LLukas22/scidocs huggingface dataset:
Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach

Current developments in ICTs such as in Internet-of-Things (IoT) and Cyber–Physical Systems (CPS) allow us to develop healthcare solutions with more intelligent and prediction capabilities both for daily life (home/office) and in-hospitals. In most of IoT-based healthcare systems, especially at smart homes or hospitals, a bridging point (i.e., gateway) is needed between sensor infrastructure network and the Internet. The gateway at the edge of the network often just performs basic functions such as translating between the protocols used in the Internet and sensor networks. These gateways have beneficial knowledge and constructive control over both the sensor network and the data to be transmitted through the Internet. In this paper, we exploit the strategic position of such gateways at the edge of the network to offer several higher-level services such as local storage, real-time local data processing, embedded data mining, etc., presenting thus a Smart e-Health Gateway. We then propose to exploit the concept of Fog Computing in Healthcare IoT systems by forming a Geo-distributed intermediary layer of intelligence between sensor nodes and Cloud. By taking responsibility for handling some burdens of the sensor network and a remote healthcare center, our Fog-assisted system architecture can cope with many challenges in ubiquitous healthcare systems such asmobility, energy efficiency, scalability, and reliability issues. A successful implementation of Smart e-Health Gateways can enable massive deployment of ubiquitous health monitoring systems especially in clinical environments. We also present a prototype of a Smart e-Health Gateway called UT-GATE where some of the discussed higher-level features have been implemented. We also implement an IoT-based Early Warning Score (EWS) health monitoring to practically show the efficiency and relevance of our systemon addressing amedical case study. Our proofof-concept design demonstrates an IoT-based health monitoring system with enhanced overall system intelligence, energy efficiency, mobility, performance, interoperability, security, and reliability. © 2017 Elsevier B.V. All rights reserved.
Predicting breast cancer survivability: a comparison of three data mining methods

OBJECTIVE
The prediction of breast cancer survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better explanatory prognostic factors are being measured and recorded; thanks to low cost computer hardware and software technologies, high volume better quality data is being collected and stored automatically; and finally thanks to better analytical methods, those voluminous data is being processed effectively and efficiently. Therefore, the main objective of this manuscript is to report on a research project where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability.


METHODS AND MATERIAL
We used two popular data mining algorithms (artificial neural networks and decision trees) along with a most commonly used statistical method (logistic regression) to develop the prediction models using a large dataset (more than 200,000 cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes.


RESULTS
The results indicated that the decision tree (C5) is the best predictor with 93.6% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), artificial neural networks came out to be the second with 91.2% accuracy and the logistic regression models came out to be the worst of the three with 89.2% accuracy.


CONCLUSION
The comparative study of multiple prediction models for breast cancer survivability using a large dataset along with a 10-fold cross-validation provided us with an insight into the relative prediction ability of different data mining methods. Using sensitivity analysis on neural network models provided us with the prioritized importance of the prognostic factors used in the study.
BUSINESS CYCLE ANALYSIS WITH MULTIVARIATE MARKOV SWITCHING MODELS

The paper aims to describe the cyclical phases of the economy by using multivariate Markov switching models. The class of Markov switching models can be extended in two main directions in a multivariate framework. In the first approach, the switching dynamics are introduced by way of one common latent factor (Diebold and Rudebusch, 1996). In the second approach, introduced by Krolzig (1997), a VAR model with parameters depending on one common Markov chain is considered (MS VAR). We will extend the MS VAR approach allowing for the presence of specific Markov chain in each equation of the VAR (Multiple Markov Switching VAR models, MMS VAR). Dynamic factor models with regime switches, MS VAR and MMS VAR models allow for a multi-country or a multi-sector simultaneous analysis in the search of common phases which are represented by the states of the switching latent factor. Moreover, in the MMS VAR approach we explore the introduction of correlated Markov chains which allow us to evaluate the relationships among phases in different economies or sectors and introduce causality relationships, which can allow more parsimonious representations. We apply the MMS model in order to study the relationship between cyclical phases in the U.S. and Euro zone industrial production. Moreover, we construct a MMS model in order to explore the cyclical relationship between the Euro zone industrial production and the industrial component of the European Sentiment Index (ESI). Subject Area N. 4: “Dating, detecting and forecasting turning points”
Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis

