nils-herrmann/clean_scirepeval huggingface dataset:
Early Detection of Heart Failure Using Electronic Health Records: Practical Implications for Time Before Diagnosis, Data Diversity, Data Quantity, and Data Density
Venue: Circulation. Cardiovascular quality and outcomes

Using electronic health records data to predict events and onset of diseases is increasingly common. Relatively little is known, although, about the tradeoffs between data requirements and model utility.  We examined the performance of machine learning models trained to detect prediagnostic heart failure in primary care patients using longitudinal electronic health records data. Model performance was assessed in relation to data requirements defined by the prediction window length (time before clinical diagnosis) the observation window length (duration of observation before prediction window) the number of different data domains (data diversity) the number of patient records in the training data set (data quantity) and the density of patient encounters (data density) A total of 1684 incident heart failure cases and 13 525 sex, age category, and clinic matched controls were used for modeling. Model performance improved as (1) the prediction window length decreases, especially when <2 years; (2) the observation window length increases but then levels off after 2 years; (3) the training data set size increases but then levels off after 4000 patients; (4) more diverse data types are used, but, in order, the combination of diagnosis, medication order, and hospitalization data was most important; and (5) data were confined to patients who had =10 phone or face to face encounters in 2 years.  These empirical findings suggest possible guidelines for the minimum amount and type of data needed to train effective disease onset predictive models using longitudinal electronic health records data.
Algorithmic Labor and Information Asymmetries: A Case Study of Uber's Drivers

Uber manages a large, disaggregated workforce through its ridehail platform, one that delivers a relatively standardized experience to passengers while simultaneously promoting its drivers as entrepreneurs whose work is characterized by freedom, flexibility, and independence. Through a nine month empirical study of Uber driver experiences, we found that Uber does leverage significant indirect control over how drivers do their jobs. Our conclusions are twofold: First, the information and power asymmetries produced by the Uber application are fundamental to its ability to structure control over its workers; second, the rhetorical invocations of digital technology and algorithms are used to structure asymmetric corporate relationships to labor, which favor the former. Our study of the Uber driver experience points to the need for greater attention to the role of platform disintermediation in shaping power relations and communications between employers and workers.
Developing a road safety review tool to identify design standard and safety deficits on high risk road sections

The European Safety Review Tool (ESReT) project has developed a tool to support the review of road sections that have been identified as 'high risk' according to network screening processes. The tool provides a methodology to practitioners for the collection and analysis of road attribute data using low cost, non specialist equipment. The overall result is a consistent and repeatable approach to the investigation and assessment of high risk road sections. There are two main parts of the assessment performed by the tool. The first part is a comparison of road attributes against design standards; the optional second part is a comparison of road attributes against 'safety rules' The road design standard check allows road authorities to compare the attributes of existing heritage roads with current design standards. This allows road authorities to identify where, and in what way, modern design rules are not met. Various design standard attributes are included in the tool including: median width, lane width, shoulder type and width, verge width, curve radius, crests and sags, stopping sight distance and intersection type. The optional 'safety' check allows the road authority to see where key safety rules are violated. This means aspects of the road that may contribute to high severity crashes are identified along with aspects of the road that may lack credibility to the road user. Attributes relating to safety include median type, clear zones (median and roadside) intersection type and frequency, stopping sight distance, adjacent land use, presence of pedestrian and cycle facilities etc. The ESReT system allows road attribute information to be visualised and reviewed by the road authority, and presents the results of the road design standard and safety checks by showing deficits both in tabular form and on interactive maps. The ESReT system does not aim to provide a definite design solution to the road authority, rather it provides experienced practitioners with standardised information about the road along with suggestions so that they can develop the best solutions based on their experience and local knowledge. This research was carried out as part of the Conference of European Directors of Roads (CEDR) Transnational Road Research Programme Call 2013. The funding for the research was provided by the national road administrations of the Netherlands, Germany and Ireland.
Research on Key Technologies of Urban Road Safety under the condition of Big Data

To solve the urban road safety, congestion, pollution and efficiency, we need grasp accurate real time traffic conditions of the city. Intelligent Traffic need to discover rules and provides various services through processing, analysis and mining massive real time and historical data. Big Data technology is an effective way to achieve this purpose. Based on the analysis and comparison of the current popular processing platforms and technologies of the big data, this paper designs a new framework for processing traffic big data that is fit for both batch processing and real time processing at the same time, and then to achieve data collection, storage, correlation, semantic processing and analysis of the traffic big data through this new framework. The experimental results show that the framework can real time, fast and accurate processing and calculating traffic big data.
The Contribution of High Order Metabolic Interactions to the Global Activity of a Four Species Microbial Community
Venue: PLoS Comput. Biol.

