxide, CO2) is a critical part of the climate system and a major factor in the biogeochemical development of the oceans. More accurate and higher resolution calculations of these gas fluxes are required if we are to fully understand and predict our future climate. Satellite Earth observation is able to provide large spatial scale datasets that can be used to study gas fluxes. However, the large storage requirements needed to host such data can restrict its use by the scientific community. Fortunately, the development of cloud-computing can provide a solution. Here we describe an open source air-sea CO2 flux processing toolbox called the ‘FluxEngine’, designed for use on a cloud-computing infrastructure. The toolbox allows users to easily generate global and regional air-sea CO2 flux data from model, in situ and Earth observation data, and its air-sea gas flux calculation is user configurable. Its current installation on the Nephalae cloud allows users to easily exploit more than 8 terabytes of climate-quality Earth observation data for the derivation of gas fluxes. The resultant NetCDF data output files contain >20 data layers containing the various stages of the flux calculation along with process indicator layers to aid interpretation of the data. This paper describes the toolbox design, the verification of the air-sea CO2 flux calculations, demonstrates the use of the tools for studying global and shelf-sea air-sea fluxes and describes future developments.

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Abstract. We combine Artificial Immune Systems (AIS) technology with Collaborative Filtering (CF) and use it to build a movie recommendation system. We already know that Artificial Immune Systems work well as movie recommenders from previous work by Cayzer and Aickelin ([3], [4], [5]). Here our aim is to investigate the effect of different affinity measure algorithms for the AIS. Two different affinity measures, Kendall's Tau and Weighted Kappa, are used to calculate the correlation coefficients for the movie recommender. We compare the results with those published previously and show that that Weighted Kappa is more suitable than others for movie problems. We also show that AIS are generally robust movie recommenders and that, as long as a suitable affinity measure is chosen, results are good.

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The international Argo program, consisting of a global array of more than 3000 free-drifting profiling floats, has now been monitoring the upper 2000 meters of the ocean for several years. One of its main proposed evolutions is to be able to reach the deeper ocean in order to better observe and understand the key role of the deep ocean in the climate system. For this purpose, Ifremer has designed the new “Deep-Arvor” profiling float: it extends the current operational depth down to 4000 meters, and measures temperature and salinity for up to 150 cycles with CTD pumping continuously and 200 cycles in spot sampling mode. High resolution profiles (up to 2000 points) can be transmitted and data are delivered in near real time according to Argo requirements. Deep-Arvor can be deployed everywhere at sea without any pre-ballasting operation and its light weight (~ 26kg) makes its launching easy. Its design was done to target a cost effective solution. Predefined spots have been allocated to add an optional oxygen sensor and a connector for an extra sensor. Extensive laboratory tests were successful. The results of the first at sea experiments showed that the expected performances of the operational prototypes had been reached (i.e. to perform up to 150 cycles). Meanwhile, the industrialization phase was completed in order to manufacture the Deep-Arvor float for the pilot experiment in 2015. In this paper, we detail all the steps of the development work and present the results from the at sea experiments.

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Several companies are trying to improve their operation efficiency by implementing an enterprise resource planning (ERP) system that makes it possible to control the resources of the company in real time. However, the success of the implementation project is not a foregone conclusion; a significant part of these projects end in a failure, one way or another. Therefore it is important to investigate ERP system implementation more closely in order to increase understanding about factors influencing ERP system success and to improve the probability of a successful ERP implementation project. Consequently, this study was initiated because a manufacturing case company wanted to review the success of their ERP implementation project. To be exact, the case company hoped to gain both information about the success of the project and insight for future implementation improvement. This study investigated ERP success specifically by examining factors that influence ERP key-user satisfaction. User satisfaction is one of the most commonly applied indicators of information system success. The research data was mainly collected by conducting theme interviews. The subjects of the interviews were six key-users of the newly implemented ERP system. The interviewees were closely involved in the implementation project. Furthermore, they act as representative users that utilize the new system in everyday business processes. The collected data was analyzed by thematizing. Both data collection and analysis were guided by a theoretical frame of reference. This frame was based on previous research on the subject. The results of the study aligned with the theoretical framework to large extent. The four principal factors influencing key-user satisfaction were change management, contractor service, key-user’s system knowledge and characteristics of the ERP product itself. One of the most significant contributions of the research is that it confirmed the existence of a connection between change management and ERP key-user satisfaction. Furthermore, it discovered two new sub-factors influencing contractor service related key-user satisfaction. In addition, the research findings indicated that in order to improve the current level of key-user satisfaction, the case company should pay special attention to system functionality improvement and enhancement of the key-users’ knowledge. During similar implementation projects in the future, it would be important to assure the success of change management and contractor service related processes.

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El modelo de investigación y pronóstico climático (WRF) es un sistema completamente funcional de modelado que permite realizar investigación atmosférica y predicción meteorológica. WRF fue desarrollado con énfasis en la eficiencia, portabilidad, facilidad de mantenimiento, escalabilidad y productividad, lo que ha permitido que sea implementado con éxito en una amplia variedad de equipos HPC. Por esta razón, el tamaño de los problemas a los que WRF da soporte ha incrementado, por lo que el entendimiento de la dependencia del WRF con los diversos elementos de clúster, como la CPU, interconexiones y librerías, son cruciales para permitir predicciones eficientes y de alta productividad. En este contexto, el presente manuscrito estudia la escalabilidad de WRF en un equipo HPC, tomando en consideración tres parámetros: número de CPUs y nodos, comunicaciones y librerías. Para esto, dos benchmarks son llevados a cabo sobre un clúster de alto rendimiento dotado de una red GigaEthernet, los cuales permiten establecer la relación entre escalabilidad y los tres parámetros estudiados, y particularmente demuestran la sensibilidad del WRF a la comunicación inter-nodo. Dicho factor es esencial para mantener la escalabilidad y el aumento de la productividad al añadir nodos en el clúster.

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Part 20: Health and Care Networks

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The concept of human capital is associated mainly with the Nobel Laureate Gary Becker and, in his usage, has a clear conceptual basis as investment in the costs of formal education. By contrast, this paper suggests that ‘intellectual capital’ is a re-branding of knowledge, skills and experience rather than re-conceptualisation of resource based learning. Becker also chose not to include informal knowledge, skills or experience within his concept of human capital, which remains limited by its constrained premises. This paper submits that both human capital and intellectual capital advocates fail to identify or measure the tacit knowledge and implicit learning which increasingly is recognised as a key to the competitive advantage of organisations. It first focuses on the conceptual basis of claims made for human capital and intellectual capital, outlines limits in their methodology, and contrasts these with insights from theories of tacit knowledge and implicit learning and the central role within them of informal or non-formal skill acquisition. It develops and illustrates instances of interfacing tacit and explicit knowledge before introducing a methodology for profiling the acquisition of knowledge, ability and skills. It does so by introducing the concepts of non-formal learningfrom- work (LfW) and informal learning-from-life (LfL), with evidence from a four country EU case study commissioned within the lifelong learning remit of the Lisbon Agenda.