With the booming of microblogs on the Web, people have begun to express their opinions on a wide variety of topics on Twitter and other similar services. Sentiment analysis on entities (e.g., products, organizations, people, etc.) in tweets (posts on Twitter) thus becomes a rapid and effective way of gauging public opinion for business marketing or social studies. However, Twitter's unique characteristics give rise to new problems for current sentiment analysis methods, which originally focused on large opinionated corpora such as product reviews. In this paper, we propose a new entity-level sentiment analysis method for Twitter. The method first adopts a lexiconbased approach to perform entity-level sentiment analysis. This method can give high precision, but low recall. To improve recall, additional tweets that are likely to be opinionated are identified automatically by exploiting the information in the result of the lexicon-based method. A classifier is then trained to assign polarities to the entities in the newly identified tweets. Instead of being labeled manually, the training examples are given by the lexicon-based approach. Experimental results show that the proposed method dramatically improves the recall and the F-score, and outperforms the state-of-the-art baselines. External Posting Date: June 21, 2011 [Fulltext] Approved for External Publication Internal Posting Date: June 21, 2011 [Fulltext]  Copyright 2011 Hewlett-Packard Development Company, L.P. Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis Lei Zhang, Riddhiman Ghosh, Mohamed Dekhil, Meichun Hsu, Bing Liu Hewlett-Packard Laboratories University of Illinois at Chicago 1501 Page Mill Rd., Palo Alto, CA 851 S. Morgan St., Chicago, IL {riddhiman.ghosh, mohamed.dekhil, {lzhang3, liub}@cs.uic.edu meichun.hsu}@hp.com
The cost-effectiveness of a treatment-based classification system for low back pain: design of a randomised controlled trial and economic evaluation

BACKGROUND
Systematic reviews have shown that exercise therapy and spinal manipulation are both more effective for low back pain (LBP) than no treatment at all. However, the effects are at best modest. To enhance the clinical outcomes, recommendations are to improve the patient selection process, and to identify relevant subgroups to guide clinical decision-making. One of the systems that has potentials to improve clinical decision-making is a treatment-based classification system that is intended to identify those patients who are most likely to respond to direction-specific exercises, manipulation, or stabilisation exercises.


METHODS/DESIGN
The primary aim of this randomised controlled trial will be to assess the effectiveness of a classification-based system. A sample of 150 patients with subacute and chronic LBP who attend a private physical therapy clinic for treatment will be recruited. At baseline, all participants will undergo a standard evaluation by trained research physical therapists and will be classified into one of the following subgroups: direction-specific exercises, manipulation, or stabilisation. The patient will not be informed about the results of the examination. Patients will be randomly assigned to classification-based treatment or usual care according to the Dutch LBP guidelines, and will complete questionnaires at baseline, and 8, 26, and 52 weeks after the start of the treatment. The primary outcomes will be general perceived recovery, functional status, and pain intensity. Alongside this trial, an economic evaluation of cost-effectiveness and cost-utility will be conducted from a societal perspective.


DISCUSSION
The present study will contribute to our knowledge about the effectiveness and cost-effectiveness of classification-based treatment in patients with LBP.