The activity of a biological community is the outcome of complex processes involving interactions between community members. It is often unclear how to accurately incorporate these interactions into predictive models. Previous work has shown a range of positive and negative metabolic pairwise interactions between species. Here we examine the ability of a modified general Lotka Volterra model with cell cell interaction coefficients to predict the overall metabolic rate of a well mixed microbial community comprised of four heterotrophic natural isolates, experimentally quantifying the strengths of two, three, and four species interactions. Within this community, interactions between any pair of microbial species were positive, while higher order interactions, between 3 or more microbial species, slightly modulated community metabolism. For this simple community, the metabolic rate of can be well predicted only with taking into account pairwise interactions. Simulations using the experimentally determined interaction parameters revealed that spatial heterogeneity in the distribution of cells increased the importance of multispecies interactions in dictating function at both the local and global scales.
Transitioning from Microbiome Composition to Microbial Community Interactions: The Potential of the Metaorganism Hydra as an Experimental Model
Venue: Front. Microbiol.

Animals are home to complex microbial communities, which are shaped through interactions within the community, interactions with the host, and through environmental factors. The advent of high throughput sequencing methods has led to novel insights in changing patterns of community composition and structure. However, deciphering the different types of interactions among community members, with their hosts and their interplay with their environment is still a challenge of major proportion. The emerging fields of synthetic microbial ecology and community systems biology have the potential to decrypt these complex relationships. Studying host associated microbiota across multiple spatial and temporal scales will bridge the gap between individual microorganism studies and large scale whole community surveys. Here, we discuss the unique potential of Hydra as an emerging experimental model in microbiome research. Through in vivo, in vitro, and in silico approaches the interaction structure of host associated microbial communities and the effects of the host on the microbiota and its interactions can be disentangled. Research in the model system Hydra can unify disciplines from molecular genetics to ecology, opening up the opportunity to discover fundamental rules that govern microbiome community stability.
A deep learning framework for hyperspectral image classification using spatial pyramid pooling

In this letter, a new deep learning framework for spectral spatial classification of hyperspectral images is presented. The proposed framework serves as an engine for merging the spatial and spectral features via suitable deep learning architecture: stacked autoencoders (SAEs) and deep convolutional neural networks (DCNNs) followed by a logistic regression (LR) classifier. In this framework, SAEs is aimed to get useful high level features for the one dimensional features which is suitable for the dimension reduction of spectral features, while DCNNs can learn rich features from the training data automatically and has achieved state of the art performance in many image classification databases. Though the DCNNs has shown robustness to distortion, it only extracts features of the same scale, and hence is insufficient to tolerate large scale variance of object. As a result, spatial pyramid pooling (SPP) is introduced into hyperspectral image classification for the first time by pooling the spatial feature maps of the top convolutional layers into a fixed length feature. Experimental results with widely used hyperspectral data indicate that classifiers built in this deep learning based framework provide competitive performance.
Vehicle detection from high resolution aerial images using spatial pyramid pooling based deep convolutional neural networks
Venue: Multimedia Tools and Applications

In recent years, vehicle detection from aerial images obtained using unmanned aerial vehicles (UAVs) has become a research focus in image processing as remote sensing platforms on UAVs are rapidly popularised. This study proposes a detection algorithm using a deep convolutional neural network (DCNN) based on multi scale spatial pyramid pooling (SPP) By using multi scale SPP models to sample characteristic patterns with different sizes, feature vectors with a fixed length are generated. This avoids the stretching or cropping induced deformation of input images of different sizes, thus improving the detection effect. In addition, an imaging pre processing algorithm based on maximum normed gradient (NG) with multiple thresholds is proposed. By using this algorithm, this research restores the edges of objects disturbed by clutter in the environment. Meanwhile, the raised candidate object extraction algorithm based on the maximum binarized NG entails fewer computations as it generates fewer candidate windows. Experimental results indicate that the multi scale SPP based DCNN can better adapt to input images of different sizes to learn of the multi scale characteristics of objects, thus further improving the detection effect.
Experiences of immigrant professors cross cultural differences, challenges, and lessons for success

