956 resultados para Applied identity-based encryption


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We empirically applied the GrooFiWorld agent-based model (Puga-González et al. 2009) in a group of captive mangabeys (Cercocebus torquatus). We analysed several measurements related to aggression and affiliative patterns. The group adopted a combination of despotic and egalitarian behaviours resulting from the behavioural flexibility observed in the Cercopithecinae subfamily. Our study also demonstrates that the GrooFiWorld agent-based model can be extended to other members of the Cercopithecinae subfamily generating parsimonious hypotheses related to the social organization.

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Objective To construct a Portuguese language index of information on the practice of diagnostic radiology in order to improve the standardization of the medical language and terminology. Materials and Methods A total of 61,461 definitive reports were collected from the database of the Radiology Information System at Hospital das Clínicas – Faculdade de Medicina de Ribeirão Preto (RIS/HCFMRP) as follows: 30,000 chest x-ray reports; 27,000 mammography reports; and 4,461 thyroid ultrasonography reports. The text mining technique was applied for the selection of terms, and the ANSI/NISO Z39.19-2005 standard was utilized to construct the index based on a thesaurus structure. The system was created in *html. Results The text mining resulted in a set of 358,236 (n = 100%) words. Out of this total, 76,347 (n = 21%) terms were selected to form the index. Such terms refer to anatomical pathology description, imaging techniques, equipment, type of study and some other composite terms. The index system was developed with 78,538 *html web pages. Conclusion The utilization of text mining on a radiological reports database has allowed the construction of a lexical system in Portuguese language consistent with the clinical practice in Radiology.

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Forensic intelligence has recently gathered increasing attention as a potential expansion of forensic science that may contribute in a wider policing and security context. Whilst the new avenue is certainly promising, relatively few attempts to incorporate models, methods and techniques into practical projects are reported. This work reports a practical application of a generalised and transversal framework for developing forensic intelligence processes referred to here as the Transversal model adapted from previous work. Visual features present in the images of four datasets of false identity documents were systematically profiled and compared using image processing for the detection of a series of modus operandi (M.O.) actions. The nature of these series and their relation to the notion of common source was evaluated with respect to alternative known information and inferences drawn regarding respective crime systems. 439 documents seized by police and border guard authorities across 10 jurisdictions in Switzerland with known and unknown source level links formed the datasets for this study. Training sets were developed based on both known source level data, and visually supported relationships. Performance was evaluated through the use of intra-variability and inter-variability scores drawn from over 48,000 comparisons. The optimised method exhibited significant sensitivity combined with strong specificity and demonstrates its ability to support forensic intelligence efforts.

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BACKGROUND: Underweight and severe and morbid obesity are associated with highly elevated risks of adverse health outcomes. We estimated trends in mean body-mass index (BMI), which characterises its population distribution, and in the prevalences of a complete set of BMI categories for adults in all countries. METHODS: We analysed, with use of a consistent protocol, population-based studies that had measured height and weight in adults aged 18 years and older. We applied a Bayesian hierarchical model to these data to estimate trends from 1975 to 2014 in mean BMI and in the prevalences of BMI categories (<18·5 kg/m(2) [underweight], 18·5 kg/m(2) to <20 kg/m(2), 20 kg/m(2) to <25 kg/m(2), 25 kg/m(2) to <30 kg/m(2), 30 kg/m(2) to <35 kg/m(2), 35 kg/m(2) to <40 kg/m(2), ≥40 kg/m(2) [morbid obesity]), by sex in 200 countries and territories, organised in 21 regions. We calculated the posterior probability of meeting the target of halting by 2025 the rise in obesity at its 2010 levels, if post-2000 trends continue. FINDINGS: We used 1698 population-based data sources, with more than 19·2 million adult participants (9·9 million men and 9·3 million women) in 186 of 200 countries for which estimates were made. Global age-standardised mean BMI increased from 21·7 kg/m(2) (95% credible interval 21·3-22·1) in 1975 to 24·2 kg/m(2) (24·0-24·4) in 2014 in men, and from 22·1 kg/m(2) (21·7-22·5) in 1975 to 24·4 kg/m(2) (24·2-24·6) in 2014 in women. Regional mean BMIs in 2014 for men ranged from 21·4 kg/m(2) in central Africa and south Asia to 29·2 kg/m(2) (28·6-29·8) in Polynesia and Micronesia; for women the range was from 21·8 kg/m(2) (21·4-22·3) in south Asia to 32·2 kg/m(2) (31·5-32·8) in Polynesia and Micronesia. Over these four decades, age-standardised global prevalence of underweight decreased from 13·8% (10·5-17·4) to 8·8% (7·4-10·3) in men and from 14·6% (11·6-17·9) to 9·7% (8·3-11·1) in women. South Asia had the highest prevalence of underweight in 2014, 23·4% (17·8-29·2) in men and 24·0% (18·9-29·3) in women. Age-standardised prevalence of obesity increased from 3·2% (2·4-4·1) in 1975 to 10·8% (9·7-12·0) in 2014 in men, and from 6·4% (5·1-7·8) to 14·9% (13·6-16·1) in women. 2·3% (2·0-2·7) of the world's men and 5·0% (4·4-5·6) of women were severely obese (ie, have BMI ≥35 kg/m(2)). Globally, prevalence of morbid obesity was 0·64% (0·46-0·86) in men and 1·6% (1·3-1·9) in women. INTERPRETATION: If post-2000 trends continue, the probability of meeting the global obesity target is virtually zero. Rather, if these trends continue, by 2025, global obesity prevalence will reach 18% in men and surpass 21% in women; severe obesity will surpass 6% in men and 9% in women. Nonetheless, underweight remains prevalent in the world's poorest regions, especially in south Asia. FUNDING: Wellcome Trust, Grand Challenges Canada.

