nils-herrmann/clean_scirepeval huggingface dataset:
Theatre, Celebrity, and Contagion: David Garrick's 1742 Dublin Visit and James R. Planche's Garrick Fever
Year: 2018

Garrick, the rising star of the London stage, tread the boards of Dublin's Theatre Royal in Smock Alley. On his way to becoming the most famous actor of his century, Garrick treated his audience to a new role that he would play to packed audiences for decades to come, Shakespeare's Prince Hamlet. In return, Dublin gave him both his permanent moniker of the "English Roscius" and a potent anecdote that mythologised his impact on theatrical audiences: Garrick fever.1 by all accounts, Dublin society's unbridled enthusiasm for the young English thespian transformed his first visit to Ireland into a significant medical as well as cultural event. Garrick's popularity apparently hastened the spread of "an epidemic fever in Dublin on account of the crowded audiences at the theatre" (Murphy 1: 34) This temporally and geographically specific illness, also dubbed Garrick fever, was conflated with the fervour stirred by Garrick's reputation and presence. The phenomenon of Garrick fever, part of the episodic narrative of the Georgian actor celebrity's meteoric rise, was used to signal that Garrick himself was a phenomenon. The actor's early biographers beginning with Arthur Murphy and Thomas Davies repeated the tantalising story of Dublin's infection, as did a bevy of late eighteenth and nineteenth century theatre historians. What is more, the prolific victorian playwright James Robinson Planche went so far as to compose Garrick Fever (1839) a one act farce inspired by this anecdote. 78 THEATRE NOTEbOOK
Influence of Cloud Based Mobile Learning Applications on User Experiences: A Review Study in the Context of Jordan
Venue: Int. J. Interact. Mob. Technol.
Year: 2017

Beside the increasing trend of cloud computing and mobile applications, the use of cloud based mobile learning applications is also mounting. Almost every e commerce service provider offers cloud based mobile learning applications so that they can target more visitors and ultimately increase their sales. The usability of cloud based mobile applications is not only grounded in e commerce platforms but it also ease out mobile learning processes. Most of the educational institutes are now offering cloud based mobile applications so their students can navigate to their knowledge portal more easily and download relevant material or submit assignments respectively. The main research area of this article is to explore how cloud based mobile learning applications can be utilized more effectively and what impact they imply on its users. Also, this research compares the mobile learning methods versus traditional learning methods. The study is evidence from Jordan and the major part of the research will be carried out through surveying literature, reports, content, and national and international databases in order to critically discuss the interactions between clouds based mobile learning application and user experiences. Published researches, published reports, books and articles has been included in the review. Review of literature shows that mobile cloud computing is rising in Jordan and have significant impact on mobile learning of Jordanian students. Further, M learning indeed an innovative tool for learning and it helps the users in many ways. In traditional learning, students used to spend their money on books and other written content. Findings of this study are helpful for the educational institution so they will come to know about user experiences of utilizing these cloud based mobile applications.
Adversarial Imitation Learning from State only Demonstrations
Venue: AAMAS
Year: 2019

Imitation from observation (IfO) is the problem of learning directly from state only demonstrations without having access to the demonstrator's actions. The lack of action information both distinguishes IfO from most of the literature in imitation learning, and also sets it apart as a method that may enable agents to learn from a large set of previously inapplicable resources such as internet videos. In this paper, we propose a new IfO approach based on generative adversarial networks called generative adversarial imitation from observation (GAIfO) We demonstrate that our approach performs comparably to classical imitation learning approaches (which have access to the demonstrator's actions) and significantly outperforms existing imitation from observation methods in high dimensional simulation environments.
Treat to target Endpoint Definitions in Systemic Lupus Erythematosus: More Is Less?
Venue: The Journal of Rheumatology
Year: 2019

The use of treat to target (T2T) strategies has revolutionized the treatment of rheumatoid arthritis (RA) and other chronic diseases such as hypertension and diabetes. Evidence that attainment of a prespecified endpoint is associated with improved longterm outcomes dates to the TICORA study of 2003, which used a prespecified definition of low disease activity1. From that auspicious beginning of T2T in rheumatology, we have now come full circle. For example, the American College of Rheumatology (ACR)/European League Against Rheumatism definition of remission in RA was grounded on empirical analysis of factors contributing to improved outcomes2. These prospectively derived and comprehensively validated endpoints form the basis of RA treatment guidelines that are now routinely applied in clinical practice3, contributing to a transformation in outcomes for patients with RA that includes significant improvements in survival4. Sadly, in systemic lupus erythematosus (SLE) the same story cannot yet be told. In the same 2 decades in which profound mortality improvements in RA were observed4, there has been virtually no improvement in mortality in SLE5, and only 1 novel target therapy has been approved6. Many trials of targeted therapies have been done, but have failed; reasons for the shortage of breakthrough medicines for SLE include the clinical and biological heterogeneity of the disease. However, a lack of well validated endpoints is certainly a contributory factor to the recurrent failure of clinical trials in SLE; a report in January 2019 in  Biotechnology 7 highlighted the lack of validated endpoints for trials in SLE as a "crisis." T2T studies such Address correspondence to E.F. Morand, Monash Medical Centre, 246 Clayton Road, Clayton 3168, Melbourne,
Design, fabrication, and bending test of shape memory polymer composite hinges for space deployable structures
Year: 2017

