Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge extraction from Chinese wiki encyclopedias

The vision of the Semantic Web is to build a ‘Web of data’ that enables machines to understand the semantics of information on the Web. The Linked Open Data (LOD) project encourages people and organizations to publish various open data sets as Resource Description Framework (RDF) on the Web, which promotes the development of the Semantic Web. Among various LOD datasets, DBpedia has proved a successful structured knowledge base, and has become the central interlinking-hub of the Web of data in English. However, in the Chinese language, there is little linked data published and linked to DBpedia. This hinders the structured knowledge sharing of both Chinese and cross-lingual resources. This paper deals with an approach for building a large-scale Chinese structured knowledge base from Chinese wiki resources, including Hudong and Baidu Baike. The proposed approach first builds an ontology based on the wiki category system and infoboxes, and then extracts instances from wiki articles. Using Hudong as our source, our approach builds an ontology containing 19 542 concepts and 2381 properties. 802 593 instances are extracted and described using the concepts and properties in the extracted ontology and 62 679 of them are linked to equivalent instances in DBpedia. As from Baidu Baike, our approach builds an ontology containing 299 concepts, 37 object properties, and 5590 data type properties. 1 319 703 instances are extracted from Baidu Baike, and 84 343 of them are linked to instances in DBpedia. We provide RDF dumps and SPARQL endpoint to access the established Chinese knowledge bases. The knowledge bases built using our approach can be used not only in Chinese linked data building, but also in many useful applications of large-scale knowledge bases, such as question-answering and semantic search.
Molecular Diagnosis of Sentinel Lymph Nodes for Breast Cancer: One Step Ahead for Standardization

Sentinel node (SN) pathologic analysis shows great limitations related to reproducibility and challenges pathologists with respect to methodology and standardization. Recently, a new, highly specific and sensitive molecular procedure has been developed, based on cytokeratin 19 messenger RNA amplification, which allows for fast and effective intraoperative evaluation of the SN. Our aim in this study was to compare 2 arms for intraoperative evaluation: one study arm with 478 cases in which an exhaustive histopathologic procedure was performed and the other study arm with 211 cases that were analyzed using the new molecular method. Our results indicate that both methods similarly distinguish between benign and malignant lymph nodes but the type of metastasis is better defined and stratified by the molecular method. We conclude that the molecular method should be recommended for SN diagnosis because besides its high sensitivity, specificity, and operating room adequacy, it may allow for standardization for SN evaluation.
A STUDY OF SCHOOL NURSE JOB SATISFACTION

This study explored job satisfaction and changes needed to help boost levels of job satisfaction. Self-reported job satisfaction data were collected from 71 school nurses employed in elementary, middle, and high school settings via interactive focus groups. The subjects participated in a 30- to 45-minute focus group session that was audiotaped and transcribed by the principal investigator. Beliefs about job satisfaction were identified and classified into exclusive categories or themes. While the majority of school nurses expressed contentment with their jobs, certain factors that would increase job satisfaction, such as salary and control issues, were discussed. Overall, 83% of school nurses in this study were satisfied in their present positions; however, issues of coping and role strain were identified as major contributors to low morale. Only 17% of the school nurses voiced job dissatisfaction, primarily attributed to low salaries and lack of trust and support from administration. As school nurses face a diverse community with complex needs, adaptation is needed for job satisfaction to be maintained. For this to occur, school nurses must take the initiative to educate administrators, parents, and communities about their role in the school setting.
Knock Out of S1P3 Receptor Signaling Attenuates Inflammation and Fibrosis in Bleomycin-Induced Lung Injury Mice Model

Sphingosine-1-phosphate (S1P) is a bioactive sphingolipid metabolite involved in many critical cellular processes, including proliferation, migration, and angiogenesis, through interaction with a family of five G protein–coupled receptors (S1P1–5). Some reports have implicated S1P as an important inflammatory mediator of the pathogenesis of airway inflammation, but the role of S1P3 in the pathogenesis of lung diseases is not completely understood. We used S1P3-deficient (knockout (KO)) mice to clarify the role of S1P3 receptor signaling in the pathogenesis of pulmonary inflammation and fibrosis using a bleomycin-induced model of lung injury. On the seventh day after bleomycin administration, S1P3 KO mice exhibited significantly less body weight loss and pulmonary inflammation than wild-type (WT) mice. On the 28th day, there was less pulmonary fibrosis in S1P3 KO mice than in WT mice. S1P3 KO mice demonstrated a 56% reduction in total cell count in bronchoalveolar lavage fluid (BALF) collected on the seventh day compared with WT mice; however, the differential white blood cell profiles were similar. BALF analysis on the seventh day showed that connective tissue growth factor (CTGF) levels were significantly decreased in S1P3 KO mice compared with WT mice, although no differences were observed in monocyte chemotactic protein-1 (MCP-1) or transforming growth factor β1 (TGF-β1) levels. Finally, S1P levels in BALF collected on the 7th day after treatment were not significantly different between WT and S1P3 KO mice. Our results indicate that S1P3 receptor signaling plays an important role in pulmonary inflammation and fibrosis and that this signaling occurs via CTGF expression. This suggests that this pathway might be a therapeutic target for pulmonary fibrosis.
{34 () a Machine Learning Approach to Pos Tagging

