Miracle-dz/UniFAR-FTUs huggingface dataset:
A semi-automatic method for ontology mapping

Ontology mapping involves the task of finding similarities among overlapping sources by using ontologies. In a Federated System in which distributed, autonomous and heterogeneous information sources must be integrated, ontologies have emerged as tools to solve semantic heterogeneity problems. In this paper we propose a three-level approach that provides a semi-automatic method to ontology mapping. It performs some tasks automatically and guides the user in performing other tasks for which his/her intervention is required. Finally, a plug-in of the ontology editor, Protege, is presented showing how the method is implemented through a case study.
Object segmentation based on Gaussian mixture model and conditional random fields

The feature representation has significantly profound impact on the segmentation accuracy. This paper proposes a feature extract method for conditional random fields (CRF) to segment image into object and background. It divides the pixel into the different semantic model via Gaussian Mixture Model (GMM), then uses the mean values and covariance of every sub-component in GMM for CRF. The proposed method takes the probability distribution of the image as the feature representation for CRF learning. Then it use the CRF to segment the image into semantic parts with the homogeneous appearance based on the minimum energy, which is computed via data costs, label costs and smooth costs. We do performance evaluation experiments on the MSRC-21 benchmark. Experimental results show the proposed method is competitive to the state-of-the-art method for object segmentation.
An approach to detect crowd panic behavior using flow-based feature

With the purpose of achieving automated detection of crowd abnormal behavior in public, this paper discusses the category of typical crowd and individual behaviors and their patterns. Popular image features for abnormal behavior detection are also introduced, including global flow based features such as optical flow, and local spatio-temporal based features such as Spatio-temporal Volume (STV). After reviewing some relative abnormal behavior detection algorithms, a brand-new approach to detect crowd panic behavior has been proposed based on optical flow features in this paper. During the experiments, all panic behaviors are successfully detected. In the end, the future work to improve current approach has been discussed.
Steps towards model-free execution monitoring on mobile robots

In this paper we present a model-free execution monitor for behavior-based mobile robots. By model-free we mean that the monitoring is based only on the actual execution, without involving any predictive models of the controlled system. Model-free monitors are especially suitable for systems where it is hard to obtain adequate models. In our approach we analyze the activation levels of the diﬀerent behaviors using a pattern recognition technique. Our model-free execution monitor, which is realized by radial basis function networks, is shown to give a high performance in several realistic simulations.
Relative response of patients with myelodysplastic syndromes and other transfusion-dependent anaemias to deferasirox (ICL670): a 1-yr prospective study

Objectives/methods: This 1‐yr prospective phase II trial evaluated the efficacy of deferasirox in regularly transfused patients aged 3–81 yrs with myelodysplastic syndromes (MDS; n = 47), Diamond–Blackfan anaemia (DBA; n = 30), other rare anaemias (n = 22) or β‐thalassaemia (n = 85). Dosage was determined by baseline liver iron concentration (LIC).
Nurse Executives Leading Change to Improve Critical Access Hospital Outcomes: A Literature Review with Research-Informed Recommendations

Background and Purpose: Nurses have been called to lead the transformation of health care to provide more efficient, safe, high quality care. However, little is known about how to prepare and enable critical access hospital (CAH) nurse executives to lead change. Research indicates that Magnet ® -designated hospitals have significantly better patient and organizational outcomes as compared to non-Magnet hospitals. The purpose of this study was to synthesize challenges faced by CAH nurse executives and provide research-informed recommendations for leading change to achieve Magnet standards in CAHs. Sample and Methods: A review of the literature was conducted to understand the historical development of CAHs and to identify challenges faced by CAH nurse executives. CINAHL, PubMed, and the Rural Health Information Hub databases were searched for relevant peer-reviewed studies and expert commentary published in English from 2007 to 2016. Thirty-four articles were synthesized. Findings: CAH nurse executives face significant challenges to ensuring their hospitals are providing high quality care including: (a) recruitment, retention, and appropriate staffing ratios; (b) the need for nursing staff with multispecialist knowledge; (c) fewer baccalaureate-prepared nurses; and (d) lack of financial and human resources to support new graduate nurse transition, continuing education, and evidence-based practice. Recommendations for CAH nurse executives seeking to achieve Magnet standards were developed from interviews with healthcare professionals ( N = 27) at the first independent CAH to achieve Magnet designation. Conclusions: CAH nurse executives may consider the Magnet standards as a blueprint for leading change to improve organizational outcomes. Consideration should be given to: (a) securing administrative leadership support; (b) strategically planning for small, incremental change; (c) building shared governance, quality improvement, research, and education; (d) harnessing collective power; and (e) believing and staying committed to the purpose of improving staff and patient outcomes. Keywords : Critical access hospital, Leading change, Magnet, Nurse executive, Quality outcomes Acknowledgements: The authors thank Dr. Kristin Stegenga, Dr. Robert Lee, Dr. Sally Barhydt, and Jennifer Nelson-Brantley for their review of the manuscript. Dr. Nelson-Brantley obtained written permission from the CNO of Waverly Health Center to use the hospital name in this published manuscript. Conflicts of Interest and Source of Funding: Dr. Nelson-Brantley received funding for this study from the Taunton Scholars program and the Delta Chapter of Sigma Theta Tau International. No conflicts of interest are declared by the authors. DOI: http://dx.doi. org/10.14574/ojrnhc.v18i1. 510
An efficient extension of N-mixture models for multi-species abundance estimation