TRIAL REGISTRATION



TRIAL REGISTRATION NUMBER
NTR1176.
Deep learning in fluid dynamics

It was only a matter of time before deep neural networks (DNNs) – deep learning – made their mark in turbulence modelling, or more broadly, in the general area of high-dimensional, complex dynamical systems. In the last decade, DNNs have become a dominant data mining tool for big data applications. Although neural networks have been applied previously to complex fluid flows, the article featured here (Ling et al., J. Fluid Mech., vol. 807, 2016, pp. 155–166) is the first to apply a true DNN architecture, specifically to Reynolds averaged Navier Stokes turbulence models. As one often expects with modern DNNs, performance gains are achieved over competing state-of-the-art methods, suggesting that DNNs may play a critically enabling role in the future of modelling complex flows.
Robust Online Motion Planning with Regions of Finite Time Invariance

In this paper we consider the problem of generating motion plans for a nonlinear dynamical system that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, disturbances, and/or errors in state estimation. Furthermore, we consider the case where these plans must be generated online, because constraints such as obstacles in the environment may not be known until they are perceived (with a noisy sensor) at runtime. Previous work on feedback motion planning for nonlinear systems was limited to offline planning due to the computational cost of safety verification. Here we take a trajectory library approach by designing controllers that stabilize the nominal trajectories in the library and precomputing regions of finite time invariance (“funnels”) for the resulting closed loop system. We leverage sums-of-squares programming in order to efficiently compute funnels which take into account bounded disturbances and uncertainty. The resulting funnel library is then used to sequentially compose motion plans at runtime while ensuring the safety of the robot. A major advantage of the work presented here is that by explicitly taking into account the effect of uncertainty, the robot can evaluate motion plans based on how vulnerable they are to disturbances. We demonstrate our method on a simulation of a plane flying through a two dimensional forest of polygonal trees with parametric uncertainty and disturbances in the form of a bounded “cross-wind”.
The ligament anatomy of the deltoid complex of the ankle: a qualitative and quantitative anatomical study.

BACKGROUND
The deltoid ligament has both superficial and deep layers and consists of up to six ligamentous bands. The prevalence of the individual bands is variable, and no consensus as to which bands are constant or variable exists. Although other studies have looked at the variance in the deltoid anatomy, none have quantified the distance to relevant osseous landmarks.


METHODS
The deltoid ligaments from fourteen non-paired, fresh-frozen cadaveric specimens were isolated and the ligamentous bands were identified. The lengths, footprint areas, orientations, and distances from relevant osseous landmarks were measured with a three-dimensional coordinate measurement device.


RESULTS
In all specimens, the tibionavicular, tibiospring, and deep posterior tibiotalar ligaments were identified. Three additional bands were variable in our specimen cohort: the tibiocalcaneal, superficial posterior tibiotalar, and deep anterior tibiotalar ligaments. The deep posterior tibiotalar ligament was the largest band of the deltoid ligament. The origins from the distal center of the intercollicular groove were 16.1 mm (95% confidence interval, 14.7 to 17.5 mm) for the tibionavicular ligament, 13.1 mm (95% confidence interval, 11.1 to 15.1 mm) for the tibiospring ligament, and 7.6 mm (95% confidence interval, 6.7 to 8.5 mm) for the deep posterior tibiotalar ligament. Relevant to other pertinent osseous landmarks, the tibionavicular ligament inserted at 9.7 mm (95% confidence interval, 8.4 to 11.0 mm) from the tuberosity of the navicular, the tibiospring inserted at 35% (95% confidence interval, 33.4% to 36.6%) of the spring ligament's posteroanterior distance, and the deep posterior tibiotalar ligament inserted at 17.8 mm (95% confidence interval, 16.3 to 19.3 mm) from the posteromedial talar tubercle.


CONCLUSIONS
The tibionavicular, tibiospring, and deep posterior tibiotalar ligament bands were constant components of the deltoid ligament. The deep posterior tibiotalar ligament was the largest band of the deltoid ligament.


CLINICAL RELEVANCE
The anatomical data regarding the deltoid ligament bands in this study will help to guide anatomical placement of repairs and reconstructions for deltoid ligament injury or instability.
Use of sequential quadrupling dose regimens to study efficacy of inhaled corticosteroids in asthma.