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PURPOSE: Advanced Practice Lung Cancer Nurses (APLCN) are well-established in several countries but their role has yet to be established in Switzerland. Developing an innovative nursing role requires a structured approach to guide successful implementation and to meet the overarching goal of improved nursing sensitive patient outcomes. The "Participatory, Evidence-based, Patient-focused process, for guiding the development, implementation, and evaluation of advanced practice nursing" (PEPPA framework) is one approach that was developed in the context of the Canadian health system. The purpose of this article is to describe the development of an APLCN model at a Swiss Academic Medical Center as part of a specialized Thoracic Cancer Center and to evaluate the applicability of PEPPA framework in this process. METHOD: In order to develop and implement the APLCN role, we applied the first seven phases of the PEPPA framework. RESULTS: This article spreads the applicability of the PEPPA framework for an APLCN development. This framework allowed us to i) identify key components of an APLCN model responsive to lung cancer patients' health needs, ii) identify role facilitators and barriers, iii) implement the APLCN role and iv) design a feasibility study of this new role. CONCLUSIONS: The PEPPA framework provides a structured process for implementing novel Advanced Practice Nursing roles in a local context, particularly where such roles are in their infancy. Two key points in the process include assessing patients' health needs and involving key stakeholders.

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The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.

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Landslide processes can have direct and indirect consequences affecting human lives and activities. In order to improve landslide risk management procedures, this PhD thesis aims to investigate capabilities of active LiDAR and RaDAR sensors for landslides detection and characterization at regional scales, spatial risk assessment over large areas and slope instabilities monitoring and modelling at site-specific scales. At regional scales, we first demonstrated recent boat-based mobile LiDAR capabilities to model topography of the Normand coastal cliffs. By comparing annual acquisitions, we validated as well our approach to detect surface changes and thus map rock collapses, landslides and toe erosions affecting the shoreline at a county scale. Then, we applied a spaceborne InSAR approach to detect large slope instabilities in Argentina. Based on both phase and amplitude RaDAR signals, we extracted decisive information to detect, characterize and monitor two unknown extremely slow landslides, and to quantify water level variations of an involved close dam reservoir. Finally, advanced investigations on fragmental rockfall risk assessment were conducted along roads of the Val de Bagnes, by improving approaches of the Slope Angle Distribution and the FlowR software. Therefore, both rock-mass-failure susceptibilities and relative frequencies of block propagations were assessed and rockfall hazard and risk maps could be established at the valley scale. At slope-specific scales, in the Swiss Alps, we first integrated ground-based InSAR and terrestrial LiDAR acquisitions to map, monitor and model the Perraire rock slope deformation. By interpreting both methods individually and originally integrated as well, we therefore delimited the rockslide borders, computed volumes and highlighted non-uniform translational displacements along a wedge failure surface. Finally, we studied specific requirements and practical issues experimented on early warning systems of some of the most studied landslides worldwide. As a result, we highlighted valuable key recommendations to design new reliable systems; in addition, we also underlined conceptual issues that must be solved to improve current procedures. To sum up, the diversity of experimented situations brought an extensive experience that revealed the potential and limitations of both methods and highlighted as well the necessity of their complementary and integrated uses.

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We present a polarimetric-based optical encoder for image encryption and verification. A system for generating random polarized vector keys based on a Mach-Zehnder configuration combined with translucent liquid crystal displays in each path of the interferometer is developed. Polarization information of the encrypted signal is retrieved by taking advantage of the information provided by the Stokes parameters. Moreover, photon-counting model is used in the encryption process which provides data sparseness and nonlinear transformation to enhance security. An authorized user with access to the polarization keys and the optical design variables can retrieve and validate the photon-counting plain-text. Optical experimental results demonstrate the feasibility of the encryption method.