We have applied the inductive learning of statistical decision trees and relaxation labelling to the Natural Language Processing (nlp) task of morphosyntactic disambiguation (Part Of Speech Tagging). The learning process is supervised and obtains a language model oriented to resolve pos ambiguities, consisting of a set of statistical decision trees expressing distribution of tags and words in some relevant contexts. The acquired decision trees have been directly used in a tagger that is both relatively simple and fast, and which has been tested and evaluated on the Wall Street Journal (wsj) corpus with remarkable accuracy. However, better results can be obtained by translating the trees into rules to feed a exible relaxation labelling based tagger. In this direction we describe a tagger which is able to use information of any kind (n{grams, automatically acquired constraints, linguistically motivated manually written constraints, etc.), and in particular to incorporate the machine{learned decision trees. Simultaneously, we address the problem of tagging when only limited training material is available, which is crucial in any process of constructing, from the scratch, an annotated corpus. We show that high levels of accuracy can be achieved with our system in this situation, and report some results obtained when using it to develop a 5.5 million words Spanish corpus from scratch.
Defending embedded systems against control flow attacks

This paper presents a control flow enforcement technique based on an Instruction Based Memory Access Control (IBMAC) implemented in hardware. It is specifically designed to protect low-cost embedded systems against malicious manipulation of their control flow as well as preventing accidental stack overflows. This is achieved by using a simple hardware modification to divide the stack in a data and a control flow stack (or return stack). Moreover access to the control flow stack is restricted only to return and call instructions, which prevents control flow manipulation. Previous solutions tackled the problem of control flow injection on general purpose computing devices and are rarely applicable to the simpler low-cost embedded devices, that lack for example of a Memory Management Unit (MMU) or execution rings. Our approach is binary compatible with legacy applications and only requires minimal changes to the tool-chain. Additionally, it does not increase memory usage, allows an optimal usage of stack memory and prevents accidental stack corruption at run-time. We have implemented and tested IBMAC on the AVR micro-controller using both a simulator and an implementation of the modified core on a FPGA. The implementation on reconfigurable hardware showed a small resulting overhead in terms of number of gates, and therefore a low overhead of expected production costs.
Using Latent Selection Difference to Model Persistence in a Declining Population

Population persistence is a direct measure of the viability of a population. Monitoring the distribution of declining populations or subpopulations over time can yield estimates of persistence, which we show can be modeled as a latent selection difference (LSD) contrasting attributes of sites where populations have persisted versus those that have not. Predicted persistence can be modeled with predictor covariates to identify factors correlated with species persistence. We demonstrate how to model persistence based on changes in occupancy that can include adjustments for detection probability. Using a known historical distribution of the western grebe (Aechmophorus occidentalis), we adapted methods originally developed for occupancy modeling to evaluate how environmental covariates including emergent vegetation and human developments have affected western grebe persistence in Alberta. The relative probability of persistence was correlated with the extent of shoreline bulrush (Scirpus lacustris), which is important vegetation for nesting cover. We also documented that western grebe populations were less likely to persist on lakes in the boreal forest, primarily located on the northern boundary of the species' range. Factors influencing occupancy were different than those determining persistence by western grebes; persistence and occupancy were not correlated. Persistence was more likely on lakes with recreational development, reflecting reliance by grebes on the larger, fish-bearing waterbodies that also are attractive for lakeshore development. Unfortunately, the correlation with recreational development on Alberta's lakes puts grebes at risk for loss of brood-rearing habitats—primary threats to altricial birds—if steps are not taken to prevent disturbance to bulrush stands. Identifying factors related to the persistence of a species—especially one in decline—is a fundamental step in conservation management.
Sensor Coverage and Evasion Constraints