BACKGROUND
Inhaled corticosteroids are widely used to treat asthma. There is a need to be able to compare different inhaled corticosteroids and different doses of an inhaled corticosteroid to determine potency and dose equivalence, but measuring efficacy in a dose related manner is difficult because of their slow onset of action. There is uncertainty about the role of sequential dosing regimens and the best end point for such studies. We have explored the use of sequential quadrupling dose regimens and a range of end points to assess the response to budesonide in subjects with asthma.


METHODS
21 subjects with mild asthma, aged 18-65, took part in a randomised three way crossover study comparing two sequential and one non-sequential regimen, separated by at least 3 weeks. The sequential regimens consisted of increasing doses of inhaled budesonide (100, 400 1600 microg/day) with each dose being given for 1 or 2 weeks; the non-sequential regimen consisted of 1600 microg/day for 2 weeks with end points measured after 1 and 2 weeks. The end points studied included the provocative dose of adenosine monophosphate causing a 20% fall in forced expiratory volume in 1 second (PD20AMP), lung function, symptoms, and bronchodilator use.


RESULTS
There was a dose related increase in PD20AMP with both sequential dose regimens. The increase in PD20AMP ranged from 1.49 doubling doses (DD) following the lowest dose (100 microg/day) to 3.1 DD following the highest dose (1600 microg/day) in the 1 week sequential regimen and from 1.98 to 4.03 DD in the 2 week sequential regimen; standard deviations (SD) for the changes in PD20AMP ranged from 1.3 to 2.6 DD. Changes in forced expiratory volume in 1 second (FEV1) and morning peak expiratory flow rate (PEFR) were dose related but small and more variable (maximum change in FEV1=148 ml, SD 228 ml), while changes in evening PEFR, symptoms, and bronchodilator use were small and not dose related. Change in PD20AMP after budesonide 1600 microg did not differ significantly between regimens.


CONCLUSION
Combining PD20AMP measurements with a sequential regimen of three quadrupling doses of an inhaled corticosteroid given for 1 or 2 weeks provides clear dose-response curves for comparative studies. PD20AMP is a more sensitive end point for this purpose than FEV1, PEFR, symptoms, or relief inhaler use.
SOCIAL CAPITAL : Its Origins and Applications in Modern Sociology

This paper reviews the origins and definitions of social capital in the writings of Bourdieu, Loury, and Coleman, among other authors. It distinguishes four sources of social capital and examines their dynamics. Applications of the concept in the sociological literature emphasize its role in social control, in family support, and in benefits mediated by extrafamilial networks. I provide examples of each of these positive functions. Negative consequences of the same processes also deserve attention for a balanced picture of the forces at play. I review four such consequences and illustrate them with relevant examples. Recent writings on social capital have extended the concept from an individual asset to a feature of communities and even nations. The final sections describe this conceptual stretch and examine its limitations. I argue that, as shorthand for the positive consequences of sociability, social capital has a definite place in sociological theory. However, excessive extensions of the concept may jeopardize its heuristic value. Alejandro Portes: Biographical Sketch Alejandro Portes is professor of sociology at Princeton University and faculty associate of the Woodrow Wilson School of Public Affairs. He formerly taught at Johns Hopkins where he held the John Dewey Chair in Arts and Sciences, Duke University, and the University of Texas-Austin. In 1997 he held the Emilio Bacardi distinguished professorship at the University of Miami. In the same year he was elected president of the American Sociological Association. Born in Havana, Cuba, he came to the United States in 1960. He was educated at the University of Havana, Catholic University of Argentina, and Creighton University. He received his MA and PhD from the University of Wisconsin-Madison. 0360-0572/98/0815-0001$08.00 1 A nn u. R ev . S oc io l. 19 98 .2 4: 124 . D ow nl oa de d fr om w w w .a nn ua lr ev ie w s. or g A cc es s pr ov id ed b y St an fo rd U ni ve rs ity M ai n C am pu s R ob er t C ro w n L aw L ib ra ry o n 03 /1 0/ 17 . F or p er so na l u se o nl y. Portes is the author of some 200 articles and chapters on national development, international migration, Latin American and Caribbean urbanization, and economic sociology. His most recent books include City on the Edge, the Transformation of Miami (winner of the Robert Park award for best book in urban sociology and of the Anthony Leeds award for best book in urban anthropology in 1995); The New Second Generation (Russell Sage Foundation 1996); Caribbean Cities (Johns Hopkins University Press); and Immigrant America, a Portrait. The latter book was designated as a centennial publication by the University of California Press. It was originally published in 1990; the second edition, updated and containing new chapters on American immigration policy and the new second generation, was published in 1996.
Interactional Coherence in CMC