Shape memory polymers are smart materials characterized by a recoverability memory effect and a large strain, but their mechanical properties such as low stiffness need to be improved for mechanical applications with large recovering force. Researchers have reported that the characteristics of shape memory polymers can be significantly improved when the shape memory polymers are used with fiber reinforced composite material. In this study, a carbon fiber fabric reinforced shape memory polymer composite hinge was designed, fabricated, and characterized for space deployable structure applications. The main idea is that the carbon epoxy composite itself and the shape memory polymer are combined, which means both epoxy resin and shape memory polymer resin are used together and the epoxy resin remains in a B stage after curing. As such, the stiffness and shape recovery ratio are increased. The shape memory polymer composite hinge specimens with four plies of carbon fiber fabric and a shape memory polymer were prepared for the experiment. The glass transition temperature, which was 70.9degC, was determined using dynamic mechanical analyzer. The effect of temperature on shape recovery capability was investigated. We investigated the reasons of damage evolution to shape memory polymer composite tapes occurring in the folding and deploying process. To do that, damage to the hinge was observed with a USB digital microscope and a scanning electron microscopy and then explained with ABAQUS analysis. The results confirm that the shape memory polymer composite hinge is a good candidate for an antenna in spacecraft in space.
Minimizing Distribution Cost of Distributed Neural Networks in Wireless Sensor Networks
Venue: IEEE GLOBECOM 2007 IEEE Global Telecommunications Conference
Year: 2007

This paper presents a novel study on how to distribute neural networks in a wireless sensor networks (WSNs) such that the energy consumption is minimized while improving the accuracy and training efficiency. Artificial neural network (ANN) learning has been shown robust to noisy and uncertain sensory data for function approximation and pattern classification applications. With the advances of miniature hardware technologies for powerful sensor nodes, embedded neural networks will emerge as important decision making brains for WSNs and vast surveillance applications to enable adaptive data quality and self managing capabilities. To distribute neural networks in WSNs in an energy efficient manner, we propose parallel transmission and adaptive neural selection algorithms(ANSA) in multilayer backpropagation(MLBP) learning process of neural networks, which is a popular supervised learning technique used for training feedforward artificial neural networks. We further analyze the energy consumption components in the online training process and evaluate the reduced energy consumption using our proposed algorithms.
Evaluating the utilization of the red edge and radar bands from sentinel sensors for wetland classification
Venue: CATENA
Year: 2019

As one of the most important ecosystems, wetlands are threatened from both natural and anthropogenic activities. Mapping wetland is one of the curtail needs in order to prevent further loss. Since the beginning of the Remote Sensing revolution, different approaches using satellite images have been used for mapping and monitoring wetlands. In this paper we investigate the potential of the recently launched Sentinel satellites, both separate and in combination, for accurately mapping of different wetland classes using Support Vector Machines (SVMs) learning classifier. For investigating the influence of the Sentinel 2 red edge bands, and the radar bands from Sentinel 1, three different datasets have been analyzed. The results showed that for more accurate mapping of different wetland classes, different datasets should be used. Thus, the red edge bands have significant influence over the intensive vegetated wetland classes such as swamps, and the radar bands have significant influence over partially decayed vegetated wetland areas such as bogs. For future studies, in addition to the analyzed datasets, we recommend adding and investigating several vegetation indices for mapping and monitoring wetland areas.
Nanotechnology Applications to Telecommunications and Networking
Year: 2006

No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading nanotechnology applications to telecommunications and networking is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.
Attributed Graph Clustering: an Attribute aware Graph Embedding Approach
Venue: ASONAM
Year: 2017