1. In this study we propose an extension of the N-mixture family of models that targets an improvement of the statistical properties of rare species abundance estimators when sample sizes are low, yet typical size for tropical studies. The proposed method harnesses information from other species in an ecological community to correct each species’ estimator. We provide guidance to determine the sample size required to estimate accurately the abundance of rare tropical species when attempting to estimate the abundance of single species. 2. We evaluate the proposed methods using an assumption of 50-m radius plots and perform simulations comprising a broad range of sample sizes, true abundances and detectability values and a complex data generating process. The extension of the N-mixture model is achieved by assuming that the detection probabilities of a set of species are all drawn at random from a beta distribution in a multi-species fashion. This hierarchical model avoids having to specify a single detection probability parameter per species in the targeted community. Parameter estimation is done via Maximum Likelihood. 3. We compared our multi-species approach with previously proposed multi-species N-mixture models, which we show are biased when the true densities of species in the community are less than seven individuals per 100-ha. The beta N-mixture model proposed here outperforms the traditional Multi-species N-mixture model by allowing the estimation of organisms at lower densities and controlling the bias in the estimation. 4. We illustrate how our methodology can be used to suggest sample sizes required to estimate the abundance of organisms, when these are either rare, common or abundant. When the interest is full communities, we show how the multi-species approaches, and in particular our beta model and estimation methodology, can be used as a practical solution to estimate organism densities from rapid inventory datasets. The statistical inferences done with our model via Maximum Likelihood can also be used to group species in a community according to their detectabilities.
A subspace approach to face detection with support vector machines

We present a subspace approach to face detection with support vector machines (SVMs). A linear SVM classifier is trained as a filter to produce a subspace in which a non-linear SVM classifier with Gaussian kernel is trained for face detection. This makes training easier and results in a very efficient face detection algorithm. Experimental results demonstrate their promising performance compared with some well-known existing detectors.
An overview of the 4 randomized trials of aspirin therapy in the primary prevention of vascular disease.

BACKGROUND In the primary prevention of cardiovascular disease, in contrast to the recommendations of the American College of Chest Physicians and the American Heart Association, the US Food and Drug Administration recently stated that there was insufficient evidence to judge whether aspirin therapy decreases the risk of a first myocardial infarction. OBJECTIVE To perform an overview of the 4 primary prevention trials of aspirin therapy to obtain the most reliable estimates of the effects of aspirin therapy on various vascular disease end points. METHODS AND RESULTS These 4 trials included more than 51,000 subjects and 2284 important vascular events. Those assigned to aspirin therapy experienced significant reductions of 32% (95% confidence interval [CI], 21%-41%) for nonfatal myocardial infarction and 13% (95% CI, 5%-19%) for any important vascular event. There were possible small but nonsignificant increases in risks of vascular disease-related death (1%; 95% CI, -12% to 16%) and nonfatal stroke (8%; 95% CI, -12% to 33%). When strokes were subdivided by type, there was no significant effect of aspirin therapy on the risk of ischemic stroke, but, while based on small numbers, there was a 1.7-fold apparent increase (95% CI, 6%-269%) in the risk of hemorrhagic stroke, which did achieve statistical significance. CONCLUSIONS For the primary prevention of vascular disease, aspirin therapy confers significant beneficial effects on first myocardial infarction and, as a result, on any important vascular event; these effects are clinically important. Whether there is any reduction in vascular disease-related death or stroke associated with treatment remains unclear because of inadequate numbers of events in the primary prevention trials completed to date. More data on hemorrhagic stroke are also needed. In addition, randomized trial data, especially in women but also in men, are needed to help to formulate a rational public health policy for individuals at usual risk. Meanwhile, these data provide evidence for a significant benefit of aspirin therapy in the primary prevention of myocardial infarction.
Simplification of 3D Triangular Mesh for Level of Detail Computation

