nils-herrmann/clean_scirepeval huggingface dataset:
Sistema de control interno con enfoque en la ISO 9001: 2015 en la bananera Monterrey
Venue: Revista Arbitrada Interdisciplinaria Koinonia
Year: 2019

Recibido: 1 de septiembre de 2019Aprobado: 30 de septiembre de 2019El presente articulo tiene como objetivo disenar un sistema de control interno bajo el enfoque de las normas ISO 9001:2015. Para ello, se desarrollo un analisis de los sistemas de control interno, los componentes del informe Coso (Committee of Sponsoring Organizations of the Treadway Comission) y las ISO 9001 2015. Se pudo concluir que la articulacion de estas teorias permite generar sistemas de control interno que garantizan la eficiencia y eficacia de las operaciones en la organizaciones. La investigacion permitio ademas evaluar las actividades de control interno que se ejecutan en la bananera Monterrey localizada en la provincia de El Oro, Ecuador, en donde se establecio que su riesgo de control es moderado y su nivel de confianza bajo. Ante estos resultados, finalmente se planteo un nuevo sistema de control interno que genere beneficios relacionados con el uso de los recursos, la optimizacion de los procesos y el aumento de la satisfaccion del cliente.
Health expenditure, human capital, and economic growth: an empirical study of developing countries
Venue: International Journal of Health Economics and Management
Year: 2019

Social security systems were successively established in most developing countries in the 1980s and 1990s. To ensure the long term sustainability of these newly established systems it is essential to carefully monitor the economic impact. Based on the panel data of 21 developing countries from 2000 to 2016, this paper is the first to apply the panel threshold model to empirically analyze the relationship between national health expenditures and economic growth under different levels of human capital. The results show that health expenditure and economic growth have significant interval effects because of the different levels of human capital. Specifically, when human capital levels are low, health expenditure is significantly negatively correlated with economic growth. When human capital is at a medium level, health expenditure has a positive but not significant impact on economic growth. When the level of human capital is high, the positive economic impact of the health expenditure is significantly enhanced. In addition, subgroup analyses indicate that population aging and low fertility aggravate the negative impact of health expenditures on economic growth. This study provides reliable analysis and can be used by developing countries to maintain a long term sustainable social security system.
Forensic analysis of System Restore points in Microsoft Windows XP
Venue: Digit. Investig.
Year: 2006

Investigating computer intrusions is becoming infinitely more complicated with the advancement of post exploitation techniques currently being used by attackers. We must continually update our traditional forensic techniques to include the more rare investigative steps. Analysis of System Restore points is one of these steps. This article will illustrate how a forensic examiner analyzed System Restore points to reveal traces of evidence which ultimately lead to the complete understanding of the computer and subsequent bank account compromises.
Speed Invariant Gait Recognition Using Single Support Gait Energy Image
Venue: Multimedia Tools and Applications
Year: 2019

Gait is one of the most popular behavioral biometrics because it can be authenticated at a distance from a camera without subject cooperation. Speed differences between matching pairs, however, cause significant performance drops in gait recognition, and gait mode difference (i.e. walking versus running) makes gait recognition further challenging. We therefore propose a speed invariant gait representation called single support GEI (SSGEI) which realizes a good trade off between speed invariance and stability by aggregating multiple frames around single support phases. In addition, to mitigate the pose differences between walking and running modes at single support phases, we morph walking and running SSGEIs into intermediate SSGEIs between walking and running mode, where we exploit a free form deformation field from the walking or running modes to the intermediate mode obtained by training data. We finally apply Gabor filtering and spatial metric learning as postprocessing for further accuracy improvement. Experiments on two publicly available datasets, the OU ISIR Treadmill Dataset A and the CASIA C Dataset demonstrate that the proposed method yields the state of the art accuracies in both identification and verification scenarios with a low computational cost.
Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling
Venue: NAACL
Year: 2019