1. The Private and Professional Lives of Foreign Born Professors: From Abstractions to the Practical. Charles B. Hutchison. 2. Foreign Born Professors and Their Quests for Social and Psychological Home: Integrating Social, Cultural, and Professional Dimensions of Life.  The "Unusual" Professors: The Experiences and Impact of Foreign Professors in Post Franco Spain. Alice Gail Bier and Louis Lemkow. 5. Conversations on Ethnicity, Adaptation and Belonging: Auto Ethnography at the Base of the Ivory Tower. Marcia D. Nichols, Aminul Huq Bijaya Aryal, and Xavier Prat Resina. 6. Voices From Behind the Scenes: How Foreign Born English Teachers Find Their Place in the French Education System. Claire Griffin. 7. Challenges and Self Efficacy of Female East Asian Born Faculty  True Meaning of Integrity: Reflections on a Professional Life in Japan. Robin Sakamoto. 9.Moroccan Imams and the "Girl Professor" Positionality and Epistemai in a Classroom Based Cross Cultural Exchange of Knowledge. Emilie Roy. 10. Not So Fast: Navigating the Complexities of Teaching in an American University as a Foreign Born Teacher Educator. Eucabeth Odhiambo. 11. My Professional Teaching Experience in the USA, 1977 2014: The Case of an African Born Immigrant. Peter F. B. Nayenga. 12. Negotiating the Trilogy of Black ness, "African ness" and "Accent ness" A "Native Alien" Professor's Tale.  From Essential and Central to Constructivist Trenches: Navigating the Transnational Contexts of the Instructional Practice of a Foreign Born Professor. Fonkem Achankeng. 14. Unpacking the Invisible Knapsack of African Born Professor's Identity in the U.S. Academe. Michael Takafor Ndemanu. 15. Not an Easy Road: Journey of a Jamaican Academic.  At Home and Abroad. Mohanalakshmi Rajakumar. 20. " come e duro calle lo scendere e 'l salir per l'altrui scale" Musings of a Foreign Born Philosopher in the American Academy.  The Unified Voices of Immigrant Professors and Their Cross Cultural Teaching Lives: Lessons Learned. Charles B. Hutchison.
Fairness and devotion go far: Integrating online justice and value co creation in virtual communities
Venue: Int. J. Inf. Manag.

We identify five distinct perceived online justice dimensions in virtual communities.We examine both in and extra role value co creation behavior in virtual communities.Sense of virtual community mediates the influence of perceived online justice on value co creation behavior.Virtual community practitioners can develop, maintain, and sustain the virtual community by helping members form fairness perceptions during participation. Although building long term, successful virtual communities is important, rare studies have examined both in and extra role value co creation behaviors from the perspective of social exchange theory and equity theory. Specially, we incorporate five different online justice perceptions into our model and examine the mediating role of "sense of virtual community" between these perceived online justice antecedents and both in and extra role online value co creation behavior (reflected by knowledge contribution and online community citizenship behaviors, respectively) We empirically examine the model using data from 278 members of virtual communities. The results reveal that perceived online justice leads to value co creation behavior through sense of virtual community. The findings elicit several implications for theory and practice.
Devoted to you my love: brand devotion amongst young consumers in emerging Indian market

Research investigating brand devotion among young consumers is scarce particularly in emerging Asian market context. The purpose of this paper is to qualitatively explore the evolutionary process leading to brand devotion among the young Indian consumers.  Semi structured depth interviews were conducted to collect data. Data were analyzed using grounded theory method.  The grounded theory framework developed shows single brand devotion in a product category as the key phenomenon along with the associated factors and evolutionary processes.  This study explores how single brand devotion is distinct from romantic brand love, as experienced by consumers. Value of this paper also lies in developing a grounded theory framework for single brand devotion in emerging Asian market context.
Video based respiration monitoring with automatic region of interest detection.
Venue: Physiological measurement

Vital signs monitoring is ubiquitous in clinical environments and emerging in home based healthcare applications. Still, since current monitoring methods require uncomfortable sensors, respiration rate remains the least measured vital sign. In this paper, we propose a video based respiration monitoring method that automatically detects a respiratory region of interest (RoI) and signal using a camera. Based on the observation that respiration induced chest/abdomen motion is an independent motion system in a video, our basic idea is to exploit the intrinsic properties of respiration to find the respiratory RoI and extract the respiratory signal via motion factorization. We created a benchmark dataset containing 148 video sequences obtained on adults under challenging conditions and also neonates in the neonatal intensive care unit (NICU) The measurements obtained by the proposed video respiration monitoring (VRM) method are not significantly different from the reference methods (guided breathing or contact based ECG; p value 0.6) and explain more than 99% of the variance of the reference values with low limits of agreement 2.67 to 2.81 bpm) VRM seems to provide a valid solution to ECG in confined motion scenarios, though precision may be reduced for neonates. More studies are needed to validate VRM under challenging recording conditions, including upper body motion types.
Combining Region of Interest Extraction and Image Enhancement for Nighttime Vehicle Detection
Venue: IEEE Intelligent Systems