Graph clustering is a fundamental problem in social network analysis, the goal of which is to group vertices of a graph into a series of densely knitted clusters with each cluster well separated from all the others. Classical graph clustering methods take advantage of the graph topology to model and quantify vertex proximity. With the proliferation of rich graph contents, such as user profiles in social networks, and gene annotations in protein interaction networks, it is essential to consider both the structure and content information of graphs for high quality graph clustering. In this paper, we propose a graph embedding approach to clustering content enriched graphs. The key idea is to embed each vertex of a graph into a continuous vector space where the localized structural and attributive information of vertices can be encoded in a unified, latent representation. Specifically, we quantify vertex wise attribute proximity into edge weights, and employ truncated, attribute aware random walks to learn the latent representations for vertices. We evaluate our attribute aware graph embedding method in real world attributed graphs, and the results demonstrate its effectiveness in comparison with state of the art algorithms.
Characterizing acoustic emission signals for the online monitoring of a fluid magnetic abrasives finishing process
Year: 2018

To implement an automated fluid magnetic abrasive finishing process, an online monitoring scheme is proposed based on characterizing acoustic emission signals in this paper. According to the material removal mechanisms during the fluid magnetic abrasive finishing process, the acoustic emission generation and characteristics are predicted analytically to be dominated by the interactions between the surface asperities and the abrasive particles. Moreover, the interactions and corresponding acoustic emission events will become weaker as the finishing process progresses and the surface becomes smoother. Experimental studies show that the amplitude and the occurrence rate of continuous acoustic emission waves and intermediate bursts reduce gradually with the progression of the finishing process. Based on these features, root mean squared values and burst occurrence rates, being of the lowest computational requirements, are suggested as online monitoring parameters for an automated and intelligent finishing in fluid magnetic abrasive manufacturing. The proposed method is verified experimentally, showing that the root mean squared values are highly consistent with the measured surface roughness values, which confirms the dynamic mechanisms between the fluid magnetic abrasive finishing and acoustic emission generation sources examined.
Professional responses to 'parental alienation' research informed practice
Year: 2020

Parental alienation was historically a term rejected by courts in England and Wales, but lawyers and social workers have noted an increase in the incidence of its use, possibly driven by campaign groups and media narratives. The two statutory services that provide independent social work advice to courts in England and Wales, respectively, on children's best interests in parenting disputes, have taken different approaches to developing practice guidance in response to concerns about the recent use of alienation terminology. A review of international research and domestic case law was undertaken as part of the development of guidance in Wales. This review revealed a dearth of reliable evidence on the concept of parental alienation, its prevalence, effects and measures for intervention. This article builds on that review and recent developments to discuss the progress being made in practice to counter myths about alienation and considers how best to support practitioners in resisting pressures to conform to these powerful narratives.
Mining affective text to improve social media item recommendation
Venue: Inf. Process. Manag.
Year: 2015

We propose a sentiment aware social media recommendation framework.An ensemble learning based method is proposed to classify sentiments from affective texts.We conduct comprehensive experiments to verify the effectiveness of the proposed methods. Social media websites, such as YouTube and Flicker, are currently gaining in popularity. A large volume of information is generated by online users and how to appropriately provide personalized content is becoming more challenging. Traditional recommendation models are overly dependent on preference ratings and often suffer from the problem of "data sparsity" Recent research has attempted to integrate sentiment analysis results of online affective texts into recommendation models; however, these studies are still limited. The one class collaborative filtering (OCCF) method is more applicable in the social media scenario yet it is insufficient for item recommendation. In this study, we develop a novel sentiment aware social media recommendation framework, referred to as SA_OCCF, in order to tackle the above challenges. We leverage inferred sentiment feedback information and OCCF models to improve recommendation performance. We conduct comprehensive experiments on a real social media web site to verify the effectiveness of the proposed framework and methods. The results show that the proposed methods are effective in improving the performance of the baseline OCCF methods.
Torque ripple minimization in dual inverter open end winding PMSM drives with non sinusoidal back EMFs by harmonic current suppression
Venue: IECON 2016 42nd Annual Conference of the IEEE Industrial Electronics Society
Year: 2016

This paper presents a closed loop control strategy for dual inverter fed, permanent magnet synchronous motors (PMSM) with non sinusoidal back electromotive force (back EMF) The inverters are connected to both ends of the machine winding but use only one power source, thus providing a path for zero sequence currents to circulate in the motor windings. Due to the non sinusoidal back EMFs and circulation of zero sequence currents, the machine electrical torque is now a function of the rotor position that leads to torque ripple oscillations. Therefore, a closed loop control strategy to suppress the circulation of zero sequence currents and amplitude of the dominant current harmonics caused by the non sinusoidal back EMFs is developed and demonstrated by experiment. The zero sequence component is considered as one additional degree of freedom that has to be managed. Therefore, the control scheme to regulate the ac signals in the regulators is developed and an approach to minimizing the torque ripples is presented.
Numerical method and calculation of two phase swirling flows with rigid particles for technical applications
Year: 2017