Recurrent Variational Autoencoder has been widely used for language modeling and text generation tasks. These models often face a difficult optimization problem, also known as KL vanishing, where the posterior easily collapses to the prior and model will ignore latent codes in generative tasks. To address this problem, we introduce an improved Variational Wasserstein Autoencoder (WAE) with Riemannian Normalizing Flow (RNF) for text modeling. The RNF transforms a latent variable into a space that respects the geometric characteristics of input space, which makes posterior impossible to collapse to the non informative prior. The Wasserstein objective minimizes the distance between marginal distribution and the prior directly and therefore does not force the posterior to match the prior. Empirical experiments show that our model avoids KL vanishing over a range of datasets and has better performance in tasks such as language modeling, likelihood approximation, and text generation. Through a series of experiments and analysis over latent space, we show that our model learns latent distributions that respect latent space geometry and is able to generate sentences that are more diverse.
W15 Improved estimation of human cortical activity and connectivity with the multimodal integration of high resolution EEG recordings and hemodynamic data
Venue: Clinical Neurophysiology
Year: 2006

Nowadays, several types of brain imaging device are available to provide images of the functional activity of the cerebral cortex based on hemodynamic, metabolic, or electromagnetic measurements. However, static images of brain regions activated during particular tasks do not convey the information of how these regions communicate with each other. In this study, advanced methods for the estimation of cortical connectivity from combined high resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data are presented. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from individual magnetic resonance images, multidipole source model, and regularized linear inverse source estimates of cortical current density. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by blockdesigned (strength of activated voxels) fMRI. We estimate functional cortical connectivity by computing the directed transfer function (DTF) on the estimated cortical current density waveforms in regions of interest (ROIs) on the modeled cortical mantle. The proposed method was able to unveil the direction of the information flow between the cortical regions of interest, as it is directional in nature. Furthermore, this method allowsto detect changes in the time course of information flow between cortical regions in different frequency bands. The reliability of these techniques was further demonstrated by elaboration of highresolution EEG and fMRI signals collected during visually triggered finger movements in four healthy subjects. Connectivity patterns estimated for this task reveal an involvement of right parietal and bilateral premotor and prefrontal cortical areas. This cortical region involvement resembles that revealed in previous studies where visually triggered finger movements were analyzed with the use of separate EEG or fMRI measurements
HUBUNGAN MOTIVASI, KECEMASAN, KONDISI FISIK, PERANAN ORANG TUA DAN PERSEPSI TERHADAP LINGKUNGAN BELAJAR DENGAN PRESTASI BELAJAR MAHASISWA DI POLITEKNIK KESEHATAN KEMENKES PALANGKA RAYA
Year: 2017

Rendahnya prestasi belajar berdampak terhadap rendahnya kualitas sumber daya manusia (SDM) lulusan tersebut. Rendahnya prestasi belajar merupakan faktor yang menjadi sorotan dunia pendidikan. Rendahnya prestasi belajar bukan hanya semata mata disebabkan oleh kurangnya kecerdasan tetapi banyak siswa yang telah memiliki kecerdasan dan motivasi tinggi namun prestasi belajarnya menurun. Hal tersebut dapat dipengaruhi oleh faktor lain, seperti kecemasan dalam menghadapi tes dan pelajaran serta keadaan psikologi dan lingkungan belajar siswa yang dapat mempengaruhi prestasi belajar siswa. 1 Tujuan Penelitian adalah untuk mengetahui hubungan antara Motivasi Belajar, Kecemasan, Kondisi Fisik, Peranan Orang Tua dan Persepsi Terhadap Lingkungan Belajar dengan prestasi belajar Mahasiswa di Politeknik Kesehatan Kemenkes Palangka Raya. Metoda Penelitian ini menggunakan penelitian kuantitatif dengan metode survei analitik menggunakan pendekatan studi Cross Sectional Data yang diperoleh pada penelitian ini dianalisis untuk mengetahui resiko variabel independen terhadap variabel dependen dengan menghitung nilai Prevalence Odd Ratio (POR) dan 95% Confidence Interval (CI) Dari hasil analisis pada variabel motivasi belajar diperoleh nilai p value sebesar 0,001, maka secara statistik dapat disimpulkan bahwa faktor motivasi belajar mempunyai hubungan yang signifikan terhadap prestasi belajar karena p value a (0,05) dengan nilai OR sebesar 4,263. Faktor kecemasan didapatkan nilai p value sebesar 0,005 maka secara statistik sisimpulkan hubungan yang signifikan terhadap prestasi belajar karena p value a (0,05) sehingga dapat disimpulkan terdapat hubungan yang signifikan antara kecemasan dengan prestasi belajar dengan nilai OR sebesar 3,202. Dari hasil analisis diatas didapatkan p value sebesar 0,049, maka secara statistik faktor kondisi fisik mempunyai hubungan yang signifikan terhadap prestasi belajar karena p a (0,05) dengan nilai OR sebesar 2,249. Secara statistik terdapat hubungan yang signifikan antara motivasi, kecemasan dan kondisi fisik dengan prestasi belajar. Kata Kunci Motivasi, Kecemasan, Kondisi Fisik, Peranan Orang Tua,Persepsi Lingkungan belajar, Prestasi Belajar.
Mirror, mirror on the wall: a comparative evaluation of composite based structural equation modeling methods
Year: 2017