In nighttime images, vehicle detection is a challenging task because of low contrast and luminosity. In this article, the authors combine a novel region of interest (ROI) extraction approach that fuses vehicle light detection and object proposals together with a nighttime image enhancement approach based on improved multiscale retinex to extract accurate ROIs and enhance images for accurate nighttime vehicle detection. Experimental results demonstrate that the proposed nighttime image enhancement method, score level multifeature fusion, and the ROI extraction method are all effective for nighttime vehicle detection. But the proposed vehicle detection method demonstrates 93.34 percent detection rate and outperforms other models, detecting blurred and partly occluded vehicles, as well as vehicles in a variety of sizes, numbers, locations, and backgrounds.
Media ethics theorizing, reoriented: A shift in focus for individual level analyses

This project argues that multidisciplinary methods and work to reconsider key concepts are critical if media ethics scholarship is to continue to mature. It identifies 3 dimensions of a reoriented framework for media ethics theory: one that conceptualizes moral motivation as the focus of inquiry at the individual level; another that focuses on promising assessments of autonomy and organizational influences for a transformed media landscape; and a third that applies formalist virtue ethics as the best framework for normative claims arising from the first 2.
Universals Without Absolutes: A Theory of Media Ethics

The global turn in media ethics has presented a tough challenge for traditional models of moral theory: How do we assert common moral standards while also showing respect for the values of those from outside the Western tradition? The danger lies in advocating for either extreme: reason dependent absolutism or cultural relativism. In this paper, I reject Cliff Christian's attempts to solve the problem and propose instead a moral theory of universal standards that are discovered via a mix of rationally grounded methods. Such universality refutes relativism but, because it is grounded in evolutionary naturalism and life world philosophy as opposed to a Kantian or theological transcendentalism it also avoids absolutism.
Lifetime Family Violence and Depression: The Case of Older Women in South Korea
Venue: Journal of Family Violence

The current study examined the prevalence of lifetime family violence among older women (N 525) and the influence of family violence on depression using data from a national survey in South Korea. The major findings were that 49.3 of respondents had been victimized from lifetime family violence, the prevalence of childhood maltreatment was 37.1 and intimate partner violence was 23.4 Approximately, 55 of participants had clinical depression, which was significantly associated with reported experiences of family violence. The findings suggest that family violence and depression are common social problems and that individualized intervention for older women in South Korea is needed.
Grammatica, gramadach and gramadeg vernacular grammar and grammarians in medieval Ireland and Wales

1. Foreword acknowledgements 2. Abbreviations 3. List of plates 4. Notes on contributors 5. Editors' introduction 6. Allegory, the aes dana and the liberal arts in Medieval Irish literature (by Boyle, Elizabeth) 7. Cryptography and the alphabet in the "Book of Adhamh O Cianain" (by Hayden, Deborah) 8. Caide Mathair Breithre "What is the Mother of a Word" Thinking about words in Medieval Ireland (by Poppe, Erich) 9. The expression of "sense, meaning, signification" in the Old Irish glosses, and particularly in the Milan and Saint Gall glosses (by Lambert, Pierre Yves) 10. The verbal paradigms in Auraicept na nEces (by Ahlqvist, Anders) 11. The glossing of the Early Irish law tracts (by Breatnach, Liam) 12. Teaching between the lines: Grammar and Grammatica in the classroom in Early Medieval Wales (by Russell, Paul) 13. The Welsh bardic grammars on Litterae (by Charles Edwards, T.M. 14. Poetry by numbers: the poetic triads in Gramadegau Penceirddiaid (by Russell, Paul) 15. Gramadeg Gwysanau: A fragment of 14th Century Welsh bardic grammar (by Parry Owen, Ann) 16. Master list of references 17. Index of manuscripts 18. Index of subjects 19. Index of terms by language
Effects of non sinusoidal pitching motion on energy extraction performance of a semi active flapping foil

Numerical simulations are used to study the energy harvester based on a semi active flapping foil, in which the profile of the pitching motion is prescribed and the heaving motion is activated by the vertical hydrodynamic force. We consider a two dimensional NACA0015 airfoil with the Reynolds number Re 1000. First, for the sinusoidal pitching, an optimal combination of the parameters of pitching amplitude th0 75deg and reduced frequency f* 0.16 is identified, with the highest energy harvesting efficiency of 32% being recorded. Then we study non sinusoidal pitching, with a gradual change from a sinusoid to a square wave as b is increased from one. We find that its effect of efficiency enhancement is limited for the parameters approaching their optimal values, and the upper boundary of the efficiency appears not to be increased. In detail, we report that when the pitching amplitude is small, non sinusoidal pitching motions can indeed improve the performance of the system. However, when both the pitching amplitude and the flapping frequency are close to their optimal values, non sinusoidal pitching motions contribute negatively to the harvesting efficiency. We suggest that a non sinusoidal profile, at least a simple trapezoidal like one is ineffective in the semi active system reported by the current study.