Text-only CMC has been claimed to be interactionally incoherent due to limitations imposed by messaging systems on turn-taking and reference, yet its popularity continues to grow. In an attempt to resolve this apparent paradox, this study evaluates the coherence of computermediated interaction by surveying research on cross-turn coherence. The results reveal a high degree of disrupted adjacency, overlapping exchanges, and topic decay. Two explanations are proposed to account for the popularity of CMC despite its relative incoherence: the ability of users to adapt to the medium, and the advantages of loosened coherence for heightened interactivity and language play.
Technical Review on Chinese Digital Terrestrial Television Broadcasting Standard and Measurements on Some Working Modes

Technical details of the recently announced Chinese Digital Terrestrial Television Broadcasting Standard which defines the physical layer transmission protocol including the frame structure, channel coding and modulation schemes are discussed in this paper. The major differences between Chinese DTV standard in multi-carrier working mode and DVB-T standard, all occupying the same 8 MHz baseband bandwidth, are addressed. The measurement results of several working modes showing satisfactory receiving performance of high and standard definition TV signals under both indoor and outdoor environments for the fixed and mobile reception are also presented
Evaluation of the impact of fibromyalgia on patients' sleep and the content validity of two sleep scales

BACKGROUND
Disturbed sleep is commonly reported in fibromyalgia (FM). Both the Sleep Quality Numeric Rating Scale (NRS) and the Medical Outcomes Study Sleep Scale (MOS-Sleep) have demonstrated positive psychometric properties in patients with FM. However, these assessments were developed prior to the current recommendation to include patient input during the concept elicitation or item generation phases. Therefore, the objective of this study was to evaluate the impact of FM on participants, including their sleep, and to test the content validity of these two sleep measures in FM patients.


METHODS
Qualitative interviews were conducted in Raleigh, North Carolina and Detroit, Michigan with 20 adults who reported a physician diagnosis of FM. Sixteen participants were female, 13 were white, and the average age was 50 years. Two researchers conducted all interviews using a structured guide.


RESULTS
Participants consistently reported that FM had a debilitating impact on their lives and their sleep, particularly getting to sleep and staying asleep. Participants responded positively to the Sleep Quality NRS as an assessment of their sleep. The majority of participants stated that they would not change the response numbering or wording of the item's anchors. Participants also responded positively to the 24-hour recall period of the Sleep Quality NRS. Participants found the 12-item MOS-Sleep to be appropriate and relevant; 19 participants indicated the measure captured all of their sleep-related symptoms. However, areas for potential modification were identified, such as the need to separate the item regarding awakening short of breath and awakening with a headache into two separate questions. Participants also questioned the relevance of the snoring and awakening short of breath items to FM. Half of participants expressed a preference for a daily rather than a weekly recall period.


CONCLUSION
This study demonstrates the significant impact that FM has on patients' lives, particularly sleep. While patients with FM were not part of the development of the generic sleep assessments that were evaluated, this study provides evidence of their content validity, supporting their use in FM studies. Modifications to the MOS-Sleep may improve the psychometric properties and relevance to patients with FM.
Generating Topical Poetry

We describe Hafez, a program that generates any number of distinct poems on a usersupplied topic. Poems obey rhythmic and rhyme constraints. We describe the poetrygeneration algorithm, give experimental data concerning its parameters, and show its generality with respect to language and poetic form.