Composite based structural equation modeling (SEM) and especially partial least squares path modeling (PLS) has gained increasing dissemination in marketing. To fully exploit the potential of these methods, researchers must know about their relative performance and the settings that favor each method's use. While numerous simulation studies have aimed to evaluate the performance of composite based SEM methods, practically all of them defined populations using common factor models, thereby assessing the methods on erroneous grounds. This study is the first to offer a comprehensive assessment of composite based SEM techniques on the basis of composite model data, considering a broad range of model constellations. Results of a large scale simulation study substantiate that PLS and generalized structured component analysis are consistent estimators when the underlying population is composite model based. While both methods outperform sum scores regression in terms of parameter recovery, PLS achieves slightly greater statistical power.
Health Care Reform, Length of Stay, and Readmissions for Child Mental Health Hospitalizations.
Venue: Hospital pediatrics
Year: 2020

Health care reform may impact inpatient mental health services by increasing access and changing insurer incentives. We examined whether implementation of the 2014 Affordable Care Act (ACA) was associated with changes in psychiatric length of stay (LOS) and 30 day readmissions for pediatric patients.  We conducted an interrupted time series analysis to evaluate LOS and 30 day readmissions during the 30 months before and 24 months after ACA implementation, with a 6 month wash out period, on patients aged 4 to 17 years who were discharged from the psychiatry unit of a children's hospital. Differences by payer (Medicaid versus non Medicaid) were examined in moderated interrupted time series. Logistic regression was used to examine the association between psychiatric LOS and 30 day readmissions.  There were 1874 encounters in the pre ACA period and 2186 encounters in the post ACA period. Compared with pre ACA implementation, post ACA implementation was associated with LOS that was significantly decreasing over time (pre ACA versus post ACA slope difference: 0.10 days per encounter per month [95% confidence interval 0.17 to 0.02] P .01) especially for Medicaid insured patients (pre ACA versus post ACA slope difference: 0.14 days per encounter per month [95% confidence interval 0.26 to 0.01] P .03) The overall proportion of 30 day readmissions increased significantly (pre ACA 6% post ACA 10% P .05 for the difference) We found no association between LOS and 30 day readmissions.  ACA implementation was associated with a decline in psychiatric inpatient LOS over time, especially for those on Medicaid, and an increase in 30 day readmissions. LOS was not associated with 30 day inpatient readmissions. Further investigation to understand the drivers of these patterns is warranted.
Learning Based Fingertip Force Estimation for Soft Wearable Hand Robot With Tendon Sheath Mechanism
Venue: IEEE Robotics and Automation Letters
Year: 2020

Soft wearable hand robots with tendon sheath mechanisms are being actively developed to assist people with lost hand mobility. For these robots, accurately estimating fingertip forces leads to successful object grasping. An approach can utilize information from actuators assuming quasi static environments. However, non linearity and hysteresis with regards to the dynamic changes of the tendon sheath mechanism hinder accurate fingertip force estimation. This paper proposes a learning based method to estimate fingertip forces by integrating dynamic information of motor encoders, wire tension, and sheath bending angles. The model is modified from Long Short Term Memory by incorporating a residual term that governs the dynamic changes in sheath bending angles. Using a tendon driven soft wearable hand robot, the proposed model obtained RMSE less than 0.44 N. It was further evaluated under criteria ranging from different object sizes, bending angle ranges, and forces. Finally, a repeatability test (0.46 N in RMSE) real time applicability (125 Hz) and force control (12.7% in MAPE) were performed to verify the feasibility of the proposed method.
DeepTracker: Visualizing the Training Process of Convolutional Neural Networks
Venue: ACM Trans. Intell. Syst. Technol.
Year: 2019