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This work investigates performance of recent feature-based matching techniques when applied to registration of underwater images. Matching methods are tested versus different contrast enhancing pre-processing of images. As a result of the performed experiments for various dominating in images underwater artifacts and present deformation, the outperforming preprocessing, detection and description methods are proposed

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En este trabajo se expone e ilustra un modelo teórico para entender las funciones de la identidad, así como los mecanismospsicosociales asociados a su construcción: “Modelo Evolutivo y Funcional de la Identidad Mediada” (MEBIM). La identidad, mediada narrativamente, cumple una función personal orientada a la dirección de la propia vida, así como una función sociocultural vinculada a la búsqueda de reconocimiento de los derechos de los grupos sociales a los que uno se siente apegado. Se ilustran los factores asociados a la construcción de la identidad personal (sí mismos posibles, transiciones vitales, vínculo afectivo) y sociocultural (acción-transformación e identificación simbólica) a partir de 12 historias de vida realizadas con mestizos e indígenasde la Universidad Intercultural de Chiapas (México). Se sugiere que en contextos educativos formales, como la escuela o la Universidad, se deben propiciar narrativas personales y socioculturales con el objetivo de optimizar la identidad en un mundo a la vez globalizado y plural

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The objective of this master’s thesis was to develop a model for mobile subscription acquisition cost, SAC, and mobile subscription retention cost, SRC, by applying activity-based cost accounting principles. The thesis was conducted as a case study for a telecommunication company operating on the Finnish telecommunication market. In addition to activity-based cost accounting there were other theories studied and applied in order to establish a theory framework for this thesis. The concepts of acquisition and retention were explored in a broader context with the concepts of customer satisfaction, loyalty and profitability and eventually customer relationship management to understand the background and meaning of the theme of this thesis. The utilization of SAC and SRC information is discussed through the theories of decision making and activity-based management. Also, the present state and future needs of SAC and SRC information usage at the case company as well as the functions of the company were examined by interviewing some members of the company personnel. With the help of these theories and methods it was aimed at finding out both the theory-based and practical factors which affect the structure of the model. During the thesis study it was confirmed that the existing SAC and SRC model of the case company should be used as the basis in developing the activity-based model. As a result the indirect costs of the old model were transformed into activities and the direct costs were continued to be allocated directly to acquisition of new subscriptions and retention of old subscriptions. The refined model will enable managing the subscription acquisition, retention and the related costs better through the activity information. During the interviews it was found out that the SAC and SRC information is also used in performance measurement and operational and strategic planning. SAC and SRC are not fully absorbed costs and it was concluded that the model serves best as a source of indicative cost information. This thesis does not include calculating costs. Instead, the refined model together with both the theory-based and interview findings concerning the utilization of the information produced by the model will serve as a framework for the possible future development aiming at completing the model.

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Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

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The counteranion exchange of quaternary 1,2,3-triazolium salts was examined using a simple method that permitted halide ions to be swap for a variety of anions using an anion exchange resin (A¯ form). The method was applied to 1,2,3-triazolium-based ionic liquids and the iodideto- anion exchange proceeded in excellent to quantitative yields, concomitantly removing halide impurities. Additionally, an anion exchange resin (N3¯ form) was used to obtain the benzyl azide from benzyl halide under mild reaction. Likewise, following a similar protocol, bis(azidomethyl)arenes were also synthesized in excellent yields. The results of a proton NMR spectroscopic study of simple azolium-based ion pairs are discussed, with attention focused on the significance of the charged-assisted (CH)+···anion hydrogen bonds of simple azolium systems such as 1-butyl-3-methylimidazolium and 1-benzyl-3-methyl-1,2,3-triazolium salts.

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The counteranion exchange of quaternary 1,2,3-triazolium salts was examined using a simple method that permitted halide ions to be swap for a variety of anions using an anion exchange resin (A¯ form). The method was applied to 1,2,3-triazolium-based ionic liquids and the iodideto- anion exchange proceeded in excellent to quantitative yields, concomitantly removing halide impurities. Additionally, an anion exchange resin (N3¯ form) was used to obtain the benzyl azide from benzyl halide under mild reaction. Likewise, following a similar protocol, bis(azidomethyl)arenes were also synthesized in excellent yields. The results of a proton NMR spectroscopic study of simple azolium-based ion pairs are discussed, with attention focused on the significance of the charged-assisted (CH)+···anion hydrogen bonds of simple azolium systems such as 1-butyl-3-methylimidazolium and 1-benzyl-3-methyl-1,2,3-triazolium salts.

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The counteranion exchange of quaternary 1,2,3-triazolium salts was examined using a simple method that permitted halide ions to be swap for a variety of anions using an anion exchange resin (A¯ form). The method was applied to 1,2,3-triazolium-based ionic liquids and the iodideto- anion exchange proceeded in excellent to quantitative yields, concomitantly removing halide impurities. Additionally, an anion exchange resin (N3¯ form) was used to obtain the benzyl azide from benzyl halide under mild reaction. Likewise, following a similar protocol, bis(azidomethyl)arenes were also synthesized in excellent yields. The results of a proton NMR spectroscopic study of simple azolium-based ion pairs are discussed, with attention focused on the significance of the charged-assisted (CH)+···anion hydrogen bonds of simple azolium systems such as 1-butyl-3-methylimidazolium and 1-benzyl-3-methyl-1,2,3-triazolium salts.