Evaluating sensor net coverage requires assuming evaders have limits. To understand sensor evaluation more thoroughly, this paper explores how different limits affect sensitivity for individual sensors We first solve the obstacle-free power law sensitivity problem under a bounded-velocity constraint on evaders, finding that the sneakiest strategy is infinite for many interesting scenarios. We then examine some geometric constraints to make such problems more well-formed. We then study the impact of dynamical constraints – namely that evaders have bounded energy/fuel under a few energy usage models.
CORRELATION BETWEEN ELEMENTARY STUDENTS’ READING ATTITUDES AND THEIR WRITING DISPOSITIONS

ABSTRACT The purpose of this study was to examine the correlation between elementary students’ reading attitudes and their writing dispositions and to determine to what extent students’ reading attitudes predict their writing dispositions. Four hundred and fifteen students from six elementary schools in Nigde, Turkey participated in the study. Students were chosen randomly. In this study, the Reading Attitudes and Writing Dispositions scales were used in order to collect data to answer the research questions. The correlative investigation model was adopted in the research. The Pearson moment’s correlation coefficient analysis showed that there was a positive significant correlation between students’ reading attitudes and their writing dispositions. It was also found that students’ reading attitudes were a significant predictor of their writing dispositions. Keywords: Reading attitude, writing disposition, elementary education, and correlation.
RoPEUS: A New Robust Algorithm for Static Positioning in Ultrasonic Systems

A well known problem for precise positioning in real environments is the presence of outliers in the measurement sample. Its importance is even bigger in ultrasound based systems since this technology needs a direct line of sight between emitters and receivers. Standard techniques for outlier detection in range based systems do not usually employ robust algorithms, failing when multiple outliers are present. The direct application of standard robust regression algorithms fails in static positioning (where only the current measurement sample is considered) in real ultrasound based systems mainly due to the limited number of measurements and the geometry effects. This paper presents a new robust algorithm, called RoPEUS, based on MM estimation, that follows a typical two-step strategy: 1) a high breakdown point algorithm to obtain a clean sample, and 2) a refinement algorithm to increase the accuracy of the solution. The main modifications proposed to the standard MM robust algorithm are a built in check of partial solutions in the first step (rejecting bad geometries) and the off-line calculation of the scale of the measurements. The algorithm is tested with real samples obtained with the 3D-LOCUS ultrasound localization system in an ideal environment without obstacles. These measurements are corrupted with typical outlying patterns to numerically evaluate the algorithm performance with respect to the standard parity space algorithm. The algorithm proves to be robust under single or multiple outliers, providing similar accuracy figures in all cases.
Use of Aspirin for Primary and Secondary Cardiovascular Disease Prevention in the United States, 2011–2012

Background Aspirin use has been shown to be an effective tool in cardiovascular disease (CVD) prevention among high‐risk patients. The patient‐reported physician recommendation for aspirin as preventive therapy among high‐ and low‐risk patients is unknown. Methods and Results We conducted an analysis of the National Health and Nutrition Examination Survey 2011–2012 to examine the use of aspirin for CVD prevention. Patients without previously diagnosed CVD were classified into high and low risk based on their Framingham Risk Score (10‐year coronary heart disease risk). Among patients without previously diagnosed CVD, 22.5% were classified as high risk. Of the high‐risk individuals, 40.9% reported being told by their physician to take aspirin, with 79.0% complying. Among those who were at low risk, 26.0% were told by their physician to take aspirin, with 76.5% complying. Logistic regression analysis indicated that age, access to a regular source of care, education, and insurance status were significant predictors of patient‐reported physician recommendations for aspirin use for primary prevention. Among high‐risk patients, age, race, and insurance status were significant predictors of reported recommendations for aspirin use. Among low‐risk patients, age, education, obesity, and insurance status were significant predictors of reported recommendations for aspirin use. Conclusions Patient reports indicate nonideal rates of being told to take aspirin, for both high‐ and low‐risk patients for primary prevention. Clinical decision support tools that could assist physicians in identifying patients at risk may increase patient reports of physician recommendations for aspirin use.
NEW THEORETICAL APPROACHES FOR HCI

The field of human-computer interaction is rapidly expanding. Alongside the extensive technological developments that are currently taking place, is the emergence of a ‘cottage industry’ culture, where a polyphony of new theories, methods and concerns have been imported into the field from a diversity of disciplines and backgrounds. An extensive critique of recent theoretical developments is presented together with what practitioner’s currently use. A significant development of importing new theories into the field has been much insightful explication of ‘HCI’ phenomena, together with extending the field’s discourse. However, at the same time, the theoretically-based approaches have had a limited impact on the practice of interaction design. This chapter discusses why this is so and suggests that different kinds of mechanisms are needed that will enable both designers and researchers to better articulate and theoretically ground the hard challenges facing them today.
A perception based selection of vector map LoDs for progressive transmission