Deep convolutional neural networks (CNNs) have achieved remarkable success in various fields. However, training an excellent CNN is practically a trial and error process that consumes a tremendous amount of time and computer resources. To accelerate the training process and reduce the number of trials, experts need to understand what has occurred in the training process and why the resulting CNN behaves as such. However, current popular training platforms, such as TensorFlow, only provide very little and general information, such as training/validation errors, which is far from enough to serve this purpose. To bridge this gap and help domain experts with their training tasks in a practical environment, we propose a visual analytics system, DeepTracker, to facilitate the exploration of the rich dynamics of CNN training processes and to identify the unusual patterns that are hidden behind the huge amount of training log. Specifically,we combine a hierarchical index mechanism and a set of hierarchical small multiples to help experts explore the entire training log from different levels of detail. We also introduce a novel cube style visualization to reveal the complex correlations among multiple types of heterogeneous training data including neuron weights, validation images, and training iterations. Three case studies are conducted to demonstrate how DeepTracker provides its users with valuable knowledge in an industry level CNN training process, namely in our case, training ResNet 50 on the ImageNet dataset. We show that our method can be easily applied to other state of the art "very deep" CNN models.
Process Mining Dashboard in Operating Rooms: Analysis of Staff Expectations with Analytic Hierarchy Process
Venue: International journal of environmental research and public health
Year: 2019

The widespread adoption of real time location systems is boosting the development of software applications to track persons and assets in hospitals. Among the vast amount of applications, real time location systems in operating rooms have the advantage of grounding advanced data analysis techniques to improve surgical processes, such as process mining. However, such applications still find entrance barriers in the clinical context. In this paper, we aim to evaluate the preferred features of a process mining based dashboard deployed in the operating rooms of a hospital equipped with a real time location system. The dashboard allows to discover and enhance flows of patients based on the location data of patients undergoing an intervention. Analytic hierarchy process was applied to quantify the prioritization of the dashboard features (filtering data, enhancement, node selection, statistics, etc. distinguishing the priorities that each of the different roles in the operating room service assigned to each feature. The staff in the operating rooms (n 10) was classified into three groups: Technical, clinical, and managerial staff according to their responsibilities. Results showed different weights for the features in the process mining dashboard for each group, suggesting that a flexible process mining dashboard is needed to boost its potential in the management of clinical interventions in operating rooms. This paper is an extension of a communication presented in the Process Oriented Data Science for Health Workshop in the Business Process Management Conference 2018.
Climate Change as Social Drama: Global Warming in the Public Sphere
Year: 2015

On a landscape of high quality science and increasing policy sophistication regarding the intricacies of 21st century anthropogenic climate change, climate conversations are generally stuck. Explained primarily through the tools of Aristotle's texts Poetics and Rhetoric, Philip Smith and Nicolas Howe posit that "although the social drama of climate change has come into being, it is by no means potent or very well organized as a cultural system. It is somewhat disorganized drama marked by incoherence, disengagement, and proliferation as much as compression, commitment and consensus" (p. 53) As such, in Climate Change as Social Drama, the authors embark on a quest to unravel blockages associated with this discursive mess so that climate change can become "a full fledged, universal social drama that would compel decisive public action and institutional reform" (p. 53) While normative undercurrents here clearly flow into movements toward downstream collective social action, Smith and Howe characterize their contribution as pointing "to the need for a considered effort at balanced communication that respects the dignity of both speakers and audience and recognizes the serious nature of the issue at hand" (p. 207) Analyses in the book are animated by an array of 'domains and illustrations focused primarily in U.S. and U.K. contexts from thematic takes (like representations of climate conferences) to episodic adventures (like the University of East Anglia email hacking scandal (affectionately dubbed "Climate gate" They approach these contemporary cases through "social drama" they argue that Aristotle's take on cultural structures, emotional impacts, performance, and claims making helps readers consider climate change more seriously. It is an ambitious treatise. In my view, the book's value derives from insights gained along the journey. Smith and Howe do well to place the many universal and predictable themes and elements of social dramas including actors (protagonists, antagonists, witnesses) rhetoric (logos, pathos, and ethos) motivations, locations, plots, genres, and audiences in contemporary and dynamic 21st century climate change contexts. For example, explorations of how youth activists "occupy a precarious position in the civil sphere" (p. 162) provide a refreshing take on important intergenerational dimensions of an unfolding climate change social drama on the stage of international policy negotiations. While vintage Aristotle helps leverage our interrogation of modern predicaments, I found that the authors' excessive attachment to the Greek philosopher at times got in the way of optimally productive critique. For example, comedic irony as a carrier of productive climatemeaningwas soundly Book Reviews