Advance three-dimensional (3D) scanning devices can create very detail complex 3D polygonal models. Though the random access memory (RAM) and parallel computing in graphics card are enormously improved, many modelers still confront with intricacy of slow computing due to exponential increase of number of polygons for realistic look of surface models. In this paper, a simple and fast triangular mesh simplification method based on half-edge collapsed scheme is proposed. The Euclidean distance of each triangle edges and the disparity between two adjacent triangles are computed for decimation validation. The to-be-collapsed vertices and edges are conducted in priority queue data structure. The empirical results show that the proposed simplified model shape is well-preserved and is computation efficient. Two existing popular methods (FMLOD and FSIMP) are to be compared with the proposed method and the result demonstrates a rational outcome and is comparable in visual quality.
Attractive Women Want it All: Good Genes, Economic Investment, Parenting Proclivities, and Emotional Commitment1

The current research tests the hypothesis that women have an evolved mate value calibration adaptation that functions to raise or lower their standards in a long-term mate according to their own mate value. A woman's physical attractiveness is a cardinal component of women's mate value. We correlated observer-assessed physical attractiveness (face, body, and overall) with expressed preferences for four clusters of mate characteristics (N = 214): (1) hypothesized good-gene indicators (e.g., masculinity, sexiness); (2) hypothesized good investment indicators (e.g., potential income); (3) good parenting indicators (e.g., desire for home and children), and (4) good partner indicators (e.g., being a loving partner). Results supported the hypothesis that high mate value women, as indexed by observer-judged physical attractiveness, expressed elevated standards for all four clusters of mate characteristics. Discussion focuses on potential design features of the hypothesized mate-value calibration adaptation, and suggests an important modification of the trade-off model of women's mating. A minority of women—notably those low in mate value who are able to escape male mate guarding and the manifold costs of an exposed infidelity—will pursue a mixed mating strategy, obtaining investment from one man and good genes from an extra-pair copulation partner (as the trade-off model predicts). Since the vast majority of women secure genes and direct benefits from the same man, however, most women will attempt to secure the best combination of all desired qualities from the same man.
Women's relative immunity to the socio-economic health gradient: artifact or real?

Background Individual and area socio-economic status (SES) are significant predictors of morbidity and mortality in developed and developing countries. However, the span in health from poorest to richest, that is, the socio-economic gradient, appears steeper for men than women. Objective Our aim is to understand women's apparent immunity to the health harms of the SES gradient. Design Findings from a non-systematic search of Medline for population-based, SES gradient studies reporting results for both men and women and with health outcomes of morbidity, mortality or self-rated health (SRH) were reflectively analyzed. Results The 36 papers reviewed generally showed women to be relatively immune to the SES gradient for all but cardiovascular health outcomes. However, addressing the interconnected nature of socio-economic circumstances, exploring whether some measures of SES had ambiguous meanings for either women or men, including modifiers of SES such as household circumstances, social capital or area gender equity, or using indicators of area SES that were contextual rather than aggregates of individual, compositional measures increased the SES gradient for women. Outcome measures that combined mental and physical health, accounted for gender differences in SRH and adjusted for sex-specific differences in causes of mortality also explained some of the observed amelioration of the SES gradient among women. Conclusions Socio-economic circumstances have a real and sustained impact on individual health. The SES gradient appears stronger for men than for women for all health outcomes other than heart disease. However, some of the observed variability between men and women may be an artifact of biased methodology. Considering webs of causation rather than individual markers of SES along with other sources of gender bias can explain much of women's blunted socio-economic gradient and deepen understanding of the pathways from SES to morbidity and mortality overall.
Probable hepatic capillariosis and hydatidosis in an adolescent from the late Roman period buried in Amiens (France)

Two calcified objects recovered from a 3rd to 4th-century grave of an adolescent in Amiens (Northern France) were identified as probable hydatid cysts. By using thin-section petrographic techniques, probable Calodium hepaticum (syn. Capillaria hepatica) eggs were identified in the wall of the cysts. Human hepatic capillariosis has not been reported from archaeological material so far, but could be expected given the poor level of environmental hygiene prevalent in this period. Identification of tissue-dwelling parasites such as C. hepaticum in archaeological remains is particularly dependent on preservation conditions and taphonomic changes and should be interpreted with caution due to morphological similarities with Trichuris sp. eggs.
A Context-Aware System Architecture for Dealing with the Emergency by the Community Service in Apartment