Swirling flow with particle deposition effects at the lateral surface is numerically investigated. The flow field calculation results have been obtained as the solutions of the Navier Stokes equations. Various flow regimes with the formation of axial recirculation zones are presented. The convection diffusion model is used for the determination of the flow particle concentration and the formation of typical sedimentation zones.
Design of Intelligent Street Lamp Control System Based on Wireless Network
Venue: 2018 IEEE 17th International Conference on Cognitive Informatics Cognitive Computing (ICCI*CC)
Year: 2018

An intelligent street lamp management system based on GPRS (General Packet Radio Service) and ZigBee communication network is designed and realized for the management of urban street lamps. The lamps can be controlled locally or remotely by the proposed system. With local control, the lamps can be on/off automatically according to the surrounding environment. With remote control, the lamps can be on/off manually. The running status of lamps can be detected and send to the upper computer on time, which give the information of the lamp fault location. With zero point detection circuit and PWM (Pulse Width Modulation) control techniques, the lamp lighting can be adjusted smoothly. With ZigBee technology, networking performance can be improved and self repairmen can be finished timely. Experimental shows that the presented system can be used to monitor and control the street lamps, which has potential applications in other similar fields.
Symptom shifting and associations with mental illness: A transdiagnostic approach applied to eating disorders.
Venue: Journal of abnormal psychology
Year: 2019

Diagnostic comorbidity is common among psychiatric patients. One approach to obtain an improved understanding of this phenomenon is to explore diagnostic transitions, across time. In the present study, we aim to take this methodology further within the specific context of eating disorders by investigating whether there are individuals who shift not only between different eating disorder symptoms, but also shift to deliberate self harm and substance use over time, and relations of this to other indicators of mental illness. Retrospective longitudinal registry data from the Swedish Stepwise national quality assurance platform for eating disorders were analyzed, including self report measurements and clinical assessments. Individuals (N 3,159 adults) were selected for analysis based on available data at admission and 12 months postadmission, and grouped based on occurrence of a "symptom shift" (defined as a decrease in one symptom and increase in another over time) In this sample of patients, 422 (13% demonstrated symptom shifting among eating disorder symptoms. As hypothesized, "symptom shifters" were more prone to engage in deliberate self harm and shifted to both deliberate self harm and substance use across time. They had higher reported levels of symptoms indicative of mental illness (e.g. anxiety and compulsivity) and more pronounced functional impairment (clinician rated and self rated) compared to nonshifters. Taken together, this study demonstrates that a subgroup of individuals diagnosed with eating disorder(s) shift between distinct psychiatric symptoms across time, indicating that they may share a common vulnerability to engaging in problem behaviors and a need for a more comprehensive and individualized treatment plan.
Who are those "risk taking adolescents" Individual differences in developmental neuroimaging research
Venue: Developmental Cognitive Neuroscience
Year: 2015

Highlights Rates of development differ across brain regions linked to reward and inhibition. Adolescent risk taking has been attributed in part to normative neurodevelopment. Significant risky behavior in mid adolescence is not characteristic of typical youth. Youth with behavior disorders show increased behavioral and brain responses to reward. Maturational theories of adolescent risk taking can consider individual differences.
A Lightweight and Discriminative Model for Remote Sensing Scene Classification With Multidilation Pooling Module
Venue: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2019

With the growing spatial resolution of satellite images, high spatial resolution (HSR) remote sensing imagery scene classification has become a challenging task due to the highly complex geometrical structures and spatial patterns in HSR imagery. The key issue in scene classification is how to understand the semantic content of the images effectively, and researchers have been looking for ways to improve the process. Convolutional neural networks (CNNs) which have achieved amazing results in natural image classification, were introduced for remote sensing image scene classification. Most of the researches to date have improved the final classification accuracy by merging the features of CNNs. However, the entire models become relatively complex and cannot extract more effective features. To solve this problem, in this paper, we propose a lightweight and effective CNN which is capable of maintaining high accuracy. We use MobileNet V2 as a base network and introduce the dilated convolution and channel attention to extract discriminative features. To improve the performance of the CNN further, we also propose a multidilation pooling module to extract multiscale features. Experiments are performed on six datasets, and the results verify that our method can achieve higher accuracy compared to the current state of the art methods.