In many daily real problems it is necessary to exchange spatial data over the network and visualize them as soon and with higher quality as possible. Unfortunately, this is not always possible. As an instance, in case of event when slow or obstructed communication lines may make exchange of information difficult or even impossible. In order to mitigate this issue, an approach is sending data in a progressive way by generating simplified versions of the map and then, transmitting the coarsest map and then adding refinements passing through intermediate versions until reaching an acceptable detail. However, choosing the first and the subsequent versions is a complex problem and designers are often let to decide on a case-by-case basis. In this paper, we present a three step schema for generating simplified versions which is based on an empirical experiment whose aim is to determine which reduction levels can be applied by analyzing the perception of map changes with respect to the original map.
Relationship between exhaled nitric oxide and mucosal eosinophilic inflammation in mild to moderately severe asthma

BACKGROUND Exhaled levels of nitric oxide (NO) are raised in asthma but the relationship between exhaled NO levels and a direct measure of airway inflammation has not been investigated in asthmatic patients treated with inhaled steroids. METHODS The relationship between exhaled NO levels, clinical measures of asthma control, and direct markers of airway inflammation were studied in patients with asthma treated with and without inhaled corticosteroids. Thirty two asthmatic patients (16 not using inhaled steroids and 16 using inhaled beclomethasone dipropionate, 400–1000 μg/day) were monitored with respect to measures of asthma control including lung function, symptom scores, medication usage, and variability of peak expiratory flow (PEF) for one month. Measurements of exhaled NO and fibreoptic bronchoscopy were performed at the end of the monitoring period. Bronchial mucosal biopsy specimens were stained with an anti-MBP antibody for quantification of eosinophils. RESULTS There was no significant difference in lung function, symptom scores, or medication usage between the two groups, but there was a significant difference in PEF variability (8.7 (1.2)% in steroid naive patients versus 13.6 (1.9)% in steroid treated patients, p<0.05) and exhaled NO levels (9.9 (3.5) ppb in steroid naive patients versus 13.6 (2.0) ppb in steroid treated patients, p<0.05). There was no correlation between exhaled NO and mucosal eosinophils, or between NO and conventional measures of asthma control. There was a significant correlation between mucosal eosinophils and lung function (r = –0.43, p<0.05). CONCLUSIONS Exhaled NO levels do not reflect airway mucosal eosinophilia and these markers reflect different aspects of airway inflammation. The clinical usefulness of exhaled NO needs to be determined in prospective longitudinal studies.
GIS-Driven classification of satellite imagery

With the increasing amount of geographic vector database available over the world, the remote sensing community has a strong potential to quickly produce up-to-date geographic products. In this paper, we propose a geographic object-based image analysis to bridge the gap between the image processing and the geographic vector database. The key component of the method is the automated sample selection refined by a statistical outlier detection algorithm. A preliminary image labelling is performed based on the intersection between a geographic vector database and the result of a multiscale image segmentation. Each class is then cleaned by a non-parametric trimming procedure that iteratively excludes outliers in order to keep only the most relevant training samples. Eventually, each image-object is classified based on a maximum likelyhood and new classes are created by decision rules. This method was succesfully applied to two study areas in a temperate and a Mediterranean region. The objectbased thematic overall accuracy was close to 75%, which was as good as the accuracy of the vector data in the temperate region and a significant improvement in the Mediterranean area. The proposed framework showed very promising results, but further work could still improve the classification step in order to take the most out of the selected training samples.
Framework for the development of a personalised recommender system with integrated web-mining functionalities

A challenge for current e-commerce systems is to improve customer satisfaction by providing instruments for the personalised offer of products. Personalised recommender systems are endowed with intelligent mechanisms to search products that users are interested in. In this paper, we integrate software engineering and web mining techniques in the development of an e-commerce recommender system capable of predicting the preferences of its users and present them a personalised catalogue. A data mining model induced by a decision tree algorithm is used to predict clients' preferences. The system also has an internationalisation automatic mechanism that facilitates the visualisation of the user's interface in different languages.
Synchronized manipulation and force measurement by optical tweezers using high-speed laser scanning

Laser micromanipulation is suitable for non-contact manipulation of a small object, such as a bacteria and microbe, at the closed space. We proposed synchronized laser micromanipulation (SLM) for the trajectory control of the multiple targets by the single laser. We used the laser scanning system for scanning of the focal point of laser in high speed to realize simultaneous trajectory control of multiple objects. Condition for secure transportation of multiple objects is derived analytically and verified through the experiment. We used SLM for indirect manipulation of the microbe by the microtools, which are directly manipulated by SLM. Here we propose the force measurement method using SLM. Recognition of the microtool is improved by using a fluorescent microbead as a microtool.