Ubiquitous technology has been developed in order to offer the adaptive services using the context of various devices and sensors. The user-centric service has been given a considerable attention in Ubiquitous computing. To be sure, there have been studies concerned specially with user-centric services, but it remains the problems to be explored with emergency situation and policy. Therefore, our system is focused on the improvement to helping people such as the elderly, young children, and the disabled according to the intelligent system architecture and application. Our system manages the service access and policy which consists of notification, association, privilege and privacy. We propose a context-aware, agent-based system architecture which performs the intelligent, comfortable and safety services to meet the respective requirements. We also propose a context model using the F-logic and an agent-based system using the OSGi. Moreover, the evaluation of the system shows evidence that our system enhances the query response time for offering adaptive services and improves the maximum 20% performance of service response time compared with the conventional system of the basic architecture and context model.
The seamless browser: enhancing the speed of web browsing by zooming and preview thumbnails

In this paper, we present a new web browsing system, Seamless Browser, for fast link traversal on a large screen like TV In navigating web, users mainly suffer from cognitive overhead of determining whether or not to follow links. This overhead can be reduced by providing preview information of the destination of links, and also by providing semantic cues on the nearest location in relation to the anchor. In order to reduce disorientation and annoyance from the preview information, we propose that users will focus on the small area nearside around a pointer, and a small number of hyperlink previews in that focused area will appear differently depending on the distances between the pointer and the hyperlinks: the nearer the distance is, the richer the content of the information scent is. We also propose that users can navigate the link paths by controlling the pointer and the zooming interface, so that users may go backward and forward seamlessly along several possible link paths. We found that combining the pointer and a zoom significantly improved performance for navigational tasks.
GALOIS MODULES, IDEAL CLASS GROUPS AND CUBIC STRUCTURES

We establish a connection between the theory of cyclotomic ideal class groups and the theory of "geometric" Galois modules and obtain results on the Galois module structure of coherent cohomology groups of Galois covers of varieties over Z. In particular, we show that an invariant that measures the obstruction to the existence of a virtual normal integral basis for the coherent cohomology of such covers is annihilated by a product of certain Bernoulli numbers with orders of even K-groups of Z. We also show that the existence of such a normal integral basis is closely connected to the truth of the Kummer-Vandiver conjecture for the prime divisors of the degree of the cover. Our main tool is a theory of "hypercubic structures" for line bundles over group schemes.
Energy, Power, and Performance Characterization of GPGPU Benchmark Programs

This paper studies the effects on energy consumption, power draw, and runtime of a modern compute GPU when changing the core and memory clock frequencies, enabling or disabling ECC, using alternate implementations, and varying the program inputs. We evaluate 34 applications from 5 benchmark suites and measure their power draw over time on a K20c GPU. Our results show that changing the frequency or the program implementation can alter the energy, power, and performance by a factor of two or more. Interestingly, some changes affect these three aspects very unevenly. ECC can greatly increase the runtime and energy consumption, but only on memory-bound codes. Compute-bound codes tend to behave quite differently from memory-bound codes, in particular regarding their power draw. On irregular programs, a small change in frequency can result in a large change in runtime and energy consumption.
A fuzzy model of a European index based on automatically extracted content information

In this paper we build on previous work related to predicting the MSCI EURO index based on content analysis of ECB statements. Our focus is on reducing the number of features employed for prediction through feature selection. For this purpose we rely on two methodologies: (stepwise) linear regression and greedy forward feature subset selection. The original dataset consists of 13 features (General Inquirer content categories). Both methodologies provide an improvement in the overall accuracy of the model, while reducing the number of features employed. Through linear regression we achieve an accuracy of 67.58% on the testing set by relying on six features, while greedy forward selection enables an accuracy on the test set of 69.50% while relying on eight features.
Fuzzy based Quantitative Evaluation of Architectures using Architectural Knowledge

Software architecture is a standard and essential part of system development. The knowledge produced and consumed during this process needs to be shared and reused among different stakeholders. Therefore, software architecture knowledge (AK) needs to be managed for improving the architectural capabilities. ADUAK (Architectural Development using Architectural Knowledge) model has been developed to support a framework for capturing and using architectural knowledge to improve the architecture design process. ADUAK guides the architect in selecting the most appropriate architecture, where the interactions between quality attributes, tactics should be analyzed and quantified and the results should be considered for the quality driven architectural design process. Ontology based AK tool is needed, because ontologies are used to combine the real world domain information and software architectural knowledge from various experts, to maintain up – to – date documentation that evolves over time. The primary focus of ADUAK is to acquire knowledge for the fuzzy based evaluation of the architectures of the system using ontologies. It is needed because the architecture represents an enormous risk in a development project. Making bad architectural designs may substantially slow down the system and even lead to failure. The quantitative impact of incorporating the quality in the software architectures is then calculated through a fuzzy based approach for the set of design alternatives. The paper aims to bring the contribution of ADUAK model for the fuzzy based quantitative evaluation of architectures with the help of architectural knowledge through a suitable case study.